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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]

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 large proportion of Pakistan's irrigation water supply is taken from the Upper Indus River Basin (UIB) in the Himalaya–Karakoram–Hindukush range. More than half of the annual flow in the UIB is contributed by five of its snow and glacier-fed sub-basins including the Astore (Western Himalaya — south latitude of the UIB) and Hunza (Central Karakoram — north latitude of the UIB) River basins. Studying the snow cover, its spatio-temporal change and the hydrological response of these sub-basins is important so as to better manage water resources. This paper compares new data from the Astore River basin (mean catchment elevation, 4100 m above sea level; m asl afterwards), obtained using MODIS satellite snow cover images, with data from a previously-studied high-altitude basin, the Hunza (mean catchment elevation, 4650 m asl). The hydrological regime of this sub-catchment was analyzed using the hydrological and climate data available at different altitudes from the basin area. The results suggest that the UIB is a region undergoing a stable or slightly increasing trend of snow cover in the southern (Western Himalayas) and northern (Central Karakoram) parts. Discharge from the UIB is a combination of snow and glacier melt with rainfall-runoff at southern part, but snow and glacier melt are dominant at the northern part of the catchment. Similar snow cover trends (stable or slightly increasing) but different river flow trends (increasing in Astore and decreasing in Hunza) suggest a sub-catchment level study of the UIB to understand thoroughly its hydrological behavior for better flood forecasting and water resources management. [ABSTRACT FROM AUTHOR]

Summary: A major proportion of flow in the Indus River is contributed by its snow and glacier-fed river catchments situated in the Karakoram Range. It is therefore essential to estimate the snowmelt runoff from these catchments (with no or scarce precipitation records) for water resources management. The snowmelt runoff model (SRM) integrated with MODIS remote-sensing snow cover products was selected to simulate the daily discharges and to study the climate change impact on these discharges in the Hunza River basin (the snow- and glacier-fed sub-catchment of the Indus River). The results obtained suggest that the SRM can be used efficiently in the snow- and glacier-fed sub-catchments of the Upper Indus River Basin (UIB). The application of the SRM under future climate (mean temperature, precipitation and snow cover) change scenarios indicates a doubling of summer runoff until the middle of this century. This analysis suggests that new reservoirs will be necessary for summer flow storage to meet with the needs of irrigation supply, increasing power generation demand, flood control and water supply. [Copyright &y& Elsevier]

A study was conducted in Sargodha, Pakistan during the year 2011 to testify irrigation practices as a method for reclaiming salt affected soil. Field experiments were conducted on saline-sodic and uncultivated land divided into 12 field plots. Six irrigation treatments, each replicated on two field plots, were applied to test the responses of soil and wheat crop yield. Six irrigation treatments combined two variables: (i) source of irrigation (canal water, brackish groundwater and mixed in 50:50 proportion) and (ii) irrigation scheduling (fixed-rotation or Traditional Warabandi (TWB) and 75% management-allowed depletion (MAD)). The electrical conductivity (ECe) and sodium adsorption ratio (SAR) of saturated paste extracts of soil were observed at four depth intervals ranging from zero to 90 cm during the four crop growth stages. Only top 15 cm soil layers of field plots could be reclaimed upto the permissible ECe level of < 4 dS/m where canal water was applied for irrigation. In deeper layers (31 to 90cm) the ECe increased from the ambient levels in response to irrigation treatments. The crop yield (wheat grains in kg/ha) was measured from all individual field plots. Canal water application with 75% MAD scheduling proved to be the optimum treatment giving average yield of 1265 kg per hectare while the treatment with groundwater application under TWB showed the lowest average yield (435 kg/ha). [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]

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]

