Final Report IRD BM 2009 10

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    IRD-WB Contract 7148343 1 Final Report, October, 2009

    Final Report of the World Bank Project"Assessing the Impacts of Climate Change on Mountain Hydrology:Development of a Methodology through a Case Study in Peru"

    October, 2009

    CONTENT

    FINAL REPORT SUMMARY

    PART 1 - DESCRIPTION OF THE TASKS ASSIGNED TO IRD

    1.1 - Task 1: Elaboration of selection criteria for the representations

    1.2 - Task 2: Evaluation and choice of the models

    1.3 - Task 3: Data acquisition for the river basins

    1.4 - Task 4: Evaluation of climate change impacts

    1.5 - Task 5: Documentation and dissemination

    PART 2 - COMPLETION OF THE ASSIGNED TASKS

    2.1 - Task 1: Elaboration of selection criteria for the representations

    2.2 - Task 2: Evaluation and choice of the models

    2.3 - Task 3: Data acquisition for the river basins

    2.4 - Task 4: Evaluation of climate change impacts

    2.5 - Task 5: Documentation and dissemination

    PART 3 MODELLING THE RIMAC-MANTARO SYSTEM

    PART 4 CONCLUSION AND PROSPECTSREFERENCES

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    FINAL REPORT SUMMARY

    This document constitutes the final report of Contract 7148343 between IRD and theWorld Bank, corresponding to the project "Assessing the Impacts of Climate Changeon Mountain Hydrology: Development of a Methodology through a Case Study inPeru". The IRD contributors are Jean-Christophe Pouget, Wilson Suarez, Thomas

    Condom, Patrick Le Goulven. In this project between July 2008 and August 2009, IRDworked in close collaboration with Stockholm Environment Institute (SEI). Due to theexisting IRD research network in Peru, IRD collaborated with several institutesincluding the Servicio Nacional de Meteorologa e Hidrologa del Per (SENAMHI), theUniversidad Nacional Agraria La Molina (UNAM), and the glacier and hydrological unitof ANA / INRENA.

    The current report begins with a summary of the tasks requested by the World Bankin IRDs original scope of work. Part 2 continues with a description of the workcompleted. Table 1 presents the work schedule, as envisaged initially, and asperformed. Due to delays encountered by the project partners charged with producingfuture climate projections, IRD could not run the elaborated models for future

    scenarios, in order to evaluate climate change impacts on Andean hydrology,corresponding to Task 4. While collaborating with SEI, IRD led the development of themodels of the Rimac and Mantaro river basins. Part 3 presents this modelling of theRimac-Mantaro system, as it corresponds to the IRD work between April and August2009.

    The report concludes with a section on prospects for future activity and includes thefollowing documents:

    Appendix 1 - Technical Report on glacier and high elevation wetlands modelselection and parameterization - Condom T., Suarez W., Pouget J.C.,Le Goulven P. June 2009

    Appendix 2 - Technical Report on data acquisition and pre-processing for the RioSanta, Rimac and Mantaro river basins in Peru - Suarez W., PougetJ.C., Condom T., Le Goulven P. September 2009

    Appendix 3 - Manuscript: Modelling the Hydrologic Role of Glaciers within a WaterEvaluation and Planning System (WEAP): A case study in the RioSanta watershed (Peru) Condom T., Escobar M., Purkey D., PougetJ.C., Suarez W., Ramos C., Apaestegui J., Zapata M., Gomez J.,Vergara W. - Submitted to Journal of Hydrology, July 2009

    8/08 9/08 10/08 11/08 12/08 1/09 2/09 3/09 4/09 5/09 6/09 7/09 8/09 9/09Task 1

    Task 2

    Task 3

    Task 4

    Rimac-Mantaro

    Task 5 p.5.1

    p.5.2

    p.5.3

    a.5.4

    Table 1 Work Schedule envisaged initially performed

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    PART 1 - DESCRIPTION OF THE TASKS ASSIGNED TO IRD

    As the IRDs primary scientific responsibility on the project consists of choosing andcalibrating appropriate models of tropical glaciers and high elevation Andeanwetlands, the IRD research network was used to validate models on reference cases inthe Rio Santa system and to extend these insights to two additional study basins

    (Figure 1.1). Although the IRD team in charge of project management operated out ofQuito, Ecuador, IRD has proposed collaboration with a Peruvian colleague whorecently completed a PhD on the Rio Santa system with the support of IRD (Suarez,2007) and with an IRD researcher who works in Peru in high sites hydrology. Thetasks initially assigned to IRD are presented below.

    Figure 1.1. Map of study river basins location in Peru

    1.1 - Task 1: Elaboration of selection criteria for representations of tropical

    glaciers and high elevation wetlandsAs there is a range of approaches available to represent tropical glaciers and highelevation wetlands, including statistical models, conceptual models, quasi-physicalmodels, and process-based models, some criteria need to be established to assesswhich approach is the most accurate in the current project. One critical considerationwill be the level of correspondence between the results expected, the variousrepresentations and the data availability in the first investigation basin which is theRio Santa.

    1.2 - Task 2: Evaluation and choice of glacier and high elevation wetlandsmodels

    In order to select modelling approaches to simulate the evolution of glaciers and highelevation wetlands, we assess the performance of the various models on one wellmeasured system (the Artesnraju glacier in the Rio Santa basin). This task will be

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    concluded with the drafting of a technical report describing the model chosen, therange of appropriate parameterizations, and on the data needed to implement theselected model in the WEAP application (Water Evaluation and Panning System).

    1.3 - Task 3: Data acquisition for the Rio Santa, Rimac and Mantaro riverbasins in Peru

    In addition to glaciers and pramos, the river basins in Peru include other land unitswhich influence the overall hydrologic response. In order to develop a tool to assessthe hydrologic implications of climate change, the land units must also becharacterized. As the investigated basins are not pristine, we have selected two otherwatersheds based on their importance to the Peruvian hydropower system. Data alsoneeds to be gathered on the actual and potential infrastructure. Finally, ashydropower is not the only important use of water in Peruvian river basins, relevantdata on other sectors could also be collected.

    In this task, the strategy will be to develop the most complete database for the RioSanta, including spatial data on land units and water management infrastructure. Afirst attempt will be made to assess water demand in the various sectors in this basinso that the eventual utility of the tool could be to explore water managementimplications under a climate change and to propose potential adaptations in apreliminary fashion. However, the primary focus of this phase of work in the Rio Santasystem will be on hydrologic change and its potential hydropower impacts.

    For the Rio Rimac and Rio Mantaro systems, database development will be moredifficult. Local institutions, such as Sedapal and the hydropower utilities, will berequested to provide most of the data following the topology and guide provided bySEI for such tasks. The application of WEAP with glacier and pramos modules willserve to test and verify the main hydrologic and hydropower utility of the glacier andpramos models developed and parameterized for the Rio Santa.

