Water Availability Under Future Climate Change: A Study of Citarum River Basin, Indonesia

This study assessed the impact of climate change on future water availability in the Citarum river basin, Indonesia. Future climate was projected based on the output of HadCM3 GCM under A2 and B2 scenarios and downscaled using SDSM package application. The hydrological processes were modelled using WEAP application. The result suggested an increase of temperature as well as precipitation in the period of the 2020s, 2050s and 2080s. The water availability is projected to increase in the future.

Comparison of Two Hydrological Models, HEC-HMS and SWAT in Runoff Estimation: Application to Huai Bang Sai Tropical Watershed, Thailand

In the present study, the streamflow simulation capacities between the Soil and Water Assessment Tool (SWAT) and the Hydrologic Engineering Centre-Hydrologic Modelling System (HEC-HMS) were compared for the Huai Bang Sai (HBS) watershed in northeastern Thailand. During calibration (2007–2010) and validation (2011–2014), the SWAT model demonstrated a Coefficient of Determination (R2) and a Nash Sutcliffe Efficiency (NSE) of 0.83 and 0.82, and 0.78 and 0.77, respectively. During the same periods, the HEC-HMS model demonstrated values of 0.80 and 0.79, and 0.84 and 0.82. The exceedance probabilities at 10%, 40%, and 90% were 144.5, 14.5, and 0.9 mm in the flow duration curves (FDCs) obtained for observed flow. From the HEC-HMS and SWAT models, these indices yielded 109.0, 15.0, and 0.02 mm, and 123.5, 16.95, and 0.02 mm. These results inferred those high flows were captured well by the SWAT model, while medium flows were captured well by the HEC-HMS model. It is noteworthy that the low flows were accurately simulated by both models. Furthermore, dry and wet seasonal flows were simulated reasonably well by the SWAT model with slight under-predictions of 2.12% and 13.52% compared to the observed values. The HEC-HMS model under-predicted the dry and wet seasonal flows by 10.76% and 18.54% compared to observed flows. The results of the present study will provide valuable recommendations for the stakeholders of the HBS watershed to improve water usage policies. In addition, the present study will be helpful to select the most appropriate hydrologic model for humid tropical watersheds in Thailand and elsewhere in the world.Comparison of Two Hydrological Models, HEC-HMS and SWAT in Runoff Estimation: Application to Huai Bang Sai Tropical Watershed, ThailandpublishedVersio

Evaluation of Ecosystem-Based Adaptation Measures for Sediment Yield in a Tropical Watershed in Thailand

Ecosystem-based adaptation (EbA) can potentially mitigate watershed degradation problems. In this study, various EbA measures were evaluated using a bio-physical model called the Soil and Water Assessment Tool (SWAT), in a small, forested watershed named Hui Ta Poe, in the northeastern region of Thailand. The developed watershed model was first used to investigate the effect of various degraded watersheds due to land-use changes on the sediment yield in the study area. The most degraded watershed produced an annual average sediment yield of 13.5 tons/ha. This degraded watershed was then used to evaluate the effectiveness of various EbA measures such as reforestation, contouring, filter strips, and grassed waterways in reducing the sediment yield. Under all individual and combined EbA scenarios analyzed, there was a significant reduction in sediment yield; however, the maximum reduction of 88% was achieved with a combined scenario of reforestation, grassed waterways, and filter strips. Reforestation alone was found to be the second-best option, which could reduce the sediment yield by 84%. Contouring alone was the least effective, with a reduction in sediment yield of only 23%. This study demonstrates the usefulness of implementing EbA measures for sediment management strategies to address watershed degradation, which is a severe problem across the globe

Future changes in extreme temperature events using the statistical downscaling model (SDSM) in the trans-boundary region of the Jhelum river basin

In the 21st century, climate change is considered to be one of the greatest environmental threats to the world, and the changes in climate extremes are estimated to have greater negative impacts on human society and the natural environment than the changes in mean climate. This study presents the projections of future changes in extreme temperature events under A2 and B2 SRES scenarios using the statistical downscaling model (SDSM) in the trans-boundary region of the Jhelum River basin. This area is located in Pakistan and India. In order to get realistic results, bias correction was also applied to downscale the daily maximum and minimum temperature values before calculating 8 intensity and 4 frequency indices. Validation (1991–2000) showed great reliability of SDSM in ascertaining changes for the periods 2011–2040, 2041–2070 and 2071–2099, relative to 1961–1990. The intensity of the highest and the lowest night time temperatures is simulated to be higher than the highest and lowest day time temperatures. In contrast, the intensity of high night time temperature (hot nights) is projected to be lower than high day time temperature (hot days). The number of hot days and hot nights is predicted to increase, and by contrast, the frequency of cold days and cold night is predicted to decrease in all three future periods. Almost all the seasons will witness warming effects in the basin. However, these effects are much more serious in spring (hot days and nights) and in winter (cold and frosty days). On the whole in the Jhelum basin, the intensity and frequency of warm temperature extremes are likely to be higher and the intensity and frequency of cold temperature extremes to be lower in the future

Assessment of temporal and spatial changes of future climate in the Jhelum river basin, Pakistan and India

