DATA-DRIVEN ANALYSIS OF WATER AND NUTRIENT ...1095755/...TRITA-LWR PhD-2017:03 ISSN 1650-8602 ISRN...

TRITA-LWR PhD-2017:03

ISSN 1650-8602

ISRN KTH/LWR/PhD

ISBN 978-91-7729-415-3

DATA-DRIVEN ANALYSIS OF WATER AND NUTRIENT FLOWS: CASE OF THE SAVA

RIVER CATCHMENT AND COMPARISON WITH OTHER REGIONS

Lea Levi

May 2017

Lea Levi TRITA LWR PhD Thesis 2017:03

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© Lea Levi 2017

PhD thesis

Division of Land and Water Resources Engineering

Department of Sustainable Development, Environmental Science and Engineering

School of Architecture and the Built Environment

Royal Institute of Technology (KTH)

SE-100 44 STOCKHOLM, Sweden

Cover: The turbines of Jaruga 1 (Croatia), the first commercial alternating current hydropower plant in Europe and the second in the world. (Photo: Lea Levi)

Reference to this publication should be written as: Levi, L. (2017). “Data-driven analysis of water and nutrient flows: Case of the Sava River Catchment and comparison with other regions”. PhD Thesis

TRITA-LWR PhD-2017:03

Data-driven analysis of water and nutrient flows: Case of the Sava River Catchment and comparison with other regions

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DEDICATION

To my family and friends

For adding colors to my life


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INSPIRATION

“Be like water making its way through cracks. Do not be assertive, but adjust to the object, and you shall find a way around or through it. If nothing within you stays rigid, outward things will disclose themselves. Empty your mind, be shapeless, formless, like water. When you pour water in a cup, it becomes the cup. When you pour water in a bottle, it becomes the bottle. When you pour water in a teapot, it becomes the teapot. Now, water can flow or drip or crash. Be water my friend.”

Brucee Lee

“If you want to find the secrets of the Universe, think in terms of energy, frequency and vibration.”

Nikola Tesla


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SUMMARY IN SWEDISH

Omfattande förändringar i floders vatten- och biogeokemiska cykler sker i avrinningsområden världen över på grund av en växande befolkning och dess ökande krav på mat, sötvatten och energi-resurser. Adresserande och undersökande av dessa förändringar är särskilt viktigt för gränsöverskridande avrinningsområden, då detta medför ytterligare risker för en regions stabilitet. Denna avhandling undersöker och utvecklar metoder för att upptäcka hydroklimat och näringsbelastnings-förändringar och dess orsaker, under förhållanden av begränsad tillgänglig data och på olika avrinningsområdes-skalor. Som fallstudie används Sava-flodens avrinningsområde och dess resultat jämförs med andra avrinningsområden världen över. En historisk-nutida-framtida utvärdering av hydro-klimat data utförs på basis av vattenbalans-beräkningar och inkluderar analyser av historiska data kring markanvändning och vattenkraft- utbredning, samt resultat från klimat-prognoser (CMIP5). Genom att använda flödesmätningar och näringsämnes-koncentrationer utvecklar vi en ny konceptuell modell. Modellen kan användas för uppskattning av belastning samt retention av totalkväve och totalfosfor, samt för fastställande av näringsämnes-hotspots, för floders avrinningsområden och nästade delavrinningsområden på olika skalor. Avhandlingen identifierar hydroklimatiska förändrings-signaler relaterade till vattenkraft-utbredning, vilket även är konsekvent med andra världsregioner. Den föreslagna näringsämnes kontroll-metoden gör det möjligt att skilja på mänskligt och landskaps-relaterade processer för näringsämnes-belastningar vid delavrinnings- till hela avrinningsområdes-skalor. En tvär-regional jämförelse av data från Sava-flodens avrinningsområdes med Östersjöområdet visar likheter mellan näringsrelevanta indikatorer och socio-ekonomiska och hydroklimatiska förhållanden. Studien belyser ett antal svårigheter avseende användande av CMIP5 modellering av vattenflöden. Den stora variationen i CMIP5 prognoser kräver försiktighet vid användande av individuella modellresultat för bedömning av pågående och framtida förändringar av vatten och näringsämnes-flöden.


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SUMMARY IN CROATIAN

Rastući broj stanovnika te povećani zahtjevi za hranom, pitkom vodom i energijom uzrokuju promjene u kruženju vode te u biogeokemijskim ciklusima rječnih slivova diljem svijeta. Istraživanje i suočavanje sa takvim promjenama od izuzetne su važnosti za rječne slivove koji se protežu preko granica više država, jer direktno utječu na stabilnost takvih regija. Ova dizertacija bavi se istraživanjem i razvojem metodologija za otkrivanje hidro-klimatskih promjena i promjena u opterećenju hranjivim solima u riječnim slivovima te njihovim uzročnicima. Metodologije su bazirane na prikupljenim podatcima i primijenjene su za različite veličine rječnih slivova. U ovom radu, kao poseban primjer analiziran je sliv rijeke Save te su njegovi rezultati uspoređeni sa drugim svjetskim regijama. Vrednovanje hidro-klimatskih podataka iz prošlosti, sadašnosti i budućnosti provedeno je korištenjem jednadžbe vodne bilance uzimajući u obzir analizu podataka vezanih za povijesni razvoj korištenja zemljišta te hidroenergetike, kao i rezultata budućih projekcija dobivenih iz faze 5 projekta Coupled Model Intercomparison Project (CMIP5). Predložen je inovativni koceptualni model za procjenu i prostornu analizu unosa te pronosa i retencije ukupnog dušika (TN) te ukupnog fosfora (TP) u riječnom slivu i njegovim podslivovima, koristeći mjerene podatke protoka te koncentracija hranjivih soli u rijeci. Metodologija također vrši identifikaciju kriznih mjesta vezanih za opterećenje hranjivim solima. Istraživanjem su uočene hidro-klimatske promjene na slivu rijeke Save povezane sa razvojem hidroenergetike pri čemu je utvrđena sličnost sa drugim regijama svijeta. Predložena metodologija za brzu procjenu stanja dinamike hranjivih soli omogućava uspješno određivanje razlike između opterećenja hranjivim solima uzrokovanih ljudskim aktivnostima ili prirodnim karakteristikama okoliša. Usporedba rezultata sliva rijeke Save sa baltičkom regijom ukazuje na sličnu povezanost i utjecaje socio-ekonomskih i hidroklimatskih uvjeta na opterećenja hranjivim solima u riječnim slivovima. Ovaj rad ukazuje na niz potencijalnih problema vezanih za modeliranje protoka, padalina te evapotranspiracije unutar CMIP5 projekta. Velike razlike u rezultatima projekcija pojedinačnih CMIP5 modela ovih varijabli upućuju na poseban oprez pri korištenju tih rezultata u svrhu određivanja kako sadašnjih tako i budućih promjena kruženja vode u prirodi te opterećenja hranjivim solima.


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ACKNOWLEDGMENTS

This research was funded through grants from the Swedish Research Council (VR; project number 2009-3221) and was linked to the Bert Bolin Centre for Climate Research supported by a Linnaeus grant from the Swedish research councils VR and Formas. I thank to the Knut and Alice Wallenberg Foundation and the Graduate Research School of the Bolin Centre for financing my travels to conferences.

I would like to express my deepest gratitude to my supervisors, Professor Vladimir Cvetkovic, Professor Georgia Destouni and Professor Roko Andričević. It has been a unique and valuable experience to learn from their different approaches to research and science. I appreciate interesting discussions with Professor Vladimir Cvetkovic, his critical thinking, patience and help to bridge the gaps when needed. I am grateful to Professor Georgia Destouni for her support, guidance and share of ideas. She has taught me by her mere example how daring to think and act out of a box, and yet being disciplined, but flexible, confident and straight forward can lead to fruitful results and life inspiration. I am thankful for enthusiasm and encouragement of Professor Roko Andričević who opened me the door to the scientific world and collaboration with Sweden, where I found not only work but also my home. I am grateful to Professor Prosun Bhattacharya for the internal review of this thesis and valuable suggestions.

The crucial part of this thesis was acquiring the data. That was possible thanks to help and cooperation of Dejan Komatina and Dragan Zeljko from the International Sava River Basin Commission, Gordana Bušelić from Croatian Meteorological and Hydrological Service, Professor Ognjen Bonacci, Professor Martina Baučić, Hrvatske vode Institute and Institute Jaroslav Černi. I thank Aira Saarelainen, Britt Chow, Jerzy Buczak, Magnus Svensson, Katrin Grünfeld, Susanna Blåndman, Sabina Prančić, Slavko Prlj, Martin Spånberg and Michael Burger for their kind help with administrative and IT issues.

As my work has been a collaboration between KTH, Stockholm University

and University of Split, I have been extremely lucky to meet extraordinary

people at all three institutions. Many of them became my friends who have

added that extra spice to my life. They are Caroline, Liangchao, Imran, Hedi,

Zahra, Prabin, Alireza, Sara, Benoit, Emad, Ezekiel, Robert, Rajabu, Marija,

Sofie, Kedar, Juan, Josephine, Fernando, Alexander, Jan, Ida, Lucille, Niels,

Juri, Emma, Morena, Veljko, Hrvoje, Neno, Vlado and Ivana. I also thank to

Maja K., Frane, Elad, Eyale, Frida, Kinga, Alev, Sheela Ivana, Rakela, Sanja,

Jonatan, Noa, Maja M., Silvana, Gordana, Dino, Sergio, Natesh, Abilash,

Renata, Slađana and Ieva for their support, friendship, guidance and

encouragement.

I would like to thank my parents, Boro and Blanka, my sister Tamara, my aunt

Rebeka, my aunt Luči and my cousin Svjetlana for their endless love, support

and understanding. Special thanks are given to my closest friend Branka for all

the shared adventures, support, laughs and overcame challenges. Despite

geographical distance or time zones between us, sharing moments with all

these wonderful people is what makes my life colorful and meaningful.

Lea Levi, Stockholm, May 2017

https://www.seed.abe.kth.se/en/om/avd/lwr/grupper/forskningsomraden/egc/personal/prosun-bhattacharya-1.329564


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LIST OF APPENDED PAPERS

This thesis is based on the following papers appended at the end of this thesis:

I Levi, L., Jaramillo, F., Andričević, R. and Destouni, G. Hydroclimatic changes and drivers in the Sava River Catchment and comparison with

Swedish catchments. Ambio, 44(7), 2015; 624-634.

II Levi, L., Cvetkovic V. and Destouni, G. Data-driven analysis of nutrient inputs and transfer through nested catchments. Under review.

III Bring, A., Asokan, S.M., Jaramillo, F., Jarsjö, J., Levi, L., Pietroń, J., Prieto, C., Rogberg, P. and Destouni, G. Implications of fresh water flux data from the CMIP5 multimodel output across a set of Northern Hemisphere drainage basins. Earth´s Future, 3(6), 2015; 206-217.

IV Levi, L. and Destouni, G. Multi-model projections of future hydro-climatic and nutrient-load evolution in the Sava River Catchment. Manuscript.

The co-authorship of the papers reflects the collaborative nature of the

underlying research. For Papers I, II and IV, I was responsible for all analysis

and was the main responsible for the study design, organization and writing.

For Paper III, I acquired, compiled and processed the historical data for the

Sava River Catchment and prepared the corresponding figures-parts of both

historical and CMIP5 data analysis. Georgia Destouni supervised the analysis

in Papers I and II, proposed the methodology of Paper II and co-wrote the

final version of these two papers. All papers have been reviewed by the co-

authors listed for each paper.


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TABLE OF CONTENT

Dedication ....................................................................................................................... iii

Inspiration ........................................................................................................................ v

Summary in Swedish ..................................................................................................... vii

Summary in Croatian ..................................................................................................... ix

Acknowledgments .......................................................................................................... xi

List of appended papers ............................................................................................... xiii

List of abbreviations .................................................................................................... xvii

Abstract ............................................................................................................................ 1

1. Introduction............................................................................................................ 1

1.1 The transboundary Sava River Catchment .......................................................... 4

2. Aims and Objectives .............................................................................................. 4

3. Materials and Methods .......................................................................................... 7

3.1 Data-driven approach ........................................................................................... 7

3.2 Catchment water-balance quantification of hydro-climatic change and its

drivers .......................................................................................................................... 7

3.3 Assessment of nutrient inputs, delivery and load changes for multi-scale

catchments .................................................................................................................. 8

3.4 Evaluation and the use of CMIP5 model data .................................................... 9

3.5 Implementation................................................................................................... 10

4. Results................................................................................................................... 11

4.1 Historical to present hydro-climatic changes and their drivers ...................... 11

4.2 Observed nutrient-related changes ................................................................... 14

4.3 Projected climate change and nutrient loading ................................................ 16

5. Discussion ............................................................................................................ 19

5.1 Detected hydro-climatic change and its drivers ............................................... 20

5.2 Data-driven nutrient analysis ............................................................................. 21

5.3 Use of CMIP5 model data for catchment-based analysis ................................ 22

6. Conclusions .......................................................................................................... 23

7. References ............................................................................................................ 24


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LIST OF ABBREVIATIONS

T Temperature

P Precipitation

R Surface runoff

ΔS Land water storage change

AET Actual evapotranspiration

AET/P Relative actual evapotranspiration

CV(R) Coefficient of variation of surface runoff

HP Hydropower production

DIN Dissolved inorganic nitrogen

TN Total nitrogen

TP Total Phosphorus

I Nutrient input

L Nutrient load


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ABSTRACT

A growing human population and demands for food, freshwater and energy are causing extensive changes in the water and biogeochemical cycles of river catchments around the world. Addressing and investigating such changes is particularly important for transboundary river catchments, where they impose additional risk to a region’s stability. This thesis investigates and develops data-driven methodologies for detecting hydro-climatic and nutrient load changes and their drivers with limited available data and on different catchment scales. As a specific case study, we analyze the Sava River Catchment (SRC) and compare its results with other world regions. A past–present to future evaluation of hydro-climatic data is done on the basis of a water balance approach including analysis of historic developments of land use and hydropower development data and projections of the Coupled Model Intercomparison Project, Phase 5 (CMIP5) output. Using observed water discharge and nutrient concentration data, we propose a novel conceptual model for estimating and spatially resolving total nitrogen (TN) and total phosphorus (TP) input and delivery-retention properties for a river catchment and its nested subcatchments, as well as detection of nutrient hotspots. The thesis identifies hydroclimatic change signals of hydropower-related drivers and finds consistency with other world regions. The proposed nutrient screening methodology provides a good distinction between human-related nutrient inputs and landscape-related transport influences on nutrient loading at subcatchment to catchment scale. A cross-regional comparison of the SRC data with the Baltic region shows similarity between nutrient-relevant indicators and driving socio-economic and hydro-climatic conditions. The study highlights a number of complexities with regard to CMIP5 model representation of water fluxes. The large intermodel range of CMIP5 future projections of fluxes calls for caution when using individual model results for assessing ongoing and future water and nutrient changes.

Key words: River catchment, Transboundary, Hydro-climatic change, Total nitrogen, Total phosphorus, CMIP5

1. INTRODUCTION

Over the past 50 years the human population on Earth has doubled (United Nations, 2015), making the increasing demands for food, energy, availability and sustainable use of freshwater the main priorities of humankind. Achieving these goals requires extensive planning and, in many cases, major changes in land and water use. These changes can often lead to detrimental impacts and irreversible consequences for the river catchments where they occur. In order to avoid negative impacts and provide support to river catchment management, essential is a good understanding of past and present

conditions in catchments, as well as detecting and identifying the causes of changes and their possible long-term legacy. Such knowledge offers a better basis for investigating possible future developments, changes projections and improved paths towards sustainable growth. Famous author Terry Pratchett’s words on the state of humankind could easily apply to the case of river catchments: “It is important that we know where we come from, because if you do not know where you come from, then you don't know where you are, and if you don't know where you are, you don't know where you're going. And if you don't know where you're going, you're probably going wrong.”


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Water circulates between the atmosphere, the ocean and the land and thus controls the exchange and partitioning of energy and mass between them. The water cycle thereby, strongly affects most biogeochemical cycles (Jacobson et al., 2000). Changes in the water cycle occur due to its constant interaction with natural and anthropogenic climate change (Hamlet and Lettenmaier 1999; Christensen et al., 2004; Nilsson et al., 2005; Seneviratne et al., 2006; Poff et al., 2007; Dyurgerov et al., 2010; Botter et al., 2013). They manifest themselves primarily as changes in the partitioning of precipitation (P), the major flux through which water moves from the atmosphere to the land. The partitioning occurs on three levels: (i) as actual evapotranspiration (AET) returning water from the land back to the atmosphere; (ii) as runoff (R), carrying water through the land back to the ocean; and (iii) land water storage change (ΔS).

Human activities and different land and water uses and their changes influence the water partitioning in various ways. For example, the expansion of agricultural land, as well as the forestation of previously sparsely vegetated areas, often increase evapotranspiration (Loarie et al., 2011; Gordon et al., 2005, 2011; Destouni et al., 2013) while deforestation might lead to its decrease with simultaneous increase in runoff. In some parts of the world agricultural developments are followed by intensified irrigation, which can cause extreme losses of water to the atmosphere and associated decrease in runoff to nearby water bodies to a great extent (Shibuo et al., 2007; Destouni et al., 2010).

Intensified agriculture can further play an important role in driving excess nutrient loading to receiving waters and enhancing the rapid eutrophication (Turner and Rabalais, 1994; Darracq et al., 2005; Aulenbach et al., 2007; Juston et al., 2016). Contrary to the slow process of natural eutrophication that occurs due to natural aging of lakes and rivers over thousands of

years (Calllisto et al., 2014), human-induced eutrophication occurs within a few decades or less, resulting in an array of changes in water quality and living organisms (Conley et al., 2009). Sources of the changes in fluxes of nitrogen (N) and phosphorus (P) can be distinguished as point and diffuse ones. Wastewater discharges from municipal and industrial facilities represent the main anthropogenic point sources. The greatest pressure from anthropogenic induced diffuse sources usually comes from agriculture through nutrients from fertilization, plant protection products and animal manure. Other diffuse sources are atmospheric deposition, urban land, forestry and rural dwellings. For example, the global production of agricultural fertilizers increased 800% within only four decades (1950-1990) and is expected to exceed 135 million metric tons of N by the year 2030, causing a considerable increase in the rate of N into the terrestrial N cycle (Vitousek et al., 1997 a, b). Also, the outputs of P from fertilizers and animal manures are often much higher than those from farm production, thus leading to substantial P accumulation in soil (Foy and Withers, 1995; Smith et al., 1999) that eventually gets exported in runoff to surface waters.