    1.4 - Task 4: Evaluation of climate change impacts on Andean hydrology

    This task will really begin the process of integrating WEAP into the Peruvian watermanagement community by running the model under the climate change scenariosdeveloped in Task 8. The major output of this task will be to characterize thehydrologic implications of climate change in the investigated basins. The implicationsof these changes on hydropower potential will also be assessed in the three basinswith a first assessment of the broader water management implications of climatechange being conducted in the Rio Santa system.

    1.5 - Task 5: Documentation and Dissemination

    This task will include the organization of briefings on project activities and outputs forthe World Bank in Washington, D.C. and for appropriate Peruvian institutions in Lima.A technical report on the application of the tool in the three basins and an assessmentof the model results will also be produced and disseminated. This report will note thepotential climate change impacts on the hydropower sector in Peru and will includerecommendations on how the hydrologic model could be used and expanded to othersectors, in other basins in Peru and the wider Andean region.

    p.5.1 - Technical report on glacier and pramos model selection and parameterization

    p.5.2 - Reports on the application of the hydrologic model to the selected pilotwatersheds

    p.5.3 - Final report

    a.5.4 - Presentations of the project and the results.

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    PART 2 - COMPLETION OF THE ASSIGNED TASKS

    2.1 - Task 1: Elaboration of selection criteria for representations of tropicalglaciers and high elevation wetlands

    This task was performed as envisaged initially (see Table 1). During the meeting inLima at the university La Molina from September 25 to 27, 2008, Wilson Suarez andThomas Condom presented various approaches for glaciers modelling. Appendix 1Technical Report on glacier and high elevation wetlands model selection andparameterization begins to present in detail several kinds of glacier modelling. Table2.1 presents the principal selection criteria of glacier modelling. According to the basindata availability, we choose the degree-day model for the glacier representation.

    Characteristics Energy Balance Degree_day, Index Hybrid (Balance+degree-day)

    Short description Model based on thestudy of the exchangeof energy between thesurface glacier and theatmosphere

    Starts by a similarenergy balance concept,but considers that allthe physical processesare summarized in thetemperature (the T is aconsequence and not acause)

    Similar to the degree-day, but to improve itsefficiency it uses thealbedo, radiation, etc.These variables areadded one by one.

    Complexity High Simple Intermediate

    Represents physicalprocesses

    Yes No Partially

    Efficiency of the model High Intermediate - high Intermediate - high

    Number of parameters 6 to 9 2 or 3 2 to 5

    Input variables for thewhole model

    More than 6:- Incident radiation- Diffuse radiation- Liquid and solidprecipitation- Humidity- Long wave radiation(incident and reflected)- Short wave radiation(incident and reflected)etc.

    3:- Precipitation- Evaporation- Temperature

    Depending on thecomplexity:- Precipitation- Evaporation- Temperature- Albedo- Radiation

    Level of spacialization Complex (generally

    grid)

    Global or semi

    distributed

    Semi distributed, global

    or gridAdvantages Its efficiency : physical

    process representationFew parametersFew input variables

    Few parametersFew input variables

    Disadvantages Needs too muchinformation (sometimesnon- inexistent)

    Does not explainphysical processes

    Explains the physicalprocesses partially

    Possible application Probably Santa Santa, Rimac, Mantaro Santa

    RecommendedBibliography

    Hook, 2005;Favier, 2004;Juen, 2006

    Hook, 2005;Schaefli and all, 2005;Martinec and Rango,1986

    Hook, 2005;Klok and all, 2001;Lang, 1990;Zhang and all, 2007

    Table 2.1.Principal selection criteria of glacier modelling.

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    2.2 - Task 2: Evaluation and choice of glacier and high elevation wetlandsmodels

    Since the meeting with Marisa Escobar and David Purkey in September 2008 at Lima,we worked in close collaboration with SEI to propose and evaluate a conceptualmodelling of mountain basins partially covered with glaciers. Also, we produced

    several versions of a working paper titled An Approach for Modelling the HydrologicRole of Glaciers in WEAP. The first proposal was sent to the World Bank on October30, 2008. The last proposal from January 30, 2009 is presented as an appendix to thefirst report (Appendix 1).

    The document Construccin del Modelo WEAP del Ro Santa from November, 2008presents, for the Santa Basin, the following: (1) data collection; (2) river basincharacterization; (3) recognition visit (September 21-24, 2008); (4) climate dataprocess; (5) demands estimation; (6) first model calibration for sub basins withoutglaciers (cf. www.mpl.ird.fr/divha/aguandes/peru/doc/Avance_RioSanta_WEAP-2008-11.pdf).

    From January to February 2009, we took an active part in the equations checking ofthe glaciers model within WEAP and in the calibration of this model for the Artesnglacier in the Rio Santa basin. From February to April 2009, we worked closely withSEI-US, leader on this task, in order to calibrate and validate a complete model forthe Santa river basin. This accurate modelling strategy caused an increase of the Task2 duration.

    Results from the final calibration-validation of the Rio Santa model are presented indetail in Appendix 3. Given the importance of the simulated flows at La Balsa in termsof assessing potential climate change impacts to the hydroelectric power station ofCaon del Pato, the performance of the Rio Santa WEAP application at that point onthe river is particularly satisfactory (Figure 2.1).

    Figure 2.1. Correspondence between simulated (continuous think line) and observed (discontinuous thickline) stream flow at Balsa gauge station between Sep 1969 Aug 1997.

    As simulated streamflow represents the combined contribution of runoff from bothglaciated and non-glaciated portions of a watershed, the simulated glacier areaevolution results were also evaluated and found satisfactory. Observations suggestthat the initial glaciated area in this watershed, 507 km2 in 1970, was reduced to 387km2 in 1999 (Table 2.2). This trend was well captured by the model. Comparison toobserved glacier areas from 1987 and 1999 indicates a good correspondence withsimulated areas (Figure 2.2; Table 2.2). In the Rio Santa watershed, the calibration oflarger glaciers is better than the calibration of smaller glaciers, likely because small

    glaciers are more likely to be dominated by unique conditions that are not wellcaptured by either the glacier module itself or the regional parameterization that wasdeveloped for the Rio Santa watershed (Appendix 3).

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    Table 2.2.Simulated and observed data of glaciers evolution between 1970 and 1999.

    Figure 2.2. Scatter plot graph with observed versus simulated glacier areasfor the two periods (1987 and 1998).

    It should be pointed out that the results in Figures 2.1 and 2.2 were achieved using asingle set of parameter values for both the rainfall-runoff and glacier routines inWEAP. A more refined calibration could be achieved if an effort was made to calibrateeach glacier and sub-watershed in the Rio Santa separately, although care would needto be taken to develop a spatially reasonable set of parameters (SEI final report, sep.2009).

    From April to August 2009, IRD led the effort to develop the Rio Mantaro and RioRimac WEAP applications. As there is a major water transfer from the Rio Mantaro tothe Rio Rimac watershed, it was decided that both rivers should be implemented in asingle WEAP application. The Rimac River was modelled to the point of diversion tothe Lima water system, and the Mantaro River was modelled down to the proposed LaGuitarra hydropower facility. We worked closely with SEI in order to calibrate andvalidate this complex application. We present this work in PART 3 MODELLING THERIMAC-MANTARO SYSTEM. Note, the results of the glacier area evolution simulation inthe Rio Mantaro/Rio Rimac system were satisfactory.

