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Assessment of Climate Change Impacts Using Hydrological Drought Index 3rd World Irrigation Forum (WIF3) International Workshop 1-7 September 2019, Bali, Indonesia CLIMATE.02 ASSESSMENT OF CLIMATE CHANGE IMPACTS USING HYDROLOGICAL DROUGHT INDEX Levina1, Brigita Diaz2 and Waluyo Hatmoko3 ABSTRACT Climate change is altering the characteristics of rainfall and consequently also the river flow. It is important to asses climate change impact on drought, especially hydrological drought in river flow. This paper proposes to quantify climate change impact using hydrological drought index, from the available flow data. Climate change impact on rainfall in the future is projected using the worst scenario Representative Concentration Pathways (RCP) 8.5 that leading in the long term to high energy demand and greenhouse gas emissions in the absence of climate change policies, as mentioned in the latest IPCC report AR 5. The monthly rainfall is projected until the year of 2045 using ensemble of seven models commonly used by Indonesian Agency for Meteorology, Climatology and Geophysics which is statistical-bias corrected by quantile mapping with observation data. Projected river discharge is calculated using an empirical equation between changes in discharge with potential evaporation and rainfall. A set of hydrological drought index are computed using the Standardized Runoff Index (SRI) method with moving average of 1, 3, 6, and 12 months. Case study of the three irrigation weirs Bodri-Juwero, Notog, and Wlingi in Java confirms that hydrological drought index can be applied to assess the climate change impact in surface water especially at irrigation weirs. It is concluded that the severity and stress of hydrological drought index follow the same pattern of climate change impact on irrigation area affected by drought. The projected hydrological drought index for the next 30 years shows increasing of drought severity with longer drought duration at irrigation weirs. Keywords: climate change, drought, hydrological drought, drought index, irrigation, irrigation weir 1. INTRODUCTION Hydrological system is highly affected by climate change. Water availability characteristics in the future will change substantially. Dry season with decreasing low flow will be in longer duration. In other word, the drought will be more severe, and the duration of the drought will be longer. Drought is a creeping disaster, originate from lack of rainfall or meteorological drought, which cause decreasing flows in the rivers and drawdown of lakes, and becoming hydrological drought. Hydrological drought index is having higher correlation with the irrigation are affected by drought than meteorological drought. Climate change impact assessment using hydrological drought index at the irrigation weir have the advantage of directly related to irrigation drought. This paper proposes to quantify climate change impact using hydrological drought index, which is simple to measure and highly related with drought. Please explain why the case study sites are selected. This concept is validated using irrigation weirs having good long time-series of monthly river flow data as well as the pairing hectares of irrigation areal affected by drought. 1 Researcher Research Center for Water Resources, Ministry of Public Works and Housing, Jalan Ir. H. Juanda 193, Bandung; E-mail: [email protected] 2 Climate Scientist, Research Center for Water Resources, Ministry of Public Works and Housing, Jalan Ir. H. Juanda 193, Bandung; E-mail: [email protected] 3 Research Professor, Research Center for Water Resources, Ministry of Public Works and Housing, Jalan Ir. H. Juanda 193, Bandung; E-mail: [email protected] 1 3rd World Irrigation Forum (WIF3) International Workshop 1-7 September 2019, Bali, Indonesia CLIMATE.02 2. METHODS 2.1 Case Study: Irrigation Weir Notog, Kragilan and Wlingi Bodri Juwero river gauging station is in the Bodri river right on Juwero weir, Central Java Province. Juwero weir is supplying the irrigation area with an area of 8,861 ha which covers Kendal Regency, Central Java. Notog river gauging station was at the Notog weir of the Pemali river. Notog weir irrigates an irrigation area of 15,180 ha which covers Brebes Regency, Tegal Regency, and Tegal City, Central Java Province. Different from Bodri-Juwero and Notog river gauging station which located in the Central Java region, Wlingi river gauging station is located in East Java around the Wlingi Dam. The Wlingi Dam is an intake from Lodoyo irrigation area with an area of 15,228 ha covering the areas of Blitar and Tulungagung Regencies. The location of the three irrigation weirs are in Figure 1. Figure 1. Location of Bodri-Juwero, Notog, and Wlingi Irrigation Weirs Source: Ministry of Agriculture Indonesia (2018) 2.2 Climate Change Projections and Datasets Climate change impact on rainfall in the future is projected using the worst scenario Representative Concentration Pathways (RCP) 8.5 that assumes high population and relatively slow income growth with modest rates of technological change and energy intensity improvements, leading in the long term