In addition, in some regions, the domestic and industrial water supplies, the protection of inhabited areas from floods (including recently more frequent flash flooding occurances) and the need for energy require the construction of dams, channels and massive water reservoirs. These have been shown to significantly contribute to changes in water fluxes (Gordon et al., 2005; Bengtsson and Berndtsson, 2006; Hossain, 2010; Degu et al., 2011; Destouni et al., 2013; Montanari et al., 2013; Jaramillo and Destouni, 2014) as well as to nutrient-related changes due to accumulation of nutrient pools within soil, aquifer, stream and reservoir systems and adding to previously mentioned nutrient sources (Grimvall et al., 2000; Baresel and Destouni, 2005; Ryder et


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al., 2007; Haag and Kaupenjohann, 2001; Stålnacke et al., 2003; Darracq et al., 2008).

Therefore, it is of interest to make a more detailed investigation of a catchment´s different responses to climate variability and the level of anthropogenic disturbance due to river flow regulation, fragmentation and excess nutrient loading in transboundary river catchments. Such catchments are controlled by several different countries that are often involved in delicate political and socio-economic conflicts. Hydro-climatic as well as water quality changes thus impose additional challenges for managing and governing their water resources on transboundary and subcatchment scales and play a key role in the stability of these areas (Varis et al., 2008; Earle et al., 2015; Abdelhady et al., 2015; Fischer et al., 2017). As such, they are of interest not only to the scientific community, but to local users, companies, governments and society at large.

To propose viable solutions for effective management of a catchment in particular on large transboundary scales, analysis of underlying physical and biogeochemical processes is needed, based on available hydrological data and water quality indicators. Yet because of the complex situation in many of the transboundary catchments, measurements are not conducted; even if data do exist, they are often unavailable or are not properly extracted. In such cases, scientists face the huge challenge of still finding ways to use scarce data or of developing methodologies that can answer questions through related proxies or comparing areas that have similar types of problems and developments but with better data coverage.

One way to learn more about catchments´ dynamics is to build distributed models and use them for understanding past impacts and possible future changes. As a mathematical representation of the water cycle, distributed hydrological models include parameters that directly represent physical properties of the system (usually values that can be measured) or parameters representing physical processes that occur in

reality but are not directly measurable (Remesar, 2015). They are grid-cell based and take into consideration the spatial variability of input data.

The outputs of existing global climate models, commonly derived through statistical or dynamic downscaling procedures (Bring et al., 2015a,b), are often used as input for regional models and for further studies of climate change and its impacts on a catchment scale (Xu and Singh, 2004). In the case of large drainage basins, downscaling can be skipped and the direct results of climate models can be used (Jarsjö et. al., 2012; Bring and Destouni, 2014; Bring et al., 2015b). Whether downscaled or directly used, the outputs of global models are primarily dependent on climate forcing and boundary conditions (Arheimer et al., 2012), and, as models in general, are subject to uncertainties and biases that might arise due to model limitations and assumptions, or the data used to calibrate and validate model performance (Kuczera et al., 1998; Jager et al., 2004; Lyon et al., 2008; Beven, 2009).

Multiple uncertainty issues are also related to the use of field-collected hydrological data due to precision of conducted measurements or (in)appropriate spatial or temporal representativity of the data (Juston, 2012). In spite of these uncertainties, it has been shown that careful analysis of flow and water quality data set in a simple water balance framework, can play a crucial role in filling the modelling gaps and for detection of occurring changes and their drivers in river catchments (Destouni et al., 2013; Jaramillo and Destouni, 2015; Du et al, 2017). Data-driven approaches can connect different types of observed data in simple ways and provide useful basis for developing more complex modeling approaches of water quality when and where needed (Alexander et al., 2008; Hägg et al., 2001, 2014). Data-based analysis has further been recognized as a useful way of understanding catchment’s mass balance in several regions of the world that have been highly-impacted by human activity, like for example the Aral Sea drainage basin (Shibuo et al., 2007),


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severely impacted by irrigation. The mass balance approach in the Aral Sea drainage basin, and several other studies, allowed for calculation of often missing, or rarely and with major difficulties measured values of actual evapotranspiration (Asokan et. al, 2010; Jaramillo and Destouni, 2015). Data-driven regionalization study by Van der Velde et al. (2013) for instance, has successfully detected energy use efficiencies of different land covers in Swedish catchments through observed river discharges. Van der Velde et al. (2013) used the later for assessing the regional climate model ensemble potential to predict changes and their drivers in the Baltic Sea region and discovered large underestimation of model outputs when compared with observation-based results.

1.1 The transboundary Sava River Catchment

The Sava River Catchment (SRC) is a transboundary catchment in the Balkans in Southeast Europe, with an area of 92,158 km2 (outlet Sremska Mitrovica) and a population of about 8,176,000 people. Depending on the elevation, there are three dominant climate zones: alpine, moderate continental and moderate continental mid-European. Only 0.6 % of the total water use in the catchment is used for irrigation, whereas only 0.28 % of the total SRC area is systematically irrigated (International Sava River Commission, 2008). Still, since the 1950s the SRC has undergone major regulation for hydropower production and flood protection which led to hydro-climatic changes driven by a combination of both human regulated and unregulated influences in the catchment (Levi et al., 2015). Due to recent social, economic and political instability, the catchment area has been regulated by six countries, each on their own level of adjustment to present European Union requirements for sustainable water management. As a result of these conditions, much of the industrial wastewaters from 266

industrial facilities in the catchment are discharged into the public sewage system or catchment environment. Being the largest tributary by discharge to the Danube River (International Commission for the Protection of the Danube River (ICPDR, 2005), Sava River and its catchment are also important contributors of nutrients for the Danube catchment and the Black Sea, water bodies that are extremely sensitive to eutrophication (Van Gils et al., 2005 a, b).

2. AIMS AND OBJECTIVES

This thesis aims to investigate, understand and quantify hydro-climatic and nutrient load changes and distinguish their drivers for a case of a transboundary river catchment with limited data availability. To do this, we propose simple observation-based screening methodologies that we test on the case study of the major transboundary Sava River Catchment (SRC, Fig. 1). The complex post-war situation present within the area has lead to relatively limited accessibility to environmental data, making the work on this thesis quite challenging. Yet finding new ways to accomplish its goals has proved instructive and fruitful. A considerable amount of temperature and water flux data in the catchment are nevertheless available for longer periods of time and as such can be a useful input for accessing possible changes and their drivers in the catchment. Such data can also be related to more scarce observations of water quality which can provide valuable insights on nutrient flow related issues. To test the generality of the applied methods, the obtained results are then compared with previously investigated, climatically different regions with better data coverage (Fig. 1).

A schematical overview of the aims of this thesis is shown in Figure 2 together with the papers that it includes.


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7

The main thesis objectives are summarized as follows:

Objective A

The first aim is to investigate past-to-present hydro-climatic changes in a transboundary Sava River Catchment based on available observed data and to relate the data to possible change drivers, including atmospheric climate change and anthropogenically induced land and water developments. This involves accounting for different methods of estimating and comparing two main components of the total AET change in the catchment: purely climate-driven AET change and AET change triggered by human-induced drivers.

In order to better relate the observed water flux changes in the landscape to possible change drivers, we analyze additional measures of relative evapotranspiration (AET/P) and coefficient of variation of daily runoff CV(R) on inter-annual scales.

This study analyzes the generality of water changes results discovered for the SRC case study by comparison with Swedish catchments. Although Sweden differs climatically from the Balkans, it has similar water and land use developments.

Objective B

The second aim of this thesis is to develop and evaluate a novel conceptual model for estimating and spatially resolving total nitrogen (TN) and total phosphorus (TP) input and delivery-retention properties for a river catchment and its nested subcatchments. The base inputs of the model are measured water discharges and nutrient concentrations data that are typically available for at least short periods along river networks.

The methodology is evaluated for the Sava River Catchment, and obtained results are further investigated in relation to discharge variation and the key indicators of human activities in the catchment and compared for the extensively monitored and researched Baltic region.

Objective C

The final aim is to expand the hydro-climatic and nutrient loading change analysis towards the future projections and change scenarios. The first and foremost, this considers the evaluation of implications of temperature and freshwater flux data from the CMIP5 multi-model output on different catchment scales and for various geographical conditions in the Northern hemisphere.

The perspectives gained further allow for a more specific analysis of projected future climate change in the Sava River Catchment itself and an investigation of its effects on nutrient loading within the catchment. Special attention is given to a more detailed comparison of model outputs with the observed data and assessment of individual models performance and their influence on nutrient estimate outcomes.

3. MATERIALS AND METHODS

3.1 Data-driven approach

The data-driven approach in this thesis refers to analysis of water and nutrient flows in a catchment by a combined use of statistical tools and physical constraints, primarily mass balance. More specifically, this thesis uses data in order to quantify mass balance of water and nutrients on a catchment scale. Such an approach allows assessing values of relevant quantities that characterize water and biogeochemical cycles, and can be indicators of their change, but are otherwise hard to measure or computed using complex, distributed modelling approaches. The data used in this thesis are primarily obtained by some type of monitoring (observation), but also from complex simulations of the climate system.

3.2 Catchment water-balance quantification of hydro-climatic change and its drivers

A river catchment is a drainage area where land and water are linked within a physical boundary that allows for closing the flow balance of water coming in and out of a catchment, following the natural topography, and the mass balances of constituents transported by waters within it.


8

It is a basic hydrological spatial unit used in this thesis to study and understand hydro-climatic and nutrient load changes.

Hydro-climatic changes in river catchments can be driven by both natural global climate change and regional land-water use changes induced by humans. The changes often manifest themselves as a decrease or an increase in AET. To distinguish between possible drivers of historical hydro-climatic changes and address the present state of a catchment, we use two different approaches for calculating the AET, following the methodology of previous studies (Shibuo et al., 2007; Asokan et al., 2010; Destouni et al., 2010, 2013; Jaramillo et al., 2013; Van der Velde et al., 2013; Asokan and Destouni, 2014; Jaramillo and Destouni, 2014). The first approach is based on calculating annual AETwb from fundamental catchment scale water balance and available time series of P and R according to (1):

𝐴𝐸𝑇𝑤𝑏 = 𝑃 − 𝑅 − ∆𝑆. (1)

ΔS is the annual change in water storage over the catchment and is assumed to be approximately equal to zero. Purely climate-related measures of AET for the second approach are calculated according to Turc (AETTclim) and Budyko (AETBclim).

𝐴𝐸𝑇𝑇𝑐𝑙𝑖𝑚 =𝑃

√0.9+𝑃2

𝑃𝐸𝑇2

, (2)

𝐴𝐸𝑇𝐵𝑐𝑙𝑖𝑚 = 𝑃 ∗ (1 − 𝑒−𝑃𝐸𝑇

𝑃 ), (3)

where PET is potential evapotranspiration, obtained from Langbein (1949) as,

𝑃𝐸𝑇 = 325 + 21 ∗ 𝑇 + 0.9 ∗ 𝑇2, (4)

and where T is mean annual temperature in degrees Celsius. To eliminate the dominating effect of P on AET and make the distinction clearer, we also analyze changes in relative evapotranspiration AET/P and a coefficient of variation of daily R, CV(R) and compare them with available data of land and water use within a catchment. Such an approach further allows for an inter-regional comparison between different catchments. In addition to analyzing a hydropower production as one of the water-use proxies researched in previous similar studies, our research takes into consideration the area and volume of man-made water reservoirs as possible valuable source of information.

3.3 Assessment of nutrient inputs, delivery and load changes for multi-scale catchments

Human land and water use in river catchments not only influence hydro-

a b

∆

∆ ∆

∆

- −

Fig. 3 Schematic illustration of a main investigated overall catchment and its (a) nested catchments and (b) incremental subcatchments The notation in the figure relates to and is as used in equations (5)-(7) in the main text.


9

climatic changes but are also reflected as the changes in a catchment´s biogeochemical responses. We propose the following methodology in order to better understand the relations between the catchment´s hydro-climatic conditions (represented by runoff) and biogeochemical dynamics (represented by nutrient-related variables). At the same time, we relate these to human-related activities (expressed through population density and farmland share) that could be possible drivers of nutrient changes.

The methodology offers a simple screening tool for estimating total nitrogen (TN) and total phosphorus (TP) loads (Li), inputs (Ii), delivery (αi) and retention factors (1- αi) on subcatchment (incremental and nested) to catchment scale (Fig. 3). Associated nested and incremental subcatchments are both defined by their respective station number i=1,n with n being the same total number for both type of subcatchments. On the basis of upstream (Li, [MT-1L-2]) and downstream (Li-1) observed nutrient loads, calculated as a product of available measured concentration (c, [ML-3]) and discharge (Q, [L3T-1] or runoff (R=Q/A (LT-1), where A is a catchment area, yielding load dimension (MT-1L-2)) along the main river, nutrient inputs and loads can be related between nested and incremental subcatchments as follows:

𝐼𝑖 =𝐿𝑖

𝛼𝑖 (5)

𝛥𝐼𝑖 =𝐿𝑖

𝛥𝛼𝑖− 𝐿𝑖− , (6)

where ΔIi (M/T) and Δαi ( - ) (0 < Δαi ≤ 1 ) are the total nutrient input and delivery factor for an incremental subcatchment i, respectively. Respective nutrient delivery factors αi and Δαi represent total delivery for both point and diffuse sources and define complementary nutrient retention factors (1-αi) and (1-Δαi) (where 0 ≤ 1-Δαi < 1 and 0 ≤ 1-αi < 1). The input to the first nested/incremental subcatchment is L0 and it is equal to zero.

Two contrasting approaches are then applied to iteratively obtain unknown variables from Eq. 5 and 6. Approach 1

assumes more or less constant ΔIi/ΔAi, accounting then for uniform human pressures over a region. Approach 2 assumes more or less constant Δαi/ΔAi, thus accounting for uniform prevailing landscape conditions. The resulting physical reasonableness and divergence or convergence of resulst based on these two approaches would potentially indicate which of the two might have more dominant nutrient loading influence. The values are finally chosen within the constraints of physically possible range 0 < Δαi ≤ 1 and minimizing the variability of incremental loads (Δαi*ΔIi)/ΔAi. The resulting total nutrient inputs Ii and corresponding total modeled loads Li in nested catchments can then be calculated as:

𝐼𝑖 = ∑ (∆𝐼𝑗)𝑗=𝑖𝑗= (7)

𝐿𝑖 = ∆𝛼𝑖(𝐿𝑖− + ∆𝐿𝑖) (8)

The initial input data of river runoff and nutrient concentrations are finally related to resulting loads within the SRC and cross-regionally compared to Baltic Sea considering observed human-related conditions of farmland share and population density

3.4 Evaluation and the use of CMIP5 model data

To extend the hydro-climatic analysis and nutrient loading changes from past to present towards the future evaluation, we use and assess the Coupled Model Intercomparison Project, Phase 5 (CMIP5) direct output of temperature and water fluxes for different catchment scales and over a range of geographical conditions (Meehl et al., 2007; Taylor et al., 2012).

The CMIP5 multimodel ensemble of climate change represents one of the latest research studies in global climate modeling. The models are driven by concentration and different emission scenarios of various atmospheric constituents (e.g., greenhouse gases) and are coupled to biogeochemical components supporting the carbon cycle. They include future projection simulations forced with representative concentration pathways (RCPs). The RCP scenarios


10

include future projections of population growth, technological development and societal responses and assume policy actions. As a novelty to all previous experiments (Meehl et al., 2000, 2005) CMIP5 takes into consideration time-evolving land cover changes (Taylor et al., 2009, 2011, 2012; Moss et al., 2010).

Unlike the many previous studies of CMIP5 model performance that do not consider catchment scale water-aspect or do not explicitly evaluate a catchment´s water balance (Alkama et al., 2013; Deng et al., 2013; Siam et al., 2013), our research uses it as a basic point for providing information on climate model reliability.

For several historical time periods, we compare the ensemble mean output of 22 models with actual observations of P, R, ET and ΔS as derived from Eq. 1. The assumption of ΔS≈0 is relaxed for the model data, as AET in that case is simulated independently of catchment-scale water balance and thus further allows for investigation of the model-implied ΔS as a residual of the remaining water fluxes. We also investigate temperature T as an important measure of climate within river catchments. To minimize the error for the historical estimate input, observed precipitation data are corrected for biases from gauge undercatch (Adam and Lettenmaier, 2003) and orographic effects (Adam et al., 2006). Considering the future projection evaluation, we analyze the results of the best-case scenario RCP2.6 and the worst-case scenario RCP8.5 (Moss et al., 2010) for two time periods, 2010-2039 and 2070-2099, and their changes from the historical period 1961-1990.

For the assessment and comparison of future nutrient load changes, we calculate the change as:

∆𝐿 = 𝑐 ∗ ∆𝑅, (9)

where c is considered constant in time and taken from historical measurements (Table 1); ΔR is derived as CMIP5 model output in 2 different ways: (a) as an ensemble mean of all the 22 models and (b) as an average of the two best performing models in terms of R and ΔR.

3.5 Implementation

The Sava River Catchment (SRC, Figure 1a) as a specific case study of this research has been analyzed in all four papers of the thesis (Table 1).

In Paper I, the hydro-climatic changes and their drivers were analyzed for the entire SRC as well as for its two major subcatchments of Slavonski Brod and Kozluk (Fig 1a.). The SRC results are further compared with nine Swedish basins (Fig. 1b, Destouni et al., 2013), which are climatically very different from the SRC because they are dominated by colder oceanic, humid continental and subarctic climates.

In Paper II, the proposed nutrient screening methodology is tested on the SRC and its seven nested and six incremental subcatchments (Fig. 1b). Due to data limitations, the analysis concentrates on dissolved inorganic nitrogen (DIN) during 1979-1991 and total phosphorus (TP) during 2001-2013. The results of the SRC nested catchments are then compared with the entire Baltic region (Fig. 1b) considering relations of nutrient concentrations with the human-related conditions of population density and farmland share.