Rapid urbanization is changing the existing patterns of Land Use Land Cover (LULC) globally which is consequently increasing the Land Surface Temperature (LST) in many regions. Present study was focused on estimating the current and simulating the future LULC and LST trends in the alpine environment of lower Himalayan region of Pakistan. Past patterns of LULC and LST were identified through the Support Vector Machine (SVM) and multi-spectral Landsat satellite images during 1987–2017 data period. The Cellular automata (CA) model and Artificial Neural Network (ANN) were applied to simulate future (years 2032 and 2047) LULC and LST changes, respectively, using their past patterns. CA model was validated for the simulated and the estimated LULC for the year 2017 with an overall Kappa (K) value of 0.77 using validation modules in QGIS and IDRISI software. ANN method was validated by correlating the observed and simulated LST for the year 2017 with correlation coefficient (R) and Mean Square Error (MSE) values of 0.81 and 0.51, respectively. Results indicated a change in the LULC and LST for instance the built-up area was increased by 4.43% while agricultural area and bare soil were reduced by 2.74% and 4.42%, respectively, from 1987 to 2017. The analysis of LST for different LULC classes indicated that built-up area has highest temperature followed by barren, agriculture and vegetation surfaces. Simulation of future LULC and LST showed that the built-up area will be increased by 2.27% (in 2032) and 4.13% (in 2047) which led 42% (in 2032) and 60% (in 2047) of the study area as compared to 26% area (in 2017) to experience LST greater than 27 °C. A strong correlation between built-up area changes and LST was thus found signifying major challenge to urban planners mitigating the consequent of Urban Heat Island (UHI) phenomenon. It is suggested that future urban planning should focus on urban plantation to counter UHI phenomena in the region of lower Himalayas. • Built-up area was increased by 4.43% from 1987 to 2017 in Abbottabad region. • Simulation indicated a further increase of 4.13% in built-up area by year 2047. • Area experiencing >27 °C surface temperature was increased by 26% over 1987 to 2017. • Simulation indicated an additional 34% area with >27 °C surface temperature in 2047. • Strong trends in land surface temperature may lead to Urban Heat Island phenomenon. [ABSTRACT FROM AUTHOR]

Highlights • Hydrological Models and GCMs were used to simulate runoff in the selected Basin. • The daily scale factors performed better than monthly scale factors. • Findings showed multiple models should be employed to best simulate actual runoff. • Multiple models are necessary for management of water resources in the basin. Abstract The Songhua River Basin (SRB) plays a vital role in supplying water resources to northeast China. This river has a substantial effect on agricultural grain producing region. Therefore, the current study was carried out to evaluate the performances of multiple hydrological models driven by bias corrected precipitation estimates from a group of General Circulation Models (GCMs). The variations in discharge obtained from multiple hydrological models were also analyzed. For this purpose, bias correction methods for GCMs and five hydrological models, lumped rainfall runoff (Nedbor Afstromnings Model (NAM), GRJ4), semi-distributed (the Water Evaluation and Planning tool (WEAP), HBV), and the distributed (Soil Water Assessment Tool (SWAT)) were used in the SRB. Before applying the bias correction method, raw GCMs were also plotted against observational data. The results revealed that none of GCM performed well against observational data. The results also showed that quantile mapping method corrects the biases of GCMs better than daily and monthly scale factors. Distributed and semi-distributed hydrological models performed better than lumped rainfall runoff models in the selected study area of SRB. By forcing hydrological models with bias corrected precipitation data, the differences in the simulated discharge by SWAT, HBV, NAM, GR4J and WEAP were −3%, −2%, −4%, −7 and +3, respectively, when compared with observed discharge. The results also show that the overall performance of SWAT, HBV, NAM, and WEAP was better than GRJ4. The results also suggest that the application of multiple hydrological models is necessary to make improvements in the planning and management of agricultural and water resources in the SRB region. [ABSTRACT FROM AUTHOR]

Swat watershed in Hindukush Mountains was selected for the evaluation of Tropical Rainfall Measuring Mission ( TRMM) Multi-satellite Precipitation Analysis ( TMPA) Versions 6 and 7 using rain-gauge data. Agreement between the satellite and gauge estimates was good at monthly scale but poor at daily and seasonal (monsoon and westerlies, particularly for monsoon) scales. Over and underestimations were observed at foothills and high-altitude areas, respectively. Although, bias was still present, but overall performance of TMPA-V7 was improved compared with TMPA-V6. Bias corrected- TMPA-V7 estimates were better than corrected- TMPA-V6. Basin average bias-corrected precipitation estimates of TMPA-V7 were estimated at daily, monthly, westerlies, monsoon, and annual scales [means (mm) 1.60, 48, 190, 173 and 582, respectively] for the period 1998-2014. Results suggest that regional bias correction of satellite-precipitation products is critical and can yield to the substantial improvement in capturing the precipitation. [ABSTRACT FROM AUTHOR]