The present study investigated future temporal and spatial changes in maximum temperature, minimum temperature, and precipitation in two sub-basins of the Jhelum River basin—the Two Peak Precipitation basin (TPPB) and the One Peak Precipitation basin (OPPB)—and in the Jhelum River basin on the whole, using the statistical downscaling model, SDSM. The Jhelum River is one of the biggest tributaries of the Indus River basin and the main source of water for Mangla reservoir, the second biggest reservoir in Pakistan. An advanced interpolation method, kriging, was used to explore the spatial variations in the study area. Validation results showed a better relationship between simulated and observed monthly time series as well as between seasonal time series relative to daily time series, with an average R2 of 0.92–0.97 for temperature and 0.22–0.62 for precipitation. Mean annual temperature was projected to rise significantly in the entire basin under two emission scenarios of HadCM3 (A2 and B2). However, these changes in mean annual temperature were predicted to be higher in the TPPB than the OPPB. On the other hand, mean annual precipitation showed a distinct increase in the TPPB and a decrease in the OPPB under both scenarios. In the case of seasonal changes, spring in the TPPB and autumn in the OPPB were projected to be the most affected seasons, with an average increase in temperature of 0.43–1.7 °C in both seasons relative to baseline period. Summer in the TPPB and autumn in the OPPB were projected to receive more precipitation, with an average increase of 4–9% in both seasons, and winter in the TPPB and spring in the OPPB were predicted to receive 2–11% less rainfall under both future scenarios, relative to the baseline period. In the case of spatial changes, some patches of the basin showed a decrease in temperature but most areas of the basin showed an increase. During the 2020s (2011–2040), about half of the basin showed a decrease in precipitation. However, in the 2080s (2071–2099), most parts of the basin were projected to have decreased precipitation under both scenarios

Assessment of risks due to climate change for the Upper Tamakoshi Hydropower Project in Nepal

Climate change poses significant challenges to hydropower development and management in mountainous basins. This study examined the impact of climate change, and the associated risks, on the energy production of the Upper Tamakoshi Hydropower Project, which is located in the Tamakoshi basin of Nepal. The outputs of three GCMs—namely MIROC-ESM, MRI-CGCM3, and MPI-ESM-M—under the Representative Concentration Pathways (RCP) scenarios were used for the projection of precipitation and temperature in the future. The minimum and maximum temperatures of the basin are projected to increase by 6.33 °C and 3.82 °C, respectively, by 2100. The projected precipitation varies from −8% to +24.8%, which is expected to alter the streamflow by −37.83% to +47% in the future. Based on the streamflow output, the risk for energy production was calculated with respect to the baseline energy production of 1963 GW h and 2281 GW h. Using the three GCMs, the risk associated with annual hydropower production under altered runoff was analyzed. The risk percentage in the future periods shows a mild risk varying from 0.69% to 6.63%. MPI-ESM-M GCM projects a higher percentage of risk for energy production during the same future periods, as compared to the baseline energy production of 1963 GW h. A mild to moderate risk, ranging from 2.73% to 13.24% can be expected when energy production in the future is compared to the baseline energy production of 2281 GW h

Manual on framework for river health assessment in Thailand

This manual has been developed by researchers from the Asian Institute of Technology (AIT) as part of project that sought to develop a river health assessment framework for Thailand. The project was supported by CGIAR’s Programme on Water, Land and Ecosystem (Greater Mekong) and Australian Aid. The authors of this Manual—Victor R. Shinde, Mukand S. Babel, and Prangpisut Suttharom—acknowledge the support provided by a number of organizations and individuals. These include the Pollution Control Department of Thailand, and Thai Water Partnership; and Oleg Shipin, Sangam Shrestha, Pinida Leelapanang Kamphaengthong, and Panpilai Sukhonthasindhu who were all part of the project. Special thanks also go to Kim Geheb and Mayvong Sayatham from the CGIAR WLE Programme for providing valuable insights during the course of the project. In early 2018, the project team presented this framework to an international group of scientists and practitioners from all over the world. Their feedback and input have helped fine-tune the content of this manual. The authors extend their sincere gratitude to these experts

An artificial neural network-based snow cover predictive modeling in the higher Himalayas

With trends indicating increase in temperature and decrease in winter precipitation, a significant negative trend in snow-covered areas has been identified in the last decade in the Himalayas. This requires a quantitative analysis of the snow cover in the higher Himalayas. In this study, a nonlinear autoregressive exogenous model, an artificial neural network (ANN), was deployed to predict the snow cover in the Kaligandaki river basin for the next 30 years. Observed climatic data, and snow covered area was used to train and test the model that captures the gross features of snow under the current climate scenario. The range of the likely effects of climate change on seasonal snow was assessed in the Himalayas using downscaled temperature and precipitation change projection from — HadCM3, a global circulation model to project future climate scenario, under the A1B emission scenario, which describes a future world of very rapid economic growth with balance use between fossil and non-fossil energy sources. The results show that there is a reduction of 9% to 46% of snow cover in different elevation zones during the considered time period, i.e., 2011 to 2040. The 4700 m to 5200 m elevation zone is the most affected area and the area higher than 5200 m is the least affected. Overall, however, it is clear from the analysis that seasonal snow in the Kaligandaki basin is likely to be subject to substantial changes due to the impact of climate change

Optimization of economic return from water using water-energy-food nexus approach: A case of Karnafuli Basin, Bangladesh

This study evaluates the existing situation of the water energy and food resource interaction using an indicator-based approach and optimizes the resource use in the Karnafuli River Basin. A water allocation model based on an optimization tool, LINDO 6.1, with an objective function to maximize the economic return, is developed to allocate water to different water use sectors (domestic, agriculture, energy, industry, and environment) in the basin. It is observed that 14.58 m3 of water is required to generate 1 kWh of energy in Kaptai hydropower plant, while 4500 m3 of water is consumed to produce 1 ton of crops in the basin. Due to improper management, around 12,500 ha of land under the Karnafuli Irrigation Project remains un-irrigated, which can be cultivated with high-yield Boro crop. Results show that by prioritizing the agriculture sector, a maximum economic return of US$ 30.3 million can be obtained; however, with this only 55% of the satisfaction level is achieved for the environment sector. Systematic and integrated management of the resources is required in Karnafuli Basin for socioeconomic and sustainable development