    Although extensive pramos landscapes are not present in the three pilot watershedsin Peru, IRD attempted to parameterize existing rainfall-runoff models in a mannerthat could capture the unique nature of hydrologic processes in watersheds dominatedby pramos. We used data of a little watershed near Quito in Ecuador, with 90% ofpramos. The existing WEAP rainfall-runoff model (Soil Moisture Model) could be

    calibrated. We also calibrated the GR2M, monthly two parameter rainfall-runoff modelof Gnie Rural (Mouelhi et al., 2006). We used these calibrations and the GR2Mcodification within WEAP as examples during the courses WEAP and Climate Change(VIII Encuentro Internacional GTNH PHI-UNESCO, Sep. 18-23, 2009, Quito).

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    2.3 - Task 3: Data acquisition for the Rio Santa, Rimac and Mantaro riverbasins in Peru

    The data acquisition is a very sensitive point of our work because the necessary datais dispersed across different institutions and is submitted to some restrictions andlimited access (public and private institutions). We have established with SEI-US a list

    of the required information for the WEAP modelling (see Appendix 1.,www.mpl.ird.fr/divha/aguandes/peru/doc/Avance_RioSanta_WEAP-2008-11.pdf)and for the glaciers models calibration.

    Initially we asked for support from the ministry of energy and mines of Peru (MINEM).We obtained recommendation letters to Duke Energy, Electroper, EDEGEL and theElectric Operation Committee (COES). We have contacted the following persons:

    - ANA / INRENA Ing. Carlos Pagador, Intendente de aguasIng. Aldrin Contreras, Responsable del area hidrolgicaIng. Marco Zapata, Director de la unidad de glaciologa

    - SENAMHI Ing. Julio Ordoez, Director de Hidrologa

    Ing. Hctor Vera, HidrologaIng. Oscar Felipe, Direccin de hidrologaIng. Waldo Lavado, Direccin de hidrologa

    - IRD Dr. Robert Gallaire, Responsable GREATICE Per

    - Duke Energy Ing. Carlos Glvez, Ing. de produccinIng. Julio Velsquez, Sub-gerente comercialIng. Abel Rodrguez, Jefe hidrolgica de la Central Huallanca

    - EDEGEL Ing. Carlos Rosas, Sub gerente de ComercializacinIng. Miguel Suarez, Centro de control y operacionesIng. Eduardo Ibarra, Coordinador

    Ing. Jhony Huaman, Coordinador

    - Electroperu Ing. Guillermo Romero, Gerente de proyectosIng. Jos Barbe, Gerente de produccinIng. Jaime Huaman, Responsable del rea hidrolgicaIng. Juan Villegas, responsable del rea de Lagunas

    In addition to the data acquisition, an important treatment work was carried out toconstitute time operational databases for the hydro-meteorological data (Hydraccess).Furthermore, an important project to compile geographic information was completed,including the processing of satellite images to reconstitute glaciers extensions and thespatialization of climate data. This work is presented in detail within the Appendix 2 -

    Technical report on data acquisition and pre-processing for the Rio Santa, Rimac andMantaro river basins in Peru - Suarez W., Pouget J.C., Condom T., Le Goulven P. September 2009.

    Tables 2.3, 2.4, and 2.5 present the collected and processed data for Santa, Rimacand Mantaro river basins.

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    Datos Requeridos para Alimentar el Modelo Prioridad Santa Fuente

    Datos de Entrada Demandas

    o Cobertura de vegetacin 1 UNALM

    o Precipitacin (series de datos histricas, i.e. promedio mensualen cada ao del periodo de modelacin) 1 32 estaciones

    IRD, INRENA,DUKE

    o Temperatura (series de datos histricas, i.e. promedio mensualen cada ao del periodo de modelacin) 1 7 estaciones

    IRD, INRENA,DUKE

    o Humedad Relativa (promedio mensual del periodo demodelacin) 1 6 estaciones

    IRD, INRENA,DUKE

    o Viento (promedio mensual del periodo de modelacin) 1 5 estacionesIRD, INRENA,

    DUKE

    o Numero de usuarios 1PoblacinUrb./rural

    INEI (paginaWEB)

    Datos de Entrada Suministro y Recursos

    - Reservorios/represas 1 4 lagunas DUKE

    Datos fsicos:o Capacidad de almacenamiento 4 lagunas DUKE

    o Volumen inicial 4 lagunas DUKE

    o Curva de volumen/elevacin 4 lagunas DUKE

    o Evaporacin

    o Perdidas a agua subterrnea

    Datos de operacin

    o Mximo nivel de conservacin Huallanca COES

    o Mximo nivel de seguridad Huallanca COES

    o Mximo nivel inactivo Huallanca COES

    - Capacidad hidroelctrica 1 Huallanca COES

    o Mnimo caudal de turbina Huallanca COES

    o Mximo cauda de turbina Huallanca COES

    o Cabeza hidrulica Huallanca COES

    o Factor de Planta Huallanca COES

    o Eficiencia Huallanca COES

    - Requerimientos de caudales mnimos 2 Huallanca COES

    Datos para Calibracin del Modelo

    - Ros

    o Series de tiempo de caudales 1 25 estacionesIRD, INRENA,

    DUKE

    SIG IRD

    O Imgenes glaciares 2006-Aster SENAMHI

    2003-SPOT5 IRD

    2000-Landsat5 SENAMHI

    1987-Landsat5 SENAMHI

    1970-Carta

    Nacional SENAMHI

    Table 2.3.Collected and processed data for the Santa river basin.

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    Datos Requeridos para Alimentar el Modelo Prioridad Rmac Fuente

    Datos de Entrada Demandas

    o Cobertura de vegetacin 1 1 carta INADE

    o Precipitacin (series de datos histricas, i.e. promedio mensual en cadaao del periodo de modelacin) 1

    24estaciones

    SENAMHI,EDEGEL

    o Temperatura (series de datos histricas, i.e. promedio mensual en cada

    ao del periodo de modelacin) 1

    5

    estaciones

    SENAMHI,

    EDEGEL

    o Humedad Relativa (promedio mensual del periodo de modelacin) 15

    estaciones

    o Viento (promedio mensual del periodo de modelacin) 1por

    determinarSENAMHI,EDEGEL

    o Numero de usuarios 1PoblacinUrb./rural

    Censonacional.

    Datos de Entrada Suministro y Recursos

    - Reservorios/represas 115 lagunasy presas EDEGEL

    Datos fsicos:

    o Capacidad de almacenamiento 15 lagunas EDEGEL

    o Volumen inicial 15 lagunas EDEGEL

    o Curva de volumen/elevacin EDEGEL

    o Evaporacin 1 laguna EDEGEL

    o Perdidas a agua subterrnea 1 laguna EDEGEL

    Datos de operacin

    o Mximo nivel de conservacin 6 centrales COES

    o Mximo nivel de seguridad 6 centrales COES

    o Mximo nivel inactivo 6 centrales COES

    - Capacidad hidroelctrica 1 6 centrales COES

    o Mnimo caudal de turbina 6 centrales COES

    o Mximo cauda de turbina 6 centrales COES

    o Cabeza hidrulica 6 centrales COES

    o Factor de Planta 6 centrales COES

    o Eficiencia 6 centrales COES

    - Requerimientos de caudales mnimos 2 6 centrales COES

    Datos para Calibracin del Modelo

    - Ros

    o Series de tiempo de caudales 1 8 estac.

