INO: Integrated Participatory Development and Management of … · 2017. 5. 23. · sustainable...

Integrated Participatory Development and Management of Irrigation Program (RRP INO 43220)

Climate Risk Vulnerability Assessment March 2017

INO: Integrated Participatory Development and Management of Irrigation Program

TABLE OF CONTENTS

Page I. OVERVIEW 1

A. Agriculture and Natural Resources .............................................................................. 1

B. Integrated Participatory Development and Management of Irrigation Program ............. 2

II. CLIMATE RISK SCREENING 5

A. Integrated Participatory Development and Management of Irrigation Program ............. 5

III. CLIMATE CHANGE SCENARIOS 6

A. Climate Change Scenarios for the Integrated Participatory Development and Management of Irrigation Program .............................................................................. 8

IV. ADAPTATION NEEDS AND OPTIONS 14

A. Integrated Participatory Development and Management of Irrigation Program ............14

APPENDIXES

1. Approximate Locations of the IPDMIP Study Catchments 2. Change Factors (For each Climate Model and Month) Used to Obtain the Multi-Model

Average Climate Change Factors 3. References

I. OVERVIEW A. Agriculture and Natural Resources

1. The contribution of the agriculture sector to national Gross Domestic Product (GDP) in Indonesia has gradually declined from 30% in 1975 to 13.7% in 2014.1 In the 1970s and 1980s, the government invested heavily in public goods including irrigation, research and development, extension services and rural infrastructure (mainly rural roads), driving the increase in agriculture productivity. By the 1990s, government spending in the agriculture sector (particularly on public goods) had decreased and there was a corresponding decline in agriculture productivity. Since 2000 the Government of Indonesia (the government) again increased spending in the agriculture sector—spending 10% and 8% of GDP on agriculture in the 1970s and 1980s, respectively, compared with 40% today—but today this goes mainly to subsidies on private inputs (fertilizers and seeds) and productivity remains sluggish. 2. Agriculture remains an important sector in terms of poverty and the paths to poverty alleviation. Poverty in Indonesia is still a predominantly rural and agricultural phenomenon – in 2010, over 60% of those earning less than $1.25 per day lived in rural areas and/or worked in agriculture. Irrigated agriculture has historically contributed to significant increases in employment and reduction of poverty; the percentage of the population living on less than $1.25 per day is down from 62% in 1984 to 12% in 2012. There is a well-established link between improving agriculture productivity and reducing poverty. 2 Agriculture is the main source of employment in rural areas, where poverty is most prevalent. Around 40% of the country’s entire work force is employed in agriculture, with about 39 million people are engaged in small-scale production. 3. Agriculture sector performance and food security is a significant government concern particularly after the world price shocks of 2008. In 2012, Indonesia produced 69 million tons of rice of which, 95% came from irrigated lands. Overall, production has been growing slowly (about 1% annually) over the last ten years mainly from increase in irrigated area and cropping intensity. With a growing urban population, rising incomes and increased levels of consumption, diets are diversifying and demand for high quality food is increasing. Despite increasing production, food security is challenged in the medium term by declining irrigation and other rural infrastructure.3 4. Irrigation management responsibilities are shared between the Directorate General of Water Resources (DGWR), Ministry of Public Works and Housing (MPWH) and provincial and district water resources agencies (WRAs).4 The total area under surface irrigation is around 7.2 million hectares (ha) of which 33% is managed by DGWR. Since 2005, the area of irrigated land with infrastructure in good condition has declined from 78% to around 55%,5 while at the same time the central government transferred around $1.2 billion to provinces and districts to improve irrigation schemes. Deteriorating infrastructure is a consequence of inadequate

1 In 2015, agriculture contributed 14.0% of the gross domestic product, with an estimated 39 million people working

in the sector. (ADB. 2016. Key Indicators for Asia and the Pacific 2016. Manila.). 2 D. Cervantes-Godoy, D. and J. Dewbre. 2010. Economic Importance of Agriculture for Poverty Reduction. OECD

Food, Agriculture and Fisheries Working Papers No. 23. Paris: OECD Publishing. doi: 10.1787/5kmmv9s20944-en. 3 In 2010, only 48% of the total irrigated area was considered in good condition. 4 System responsibilities are: district governments (systems serving less than 1,000 ha); provincial governments

(1,001 to 3,000 ha); and national government (more than 3,000 ha). Many systems lack staff and resources and ad hoc arrangements, between the various levels of government, are often in place to share responsibilities.

5 It is estimated that: (i) 33% of the 2,650,708 ha managed by DGWR; (ii) more than 53% of the 1,105,475 ha under province authority; and (iii) 59% of the 3,663,175 ha under the district authority are degraded and in poor condition.

2

financing and delivery of operations and maintenance (O&M), which limits possible rice and other high value crop productivity increases. High transport and logistics costs are a serious constraint on business operations and undermine the competitiveness of agricultural value chains. In addition, a need exists to increase higher value cropping and to commercialize agriculture to improve rural livelihoods. 5. The government has the ambitious target to reach rice self-sufficiency through modernizing 3.2 million ha of irrigation systems for rice production.6 In January 2015, it also raised the government rice-purchasing price by roughly 10% to Indonesian Rupiah (Rp) 7,260/kilogram. Recent irrigation project experience has shown positive impacts on smallholder productivity and on rural poverty. The recently completed Asian Development Bank (ADB) financed Participatory Irrigation Support Project (PISP), estimated that average rice yields increased by nearly 20% in both the wet and dry seasons. The average annual household income increased from Rp26.7 million in the 2008 baseline survey to Rp35.3 million in 2012 (32.2%). The overall incidence of poverty was reduced from 24.4% in the 2008 baseline survey to 6.8% in 2012. Similar results are reported for the Water Resources and Irrigation Sector Management Project funded by the World Bank. 6. Water security and food security are priorities in the government’s long-term National Development Plan (RPJPN), 2005-2025 7 and National Medium Term Development Plan (RPJMN) 2015–2019. 8 All sectoral plans, including the 2015-2025 Irrigation Improvement Plan (IIP), are aligned with these national medium- and long term plans.9 To meet the national RPJPN goals of increased rice production, improved rural incomes and more productive and sustainable irrigation infrastructure, Indonesia’s IIP calls for participatory irrigation management, asset management systems, needs-based budgeting, and strengthening of water user associations (WUAs) and WRAs. The IIP also calls for the rehabilitation and modernization of 3.2 million ha of irrigation systems. 7. The rising trend of allocation for irrigation rehabilitation and O&M provides a good opportunity to take a programmatic and results-oriented approach to the rehabilitation and modernization of irrigation systems. Investment focused on improving productivity of irrigated agriculture to enhance food security and improve rural livelihoods is essential for sustainability. B. Integrated Participatory Development and Management of Irrigation Program

8. The Integrated Participatory Development and Management of Irrigation Program (IPDMIP – the program) will support the Indonesian Government’s 2015-2025 RPJPN to (i) increase rice production for food security; (ii) develop higher value cropping to improve rural livelihoods; and (iii) promote more productive irrigation infrastructure and its sustainable management. The program will improve the performance of irrigated agriculture through the delivery of an integrated modernization package of infrastructure, improved system management (O&M and asset management), strengthening of WUAs, and agriculture services to implement sector reforms. 9. A results-based lending program was adopted because it provides or facilitates: (i) a holistic approach to irrigation system modernization with an institutional development plan for

6 Ministry of Public Works and Housing. 2012. Jakarta; and World Bank. 2013. 7 Government of Indonesia. 2005. Long-term Development Plan: RPJPN 2005-2025. Jakarta. 8 Government of Indonesia. 2015. Medium-term Development Plan: RPJMN 2015-2019. Jakarta. 9 The IIP is a subprogram of the Sector Plan for Water Resources from the Ministry of Public Works and Housing.

3

better alignment with government policy; (ii) greater flexibility and leeway to prioritize investments; (iii) better efficiency in public spending on irrigation system modernization; and (iv) predictability of maintenance financing whereby other development partners can participate with a harmonized programmatic results-based approach. 10. The recent ADB supported PISP and other development partners’ projects in the irrigation subsector indicate that the following innovations should be pursued: (i) empowered farmer WUAs for management and contracting civil works; (ii) support district irrigation commission and irrigation development and management plans (RP2I) with irrigation asset management information systems (IAMIS) and improved budgeting; (iii) improved institutional coordination at different levels of government; (iv) agricultural support services delivered through WUAs; and (v) improved water management and O&M practices. Based on PISP innovations the government is adopting RP2Is, contracting WUAs for civil works, and enhancing management devolution as policy. 11. Consistent with the Indonesian Government’s program the following outputs are expected to achieve improved productivity from irrigated agriculture:

(i) systems and institutional capacity for sustainable irrigated agriculture strengthened;

(ii) irrigation O&M and management improved; (iii) irrigation infrastructure improved; and (iv) increased agriculture production and market access (including irrigation and

agriculture infrastructure, production and productivity and value chain enhancement) to be supported by International Fund for Agricultural Development.

12. The project preparatory technical assistance (PPTA) prepared three representative subprojects and demonstrated the economic viability of interventions to modernize them. In order of increasing water scarcity they are:

(i) Karowa subproject, Karowa system and catchment (net irrigable area [NIA] = 258 ha), North Sulawesi;

(ii) Mon Sukon subproject and sub-system (NIA = 3,985 ha) in Jambo Aye irrigation system, Jambo Aye River Basin (NIA = 17,931 ha), Special Region of Aceh; and

(iii) Lembor subproject, Lembor system and catchment (NIA = 4,483 ha), Flores Island.

4

Figure 1: The three dominate Indonesian climate regions (Monsoon Type in yellow, Equatorial type in green and Local Type in red) according to the mean annual rainfall

patterns. Shading in the mean annual rainfall patterns (bottom) indicates one standard deviation.

Source: E. Aldrian and D. Susanto. 2003. Identification of Three Dominant Rainfall Regions Within Indonesia and their Relationship to Sea Surface Temperature. International Journal of Climatology. 23. 1435-1452. doi:10.1002/joc.950.

