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Water-related soil erosion is a major environmental concern for catchments with barren topography in arid and semi-arid regions. With the growing interest in irrigation infrastructure development in arid regions, the current study investigates the runoff and sediment yield for the Gomal River catchment, Pakistan. Data from a precipitation gauge and gridded products (i.e., GPCC, CFSR, and TRMM) were used as input for the SWAT model to simulate runoff and sediment yield. TRMM shows a good agreement with the data of the precipitation gauge (≈1%) during the study period, i.e., 2004–2009. However, model simulations show that the GPCC data predicts runoff better than the other gridded precipitation datasets. Similarly, sediment yield predicted with the GPCC precipitation data was in good agreement with the computed one at the gauging site (only 3% overestimated) for the study period. Moreover, GPCC overestimated the sediment yield during some years despite the underestimation of flows from the catchment. The relationship of sediment yields predicted at the sub-basin level using the gauge and GPCC precipitation datasets revealed a good correlation (R2 = 0.65) and helped identify locations for precipitation gauging sites in the catchment area. The results at the sub-basin level showed that the sub-basin located downstream of the dam site contributes three (3) times more sediment yield (i.e., 4.1%) at the barrage than its corresponding area. The findings of the study show the potential usefulness of the GPCC precipitation data for the computation of sediment yield and its spatial distribution over data-scarce catchments. The computations of sediment yield at a spatial scale provide valuable information for deciding watershed management strategies at the sub-basin level. [ABSTRACT FROM AUTHOR]

Seasonal and annual water supplies of the rivers originating in the Hindukush-Karakoram-Himalaya (HKH) region of Pakistan are important to manage the Indus basin irrigation system for better agricultural production and its dependent agrarian economy. In this study, we simulated the current and future snowmelt runoff in a poorly gauged river basin of the Hindukush region under Representative Concentration Pathways (RCP) climate change scenarios. Snowmelt Runoff Model (SRM) furnished with satellite snow cover maps and hydro-meteorological data were used to simulate the daily river discharge for the period 2000‒2005. The results indicated that SRM has effectually simulated the runoff in Chitral River with Nash-Sutcliffe model efficiency coefficient of 0.85 (0.84) and 0.88 (0.83) in the basin-wide (zone-wise) application during the calibration and validation periods, respectively. The results obtained under future climate change scenario showed ∼14‒19% increase in mean summer discharge under three mid-21st century RCP (2.6, 4.5 and 8.5) scenarios. While an increase of ∼13‒37% is expected under late-21st century RCP scenarios. This study can help water resource managers to plan and manage peak discharges from the Chitral River Basin in the future and can thus prevent major losses due to floods in the area. [ABSTRACT FROM AUTHOR]

In many parts of the world, including the Hindukush-Karakoram-Himalayan (HKH) region, the population depends on snow and glaciers-melt waters to grow food, generate electricity and store water for their use throughout the year. The annual and seasonal variation in the snow cover area (SCA) due to its response to the climatic variables directly influences the water supplies. The aim of current study is to evaluate the spatio-temporal trends in the annual and seasonal snow cover at basin-wide scale. Impact of topography on the SCA is analyzed by revealing the trends in SCA in different elevation bands. It also investigates the trends in hydro-climatic factors (temperature, precipitation and river flows) in the Chitral River basin (Hindukush region) and their linkage to the SCA variations. Snow cover is estimated using cloud-free 8-day MODIS snow products on 17-year time-period (2000–2016). Hydro-climatic data of the river flow (1989–2014), temperature, and precipitation (1965–2013) were obtained from Water and Power Development Authority (WAPDA), and Pakistan Meteorological Department (PMD). Trend analysis of the SCA and hydro-climatic variables was carried out using Mann-Kendall's trend test and Sen's slope. The results reveal: (1) a significant increasing trend in the SCA at basin-wide scale and at all elevation zones i.e. A to E (1471–7708 m ASL), except zone B (2500–3500 m ASL), (2) decreasing and constant trends in the mean temperature and total precipitation, respectively, over the same time-period as SCA indicating possible reasons for increasing SCA, (3) a slight decrease in the mean annual and summer flow (1989–2014) possibly due to the summer cooling, reduced snowmelt and slightly decreasing summer precipitation over this time-period, and (4) strong dependency of the Chitral River flow on snowmelt, driven by the temperature seasonality. Modeling snowmelt runoff under future climate projections in the study area may help to manage the water resources properly. [ABSTRACT FROM AUTHOR]

