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]

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

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.

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.

(a) Notog Gauging Station (b) Juwero Gauging Station

Figure 2. Projection and Observation Flow Duration Curve in 2006-2015

In 2026-2035 at Wlingi Gauging Station four models shows decreased Q50% discharge value varies from -5% to -37%, Juwero Gauging station six models will decrease from -5% to -31%, and Notog Gauging Station four models a slight increased +0.3% to 12%. Furthermore in 2036-2045 period five models at Wlingi Gauging Station shows decreased discharge from -2% to -38%, four models at Juwero Gauging Station four models decreased from -4% to -55%, and Notog Gauging Station four models decreased -1% to 55%. It can be concluded mostly that discharge at Wlingi Gauging Station, Juwero Gauging Station, and Notog Gauging Station would be decreased in 2026-2035 and 2036-2045, except at Notog Gauging Station in 2026-2035 compared to present condition.

3.2 Hydrological Drought Index

Case study of the three irrigation weirs (Bodri-Juwero, Notog, and Wlingi) in Java is to demonstrate that hydrological drought index can be applied to assess the climate change impact in surface water especially drought at irrigation weirs. The calculation of the hydrological drought index using the SRI method is carried out on 1, 3, 6, 9, and 12 months’ time scales by using observed monthly discharge data from river gauging stations at each irrigation weirs.

3rd World Irrigation Forum (WIF3) International Workshop 1-7 September 2019, Bali, Indonesia CLIMATE.02

Table 1. Q50% Projected Monthly Discharge

Irrigation CNRM CNRM CNRM v2 CSIRO EC GFDL Period IPSL Weir CM5 RCA RegCM MK3.6 EARTH ESM 2006-2015 129.95 105.66 133.63 82.65 71.95 96.03 156.17 Wlingi 2026-2035 122.92 97.45 121.53 89.78 76.07 122.99 98.99 2036-2045 121.91 103.66 99.17 106.18 93.75 59.69 128.55 2006-2015 23.18 23.30 22.34 19.66 14.43 22.97 24.62 Juwero 2026-2035 26.61 20.48 26.44 22.89 13.72 21.76 17.01 2036-2045 18.94 22.45 19.80 23.49 16.97 10.42 20.70 2006-2015 53.93 51.64 56.26 53.92 40.99 49.96 59.83 Notog 2026-2035 59.32 57.69 56.76 54.10 40.55 52.90 56.74 2036-2045 51.18 54.20 48.76 53.46 48.40 22.26 61.66

Figure 3. The SRI Values on Various Time Scales at Bodri-Juwero River Gauging Station

Figure 3 shows the severity of hydrological drought at Bodri Weir, which is stable in the near normal to severely dry all time scale within a period of 20 years (1981-2002), but from 2003-2008 the severity of drought in the Bodri Weir increased sharply to an extreme dry level, and in that period Bodri irrigation area is suffering a water crisis.

For Notog weir which covering Notog irrigation areas (Figure 4a), in the period 1991- 2013 almost all SRI (except SRI-1) showed the severity of drought was in normal conditions.

Whereas of the Lodoyo irrigation area with the intake from Wlingi Dam (Figure 4b), the average severity of drought is in a normal condition to severely drought. Only in 1997-1998 and 2007 were in extreme dry conditions. The condition of the severity of the drought applies to all SRI time scales.

3.3 Correlation between Hydrological Drought and Climate Change Impact

The relation between hydrological drought index and the impact of drought is represented using a correlation coefficient between the severity, duration and stress the drought versus areal affected by drought. Drought stress is the multiplication product between drought severity and drought duration. The rice field affected by drought was obtained from the Ministry of Agriculture with data period from 1997 to 2012. The results of the correlation coefficient are presented in Figure 5 and Table 2.

3rd World Irrigation Forum (WIF3) International Workshop 1-7 September 2019, Bali, Indonesia CLIMATE.02

(a) (b)

Figure 4. The SRI Values on Various Time Scales at Notog (a) and Wlingi (b) Weirs

From the Table 2, in the Bodri-Juwero irrigation weir shows that there is a strong correlation between drought stress and the hectares of rice fields affected by drought, which is SRI-12, while for the Notog irrigation weir which gives a strong correlation, is SRI 1.

In Wlingi area, the “best correlation” of drought intensity to the acres of paddy fields affected occurred in SRI-12, while for the best correlation in stress is SRI-3. In other word, the highest correlation of hydrological drought index (SRI) time scale to the rice fileds affected drought means the better index in expressing he impact of hydrological drought

These drought analysis at present control period, suggest the possibility to predict the impact of climate change scenarios in the next few decades for the Bodri Juwero irrigation weir focusing on SRI 12, Notog on SRI 1, and for the Wlingi irrigation weir focus on SRI 3. Predicted SRI projections are calculated using GCM RCP 8.5 projection on monthly discharge data from the average and minimum value of the seven GCM models.