Figure 2: Oldeman Agro-Climatic Classes

Irrigation System

Annual rainfall

(Jan-Dec)

# of wet

month

# of dry

month

OACC

Jambo Aye 2,850 6 0 C1 Karowa 1,829 0 3 E2 Lembor 1,249 1 6 E3

Source: Deltares. 2015.Country Water Assessment: Indonesia, Extended Report. Final Draft.; Deltares. 2015. TRMM Satellite Rainfall Characteristics in Karowa, Lembor and Jambo Aye Irrigation Areas, Indonesia. (available on request from [email protected]).

5

13. Figure 1 indicates that: (i) Jambo Aye and Lembor irrigation systems, located at opposite ends of the archipelago in Aceh Special Region and Flores in East Nusa Tenggara respectively, both have the predominant monsoon rainfall pattern (in yellow); (ii) Karowa (North Sulawesi) has the other major equatorial rainfall pattern (green); and (iii) only the relatively minor and more variable local rainfall pattern (red) is unrepresented by these three irrigation systems. See Appendix 1 for further information on the location of the three catchments investigated in this study. 14. Figure 2 indicates that the sum of mean annual rainfall generally decreases from west (2,850 millimeter [mm] in Aceh Special Region) to east (1,249 mm in Flores, East Nusa Tenggara) across the archipelago. Based on the number of dry months (rainfall less than 100 mm) and consecutive wet months (rainfall more than 200 mm) the Oldeman agro-climate classes (OACC) are: (i) bordering on E3 and E4 (Lembor); and (ii) E2 (Karowa) and C1 (Jambo Aye). However, if it were not for rainfall of slightly less than 200 mm in June, the latter (Jambo Aye) might be classified as A1. This indicates that, except for E2 instead of E1, the three irrigation systems are located in the apexes of the triangle shown in Figure 2 and represent the full range of all 17 Oldeman agro-climate classes. The implications are:

(i) The soil and water assessment tool (SWAT) data and model, used herein, offer a rapid objective method of estimating the without climate change water balances and cropping patterns required to compare, screen, rank, select, and prepare other irrigation systems or subprojects.

(ii) However, estimation of with climate change water balances and cropping patterns, and preparation of climate risk and vulnerability assessments (CRVAs), will not be necessary as climate change impacts and adaptation interventions will be similar, for similar agro-climate conditions, and the three CRVAs, prepared herein, are representative of the full range of present agro-climate conditions.

II. CLIMATE RISK SCREENING

A. Integrated Participatory Development and Management of Irrigation Program

15. Irrigated agriculture in Indonesia (and other countries also) is inherently sensitive to many of the anticipated impacts of climate change. For example:

(i) Changes in crop evapotranspiration and in the quantity and/or timing of rainfall may alter demand for irrigation water;

(ii) Increases in extreme temperatures may affect crop development; (iii) Changing precipitation patterns may alter the supply of water available for

irrigation; (iv) Changes in the frequency and intensity of climate-related hazards (e.g. droughts,

floods, tropical storms) may result in increased crop losses; (v) Climate change may influence the prevalence, spatial distribution and behavior of

agricultural pests and diseases; and (vi) Sea level rise may increase the risk of salinization of coastal aquifers and

agricultural soils in low-lying coastal areas such as river deltas.

6

III. CLIMATE CHANGE SCENARIOS 16. To obtain information about future climate conditions, outputs from General Circulation Models (GCMs) are required. The GCMs, combined with the greenhouse gas (GHG) emission scenarios, provide the climate projections which form a critical part of climate adaptation planning. GCMs make it possible to provide scenarios about the future climate that move beyond simply assuming that future climate will remain similar to the past, or that it will unpredictably fluctuate within the bounds of natural variability as assumed in non-GCM based approaches. GCMs provide possible answers to the question: given current understanding of climate drivers, and plausible future scenarios of GHG emissions, how will the climate system respond twenty, fifty or a hundred years into the future. However, in addition to uncertainties surrounding the GHG emission scenarios and uncertainty inherent in any model (due to the assumptions that need to be made), GCM outputs are associated with a number of limitations which reduces their usefulness for climate change adaptation and other decision making, especially at the regional or local scale. These uncertainties/limitations are comprehensively discussed in numerous papers10 and include:

(i) while GCMs are reasonable at simulating temperature, there is less confidence in their simulations of precipitation and other climate variables, particularly at the regional and local scale and especially for extreme rainfall events (i.e. extremely high rainfall events or persistently dry conditions);

(ii) GCMs are poor at realistically simulating the large-scale physical processes known to be responsible for natural climate variability (e.g. El Niño/Southern Oscillation, North Atlantic Oscillation, Asian monsoons);

(iii) GCM output is provided in grids that typically have a resolution of approximately 250 km x 250 km. Outputs at such a coarse scale are not able to factor in topographical or land-surface (i.e. vegetation) variation, which we know plays a very important role in determining regional and local climate; and

(iv) GCMs do not capture important offshore ocean processes and currents, which are extremely influential on climate in areas like Southeast Asia, including Indonesia. For example, a recent paper by H.M. Kim et al.11 found that the world’s most advanced climate models “perform poorly” in simulating the physical processes that determine the character of monsoon systems, specifically focussing on the

10 M.L. Parry et al. 2007. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group

II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press.; D.A. Randall et al. 2007. Climate Models and Their Evaluation in Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK and New York, USA: Cambridge University Press.; D.A. Stainforth et al. 2007. Confidence, Uncertainty and Decision-Support Relevance in Climate Predictions. Philosophical Transactions of the Royal Society A. pp. 365, 2145-2161; doi:10.1098/rsta.2007.2074.; Koutsoyiannis et al. 2008 and 2009.; G. Blöschl and A. Montanari. 2010. Climate Change Impacts - Throwing the Dice? Hydrological Processes. pp. 24, 374–381,10.1002/hyp.7574.; A. Montanari et al. 2010, Getting on Target. Public Service Review: Science and Technology. pp 7, 167-169.; R.L. Wilby and S. Dessai. 2010. Robust Adaptation to Climate Change. Weather. pp. 65, 180-185, doi: 10.1002/wea.543.; A.S. Kiem and D.C. Verdon-Kidd. 2011. Steps towards ‘Useful’ Hydroclimatic Scenarios for Water Resource Management in the Murray-Darling Basin. Water Resources Research. pp. 47. W00G06, doi:10.1029/2010WR009803; and E.M. Stephens, T.L. Edwards and D. Demeritt. 2012. Communicating Probabilistic Information from Climate Model Ensembles—Lessons from Numerical Weather Prediction. WIREs Climate Change. Pp. 3, 409–426, doi:10.1002/wcc.187.

11 H.M. Kim et al. 2012. Asian Summer Monsoon Prediction in ECMWF System 4 and NCEP CFSv2 Retrospective Seasonal Forecasts. Climate Dynamics. pp. 39, 2975-2991.

7

Asian summer monsoon. 17. In addition to requiring information about future climate conditions available from GCMs, this study also needs that information to be representative of the different climate patterns that exist across and within each of the case study locations. This necessitates some form of downscaling. 18. The simplest form of downscaling is the ‘delta-change’ approach (also called the ‘perturbation’ method). The ‘delta-change’ approach constructs future climate time series by perturbing the historical observed climate time series by change factors.12 The change factors are the differences between GCM future and GCM historical climate simulations and can be based on differences in annual, seasonal, monthly or daily statistics. Since the same change factors are used to perturb both the average and extreme values of a historical observed climate change time series, this approach is also called ‘constant scaling’. This method is simple and therefore enables several different GHG emission scenarios and the output from several GCMs to be considered within a relatively short amount of time. The main limitations of the ‘constant scaling’, or the related ‘pattern scaling’, method are that it assumes that even though GCMs do not adequately simulate current climate (especially at the provincial scale and especially for precipitation) the GCM-simulated change will be realistic (e.g. the change between current and future GCM outputs will be realistic even if the absolute values of the GCM outputs are not). Applying the same change factor to all parts of the climate variable’s distribution also does not account for the fact that changes in extremes are likely to be different to changes in averages, nor does it consider changes in the frequency or temporal distribution of climate events13 (number of wet days, sequencing or clustering of wet spells or dry spells, etc.). Significant uncertainty also exists around the selection of an appropriate climate baseline and in some places (e.g. Indonesia) there are constraints surrounding availability, accessibility and quality of the historically observed data that is needed to apply the change factors to. 19. ‘Delta change’ (or constant scaling) approaches are frequently used due to their simplicity and the minimal financial, human, and technological resources required to apply them. However, an increasing number of studies that require regional or local scale climate scenario information are now using either statistical or dynamical downscaling or a combination of both. The latter overcomes the limitations of the delta change approaches but also decreases the number of GCMs and emission scenarios that can be used due to the time and extra complexity associated with the more sophisticated statistical and dynamical downscaling approaches. Numerous studies 14 have compared the results obtained from statistical and dynamical

12 A.S. Kiem et al. 2008. Future Hydroclimatology of the Mekong River Basin Under Climate Change Simulated

Using the High Resolution Japan Meteorological Agency (JMA) AGCM. Hydrological Processes. pp. 22(9), 1382-1394; and A.J. Frost et al. 2011. A Comparison of Multi-site Daily Rainfall Downscaling Techniques Under Australian Conditions. Journal of Hydrology. pp. 408(1-2), 1-18, doi:10.1016/j.jhydrol.2011.06.021.

13 J.F. B. Mitchell et al. 1999. Towards the Construction of Climate Change Scenarios. Climatic Change. pp. 41, 547-581.; E.P. Maurer and H.G. Hidalgo. 2007. Utility of Daily vs. Monthly Large-Scale Climate Data: An Intercomparison of Two Statistical Downscaling Methods. Hydrology and Earth Systems Sciences Discussions. pp. 12, 551-563.; A.S. Kiem et al. 2008. Future Hydroclimatology of the Mekong River Basin Under Climate Change Simulated Using the High Resolution Japan Meteorological Agency (JMA) AGCM. Hydrological Processes. pp. 22(9), 1382-1394; and A.J. Frost et al. 2011. A Comparison of Multi-site Daily Rainfall Downscaling Techniques Under Australian Conditions. Journal of Hydrology. pp. 408(1-2), 1-18, doi:10.1016/j.jhydrol.2011.06.021.

