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

Ali, Ayaz Fateh; Xiao, Cunde; Zhang, Xiao-Peng; Adnan, Muhammad; Iqbal, Mujahid; Khan, Garee

doi:10.1007/s11629-018-4907-4

Cited by 27 publications

(15 citation statements)

References 47 publications

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“…The deviation of the runoff from the threshold was assessed based on hydrological drought characteristics. We used this approach because the high-flow season is important for hydropower generation, where high-altitude catchments such as the one investigated in this study have strong hydropower generation potential [64,65]. The deviation from the threshold is governed by climate and weather patterns, which influence a range of hydrological events.…”

Section: Discussionmentioning

confidence: 99%

Development of Threshold Levels and a Climate-Sensitivity Model of the Hydrological Regime of the High-Altitude Catchment of the Western Himalayas, Pakistan

Saifullah

Liu

Tahir

et al. 2019

Water

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Water shortages in Pakistan are among the most severe in the world, and its water resources are decreasing significantly due to the prevailing hydro-meteorological conditions. We assessed variations in meteorological and hydrological variables using innovative trend analysis (ITA) and traditional trend analysis methods at a practical significance level, which is also of practical interest. We developed threshold levels of hydrological variables and developed a non-parametric climate-sensitivity model of the high-altitude catchment of the western Himalayas. The runoff of Zone I decreased, while the temperature increased and the precipitation increased significantly. In Zone II, the runoff and temperature increased but the precipitation decreased. A two-dimensional visualization of the Pardé coefficient showed extreme drought events, and indicated greater sensitivity of the hydrological regime to temperature than to precipitation. The threshold levels of runoff for Zones I and II were 320 and 363 mm using the Q 80 fixed method, while the mean runoff amounts were estimated to be 79.95 and 55.61 mm, respectively. The transient threshold levels varied by month, and the duration of droughts in Zones I and II ranged from 26.39 to 78.98 days. The sensitivity of the hydrological regime was estimated based on a modified climate-elasticity model (ε p = 0.11-0.23, ε t = −0.04-2.39) for Zones I and II, respectively. These results highlight the sensitivity of the hydrological regime to temperature, which influences the melting process. However, it is important to establish thresholds for hydrological variables and understand the climate sensitivity of the hydrological regime of the entire basin, so that policy makers and water managers can make sustainable water-resource-management decisions for this region.

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Section: Discussionmentioning

confidence: 99%

Development of Threshold Levels and a Climate-Sensitivity Model of the Hydrological Regime of the High-Altitude Catchment of the Western Himalayas, Pakistan

Saifullah

Liu

Tahir

et al. 2019

Water

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“…In most of the studies, internal processes and streamflow output are simulated using a daily time step (98 studies), but also sub-daily (5 studies), hourly (28 studies), and monthly (11 studies) time steps are used. Sometimes models are validated for different time resolutions, for example, daily and monthly (Ali et al, 2018). Frans et al (2015), for instance, used different time steps for different parts of the models, that is, hourly in general but glacier dynamics were modeled monthly.…”

Section: Glacio-hydrological Models and Scalesmentioning

confidence: 99%

Glacio‐hydrological model calibration and evaluation

et al. 2020

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Glaciers are essential for downstream water resources. Hydrological modeling is necessary for a better understanding and for future projections of the water resources in these rapidly changing systems, but modeling glacierized catchments is especially challenging. Here we review a wealth of glacio-hydrological modeling studies (145 publications) in catchments around the world. Major model challenges include a high uncertainty in the input data, mainly precipitation, due to scarce observations. Consequently, the risk of wrongly compensating input with model errors in competing snow and ice accumulation and melt process parameterization is particularly high. Modelers have used a range of calibration and validation approaches to address this issue. The review revealed that while a large part ($35%) of the reviewed studies used only streamflow data to evaluate model performances, most studies ($50%) have used additional data related to snow and glaciers to constrain model parameters. These data were employed in a variety of calibration strategies, including stepwise and multi-signal calibration. Although the primary aim of glaciohydrological modeling studies is to assess future climate change impacts, longterm changes have rarely been taken into account in model performance evaluations. Overall, a more precise description of which data are used how for model evaluation would facilitate the interpretation of the simulation results and their uncertainty, which in turn would support water resources management. Moreover, there is a need for systematic analyses of calibration approaches to disentangle what works best and why. Addressing this need will improve our system understanding and model simulations of glacierized catchments.

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“…Types of land cover in the basin include forest (0.36%), shrubs (16.12%), farmland (0.76%), and barren land (82.78%). The main soil types include Leptosols (LP), rock outcrop soil (RK), and glaciated soil (GG); further, the major soil component in the region is highly active clay, followed by rock outcrops and glacial soil (Ali et al, 2018;Garee et al, 2017). Surging glaciers are marked with triangles (Bhambri et al, 2017).…”

Section: Study Areamentioning

confidence: 99%

“…Training data were visually sampled based on high-spatial-resolution Sentinel-2 and Google Earth images. Samples in the regions of interest (ROIs) were divided into clean ice or snow (ice/snow), debris-covered ice (debris), bare land, and others (e.g., vegetation, villages, rivers and lakes, and shadows) according to land cover types in the Hunza Valley (Ali et al, 2018). For example, for 2019, 1,024 sample points were selected, including 373 debris, 356 ice/snow, 270 bare land, and 205 other points.…”

Section: Debris-covered Ice Extractionmentioning

confidence: 99%

Derivation of Supraglacial Debris Cover by Machine Learning Algorithms on the Gee Platform: A Case Study of Glaciers in the Hunza Valley

Xie

Liu

Gao

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Calculating the spatial-temporal distribution of supraglacial debris cover on glaciers is essential for understanding mass balance processes, glacier lake outburst floods, hydrological predictions, and glacier fluctuations that have attracted attention in recent years. However, due to the reflectance of supraglacial debris is similar to that of non-glacier slopes, mapping supraglacial debris cover based on optical remote sensing remains challenging. In this paper, we used NDSI and machine learning algorithm to extract debris cover on glaciers in Hunza Valley, Pakistan. Our result showed that the RF model has the best classification accuracy with kappa coefficient of 0.94 and overall accuracy of 96%. The debris-covered area increased by 21.31% from 1990 to 2019 (394.76 km2 – 478.88 km2) in the study area. Results and the method are of significance in the assessment of meltwater modeling for glaciers with debris cover.

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

Cited by 27 publications

References 47 publications

Development of Threshold Levels and a Climate-Sensitivity Model of the Hydrological Regime of the High-Altitude Catchment of the Western Himalayas, Pakistan

Development of Threshold Levels and a Climate-Sensitivity Model of the Hydrological Regime of the High-Altitude Catchment of the Western Himalayas, Pakistan

Glacio‐hydrological model calibration and evaluation

Derivation of Supraglacial Debris Cover by Machine Learning Algorithms on the Gee Platform: A Case Study of Glaciers in the Hunza Valley

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