Assessment of the spatial extent of permafrost in the Upper Indus Basin(UIB)

Climate change differentially influences the frozen ground,a major dynamic component of the cryosphere,on a local and regional scale.Under the warming climate with pronounced effects reported at higher altitudes,the characterization of the frozen ground is very important in the Upper Indus Basin(UIB),an important and critical region with respect to climate and hydro-glaciological dynamics.In this study,the efficiency and reliability of the surface frost number model are assessed in delineating the spatial extent of different classes of frozen ground in the region.The daily MODIS land surface temperature(LST)with ground surface temperature(GST)and surface geomorphological expressions as ground validation datasets are used jointly in efficiently determining the extent of different classes of frozen ground(continuous and discontinuous permafrost and seasonal frost).The LST and GST resonate with each other in the annual cycle of temperature variation,however,with mean annual LST exhibiting an offset(cold bias)of 5 to 7℃relative to mean GST.This study shows that the highest permafrost extent is observed in areas where the lowest thinning rates of glacier ice are reported and vice versa.The surface frost number model categorizes an area of 38%±3%and 15%±3%in the UIB as permafrost and seasonal frost,respectively.Based on the altitude model,the lower limit of alpine permafrost is approximated at a mean altitude of 4919±590 m a.s.l.in the UIB.The present study acts as preliminary work in the data sparse and inaccessible regions of the UIB in characterizing the frozen and unfrozen ground and may act as a promising input data source in glaciohydro-meteorological models for the Himalaya and Karakoram.In addition,the study also underlines the consideration of this derelict cryospheric climatic variable in defining and accounting for the sustainable development of socio-economic systems through its intricate ramification on agricultural activity,landscape stability and infrastructure.

Generation of a long-term daily gridded precipitation dataset for the rTjSj-Upper Indus Basin(UIB)through temporal Reconstruction,Correction I sag&Informed Regionalization-“ReCIR”

This study attempted to generate a long-term(1961-2010)daily gridded precipitation dataset for the Upper Indus Basin(UIB)with orographic adjustments so as to generate realistic precipitation estimates,enabling hydrological and water resource investigations that can close the water balance,that is difficult,if not impossible to achieve with the currently available precipitation data products for the basin.The procedure includes temporal reconstruction of precipitation series at points where data were not recorded prior to the mid-nineties,followed by a regionalization of the precipitation series to a smaller scale across the basin(0.125°x 0.125°),while introducing adjustments for the orographic effect and changes in glacier storage.The reconstruction process involves interpolation of the precipitation at virtual locations of the current(1995-)dense observational network,followed by corrections for frequency and intensity and adjustments for temporal trends at these virtual locations.The data generated in this way were further validated for temporal and spatial representativeness through evaluation of SWAT-modelled streamflow responses against observed flows across the UIB.The results show that the calibrated SWAT-simulated daily discharge at the basin outlet as well as at different sub-basin outlets,when forcing the model with the reconstructed precipitation of years 1973—1996,is almost identical to that when forcing it with the reference precipitation data(1997-2008).Finally,the spatial distribution pattern of the reconstructed(1961—1996)and reference(1997—2008)precipitation were also found consistent across the UIB,reflecting well the large-scale atmospheric-circulation pattern in the region.

Hydrological response under CMIP6 climate projection in Astore River Basin,Pakistan