14 I.Hanssen-Bauer et al. 2003. Temperature and Precipitation Scenarios for Norway: Comparison of Results from Dynamical and Empirical Downscaling. Climate Research. pp. 25, 15–27; M.R. Haylock et al. 2006, Downscaling Heavy Precipitation Over the United Kingdom: A Comparison of Dynamical and Statistical Methods and Their Future Scenarios. International Journal of Climatology. pp. 26, 1397-1415. doi: 10.1002/joc.1318; W. Buytaert et al. 2010. Uncertainties in Climate Change Projections and Regional Downscaling in the Tropical Andes: Implications for Water Resources Management. Hydrology and Earth Systems Sciences. pp. 14, 1247-1258, doi:10.5194/hess-

8

downscaling with the main conclusion being that the results are usually quite similar for present-day climate, but often differ significantly for future climate projections. It is suggested that these differences are due to unwise choice of predictors in the statistical downscaling or because the projected climate change exceeds the range of data used to develop the statistical relationships. 15 However, differences between results from statistical downscaling and dynamical downscaling are also because statistical downscaling implicitly incorporates local climatic and geographical characteristics (e.g. topography, land surface-atmosphere interactions) that are not very well resolved in the regional climate models used for dynamical downscaling.16 In short, both statistical and dynamical downscaling approaches provide different information about the future and it is not yet certain which approach is more realistic or reliable. 20. In summary, it is not possible to say whether statistical or dynamical downscaling is the superior approach when looking to obtain climate scenario information at the regional or local scale – both methods have their strengths and weaknesses. For climate change impact assessment, vulnerability, and adaptation studies where the quantification and management of uncertainty is required it is critical to depict the range of uncertainty in GCM projections and to consider multiple emission scenarios and time horizons. Hence, constant or pattern scaling (or simple statistical downscaling) for such applications has obvious benefits compared to dynamic downscaling and is recommended at least until it can be demonstrated that the statistical relationships utilized in constant or pattern scaling statistical downscaling are no longer reliable – this, however, can only be achieved by regional climate modelling and is the reason why both dynamical and statistical downscaling studies should continue.. 21. Given these uncertainties, combined with data, time and budget constraints, a dynamical or sophisticated statistical downscaling approach was not feasible or justifiable. Therefore, the delta-change (or constant scaling) approach was employed to develop future climate scenarios for the case study locations. A. Climate Change Scenarios for the Integrated Participatory Development and

Management of Irrigation Program

22. To identify and manage climate-related risks to the Program and its integrated outputs information is required for the variables that irrigation is most sensitive to (i.e. rainfall, evaporation, water available for irrigation) and how they change in space and time under baseline (i.e. current without climate change impacts) and future (i.e. with climate change)

14-1247-2010; and and A.J. Frost et al. 2011. A Comparison of Multi-site Daily Rainfall Downscaling Techniques Under Australian Conditions. Journal of Hydrology. pp. 408(1-2), 1-18, doi:10.1016/j.jhydrol.2011.06.021.

15 J. Murphy. 2000. Predictions of Climate Change Over Europe Using Statistical and Dynamical Downscaling Techniques. International Journal of Climatology. pp. 20, 489–501; L.O. Mearns et al. 2001. Climate Scenario Development in Climate Change 2001:The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press. pp. 739–768; I.Hanssen-Bauer et al. 2003. Temperature and Precipitation Scenarios for Norway: Comparison of Results from Dynamical and Empirical Downscaling. Climate Research. pp. 25, 15–27; and W. Buytaert et al. 2010. Uncertainties in Climate Change Projections and Regional Downscaling in the Tropical Andes: Implications for Water Resources Management. Hydrology and Earth Systems Sciences. pp. 14, 1247-1258, doi:10.5194/hess-14-1247-2010.

16 E.P. Maurer and H.G. Hidalgo. 2007. Utility of Daily vs. Monthly Large-Scale Climate Data: An Intercomparison of Two Statistical Downscaling Methods. Hydrology and Earth Systems Sciences Discussions. pp. 12, 551-563; A.S. Kiem et al. 2008. Future Hydroclimatology of the Mekong River Basin Under Climate Change Simulated Using the High Resolution Japan Meteorological Agency (JMA) AGCM. Hydrological Processes. pp. 22(9), 1382-1394; and A.J. Frost et al. 2011. A Comparison of Multi-site Daily Rainfall Downscaling Techniques Under Australian Conditions. Journal of Hydrology. pp. 408(1-2), 1-18, doi:10.1016/j.jhydrol.2011.06.021.

9

climate conditions. This information is required to assess the resilience and utility of existing and potential irrigation adaptation options for the three case study locations:

(i) Karowa subproject, Karowa system and catchment (NIA = 258 ha), North Sulawesi;

(ii) Mon Sukon subproject and sub-system (net irrigable area NIA = 3,985 ha) in Jambo Aye irrigation system, Jambo Aye River Basin (NIA = 17,931 ha), Special Region of Ache; and

(iii) Lembor subproject, Lembor system and catchment (NIA = 4,483 ha), Flores Island.

1. Baseline climate data (without climate change)

23. Minimal reliable observational climate data exists in the three IPDMIP case study locations. Therefore, daily precipitation, temperature (maximum and minimum), wind speed, relative humidity and solar radiation was obtained from the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR)17 for the 36-year period 1979–2014 for several locations within each the three case study catchments. The CFSR was designed and executed as a global, high resolution (grids ~50 km x 50 km), coupled atmosphere-ocean-land surface-sea ice system to provide the best estimate of the state of these coupled domains over this period. 24. Table 1 shows the summary statistics of the baseline climate precipitation and temperature data used. It is acknowledged that uncertainties exist with this baseline climate dataset and this undoubtedly introduces uncertainty into both the baseline and future climate scenarios. However, this situation can only be resolved via ground truthing with actual observed data (which in turn requires a comprehensive effort to improve the coverage and access to hydroclimatic observations across Indonesia – see Section IV.A.4 below for further discussion and recommendations on this).

17 Y.T. Dile and R. Srinivasan. 2014. Evaluation of CFSR Climate Data for Hydrologic Prediction in Data-Scarce

Watersheds: An Application in the Blue Nile River Basin. Journal of the American Water Resources Association (JAWRA). pp. 50(5), 1226-1241. doi: 10.1111/jawr.12182; and D.R. Fuka et al. 2014. Using the Climate Forecast System Reanalysis as Weather Input Data for Watershed Models. Hydrological Processes. pp. 28, 5613–5623, doi: 10.1002/hyp.10073.

10

Table 1: Monthly and Annual Average Rainfall (mm) and Monthly and Annual Average Daily Temperature (°C) for the Three Study Locations (Obtained from NCEP CFSR for the Period 1979–2014)

RAINFALL TEMP – daily maximum TEMP – daily minimum

Karowa Jambo Aye

Lembor Karowa Jambo Aye

Lembor Karowa Jambo Aye

Lembor

Jan 331.9 118.0 417.3 26.96 23.39 28.99 19.58 15.60 23.60 Feb 327.2 73.8 424.8 26.95 23.87 28.08 19.50 15.38 23.37 Mar 345.3 106.4 268.8 27.47 23.45 28.70 19.39 15.85 22.94 Apr 341.1 130.1 131.2 27.50 22.66 29.30 19.34 16.36 23.23 May 308.3 195.1 48.9 27.43 22.78 29.67 19.79 15.52 23.33 Jun 263.7 139.7 34.4 26.84 22.93 28.77 19.90 14.03 22.91 Jul 223.3 180.6 20.6 26.52 22.54 28.75 19.58 13.96 22.37 Aug 168.7 162.2 10.5 27.22 22.46 30.45 19.49 14.17 21.96 Sep 188.1 203.3 23.3 28.37 21.93 32.90 19.17 15.19 22.43 Oct 250.7 227.9 59.7 28.87 21.44 33.95 19.30 15.76 23.28 Nov 340.4 232.0 145.3 28.17 21.57 33.43 19.48 16.00 23.64 Dec 343.7 264.0 300.7 27.26 22.08 30.66 19.64 15.88 23.55 ANN 3394.5 2001.0 1857.21 27.46 22.59 30.30 19.51 15.31 23.05

2. Baseline Runoff and Water Balance Modelling Using the Soil and Water

Assessment Tool 25. In addition to rainfall and temperature, the other critical variables for irrigation are evaporation and water availability. As with the climatological data minimal observations exist for evaporation or water availability. Therefore, these variables were modelled using the Soil and Water Assessment Tool (SWAT). SWAT is a public domain model jointly developed by United States Department of Agriculture Agricultural Research Service and Texas A&M University. SWAT is a small watershed to river basin-scale model that can simulate the quality and quantity of surface and ground water and predict the environmental impact of land use, land management practices, and climate change. SWAT is widely used in assessing soil erosion prevention and control, non-point source pollution control and regional management in watersheds18 (also see https://www.card.iastate.edu/swat_articles/ for a list of peer-reviewed journal articles describing applications of SWAT). 26. Catchment boundaries for the three case study locations were determined and the baseline data obtained from NCEP CFSR (daily precipitation, temperature (maximum and minimum), wind speed, relative humidity and solar radiation) was input to SWAT to obtain daily potential evapotranspiration (PET) and streamflow (Q) from 1979–2014. The summary statistics of the baseline (without climate change) average monthly and annual PET and Q are shown in Table 2.

18 P.W. Gassman et al. 2007. The Soil and Water Assessment Tool: Historical Development, Applications, and Future

Research Directions. Transactions of the ASABE. pp. 50(4), 1211-1250, doi: 10.13031/2013.23637; and J.G. Arnold et al. 2012. SWAT: Model Use, Calibration, and Validation. Transactions of the ASABE. pp. 55(4), 1491 1508, doi: 10.13031/2013.42256.