    SIG IRD

    Imgenes glaciares2008-

    Landsat5 INPE

    1998-Landsat5 INPE

    1988-Landsat5

    INPE-Maryland

    1980-Landsat2 INPE

    1970-Carta

    Nacional

    Table 2.4. Collected and processed data for the Rimac river basin

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    Datos Requeridos para Alimentar el Modelo Prioridad Mantaro Fuente

    Datos de Entrada Demandas

    o Cobertura de vegetacin 1

    o Precipitacin (series de datos histricas, i.e. promedio mensual encada ao del periodo de modelacin) 1

    172estaciones

    SENAMHI,ELECTROPERU

    o Temperatura (series de datos histricas, i.e. promedio mensual encada ao del periodo de modelacin) 1 Evaluacion

    SENAMHI,ELECTROPERU

    o Humedad Relativa (promedio mensual del periodo de modelacin) 1 EvaluacionSENAMHI,

    ELECTROPERU

    o Viento (promedio mensual del periodo de modelacin) 1 EvaluacionSENAMHI,

    ELECTROPERU

    o Numero de usuarios 1PoblacinUrb./rural

    Sensonacional

    Datos de Entrada Suministro y Recursos

    - Reservorios/represas 121 constr.-28 proyecto

    ElectroperuCOES

    Datos fsicos:

    o Capacidad de almacenamiento 19 lagunasy presas ElectroperuCOES

    o Volumen inicial 19 lagunasy presas

    ElectroperuCOES

    o Curva de volumen/elevacin 19 lagunasy presas

    ElectroperuCOES

    o Evaporacin

    o Perdidas a agua subterrnea

    Datos de operacin

    o Mximo nivel de conservacin 3 centrales COES

    o Mximo nivel de seguridad 3 centrales COES

    o Mximo nivel inactivo 3 centrales COES

    - Capacidad hidroelctrica 1 3 centrales COES

    o Mnimo caudal de turbina 3 centrales COES

    o Mximo cauda de turbina 3 centrales COES

    o Cabeza hidrulica 3 centrales COES

    o Factor de Planta 3 centrales COES

    o Eficiencia 3 centrales COES

    - Requerimientos de caudales mnimos 2 3 centrales COES

    Datos para Calibracin del Modelo

    o Series de tiempo de caudales 120

    estaciones

    SIG IRD

    Imagenes glaciares2008-

    Landsat5 INPE

    1998-Landsat5 INPE

    1988-Landsat5

    INPE-Marilland

    1980-Landsat2 INPE

    1970-CartaNacional

    Table 2.5. Collected and processed data for the Mantaro river basin

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    2.4 - Task 4: Evaluation of climate change impacts on Andean hydrology

    Due to delays encountered by the project partners charged with producing futureclimate projections, we could not run the elaborated models for future simulations. Inconnection with SEI-US, we took the lead in developing the models of the Rimac andMantaro river basins.

    Although it was not possible to perform detailed climate change analysis during theperiod of project implementation, SEI performed a preliminary exercise using stylizedclimate projections derived from available datasets. Section 3 of the SEI final reportdescribes the results of this preliminary assessment which was carried out using theRio Santa WEAP application and provided insight into how the types of analysis of thepotential impacts of climate change in the region can be supported using the WEAPsoftware enhanced to represent unique features of Andean hydrology.

    2.5 - Task 5: Documentation and Dissemination

    We organized several presentations: projects management unit of Electroperu (July,2008), management unit of EDEGEL (July, 2008), production unit of Duke Energy

    (July, 2008), production unit of Electroperu (Enero, 2008), etc.

    Important steps of the dissemination were:

    - The mission organized by IRD in the Santa river basin, September 21-24, 2008, withthe participation of Adriana Valencia, World Bank; Marisa Escobar, SEI-US; CayoRamos, Universidad La Molina; Thomas Condom, Jean-Christophe Pouget, andWilson Suarez, IRD, as well as with visits to the water collecting unit of theCHAVIMOCHIC irrigation project; hydro power unit of Caon del Pato with the DukeEnergy staff; and several characteristic parts of the river basin with notablyregulated lakes downstream of glaciers (seewww.mpl.ird.fr/divha/aguandes/peru/santa/mision-2008-09/);

    - The training course Sistema de Evaluacin y Planeacin de Agua - Una Herramientapara el Anlisis de Sostenibilidad del Agua, Universidad La Molina Lima, September25-27, 2008, co-organized by IRD, SEI-US and Universidad Nacional Agraria LaMolina, with 45 participants from public and private institutions related to waterresources management in Peru, notably from SENAMHI (two persons), Electroperu(three persons), INRENA (four persons), DUKE Energy (one person), EDEGEL (oneperson), CHAVIMOCHIC (one person), Ministerio del Medio Ambiente (one person).

    The use of the WEAP Santa river basin model has been used for several presentations:

    Apoyo a la Gestin de los Recursos Hdricos Introduccin a la Herramienta

    WEAP., J.C. Pouget, Universidad Nacional de San Agustn de Arequipa, 28 y 29Septiembre, 2008 (ver www.mpl.ird.fr/divha/aguandes/peru/arequipa/mision-JCP-UNSA-Arequipa-2008-09-29.pdf)

    Curso-Taller IRD, EPN, FONAG, Sistema de Apoyo a la Planificacin de losRecursos Hdricos - Capacitacin Basica a la Herramienta WEAP, J.C. Pouget,Escuela Politcnica Nacional de Quito, Febrero 19 y 20, 2009

    Presentacin al 1er Congreso Nacional del Agua de Peru Variaciones glaciaresy disponibilidad del agua en la Cordillera Blanca del Per desde hace 40 aos-,T. Condom, Universidad Nacional La Molina, Lima, Marzo 19 al 21, 2009

    VIII Encuentro Internacional de Investigadores del Grupo de Trabajo Nieves yHielos (GTNH) de Amrica Latina del PHI-UNESCO, Curso taller de Hidroglaciologa - WEAP y el Cambio Climtico, Organizadores: B. Cceres,

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    M. Villacis, B. Francou, J.C. Pouget, E. Ramirez, 18-23 septiembre 2009, HotelMercure, Quito Ecuador - Modelacin hidroglaciolgica orientada a la gestinde cuencas hidrogrficas con cobertura parcialmente glaciar. Expositores:Thomas Condom (IRD, Peru), Edson Ramrez (UMSA, Bolivia) - Ejemplos yejercicios de calibracin en Ecuador y Per - Adaptacin y Construccin denuevos mdulos en WEAP, Expositores: J.C. Pouget (IRD, Ecuador), David

    Purkey (SEI, USA).

