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Climate change is currently a dangerous phenomenon that has deleterious economic, social and environmental impacts. Climatic fluctuations directly affect the hydrology of an area, which in turn plays a significant role in economy of any country. In Pakistan, River Indus serves as a major fresh water resource, the flow of this river is fed by glacia...
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The rising global temperature is shifting the runoff patterns of snowmelt-dominated alpine watersheds, resulting in increased cold season flows, earlier spring peak flows, and reduced summer runoff. Projections of future runoff are beneficial in preparing for the anticipated changes in streamflow regimes. This study applied the degree–day Snowmelt...
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... On the other hand, it highlighted the ability of SRM to predict snow and ice meltwater runoff under climate change. Javeria Saleem et al. [35] performed SRM in the Hunza watershed and the results showed that regional warming affects local hydrological characteristics. Caitriona Steele et al. [36] compared the accuracy of the application of snow cover product from MODIS and Landsat TM in the SRM, and the two products were in high agreement in terms of snow cover area, but Landsat TM snow cover product MODSCAG was more suitable for the SRM when the study watershed area was less than 4000 km 2 . ...
Global warming affects the hydrological characteristics of the cryosphere. In arid and semi-arid regions where precipitation is scarce, glaciers and snowmelt water assume important recharge sources for downstream rivers. Therefore, the simulation of snowmelt water runoff in mountainous areas is of great significance in hydrological research. In this paper, taking the Hutubi River Basin in the Tianshan Mountains as the study area, we used the “MODIS Daily Cloud-free Snow Cover 500 m Dataset over China” (MODIS_CGF_SCE) to carry out the Snowmelt Runoff Model (SRM) simulation and evaluated the simulation accuracy. The results showed that: (1) The SRM preferably simulated the characteristics of the average daily flow variation of the Hutubi River from May to October, from 2003–2009. The monthly total runoff was maximum in July and minimum in October. Extreme precipitation events influenced the formation of flood peaks, and the interannual variation trend of total runoff from May to October was increased. (2) The mean value of the volume difference (DV) during the model validation period was 8.85%, and the coefficient of determination (R2) was 0.73. In general, the SRM underestimates the runoff of the Hutubi River, and the simulation accuracy is more accurate in the normal water period than in the high-water period. (3) By analyzing MODIS_CGF_SCE from 2003 to 2009, areas above 3200 m elevation in the Hutubi River Basin were classified as permanent snow areas, and areas below 3200 m were classified as seasonal snow areas. In October, the snow area in the Hutubi River Basin gradually increased, and the increase in snow cover in the permanent snow area was greater than that in the seasonal snow area. The snowmelt period was from March to May in the seasonal snow area and from May to early July in the permanent snow area, and the minimum snow cover was 0.7%.
... Nevertheless, they can be affected by high cloud coverage, preventing an accurate estimation of the SCA [51]. Many studies have applied techniques to improve the images and obtain better quality SCA data [52,53]. In the study area, SCA values fluctuated between 5.1 and 7.4% over the total study area, therefore it was necessary to compare with SCA values obtained from Landsat data [54]. ...
Effects of climate change have led to a reduction in precipitation and an increase in temperature across several areas of the world. This has resulted in a sharp decline of glaciers and an increase in surface runoff in watersheds due to snowmelt. This situation requires a better understanding to improve the management of water resources in settled areas downstream of glaciers. In this study, the snowmelt runoff model (SRM) was applied in combination with snow-covered area information (SCA), precipitation, and temperature climatic data to model snowmelt runoff in the Santa River sub-basin (Peru). The procedure consisted of calibrating and validating the SRM model for 2005–2009 using the SRTM digital elevation model (DEM), observed temperature, precipitation and SAC data. Then, the SRM was applied to project future runoff in the sub-basin under the climate change scenarios RCP 4.5 and RCP 8.5. SRM patterns show consistent results; runoff decreases in the summer months and increases the rest of the year. The runoff projection under climate change scenarios shows a substantial increase from January to May, reporting the highest increases in March and April, and the lowest records from June to August. The SRM demonstrated consistent projections for the simulation of historical flows in tropical Andean glaciers.
Streamflow is increasingly vulnerable to climate change over cold-arid regions. To understand the underlying mechanisms of streamflow changes, it is essential to distinguish streamflow components and identify their drivers due to various meltwater and rainfall runoff processes. This study combines the conceptual temperature-index Snowmelt Runoff Model (SRM) and wavelet coherence analysis to unravel the impacts of environmental factors on streamflow and its components over the semiarid-and-cold headwater catchment of the Manas River in Northwest China during 2001–2015. Our results show that SRM reproduces monthly observed streamflow with determination coefficients of > 0.91, Nash-Sutcliffe Efficiency of > 0.82, and absolute relative error of < 5 % during SRM calibration (2001–2007) and validation (2008–2015) periods. The meltwater runoff and rainfall runoff contribute to 42 % and 31 % of the total streamflow, respectively. The wavelet-based spectral analysis, partial wavelet coherency analysis, and multiple-wavelet coherence together demonstrate that total streamflow (rainfall runoff) is predominantly influenced by snow cover area (precipitation) at a scale of 32 (4–16) months. But the meltwater runoff is simultaneously controlled by precipitation, potential evapotranspiration, and normalized difference vegetation index on scales of 4–16 and > 32 months. Our study provides technical support for streamflow partitioning and controlling factors identification, and has implication to sustainable water resources management in cold and arid regions.