11

Table 2: Monthly and Annual Average Evapotranspiration (PET, mm) and Streamflow (Q, m3/s) for the Three Study Locations

(Obtained Using SWAT and Inputs Obtained from NCEP CFSR for the period 1979-2014)

Jambo Aye Karowa Lembor

PET, mm Q, m3/s PET, mm Q, m3/s PET, mm Q, m3/s

Jan 136.72 592.26 122.62 6.96 166.5 6.48 Feb 147.67 526.07 114.51 7.47 143.2 8.68 Mar 176.00 550.39 130.74 7.21 156.7 5.77 Apr 167.98 652.71 121.71 8.16 150.6 3.05 May 161.30 599.84 129.90 7.73 154.7 1.27 Jun 157.64 404.81 120.59 7.19 148.8 0.61 Jul 160.79 322.73 131.53 5.67 163.6 0.17 Aug 162.67 304.73 147.14 4.44 182.3 0.06 Sep 152.17 407.31 144.50 4.05 190.5 0.10 Oct 147.00 587.98 142.38 4.93 204.1 0.18 Nov 127.62 703.80 118.92 7.07 187.3 0.86 Dec 117.27 719.48 117.38 7.67 173.6 3.36

ANNUAL 1810.09 532.76 1522.19 6.58 1994.2 2.56

3. Future Hydroclimatic Scenarios (with Climate Change)

27. The climate change factors used for the study catchments were developed based on climate model data available through the World Bank’s Climate Change Knowledge Portal (CCKP, http://sdwebx.worldbank.org/climateportal/); the background and methods are described in Thrasher et al. (2012) and Girvetz et al. (2013).19 GCM outputs from the most comprehensive physically-based models of climate change (i.e. those referenced in the Intergovernmental Panel on Climate Change (IPCC) Assessment Reports) are available for:

(i) precipitation and temperature; (ii) 15 GCMs; (iii) current/historical (1961-2000) and projected (2046-2064 and 2081-2100)

conditions; (iv) two GHG emissions scenarios (B1, A1B, A2); and (v) a common 2° grid – because the resolution of each GCM varies, the data available

in the CCKP were re-gridded to a common 2° grid to allow multi-model comparisons and assessments.

28. In this study, the future hydroclimatic scenarios are based on model outputs from 2046–2064 under the A2 GHG emissions scenario. The A2 GHG emissions scenario is similar to the newer Representative Concentration Pathway (RCP) 8.5, in that they are both at the upper end of what is considered plausible in terms of projected increases to GHGs and global average temperatures (IPCC, 2013). The B1 scenario is at the lower end of what is plausible and A1B is somewhere in the middle. Therefore, by using GCM outputs based on the A2 GHG emissions scenario we are able to consider changes to rainfall and temperature that are at the upper bound of what is considered plausible by the best available science and GCMs – with the assumption being that if adaptation strategies can be developed and implemented to cope with the “worst case” impacts projected under the A2 or (RCP8.5) scenarios then the irrigation

19 B.Thrasher et al. 2012. Technical Note: Bias Correcting Climate Model Simulated Daily Temperature Extremes with

Quantile Mapping. Hydrology and Earth Systems Sciences. pp 16, 3309-3314, doi:10.5194/hess-16-3309-2012; E.H. Girvetz et al. 2013. Making Climate Data Relevant to Decision Making: the Important Details of Spatial and Temporal Downscaling. The World Bank. pp. 43 pages. (27 March 2013).

12

systems will be less vulnerable to the reality that eventuates than they would be if the lower (B1) or moderate (A1B) GHG emissions scenarios were used. 29. Climate change factors (Table 3) for monthly average rainfall (mm) and monthly average daily temperature were derived as follows for each of the input locations used in the SWAT modelling:

For each GCM (M, where M = 1 to 15) and for each month (m, where m = 1 to 12):

��,�� = ��,��

��,��

�� − �� = ∑�� ,��

15

Where: CCF = climate change factor (a CCF = 1 implies no change)

Baseline = GCM outputs from the period 1961-2000. Future = GCM outputs from the period 2046-2064. GCM outputs for both Baseline and Future are obtained from the 2° grid that encompasses the location where climate change information is required (i.e. the NCEP CFSR “observed” rainfall and temperature that is used as inputs to the SWAT modelling (see Section III.A.1 and III.A.2).

Table 3: Climate Change Factors Used for the Three Study Locations – Where the Factor Indicates the Relative Increase (> 1) or Decrease (< 1) in Future (2046-2064) Compared

with Current (1961-2000) Monthly Averages (e.g. a Factor of 1.05 Represents a 5% Increase by 2046-2064 while 0.95 Represents a 5% Decrease by 2046-2064) 20


Rain Tmax Tmin Rain Tmax Tmin Rain Tmax Tmin


30. The monthly climate change factors (multi-model averages as explained in the previous paragraph) were then used to scale the baseline daily rainfall and temperature (minimum and

20 Regarding the multi-model average CCFs shown in Table 3, it is important to note that these multi-model averages

may sometimes be misleading, especially for precipitation where some GCMs project reductions in seasonal/annual totals and some GCMs project increases (and some of the projected reductions or increases are significantly larger than those indicated by the multi-model average CCFs shown in Table 3. To put the multi-model average CCFs shown in Table 3 into context Appendix B shows the change factors by model and by month for each of the locations investigated in this CRVA.

13

maximum) data (see Section III.A.1.). The SWAT modelling explained in Section III.A.2 was then conducted again using the climate change impacted daily precipitation and temperature (maximum and minimum) combined with the same daily wind speed, relative humidity and solar radiation data as was used in the without climate change PET and Q calculation. As such the climate change impacted PET and Q used in this study is only sensitive to changes in rainfall and temperature – a simplification that was necessary due to the lack of data on the impacts of climate change on the other SWAT input variables (i.e. wind speed, relative humidity and solar radiation). Table 4 shows the PET with and without climate change for the three study locations and shows the same for Q.

Table 4: Monthly and Annual Average Evapotranspiration (PET, mm) Used for the Three Study Locations

(Obtained Using SWAT and Inputs Obtained from NCEP CFSR without (PETbas where bas refers to baseline) and with Climate Change (PETfut where fut refers to future)


PETbas PETfut %

change PETbas PETfut %

change PETbas PETfut %

change


Table 5: Monthly and Annual Average Streamflow (Q, m3/s) Used in the Study

(Obtained using SWAT and Inputs Obtained from NCEP CFSR without (Qbas where bas refers to baseline) and with climate change (Qfut where fut refers to future))


Qbas Qfut %

change Qbas Qfut %

change Qbas Qfut %

change

Jan 592.26 640.95 8.22 6.96 6.91 -0.72 6.48 7.87 21.45 Feb 526.07 508.33 -3.37 7.47 8.02 7.36 8.68 10.69 23.16 Mar 550.39 529.93 -3.72 7.21 7.62 5.69 5.77 8.48 46.97 Apr 652.71 499.22 -23.52 8.16 8.22 0.74 3.05 4.90 60.66 May 599.84 601.98 0.36 7.73 8.77 13.45 1.27 2.14 68.50 Jun 404.81 414.06 2.29 7.19 9.30 29.35 0.61 1.09 78.69 Jul 322.73 342.74 6.20 5.67 7.06 24.51 0.17 0.30 76.47 Aug 304.73 294.18 -3.46 4.44 5.51 24.10 0.06 0.12 100.00 Sep 407.31 410.98 0.90 4.05 4.53 11.85 0.10 0.14 40.00 Oct 587.98 597.52 1.62 4.93 4.40 -10.75 0.18 0.18 0.00 Nov 703.80 681.24 -3.21 7.07 6.85 -3.11 0.86 0.77 -10.47 Dec 719.48 739.05 2.72 7.67 7.62 -0.65 3.36 3.30 -1.79 ANN 532.76 523.25 -1.79 6.58 7.11 8.05 2.56 3.35 30.86

14

IV. ADAPTATION NEEDS AND OPTIONS A. Integrated Participatory Development and Management of Irrigation Program

31. This section proposes specific implementation interventions to adapt to climate change on the basis of preceding assessments of the agricultural production risks (Section IV.A.1), vulnerabilities (Section IV.A.2) and adaptation options (Section IV.A.3). 21 It considers (i) cropping patterns; (ii) crop yields; (iii) irrigation; and (iv) drainage design criteria.

1. Risk Assessment 32. The irrigation design standard KP-01 (DGWR, 1986) requires a half-monthly period of water balance analysis using the following hydroclimate parameters and levels of risk: (i) mean potential evapotranspiration (ET0) and one-in-five year low (20% probability) dependable (ii) rainfall; and (iii) streamflow. Dependable values are to be estimated on the basis of either normal or log-normal frequency distributions.22 33. Net Irrigable Areas (NIAs). This CRVA estimates the vulnerability (drought disaster risk) of cropping patterns to climate change in the irrigation systems in which the three subprojects are located. However, on the supply side of the irrigation system water balances, available runoff or discharge is estimated in Section III on the basis of the river basin, catchment or watershed water balances (runoff/discharge = rainfall – actual evapotranspiration). The three irrigation systems irrigate most, if not all, the NIA in their respective catchments. Total NIA areas served are 258 ha (Karowa), 4,483 ha (Lembor) and 17,931 ha (Jambo Aye) (MMD 2015). 34. Irrigation Efficiencies. KP-01 indicates average irrigation design efficiencies of: (i) 65% (range 59% to 73%) for normal systems (Lembor 4,483 ha); (ii) 70% for small systems (Karowa 258 ha); and (iii) 60% for large systems, serving more than 10,000 ha, (Jambo Aye 17,931 ha). These operationally realistic to optimistic values are used herein to estimate irrigation system water balances and compare potential cropping patterns with and without climate change.

Table 6: Present Stationary Annual Water Balances Variable Unit Karowa Jambo Aye Lembor

R20 mm 2,347.6 1,231.6 996.2 ET0 mm 1,541.8 1,814.9 2,021.9 R20 – ET0 mm 805.8 -583.3 -1,025.7 Q20 mm 37,728.7 41,588.9 504.8 Q20 + R20 – ET0 mm 38,534.5 41,005.6 -520.9 (Q20 + R20)/ET0 ratio 26.0 23.6 0.74

ET0 = annual sum of mean monthly PET; PET = potential (reference crop) evapotranspiration; Q20 = annual sum of dependable monthly streamflow; adjusted for irrigation efficiency, over the service area; R20 = annual sum of dependable monthly rainfall.

21 Climate change science risk and vulnerability definitions, used herein, are similar to the hazard and disaster risks

in: ADB. 2014. Operational Plan for Integrated Disaster Risk Management 2014-2020, which defines disaster risk p(d) as a function of the probability of occurrence of a hazard p(h); the people and physical assets situated in that location and therefore exposed to the hazard e; and the level of vulnerability of those people and physical assets to that hazard v. This relationship can be expressed mathematically as p(d) = fn (p(h), e, v).