11

In Paper III, the Sava River Catchment is part of a regional comparison of a number of temperate catchments in the Northern Hemisphere (Fig. 1c) for which CMIP5 model output is assessed. In half of the catchments, warm temperate climates prevail (Aral Sea, SRC and Greece), and cold climates prevail in the other half (the Arctic, Sweden and Selenga). The investigated catchment areas span from 0.09*106 km2 (SRC) to 10.2*106 km2 (Arctic).

The final paper, Paper IV, deals with the more detailed CMIP5 subset output analysis particularly for the SRC considering both hydro-climatic and nutrient load changes from the historical period 1961-1990 to two future time periods 2010-2039 and 2070-2099 and for both scenarios.

Table 1 contains all the data references used in all four papers of this thesis.

4. RESULTS

4.1 Historical to present hydro-climatic changes and their drivers

We assessed hydro-climatic changes during the most of the 20th century in the SRC and its two major subcatchments of Slavonski Brod and Kozluk (Fig. 1a) by analyzing 20-year running averages of continuous hydro-climatic, land- and water-use data (Fig. 4) in Paper I. The two subcatchments are particularly different in terms of hydropower development that they have undergone since 1950s.

In the both subcatchments we find AETwb/P shifts to a higher level, but with a 2.7 smaller change magnitude in the Slavonski Brod subcatchment in which these changes can also be explained by concurrent climate change in T and P (Fig. 5). Whereas in the Kozluk subcatchment, with 16 times

Table 1. Sources of input datasets used in this work DATA PAPER I PAPER II PAPER III PAPER IV

Studied catchments

SRC, Kozluk, Slavonski Brod, 9

Swedish catchments

SRC and its 13 subcatchments,

Baltic Sea catchments

Aral Sea, Arctic, Greece, Selenga, Sweden and SRC

SRC and its 7 subcatchments

Time periods 1931-1960, 1964-1993, year 2000

1979-1991, 2001-2013

1961-1990, 2010-2039, 2070-2099

T time series CRU 2006; Mitchell and Jones 2005.

Coupled Model Intercomparison

Project CMIP5 (Taylor et al. 2012)

Coupled Model

IntercomparisonProject CMIP5 (Taylor et al.

2012)

P time series

R time series

Levi et al. 2015 (Supplementary table S1)

Levi et al. 2015 (Supplementary table S1), Coupled Model

IntercomparisonProject CMIP5 (Taylor et al.

2012)

DIN Hrvatske vode (2015)

Hrvatske vode (2015) TP

Hydropower related developments

Levi et al. 2015 (Supplementary

table S3)

Population density

SEDAC (1990, 2000)

Farmland share

USGS (2000), European

Comission (2012)


12

Fig. 4 Twenty-year running averages of hydroclimatic and hydropower related changes in the SRC subcathcments: a,c Slavonski Brod. b,d Kozluk. AETTclim and AETBclim have been scaled by the ratio of average AETwb in 1931–1993 and corresponding average AETTclim and AETBclim, respectively. Hydropower development is represented by annual hydropower production per unit catchment area, water surface area and volume of man-made water reservoirs.

200 200

a) b)Slavonski Brod Kozluk

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0

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100

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1900 1920 1940 1960 1980 2000

CV

(R)

Time (year)

x

HE prod run avg MWh/km2

ET/P20 year-window

AETturc/P-20 year

CV(R) 20 year-window

AETBudyko/P 20 year

Hydro

po

wer

pro

duction

(MW

hkm

-2)

540

550

560

570

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630

640

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Time (year)

Scaled ET (Turc)

Scaled ET (Budyko)

ET - data

Eva

po

tra

nsp

iration

(m

mye

ar-

1)

AETTclim

AETBclim

AETwb

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0.85

0.95

0

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Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2)

Time (year)



CV(R)

CV

(R)

0,48

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0,58

0,60

0,62

0

10

20

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Hydropower production

CV(R)

ET/P

Hydro

pow

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pro

duction

(MW

hkm

-2)

Time (year)

AE

Tw

b/P

; C

V(R

)

d)

CV(R)

AETwb/P

Hydropower

0.5

0.51

0.52

0.53

0.54

0.55

0.56

0.57

0.58

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530

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Time (year)

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Scaled ET (Budyko)

ET - data

ET/P

Eva

po

tra

nsp

iration

(m

mye

ar-1

)

AE

Tw

b/P

AE

Tw

b/P

AETwb/P

540

550

560

570

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620

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Time (year)

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Scaled ET (Budyko)

ET - data

Eva

po

tra

nsp

iration

(m

mye

ar-

1)

AETTclim

AETBclim

AETwb

/P

/P

0.30

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0.65

0.70

0

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AE

Tw

b/P

; C

V(R

)

Time (year)


ET/P20 year-window

Scaled Turc/P run 20 year


Scaled Budyko/P run 20 year

Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2)

0.30

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0.70

0

50

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350

1900 1920 1940 1960 1980 2000

AE

Tw

b/P

; C

V(R

)

Time (year)


x

ET/P20 year-window




Hydro

pow

er

pro

duction

(MW

hkm

-2)

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0

50

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1900 1920 1940 1960 1980 2000

AE

Tw

b/P

; C

V(R

)

Time (year)

x


ET/P20 year-window




Hydro

pow

er

pro

duction

(MW

hkm

-2)

350

300

250

1900

200

150

100

50

01920 1940 1960 1980 2000

0.70

0.65

0.60

0.55

0.50

0.45

0.40

0.30

0.35

Hyd

rop

ow

er p

rod

uct

ion

(M

Wh

km

-2)

AET

wb

/P; C

V(R

)

Slavonski Brod

0

200

400

600

800

1000

1200

1400

1600

1800

0

50

100

150

200

250

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350

1900 1920 1940 1960 1980 2000

Volu

me o

f m

an m

adew

ate

r r

eservoirs

( 1

0 -

3km

3)

Hydropow

er p

roduction

(M

Wh

km

-2);

surfa

ce a

rea o

f w

ate

r r

eservoirs (

km

2)

Time (year)

HE prod run avgMWh/km2area cumul run avg

volume cum run avg

0

200

400

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800

1000

1200

1400

1600

1800

0

50

100

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350

1900 1920 1940 1960 1980 2000

Hydropow

er p

roduction

(M

Wh

km

-2);

surfa

ce a

rea o

f w

ate

r r

eservoirs (

km

2)

Time (year)

HE prod run avgMWh/km2

area cumul run avg

volume cum run avgV

olu

me

ofm

an m

adew

ate

rreservoirs

( 1

0 -

3km

3)

0

200

400

600

800

1000

1200

1400

1600

1800

0

50

100

150

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250

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Hydropow

er p

roduction

(M

Wh

km

-2);

surfa

ce a

rea o

f w

ate

r r

eservoirs (

km

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Time (year)


area cumul run avg

volume cum run avg

Volu

me

ofm

an m

adew

ate

rreservoirs

( 1

0 -

3km

3)

0

200

400

600

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1000

1200

1400

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1800

0

50

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250

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350

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Hydropow

er p

roduction

(M

Wh

km

-2);

surfa

ce a

rea o

f w

ate

r r

eservoirs (

km

2)

Time (year)


area cumul run avg

volume cum run avg

Volu

me

ofm

an m

adew

ate

rreservoirs

( 1

0 -

3km

3)

0

200

400

600

800

1000

1200

1400

1600

1800

0

50

100

150

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250

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350

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Volu

me o

f m

an m

adew

ate

r re

serv

oirs

( 10 -

3km

3)

Hydro

pow

er

pro

duction

(MW

hkm

-2);

surf

ace a

rea o

f w

ate

r re

serv

oirs (

km

2)

Time (year)


volume cum run avg

Area

Volume

HydropowerSlavonski Brod Kozluka) b)

350

300

250

1900

200

150

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50

01920 1940 1960 1980 2000

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1000

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400

Hyd

rop

ow

er p

rod

uct

ion

(M

Wh

km

-2)

Surf

ace

area

of

wat

er r

eser

voir

s (k

m-2

)

Vo

lum

e o

f m

an m

ade

rese

rvo

irs

(10-3

km3)

Slavonski Brod


0.30

0.35

0.40

0.45

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0.60

0.65

0.70

0

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100

150

200

250

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350

1900 1920 1940 1960 1980 2000

CV

(R)

Time (year)

x


ET/P20 year-window

AETturc/P-20 year


AETBudyko/P 20 year

Hydro

pow

er

pro

duction

(MW

hkm

-2)

540

550

560

570

580

590

600

610

620

630

640

1900 1920 1940 1960 1980 2000

Time (year)

Scaled ET (Turc)

Scaled ET (Budyko)

ET - data

Eva

po

tra

nsp

iration

(m

mye

ar-

1)

AETTclim

AETBclim

AETwb

0.25

0.35

0.45

0.55

0.65

0.75

0.85

0.95

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2)

Time (year)



CV(R)

CV

(R)

0,48

0,50

0,52

0,54

0,56

0,58

0,60

0,62

0

10

20

30

40

50

60

70

80

90

100

1940 1960 1980 2000


CV(R)

ET/P

Hydro

pow

er

pro

duction

(MW

hkm

-2)

Time (year)

AE

Tw

b/P

; C

V(R

)

d)

CV(R)

AETwb/P

Hydropower

0.5

0.51

0.52

0.53

0.54

0.55

0.56

0.57

0.58

520

530

540

550

560

570

580

590

600

1900 1920 1940 1960 1980 2000

Time (year)

Scaled ET (Turc)

Scaled ET (Budyko)

ET - data

ET/P

Evapotr

anspiration

(m

myear-

1)

AE

Tw

b/P

AE

Tw

b/P

AETwb/P

540

550

560

570

580

590

600

610

620

630

640

1900 1920 1940 1960 1980 2000

Time (year)

Scaled ET (Turc)

Scaled ET (Budyko)

ET - data

Eva

po

tra

nsp

iration

(m

mye

ar-

1)

AETTclim

AETBclim

AETwb

/P

/P

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

AE

Tw

b/P

; C

V(R

)

Time (year)


ET/P20 year-window




Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2)

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

AE

Tw

b/P

; C

V(R

)

Time (year)


x

ET/P20 year-window




Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2)

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

AE

Tw

b/P

; C

V(R

)

Time (year)

x


ET/P20 year-window




Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2)

350

300

250

1900

200

150

100

50

01920 1940 1960 1980 2000

0.70

0.65

0.60

0.55

0.50

0.45

0.40

0.30

0.35

Hyd

rop

ow

er p

rod

uct

ion

(M

Wh

km

-2)

AET

wb

/P; C

V(R

)

Kozluk

0

200

400

600

800

1000

1200

1400

1600

1800

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

Volu

me o

f m

an m

adew

ate

r r

eservoirs

( 1

0 -

3km

3)

Hydropow

er p

roduction

(M

Wh

km

-2);

surfa

ce a

rea o

f w

ate

r r

eservoirs (

km

2)

Time (year)


volume cum run avg

0

200

400

600

800

1000

1200

1400

1600

1800

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

Hydropow

er p

roduction

(M

Wh

km

-2);

surfa

ce a

rea o

f w

ate

r r

eservoirs (

km

2)

Time (year)


area cumul run avg

volume cum run avg

Volu

me

ofm

an m

adew

ate

rreservoirs

( 1

0 -

3km

3)

0

200

400

600

800

1000

1200

1400

1600

1800

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

Hydropow

er p

roduction

(M

Wh

km

-2);

surfa

ce a

rea o

f w

ate

r r

eservoirs (

km

2)

Time (year)


area cumul run avg

volume cum run avg

Volu

me

ofm

an m

adew

ate

rreservoirs

( 1

0 -

3km

3)

0

200

400

600

800

1000

1200

1400

1600

1800

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

Hydropow

er p

roduction

(M

Wh

km

-2);

surfa

ce a

rea o

f w

ate

r r

eservoirs (

km

2)

Time (year)


area cumul run avg

volume cum run avg

Volu

me

ofm

an m

adew

ate

rreservoirs

( 1

0 -

3km

3)

0

200

400

600

800

1000

1200

1400

1600

1800

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

Volu

me o

f m

an m

adew

ate

r re

serv

oirs

( 10 -

3km

3)

Hydro

pow

er

pro

duction

(MW

hkm

-2);

surf

ace a

rea o

f w

ate

r re

serv

oirs (

km

2)

Time (year)


volume cum run avg

Area

Volume


350

300

250

1900

200

150

100

50

01920 1940 1960 1980 2000

1800

1600

1400

1200

1000

800

600

0

400

Hyd

rop

ow

er p

rod

uct

ion

(M

Wh

km

-2)

Surf

ace

area

of

wat

er r

eser

voir

s (k

m-2

)

Vo

lum

e o

f m

an m

ade

rese

rvo

irs

(10-3

km3)

Kozluk

a b

c dTime (year) Time (year)

Time (year) Time (year)


0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

CV

(R)

Time (year)

x


ET/P20 year-window

AETturc/P-20 year


AETBudyko/P 20 year

Hydro

pow

er

pro

duction

(MW

hkm

-2)

540

550

560

570

580

590

600

610

620

630

640

1900 1920 1940 1960 1980 2000

Time (year)

Scaled ET (Turc)

Scaled ET (Budyko)

ET - data

Eva

po

tra

nsp

iration

(m

mye

ar-

1)

AETTclim

AETBclim

AETwb

0.25

0.35

0.45

0.55

0.65

0.75

0.85

0.95

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2)

Time (year)



CV(R)

CV

(R)

0,48

0,50

0,52

0,54

0,56

0,58

0,60

0,62

0

10

20

30

40

50

60

70

80

90

100

1940 1960 1980 2000


CV(R)

ET/P

Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2)

Time (year)

AE

Tw

b/P

; C

V(R

)

d)

CV(R)

AETwb/P

Hydropower

0.5

0.51

0.52

0.53

0.54

0.55

0.56

0.57

0.58

520

530

540

550

560

570

580

590

600

1900 1920 1940 1960 1980 2000

Time (year)

Scaled ET (Turc)

Scaled ET (Budyko)

ET - data

ET/P

Evapotr

anspiration (

mm

year-

1)

AE

Tw

b/P

AE

Tw

b/P

AETwb/P

540

550

560

570

580

590

600

610

620

630

640

1900 1920 1940 1960 1980 2000

Time (year)

Scaled ET (Turc)

Scaled ET (Budyko)

ET - data

Eva

po

tra

nsp

iration

(m

mye

ar-

1)

AETTclim

AETBclim

AETwb

/P

/P

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

AE

Tw

b/P

; C

V(R

)

Time (year)


ET/P20 year-window




Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2)

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

AE

Tw

b/P

; C

V(R

)

Time (year)


x

ET/P20 year-window




Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2)

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

AE

Tw

b/P

; C

V(R

)

Time (year)

x


ET/P20 year-window




Hydro

pow

er

pro

duction

(MW

hkm

-2) AETTclim/P

AETwb/P

Hydropower

CV(R)

AETBclim/P


0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

CV

(R)

Time (year)

x


ET/P20 year-window

AETturc/P-20 year


AETBudyko/P 20 year

Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2)

540

550

560

570

580

590

600

610

620

630

640

1900 1920 1940 1960 1980 2000

Time (year)

Scaled ET (Turc)

Scaled ET (Budyko)

ET - data

Evapotr

anspiration (

mm

year-

1)

AETTclim

AETBclim

AETwb

0.25

0.35

0.45

0.55

0.65

0.75

0.85

0.95

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2)

Time (year)



CV(R)

CV

(R)

0,48

0,50

0,52

0,54

0,56

0,58

0,60

0,62

0

10

20

30

40

50

60

70

80

90

100

1940 1960 1980 2000


CV(R)

ET/P

Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2)

Time (year)

AE

Tw

b/P

; C

V(R

)

d)

CV(R)

AETwb/P

Hydropower

0.5

0.51

0.52

0.53

0.54

0.55

0.56

0.57

0.58

520

530

540

550

560

570

580

590

600

1900 1920 1940 1960 1980 2000

Time (year)

Scaled ET (Turc)

Scaled ET (Budyko)

ET - data

ET/P

Evapotr

anspiration (

mm

year-1

)

AE

Tw

b/P

AE

Tw

b/P

AETwb/P

540

550

560

570

580

590

600

610

620

630

640

1900 1920 1940 1960 1980 2000

Time (year)

Scaled ET (Turc)

Scaled ET (Budyko)

ET - data

Evapotr

anspiration (

mm

year-

1)

AETTclim

AETBclim

AETwb

/P

/P

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

AE

Tw

b/P

; C

V(R

)

Time (year)


ET/P20 year-window




Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2)

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

AE

Tw

b/P

; C

V(R

)

Time (year)


x

ET/P20 year-window




Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2)

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

AE

Tw

b/P

; C

V(R

)

Time (year)

x


ET/P20 year-window




Hydro

pow

er

pro

duction

(MW

hkm

-2) AETTclim/P

AETwb/P

Hydropower

CV(R)

AETBclim/P


0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

CV

(R)

Time (year)

x


ET/P20 year-window

AETturc/P-20 year


AETBudyko/P 20 year

Hydro

pow

er

pro

duction

(MW

hkm

-2)

540

550

560

570

580

590

600

610

620

630

640

1900 1920 1940 1960 1980 2000

Time (year)

Scaled ET (Turc)

Scaled ET (Budyko)

ET - data

Eva

po

tra

nsp

iration

(m

mye

ar-

1)

AETTclim

AETBclim

AETwb

0.25

0.35

0.45

0.55

0.65

0.75

0.85

0.95

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2)

Time (year)



CV(R)

CV

(R)

0,48

0,50

0,52

0,54

0,56

0,58

0,60

0,62

0

10

20

30

40

50

60

70

80

90

100

1940 1960 1980 2000


CV(R)

ET/P

Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2)

Time (year)

AE

Tw

b/P

; C

V(R

)

d)

CV(R)

AETwb/P

Hydropower

0.5

0.51

0.52

0.53

0.54

0.55

0.56

0.57

0.58

520

530

540

550

560

570

580

590

600

1900 1920 1940 1960 1980 2000

Time (year)

Scaled ET (Turc)

Scaled ET (Budyko)

ET - data

ET/P

Evapotr

anspiration (

mm

year-

1)

AE

Tw

b/P

AE

Tw

b/P

AETwb/P

540

550

560

570

580

590

600

610

620

630

640

1900 1920 1940 1960 1980 2000

Time (year)

Scaled ET (Turc)

Scaled ET (Budyko)