(a) (b)

(c)

Figure 5. Comparison SRI Annual Drought Stress in The Bodri-Juwero (a), Notog (b), and Wlingi Irrigation Weir (c) to number of hectares of rice fields affected by drought

3rd World Irrigation Forum (WIF3) International Workshop 1-7 September 2019, Bali, Indonesia CLIMATE.02

Table 2. Correlation between drought stress and the number of hectares of rice fields affected by drought for different time-scale of SRI

Stress SRI Bodri-Juwero Notog Wlingi 1 27.3% 70.8% 48.8% 3 29.3% 61.1% 52.9% 6 30.8% 49.4% 49.5% 9 33.3% 36.4% 47.8% 12 33.8% 32.0% 40.0%

Figure 6. SRI-12 Projection in Bodri-Juwero

(a)

(b)

Figure 7. SRI-1 Projection in Notog (a) and SRI-3 Projection in Wlingi (b)

3rd World Irrigation Forum (WIF3) International Workshop 1-7 September 2019, Bali, Indonesia CLIMATE.02

In Bodri-Juwero irrigation area in the next 20 years ahead, it is indicated that the extreme drought with duration of 2 years will occur twice, on the year of 2022 – 2023, and on the year of 2041 – 2043 (Figure 6), .

Based on Figure 7a, in the next 20 years the extreme drought of Notog irrigation weir will occur more frequently compared to the historical drought as presented in Figure 4a, where the extreme drought in the historical drought only occurred at the El-Nino event.

In the Wlingi irrigation weir (Fig. 7b), hydrological drought is predicted to occur in almost all of the decades, and extreme drought might at all drought time scale in some years, a similar hydrological drought pattern as predicted in Bodri-Juwero. Unlike present situation of relatively rare and mild hydrological drought, the climate projection for the next decades predict the increase of hydrological drought severity and duration for the three irrigation weirs at Bodri-Juwero, Notog, and Wlingi.

4. CONCLUSIONS

It is concluded that the validation of present data confirms that the drought severity and stress of hydrological drought index follows the same pattern of climate change impact on the area of rice field affected by drought. The best correlations are achieved for drought stress of SRI-12, SRI-1, and SRI-3 for Bodri Juwero, Notog, and Wlingi irrigation weirs consequently. Therefore, hydrological drought can be applied to identify climate change impact in the future.

The projected hydrological drought index for the next 30 years at the three irrigation weirs in Java identifies an increasing of drought severity with longer drought duration and worse severity, and consequently more area of irrigated of rice fields will be affected by drought. Extreme drought in Bodri-Juwero irrigation area is predicted twice with duration of two years, while extreme drought at Notog and Wlingi irrigation weirs will occur more frequently compared to the historical drought. Adaptive strategy should be developed to maintain irrigation productivity under these predicted drought condition.

5. REFERENCES

Fadholi, A., & Adzani, R. (2018). Analisis Frekuensi Curah Hujan Ekstrem Kepulauan Bangka Belitung Berbasis Data Climate Hazards Group Infra-red Precipitation With Stations (CHIRPS). Gea: Jurnal Pendidikan Geografi Vol. 18 No. 1. Fu, G., Charles, S., & Chiew, F. (2007). A Two-Parameter Climate Elasticity of Streamflow Index to Assess Climate. Water Resources Research Vol. 43. IPCC (Intergovernmental Panel on Climate Change). 2000 Emission scenarios. A special report of Working Group III of the Intergovernmental Panel on Climate Change. Nakicenovic N. Coordinating lead author. Cambridge University Press, Cambridge, UK, and New York, NY, USA. IPCC. 2007 Climate Change 2007: The physical science basis. Cambridge University Press, Cambridge, and New York, NY, USA. Ministry of Agriculture Indonesia. 2018. Peta Irigasi Pertanian. Retrieved from http://sig.pertanian.go.id/sikpv3/. Risbey, J., & Entekhabi, D. 1996. Observed Sacramento Basin streamflow response to precipitation and temperature changes and its relevance to climate impacts studies. J. Hydrol., 184(3–4), 209-223. Shukla, S and Wood, Andrew W. 2008. Use of a standardized runoff index for characterizing hydrological drought. Geophysical Research Letters, Vol. 35, L02405, doi:10.1029/2007GL032487, 2008 University of East Anglia. (n.d.). Retrieved from Climatic Research Unit: https://crudata.uea.ac.uk/cru/data/hrg/.