22 The assessment herein employs both the more conservative log-normal distribution and less conservative monthly period of analysis. In the absence of readily available and reliable hydro-climate data, the latter is preferred to develop a generic method for rapid assessment of potential non-core subprojects during IDPMIP implementation. Irrigation drought occurs when demand (evapotranspiration) exceeds dependable supply (discharge plus rainfall).

15

35. Water Balances, Drought Hazard Risks and Growing Seasons. Table 6 indicates the present rainfed drought hazard risk is relatively low at Karowa, high at Jambo Aye and very high at Lembor. This is because Karowa has a present stationary annual rainfed water balance (R20 – ET0) surplus of 805.8 mm and Lembor has a large rainfed water balance deficit of 1,025.7 mm. 36. Table 6 also indicates that the relative present irrigated drought hazard risk is very low at Karowa and Jambo Aye but remains quite high at Lembor. Both Jambo Aye and Karowa have favorable irrigation water balances, with high supply (Q20 + R20)/demand (ET0) ratios and large (Q20 + R20 – ET0) surpluses, whereas the annual Lembor irrigation supply (Q20 + R20) is only 74% of annual demand (ET0) and the irrigated water balance deficit (Q20 + R20 – ET0) is still 520.9 mm. 37. Therefore, the relative rainfed crop production potential is high at Karowa, low at Jambo Aye and very low at Lembor. Conversely the relative need for irrigation is low at Karowa, high at Jambo Aye and very high at Lembor. However the irrigated crop production potential, while high at Jambo Aye, remains low at Lembor because of its unfavorable irrigated water balance. 38. The Jambo Aye irrigation system has the largest catchment area, relative to its irrigation system service area, (414,620/17,931 = 23.1) whereas Karowa (656/258 = 2.54) and Lembor (7,826/4,483 = 1.75) are much smaller. At Karowa the rainfed water balance (R20 – ET0) surplus compensates for the small catchment area whereas the large Jambo Aye catchment area compensates for the rainfed water balance (R20 – ET0) deficit. However, Lembor suffers from both a very small catchment and a large rainfed water balance (R20 – ET0) deficit (Table 6).

16

Table 7: Present and Future Hydroclimate Frequency Estimates and Water Balances V U Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

JA

MB

O A

YE

PR

ES

EN

T

Q20 m3/s 454.0 392.0 393.7 457.5 447.8 310.0 233.3 188.0 293.0 427.0 567.9 563.5 R20 Mm 71.8 29.6 45.5 57.4 116.5 92.8 127.2 109.3 126.3 140.9 144.4 169.9 ET0 Mm 136.7 147.7 176.0 168.0 161.3 157.6 160.8 162.7 152.2 147.0 127.6 117.3 Q20 MCM 729.6 574.1 632.7 711.5 719.6 482.1 374.9 302.1 455.7 686.2 883.2 905.6 R20 MCM 12.9 5.3 8.2 10.3 20.9 16.6 22.8 19.6 22.6 25.3 25.9 30.5 ET0 MCM 24.5 26.5 31.6 30.1 28.9 28.3 28.8 29.2 27.3 26.4 22.9 21.0 S-D MCM 718.0 552.9 609.3 691.7 711.6 470.4 368.9 292.5 451.0 685.1 886.2 915.1 S/D ratio 30.3 21.9 20.3 24.0 25.6 17.6 13.8 11.0 17.5 27.0 39.7 44.6

JA

MB

O A

YE

FU

TU

RE


KA

RO

WA

PR

ES

EN

T


KA

RO

WA

FU

TU

RE


LE

MB

OR

PR

ES

EN

T

Q20 m3/s 2.79 4.16 2.96 1.57 0.58 0.09 0.03 0.01 0.01 0.03 0.17 1.02 R20 Mm 244.5 257.2 146.1 63.9 15.6 5.7 5.2 2.6 4.2 24.4 65.2 161.6 ET0 Mm 166.5 143.2 156.7 150.6 154.7 148.8 163.6 182.3 190.5 204.1 187.3 173.6 Q20 MCM 4.86 6.60 5.15 2.65 1.01 0.15 0.05 0.02 0.02 0.05 0.29 1.78 R20 MCM 10.96 11.53 6.55 2.86 0.70 0.26 0.23 0.12 0.19 1.09 2.92 7.24 ET0 MCM 7.46 6.42 7.02 6.75 6.94 6.67 7.33 8.17 8.54 9.15 8.40 7.78 S-D MCM 8.36 11.71 4.68 -1.24 -5.23 -6.26 -7.05 -8.03 -8.33 -8.01 -5.19 1.24 S/D ratio 2.12 2.82 1.67 0.82 0.25 0.06 0.04 0.02 0.02 0.12 0.38 1.16

LE

MB

OR

FU

TU

RE

Q20 m3/s 3.45 5.28 4.33 2.40 0.91 0.19 0.05 0.02 0.01 0.02 0.15 1.02 R20 Mm 281.7 302.1 210.3 103.0 26.8 7.1 5.0 3.7 4.9 22.9 60.3 159.3 ET0 Mm 175.2 150.4 164.9 158.3 163.5 158.0 173.1 191.1 198.6 212.3 195.8 182.2 Q20 MCM 6.01 8.38 7.54 4.04 1.58 0.32 0.09 0.03 0.02 0.03 0.25 1.78 R20 MCM 12.63 13.54 9.43 4.62 1.20 0.32 0.22 0.17 0.22 1.03 2.70 7.14 ET0 MCM 7.85 6.74 7.39 7.10 7.33 7.08 7.76 8.57 8.90 9.52 8.78 8.17 S-D MCM 10.79 15.18 9.58 1.56 -4.55 -6.44 -7.45 -8.37 -8.66 -8.46 -5.83 0.75 S/D ratio 2.37 3.25 2.30 1.22 0.38 0.09 0.04 0.02 0.03 0.11 0.34 1.09 D = demand; = ET0 = mean potential (reference crop) evapotranspiration; e = efficiency (60% at Jambo Aye, 65% at Lembor and 70% at Karowa); MCM = million cubic meters (m3); NIA = net irrigable area (17,931ha at Jambo Aye, 4,483ha at Lembor and 258ha at Karowa); Q20 = dependable discharge; R20 = dependable rainfall; S = supply (Q20 + R20). To convert to m3, Q20 (m3/sec) is multiplied by e x 3,600 x 24 x days/month and R20 and ET0 (mm) by 10 x NIA.

17

39. Table 7 above indicates that, as monthly supply is more than an order of magnitude greater than demand in the driest months (August and September respectively), 23 Karowa and Jambo Aye both have potential 12-month irrigable growing seasons. Lembor, however, where demand exceeds supply for eight months: (i) has a very short potential four-month irrigable growing season; and (ii) supply is only 2% of demand in the critical month of August. 24 Increased rice yield, due to improved supplementary irrigation of the wet season crop, is likely to be the main benefit of improved run-of-the-river diversion irrigation at Lembor. If stored without losses, the present irrigation surplus (Q20 + R20 – ET0) of 25.99 MCM, from December to March, would meet the deficit of 19.78 MCM, from April to July, and irrigate an increased dry season rice area. 40. Table 8 indicates that the annual sums of mean monthly potential (reference crop) evapotranspiration (PET)25 and dependable monthly rainfall are both projected to increase at all three sites. System specific projections (Table 8) are:

(i) Karowa: Annual rainfall will increase more than annual PET. Annual runoff/streamflow and the irrigation system water balance (Q20 + R20 – ET0) surplus will both increase by 7%;

(ii) Jambo Aye: Annual PET will increase more than rainfall. The irrigation system water balance (Q20 + R20 – ET0) surplus will decrease by 2.5% as a result. However irrigated drought hazard risk and optimum cropping pattern impacts are likely to be insignificant (within 95% confidence limit of present hydroclimate estimates without climate change);

(iii) Lembor: Annual rainfall will increase by 19%, from 996.2 mm to 1,187.1 mm, while annual PET increases by only 5% from 2,021.9 mm to 2,123.4 mm. Therefore annual runoff or streamflow will increase by 33%, from 504.8 mm to 670.8 mm, and the irrigation water balance (Q20 + R20 – ET0) deficit will decrease by 51% from 520.9 mm to 265.5 mm. Table 7 indicates that the potential irrigated growing season will increase by 25%, from 4 to 5 months, but this is not enough for double cropping (at least seven months for 90-day rice varieties). This indicates that most irrigation water balance (Q20 + R20 – ET0) increases will occur in the wet season and main climate change benefits will be either:

a. Wet season rice yield increases, under run-of-the-river diversion irrigation; or b. Dry season area and cropping intensity increases if a storage dam proves

feasible. Table 7 indicates that the annual irrigation surplus (Q20 + R20 – ET0) will increase by 46% from 25.99 MCM to 37.86 MCM. If stored this surplus will extend the potential irrigated growing season by 25% from eight (December to July) to 10 months (December to September).

23 Supply (discharge and rainfall) and demand (evapotranspiration) are converted to a common metric (m3).

Discharge (m sec-1) is multiplied by irrigation efficiency x 3,600 x 24 x days/month and rainfall and ET0 (mm) by 10 x NIA.

24 Small East Indonesian catchments are generally limited to either: (i) rain-fed wet season rice production; (ii) run-of-the-river diversion irrigation systems, e.g., Lembor, with low cropping intensities; or (iii) storage irrigation systems.

25 Potential evapotranspiration increases, projected herein, are based solely on temperature projections as available sources do not provide projections of changes in wind speed, relative humidity, solar radiation or cloud cover.

18

Table 8: Present and Future Annual Irrigation Water Balances

Scenario Variable Unit Karowa Jambo Aye Lembor

Present Stationary Climate

Q20 mm 37,728.7 41,588.9 504.8

R20 mm 2,347.6 1,231.6 996.2

ET0 mm 1,541.8 1,814.9 2,021.9

Q20 + R20 – ET0 mm 38,534.5 41,005.6 -520.9

Future With

Climate Change

Q20 mm 40,426.4 40,625.2 670.8

R20 mm 2,497.6 1,251.5 1,187.1

ET0 mm 1,617.3 1,907.1 2,123.4

Q20 + R20 – ET0 mm 41,306.7 39,969.6 - 265.5

ET0 = annual sum of mean monthly PET; PET = potential (reference crop) evapotranspiration; Q20 = annual sum of dependable monthly streamflow; adjusted for irrigation efficiency, over the service area; R20 = annual sum of dependable monthly rainfall.