ET - data

Eva

po

tra

nsp

iration

(m

mye

ar-

1)

AETTclim

AETBclim

AETwb

/P

/P

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

AE

Tw

b/P

; C

V(R

)

Time (year)


ET/P20 year-window




Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2)

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

AE

Tw

b/P

; C

V(R

)

Time (year)


x

ET/P20 year-window




Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2)

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

AE

Tw

b/P

; C

V(R

)

Time (year)

x


ET/P20 year-window




Hydro

pow

er

pro

duction

(MW

hkm

-2) AETTclim/P

AETwb/P

Hydropower

CV(R)

AETBclim/P

0

10

20

30

40

50

60

1900 1920 1940 1960 1980 2000

Cultivated land area

Pasture area

Boreal forest area

Temperate mixed forestarea

Temperate deciduous forestarea

Rela

tive

are

a c

ove

rag

e(%

)

Time (year)

0

1

2

3

4

5

6

7

8

9

10

11

200

400

600

800

1000

1200

1400

1600

1800

1900 1920 1940 1960 1980 2000

P a

nd R

(m

myear-

1)

Time (year)

P (mm/year)

P 20 year-window

R (mm/year)

R 20 year-window

T (C)

T 20 year-window

T ( C

)

a) b)

c) d)

-

1

2

3

4

5

6

7

8

9

10

11

200

300

400

500

600

700

800

900

1000

1100

1200

1300

1400

1500

1600

1700

1800

1900 1920 1940 1960 1980 2000

P, R

an

d A

ET

wb

(mm

ye

ar-

1)

Time (year)

Annual P

20-year running average P

Annual R

20-year running average R

Mean annual T

20-year running average T

T ( C

)

0.5

0.51

0.52

0.53

0.54

0.55

0.56

0.57

0.58

520

530

540

550

560

570

580

590

600

1900 1920 1940 1960 1980 2000

Time (year)

Scaled ET (Turc)

Scaled ET (Budyko)

ET - data

ET/P

Eva

po

tra

nsp

iration

(m

mye

ar-

1)

AE

Tw

b/P

AE

Tw

b/P

AETwb/P

540

550

560

570

580

590

600

610

620

630

640

1900 1920 1940 1960 1980 2000

Time (year)

Scaled ET (Turc)

Scaled ET (Budyko)

ET - data

Eva

po

tra

nsp

iration

(m

mye

ar-

1)

AETTclim

AETBclim

AETwb

0

10

20

30

40

50

60

1900 1920 1940 1960 1980 2000

Cultivated land area

Pasture area

Boreal forest area

Temperate mixed forestarea

Temperate deciduous forestarea

Re

lative

are

a c

ove

rag

e (

%)

Time (year)

0

1

2

3

4

5

6

7

8

9

10

11

200

400

600

800

1000

1200

1400

1600

1800

1900 1920 1940 1960 1980 2000

P, R

an

d T

wb

(mm

ye

ar-

1)

Time (year)

P (mm/year)

P 20 year-window

R (mm/year)

R 20 year-window

T (C)

T 20 year-window

T ( C

)

0

200

400

600

800

1000

1200

1400

1600

1800

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

Hyd

rop

ow

er

pro

ductio

n(M

Wh

km

-2);

su

rface

are

a o

f w

ate

r re

serv

oir

s (

km

2)

Time (year)

HE prodrun avgMWh/km2

Vo

lum

eofm

an m

ade

wa

ter

reserv

oir

s

( 1

0 -

3km

3)

0

200

400

600

800

1000

1200

1400

1600

1800

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

Vo

lum

e o

f m

an m

ade

wa

ter

reserv

oirs

( 1

0 -3

km

3)

Hyd

rop

ow

er

pro

ductio

n(M

Wh

km

-2);

su

rface

are

a o

f w

ate

r re

serv

oirs (

km

2)

Time (year)


volume cum run avg

Area

Volume

Hydropower

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

AE

Tw

b/P

; C

V(R

)

Time (year)


ET/P20 year-window




Hydro

pow

er

pro

duction

(MW

hkm

-2)

0.5

0.52

0.54

0.56

0.58

0.6

520

530

540

550

560

570

580

590

600

1900 1920 1940 1960 1980 2000

Eva

potr

ansp

iratio

n (

mm

ye

ar-

1)

Time (year)

AETturc 20 year-window

AETbudyko 20 year-window

ET 20 year-window

ET/P20 year-window

AE

Tw

b/P

0.5

0.52

0.54

0.56

0.58

0.6

520

530

540

550

560

570

580

590

600

1900 1920 1940 1960 1980 2000

Evapotr

anspiration (

mm

year-

1)

Time (year)

AETturc 20 year-window

AETbudyko 20 year-window

ET 20 year-window

ET/P20 year-window

AE

Tw

b/P


0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

CV

(R)

Time (year)

x


ET/P20 year-window

AETturc/P-20 year


AETBudyko/P 20 year

Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2)

540

550

560

570

580

590

600

610

620

630

640

1900 1920 1940 1960 1980 2000

Time (year)

Scaled ET (Turc)

Scaled ET (Budyko)

ET - data

Eva

po

tra

nsp

iration

(m

mye

ar-

1)

AETTclim

AETBclim

AETwb

0.25

0.35

0.45

0.55

0.65

0.75

0.85

0.95

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2)

Time (year)



CV(R)

CV

(R)

0,48

0,50

0,52

0,54

0,56

0,58

0,60

0,62

0

10

20

30

40

50

60

70

80

90

100

1940 1960 1980 2000


CV(R)

ET/P

Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2)

Time (year)

AE

Tw

b/P

; C

V(R

)

d)

CV(R)

AETwb/P

Hydropower

0.5

0.51

0.52

0.53

0.54

0.55

0.56

0.57

0.58

520

530

540

550

560

570

580

590

600

1900 1920 1940 1960 1980 2000

Time (year)

Scaled ET (Turc)

Scaled ET (Budyko)

ET - data

ET/P

Evapotr

anspiration (

mm

year-

1)

AE

Tw

b/P

AE

Tw

b/P

AETwb/P

540

550

560

570

580

590

600

610

620

630

640

1900 1920 1940 1960 1980 2000

Time (year)

Scaled ET (Turc)

Scaled ET (Budyko)

ET - data

Eva

po

tra

nsp

iration

(m

mye

ar-

1)

AETTclim

AETBclim

AETwb

/P

/P

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

AE

Tw

b/P

; C

V(R

)

Time (year)


ET/P20 year-window




Hydro

pow

er

pro

duction

(MW

hkm

-2)

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

AE

Tw

b/P

; C

V(R

)

Time (year)


x

ET/P20 year-window




Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2)

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

AE

Tw

b/P

; C

V(R

)

Time (year)

x


ET/P20 year-window




Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2) AETTclim/P

AETwb/P

Hydropower

CV(R)

AETBclim/P


0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

CV

(R)

Time (year)

x


ET/P20 year-window

AETturc/P-20 year


AETBudyko/P 20 year

Hydro

pow

er

pro

duction

(MW

hkm

-2)

540

550

560

570

580

590

600

610

620

630

640

1900 1920 1940 1960 1980 2000

Time (year)

Scaled ET (Turc)

Scaled ET (Budyko)

ET - data

Evapotr

anspiration (

mm

year-

1)

AETTclim

AETBclim

AETwb

0.25

0.35

0.45

0.55

0.65

0.75

0.85

0.95

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2)

Time (year)



CV(R)

CV

(R)

0,48

0,50

0,52

0,54

0,56

0,58

0,60

0,62

0

10

20

30

40

50

60

70

80

90

100

1940 1960 1980 2000


CV(R)

ET/P

Hydro

pow

er

pro

duction

(MW

hkm

-2)

Time (year)

AE

Tw

b/P

; C

V(R

)

d)

CV(R)

AETwb/P

Hydropower

0.5

0.51

0.52

0.53

0.54

0.55

0.56

0.57

0.58

520

530

540

550

560

570

580

590

600

1900 1920 1940 1960 1980 2000

Time (year)

Scaled ET (Turc)

Scaled ET (Budyko)

ET - data

ET/P

Evapotr

anspiration (

mm

year-1

)

AE

Tw

b/P

AE

Tw

b/P

AETwb/P

540

550

560

570

580

590

600

610

620

630

640

1900 1920 1940 1960 1980 2000

Time (year)

Scaled ET (Turc)

Scaled ET (Budyko)

ET - data

Evapotr

anspiration (

mm

year-

1)

AETTclim

AETBclim

AETwb

/P

/P

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

AE

Tw

b/P

; C

V(R

)

Time (year)


ET/P20 year-window




Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2)

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

AE

Tw

b/P

; C

V(R

)

Time (year)


x

ET/P20 year-window




Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2)

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

AE

Tw

b/P

; C

V(R

)

Time (year)

x


ET/P20 year-window




Hyd

rop

ow

er

pro

duction

(MW

hkm

-2) AETTclim/P

AETwb/P

Hydropower

CV(R)

AETBclim/P


0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

CV

(R)

Time (year)

x


ET/P20 year-window

AETturc/P-20 year


AETBudyko/P 20 year

Hydro

pow

er

pro

duction

(MW

hkm

-2)

540

550

560

570

580

590

600

610

620

630

640

1900 1920 1940 1960 1980 2000

Time (year)

Scaled ET (Turc)

Scaled ET (Budyko)

ET - data

Eva

po

tra

nsp

iration

(m

mye

ar-

1)

AETTclim

AETBclim

AETwb

0.25

0.35

0.45

0.55

0.65

0.75

0.85

0.95

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2)

Time (year)



CV(R)

CV

(R)

0,48

0,50

0,52

0,54

0,56

0,58

0,60

0,62

0

10

20

30

40

50

60

70

80

90

100

1940 1960 1980 2000


CV(R)

ET/P

Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2)

Time (year)

AE

Tw

b/P

; C

V(R

)

d)

CV(R)

AETwb/P

Hydropower

0.5

0.51

0.52

0.53

0.54

0.55

0.56

0.57

0.58

520

530

540

550

560

570

580

590

600

1900 1920 1940 1960 1980 2000

Time (year)

Scaled ET (Turc)

Scaled ET (Budyko)

ET - data

ET/P

Evapotr

anspiration (

mm

year-1

)

AE

Tw

b/P

AE

Tw

b/P

AETwb/P

540

550

560

570

580

590

600

610

620

630

640

1900 1920 1940 1960 1980 2000

Time (year)

Scaled ET (Turc)

Scaled ET (Budyko)

ET - data

Eva

po

tra

nsp

iration

(m

mye

ar-

1)

AETTclim

AETBclim

AETwb

/P

/P

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

AE

Tw

b/P

; C

V(R

)

Time (year)


ET/P20 year-window




Hydro

pow

er

pro

duction

(MW

hkm

-2)

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

AE

Tw

b/P

; C

V(R

)Time (year)


x

ET/P20 year-window




Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2)

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

AE

Tw

b/P

; C

V(R

)

Time (year)

x


ET/P20 year-window




Hydro

pow

er

pro

duction

(MW

hkm

-2) AETTclim/P

AETwb/P

Hydropower

CV(R)

AETBclim/P

0

200

400

600

800

1000

1200

1400

1600

1800

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

Volu

me o

f m

an m

adew

ate

r re

serv

oirs

( 10 -3

km

3)

Hydro

pow

er

pro

duction

(MW

hkm

-2);

surf

ace a

rea o

f w

ate

r re

serv

oirs (

km

2)

Time (year)


volume cum run avg

0

200

400

600

800

1000

1200

1400

1600

1800

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2);

su

rfa

ce

are

a o

f w

ate

r re

se

rvo

irs (

km

2)

Time (year)


area cumul run avg

volume cum run avg

Vo

lum

eofm

an

ma

de

wa

ter

rese

rvo

irs

( 1

0 -

3km

3)

0

200

400

600

800

1000

1200

1400

1600

1800

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2);

su

rfa

ce

are

a o

f w

ate

r re

se

rvo

irs (

km

2)

Time (year)


area cumul run avg

volume cum run avg

Vo

lum

eofm

an

ma

de

wa

ter

rese

rvo

irs

( 1

0 -

3km

3)

0

200

400

600

800

1000

1200

1400

1600

1800

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2);

su

rfa

ce

are

a o

f w

ate

r re

se

rvo

irs (

km

2)

Time (year)


area cumul run avg

volume cum run avg

Vo

lum

eofm

an

ma

de

wa

ter

rese

rvo

irs

( 1

0 -

3km

3)

0

200

400

600

800

1000

1200

1400

1600

1800

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

Volu

me

of m

an m

adew

ater

res

ervo

irs( 1

0 -3

km3 )

Hyd

ropo

wer

pro

duct

ion

(MW

hkm

-2);

surfa

ce a

rea

of w

ater

rese

rvoi

rs (

km2 )

Time (year)


volume cum run avg

Area

Volume


Area

Volume

0

200

400

600

800

1000

1200

1400

1600

1800

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

Vo

lum

e o

f m

an m

ade

wa

ter

reserv

oirs

( 1

0 -3

km

3)

Hyd

rop

ow

er

pro

ductio

n(M

Wh

km

-2);

su

rface

are

a o

f w

ate

r re

serv

oirs (

km

2)

Time (year)


volume cum run avg

0

200

400

600

800

1000

1200

1400

1600

1800

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2);

su

rfa

ce

are

a o

f w

ate

r re

se

rvo

irs (

km

2)

Time (year)


area cumul run avg

volume cum run avg

Vo

lum

eofm

an

ma

de

wa

ter

rese

rvo

irs

( 1

0 -

3km

3)

0

200

400

600

800

1000

1200

1400

1600

1800

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2);

su

rfa

ce

are

a o

f w

ate

r re

se

rvo

irs (

km

2)

Time (year)


area cumul run avg

volume cum run avg

Vo

lum

eofm

an

ma

de

wa

ter

rese

rvo

irs

( 1

0 -

3km

3)

0

200

400

600

800

1000

1200

1400

1600

1800

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

Hyd

rop

ow

er

pro

du

ctio

n(M

Wh

km

-2);

su

rfa

ce

are

a o

f w

ate

r re

se

rvo

irs (

km

2)

Time (year)


area cumul run avg

volume cum run avg

Vo

lum

eofm

an

ma

de

wa

ter

rese

rvo

irs

( 1

0 -

3km

3)

0

200

400

600

800

1000

1200

1400

1600

1800

0

50

100

150

200

250

300

350

1900 1920 1940 1960 1980 2000

Volu

me

of m

an m

adew

ater

res

ervo

irs( 1

0 -3

km3 )

Hyd

ropo

wer

pro

duct

ion

(MW

hkm

-2);

surfa

ce a

rea

of w

ater

rese

rvoi

rs (

km2 )

Time (year)


volume cum run avg

Area

Volume


Area

Volume

Fig. 5 Change in hydroclimatic variables and hydropower production development in the SRC and its subcatchments. a Temperature (T), precipitation (P), runoff (R). b Relative actual evapotranspiration (AETwb/P), coefficient of variation of monthly runoff CV(R) and developed hydropower production per catchment area (HP). Error bars show 95 % confidence intervals for the hydroclimatic and hydropower changes

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

-0,40

-0,35

-0,30

-0,25

-0,20

-0,15

-0,10

-0,05

0,00

0,05

0,10

0,15

0,20

0,25

0,30

0,35

0,40

ΔT

( C

)

ΔP

and

ΔR

(m

m y

ear-

1)

0.40

0.30

ΔT (°

C)

0.20

0.10

0.00

-0.10

-0.20

-0.30

-0.40

120

80

ΔP and ΔR(m

m yea

r-1)

40

0

-40

-80

-120

Slavonski Brod KozlukSRC

-280

-240

-200

-160

-120

-80

-40

0

40

80

120

160

200

240

280

-0,15

-0,13

-0,11

-0,09

-0,07

-0,05

-0,03

-0,01

0,01

0,03

0,05

0,07

0,09

0,11

0,13

0,15Δ

HP

(M

Wh

km

-2)

Δ(A

ET

wb/P

) and Δ

CV

(R)

0.15

0.11

Δ(A

ETw

b/P

) an

d Δ

CV

(R)

0.07

0.01-0.01

-0.05

-0.09

-0.13

0.13

0.09

0.050.03

-0.03

-0.07

-0.11

-0.15

280

200

120

40

-40

-120

-200

-280

ΔHP (

MW

h k

m-2

)Slavonski Brod KozlukSRC

-250

-200

-150

-100

-50

0

50

100

150

200

250

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11

0.13Δ(AETwb/P)ΔCV(R )

grrr

brr

ΔHP

SRCKozluk Slavonski

Brod

ΔHP (MWhkm-2

)

Δ(AETwb/P) and ΔCV(R)

c)

a)

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

ΔT ( C)

ΔP (mm yr-1)

ΔR (mm yr-1)

( C)

(mmyear -1)

Sremska

MitrovicaKozluk Slavonski

Broda)

(mm yr-1)

(mm yr-1)

-0.15

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11

0.13

0.15

Δ(AETwb/P)

ΔCV(R )

Sremska


Brodb)

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

ΔT ( C)

ΔP (mm yr-1)

ΔR (mm yr-1)

( C)

(mmyear -1)

Sremska


Broda)

(mm yr-1)

(mm yr-1)

-0,15

-0,13

-0,11

-0,09

-0,07

-0,05

-0,03

-0,01

0,01

0,03

0,05

0,07

0,09

0,11

0,13

0,15

Δ(AETwb/P)

ΔCV(R )

SRBKozluk Slavonski

Brod

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30ΔT (°C)

ΔP (mm yr-1)

SRCKozluk Slavonski

Brod

ΔT ( C)

ΔP and ΔR (mm year-1)

-250

-200

-150

-100

-50

0

50

100

150

200

250

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11


grrr

brr

ΔHP

SRCKozluk Slavonski

Brod

ΔHP (MWhkm-2

)


b)

a)

b)

a)ΔT

ΔP

ΔR

Δ(AETwb/P)

ΔCV(R)

ΔHP

-250

-200

-150

-100

-50

0

50

100

150

200

250

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11


grrr

brr

ΔHP

SRCKozluk Slavonski

Brod

ΔHP (MWhkm-2

)


c)

a)

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

ΔT ( C)

ΔP (mm yr-1)

ΔR (mm yr-1)

( C)

(mmyear -1)

Sremska


Broda)

(mm yr-1)

(mm yr-1)

-0.15

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11

0.13

0.15

Δ(AETwb/P)

