Simulating the hydrological regime of the snow fed and glaciarised Gilgit Basin in the Upper Indus using global precipitation products and a data parsimonious precipitation-runoff model

Nazeer, Aftab; Maskey, Shreedhar; Skaugen, Thomas; McClain, Michael E.

doi:10.1016/j.scitotenv.2021.149872

Cited by 32 publications

(21 citation statements)

References 62 publications

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“…The 1966–2010 Hunza river flow data collected by Pakistan’s Water and Power development authority (WAPDA) showed an average flow of 304 m 3 /s (~ 710 mm). The climate in the Hunza basin is arid to semiarid and is generally divided into four seasons; winter (Dec–Feb), spring (March–May), monsoon (June–Sep), and post-monsoon season (Oct–Nov) 43 . The HKH precipitation has two primary sources; summer monsoons and winter westerlies.…”

Section: Methodsmentioning

confidence: 99%

Changes in the hydro-climatic regime of the Hunza Basin in the Upper Indus under CMIP6 climate change projections

Nazeer

Maskey

Skaugen

et al. 2022

Sci Rep

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The Upper Indus Basin (UIB) heavily depends on its frozen water resources, and an accelerated melt due to the projected climate change may significantly alter future water availability. The future hydro-climatic regime and water availability of the Hunza basin (a sub-basin of UIB) were analysed using the newly released Coupled Model Intercomparison Project Phase 6 (CMIP6) climate projections. A data and parameter parsimonious precipitation-runoff model, the Distance Distribution Dynamics (DDD) model, was used with energy balance-based subroutines for snowmelt, glacier melt and evapotranspiration. The DDD model was set up for baseline (1991–2010), mid-century (2041–2060) and end-century (2081–2100) climates projections from two global circulation models (GCM), namely EC-Earth3 and MPI-ESM. The projections indicate a substantial increase in temperature (1.1–8.6 °C) and precipitation (12–32%) throughout the twenty-first century. The simulations show the future flow increase between 23–126% and the future glacier melt increase between 30–265%, depending on the scenarios and GCMs used. Moreover, the simulations suggest an increasing glacier melt contribution from all elevations with a significant increase from the higher elevations. The findings provide a basis for planning and modifying reservoir operation strategies with respect to hydropower generation, irrigation withdrawals, flood control, and drought management.

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Section: Methodsmentioning

confidence: 99%

Changes in the hydro-climatic regime of the Hunza Basin in the Upper Indus under CMIP6 climate change projections

Nazeer

Maskey

Skaugen

et al. 2022

Sci Rep

Self Cite

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“…Flash floods, supraglacial lakes, glacier lake outburst floods, and landslides are common in Pakistan's northern highlands (Kanwal et al, 2017;Qaisar et al, 2019;Rahim et al, 2018). 72°25′02′′ to 74°19′25′′ E, spanning an approximate area of 12,726 km 2 with a mean elevation of 4,054 masl, according to the SRTM 30m digital elevation model (DEM) (Nazeer et al, 2022).…”

Section: Study Areamentioning

confidence: 99%

Spatiotemporal Variability Analysis of Glaciers in the Hindukush Region of Pakistan using Remote Sensing Data