41. Rice Yields: With future climate change local rice yields may be sensitive to increased temperatures, particularly at night (Tmin). 26 Mean Tmin values, assessed in Section III, are summarized in Table 9 below.

Table 9: Mean Minimum Temperature (Tmin)

System

Wet Season Rice Dry Season Rice

Months Tmin (0C)

Months Tmin (0C)

W/O CC With CC W/O CC With CC

Karowa Dec-Mar 19.5 21.1 May-Aug 19.7 21.3 Jambo Aye Dec-Mar 15.7 17.0 May-Aug 14.4 15.8 Lembor Jan-Apr 23.3 25.4 May-Aug 22.6 24.7

42. Drainage Design Criteria: Drainage design criteria for paddy field will follow the standard from KP-01. According to KP-01, the amount of excess rainfall that should be removed from paddy field could be calculated using maximum 3-consecutive days of rain with non-exceeding probability 20% using normal/log-normal frequency distribution.27 This variable is represented as "#

$ . Then Drainage Modulus (%&) is calculated using the following formula:

%& = 1

�%#

%# ="#$ + ��I − ET − P� − S

Where: %& : Drainage modulus (mm/day) %# : Excess surface water to be drain in n days "#$ : maximum n-consecutive days of rain with non-exceeding probability 20% using

26 Peng et al. 2004. Rice Yields Decline with Higher Night Temperature from Global Warming; Laza et al. 2015.

Different Response of Rice Plants to High Night Temperatures Imposed at Varying Developmental Stages; and UK Met Office. 2011.

27 This is counter-intuitive as 20% non-exceedance is often well below the mean. While using 20% non-exceedance precipitation as a basis for irrigation design makes sense, as the four out of five years with wetter conditions are presumably less challenging, the reverse is true with respect to drainage: four out of five years would present more difficult conditions. Re-stated, using this design criteria drainage would only be assumed to function properly in one year out of five.

19

normal/log-normal frequency distribution � : Number of days I : Irrigation (mm/day) ET : Evapotranspiration (mm/day) P : Percolation (mm/day) S : Storage (mm/day)

43. Using current and future daily precipitation data, the amount of maximum 3-consecutive days of rain with non-exceeding probability 20% using log-normal frequency distribution for Jambo Aye, Lembor and Karowa are given in Table 10.

Table 10: Maximum 3-consecutive Days of Rain with Non-exceeding Probability 20% Using Log-Normal Frequency Distribution (mm/day)

Jambo Aye Lembor Karowa

Present Future Present Future Present Future

January 19.9 23.8 71.4 81.9 55.6 56.5 February 14.9 15.0 77.0 90.3 55.2 60.3 March 22.1 21.1 48.6 69.1 61.8 65.5 April 26.1 19.8 23.3 37.4 58.6 59.2 May 36.0 39.0 6.6 11.4 60.2 68.5 June 28.7 32.0 2.6 3.3 40.4 52.4 July 36.1 39.3 3.6 3.7 35.3 40.5 August 32.6 29.6 1.4 2.0 26.0 30.9 September 39.4 42.4 2.4 3.0 28.7 31.2 October 42.9 44.4 8.8 8.3 43.6 37.9 November 46.6 45.0 18.1 16.8 59.5 59.3 December 43.1 44.9 46.6 45.6 52.0 53.0

44. During rainy days, average irrigation (I) requirements in three locations could be approached from field irrigation requirements (FIR) values as summarized in Table 11.

Table 11: Current and Future Field Irrigation Requirements (FIR)

Variable Unit Dec Jan Feb Mar Apr May Jun Jul Aug

Pattern Rice 120-day 120-day Jambo Aye present FIR mm/d 7.32 5.84 7.66 5.37 0 9.98 6.28 5.32 3.52 Jambo Aye future FIR mm/d 6.96 5.85 7.98 5.69 0 9.48 6.35 5.39 4.01 Lembor present FIR mm/d 6.3 4.8 9.2 11.8 13.4 13.7 13.9 14.4 14.7 Lembor future FIR mm/d 4.9 3.1 7.0 10.3 12.9 13.5 13.8 14.2 14.4 Karowa present FIR mm/d 4.61 1.82 2.04 0.00 0 5.84 4.07 4.80 4.24 Karowa future FIR mm/d 4.32 1.93 1.70 0.00 0 4.57 3.23 4.61 4.12

45. Using average ET values as in the previous analysis (4.0 mm/day) and average P = percolation and seepage (1.5 mm/day at Karowa and Lembor and 1.0 mm/day at Jambo Aye), and also with the value of additional storage as zero (0) then excess surface water to be drained in n days (D_n) and the drainage modulus (D_m) for present and future in Jambo Aye, Lembor and Karowa are given in Table 12 and Table 13.

20

Table 12: Excess Surface Water to be Drained in 3 days (Dn) for Present and Future (mm/3 days)


Pattern Rice 120-day 120-day

Jambo Aye present FIR

mm/d 42.3 19.6 14.0 22.0 27.7 34.4 28.3 36.0 33.1

Jambo Aye future FIR

mm/d 44.3 23.5 14.1 20.9 21.4 37.5 31.6 39.1 29.9

Lembor present FIR

mm/d 46.3 71.6 75.7 46.5 20.7 3.9 -0.2 0.7 -1.7

Lembor future FIR

mm/d 45.8 82.7 89.8 67.5 34.9 8.7 0.6 0.8 -1.0

Karowa present FIR

mm/d 52.3 56.9 56.4 63.6 60.5 60.0 40.9 35.6 26.4

Karowa future FIR

mm/d 53.4 57.7 61.6 67.3 61.0 68.8 53.2 40.8 31.4

Table 13: The Drainage Modulus (Dm) for Present and Future (mm/day)



Jambo Aye present FIR

mm/d 14.1 6.5 4.7 7.3 9.2 11.5 9.4 12.0 11.0

Jambo Aye future FIR

mm/d 14.8 7.8 4.7 7.0 7.1 12.5 10.5 13.0 10.0

Lembor present FIR

mm/d 15.4 23.9 25.2 15.5 6.9 1.3 -0.1 0.2 -0.6

Lembor future FIR

mm/d 15.3 27.6 29.9 22.5 11.6 2.9 0.2 0.3 -0.3

Karowa present FIR

mm/d 17.4 19.0 18.8 21.2 20.2 20.0 13.6 11.9 8.8

Karowa future FIR

mm/d 17.8 19.2 20.5 22.4 20.3 22.9 17.7 13.6 10.5

46. Table 13 shows that the highest increase of drainage modulus is in Lembor (from 25.2 mm/day to 29.9 mm/day) and then in Karowa (from 21.2 mm/day to 22.4 mm/day). The increase of drainage modulus in Jambo Aye is the smallest one among the three (14.1 mm/day to 14.8 mm/day). The estimated increases in peak drainage might be accommodated within the existing drainage system. In that case, the incremental cost of drainage service in response to climate change would be zero. The estimated increases in peak drainage flow are particularly small in Jambo Aye and Karowa (about 5% above the current drainage flow, in December and March, respectively). It is not clear that new construction would be needed to accommodate those increases. The estimated increase is somewhat larger in Lembor (19% above the current drainage flow in February). We have not estimated the incremental cost of accommodating the higher flow in February, as we are not aware of the current drainage system capacity. If the current capacity is sufficient, the incremental cost would be zero. If new construction is required, the incremental benefit would include sustaining successful production of rice during the December to February season.

21

2. Vulnerability Assessment 47. Land Preparation Requirements. To prepare rice land (saturate the soil) and establish the water layer, KP-01 requires 300 mm or 250 mm after fallow of more or less than 2.5 months. KP-01 suggests land preparation over 30 to 45 days. Shorter durations are more efficient and a one-month duration is adopted herein. Based on soil texture percolation the seepage rates (P) are estimated as 1.5 mm day-1 (Lembor, Karowa) or 1.0 mm day-1 (Jambo Aye).28 48. Golongan Rotations and Field Drainage. Golongan29 land preparation rotations allow earlier wet season land preparation (and higher cropping intensities), by reducing peak water requirements (DGWR, 1986). KP-01 recommends drainage (and replacement) of the 50 mm water layer before (and after) two fertilizer applications in the second and third crop months. However, where water is limiting, this practice will reduce the cropping intensity appreciably and it is not clear if the increased rice yield benefits outweigh the reduced cropping intensity costs. 49. Cropping Patterns, Water Balances, Risks and Vulnerabilities. The maximum water requirement, for any crop at any growth stage (including the above field drainage requirement) in any month, is for initial (pre-saturation and water layer of 300 mm) land preparation for rice using only one Golongan. These maximum monthly Karowa and Jambo Aye water requirements, and associated irrigated water balances, are in Table 14 for both the present stationary situation and projected future scenario (without and with climate change). 50. Table 14 indicates that there is presently enough water, at both Jambo Aye and Karowa, for initial rice land preparation in any month. The minimum water supply surpluses (and their proportions of dependable supply) are 153 m3/sec, (81%), at Jambo Aye in August, and 1.53 m3/sec (78%) at Karowa in September. These large surpluses will increase further for any other crop and/or growth stage. This confirms present water balances are not constraints. Table 14 also confirms that the future (with climate change) water balances will not be constraints either as the above minimum surpluses are expected to: (i) increase by 18%, from 1.53-1.80 m3/sec, at Karowa; and (ii) only decrease by 1.3%, from 153-151 m3/sec, at Jambo Aye.

28 MMD. 2015. Summary Sub-Project Report for DI Mon Sukon; MMD. 2015. Summary Sub-Project Report for DI

Karowa; and MMD. 2015. Summary Sub-Project Report for DI Lembor. 29 Golongan are groups of tertiary units that are: (i) distributed uniformly over the service area and (ii) arranged, for

three Golongan for example, so that each Golongan serves about a third of the total service area (DGWR 1986).