ΔCV(R )

Sremska


Brodb)

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

ΔT ( C)

ΔP (mm yr-1)

ΔR (mm yr-1)

( C)

(mmyear -1)

Sremska


Broda)

(mm yr-1)

(mm yr-1)

-0,15

-0,13

-0,11

-0,09

-0,07

-0,05

-0,03

-0,01

0,01

0,03

0,05

0,07

0,09

0,11

0,13

0,15

Δ(AETwb/P)

ΔCV(R )

SRBKozluk Slavonski

Brod

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30ΔT (°C)

ΔP (mm yr-1)

SRCKozluk Slavonski

Brod

ΔT ( C)


-250

-200

-150

-100

-50

0

50

100

150

200

250

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11


grrr

brr

ΔHP

SRCKozluk Slavonski

Brod

ΔHP (MWhkm-2

)


b)

a)

b)

a)ΔT

ΔP

ΔR

Δ(AETwb/P)

ΔCV(R)

ΔHP

-250

-200

-150

-100

-50

0

50

100

150

200

250

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11


grrr

brr

ΔHP

SRCKozluk Slavonski

Brod

ΔHP (MWhkm-2

)


c)

a)

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

ΔT ( C)

ΔP (mm yr-1)

ΔR (mm yr-1)

( C)

(mmyear -1)

Sremska


Broda)

(mm yr-1)

(mm yr-1)

-0.15

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11

0.13

0.15

Δ(AETwb/P)

ΔCV(R )

Sremska


Brodb)

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

ΔT ( C)

ΔP (mm yr-1)

ΔR (mm yr-1)

( C)

(mmyear -1)

Sremska


Broda)

(mm yr-1)

(mm yr-1)

-0,15

-0,13

-0,11

-0,09

-0,07

-0,05

-0,03

-0,01

0,01

0,03

0,05

0,07

0,09

0,11

0,13

0,15

Δ(AETwb/P)

ΔCV(R )

SRBKozluk Slavonski

Brod

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30ΔT (°C)

ΔP (mm yr-1)

SRCKozluk Slavonski

Brod

ΔT ( C)


-250

-200

-150

-100

-50

0

50

100

150

200

250

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11

0.13Δ(AETwb/P)

ΔCV(R )

grrr

brr

ΔHP

SRCKozluk Slavonski

Brod

ΔHP (MWhkm-2

)


b)

a)

b)

a)ΔT

ΔP

ΔR

Δ(AETwb/P)

ΔCV(R)

ΔHP

-250

-200

-150

-100

-50

0

50

100

150

200

250

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11


grrr

brr

ΔHP

SRCKozluk Slavonski

Brod

ΔHP (MWhkm-2

)


c)

a)

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

ΔT ( C)

ΔP (mm yr-1)

ΔR (mm yr-1)

( C)

(mmyear -1)

Sremska


Broda)

(mm yr-1)

(mm yr-1)

-0.15

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11

0.13

0.15

Δ(AETwb/P)

ΔCV(R )

Sremska


Brodb)

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

ΔT ( C)

ΔP (mm yr-1)

ΔR (mm yr-1)

( C)

(mmyear -1)

Sremska


Broda)

(mm yr-1)

(mm yr-1)

-0,15

-0,13

-0,11

-0,09

-0,07

-0,05

-0,03

-0,01

0,01

0,03

0,05

0,07

0,09

0,11

0,13

0,15

Δ(AETwb/P)

ΔCV(R )

SRBKozluk Slavonski

Brod

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30ΔT (°C)

ΔP (mm yr-1)

SRCKozluk Slavonski

Brod

ΔT ( C)


-250

-200

-150

-100

-50

0

50

100

150

200

250

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11


grrr

brr

ΔHP

SRCKozluk Slavonski

Brod

ΔHP (MWhkm-2

)


b)

a)

b)

a)ΔT

ΔP

ΔR

Δ(AETwb/P)

ΔCV(R)

ΔHP

-250

-200

-150

-100

-50

0

50

100

150

200

250

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11


grrr

brr

ΔHP

SRCKozluk Slavonski

Brod

ΔHP (MWhkm-2

)


c)

a)

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

ΔT ( C)

ΔP (mm yr-1)

ΔR (mm yr-1)

( C)

(mmyear -1)

Sremska


Broda)

(mm yr-1)

(mm yr-1)

-0.15

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11

0.13

0.15

Δ(AETwb/P)

ΔCV(R )

Sremska


Brodb)

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

ΔT ( C)

ΔP (mm yr-1)

ΔR (mm yr-1)

( C)

(mmyear -1)

Sremska


Broda)

(mm yr-1)

(mm yr-1)

-0,15

-0,13

-0,11

-0,09

-0,07

-0,05

-0,03

-0,01

0,01

0,03

0,05

0,07

0,09

0,11

0,13

0,15

Δ(AETwb/P)

ΔCV(R )

SRBKozluk Slavonski

Brod

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30ΔT (°C)

ΔP (mm yr-1)

SRCKozluk Slavonski

Brod

ΔT ( C)


-250

-200

-150

-100

-50

0

50

100

150

200

250

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11

0.13Δ(AETwb/P)

ΔCV(R )

grrr

brr

ΔHP

SRCKozluk Slavonski

Brod

ΔHP (MWhkm-2

)


b)

a)

b)

a)ΔT

ΔP

ΔR

Δ(AETwb/P)

ΔCV(R)

ΔHP

-250

-200

-150

-100

-50

0

50

100

150

200

250

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11


grrr

brr

ΔHP

SRCKozluk Slavonski

Brod

ΔHP (MWhkm-2

)


c)

a)

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

ΔT ( C)

ΔP (mm yr-1)

ΔR (mm yr-1)

( C)

(mmyear -1)

Sremska


Broda)

(mm yr-1)

(mm yr-1)

-0.15

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11

0.13

0.15

Δ(AETwb/P)

ΔCV(R )

Sremska


Brodb)

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

ΔT ( C)

ΔP (mm yr-1)

ΔR (mm yr-1)

( C)

(mmyear -1)

Sremska


Broda)

(mm yr-1)

(mm yr-1)

-0,15

-0,13

-0,11

-0,09

-0,07

-0,05

-0,03

-0,01

0,01

0,03

0,05

0,07

0,09

0,11

0,13

0,15

Δ(AETwb/P)

ΔCV(R )

SRBKozluk Slavonski

Brod

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30ΔT (°C)

ΔP (mm yr-1)

SRCKozluk Slavonski

Brod

ΔT ( C)


-250

-200

-150

-100

-50

0

50

100

150

200

250

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11


grrr

brr

ΔHP

SRCKozluk Slavonski

Brod

ΔHP (MWhkm-2

)


b)

a)

b)

a)ΔT

ΔP

ΔR

Δ(AETwb/P)

ΔCV(R)

ΔHP

a b

-250

-200

-150

-100

-50

0

50

100

150

200

250

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11


grrr

brr

ΔHP

SRCKozluk Slavonski

Brod

ΔHP (MWhkm-2

)


c)

a)

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

ΔT ( C)

ΔP (mm yr-1)

ΔR (mm yr-1)

( C)

(mmyear -1)

Sremska


Broda)

(mm yr-1)

(mm yr-1)

-0.15

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11

0.13

0.15

Δ(AETwb/P)

ΔCV(R )

Sremska


Brodb)

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

ΔT ( C)

ΔP (mm yr-1)

ΔR (mm yr-1)

( C)

(mmyear -1)

Sremska


Broda)

(mm yr-1)

(mm yr-1)

-0,15

-0,13

-0,11

-0,09

-0,07

-0,05

-0,03

-0,01

0,01

0,03

0,05

0,07

0,09

0,11

0,13

0,15

Δ(AETwb/P)

ΔCV(R )

SRBKozluk Slavonski

Brod

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30ΔT (°C)

ΔP (mm yr-1)

SRCKozluk Slavonski

Brod

ΔT ( C)


-250

-200

-150

-100

-50

0

50

100

150

200

250

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11


grrr

brr

ΔHP

SRCKozluk Slavonski

Brod

ΔHP (MWhkm-2

)


b)

a)

b)

a)ΔT

ΔP

ΔR

Δ(AETwb/P)

ΔCV(R)

ΔHP

-250

-200

-150

-100

-50

0

50

100

150

200

250

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11


grrr

brr

ΔHP

SRCKozluk Slavonski

Brod

ΔHP (MWhkm-2

)


c)

a)

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

ΔT ( C)

ΔP (mm yr-1)

ΔR (mm yr-1)

( C)

(mmyear -1)

Sremska


Broda)

(mm yr-1)

(mm yr-1)

-0.15

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11

0.13

0.15

Δ(AETwb/P)

ΔCV(R )

Sremska


Brodb)

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

ΔT ( C)

ΔP (mm yr-1)

ΔR (mm yr-1)

( C)

(mmyear -1)

Sremska


Broda)

(mm yr-1)

(mm yr-1)

-0,15

-0,13

-0,11

-0,09

-0,07

-0,05

-0,03

-0,01

0,01

0,03

0,05

0,07

0,09

0,11

0,13

0,15

Δ(AETwb/P)

ΔCV(R )

SRBKozluk Slavonski

Brod

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30ΔT (°C)

ΔP (mm yr-1)

SRCKozluk Slavonski

Brod

ΔT ( C)


-250

-200

-150

-100

-50

0

50

100

150

200

250

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11


grrr

brr

ΔHP

SRCKozluk Slavonski

Brod

ΔHP (MWhkm-2

)


b)

a)

b)

a)ΔT

ΔP

ΔR

Δ(AETwb/P)

ΔCV(R)

ΔHP

-250

-200

-150

-100

-50

0

50

100

150

200

250

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11


grrr

brr

ΔHP

SRCKozluk Slavonski

Brod

ΔHP (MWhkm-2

)


c)

a)

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

ΔT ( C)

ΔP (mm yr-1)

ΔR (mm yr-1)

( C)

(mmyear -1)

Sremska


Broda)

(mm yr-1)

(mm yr-1)

-0.15

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11

0.13

0.15

Δ(AETwb/P)

ΔCV(R )

Sremska


Brodb)

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

ΔT ( C)

ΔP (mm yr-1)

ΔR (mm yr-1)

( C)

(mmyear -1)

Sremska


Broda)

(mm yr-1)

(mm yr-1)

-0,15

-0,13

-0,11

-0,09

-0,07

-0,05

-0,03

-0,01

0,01

0,03

0,05

0,07

0,09

0,11

0,13

0,15

Δ(AETwb/P)

ΔCV(R )

SRBKozluk Slavonski

Brod

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30ΔT (°C)

ΔP (mm yr-1)

SRCKozluk Slavonski

Brod

ΔT ( C)


-250

-200

-150

-100

-50

0

50

100

150

200

250

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11

0.13Δ(AETwb/P)

ΔCV(R )

grrr

brr

ΔHP

SRCKozluk Slavonski

Brod

ΔHP (MWhkm-2

)


b)

a)

b)

a)ΔT

ΔP

ΔR

Δ(AETwb/P)

ΔCV(R)

ΔHP

-250

-200

-150

-100

-50

0

50

100

150

200

250

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11


grrr

brr

ΔHP

SRCKozluk Slavonski

Brod

ΔHP (MWhkm-2

)


c)

a)

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

ΔT ( C)

ΔP (mm yr-1)

ΔR (mm yr-1)

( C)

(mmyear -1)

Sremska


Broda)

(mm yr-1)

(mm yr-1)

-0.15

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11

0.13

0.15

Δ(AETwb/P)

ΔCV(R )

Sremska


Brodb)

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

ΔT ( C)

ΔP (mm yr-1)

ΔR (mm yr-1)

( C)

(mmyear -1)

Sremska


Broda)

(mm yr-1)

(mm yr-1)

-0,15

-0,13

-0,11

-0,09

-0,07

-0,05

-0,03

-0,01

0,01

0,03

0,05

0,07

0,09

0,11

0,13

0,15

Δ(AETwb/P)

ΔCV(R )

SRBKozluk Slavonski

Brod

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30ΔT (°C)

ΔP (mm yr-1)

SRCKozluk Slavonski

Brod

ΔT ( C)


-250

-200

-150

-100

-50

0

50

100

150

200

250

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11


grrr

brr

ΔHP

SRCKozluk Slavonski

Brod

ΔHP (MWhkm-2

)


b)

a)

b)

a)ΔT

ΔP

ΔR

Δ(AETwb/P)

ΔCV(R)

ΔHP

-250

-200

-150

-100

-50

0

50

100

150

200

250

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11


grrr

brr

ΔHP

SRCKozluk Slavonski

Brod

ΔHP (MWhkm-2

)


c)

a)

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

ΔT ( C)

ΔP (mm yr-1)

ΔR (mm yr-1)

( C)

(mmyear -1)

Sremska


Broda)

(mm yr-1)

(mm yr-1)

-0.15

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11

0.13

0.15

Δ(AETwb/P)

ΔCV(R )

Sremska


Brodb)

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

ΔT ( C)

ΔP (mm yr-1)

ΔR (mm yr-1)

( C)

(mmyear -1)

Sremska


Broda)

(mm yr-1)

(mm yr-1)

-0,15

-0,13

-0,11

-0,09

-0,07

-0,05

-0,03

-0,01

0,01

0,03

0,05

0,07

0,09

0,11

0,13

0,15

Δ(AETwb/P)

ΔCV(R )

SRBKozluk Slavonski

Brod

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30ΔT (°C)

ΔP (mm yr-1)

SRCKozluk Slavonski

Brod

ΔT ( C)


-250

-200

-150

-100

-50

0

50

100

150

200

250

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11

0.13Δ(AETwb/P)

ΔCV(R )

grrr

brr

ΔHP

SRCKozluk Slavonski

Brod

ΔHP (MWhkm-2

)


b)

a)

b)

a)ΔT

ΔP

ΔR

Δ(AETwb/P)

ΔCV(R)

ΔHP

-250

-200

-150

-100

-50

0

50

100

150

200

250

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11


grrr

brr

ΔHP

SRCKozluk Slavonski

Brod

ΔHP (MWhkm-2

)


c)

a)

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

ΔT ( C)

ΔP (mm yr-1)

ΔR (mm yr-1)

( C)

(mmyear -1)

Sremska


Broda)

(mm yr-1)

(mm yr-1)

-0.15

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11

0.13

0.15

Δ(AETwb/P)

ΔCV(R )

Sremska


Brodb)

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.80

-0.60

-0.40

-0.20

0.00

0.20

0.40

0.60

0.80

ΔT ( C)

ΔP (mm yr-1)

ΔR (mm yr-1)

( C)

(mmyear -1)

Sremska


Broda)

(mm yr-1)

(mm yr-1)

-0,15

-0,13

-0,11

-0,09

-0,07

-0,05

-0,03

-0,01

0,01

0,03

0,05

0,07

0,09

0,11

0,13

0,15

Δ(AETwb/P)

ΔCV(R )

SRBKozluk Slavonski

Brod

-140

-120

-100

-80

-60

-40

-20

0

20

40

60

80

100

120

140

-0.30

-0.25

-0.20

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30ΔT (°C)

ΔP (mm yr-1)

SRCKozluk Slavonski

Brod

ΔT ( C)


-250

-200

-150

-100

-50

0

50

100

150

200

250

-0.13

-0.11

-0.09

-0.07

-0.05

-0.03

-0.01

0.01

0.03

0.05

0.07

0.09

0.11


grrr

brr

ΔHP

SRCKozluk Slavonski

Brod

ΔHP (MWhkm-2

)


b)

a)

b)

a)ΔT

ΔP

ΔR

Δ(AETwb/P)

ΔCV(R)

ΔHP


13

higher hydropower production, we find a parallel significant decrease of CV(R) (Fig. 5) unsustained in terms of T and P or modeled atmospheric climate change represented by AETTclim and AETBclim (Fig. 4). From these results, it is indicative that various related

changes in landscape and atmospheric water, proxied here by hydropower production, area and volume of man-made reservoirs, might have caused the AETwb/P and CV(R) shifts in Kozluk.

Fig. 6 Cross-regional relation between changes. a Changes in relative evapotranspiration (AETwb/P) and hydropower production between periods (1931–1960) and (1971–2000) [(1964–1993) for Slavonski Brod and Kozluk.] b Changes in coefficient of variation of runoff CV(R) and hydropower production for the same periods as in a. Results are shown for the two different SRC subcatchments (Fig. 1a) (purple symbols) and compared with previously reported results (Destouni et al. 2013) for different Swedish catchments (green symbols) and predicted results for the SRC catchments (red symbols) calculated on the basis of Swedish catchments results. Regression lines are shown for the Swedish catchments’ results. Illustrated are also values of average AETwb/P and CV(R) change for the four catchments with hydropower production change of more than 100 MWh km-2 (blue square) and the seven catchments with less than 100 MWh km-2 (yellow rectangle).