Climate change strongly influences the available water resources in a watershed due to direct linkage of atmospheric driving forces and changes in watershed hydrological processes.Understanding how these climatic changes affect watershed hydrology is essential for human society and environmental processes.Coupled Model Intercomparison Project phase 6(CMIP6)dataset of three GCM's(BCC-CSM2-MR,INM-CM5-0,and MPIESM1-2-HR)with resolution of 100 km has been analyzed to examine the projected changes in temperature and precipitation over the Astore catchment during 2020-2070.Bias correction method was used to reduce errors.In this study,statistical significance of trends was performed by using the Man-Kendall test.Sen's estimator determined the magnitude of the trend on both seasonal and annual scales at Rama Rattu and Astore stations.MPI-ESM1-2-HR showed better results with coefficient of determination(COD)ranging from 0.70-0.74 for precipitation and 0.90-0.92 for maximum and minimum temperature at Astore,Rama,and Rattu followed by INM-CM5-0 and BCC-CSM2-MR.University of British Columbia Watershed model was used to attain the future hydrological series and to analyze the hydrological response of Astore River Basin to climate change.Results revealed that by the end of the 2070s,average annual precipitation is projected to increase up to 26.55%under the SSP1-2.6,6.91%under SSP2-4.5,and decrease up to 21.62%under the SSP5-8.5.Precipitation also showed considerable variability during summer and winter.The projected temperature showed an increasing trend that may cause melting of glaciers.The projected increase in temperature ranges from-0.66℃ to 0.50℃,0.9℃ to 1.5℃ and 1.18℃ to 2℃ under the scenarios of SSP1-2.6,SSP2-4.5 and SSP5-8.5,respectively.Simulated streamflows presented a slight increase by all scenarios.Maximum streamflow was generated under SSP5-8.5 followed by SSP2-4.5 and SSP1-2.6.The snowmelt and groundwater contributions to streamflow have decreased whereas rainfall and glacier melt components have increased on the other hand.The projected streamflows(2020-2070)compared to the control period(1990-2014)showed a reduction of 3%-11%,2%-9%,and 1%-7%by SSP1-2.6,SSP2-4.5,and SSP5-8.5,respectively.The results revealed detailed insights into the performance of three GCMs,which can serve as a blueprint for regional policymaking and be expanded upon to establish adaption measures.

Projection of future streamflow of the Hunza River Basin,Karakoram Range(Pakistan)using HBV hydrological model

Hydrologiska Byrans Vattenbalansavdeling(HBV) Light model was used to evaluate the performance of the model in response to climate change in the snowy and glaciated catchment area of Hunza River Basin. The study aimed to understand the temporal variation of streamflow of Hunza River and its contribution to Indus River System(IRS). HBV model performed fairly well both during calibration(R2=0.87, Reff=0.85, PBIAS=-0.36) and validation(R2=0.86, Reff=0.83, PBIAS=-13.58) periods on daily time scale in the Hunza River Basin. Model performed better on monthly time scale with slightly underestimated low flows period during bothcalibration(R2=0.94, Reff=0.88, PBIAS=0.47) and validation(R2=0.92, Reff=0.85, PBIAS=15.83) periods. Simulated streamflow analysis from 1995-2010 unveiled that the average percentage contribution of snow, rain and glacier melt to the streamflow of Hunza River is about 16.5%, 19.4% and 64% respectively. In addition, the HBV-Light model performance was also evaluated for prediction of future streamflow in the Hunza River using future projected data of three General Circulation Model(GCMs) i.e. BCC-CSM1.1, CanESM2, and MIROCESM under RCP2.6, 4.5 and 8.5 and predictions were made over three time periods, 2010-2039, 2040-2069 and 2070-2099, using 1980-2010 as the control period. Overall projected climate results reveal that temperature and precipitation are the most sensitiveparameters to the streamflow of Hunza River. MIROC-ESM predicted the highest increase in the future streamflow of the Hunza River due to increase in temperature and precipitation under RCP4.5 and 8.5 scenarios from 2010-2099 while predicted slight increase in the streamflow under RCP2.6 during the start and end of the 21 th century. However, BCCCSM1.1 predicted decrease in the streamflow under RCP8.5 due to decrease in temperature and precipitation from 2010-2099. However, Can ESM2 predicted 22%-88% increase in the streamflow under RCP4.5 from 2010-2099. The results of this study could be useful for decision making and effective future strategic plans for water management and their sustainability in the region.

Simulation of snowmelt-runoff under climate change scenarios in a data-scarce mountain environment

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℃(by 2025),2℃(by 2050),3℃(by 2075)and 4℃(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.