Khan¹,

Syed²,

Irfan³

et al. 2022

Preprint

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Glaciers in northern Pakistan are a prime source of freshwater, providing headwater in the Indus river system and serving as a lifeline to millions of people in the region. These glaciers undergo continuous changes by melting due to global warming or accumulation due to snowfall/precipitation at higher altitudes. In this study, we used remote sensing data to quantify glacier changes in spatiotemporal variability in the past three decades. Five glaciers in the Gilgit region (near the junction of the Hindukush and Karakoram Mountains) with an extent of less than 5 square kilometers were selected, namely Phakor glacier, Karamber glacier, East Gammu glacier, Bhort glacier, and Bad-e-Swat glacier. The fluctuations in these glaciers were monitored using a digital elevation model (DEM) and a cloud-free continuous series of Landsat satellite pictures from the minimal snow cover season. The annual climatic trends were studied through spatially interpolated gridded climate data WοrldClim version-1 climate database for 1970 – 2000. We used it to study the variations of minimum and maximum temperature, solar radiation, and precipitation through the preparation of sub-sets from the original global grids. Because of its exact delineation in the Gilgit sub-basin, the characterized watersheds were visually compared to optical Landsat 8 OLI data for mountainous ridge matching, revealing that SRTM 30m (radar-based) demonstrated greater accuracy than other DEMs. The temporal assessment of Bhort, Bad-e-Sawat, East Gammu, Karamber, and other rivers was also carried out. It is observed that the glaciers in the Gilgit watershed are rather stable. The little variability of glaciers is due to their geographic condition, altitude, topography, and orientation. Validation of the mapped glacier classes has been performed to check the accuracy assessment through an error matrix method. The kappa coefficient from the error matrix has been calculated to be 84 %. The study makes a critical input to a greater understanding of watershed controlling and hydrological processes in the upper Indus catchment's Gilgit watershed.

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“…Rainfall-runoff modeling is of great importance for water resource management practices, such as flood protection, reservoir operation, inland shipping, irrigation, and drought mitigation [1][2][3][4][5][6]. Runoff forecasting models can be divided into three categories: conceptual models, physically-based models, and empirical models [7].…”

Section: Introductionmentioning

confidence: 99%

“…The short lag-times are only involved in runoff modeling [42], and not employed in rainfall-runoff models; (2) To further improve rainfall-runoff forecasting performance, a self-attention mechanism is employed to simultaneously model the temporal dependencies within both long and short lag-times, by jointly extracting better features. The previous work only uses self-attention mechanism in short lag-times [42]; (3) The LSTM-ALSL model is modified from SA-LSTM [42] to obtain better results. The LSTM-ALSL is compared with an SVR, convolutional neural network (CNN), random forest (RF), and LSTM models by forecasting runoff 1~7 days ahead.…”

Section: Introductionmentioning

confidence: 99%

A New Rainfall-Runoff Model Using Improved LSTM with Attentive Long and Short Lag-Time

Chen

Huang

Wang

et al. 2022

Water

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It is important to improve the forecasting performance of rainfall-runoff models due to the high complexity of basin response and frequent data limitations. Recently, many studies have been carried out based on deep learning and have achieved significant performance improvements. However, their intrinsic characteristics remain unclear and have not been explored. In this paper, we pioneered the exploitation of short lag-times in rainfall-runoff modeling and measured its influence on model performance. The proposed model, long short-term memory with attentive long and short lag-time (LSTM-ALSL), simultaneously and explicitly uses new data structures, i.e., long and short lag-times, to enhance rainfall-runoff forecasting accuracy by jointly extracting better features. In addition, self-attention is employed to model the temporal dependencies within long and short lag-times to further enhance the model performance. The results indicate that LSTM-ALSL yielded superior performance at four mesoscale stations (1846~9208 km2) with humid climates (aridity index 0.77~1.16) in the U.S.A., for both peak flow and base flow, with respect to state-of-the-art counterparts.

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Simulating the hydrological regime of the snow fed and glaciarised Gilgit Basin in the Upper Indus using global precipitation products and a data parsimonious precipitation-runoff model

Cited by 32 publications

References 62 publications

Changes in the hydro-climatic regime of the Hunza Basin in the Upper Indus under CMIP6 climate change projections

Changes in the hydro-climatic regime of the Hunza Basin in the Upper Indus under CMIP6 climate change projections

Spatiotemporal Variability Analysis of Glaciers in the Hindukush Region of Pakistan using Remote Sensing Data

A New Rainfall-Runoff Model Using Improved LSTM with Attentive Long and Short Lag-Time

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