Quantifying future changes in glacier melt and river runoff in the headwaters of the Urumqi River, China

Zhang, Yiqing; Luo, Yi; Sun, Lin

doi:10.1007/s12665-016-5563-z

Cited by 16 publications

(7 citation statements)

References 48 publications

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“…The PBIAS values from verification using the modified model were better than that of the model before (Tables 4 and 5). The temperatures in Issyk-Kul's northern and southern catchments begin to rise in early April, snow begins to melt into stream flow, and the water levels in the catchments begin to rise rapidly [25], eventually leading to floods [97][98][99]. Our results showed that the participation of snowmelt from October to March was limited to the modified degree-day method, which increased from April to September.…”

Section: Model Performancementioning

confidence: 79%

Modifications to Snow-Melting and Flooding Processes in the Hydrological Model—A Case Study in Issyk-Kul, Kyrgyzstan

Uwamahoro

Nzabarinda

et al. 2021

Atmosphere

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Streamflow impacts water supply and flood protection. Snowmelt floods occur frequently, especially in mountainous areas, and they pose serious threats to natural and socioeconomic systems. The current forecasting method relies on basic snowmelt accumulation and has geographic limitations that restrict the accuracy and timeliness of flood simulation and prediction. In this study, we clarified the precipitation types in two selected catchments by verifying accumulated and maximum temperatures’ influences on snow melting using a separation algorithm of rain and snow that incorporates with the temperatures. The new snow-melting process utilizing the algorithm in the soil and water assessment tool model (SWAT) was also developed by considering the temperatures. The SWAT model was used to simulate flooding and snowmelt in the catchments. We found that the contributions of snowmelt to the river flow were approximately 6% and 7% higher, according to our model compared to the original model, for catchments A and B, respectively. After the model improvement, the flood peaks increased by 49.42% and 43.87% in A and B, respectively. The contributions of snowmelt to stream flow increased by 24.26% and 31% for A and B, respectively. Generally, the modifications improved the model accuracy, the accuracy of snowmelt’s contributions to runoff, the accuracy of predicting flood peaks, the time precision, and the flood frequency simulations.

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Section: Model Performancementioning

confidence: 79%

Modifications to Snow-Melting and Flooding Processes in the Hydrological Model—A Case Study in Issyk-Kul, Kyrgyzstan

Uwamahoro

Nzabarinda

et al. 2021

Atmosphere

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“…In the Tizinafu River Basin, temperatures usually begin to increase at the end of the March, and snow at the lower elevations starts to melt into the streamflow [8]. This leads to a rapid increase in the water level of the river, an increase in water flow, and a tendency for snow-melting floods [37,[91][92][93]. The contribution of snowmelt to runoff during the non-snow-melting season was limited in the modified degree-day method, which increased peak flooding during the snow-melting season and brought it closer to the measured discharge values.…”

Section: Model Performancementioning

confidence: 99%

Inclusion of Modified Snow Melting and Flood Processes in the SWAT Model

Duan

Meng

et al. 2018

Water

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Flooding, one of the most serious natural disasters, poses a significant threat to people’s lives and property. At present, the forecasting method uses simple snowmelt accumulation and has certain regional restrictions that limit the accuracy and timeliness of flood simulation and prediction. In this paper, the influence of accumulated temperature (AT) and maximum temperature (MT) on snow melting was considered in order to (1) reclassify the precipitation categories of the watershed using a separation algorithm of rain and snow that incorporates AT and MT, and (2) develop a new snow-melting process utilizing the algorithm in the Soil and Water Assessment Tool Model (SWAT) by considering the effects of AT and MT. The SWAT model was used to simulate snowmelt and flooding in the Tizinafu River Basin (TRB). We found that the modified SWAT model increased the value of the average flood peak flow by 43%, the snowmelt amounts increased by 45%, and the contribution of snowmelt to runoff increased from 44.7% to 54.07%. In comparison, we concluded the snowmelt contribution to runoff, flood peak performance, flood process simulation, model accuracy, and time accuracy. The new method provides a more accurate simulation technique for snowmelt floods and flood simulation.

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“…In previous studies, the analysis of historical climate change trends has mainly based on historical long‐term observational data covering the entire scale of Xinjiang (Jiang and Hu, ; Wu et al ., ; Campbell et al ., ). For the future climate change projections, most studies have mainly focused on watershed scales, including Bosten Lake (Chen et al ., ; Qiu et al ., ) as well as in the Urumqi (Zhang et al ., ), Kaidu (Liu et al ., ; Fang et al ., ), Tarim (Xu et al ., ; Xu et al ., ; Liu et al ., ), and Hotan (Luo et al ., ) river basins. Fewer studies have paid attention to the future patterns of climate change across the entire region as well as the possible circulation mechanisms response for the local climate change.…”

Section: Introductionmentioning

confidence: 99%

Defining spatiotemporal characteristics of climate change trends from downscaled GCMs ensembles: how climate change reacts in Xinjiang, China

Luo

Frankl

Duan

et al. 2018

Intl Journal of Climatology

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The fragile ecosystem, scarce water resources, and limited ecosystem resilience of Xinjiang, China make the region especially vulnerable to climate change. Researchers need reliable analyses of climate change trends to formulate regional mitigation and adaption strategies and to support sustainable development. Therefore, two statistical downscaling methods and the combination of a Mann–Kendall test with Co‐Kriging interpolation were used to investigate the spatiotemporal characteristics of climate change in Xinjiang based on the ensemble of 37 general circulation models (GCMs) in 2021–2060. The reliabilities of single run and ensemble downscaling results of GCMs were evaluated by 69 meteorological stations over the period 1965–2004. The correlation coefficients (CC) with the observations for precipitation and temperature ranged between 0.4–0.8 and 0.9–0.99 in the reference period, respectively. CC improved to 0.87 and was close to 1 after downscaling for precipitation and temperature, respectively. A pronounced increase of ca. 0.27–0.51 °C decade−1 was projected overall, and especially for Representative Concentration Pathway (RCP) 8.5 in northern Xinjiang. In general, the precipitation changed by −1.66 to 6.83% decade−1 while varying seasonally and spatially; a declining tendency emerged in the western regions of Xinjiang during summer. More extreme rainfall events are predicted to occur in summer and autumn months, while warmer extremes would be concentrated in August. The climate in Xinjiang will continue to become warmer and wetter. Nevertheless, Western Xinjiang will experience a warmer and drier climate in summer and autumn. These projections of climate change in the near future are able to provide useful information for the development of potential mitigation measures and adaptation strategies. Multiple circulation mechanisms are also suggested accounting for the changes in precipitation and temperature which will benefit the understanding of the possible drivers of climate variability in this region.

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Quantifying future changes in glacier melt and river runoff in the headwaters of the Urumqi River, China

Cited by 16 publications

References 48 publications

Modifications to Snow-Melting and Flooding Processes in the Hydrological Model—A Case Study in Issyk-Kul, Kyrgyzstan

Modifications to Snow-Melting and Flooding Processes in the Hydrological Model—A Case Study in Issyk-Kul, Kyrgyzstan

Inclusion of Modified Snow Melting and Flood Processes in the SWAT Model

Defining spatiotemporal characteristics of climate change trends from downscaled GCMs ensembles: how climate change reacts in Xinjiang, China

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