to high energy demand and greenhouse gas emissions in the absence of climate change policies, as mentioned in the latest IPCC report AR 5. The monthly rainfall is projected until the year of 2045 using ensemble of seven models commonly used by Indonesian Agency for Meteorology, Climatology and Geophysics, those are CNRM CM5, CNRM RCA, CNRM v2 RegCM, CSIRO MK3,6, EC EARTH, GFDL ESM, and IPSL. Data were divided into two groups consists of baseline periods (1981-2005) and projection period (2006-2045). Projection rainfall data is bias-corrected by using statistical bias corrected methods, quantile mapping. We use CHIRPS dataset as observation rainfall data. CHIRPS data has high resolution and long data sequences so able to cover blank areas, disconnected data, and data inconsistencies in Indonesia area (Sutikno et al (2014) in Fadholi & Adzani, 2018)). To project discharge, empirical projection method is used with observation rainfall and potential evaporation obtained from Potential Evaporation Climatic Research Unit Time Series (CRU TS) version 4.01 (University of East Anglia, n.d.). The empirical 2 3rd World Irrigation Forum (WIF3) International Workshop 1-7 September 2019, Bali, Indonesia CLIMATE.02 methods assumed that changes in discharge for each month are caused by changes in monthly rainfall and potential evaporation. Empirical methods steps to make the discharge projection is further explained in Risbey & Entekhabi (1996) and Fu, Charles, & Chiew (2007). In the projection period, year of 2006-2015 is used as the control period to compare the results of discharge projections with gauging station observational data. Furthermore, for the SRI calculation, we use projected models from seven models and the average of the seven models. 2.3 Hydrological Drought Index Based on the seven projected discharge models, a set of hydrological drought index are computed using the Standardized Runoff Index (SRI) method with moving average of 1, 3, 6, and 12 months. This method applies the concept employed by McKee et al. (1993) for the SPI in defining a standardized runoff index (SRI) as the unit standard normal deviation associated with the percentile of hydrologic runoff accumulated over a specific duration (Shukla and Wood, 2008). The procedure for calculating the SRI includes the following steps: (1) A retrospective time series of runoff is obtained by simulation, and a probability distribution is fit to the sample represented by the time series values. (2) The distribution is used to estimate the cumulative probability of the runoff value of interest (either the current accumulation or one from a retrospective date). (3) The cumulative probability is converted to a standard normal deviation (with zero mean and unit variance), which can either be calculated from a numerical approximation to the normal cumulative distribution function (CDF) or extracted from a table of values for the normal CDF that is already available in statistics text books or on the World Wide Web (Shukla and Wood, 2008). 3. RESULTS AND DISCUSSION 3.1 Climate Change Projections As shown in Figure 1, at high discharge level the seven models give overestimate value with the discharge measured by gauging. The difference between projection with observation is especially shown at the high discharge at Wlingi Weir. As with the probability value of 10%, the average value of the seven models reaches 300 m3/s, while the observation value only ranges from 190 m3/s. At Juwero Weir there is also a significant difference between the calculated discharge and what happens in the field where the probability value of 10% the discharge projection is at the value of 70.4 m3/s while the observation discharge value is 51.65 m3/s. But at Notog Weir from the seven models there are several models that can provide results that are closer to the discharge observation value at high discharge values, such as the EARTH EC model and GFDL ESM. While at the low discharge value, the projection discharge can give better results with the observation discharge value of each station gauging. At Q50% value the value tends to be very close (Wlingi Gauging Station discharge average 110.86 m3/s, observation 108.75 m3/s, Juwero Gauging Station discharge average 21.49 m3/s, observation 22.24 m3/s, and Notog Gauging Station discharge average 52.36 m3/s, observation 52.21 m3/s). Whereas the Q80% discharge projection tends to underestimate two stations and overestimate one station (Wlingi Gauging Station discharge average 42.08 m3/s, observation 62.17 m3/s, Juwero Gauging Station average discharge 7.57 m3/s, observation 11.14 m3/s, and the average Notog Gauging Station discharge is 19.60 m3/s, observation is 16.52 m3/s). But in general, the projection debit of the seven models can provide good results on the average discharge value to low discharge. 3 3rd World Irrigation Forum (WIF3) International Workshop 1-7 September 2019, Bali, Indonesia CLIMATE.02 Due to the fact that the projections give better results in Q50% value, then for comparing value between 2006-2015, 2026-2035, and 2036-2045 average value Q50% will be applied.
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