Attribution of Runoff Decline in the Amu Darya River in Central Asia during 1951–2007

Wang, Xiaolei; Luo, Yi; Sun, Lin; He, Chansheng; Zhang, Yiqing; Liu, Shiyin

doi:10.1175/jhm-d-15-0114.1

Cited by 47 publications

(39 citation statements)

References 45 publications

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“…Glacier volume is estimated using glacier area based on the volume‐area scaling relation (Radic et al, ; Wang et al, ) and expressed as

V = 0.04 \times {(\frac{A}{1, 000, 000})}^{1.35},

where V (m 3 ) is the glacier volume of the individual glacier. A (m 2 ) is the glacier area derived from the CGI or RGI.…”

Section: Study Area and Extended Hydrologic Modelmentioning

confidence: 99%

“…Corresponding relative changes of glacier volume (δ V) and glacier area (δ A ) are updated each day based on the area‐volume relationship (Liu et al, ; Wang et al, ). These changes are formulated as

normalδV = \frac{{GLA}_{mlt} \times A}{V}

…”

Section: Study Area and Extended Hydrologic Modelmentioning

confidence: 99%

“…Glacier volume is estimated using glacier area based on the volume-area scaling relation (Radic et al, 2008;Wang et al, 2016) and expressed as…”

Section: Data Collectionmentioning

confidence: 99%

“…where Gmtmp is the glacier melt base temperature (°C), with Gmfmx and Gmfmn (mm·H 2 O·day À1 ·°C À1 ) being the melt factors for 1 August and 1 February, respectively; d n is the day of year; gla cov is the fraction of glaciercovered area in a subbasin; and T max is the maximum air temperature. Corresponding relative changes of glacier volume (δV) and glacier area (δA) are updated each day based on the area-volume relationship (Liu et al, 2003;Wang et al, 2016). These changes are formulated as…”

Section: Data Collectionmentioning

confidence: 99%

“…Hydrological models are important tools for analyzing hydrological processes and planning sustainable water use to meet various demands with high accuracy and reliability (Abbaspour et al, ). Over the past decade, hydrologists have implemented models to investigate the hydrological processes in several typical watersheds in Central Asia (Chen et al, ; Fang et al, ; Liu et al, , ; Sidike et al, ), including the main headwaters of the Tarim River (Duethmann et al, ; He et al, ; Zhao et al, ), the Amudarya (Wang et al, ), and the Syrdarya (Gan et al, ). However, as it is challenging to deal with endorheic large basins characterized by diverse terrain and complex hydrological processes, most of these studies were carried out for single catchments only and used models that did not take glacier melt processes into consideration.…”

Section: Introductionmentioning

confidence: 99%

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How Hydrologic Processes Differ Spatially in a Large Basin: Multisite and Multiobjective Modeling in the Tarim River Basin

et al. 2018

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Water resources are essential to ecosystems and social economies worldwide, especially in the deserts and oases of the Tarim River Basin, whose water originates largely from alpine mountains characterized by complicated hydrological processes and scarce hydrometeorological observations. This paper presents multisite and multiobjective modeling of hydrological processes in the whole Tarim River Basin, covering 32 catchments in total. The study uses the Soil and Water Assessment Tool, extended by incorporating a degree‐day glacier melt module to enable modeling of glacier melt in the alpine mountains. The multiobjective calibration approach of ε‐Nondominated Sorting Genetic Algorithm‐II was implemented with the two objective functions of the Nash‐Sutcliffe value of daily streamflow and the bias of simulated glacier melt contribution to streamflow. Based on the combined use of the Morris sensitivity technique and hierarchy cluster, the 32 catchments in the study area are divided into six groups according to their dominating hydrological processes, for example, glacier melt, snowmelt, groundwater, and routing. The multiobjective calibration was satisfactory, with 22 of the 32 catchments showing Nash‐Sutcliffe values of daily streamflow larger than 0.6 and the bias of simulated glacier melt contribution to streamflow values smaller than 0.05. Model performance was highly dependent on meteorological data availability, in that low data availability led to poor model performance, while factors such as catchment area and mean annual snowfall had little influence on model performance. The results indicate that multisite and multiobjective calibration enables consistent and comprehensive examination of the spatially different hydrological processes in a large basin and provides information for further assessment of the impact of climate change on water availability.

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“…Glacier volume is estimated using glacier area based on the volume‐area scaling relation (Radic et al, ; Wang et al, ) and expressed as

V = 0.04 \times {(\frac{A}{1, 000, 000})}^{1.35},

where V (m 3 ) is the glacier volume of the individual glacier. A (m 2 ) is the glacier area derived from the CGI or RGI.…”

Section: Study Area and Extended Hydrologic Modelmentioning

confidence: 99%

normalδV = \frac{{GLA}_{mlt} \times A}{V}

…”

Section: Study Area and Extended Hydrologic Modelmentioning

confidence: 99%

“…Glacier volume is estimated using glacier area based on the volume-area scaling relation (Radic et al, 2008;Wang et al, 2016) and expressed as…”

Section: Data Collectionmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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How Hydrologic Processes Differ Spatially in a Large Basin: Multisite and Multiobjective Modeling in the Tarim River Basin

et al. 2018

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Projection of future water availability in the Amu Darya Basin

Salehie,

Jamal,

Ismail

et al. 2024

Intl Journal of Climatology

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Water scarcity is a major challenge facing many regions worldwide, especially arid and semi‐arid areas that are increasingly vulnerable to climate change. This study aimed to project water availability in the Amu Darya Basin (ADB) of Central Asia under four Shared Socioeconomic Pathways (SSPs) from the Coupled Model Intercomparison Project Phase Six (CMIP6) during two upcoming periods (2020–2059 and 2060–2099). The study used a robust machine learning approach, namely a Random Forest (RF) model, to simulate Gravity Recovery and Climate Experiment (GRACE) Terrestrial Water Storage (TWS) data from precipitation and maximum and minimum temperatures (Tmax and Tmin). It then incorporated precipitation, Tmax and Tmin from four selected CMIP6 GCMs, into a water storage model to project spatiotemporal changes in water availability across the basin. The study also evaluated the relative impacts of land use and population on TWS. Results indicate an increase in TWS by approximately 4 cm in the basin's eastern, northwestern and southwestern regions in both future periods, while a decrease by approximately −4 cm in the remaining areas. These projections suggest that TWS will decline in densely populated regions and increase in certain intensively cultivated areas. The most pronounced increase in TWS is anticipated in the snow‐covered Tundra climate zone of the basin. This is attributed to the melting of glaciers, which contributes to runoff in the tributaries of the Amu River. The findings highlight the importance of considering climate change and socioeconomic factors when projecting water availability in arid and semi‐arid regions. The projected changes in TWS have important implications for water resources management in the ADB, particularly in densely populated and intensively cultivated areas.

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