    Taller internacional sobre cambio climtico en los Andes - Estado delconocimiento y enfoques para el futuro - Lima 24 - 26 septiembre 2009 MINAM CAN - IRD - Cooperacin regional Francia - Lugar: Sede CAN, Lima -Cambios climticos y recursos agua de origen glaciar: ejemplos tomados en laCordillera Real de Bolivia y en la Cordillera Blanca del Per. Expositores: EdsonRamrez (UMSA, Bolivia), Thomas Condom (IRD, Peru) - El Cambio climtico, laregresin de los glaciares y la definicin de un nuevo manejo del agua en lascuencas. Expositor: David Purkey (SEI, USA)

    Beyond this final report, the most important documentation produced during thisstudy is the manuscript for scientific publication entitled Modelling the Hydrologic Roleof Glaciers within a Water Evaluation and Planning System (WEAP): A case study inthe Rio Santa watershed (Peru), which was submitted to peer-reviewed Journal ofHydrology (see Appendix 3). SEI and IRD intend to revise this article, as needed, untilit is published in a peer-reviewed journal. The article does not include, however, anystatement on potential climate change impacts on the hydropower sector in Peru dueto delays encountered by the project partners charged with producing future climateprojections. While it was anticipated that these projections would be available prior tothe end of the current project in July 2009, this did not happen. Such analysis wouldcertainly constitute suitable material for drafting a subsequent journal article.

    We can also note the creation of the Santa, Rimac and Mantaro river basinspresentation with interactive maps from the site www.mpl.ird.fr/divha/aguandes/.These applications were developed with Google Maps technology and optimized withFirefox version 3. Figure 2.3 presents the interactive map of the Santa river basin,which permits access to data on the following: glaciers extensions, rainfall andhydrometric stations, irrigation areas, hydro power units, etc.

    Figure 2.3. Santa river basin www.mpl.ird.fr/divha/aguandes/

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    PART 3 - MODELLING THE RIMAC-MANTARO SYSTEM

    In close collaboration with SEI, IRD took the lead in developing the models of theRimac and Mantaro river basins. As there is a major water transfer from the RioMantaro to the Rio Rimac watershed, it was decided that both rivers should beimplemented in a single WEAP application. The Rimac basin river was modelled to thepoint of diversion to the Lima water system and the Mantaro basin river was modelled

    down to the proposed La Guitarra hydropower facility.

    3.1 Study area data

    The description of the study area and the pre-processing data of this complex systemwere presented within the Technical Report on data acquisition and pre-processing forthe Rio Santa, Rimac and Mantaro river basins in Peru (Appendix 2). Figure 3.1presents the rainfall areas and the location of data stations.

    Figure 3.1. Map of rainfall areas and location of data stations of the Rimac and Mantaro river basins.

    The optimal period of simulation is between September 1966 and August 1996

    (Appendix 2). We considered: (1) a calibration period between September 1970 andAugust 1981; 4 years, between 1966 and 1970, used to stabilize the model;(2) a validation period between September 1981 and August 1996.

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    As for the temperature and humidity data, only one good quality set of long andcontinuous time-series data exists, collected from the Cercapuquio station (12.422S,75.417W). Continuous temperature data for each catchment was obtained using atemperature gradient of 0.6C/100m applied to the temperature observed atCercapuquio. For the humidity and wind speed, we assumed that the long-termmonthly time series at Cercapuquio applied to all catchments.

    3.2 Proposed modelling and parameters

    The combined Rimac-Mantaro WEAP application (Figure 3.2) includes 38 WEAPreservoir objects, suggesting a much more significant level of hydraulic manipulationthan that which exists in the Rio Santa system where extensive glaciers provide muchof the water storage service. Twenty-two WEAP demand sites represent the urban andrural water demands in individual provinces, along with 276 (102 for Rimac and 174for Mantaro) WEAP catchment objects that are used to simulate rainfall-runoffprocess. Five WEAP diversion objects and nine WEAP run of river hydropower objectsare used to represent the hydropower production system and 28 WEAP streamflowgauge objects were available for calibration-validation of the hydrologic routines.

    Figure 3.2. Schematic of the Rimac and Mantaro system within WEAP.

    The Rio Mantaro and Rio Rimac watersheds are more complex than the Rio Santa,with many more subwatersheds and a higher level of hydraulic manipulationaccomplished via reservoir storage and release (see Section 3.5). As such, the finalcalibration-validation of the Rio Mantaro/Rio Rimac model focused on obtaining a setof parameters to reasonably represent the hydrology of the mainstream Mantaro andRimac rivers. Given that the rivers are located in dissimilar watersheds (the Mantaro isin the Amazon Basin, the Rimac drains to the Pacific), the project team, after ahundred simulations, did not attempt to define a uniform set of parameters. Instead,parameter values for each watershed were adjusted separately to represent the

    different physical processes of each basin, although each basin arrived at an internallyuniform set of parameters, presented in Table 3.1.

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    Land use parameters (part without glacier)Parameter unit Rimac Mantaro

    Crop coefficient 0.9 1.2Root zone capacity mm 425 425

    Root zone conductivity Cultivos mm/month 840 420others mm/month 600 300

    Deep water capacity mm 7500 300

    Deep water conductivity mm/month 800 300Runoff resistance factor Cultivos 1.2 1.2

    Matorral 0.9 0.9Planicie costera 0.6 0.6

    Tundra 0.6 0.6Flow direction % horizontal 0.2 0.2

    Initial storage fractions z1 % 30 30z2 % 10 30

    Glacier parametersParameter unit Rimac Mantaro

    T0 C 1.7 1.7asnow mm.month

    -1.C-1 300 300

    aice mm.month-1.C-1 600 600

    Table 3.1. Land use parameter values for the non glacial partand parameter values for the glacier module

    Although the Rio Mantaro modelling domain extends to the location of the projectedhydropower facility at La Guitarra, as there are no historical streamflow records atthat site model calibration could not be attained at this most downstream point.Similarly, while the Rio Rimac modelling domain extends to the point of waterdiversion to the city of Lima, the most downstream gauge in the system was locatedupstream, once again limiting the ability to calibrate the model at this key point of

    management interest.Detailed in Section 3.3, the assessment of model performance for the calibrationperiod 1970-1981 and validation period 1981-1996 was done at several gaugestations (19 stations for Mantaro, six stations for Rimac). But to retain the parameterspresented in Table 3.1, the focus was notably on the bigger downstream stations. Forthe Rio Mantaro, working upstream from La Guitarra, these stations include Pongor;Mejorada; Moya; Puente Stuart; Puente Chulec; and Upamayo. For the Rio Rimac,working upstream from the Lima diversion, the main gauging stations include Chosicaand Surco.

    Table 3.1 shows that the Runoff resistance factor and the Root zone conductivityparameters were defined considering the land cover (Cultivos, Matorral, Planiciecostera, Tundra).