Slavonski Brod

KozlukSlavonski Brod

Kozluk

y = 7E-05x + 0.0509R² = 0.27

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0 100 200 300 400 500 600

SRC catchments

SRC catchments (predicted)

Swedish Catchments

catchments with HP change >100MWh km-2catchments with HP change <100MWh km-2

Change in hydropower production (MWh km-2)

Ch

an

ge

in r

ela

tive

eva

po

tra

nsp

ira

tion

(AE

Tw

b/P

) (

mm

ye

ar-

1)

SRC catchments

Swedish catchments

Catchments with HP change ≥ 100

MWh km-2

Catchments with HP change <100

MWh km-2


Slavonski Brod


Kozluk

y = 7E-05x + 0.0509R² = 0.27

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0 100 200 300 400 500 600

SRC catchments


Swedish Catchments



Ch

an

ge

in r

ela

tive

eva

po

tra

nspira

tion

(AE

Tw

b/P

) (

mm

ye

ar-1

)

a)

b)

Slavonski Brod

Kozluk

Slavonski Brod

Kozluk

y = -0.0019x + 0.1465R² = 0.83

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0 100 200 300 400 500 600


Ch

an

ge

in C

V(R

)

Slavonski Brod

Kozluk

y = 7E-05x + 0.0509R² = 0.27

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0 100 200 300 400 500 600

SRC catchments


Swedish Catchments

catchments with HP change >100 MWhkm-2catchments with HP change <100MWh km-2


Change

in r

ela

tive

eva

po

tra

nspira

tion

(AE

Tw

b/P

) (

mm

ye

ar-1

)

Slavonski Brod Kozluk

y = -0.0019x + 0.1465R² = 0.83

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0 100 200 300 400 500 600

SRC catchments


Swedish Catchments

catchments with HP change >100 MWhkm-2

catchments with HP change <100MWh km-2

Linear (Swedish Catchments)


Change

in C

V(R

)

a)

b)

0.12

0.10

0.08

0

0.06

0.04

0.02

0.00

Ch

ange

s in

rel

ativ

e ev

apo

tran

spir

atio

nA

ETw

b/P

(mm

yea

r-1)

100 200 300 400 500 600

Slavonski Brod

Kozluk


y=7E-05x+0.0509R2=0.27

Slavonski Brod


Kozluk

y = 7E-05x + 0.0509R² = 0.27

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0 100 200 300 400 500 600

SRC catchments


Swedish Catchments



Ch

an

ge

in r

ela

tive

eva

po

tra

nspira

tion

(AE

Tw

b/P

) (

mm

ye

ar-

1)

SRC catchments

Swedish catchments


MWh km-2


MWh km-2


Slavonski Brod


Kozluk

y = 7E-05x + 0.0509R² = 0.27

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0 100 200 300 400 500 600

SRC catchments


Swedish Catchments



Ch

an

ge

in r

ela

tive

eva

po

tra

nspira

tion

(AE

Tw

b/P

) (

mm

ye

ar-1

)

a)

b)

Slavonski Brod

Kozluk

Slavonski Brod

Kozluk

y = -0.0019x + 0.1465R² = 0.83

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0 100 200 300 400 500 600


Ch

an

ge

in C

V(R

)

Slavonski Brod

Kozluk

y = 7E-05x + 0.0509R² = 0.27

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0 100 200 300 400 500 600

SRC catchments


Swedish Catchments



Change

in r

ela

tive

evapotr

anspiration

(AE

Tw

b/P

) (

mm

year-1

)


y = -0.0019x + 0.1465R² = 0.83

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0 100 200 300 400 500 600

SRC catchments


Swedish Catchments





Change

in C

V(R

)

a)

b)

SRC catchments


Catchments with HP change ≥100 MWh km-2

Catchments with HP change ≤100 MWh km-2

Swedish catchments

Slavonski Brod


Kozluk

y = 7E-05x + 0.0509R² = 0.27

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0 100 200 300 400 500 600

SRC catchments


Swedish Catchments



Ch

an

ge

in r

ela

tive

eva

po

tra

nspira

tion

(AE

Tw

b/P

) (

mm

ye

ar-

1)

SRC catchments

Swedish catchments


MWh km-2


MWh km-2


Slavonski Brod


Kozluk

y = 7E-05x + 0.0509R² = 0.27

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0 100 200 300 400 500 600

SRC catchments


Swedish Catchments



Ch

an

ge

in r

ela

tive

eva

po

tra

nspira

tion

(AE

Tw

b/P

) (

mm

ye

ar-1

)

a)

b)

Slavonski Brod

Kozluk

Slavonski Brod

Kozluk

y = -0.0019x + 0.1465R² = 0.83

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0 100 200 300 400 500 600


Ch

an

ge

in C

V(R

)

Slavonski Brod

Kozluk

y = 7E-05x + 0.0509R² = 0.27

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0 100 200 300 400 500 600

SRC catchments


Swedish Catchments



Change

in r

ela

tive

evapotr

anspiration

(AE

Tw

b/P

) (

mm

year-1

)


y = -0.0019x + 0.1465R² = 0.83

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0 100 200 300 400 500 600

SRC catchments


Swedish Catchments





Change

in C

V(R

)

a)

b)

SRC catchments




Swedish catchments

Slavonski Brod


Kozluk

y = 7E-05x + 0.0509R² = 0.27

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0 100 200 300 400 500 600

SRC catchments


Swedish Catchments



Ch

an

ge

in r

ela

tive

eva

po

tra

nsp

ira

tion

(AE

Tw

b/P

) (

mm

ye

ar-

1)

SRC catchments

Swedish catchments


MWh km-2


MWh km-2


Slavonski Brod


Kozluk

y = 7E-05x + 0.0509R² = 0.27

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0 100 200 300 400 500 600

SRC catchments


Swedish Catchments



Ch

an

ge

in r

ela

tive

eva

po

tra

nsp

ira

tion

(AE

Tw

b/P

) (

mm

ye

ar-1

)

a)

b)

Slavonski Brod

Kozluk

Slavonski Brod

Kozluk

y = -0.0019x + 0.1465R² = 0.83

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0 100 200 300 400 500 600


Ch

an

ge

in C

V(R

)

Slavonski Brod

Kozluk

y = 7E-05x + 0.0509R² = 0.27

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0 100 200 300 400 500 600

SRC catchments


Swedish Catchments



Change

in r

ela

tive

evapotr

anspiration

(AE

Tw

b/P

) (

mm

year-1

)


y = -0.0019x + 0.1465R² = 0.83

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0 100 200 300 400 500 600

SRC catchments


Swedish Catchments





Change

in C

V(R

)

a)

b)

SRC catchments




Swedish catchments

Slavonski Brod


Kozluk

y = 7E-05x + 0.0509R² = 0.27

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0 100 200 300 400 500 600

SRC catchments


Swedish Catchments



Ch

an

ge

in r

ela

tive

eva

po

tra

nspira

tion

(AE

Tw

b/P

) (

mm

ye

ar-

1)

SRC catchments

Swedish catchments


MWh km-2


MWh km-2


Slavonski Brod


Kozluk

y = 7E-05x + 0.0509R² = 0.27

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0 100 200 300 400 500 600

SRC catchments


Swedish Catchments



Ch

an

ge

in r

ela

tive

eva

po

tra

nspira

tion

(AE

Tw

b/P

) (

mm

ye

ar-1

)

a)

b)

Slavonski Brod

Kozluk

Slavonski Brod

Kozluk

y = -0.0019x + 0.1465R² = 0.83

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0 100 200 300 400 500 600


Ch

an

ge

in C

V(R

)

Slavonski Brod

Kozluk

y = 7E-05x + 0.0509R² = 0.27

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0 100 200 300 400 500 600

SRC catchments


Swedish Catchments



Change

in r

ela

tive

evapotr

anspiration

(AE

Tw

b/P

) (

mm

year-1

)


y = -0.0019x + 0.1465R² = 0.83

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0 100 200 300 400 500 600

SRC catchments


Swedish Catchments





Change

in C

V(R

)

a)

b)

SRC catchments




Swedish catchments

Slavonski Brod


Kozluk

y = 7E-05x + 0.0509R² = 0.27

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0 100 200 300 400 500 600

SRC catchments


Swedish Catchments



Ch

an

ge

in r

ela

tive

eva

po

tra

nsp

ira

tion

(AE

Tw

b/P

) (

mm

ye

ar-

1)

SRC catchments

Swedish catchments


MWh km-2


MWh km-2


Slavonski Brod


Kozluk

y = 7E-05x + 0.0509R² = 0.27

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0 100 200 300 400 500 600

SRC catchments


Swedish Catchments



Ch

an

ge

in r

ela

tive

eva

po

tra

nspira

tion

(AE

Tw

b/P

) (

mm

ye

ar-1

)

a)

b)

Slavonski Brod

Kozluk

Slavonski Brod

Kozluk

y = -0.0019x + 0.1465R² = 0.83

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0 100 200 300 400 500 600


Ch

an

ge

in C

V(R

)

Slavonski Brod

Kozluk

y = 7E-05x + 0.0509R² = 0.27

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0 100 200 300 400 500 600

SRC catchments


Swedish Catchments



Change

in r

ela

tive

eva

po

tra

nspira

tion

(AE

Tw

b/P

) (

mm

ye

ar-1

)


y = -0.0019x + 0.1465R² = 0.83

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0 100 200 300 400 500 600

SRC catchments


Swedish Catchments





Change

in C

V(R

)

a)

b)

SRC catchments




Swedish catchments

Slavonski Brod


Kozluk

y = 7E-05x + 0.0509R² = 0.27

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0 100 200 300 400 500 600

SRC catchments


Swedish Catchments



Ch

an

ge

in r

ela

tive

eva

po

tra

nspira

tion

(AE

Tw

b/P

) (

mm

ye

ar-

1)

SRC catchments

Swedish catchments


MWh km-2


MWh km-2


Slavonski Brod


Kozluk

y = 7E-05x + 0.0509R² = 0.27

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0 100 200 300 400 500 600

SRC catchments


Swedish Catchments



Ch

an

ge

in r

ela

tive

eva

po

tra

nspira

tion

(AE

Tw

b/P

) (

mm

ye

ar-1

)

a)

b)

Slavonski Brod

Kozluk

Slavonski Brod

Kozluk

y = -0.0019x + 0.1465R² = 0.83

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0 100 200 300 400 500 600


Ch

an

ge

in C

V(R

)

Slavonski Brod

Kozluk

y = 7E-05x + 0.0509R² = 0.27

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0 100 200 300 400 500 600

SRC catchments


Swedish Catchments



Change

in r

ela

tive

evapotr

anspiration

(AE

Tw

b/P

) (

mm

year-1

)


y = -0.0019x + 0.1465R² = 0.83

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0 100 200 300 400 500 600

SRC catchments


Swedish Catchments





Change

in C

V(R

)

a)

b)0.4

0.2

0.00

-0.2

-0.4

-0.6

-1.0

Ch

ange

in C

V(R

)

100 200 300 400 500 600

Slavonski Brod

Kozluk


y=-0.0019x+0.1465R2=0.83

-0.8

a

b


14

When analyzing changes in averages of all the hydro-climatic variables between two time periods 1931-1960 and 1964-1993 (Fig. 5), the entire SRC appears as a combination of unregulated (Slavonski Brod) and hydropower dominated (Kozluk) signal. From Fig. 6, where the SRC is compared with hydro-climatically different Swedish catchments previously studied by Destouni et al. (2013), consistency in terms of hydro-climatic change for similar hydropower conditions is evident.

4.2 Observed nutrient-related changes

The proposed methodology for the assessment of nutrient inputs, delivery and load changes has been tested in the case of the SRC and its seven nested and six incremental subcatchments defined by the multiple measurement stations along the Sava River (Fig. 1a). This has been done as part of Paper II.

Respecting physically possible range 0 < Δαi ≤ 1 and minimizing the variability of

Inc1 Inc2 Inc3 Inc4 Inc5 Inc6

1

10

100

1000

10000

Incr

emen

tal i

nput

per

are

a(T

yea

r-1km

-2)

DIN

Inc1 Inc2 Inc3 Inc4 Inc5 Inc60.000001

0.00001

0.0001

0.001

0.01In

crem

enta

l del

iver

y fa

ctor

pe

r ar

ea(k

m-2

)

DIN

Zagreb

0306090

120150180

Inpu

t pe

r ar

ea(T

yea

r-1km

-2)

DIN

Rugvica Davor SBrod SKobaš Županja0.000.050.10

Del

iver

y fa

ctor

0.150.200.250.300.350.40 DIN

Rugvica Davor SBrod SKobaš ŽupanjaZagreb

Zagreb0.000.20Lo

adpe

r ar

ea(T

yea

r-1km

-2)

DIN

Rugvica Davor SBrod SKobaš Županja

1.00

0.400.600.80

1.201.401.601.80 DIN

20,0000.00A

pp-m

ean

quan

tity

Load

per

area

and

del

iver

y fa

ctor

(T y

ear-1

km-2

)

0.400.801.20

2.40

1.60

2.00

2.80

40,000 60,000 80,000

Catchment area (km2)

0

30

60

90

120

150

180

Zagreb Rugvica Davor SlavonskiKobaš

SlavonskiBrod

Županja

Inp

ut

per

are

a (T

yea

r -1

km-2

)

App 1

App 2

Average

App1

App2

Average

y = 16.634x-0.25

R² = 0.9002

y = 24.7x-0.524

R² = 0.66

0.00

0.40

0.80

1.20

1.60

2.00

2.40

2.80

-20,000 30,000 80,000

Ap

p-m

ean

qu

anti

ty

Catchment area (km-2)

Measured load per area (T year-1km-2)

Load per area (T year-1 km-2)

Delivery factor

Measured load per area (T year-1 km-2)

Load per area(T year-1 km-2)

Delivery factor

0

30

60

90

120

150

180


SlavonskiBrod

Županja

Inp

ut

per

are

a (T

yea

r -1

km-2

)

App 1

App 2

Average

App1

App2

Average

y = 16.634x-0.25

R² = 0.9002

y = 24.7x-0.524

R² = 0.66

0.00

0.40

0.80

1.20

1.60

2.00

2.40

2.80

-20,000 30,000 80,000

Ap

p-m

ean

qu

anti

ty




Delivery factor



Delivery factor

0

30

60

90

120

150

180


SlavonskiBrod

Županja

Inp

ut

per

are

a (T

yea

r -1

km-2

)

App 1

App 2

Average

App1

App2

Average

y = 16.634x-0.25

R² = 0.9002

y = 24.7x-0.524

R² = 0.66

0.00

0.40

0.80

1.20

1.60

2.00

2.40

2.80

-20,000 30,000 80,000

Ap

p-m

ean

qu

anti

ty




Delivery factor



Delivery factor

0

30

60

90

120

150

180


SlavonskiBrod

Županja

Inp

ut

per

are

a (T

yea

r -1

km-2

)

App 1

App 2

Average

App1

App2

Average

y = 16.634x-0.25

R² = 0.9002

y = 24.7x-0.524

R² = 0.66

0.00

0.40

0.80

1.20

1.60

2.00

2.40

2.80

-20,000 30,000 80,000

Ap

p-m

ean

qu

anti

ty




Delivery factor



Delivery factor

0

30

60

90

120

150

180


SlavonskiBrod

Županja

Inp

ut

per

are

a (T

yea

r -1

km-2

)

App 1

App 2

Average

App1

App2

Average

y = 16.634x-0.25

R² = 0.9002

y = 24.7x-0.524

R² = 0.66

0.00

0.40

0.80

1.20

1.60

2.00

2.40

2.80

-20,000 30,000 80,000

Ap

p-m

ean

qu

anti

ty




Delivery factor



Delivery factor

0

30

60

90

120

150

180


SlavonskiBrod

Županja

Inp

ut

per

are

a (T

yea

r -1

km-2

)

App 1

App 2

Average

App1

App2

Average

y = 16.634x-0.25

R² = 0.9002

y = 24.7x-0.524

R² = 0.66

0.00

0.40

0.80

1.20

1.60

2.00

2.40

2.80

-20,000 30,000 80,000

Ap

p-m

ean

qu

anti

ty




Delivery factor



Delivery factor

a b

c d

e f

y=16.634x-0.25

R2=0.90

y=24.7x-0.524

R2=0.66

Fig. 7 Calculation results for DIN for the incremental and nested subcatchments of the total Sava River Catchment (Fig. 1a). Results are shown for (a-e) each subcatchment and (f) versus subcatchment scale (area), with regard to nutrient input per unit area (a,c), nutrient delivery factor (b,d), as obtained in the present methodology for approach 1 (App1, blue circles in (a-d)), approach 2 (App2, orange squares in (a-d)) and their average value (“x” symbols in (a-e)). Results in (f) are shown for approach-average values of delivery factor (green triangles) and loads (“+”) with the latter also compared to observation-based data (red circles).


15

incremental loads (Δαi*ΔIi)/ΔAi, iterative process yielded values of ΔIi/ΔAi = 7.7 T/yr/km2 for DIN and ΔIi/ΔAi =1.9 T/yr/km2 for TP in App1, and Δαi/ΔAi = 3*10-5 per km2 for both DIN and TP in App2.

Figures 7 and 8 show extremely large difference between the two approaches considering the smallest and the most densely populated subcatchment of Inc2 for both DIN and TP (panels a, b), identifying it as a hotspot within the whole catchment. As Rugvice catchment is mostly influenced by the Inc1 incremental hotspot (containing 97% of its area), it thus exhibits particularly high values of Ii/Ai of 158 T/yr/km2 for DIN and 13 T/yr/km2 for TP for App 2 (Fig. 7c and 8c). Both approaches show reasonable consistency of DIN and TP

nutrient input per unit area and delivery factors, but again particularly high results for the two smallest catchments. Measured and modeled loads normalized with catchment area show consistency exhibiting non-linear power-law decay with increasing catchment scale.