22

Table 14: Present and Future Maximum Monthly Water Requirements – Jambo Aye and Karowa

V U Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

JA

MB

O A

YE

PR

ES

EN

T

Q20 m3/s 454 392 394 458 448 310 233 188 293 427 568 564 R20 mm 72 30 46 57 117 93 127 109 126 141 144 170 ET0 mm 137 148 176 168 161 158 161 163 152 147 128 117

CWR mm/d 5.9 6.8 7.3 7.2 6.7 6.8 6.7 6.8 6.6 6.2 5.7 5.2 LPR mm/d 13.2 13.8 14.1 14.0 13.7 13.8 13.7 13.8 13.7 13.4 13.1 12.8 FIR mm/d 10.9 12.7 12.6 12.1 10.0 10.7 9.6 10.2 9.4 8.9 8.3 7.3 FIR lps/ha 1.26 1.47 1.46 1.40 1.15 1.24 1.11 1.19 1.09 1.03 0.96 0.85 DIR lps/ha 2.10 2.45 2.43 2.33 1.92 2.06 1.86 1.98 1.82 1.71 1.60 1.41

Surplus m3/s 416 348 350 416 413 273 200 153 260 396 539 539

JA

MB

O A

YE

FU

TU

RE

Q20 m3/s 483 380 379 356 435 308 245 187 295 427 550 572 R20 mm 81 27 44 43 128 104 138 99 129 144 139 178 ET0 mm 143 154 185 177 171 166 170 171 159 154 134 124


Surplus m3/s 447 337 335 313 402 273 213 151 263 397 521 548

KA

RO

WA

PR

ES

EN

T

Q20 m3/s 5.18 5.44 5.19 5.98 6.07 4.58 3.48 2.21 1.97 2.57 4.75 5.55 R20 mm 250 226 250 252 233 151 127 78 102 160 256 263 ET0 mm 123 115 131 122 130 121 132 147 145 142 119 117


Surplus m3/s 4.96 5.21 4.96 5.77 5.82 4.23 3.08 1.73 1.53 2.20 4.55 5.35

KA

RO

WA

FU

TU

RE

Q20 m3/s 5.11 5.77 5.47 6.02 6.83 5.76 4.49 2.82 2.22 2.24 4.55 5.46 R20 mm 254 247 264 252 269 197 146 94 109 140 257 268 ET0 mm 128 120 138 128 137 127 138 155 151 149 125 123


Surplus m3/s 4.90 5.58 5.27 5.82 6.64 5.48 4.12 2.37 1.80 1.86 4.36 5.27 CWR = crop (maintenance) water requirement = (1.10 x ET0 + P) during land preparation; DIR = diversion irrigation requirement = (FIR/e); ET0 = mean potential (reference crop) evapotranspiration; e = efficiency (60% at Jambo Aye, 65% at Lembor and 70% at Karowa); FIR = field-level irrigation requirement = (LPR – R20); k = CWR x T/S: LPR = land preparation water requirement = CWR x ek/(ek -1); lps/ha = litres per second per hectare; NIA = net irrigable area (17,931 ha at Jambo Aye, 4,483 ha at Lembor and 258 ha at Karowa); P = percolation and seepage (1.5 mm/day at Karowa and Lembor and 1.0 mm/day at Jambo Aye); Q20 = dependable discharge; R20 = dependable rainfall; S = initial pre-saturation + water layer (300 mm); Surplus = (Q20 - DIR) and T = land preparation duration (one month). To convert: (i) mm/d to lps/ha divide mm/d by 8.64; and (ii) lps/ha to m3/sec multiply lps/ha by NIA/1,000.

23

Table 15: Present and Future Irrigated Cropping Patterns and Water Balances



Kc Unit LP 1.10 1.08 0.95 0 LP 1.10 1.08 0.95 K

AR

OW

A

PR

ES

EN

T

Q20 m3/s 5.55 5.18 5.44 5.19 5.98 6.07 4.58 3.48 2.21 R20 mm 263 250 226 250 252 233 151 127 78 ET0 mm 117 123 115 131 122 130 121 132 147

CWR mm/d 13.1 7.46 7.63 5.51 0 13.4 7.59 7.67 6.01 FIR mm/d 4.61 1.82 2.04 0.00 0 5.84 4.07 4.80 4.24 DIR m3/s 0.20 0.08 0.09 0.00 0 0.25 0.17 0.20 0.18

Q20 - DIR m3/s 5.35 5.10 5.35 5.25 5.98 5.82 4.41 3.28 2.35

KA

RO

WA

FU

TU

RE

Q20 m3/s 5.46 5.11 5.77 5.47 6.02 6.83 5.76 4.49 2.82 R20 mm 268 254 247 264 252 269 197 146 94 ET0 mm 123 128 120 138 128 137 127 138 155

CWR mm/d 13.0 7.66 7.82 5.71 0 13.2 7.83 7.91 6.25 FIR mm/d 4.32 1.93 1.70 0.00 0 4.57 3.23 4.61 4.12 DIR m3/s 0.18 0.08 0.07 0.00 0 0.19 0.14 0.20 0.18

Q20 - DIR m3/s 5.27 5.03 5.70 5.55 6.02 6.64 5.62 4.29 3.06

JA

MB

O A

YE

PR

ES

EN

T

Q20 m3/s 564 454 392 394 458 448 310 233 188 R20 mm 170 72 30 46 57 117 93 127 109 ET0 mm 117 137 148 176 168 161 158 161 163

CWR mm/d 12.8 7.46 8.39 6.39 0 13.7 8.45 8.19 5.99 FIR mm/d 7.32 5.84 7.66 5.37 0 9.98 6.28 5.32 3.52 DIR m3/s 25 20 26 19 0 35 22 18 12

Q20 – DIR m3/s 538 434 366 375 458 413 288 215 187

JA

MB

O A

YE

FU

TU

RE

Q20 m3/s 572 483 380 379 356 435 308 245 187 R20 mm 178 81 27 44 43 128 104 138 99 ET0 mm 124 143 154 185 177 171 166 170 171

CWR mm/d 12.7 7.68 8.63 6.68 0 13.6 8.76 8.51 6.24 FIR mm/d 6.96 5.85 7.98 5.69 0 9.48 6.35 5.39 4.01 DIR m3/s 24 20 28 20 0 33 22 19 14

Q20 – DIR m3/s 548 463 353 359 356 402 286 226 188

Land Preparation Crop Development Crop Ripening

CWR = crop water requirement = (Kc x ET0 + P); DIR = diversion irrigation requirement = (NIA x FIR/e); ET0 = mean potential evapotranspiration; e = efficiency (60% at Jambo Aye, 65% at Lembor and 70% at Karowa); FIR = field-level irrigation requirement = (LPR – R20) or (CWR – Re); Kc = crop coefficient; LPR = land preparation water requirement (Table 14); NIA = net irrigable area (17,931 ha at Jambo Aye, 4,483 ha at Lembor and 258 ha at Karowa); P = percolation and seepage (1.5 mm/day at Karowa and Lembor and 1.0 mm/day at Jambo Aye); Q20 = dependable discharge; R20 = dependable rainfall; Re = effective rainfall = 0.70 x R20. To convert mm/d to m3/s multiply mm/d by NIA/(e x 8,640).

51. Table 15 includes replacement of the water layer, following drainage for efficient fertilizer applications, and confirms there is presently more than enough water, at both Jambo Aye and Karowa, for the standard cropping pattern (two 120-day rice varieties) and a cropping intensity of 200%. The minimum water supply surpluses (and their proportions of dependable supply) are presently 187 m3/sec, (94%) and 2.35 m3/sec (93%) at Jambo Aye and Karowa (in August).30 This confirms that these systems have a very low present risk of drought disaster (vulnerability).

30 Water is only required during the first half of the last month (during ripening). The (Q20 – DIR) surpluses (Table 6)

are adjusted accordingly. For Karowa, for example, the present early August discharge is (3x2.21 + 3.48)/4 = 2.53 cumec. Starting initial land preparation in November, instead of December, would further increase these large surpluses. However, at Jambo Aye but not Karowa, the peak DIR, and hence the required irrigation system capacity, would also increase by 13% from 1.41 to 1.60 liter per second /ha (Table 5).

24

52. Table 15 also indicates that, with future climate change, the above minimum water supply surpluses are expected to: (i) be little changed at Jambo Aye but (ii) increase by 30%, from 2.35 m3/sec (93%) to 3.06 m3/sec (94%), at Karowa. This indicates that future climate change will: (i) not affect the present very low risk of drought disaster (vulnerability) at Jambo Aye and (ii) will further reduce it at Karowa. This is consistent with the interpretation in the paragraph 51. 53. At Lembor, Table 16 indicates that, there is presently only enough water for initial land preparation in February when the DIR of 0.86 liter per second/ha is a minimum and there is a small surplus of 0.29 m3/sec representing only 7% of the dependable monthly discharge of 4.16 m3/sec. There is a deficit in the remaining 11 months of the year. Future climate change is expected to be beneficial. The minimum DIR will decrease by 36%, from 0.86 to 0.55 lps/ha, the surplus will increase by nearly an order of magnitude, from 0.29 to 2.80 m3/sec, in February and the other deficits will all decrease. However, the water balance improves more in the wet season, than in the dry season, and the short 11-month growing season will remain a considerable constraint.