Ethnopharmacological relevance Malaria is among the most prevalent infectious diseases in the developing countries of world. Estimated number of annual malaria episodes in Pakistan is 1.5 million, but very little is known about medicinal plant species of Pakistan, which have great potential against malarial disease. Present study was aimed to document medicinal plant species used by the local inhabitants of Lesser Himalayas-Pakistan to treat malaria. Materials and methods Data were collected through interviews, questionnaires and contributor observation. A total of 55 informants aged between 25 and 80 years who were familiar with malarial disease participated in the survey. Results A total of 84 plant species belonging to 69 genera and 50 families were recorded to treat malaria. Asteraceae was found as most cited botanical family with (11.9%) representation, followed by Lamiaceae (5.9%), Solanaceae and Verbenaceae (4.7%) and Violaceae (3.5%) respectively. About 60% of the inhabitants prefer herbal treatment by local herbalists or self-treatment with locally available medicinal plant species. Of the plants identified during present investigation against malaria, Azadirachta indica, Swertia chirayita and Swertia ciliata exhibited uppermost frequency of encounter (36.3%) and corresponding PR value 5. About 67.2% of the botanical taxa are reported for the first time in the treatment of malaria. It was observed thatover harvesting is the foremost threat to medicinal plant species of the study area. Conclusion Present survey indicates that traditional knowledge about the use of plant species against various diseases and particularly to treat malaria is in decline. Similarly anthropogenic pressure, over exploitation and grazing of the botanical taxa are the major concerns regarding medicinal plant biodiversity loss. Frequently utilized plant species with significant malarial reduction should be authenticated by in vitro and in vivo standard tests [ABSTRACT FROM AUTHOR]

The glaciers in the Nepalese Himalayas are retreating due to rising temperatures. Lack of data and information on Nepal’s cryosphere has impeded scientific studies and field investigations in the Nepalese Himalayas. Therefore, IRD France and Ev-K2 CNR Italy have conducted the PAPRIKA (CryosPheric responses to Anthropogenic PRessures in the HIndu Kush-Himalaya regions: impacts on water resources and society adaptation in Nepal) project in Nepal with the financial support of the French and Italian scientific agencies. This project aims to address the current and future evolution of the cryosphere in response to overall environmental changes in South Asia, and its consequences for water resources in Nepal. Thus, two hydrological models, the GR4J lumped precipitation–runoff model and the snowmelt runoff model (SRM), were used in the Dudh Koshi basin. The GR4J model has been successfully applied in different parts of Europe. To obtain better results in such a harsh and rugged topography, modifications needed to be made, particularly in the snow module. The runoff pattern is analysed herein both for past years and, in a sensitivity analysis, for possible future climatic conditions (i.e. precipitation and temperature) using the SRM and GR4J modelling approaches. The results reveal a significant contribution of snow- and glacier-melt to runoff, and the SRM model shows better performance in Nepalese catchments than the GR4J model.Editor D. Koutsoyiannis; Associate editor D. GertenCitation Pokhrel, B.K., Chevallier, P., Andréassian, V., Tahir, A.A., Arnaud, Y., Neppel, L., Bajracharya, O.R., and Budhathoki, K.P., 2014. Comparison of two snowmelt modelling approaches in the Dudh Koshi basin (eastern Himalayas, Nepal). Hydrological Sciences Journal, 59 (8), 1507–1518. http://dx.doi.org/10.1080/02626667.2013.842282 [ABSTRACT FROM PUBLISHER]

Previous studies have shown that glacier changes were heterogeneous in the western Karakoram, with the coexistence of retreating, advancing, and surging glaciers. However, it remains unclear that the mechanisms driving these changes. Based on the Shuttle Radar Topography Mission (SRTM) DEM and TerraSAR-X/TanDEM-X images (2014), this study presents glacier surface height changes in the Hunza Basin of the western Karakoram, employing the method of differential synthetic aperture radar interferometry (DInSAR). A slight negative glacier mass balance was observed in the Hunza Basin during 2000–2014. Surge-type glaciers would not have an obvious effect on overall mass balance in regional assessments over long-time scales. Further, glacier surface velocities in the Hunza Basin were estimated from Landsat images for the period of 1990–2018 by utilizing published data sets and Landsat images. Compared to the annual glacier surface velocities, 22 surge events were observed in seven surge-type glaciers in the Hunza Basin. Glacier flow can be attributed to thermally and hydrologically control, and the geomorphological characteristics of different individuals. This study gives us a new insight into the situation of the "Karakoram anomaly" under the background of glacier mass loss in the high mountains of Asia (HMA). [ABSTRACT FROM AUTHOR]