The results of the SRC nested catchments further show good correlation of nutrient loads with runoff, while being essentially independent of nutrient concentration (Fig. 9). Such results are consistent with previous results from the Baltic (Selroos and Destouni, 2015) and other world regions (Basu et al., 2010), indicating that the nutrient transport may be primarily hydro-climatically driven, through runoff dynamics. From Fig. 10, it is evident that there is consistency between the SRC results and the

Inc1 Inc2 Inc3 Inc4 Inc5 Inc6

0.1

1

10

100

1000

Incr

emen

tal i

nput

per

are

a(T

yea

r-1km

-2)

TP

Inc1 Inc2 Inc3 Inc4 Inc5 Inc60.000001

0.00001

0.0001

0.001

0.01

Incr

emen

tal d

eliv

ery

fact

or

per

area

(km

-2)

TP

0.000.050.10

Del

iver

y fa

ctor

0.150.200.250.300.350.40

TP

Rugvica Davor SBrod SKobaš ŽupanjaZagreb

TP

20,0000.00

0.04

0.08

0.12

0.240.20

0.16

40,000 60,000 80,000

Catchment area (km2)

Inc7 Inc7

Zagreb

0

3Inpu

t pe

r ar

ea(T

yea

r-1km

-2)

TP

Rugvica Davor SBrod SKobaš Županja SremskaMitrovica

SremskaMitrovica

Zagreb0.000.02Lo

adpe

r ar

ea(T

yea

r-1km

-2)

TP

Rugvica Davor SBrod SKobaš Županja

0.040.06

SremskaMitrovica

ab

c d

e f

0.01

6

9

12

15

0.080.100.120.140.16

100,000

0

30

60

90

120

150

180


SlavonskiBrod

Županja

Inpu

t per

are

a (T

yea

r -1km

-2)

App 1

App 2

Average

App1

App2

Average

y = 16.634x-0.25

R² = 0.9002

y = 24.7x-0.524

R² = 0.66

0.00

0.40

0.80

1.20

1.60

2.00

2.40

2.80

-20,000 30,000 80,000

App

-mea

n qu

anti

ty




Delivery factor



Delivery factor

0

30

60

90

120

150

180


SlavonskiBrod

Županja

Inpu

t per

are

a (T

yea

r -1km

-2)

App 1

App 2

Average

App1

App2

Average

y = 16.634x-0.25

R² = 0.9002

y = 24.7x-0.524

R² = 0.66

0.00

0.40

0.80

1.20

1.60

2.00

2.40

2.80

-20,000 30,000 80,000

App

-mea

n qu

anti

ty




Delivery factor



Delivery factor

0

30

60

90

120

150

180


SlavonskiBrod

Županja

Inpu

t per

are

a (T

yea

r -1km

-2)

App 1

App 2

Average

App1

App2

Average

y = 16.634x-0.25

R² = 0.9002

y = 24.7x-0.524

R² = 0.66

0.00

0.40

0.80

1.20

1.60

2.00

2.40

2.80

-20,000 30,000 80,000

App

-mea

n qu

anti

ty




Delivery factor



Delivery factor

0

30

60

90

120

150

180


SlavonskiBrod

Županja

Inpu

t per

are

a (T

yea

r -1km

-2)

App 1

App 2

Average

App1

App2

Average

y = 16.634x-0.25

R² = 0.9002

y = 24.7x-0.524

R² = 0.66

0.00

0.40

0.80

1.20

1.60

2.00

2.40

2.80

-20,000 30,000 80,000

App

-mea

n qu

anti

ty




Delivery factor



Delivery factor

0

30

60

90

120

150

180


SlavonskiBrod

Županja

Inpu

t per

are

a (T

yea

r -1km

-2)

App 1

App 2

Average

App1

App2

Average

y = 16.634x-0.25

R² = 0.9002

y = 24.7x-0.524

R² = 0.66

0.00

0.40

0.80

1.20

1.60

2.00

2.40

2.80

-20,000 30,000 80,000

App

-mea

n qu

anti

ty




Delivery factor



Delivery factor

0

30

60

90

120

150

180


SlavonskiBrod

Županja

Inpu

t per

are

a (T

yea

r -1km

-2)

App 1

App 2

Average

App1

App2

Average

y = 16.634x-0.25

R² = 0.9002

y = 24.7x-0.524

R² = 0.66

0.00

0.40

0.80

1.20

1.60

2.00

2.40

2.80

-20,000 30,000 80,000

App

-mea

n qu

anti

ty




Delivery factor



Delivery factor

y=3.892x-0.377

R2=0.77 y=2159.57x-0.796

R2=0.63

App

-mea

nqu

anti

tyLo

adpe

r ar

ea a

nd d

eliv

ery

fact

or(T

yea

r-1km

-2)

Fig. 8 Calculation results for TP for the incremental and nested subcatchments of the total Sava River Catchment (Fig. 1a). Results are shown for (a-e) each subcatchment and (f) versus subcatchment scale (area), with regard to nutrient input per unit area (a,c), nutrient delivery factor (b,d), as obtained in the present methodology for approach 1 (App1, blue circles in (a-d)), approach 2 (App2, orange squares in (a-d)) and their average value (“x” symbols in (a-e)). Results in (f) are shown for approach-average values of delivery factor (green triangles) and loads (“+”) with the latter also compared to observation-based data (red circles).


16

entire Baltic region (Fig. 1b) considering the relations of nutrient concentrations with the human-related conditions of population density and farmland share.

4.3 Projected climate change and nutrient loading

In order to evaluate CMIP5 data reliability, in Paper III we analyzed its output on a range of six Northern Hemisphere catchments (Fig. 1c).

Figure 11 shows the results of modeled temperature and water fluxes for the catchments for the historical period 1961-1990 and RCP8.5 scenario. Temperature projections show an increase across all the catchments with a relatively small intermodel standard deviation. In contrast, projected water fluxes of P, R, ET and ΔS show a large range and standard deviation. For both the historical experiment and RCP8.5

scenario, the mentioned statistics are particularly large for runoff. Even though the catchments vary significantly in size (for example, the Arctic is 30 times bigger than Sweden), they show an almost identical standard deviation for some water fluxes, indicative that there is no catchment scale influence on their intermodel variability. The model-implied long-term average net water balance ΔS exhibits an even larger intermodel standard deviation than runoff. The extreme ΔS historical change results have been revealed for the Greek catchment, implying a total decrease of 4.4 m in surface

water level over the 30-year period. For the specific case study of the SRC, the CMIP5 ensemble mean of the historical long-term average water storage change is at -23 mm yr-1, which is 2.5 times larger than the corresponding runoff (-9 mm yr-1). If

a

d

b

c

DIN (1979-1991) TP (2001-2013)

R² = 0.0153

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

200 400 600 800 1000 1200

Zagreb

Rugvica

Davor

Slavonsk Kobaš

Slavonski Brod

Županja

Sremska Mitrovica

R (mm year-1)

Co

nce

ntr

ati

on

(m

gL-

1)

R² = 0.3151

0.0E+00

5.0E-01

1.0E+00

1.5E+00

2.0E+00

2.5E+00

3.0E+00

200 400 600 800 1000 1200

R (mm year-1)

Loa

d p

er

un

it a

rea

(T

ye

ar-1

km-2

) R² = 0.3804

0.0E+00

5.0E-02

1.0E-01

1.5E-01

2.0E-01

2.5E-01

200 400 600 800 1000 1200

R (mm year-1)

Loa

d p

er

un

it

are

a (

T y

ea

r-1k

m-

2)

R² = 0.0254

0

0.5

1

1.5

2

2.5

3

3.5

4

200 400 600 800 1000 1200

R (mm year-1)

Co

nce

ntr

ati

on

(m

gL-

1)

R² = 0.0153

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

200 400 600 800 1000 1200

Zagreb

Rugvica

Davor

Slavonsk Kobaš

Slavonski Brod

Županja

Sremska Mitrovica

R (mm year-1)

Co

nce

ntr

ati

on

(m

gL-

1)

Loa

d p

er

un

it a

rea

(T y

ea

r-1km

2)

3.0

2.5

2.0

1.5

1.0

0.5

0.0200 400 600 800 1000 1200

R (mm year-1)

R2=0.32

R2=0.38

DIN (1979-1991) TP (2001-2013)

2.5

2.0

1.5

1.0

0.5

0.0200 400 600 800 1000 1200

R (mm year-1)

Loa

d p

er

un

it a

rea

(T y

ea

r-1km

2)

2.5

2.0

1.5

1.0

0.5

0.0200 400 600 800 1000 1200

R (mm year-1)

Co

nce

ntr

ati

on

(mg

L-1

)

0.35

0.00200 400 600 800 1000 1200

R (mm year-1)

Co

nce

ntr

ati

on

(mg

L-1

)

3.0

3.5

4.0

0.30

0.25

0.20

0.15

0.10

0.05

R2=0.02

R2=0.03

Fig. 9 Annual nutrient data for the nested catchmnets of the total Sava River Catchment (Fig. 1). The available annual data are for (a,b) nutrient load per unit area and (c,d) concentration, with regard to DIN (a,c) in the period 1979-1991 and TP(b.d) in the period 2001-2013, plotted against annual runoff for each period. Regression lines are based on all data points within each panel.


17

continuous over the period of 30 years, it would imply a total decrease of 0.7 m in surface water level and around 2.3 meter in ground water level (with 30% soil porosity). As no such changes have been reported or detected within the Greek catchment or the SRC during the modeled time period (1961-1990), this indicates deviations between observations and climate model outputs.

Runoff change projections in the SRC show on average for both scenarios a decrease of 24 mm yr-1 with a corresponding average

evapotranspiration increase of 44 mm yr-1 and an average decrease in net water change of 29 mm yr-1for both scenarios.

The estimation of runoff and discharge change projections from the CMIP5 ensemble subset exhibits their decrease for

all the subcatchments as well as a large variation among different models for both scenarios (Fig. 12), with a standard deviation two times larger than the estimation of the ensemble mean change for the smallest catchments (Zagreb and Rugvica). This

y = 0.0684x + 0.0801R² = 0.79

0

1

2

3

4

5

6

7

0 10 20 30 40 50 60 70 80

Co

nce

ntr

atio

n (

mg

/L)

Farmland share (%)

y = 0.0023x + 0.0173R² = 0.75

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0 10 20 30 40 50 60 70 80

Co

nce

ntr

atio

n (

mg

/L)

Farmland share (%)0

Co

nce

ntra

tio

n (

mg

L-1)

Farmland share (%)10 20 30 40

2

0

1

5

3

4

6

7

50 60 70 80

0.10

0.15

0.20

0.25

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0 10 20 30 40 50 60 70 80Farmland share (%)

y = 0.0012x + 0.0238R² = 0.90

0.00

0.05

0.10

0.15

0.20

0.25

0 20 40 60 80 100 120 140 160 180

Co

nce

ntr

atio

n (

mg

/L)

Population density (people/km2)

0

Co

nce

ntra

tio

n (

mg

L-1)

Population density (people km-2 )

20 40 60 80 100 120 140 160 180

0.00

0.05

TP

DIN for SRC, TN for Baltic TP

y = 0.0324x + 0.4412R² = 0.82

0

1

2

3

4

5

6

7

0 20 40 60 80 100 120 140 160 180

Co

nce

ntr

atio

n (

mg

/L)


0

Co

nce

ntra

tio

n (

mg

L-1)

Population density (people km-2 )

20 40 60 80 100 120 140 160 180

2

0

1

5

3

4

6

7DIN for SRC, TN for Baltic

a b

c d

TP (2001-2013)a)

d)

b)

c)

DIN (1979-1991)

TN Baltic

DIN SRC

TP Baltic

TP SRC

y = 0.0012x + 0.0238R² = 0.90

y = 0.0016x + 0.0358R² = 0.64

0.00

0.05

0.10

0.15

0.20

0.25

0 20 40 60 80 100 120 140 160 180

Co

nce

ntr

atio

n (

mg/

L)


TP Baltic

TP SRC

Linear (TP Baltic)

Linear (TP SRC)

y = 0.0324x + 0.4412R² = 0.82

y = 0.005x + 1.7523R² = 0.43

0

1

2

3

4

5

6

7

0 20 40 60 80 100 120 140 160 180

Co

nce

ntr

atio

n (

mg/

L)


TN Baltic

TN SRC

y = 0.0023x + 0.0173R² = 0.75

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0 10 20 30 40 50 60 70 80

Co

nce

ntr

atio

n (

mg/

L)

Farmland share (%)

y = 0.0012x + 0.0238R² = 0.90

0.00

0.05

0.10

0.15

0.20

0.25

0 20 40 60 80 100 120 140 160 180

Co

nce

ntr

atio

n (

mg/

L)


y = 0.0684x + 0.0801R² = 0.79

0

1

2

3

4

5

6

7

0 10 20 30 40 50 60 70 80

Co

nce

ntr

atio

n (

mg/

L)

Farmland share (%)

y = 0.0324x + 0.4412R² = 0.82

0

1

2

3

4

5

6

7

0 20 40 60 80 100 120 140 160 180

Co

nce

ntr

atio

n (

mg/

L)



TN Baltic

DIN SRC

TP Baltic

TP SRC

TP (2001-2013)a)

d)

b)

c)

DIN (1979-1991)

TN Baltic

DIN SRC

TP Baltic

TP SRC

y = 0.0012x + 0.0238R² = 0.90

y = 0.0016x + 0.0358R² = 0.64

0.00

0.05

0.10

0.15

0.20

0.25

0 20 40 60 80 100 120 140 160 180

Co

nce

ntr

atio

n (

mg/

L)


TP Baltic

TP SRC

Linear (TP Baltic)

Linear (TP SRC)

y = 0.0324x + 0.4412R² = 0.82

y = 0.005x + 1.7523R² = 0.43

0

1

2

3

4

5

6

7

0 20 40 60 80 100 120 140 160 180

Co

nce

ntr

atio

n (

mg/

L)


TN Baltic

TN SRC

y = 0.0023x + 0.0173R² = 0.75

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0 10 20 30 40 50 60 70 80

Co

nce

ntr

atio

n (

mg/

L)

Farmland share (%)

y = 0.0012x + 0.0238R² = 0.90

0.00

0.05

0.10

0.15

0.20

0.25

0 20 40 60 80 100 120 140 160 180

Co

nce

ntr

atio

n (

mg/

L)


y = 0.0684x + 0.0801R² = 0.79

0

1

2

3

4

5

6

7

0 10 20 30 40 50 60 70 80

Co

nce

ntr

atio

n (

mg/

L)

Farmland share (%)

y = 0.0324x + 0.4412R² = 0.82

0

1

2

3

4

5

6

7

0 20 40 60 80 100 120 140 160 180

Co

nce

ntr

atio

n (

mg/

L)



TN Baltic

DIN SRC

TP Baltic

TP SRC

TP (2001-2013)a)

d)

b)

c)

DIN (1979-1991)

TN Baltic

DIN SRC

TP Baltic

TP SRC

y = 0.0012x + 0.0238R² = 0.90

y = 0.0016x + 0.0358R² = 0.64

0.00

0.05

0.10

0.15

0.20

0.25

0 20 40 60 80 100 120 140 160 180

Co

nce

ntr

atio

n (

mg/

L)


TP Baltic

TP SRC

Linear (TP Baltic)

Linear (TP SRC)

y = 0.0324x + 0.4412R² = 0.82

y = 0.005x + 1.7523R² = 0.43

0

1

2

3

4

5

6

7

0 20 40 60 80 100 120 140 160 180

Co

nce

ntr

atio

n (

mg/

L)


TN Baltic

TN SRC

y = 0.0023x + 0.0173R² = 0.75

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0 10 20 30 40 50 60 70 80

Co

nce

ntr

atio

n (

mg/

L)

Farmland share (%)

y = 0.0012x + 0.0238R² = 0.90

0.00

0.05

0.10

0.15

0.20

0.25

0 20 40 60 80 100 120 140 160 180

Co

nce

ntr

atio

n (

mg/

L)


y = 0.0684x + 0.0801R² = 0.79

0

1

2

3

4

5

6

7

0 10 20 30 40 50 60 70 80

Co

nce

ntr

atio

n (

mg/

L)

Farmland share (%)

y = 0.0324x + 0.4412R² = 0.82

0

1

2

3

4

5

6

7

0 20 40 60 80 100 120 140 160 180

Co

nce

ntr

atio

n (

mg/

L)



TN Baltic

DIN SRC

TP Baltic

TP SRC

TP (2001-2013)a)

d)

b)

c)

DIN (1979-1991)

TN Baltic

DIN SRC

TP Baltic

TP SRC

y = 0.0012x + 0.0238R² = 0.90

y = 0.0016x + 0.0358R² = 0.64

0.00

0.05

0.10

0.15

0.20

0.25

0 20 40 60 80 100 120 140 160 180

Co

nce

ntr

atio

n (

mg/

L)


TP Baltic

TP SRC

Linear (TP Baltic)

Linear (TP SRC)

y = 0.0324x + 0.4412R² = 0.82

y = 0.005x + 1.7523R² = 0.43

0

1

2

3

4

5

6

7

0 20 40 60 80 100 120 140 160 180

Co

nce

ntr

atio

n (

mg/

L)


TN Baltic

TN SRC

y = 0.0023x + 0.0173R² = 0.75

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0 10 20 30 40 50 60 70 80

Co

nce

ntr

atio

n (

mg/

L)

Farmland share (%)

y = 0.0012x + 0.0238R² = 0.90

0.00

0.05

0.10

0.15

0.20

0.25

0 20 40 60 80 100 120 140 160 180

Co

nce

ntr

atio

n (

mg/

L)


y = 0.0684x + 0.0801R² = 0.79

0

1

2

3

4

5

6

7

0 10 20 30 40 50 60 70 80

Co

nce

ntr

atio

n (

mg/

L)

Farmland share (%)

y = 0.0324x + 0.4412R² = 0.82

0

1

2

3

4

5

6

7

0 20 40 60 80 100 120 140 160 180

Co

nce

ntr

atio

n (

mg/

L)



TN Baltic

DIN SRC

TP Baltic

TP SRC

y=10.0324x+0.4412R2=0.82

y=0.0684x+0.0801R2=0.79

y=0.0023x+0.0173R2=0.75

y=0.0012x+0.0238R2=0.90

Fig. 10 Relation of flow-weighted annual average nutrient concentration to (a,b) population density and (c,d) farmland share. The plotted data are for the nested catchments of the Sava River Catchment (SRC, Fig. 1); blue symbols here) and the country and national catchments of the Baltic Sea (red symbols) for (a,c) DIN (SRC) or total nitrogen (TN, Baltic) and (b,d) TP. Data for the SRC are for the period 1979-1991 for DIN and the period 2001-2013. Data for the Baltic region are from Destouni et al. (2015; their Fig. 4 and their Appendix: Supplementary Material Section SM2 and Table SM2) for the period 1994-2006 and the country catchments of Denmark, Estonia, Finland, Germany, Latvia, Lithuania, Poland, Russia and Sweden, and the marine-basin catchments of Gulf of Bothnia, Gulf of Finland, Gulf of Riga, Baltic Proper, Danish Straits and Kattegatt (Fig. 1); among the Baltic data points are also total values for the entire Baltic Sea Catchment, within which all other country and marine-basin catchments are nested.


18

Fig

. 11

Mo

del

ed t

emp

erat

ure

an

d w

ater

flu

xes

fo

r th

e se

t o

f N

ort

her

n H

emis

ph

ere

dra

inag

e b

asin

s (F

ig.

1).

En

sem

ble

mea

n,

stan

dar

d d

evia

tio

n,

and

ran

ge

of

CM

IP5

resu

lts

for

tem

per

atu

re (

T),

an

d w

ater

flu

xes

of

pre

cip

itat

ion

(P

), s

urf

ace

run

off

(R

),

evap

otr

ansp

irat

ion

(E

T),

an

d n

et a

nn

ual

wat

er b

alan

ce (

ΔS

) fo

r a

set

of

six N

ort

her

n H

emis

ph

ere

dra

inag

e b

asin

reg

ion

s. E

rro

r b

ars

den

ote

on

e st

and

ard

dev

iati

on

of

mo

del

mea

ns,

an

d o

pen

cir

cles

th

e w

ho

le r

ang

e o

f in

div

idu

al m

od

el r

esu

lts.