Table 16: Present and Future Maximum Monthly Water Requirements – Lembor

V U Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

LE

MB

OR

PR

ES

EN

T Q20 m3/s 2.79 4.16 2.96 1.57 0.58 0.09 0.03 0.01 0.01 0.03 0.17 1.02

R20 mm 245 257 146 64 16 6 5 3 4 24 65 162 ET0 mm 167 143 157 151 155 149 164 182 191 204 187 174


Deficit m3/s 2.22 0.29 4.41 7.84 10.1 10.8 11.1 11.5 11.7 11.3 9.88 6.25

LE

MB

OR

FU

TU

RE

Q20 m3/s 3.45 5.28 4.33 2.40 0.91 0.19 0.05 0.02 0.01 0.02 0.15 1.02 R20 mm 282 302 210 103 27 7 5 4 5 23 60 159 ET0 mm 175 150 165 158 164 158 173 191 199 212 196 182


Deficit m3/s 0.47 2.80 1.27 5.85 9.39 10.6 11.0 11.3 11.5 11.2 9.86 6.16 CWR = crop (maintenance) water requirement = (1.10 x ET0 + P) during land preparation; DIR = diversion irrigation requirement = (FIR/e); ET0 = mean potential (reference crop) evapotranspiration; e = efficiency (60% at Jambo Aye, 65% at Lembor and 70% at Karowa); FIR = field-level irrigation requirement = (LPR – R20); k = CWR x T/S: LPR = land preparation water requirement = CWR x ek/(ek -1); lps/ha = litres per second per hectare; NIA = net irrigable area (17,931 ha at Jambo Aye, 4,483 ha at Lembor and 258 ha at Karowa); P = percolation and seepage (1.5 mm/day at Karowa and Lembor and 1.0 mm/day at Jambo Aye); Q20 = dependable discharge; R20 = dependable rainfall; S = initial pre-saturation + water layer (300 mm); Surplus = (Q20 - DIR) and T = land preparation duration (one month). To convert: (i) mm/d to lps/ha divide mm/d by 8.64; and (ii) lps/ha to m3/sec multiply lps/ha by NIA/1,000.

54. At Lembor, present diversion irrigation requirements are 7.3, 3.6 or 2.4 m3/sec for land preparation, with one, two or three Golongan, starting in December. However dependable discharge is then only 1.02 m3/sec. More than three Golongan is operationally impractical and, as indicated in Table 17, optimum land preparation requires two Golongan starting in January. There is enough water to drain and replace the water layer (not included in Table 17) in February, but not in March, and even a short 90-day high yielding variety (HYV) will only just have enough water in May (Table 17). Other estimates indicate that a second 90-day HYV, from

25

May to July, could be grown on less than 1% of the area. This is negligible and the present design irrigated cropping pattern is effectively limited to a single 90-day HYV and a cropping intensity of 100%. 55. At Lembor, future (with climate change) diversion irrigation requirements are expected to be 7.2, 3.6 or 2.4 m3/sec in December, when dependable discharge will remain at 1.02 m3/sec, (Table 17) and optimum land preparation will still require two Golongan starting in January. There is expected to be enough water to drain and replace the water layer in both February and March and the minimum surplus (0.76 m3/sec in April) will be a comfortable 32% of dependable supply (2.40 m3/sec). However other estimates indicate that a second 90-day HYV, from May to July, could be grown on less than 2% of the area. This is still negligible and the present single 90-day HYV design cropping pattern and 100% cropping intensity are unlikely to change in future.

Table 17: Present and Future Irrigated Cropping Patterns – Lembor

Variable Unit Dec Jan Feb Mar Apr May Crop

Gol 1 Rice 90 Kc Unit LP 1.05 0.95

Gol 2 90 Kc LP 1.05 0.95

LE

MB

OR

PR

ES

EN

T Q20 m

3/s 1.02 2.79 4.16 2.96 1.57 0.58 R20 mm 162 245 257 146 64 16 ET0 mm 174 167 143 157 151 155

CWR1 mm/d 14.3 14.2 6.82 6.30 809 mm

CWR2 mm/d 0 14.0 6.81 6.27 FIR1 mm/d 9.10 6.27 0.45 3.00 FIR2 mm/d 0 4.85 3.51 4.78 FIRav mm/d 3.14 2.65 3.26 2.39 429 mm DIRav m3/s 2.50 2.11 2.60 1.91

Q20 - DIR m3/s 0.29 2.05 0.36 0.01 0 mm

LE

MB

OR

FU

TU

RE

Q20 m3/s 1.02 3.45 5.28 4.33 2.40 0.91 R20 mm 159 282 302 210 103 27 ET0 mm 182 175 150 165 158 164

CWR1 mm/d 14.1 14.0 7.09 6.55 819 mm

CWR2 mm/d 13.8 7.09 6.51 FIR1 mm/d 9.00 4.91 0.00 1.80 0.00 FIR2 mm/d 0.00 3.10 2.34 4.11 FIRav mm/d 2.46 1.55 2.07 2.05 245 mm DIRav m3/s 1.96 1.24 1.65 1.64

Q20 - DIR m3/s 1.49 4.04 2.68 0.76 0 mm

Land Preparation Crop Development Crop Ripening

CWR = crop water requirement = (Kc x ET0 + P); DIR = diversion irrigation requirement = (NIA x FIR/e); ET0 = mean potential evapotranspiration; e = efficiency (60% at Jambo Aye, 65% at Lembor and 70% at Karowa); FIR = field-level irrigation requirement = (LPR – R20) or (CWR – Re); Kc = crop coefficient; LPR = land preparation water requirement (Table 14); NIA = net irrigable area (17,931 ha at Jambo Aye, 4,483 ha at Lembor and 258 ha at Karowa); P = percolation and seepage (1.5 mm/day at Karowa and Lembor and 1.0 mm/day at Jambo Aye); Q20 = dependable discharge; R20 = dependable rainfall; Re = effective rainfall = 0.70 x R20. To convert mm/d to m3/s multiply mm/d by NIA/(e x 8,640).

56. Rice Yields. At the International Rice Research Institute (IRRI), Los Baños, Philippines, from 1979 to 2003, rice yield declined by 10% for each 1oC increase in Tmin above 22oC.31 A recent controlled experiment in Japan found increasing Tmin, during reproduction, had a negative 31 Peng, et al. 2004. Rice Yields Decline with Higher Night Temperature from Global Warming.

26

effect, on dark respiration and spikelet degeneration, “which may explain yield reductions reported in previous studies”.32 For Indonesia, however, the consensus is that climate change “could be associated with increases in rice yields from 2050”33 and, while Tmin is expected to increase, there is conflicting evidence regarding the direction, let alone magnitude, of its expected effect on Indonesian rice yields. 57. The main benefit of irrigation at Lembor is to reduce the rain-fed wet season drought risk (vulnerability). Table 17 indicates that the present crop water requirement is 809 mm. Rainfall meets only 47% (380 mm) leaving 429 mm that is fully met by irrigation. It is likely that actual rain-fed crop evapotranspiration is also about 47% of potential crop evapotranspiration but actual rain-fed yield is much less than 47% of fully irrigated potential yield.34 58. The main impact of future climate change at Lembor is expected to be the reduced rain-fed drought disaster risk (vulnerability) and increased rain-fed rice yields. Table 17 indicates that expected future rainfall could meet 70% (574 mm) of the total crop water requirement (819 mm). Thus actual rain-fed yield is likely to be about 70% of fully irrigated potential yield. 59. Irrigation Design Criteria. Table 18 indicates that existing irrigation system capacity (ISC) is sufficient, to supply present peak irrigation requirements (PIRs), for cropping patterns at Karowa and Lembor (as ISC is more than PIR) but not at Jambo Aye (where ISC is less than PIR) (Table 15 and Table 17). The practical solution is to operate the Jambo Aye irrigation system using two or three Golongan (rather than increase design capacity). Climate change is not expected to constrain future operations as the PIR is expected to decline in all three cases.

Table 18: Irrigation Design Criteria

Variable Unit Situation Irrigation System

Karowa Jambo Aye Lembor

ISC lps/ha Present 2.0 1.42 1.4 PIR Month Present May May January PIR lps/ha Present 0.97 1.93 0.56 PIR Month Future May May January PIR lps/ha Future 0.76 1.83 0.44

lps/ha = litres per second per hectare; ISC = irrigation system capacity; PIR = peak irrigation requirement. Sources: ISC = MMD 2015 and PIR = Table 15 and Table 17 herein.

3. Adaptation Assessment

60. Alternative cropping patterns were also considered, but are not recommended because:

(i) Rice-Rice-Rice Cropping Patterns. Where water balances improve with climate change, as expected herein, rice intensification might be an effective adaptation option. This involves three 90-day variety rice crops with a month between each of them or, if farmers can "turn crops around" faster, three 105-day variety rice crops with two weeks between each of them. However: (i) long growing season rice varieties help avoid wet conditions during ripening and harvest; and (ii) heavy rice soils benefit from, and rice yields decline appreciably without, periodic fallow and soil drying, cracking and aeration.

32 Laza et al. 2015. Different Response of Rice Plants to High Night Temperatures Imposed at Varying

Developmental Stages. 33 U.M.Office.2011. Climate: Observations, Projections and Impacts – Indonesia. 34 FAO. 2012. Crop Yield Response to Water. Irrigation and Drainage Paper 66.

27

(ii) Rice-Palawija Cropping Patterns: Where water balances are limiting and/or expected to deteriorate with climate change, crop diversification is often advocated as an irrigation management (O&M) and/or climate change adaptation intervention to “save” water. From 1985 to 1992 the world price of rice was low. 35 In Bangladesh, Indonesia, and the Philippines, government policy and International Irrigation Management Institute (IIMI) research was focused on palawija (non-rice) crop diversification in rice-based cropping systems36 which have predominantly heavy-textured soils.37 This research had mixed results but confirmed potential to diversify into rice-palawija cropping patterns especially in those (less frequently occurring) areas with well-drained (light-textured) soils. However, it also confirmed low farmer adoption and crop diversification rates. This crop diversification research was discontinued,38 when the world price of rice rose throughout the 1990s, and Indonesian government policy now promotes rice production. The following factors constrain crop diversification in most rice-based cropping systems:

a. Agronomy: Land preparation for paddy rice (puddling) destroys soil

structure, increases saturation, following rainfall, and reduces soil aeration and palawija yields;

b. Farm-level irrigation: If PET is 5.0 mm/day and Kc is 1.2 (rice) and 1.0 (palawija) in the predominant heavy-textured soils (e.g. percolation and seepage = 1.5 mm/day) palawija needs to achieve a field application efficiency (FAE) of 67% (1.0 x 5.0) / (1.2 x 5.0 + 1.5) or more before it withdraws less water than rice (except during land soaking).39 This high FAE is hard to achieve because the farmer needs to stay up all night shifting irrigation between rice bays every 10 to 20 minutes; 40

c. Farmer Disincentives: Farmers have to construct temporary: (i) farm ditches, for intermittent irrigation; and (ii) beds-a

INO: Integrated Participatory Development and Management of … · 2017. 5. 23. · sustainable...

Documents

Transcript of INO: Integrated Participatory Development and Management of … · 2017. 5. 23. · sustainable...