Pakistan is an agriculture-based economy and major proportion of irrigation water for its cultivated lands is abstracted from the Upper Indus Basin (UIB). UIB water supplies are mostly contributed from the high-altitude snow and glacier fields situated in the Hindukush–Karakoram–Himalayan ranges. Any change in the flows of these river catchments due to climate variability may result in the form of catastrophic events like floods and droughts and hence will adversely affect the economy of Pakistan. This study aims to simulate snowmelt runoff in a mountainous sub-catchment (Shyok River basin) of the UIB under climate change scenarios. Snowmelt Runoff Model (SRM) coupled with remotely sensed snow cover product (MOD10A2) is used to simulate the snowmelt runoff under current and future climate scenarios in the study area. The results indicate that (a) SRM has efficiently simulated the flow in Shyok River with average Nash–Sutcliff coefficient value (R2) of 0.8 (0.63–0.93) for all six years (2000–2006) of basin-wide and zone-wise simulations, (b) an increase of 10% (by 2050) and 20% (by 2075) in SCA will result in a flow rise of ∼11% and ∼20%, respectively, and (c) an increase of 1°C (by 2025), 2°C (by 2050), 3°C (by 2075) and 4°C (by 2100) in mean temperature will result in a flow rise of ∼26%, ∼54%, ∼81% and ∼118%, respectively. This study suggests that SRM equipped with remotely sensed snow cover data is an effective tool to estimate snowmelt runoff in high mountain data-scarce environments. [ABSTRACT FROM AUTHOR]

A major portion of Pakistan's economy is based on cultivated lands which are irrigated from the supply of water from Upper Indus River Basins (UIB). Any change in UIB rivers flows may come with catastrophic events and therefore, will destructively affect Pakistan's economy. By aiming this scenario, an uneven and important climate variable (i.e., precipitation) obtained from different gridded and satellite datasets were used for its statistical and hydrological performance evaluation in UIB catchments for the period of 2000 to 2004. In addition, a bias corrected technique and snow cover product (MOD10A2) was also used to enhance the performance of precipitation data sets to obtain realistic discharge simulations. The results indicated that without correcting the biases from the datasets, only APHRODITE precipitation dataset showed higher correlation with observations compared to other precipitation datasets in Hunza River Basin (HRB) with correlation coefficient of (0.44) & and in Gilgit River Basin (GRB) (0.35), respectively. However, after applying bias correction technique (quantile mapping), the performance of precipitation datasets significantly improved. For GRB, correlation coefficient and root mean square values improved up to 48% & 55%, while for HRB up to 53% & 51%, respectively. Likewise, based on hydrological utility which was implied by the well-known hydrological model (snowmelt runoff model), bias corrected CHIRPS and APHRODITE precipitation datasets displayed best performance in simulating the discharge with Nash–Sutcliffe coefficient (0.82 & 0.90) & correlation coefficient (0.83 & 0.84) in HRB and (0.84 & 0.80) and (0.86 & 0.82) in GRB, respectively. Moreover, recalibration was also carried out to assess how the hydrological model can adjust and tolerate the errors of different precipitation data products. The results revealed that after adjusting the model parameters particularly coefficient of rainfall and coefficient of snow, the performance of data products significantly improved in terms of the difference in volumes against in situ measurements. Overall, this study may assist, provide guidelines and efficiently used for snowmelt runoff model coupled with different precipitation datasets for management of Indus River irrigation system of Pakistan. • 0.25° precipitation datasets are evaluated with observations over high altitudes of Pakistan. • CHIRPS and APHRODITE precipitation datasets show best performance in simulating the stream flow. • Bias corrected precipitation datasets show significant improvement in simulating stream flows. [ABSTRACT FROM AUTHOR]