Leg

en

d

ab

c d

fe

mm

yr-

1m

m y

r-1

mm

yr-

1

mm

yr-

1

mm

yr-

1

mm

yr-

1


19

implies a particularly large spread of estimations of model projections of future runoff changes among all the subcatchments.

Those two subcatchments exhibit the biggest future DIN and TP load decrease based on CMIP5 ensemble mean estimation (Fig. 13 a and b), particularly for DIN. It is on average 1.5 times higher than for all other subcatchments for both change periods and scenarios, and 3 times larger than for the entire SRC. A high quantitative and qualitative difference is apparent for the load estimations based on the average of the two best-performing models (Fig. 13 c and d).

5. DISCUSSION

Disruptive changes in water cycle have been detected in many regions of the world during the past hundred years in the form of higher climatic, hydrological and biogeochemical variability and shifts. These have the potential of being the main cause of insecurities in such regions, leading to escalating conflicts and even threatening peace. A good scientific understanding of hydro-climatic and biogeochemical changes and their drivers is thus the first crucial step towards properly addressing the challenges raised and developing adequate management and allocation systems and policies. This thesis has aimed to contribute to such knowledge by developing methods and approaches for detecting the drivers behind observed hydro-climatic and nutrient load

Fig. 12 Projected model (N=22) changes to a,b runoff and c,d discharge for the SRC and its six subcatchments, from the period 1961-1990 to future periods 2010-2039 and 2070-2099, and for emission scenarios RCP2.6 (a,c) and RCP8.5 (b,d). Future runoff and discharge for each subcatchment have been assessed on the basis of (discharge) CMIP5 ensemble mean of SRC runoff multiplied by a ratio of observed runoff (discharge) data for each subcatchment in period 1961-1990 with observed runoff (discharge) for the SRC in the same period. Error bars denote one standard deviation of individual model means (22 models).

ΔQ

(m3

year

-1)

-1.2E+10

-1.0E+10

-8.0E+09

-6.0E+09

-4.0E+09

-2.0E+09

0.0E+00

2.0E+09

4.0E+09

Zagreb Rugvice DavorSlavonskiKobaš

SlavonskiBrod Županja SRC

ΔQ(m

3 /yea

r)

RCP8.5 A RCP8.5 B

-1.2E+10

-1.0E+10

-8.0E+09

-6.0E+09

-4.0E+09

-2.0E+09

0.0E+00

2.0E+09

4.0E+09

Zagreb Rugvice DavorSlavonskiKobaš Slavonski Brod Županja SRC

ΔQ

(m3/y

ea

r)

RCP2.6 A RCP 2.6 B

-200

-150

-100

-50

0

50



ΔR

(mm

/yea

r)

RCP8.5 A RCP8.5 B

-200

-150

-100

-50

0

50

Zagreb Rugvice DavorSlavonskiKobaš Slavonski Brod Županja SRC

ΔR

(m

m/y

ea

r)

RCP2.6 A RCP 2.6 B

Zagreb Rugvica Davor SBrod SKobaš Županja SRC

ΔR

(mm

year

-1)

50

0

-50

-100



ΔQ

(m3

year

-1)

4.0E+09Zagreb Rugvica Davor SBrod SKobaš Županja SRC

RCP 2.6 RCP 8.5

-0.400

-0.350

-0.300

-0.250

-0.200

-0.150

-0.100

-0.050

0.000

0.050

0.100



ΔY c

lim(T

/ye

ar/k

m2)

rcp2.6 A

RCP 2.6 B

(2010-2039)

(2070-2099)

-0.400

-0.350

-0.300

-0.250

-0.200

-0.150

-0.100

-0.050

0.000

0.050

0.100



ΔY c

lim(T

/ye

ar/k

m2)

rcp2.6 A

RCP 2.6 B

(2010-2039)

(2070-2099)

a b

c d

-150

-200

-150

-200

50

0

-50

-100

ΔR

(mm

year

-1)

2.0E+09

0.0E+09

-2.0E+09

-4.0E+09

-6.0E+09

-8.0E+09

-1.0E+10

-1.2E+10

2.0E+09

0.0E+09

-2.0E+09

-4.0E+09

-6.0E+09

-8.0E+09

-1.0E+10

-1.2E+10

4.0E+09


20

changes. The proposed approaches have been tested on the transboundary Sava River Catchment. Because of limited data availability, the thesis also attempted to evaluate the complexities of using CMIP5 odel output for detecting historical and future hydro-climatic changes and any related values that could potentially be further estimated from those.

5.1 Detected hydro-climatic change and its drivers

Considering past to present hydro-climatic changes in the study case of the Sava River Catchment, Paper I has detected two particular signals concerning water-balance-based relative evapotranspiration. The first signal manifests itself in the hydropower-dominated subcatchment Kozluk as parallel shifts in AETwb/P to higher level and runoff variability to lower level, not sustained by

observed climate changes in T and P, or their dependent modeled estimates of AETBclim/P and AETTclim/P. The second signal revealed in the Slavonski Brod subcatchment shows a sustained increase in AETwb/P from 1960, followed by similar behavior of AETBclim/P and AETTclim/P but with no significant increase in CV(R). Both subcatchments experienced the same changes in terms of total area coverage by different land uses. If the two are compared in terms of water-use-related changes, Kozluk has experienced on average 43 times more increase in water surface area of man-made reservoirs, 20 times more increase in their volume and 16 times increase in hydropower production per subcatchment area between time periods 1931-1960 and 1964-1990.

Fig. 13 Modeled load changes for DIN and TP in the SRC nested subcatchments from historical period (1961-1990) to time periods (2010-2039) denoted as period A, and (2070-2099) denoted as period B, for the two emissions scenarios RCP2.6 and RCP8.5. Results are shown for the ensemble mean and the average of two best performing models in terms of runoff (GISS-E2_H) and runoff change (IPSL-CM5A-LR). For the ensemble mean predictions shown are error bars of models range.

-0.400

-0.300

-0.200

-0.100

0.000

0.100

0.200



-0.040

-0.030

-0.020

-0.010

0.000

0.010

0.020



-0.400

-0.300

-0.200

-0.100

0.000

0.100

0.200



-0.040

-0.030

-0.020

-0.010

0.000

0.010

0.020



Zagreb Rugvica Davor SBrod SKobaš Županja SRCΔ

L clim

(T y

ear-1

km-2

)

DIN changes-ensemble mean

0.200

0.100

0.000

-0.100

-0.200

-0.300

-0.400


ΔL c

lim(T

yea

r-1km

-2)

DIN changes-mean of the two best perfroming models

0.200

0.100

0.000

-0.100

-0.200

-0.300

-0.400


ΔL c

lim(T

yea

r-1km

-2)

TP changes-ensemble mean

0.020

0.010

0.000

-0.010

-0.020

-0.030

-0.040


ΔL c

lim(T

yea

r-1km

-2)

0.020

0.010

0.000

-0.010

-0.020

-0.030

-0.040

TP changes-mean of the two best perfroming models

-0.400

-0.300

-0.200

-0.100

0.000

0.100

0.200



ΔY c

lim(T

/yea

r/km

2) rcp2.6 A

RCP8.5 A

RCP 2.6 B

RCP8.5 B

DIN

RCP 2.6 A

RCP 8.5 A

RCP 2.6 B

RCP 8.5 B

-0.400

-0.300

-0.200

-0.100

0.000

0.100

0.200



ΔY c

lim(T

/yea

r/km

2) rcp2.6 A

RCP8.5 A

RCP 2.6 B

RCP8.5 B

DIN

RCP 2.6 A

RCP 8.5 A

RCP 2.6 B

RCP 8.5 B

-0.400

-0.300

-0.200

-0.100

0.000

0.100

0.200



ΔY c

lim(T

/yea

r/km

2) rcp2.6 A

RCP8.5 A

RCP 2.6 B

RCP8.5 B

DIN

RCP 2.6 A

RCP 8.5 A

RCP 2.6 B

RCP 8.5 B

-0.400

-0.300

-0.200

-0.100

0.000

0.100

0.200



ΔY c

lim(T

/yea

r/km

2) rcp2.6 A

RCP8.5 A

RCP 2.6 B

RCP8.5 B

DIN

RCP 2.6 A

RCP 8.5 A

RCP 2.6 B

RCP 8.5 B

a b

c d


21

A concurrent increase in AETwb/P with a decrease in CV(R), together with expanding hydropower development, have also been reported by Destouni et al. (2013) for a set of Swedish basins. In both the SRC and Sweden cases, hydropower production, along with an area and a volume of man-made water reservoirs, added in our study, are used as proxies representing the whole range of related changes that might occur due to water-use developments. Building dams and multi-purpose water reservoirs (for household and industrial water supply, flood protection, irrigation) result in quite invasive disruptions to the landscape including diversions of rivers, flooding of large areas and changes in groundwater levels. These can be reflected in changes in atmospheric water. For example, Hossain et al. (2010) have detected considerable alterations of extreme precipitation due to the presence of large dams in South Africa, India, the western United States and Central Asia. Large dams in different parts of the United States (Degu et al., 2011) have further been shown to influence spatial gradients of surface evaporation, specific humidity and available potential energy over distances of up to 100 km from related water reservoirs. The consistency of the SRC results with the Swedish basins and other parts of the world (Jaramillo and Destouni, 2014) open a further possibility for the approach to be tested on other hydropower-influenced catchments in order to quantify different types of changes for which direct data are not available, but might be presented through the proposed proxies. A question that might arise here is whether different types of hydropower development would still show the same signal in terms of water cycle changes.

The catchment water-balance quantification of hydro-climatic change used in this study has been previously implemented for regions around the world, such as the Aral Sea drainage basin (Shibuo et al., 2007), the Mahanadi River in India (Asokan et al., 2010) and Sweden (Jaramillo et al., 2013; Destouni et al., 2013; Jaramillo and Destouni, 2014). These regions underwent

very different land- and water-use developments (intensive agriculture and irrigation, for example) than those in the SRC. Despite this, in all the cases including the SRC this simple approach has been shown as a good tool for distinguishing the potential effects of natural climate change from those induced by human land and water use.

Assumed stationary conditions in inter-annual hydroclimate implied by negligible water storage in Eq. 1. allow for calculation of AETwb, data that in general is rarely measured in any river catchment and particularly over longer time periods. The assumption of ΔS≈0 has been used even in the case of surface water storage of Aral Sea Basin, severely influenced by irrigation. Still, for any future study we would advise verifying its accountability. In cases where data on water storage changes are available, which was not the case for the SRC, these should be included directly in the water balance equation. If the data are not available, alternative indirect methods of checking the validity of the assumption are advisable. One example would be an approach implemented by Jaramillo et al. (2013) in which potential water storage changes in catchments were analyzed in relation to observed water levels change in major lakes. Another interesting approach is taking into consideration apparent actual evapotranspiration AETA, which differs from AETwb as it includes a component of non-zero water storage change (Jaramillo and Destouni, 2014).

5.2 Data-driven nutrient analysis

The results of our proposed data-driven methodology for spatial-resolving total nitrogen (TN) and total phosphorus (TP) in river catchments has identified incremental subcatchment Inc2 as a particular hotspot in the case study of the SRC. This catchment exhibits 15 times higher population density than any other SRC catchment, thus implying large associated nutrient inputs per unit area indicated by App2. The large nutrient delivery factor of Inc2 retrieved by App1 for both DIN and TP further implies


22

relative large fractions of impervious land cover and flow, often detected in highly urbanized areas. As the nutrient monitoring station of this subcatchment is just downstream of Croatian capital Zagreb, urbanization’s influence on the nutrient loading could be a fairly justifiable reason. This reasoning is also backed up by the fact that the capital had no wastewater treatment plant prior to 2004. Besides, according to Tušar (2009), most of the storm water and industrial wastewater were discharged directly into the Sava River.

A combination of both indications implied by the two different approaches is then the most likely cause of high DIN and TP loadings’ contribution of contaminants from Inc2 to the entire catchment as well as to the Rugvica subcatchment, which emerges as a hotspot among nested catchments.

Overall for all the incremental subcatchments discovered is a decrease in both the input (ΔIi/ΔAi) and the delivery factor per unit area (Δαi/ΔAi ) with an increase in subcatchment scale.

The scale dependence of nutrient transport and delivery in terms of αi are recognized as the main drivers of non-linear power-law decay of Li/Ai with increasing catchment scale. This is also consistent with the results of the SRC nutrient loads exhibiting primarily hydro-climatic-driven dependency, through the dynamics of runoff.

On the other hand, nutrient concentration dynamics have been shown essentially independent of runoff as found also in other world regions (Basu et al., 2010) as well as in the Baltic, to which the SRC results have been directly compared. Consistent close relations of the SRC nutrient concentration to human-related characteristics of farmland share and population density, previously also found in other regions (Destouni et al., 2015; Juston et al., 2016) might further explain why Inc2 and Rugvica emerge as nutrient hotspots in the SRC.

Generally, the proposed methodology has managed not only to identify subcatchments

that potentially contribute high loadings of contaminants, but to also provide useful estimates of characteristic regional values and possible scale-dependencies among them.

5.3 Use of CMIP5 model data for catchment-based analysis

The thesis also analyzed CMIP5 output results for historical catchment-scale implications and possible future hydro-climatic and nutrient load changes specifically investigated in the SRC.

The ensemble mean results of CMIP5 historical experiment for the period 1961-1990 for the six Northern Hemisphere catchments show a relatively large change in the long-term average net water balance of ΔS=10 mm yr-1, with the extreme result of ΔS=-146 mm yr-1 for Greece. Reasons for nonzero balance in models might involve several factors and might differ among the catchments but with groundwater change playing an important role in both warm and cold temperate regions. Nonconsumptive water addition (e.g., by permafrost thaw) might be another valid reason for the change in groundwater level and also in average R from the catchment, without sustained changes in P or ET (Milliman et al., 2008; Bring and Destouni, 2011; Karlsson et al., 2012; Mazi et al., 2014). For warm temperate areas, such as Greece with extreme results and the SRC as in the case study, shifts in the long-term average amount of groundwater or surface water level may occur due to irrigation, hydropower or other changes related to water use.

If the more detailed analysis of the SRC change projections are compared with similar studies for the catchment, a certain consistency is found considering temperature and evapotranspiration change projections (World Bank Group, 2015; Gampe et al., 2016).

A large range and standard deviation of all water fluxes, together with large water changes of magnitude and direction of water storage changes for the SRC and other


23

catchments, indicate an unsatisfactory process presentation of freshwater balance within CMIP5. Model-observation differences of AET are shown to be largest in Greece, possibly due to the surrounding sea that dominates the water-related representations in CMIP5 for this area or due to insufficient resolution (Arakawa, 2004; Christensen et al., 2007; Stevens and Bony, 2013; Palazzi et al., 2015). All the problems mentioned indicate an issue with driver process representation and resolution of the catchment-scale freshwater systems in CMIP5.

A lack of consistency in the results between different models implies difficulty in finding the best performance model for the region, exposing the ensemble mean as more useful for hydro-climatic and nutrient change assessment and projection.

6. CONCLUSIONS

This thesis identified and quantified hydro-climatic changes in the Sava River Catchment using a catchment-based, data-driven analysis. We succeeded to distinguish between natural climate and human induced drivers of change and compare these for the SRC with Baltic Sea and Swedish catchments. On the basis of water discharge and nutrient concentration data over a given time period, we developed a simple methodology for estimating characteristic regional nutrient loads, inputs and retention- delivery factors and their catchment-based dependencies, as well as to relate them to anthropogenic and landscape drivers for the SRC. As the first study to explicitly evaluate catchment-constrained water balances for several climatically different catchments, we highlighted a number of complexities with regard to CMIP5 model representation of the freshwater exchanges between land, atmosphere and the ocean and their use for predictions of future water fluxes and nutrient loads. The present results provide a new and extended basis for further assessment of water and nutrient flows and related questions on subcatchment to catchment scales for other world regions.

Specific conclusions are related to the three thesis objectives as follows:

Objective A

This thesis has developed a catchment-wise data-driven approach for detecting climate-and human-induced drivers of past-to-present hydro-climatic change.

For the Sava River Catchment, the approach revealed the hydro-climatic change as a combination of two distinct signals within the catchment.

The first signal manifests itself as shifts of relative evapotranspiration AETwb/P to higher level and runoff variability CV (R) to lower level due to dominant hydropower development activities in the Kozluk subcatchment.

The second signal of unregulated Slavonski Brod catchment exhibits AETwb/P shifts explainable by observed climate change and with concurrent stable values of CV(R).

The revealed consistency of the SRC results with Swedish catchments represents an important step towards possible generalization of the approach and its application for other world regions.

Objective B

The proposed nutrient screening methodology in this thesis has been shown to provide useful and realistic estimates of characteristic regional values of nutrient loads, inputs and retention-delivery factors and their catchment-based dependencies.

Two different proposed approaches provided a good distinction between human-related nutrient inputs and landscape-related transport influences on nutrient loading at subcatchment to catchment scale.

The data-driven analysis has also managed to detect incremental subcatchment Inc2 and the nested catchment Rugvica as specific nutrient hotspots within the SRC.

A cross-regional comparison of the SRC data with the Baltic region shows a similarity between nutrient-relevant indicators and driving socio-economic and hydro-climatic conditions.


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Similarly to previous investigations in the Baltic and other parts of the world (Basu et al., 2010), population density and farmland share in the SRC emerge as main drivers for nutrient concentration and runoff as a main hydro-climatic driver of nutrient loads.

Thus, the developed methodology is proposed as a simple tool for first-order estimates of nutrient dynamics in other regional catchments.

Objective C

As the first study to explicitly evaluate catchment-constrained water balances for several climatically different catchments, we have managed to highlight a number of complexities with regard to CMIP5 model representation of the freshwater exchanges between land, atmosphere and ocean.

We find no realistic predictions for model ensemble means and a number of individual climate models over the regions of the study.

Catchment scale has not been shown to be the crucial factor for the complexities found in terms of CMIP5 model representation of the water fluxes.

A projected temperature increase, followed by evapotranspiration increase and runoff decrease, indicate possible water scarcity issues for the case of the Sava River Catchment.

A more detailed investigation of individual models to estimate observed data does not exclude any of the models outperforming each other, indicating high inaccuracy and uncertainty.

The large intermodal range of modeled fluxes calls for caution when using individual model results for assessing ongoing and future water and nutrient changes.

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