    3.3 Calibration and validation streamflows results

    Efficiency criterions

    In order to test the validity of the different simulations scenarios and to calibrate thedifferent parameters values, we use three statistics: (a) the Root Mean Square Error(RMSE); (b) the BIAS; and (c) the Nash-Sutcliffe parameter (Nash and Sutcliffe,1970).

    n

    QQ

    QRMSE

    n

    i iois

    o

    =

    =1

    2

    ,,)(100

    (a)

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    ]/)[(100oos

    QQQBIAS =(b)

    =

    =

    =n

    i

    oio

    n

    i

    iois

    f

    QQ

    QQ

    E

    1

    2

    ,

    1

    2

    ,,

    )(

    )(

    1

    (c)Where Qs,i and Qo,i are simulated and observed outflow data for each time step i.

    For the calibration period 1970-1981, n corresponds to a maximum value of 144.

    For the validation period 1981-1996, n corresponds to a maximum value of 196.

    Calibration and validation results

    Calibration and validation statistics for all stations are presented in Table 3.2 for theRio Mantaro and Table 3.4 for the Rio Rimac, indicating a satisfactory performance ofthe model for the bigger downstream stations. The stations are classified from thebiggest watershed to the smallest one. For the calibration and the validation periodand for each station, the following information is presented:

    - RMSE, BIAS, Ef: the efficiency criterions;

    - n: the time steps number of monthly observed flows;

    - Qo m3/s: the corresponding observed flow average.

    Calibration period 1970-1981 Validation period 1981-1996

    station n Qo m3/s RMSE BIAS Ef n Qo m3/s RMSE BIAS Ef

    Pongor 90 310.2 53.76 -14% 0.74 39 375.2 44.57 5% 0.68

    Mejorada 90 176.0 37.91 16% 0.81 158 170.6 48.14 36% 0.65Puente Stuart 107 91.8 37.16 10% 0.81 142 81.9 72.33 50% -0.25

    Puente Chulec 91 57.5 37.40 5% 0.73 113 50.4 51.67 30% -0.03

    Moya 119 24.5 57.52 14% 0.62 115 23.7 65.82 40% 0.31

    Upamayo 124 25.9 45.72 14% 0.65 161 23.5 84.65 41% 0.06

    Chinchi 130 15.7 61.03 -5% 0.68 151 17.7 86.15 -8% 0.55

    Angasmayo 132 17.5 52.95 10% 0.77 159 12.2 84.62 67% 0.46

    Quillon 122 11.3 76.59 53% 0.36 99 8.2 128.62 98% -0.55

    Yanacocha 128 6.9 149.65 130% -0.53 173 6.1 216.42 189% -1.16

    Pachacayo 131 10.7 61.60 -3% 0.60 174 9.4 107.50 82% -0.26

    Huapa 131 10.7 59.35 8% 0.68 164 11.0 54.47 3% 0.72Santa Elena 126 8.4 78.52 -17% 0.59 174 10.7 115.89 -39% 0.33

    Huari 131 6.8 54.37 -11% 0.70 174 6.1 131.36 97% -0.69

    Cochas Tunel 132 6.1 73.52 11% 0.36 156 2.3 79.91 33% 0.52

    Yulapuquio 127 5.8 83.14 -13% 0.48 172 4.9 82.41 24% 0.47

    Canipaco 119 3.8 88.02 -13% 0.55 159 4.2 163.14 -10% 0.31

    Piascocha 132 1.8 95.94 62% 0.17 177 1.5 225.38 181% -2.94

    Casaracra 122 1.8 82.99 49% 0.07 156 2.3 79.91 33% 0.52

    Table 3.2. Criteria for the calibration and validation periods for the Mantaro sub-watersheds.

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    Table 3.2 shows that, for the Mantaro calibration, almost 70% of the stations (13stations / 19) have a BIAS less than 15% and with an efficiency (Ef Nash-Sutcliffe)greater than 0.5; for the validation, almost 60% of the stations (11 stations / 19)have a BIAS less than 40% and with an efficiency greater than 0.3.

    Figure 3.3 presents the seasonal fluctuations during the calibration period of observedand simulated stream flows for the bigger downstream stations of Mantaro.

    Figure 3.3. Monthly averages during the calibration period (1970-1981) of observed and simulatedstream flows for the Mantaro river basin.

    Figure 3.4. Observed and simulated stream flows at Pongor station in Mantaro River system.

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    Figures 3.4 and 3.5 present the observed and simulated stream flows of the Pongorand Mejorada stations.

    Figure 3.5. Observed and simulated stream flows at Mejorada station in Mantaro River system.

    Table 3.3 shows that, for the Rimac calibration, 50% of the stations (3 stations / 6)have a BIAS less than 25% and with an efficiency (Ef Nash-Sutcliffe) greater than 0.6;for the validation, four stations have a BIAS less than 20%, but only two stationspresent an efficiency greater than 0.4.

    Calibration period 1970-1981 Validation period 1981-1996

    station n Qo m3/s RMSE BIAS Ef n Qo m3/s RMSE BIAS Ef

    Chosica 132 28.6 36.26 -3% 0.62 180 27.7 61.18 17% -0.16

    Surco 132 17.0 41.29 -24% 0.66 124 16.0 42.16 -11% 0.60

    Tamboraque 110 14.3 49.93 -34% 0.46 180 14.0 51.29 -11% 0.44

    San Mateo 130 12.7 51.37 -42% -0.10 99 13.1 48.06 -35% -0.02

    Sheque 132 11.5 45.43 -32% -0.04 180 11.2 51.74 -16% -0.54

    Rio Blanco 131 3.3 61.87 21% 0.63 64 3.0 91.77 61% 0.10

    Table 3.3. Criteria for the calibration and validation periods for the Rimac sub-watersheds.

    Figure 3.6 presents the seasonal fluctuations during the calibration period of observedand simulated stream flows at Chosica and Surco stations. All observed and simulatedstream flows are presented in Figures 3.7 and 3.8 for the Chosica and Surco stations.

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    3.4 Simulation of the glacier area evolution since the 70s

    The results of the glacier area evolution simulation in the Rimac-Mantaro system weresatisfactory (see glacier parameters in Table 3.1). The observed initial glacier area ofthese watersheds in 1970 was 113 km2, which decreased to roughly 40 km2 in 1997(Table 3.4). This trend was well captured by the model when comparing simulatedand observed glaciated areas at discrete times during the calibration-validation period(Table 3.4 and Figure 5). In contrast to the results obtained in the Rio Santa, in theRimac-Mantaro system there is no apparent performance trend of the glacier moduleas a function of the extent of individual glacier extent. Note, however, that theglaciers in the Rio Mantaro and Rio Rimac watersheds are much smaller than those ofthe Rio Santa.

    Medido (Km) Simulado (KM2)

    1970 1988 1997 1988 1996

    Azu11 14.42 11.49 10.16 9.1 7.7Carh10 4.32 2.77 2.2 0.0 0.0

    Hua09 19.73 7.58 6.38 11.3 9.7

    Huap08 3.66 0 0 0.0 0.0

    Huay10 3.13 1.53 1.21 1.8 1.8

    Mej07 12.84 6.56 5.25 6.2 5.3

    Pte.St08 17.55 5.74 4.33 7.5 5.8

    Pte.chu09 12.16 2.84 1.9 0.0 0.0

    Sma08 6.25 0.88 0.46 3.2 2.3

    Tem10 4.79 3.75 3.04 3.5 3.3

    Upa10 4.63 0 0 0.0 0.0Yurac10 9.67 4.38 3.87 5.0 3.7

    Table 3.4. Simulated and observed data of glaciers evolution between 1970 and 1997.

    Figure 3.9. Scatter plot graph with observed versus simulated glacier areasfor the two periods (1987 and 1996).

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    3.5 Conclusions

    A double validation of the model was done by comparing (1) observed and simulatedstreamflows at 25 control points in the Rimac-Mantaro system; (2) the glacier areacalculated by the model and that observed with Landsat images for two periods (1988and 1997). This validation gave reasonable results. But the Rimac-Mantaro systemfuture simulations have to be able to reproduce the reservoirs operation management,

    16 reservoirs operational since 1995-2000, and 28 reservoirs planned for the future(Table 3.5).

    nombre tipo empresa Vol (MMC) Inicio operacin

    Upamayo represa Electroperu 441

    Tablachaca represa Electroperu 7 1973

    Malpaso represa Electroandes 2.76

    Huaylacancha laguna regulada Electroperu 22.4 1995

    Carhuacocha laguna regulada Electroperu 23.0 1995

    Tembladera laguna regulada Electroperu 5.0 1997

    Azulcocha laguna regulada Electroperu 6.0 1997

    Yuraccocha laguna regulada Electroperu 2.2 1995

    Vichecocha laguna regulada Electroperu 10.6 1995Nahuincocha laguna regulada Electroperu 1.4 1995

    Yanacoha-Palcn laguna regulada Electroperu 7.6 2000

    Huacracocha laguna regulada Electroperu 4.9 2000

    Hueghue laguna regulada Electroperu 18.4 2000

    Chilicocha laguna regulada Electroperu 42.8 1999

    Nahuincocha laguna regulada Electroperu 7.0 1999

    Balsacocha laguna regulada Electroperu 3.0 1999

    Yurajcocha laguna regulada Electroperu 16.0 1999

    Hulchicocha laguna regulada Electroperu 19.0 1999

    Coyllorcocha laguna regulada Electroperu 11.0 1999

    Antacocha laguna regulada Electroperu 2.4 estudio definitivo

    Tunshu laguna regulada Electroperu 3.5 estudio definitivoNorma laguna regulada Electroperu 3.0 estudio definitivo

    Paucara laguna regulada Electroperu 4.4 estudio factibilidad definitivo

    Llacsa laguna regulada Electroperu 3.3 estudio factibilidad definitivo

    Parlona I laguna regulada Electroperu 2.5 estudio factibilidad definitivo

    Calzada laguna regulada Electroperu 2.3 estudio factibilidad definitivo

    Caullau laguna regulada Electroperu 5.6 estudio factibilidad definitivo

    Huarmicocha laguna regulada Electroperu 1.5 estudio factibilidad definitivo

    Luquina laguna regulada Electroperu 11.1 estudio definitivo

    Habascocha laguna regulada Electroperu 3.1 estudio definitivo

    Tipicocha laguna regulada Electroperu 3.5 estudio definitivo

    Tranca Grande laguna regulada Electroperu 14.5 estudio definitivo

    Paccha laguna regulada Electroperu 11.6 estudio definitivoLacsacocha laguna regulada Electroperu 4.6 estudio factibilidad definitivo

    Huacracocha-Huari laguna regulada Electroperu 11.0 estudio factibilidad definitivo

    Abascocha laguna regulada Electroperu 2.4 estudio factibilidad definitivo

    Inticojasa laguna regulada Electroperu 1.3 estudio factibilidad definitivo

    Ampacocha laguna regulada Electroperu 4.7 estudio factibilidad definitivo

    Tutayac II laguna regulada Electroperu 1.0 estudio factibilidad definitivo

    Aclacocha laguna regulada Electroperu 5.2 estudio factibilidad definitivo

    Huascacocha laguna regulada Electroperu 26.0 estudio factibilidad definitivo

    Yanacoha-Pampahua laguna regulada Electroperu 6.1 estudio factibilidad definitivo

    Tanserococha laguna regulada Electroperu 13.1 estudio factibilidad

    Tipicocha laguna regulada Electroperu 10.1 estudio factibilidad

    Turmanya laguna regulada Electroperu 46.9 estudio factibilidadPajaco laguna regulada Electroperu 61.1 estudio factibilidad

    Table 3.5. Existing and projected reservoirs of the Mantaro river basin.

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    CONCLUSION AND PROSPECTS

    Understanding and modelling hydrology are crucial in Andean tropical mountains aspart of efforts to plan and manage water resources.

    One of the main challenges in this region is to be able to simulate the hydrology withscarce availability of meteorological and hydrological data which has high spatial

    variability similar to the temperature and precipitation gradients observed in Andeanmountains watersheds. Several assumptions need to be made and interpolationmethods need to be implemented in order to obtain continuous climate time-seriesthat can feed hydrologic models. The historical studies of IRD (Pouyaud et al., 2005;Suarez, 2007; Suarez et al., 2008) facilitated the data processing on the Santa riverbasin. For the Rimac and Mantaro river basins, database development was moredifficult due to the system complexity and the lack of reference studies. For instance,we needed to process satellite images to reconstitute glacier extensions and landcovers.

    One of the difficulties in constituting databases came from the data dispersion acrossthe various institutions. It is worth reiterating that (1) certain institutions (unidad

    de glaciologa del ANA - ex- INRENA) and companies (Electroper) supporting thisproject have a data confidentiality commitment, in which the IRD guarantees data useonly for the project and that this data cannot be transferred to another institutionwithout their authorization; (2) within this cooperation framework, the differentinstitutions supporting the project have access to the final technical report.

    The originality of this work rests on the successful linkage of a glacier evolutionmodule based on the degree-day method to a WEAPs integrated rainfall-runoff/waterresource systems modelling framework. A double validation of the model was done bycomparing the glacier area calculated by the model and that observed with Landsatimages for two periods (1987 and 1998) and observed and simulated streamflows at16 control points in the Rio Santa watershed. Modelling the Rimac-Mantaro system,

    presented in this report, confirmed the effectiveness of the glacier evolutionsimulation (1988, 1996) and gave reasonable flow results for the downstream gaugestations.

    Although extensive pramos landscapes are not present in the three pilot watershedsin Peru, IRD and SEI attempted to parameterize existing rainfall-runoff models in amanner that could capture the unique nature of hydrologic processes in watershedsdominated by pramos. IRD used data of Antisana upstream watersheds near Quito inEcuador, with more than 70% of pramos land cover (collaboration GreatIce IRD unit,EMAAP-Q, INAMHI). The existing WEAP rainfall-runoff model (Soil Moisture Model) andthe GR2M, monthly two parameter rainfall-runoff model of Gnie Rural (Mouelhi et al.,

    2006) could be calibrated. As the World Bank supported Proyecto Regional AdaptacinAndina (PRAA) in Ecuador used Antisana watersheds as the pilot area, it seemsreasonable to envisage an active collaboration.

    Due to delays encountered by the project partners charged with producing futureclimate projections, IRD could not run the elaborated models for future scenarios, inorder to evaluate climate change impacts on Andean hydrology, corresponding to Task4. Clearly the preliminary climate change analysis carried out by SEI based on thedevelopment of two stylized future climate projections is not sufficient. The WEAPapplications developed under this project should be run using scientifically rigorousclimate projections like the ones that are being developed by NCAR and PNNL.Developing the ability to simulate the impacts of a set of assumed future climate

    projections is an important step in creating the capacity for Andean water managersto plan for climate change.

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    This is critical, for in addition to the hydrologic dimension of the model, the WEAPsoftware provides the ability to represent and simulate different water uses and watersystem elements. Further steps into this modelling exercise should focus on detailingthe implications of hydrologic change on water demands including (1) hydropower, (2)agriculture, and (3) ecosystem flow and the consequent economic implications.

    (1) For hydropower generation, in cooperation with Duke Energy, the operators of

    the Caon del Pato hydropower project, it was possible to assess theperformance of the final WEAP Rio Santa application in terms of its utility insimulating the hydropower system. The production simulation for 1997-1998gave a reasonable result. As such, no substantial effort could be made to verifythe veracity of simulated hydropower operations in the Rimac-Mantaro systembeyond a simple assessment that the numbers were realistic. As the World Banksupported Proyecto Regional Adaptacin Andina (PRAA) in Peru and uses theMantaro river basin as one of its pilot areas, it seems reasonable to envisage theperformance evaluation of the hydropower routines at some point in the future,depending on a collaborative relationship with the system operators. It is worthreiterating that the Mantaro system future simulations have to be able to

    reproduce the reservoirs operation management, 16 reservoirs operational since1995-2000, and 28 reservoirs planned for the future.

    (2) For agriculture, it would be beneficial to refine the representation of agriculturalwater demands in these systems under different scenarios regarding theevolution of irrigated areas and the climate driven water demand associated withthese changes. An IRD research project in Quito focuses on the competitionbetween demands for drinking water and irrigation leading to significant transfersfrom Amazonian high-altitude watersheds with pramos. We propose anirrigation representation combining water rights practices and crops-soils waterbudget (www.mpl.ird.fr/divha/aguandes/ecuador/hoya-quito).

    (3) For ecosystem flow, it may be useful to begin to introduce this consideration intothe analysis and to see how these targets might constrain system operationsunder alternative future climate projections. WEAP is being used extensively inthis manner in numerous locations around the world. An IRD research project inQuito aims to define ecosystem flows for Andean mountains watersheds(CAUdales ECOlgicos, www.mpl.ird.fr/divha/aguandes/ecuador/papallacta/).

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    REFERENCES

    Bahr, D. B., M. F. Meier, and S. D. Peckham. 1997. The physical basis for glaciervolume-area scaling. Journal of Geophysical Research 102:20355-20362.

    Hock, R., 2005. "Glacier melt: a review of processes and their modelling." Progress inPhysical Geography 29: 362-391.

    Mouelhi, S., Michel C., Perrin C., Andrassian V., 2006. Stepwise development of atwo-parameter monthly water balance model, J. Hydrol., 318, 200-214,doi:10.1016/j.jhydrol.2005.1006.1014

    Nash, J.E., Sutcliffe, J.V., 1970. River flow forecasting through conceptual models.Part 1 A discussion of principles. Journal of hydrology, 27 (3).

    Pouyaud, B., M. Zapata, J. Yerren, J. Gomez, G. Rosas, W. Suarez and P. Ribstein,2005. "Devenir des ressources en eau glaciaire de la Cordillre Blanche." HydrologicalSciences Journal 50: 999-1022.

    Suarez, W., 2007. "Le bassin versant du fleuve Santa (Andes du Prou) : dynamique

    des coulements en contexte glacio-pluvio-nival", Thse Universit Montpellier 2,(2007), 290 p.

    Suarez, W., P. Chevallier, B. Pouyaud, and P. Lopez. 2008. Modelling the waterbalance in the glacierized Paron Lake basin (White Cordillera, Peru). HydrologicalSciences 53.

    Yates, D., J. Sieber, D. Purkey, and A. Huber-Lee. 2005. WEAP21 - A demand-,priority-, and preference-driven water planning model Part 1: Model characteristics.Water International 30:487-500.

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    LIST OF TABLES

    Table 1Work schedule, envisaged initially and performed..................................... 2Table 2.1 Principal selection criteria of glacier modelling....................................... 5

    Table 2.2. Simulated and observed data of glaciers evolutionbetween 1970 and 1999.................................................................... 7

    Table 2.3. Collected and processed data for the Santa river basin.......................... 9

    Table 2.4. Collected and processed data for the Rimac river basin. .......................10

    Table 2.5. Collected and processed data for the Mantaro river basin. ....................11

    Table 3.1. Land uses parameters values for the non glacial partand parameters values for the glacier module .....................................16

    Table 3.2. Criterions for the calibration and validation periodsfor the Mantaro and Rimac sub-watersheds ........................................17

    Table 3.3. Criterions for the calibration and validation periodsfor the Rimac sub-watersheds...........................................................19

    Table 3.4. Simulated and observed data of glaciers evolutionbetween 1970 and 1997...................................................................21

    Table 3.5. Existing and projected reservoirs of the Mantaro river basin .................22

    LIST OF FIGURES

    Figure 1.1. Map of study river basins location in Peru........................................... 3

    Figure 2.1. Correspondence between, simulated and observed stream flowat Balsa gauge station between Sep 1969 Aug 1997........................ 6

    Figure 2.2. Scatter plot graph with observed versus simulated glacier areasfor the two periods (1987 and 1998) ............................................................7

    Figure 2.3. Santa river basin www.mpl.ird.fr/divha/aguandes/ ..........................13

    Figure 3.1. Map of rainfall areas and location of data stations of the Rimacand Mantaro river basins...............................................................14

    Figure 3.2. Schematic of the Rimac and Mantaro system within WEAP...................15

    Figure 3.3. Monthly averages during the calibration period (1970-1981)of observed and simulated stream flows for the Mantaro river basin....18

    Figure 3.4. Observed and simulated stream flows at Pongor stationin Mantaro River system ................................................................18

    Figure 3.5. Observed and simulated stream flows at Mejorada stationin Mantaro River system ................................................................19

    Figure 3.6. Monthly averages during the calibration period (1970-1981)of observed and simulated stream flows for the Rimac river basin.......20

    Figure 3.7. Observed and simulated stream flows at Chosica stationin Rimac River basin......................................................................20

    Figure 3.8. Observed and simulated stream flows at Surco stationin Rimac River basin......................................................................20

    Figure 3.9. Scatter plot graph with observed versus simulated glacier areasfor the two periods (1987 and 1996). ..............................................21