Modelling of snowmelt runoff across the Himalayan Region

The Snowmelt-Runoff Model (SRM) was used to evaluate the impact of climate change on hydrological aspects of Lidder River Catchment of the Himalayan Region. It was observed that the river has an average discharge of 1082.49 cusecs. The coefficient of determination (R2) was varies in the range 0.90-0.95 during model validation period (2013-2018).The average coefficient of determination 0.926 and average seasonal volume difference (Dv) was obtained (-) 0.83%. The snow melt runoff harvested water can be used to bring 10 per cent more area under irrigation and water use efficiency which can be increased to an extent of 12-15 per cent for sustainable agriculture production in the Himalayan Region.

Assessment of Climate Change Impact on Snowmelt Runoff in Himalayan Region

The current study was planned to simulate runoff due to the snowmelt in the Lidder River catchment of Himalayan region under climate change scenarios. A basic degree-day model, Snowmelt-Runoff Model (SRM) was utilized to assess the hydrological consequences of change in climate. The SRM model performance during the calibration and validation was assessed using volume difference (Dv) and coefficient of determination (R2). The Dv was found as 11.7, -10.1, -11.8, 1.96, and 8.6 during 2009-2014, respectively, while the R2 is 0.96, 0.92, 0.95, 0.90, and 0.94, respectively. The Dv and R2 values indicating that the simulated snowmelt runoff has a close agreement with the observed value. The simulated findings were also assessed under the different scenarios of climate change: a) increases in precipitation by +20 %, b) temperature rise of +2 °C, and c) temperature rise of +2 °C with a 20 % increase in snow cover. In scenario "b", the simulated results showed that runoff increased by 53 % in summer (April–September). In contrast, the projected increased discharge for scenarios "a" and "c" was 37 % and 67 %, respectively. In high elevation data-scarce mountain environments, the SRM is efficient in forecasting future water supplies due to the snowmelt runoff.

Modelling Snowmelt Runoff from Tropical Andean Glaciers under Climate Change Scenarios in the Santa River Sub-Basin (Peru)

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.

Manure Application Timing and Tillage Influence on Nutrient Loss from Snowmelt Runoff

Winter manure application contributes substantial nutrient loss during snowmelt and influences water quality. The goal of this study is to develop best management practices (BMPs) for winter manure management. We compared nutrient concentrations in snowmelt runoff from three dates of feedlot solid beef manure application (November, January, and March) at 18 tons ha−1 on untilled and fall-tilled plots. The manure was applied at a single rate. Sixteen 4 m2 steel frames were installed in the fall to define individual plots. Treatments were randomly assigned so that each tillage area had two control plots, two that received manure during November, two in January, and two in March. Snowmelt runoff from each individual plot was collected in March and analyzed for runoff volume (RO), ammonium-nitrogen (NH4-N), nitrate-nitrogen (NO3-N), total suspended solids (TSS), total Kjeldahl nitrogen (TKN), total phosphorus (TP), and total dissolved phosphorus (TDP). Snowmelt runoff concentrations and loads of NH4-N, TKN, TP, and TDP were significantly higher in runoff from manure application treatments compared to control. The concentration of NH4-N and loads of NH4-N and TDP were significantly (p = 0.05) greater (42%, 51%, and 47%, respectively) from untilled compared to fall-tilled plots. The November application significantly increased RO, NH4-N, and TDP concentrations and loads in the snowmelt runoff compared to January and March applications. Results showed that nutrient losses in snowmelt runoff were reduced from manure applications on snow compared to non-snow applications. The fall tillage before winter manure application decreased nutrient losses compared to untilled fields.

Multi-station calibration strategy for evaluation and sensitivity analysis of the snowmelt runoff model using MODIS satellite images

In this study, the snowmelt runoff model (SRM) was employed to estimate the effect of snow on the surface flow of Aji-Chay basin, northwest Iran. Two calibration techniques were adopted to enhance the calibration. The multi-station calibration (MSC) and single-station calibration (SSC) strategies applied to investigate their effects on the modeling accuracy. The runoff coefficients (cs and cr) were selected as calibration parameters because of their uncertainty in such an extended basin. To determine the most substantial input of the model which is the snow-covered area (SCA) from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor imagery, MOD10A2 images were collected with spatial and temporal resolutions of 500 meters and 8 days, respectively. The results show an average of 15% improvement in the model performance in the MSC strategy from the data period of 2008–2012. Also, an appropriate agreement with physical characteristics of the study area could be seen for the calibration parameters. The contribution of snowmelt in the river flow reaches its peak in April and May, then with increasing temperature, the contribution decreased gradually. Furthermore, analysis of parameters indicates that the SRM is sensitive to recession coefficient and runoff coefficients.

Snowmelt and Snow Sublimation in the Indus Basin

The Indus basin is considered as the one with the highest dependence on snowmelt runoff in High Mountain Asia. The recent High Mountain Asia snow reanalysis enables us to go beyond previous studies by evaluating both snowmelt and snow sublimation at the basin scale. Over 2000–2016, basin-average snowmelt was 101 ± 11 Gt.a−1 (121 ± 13 mm.a−1), which represents about 25–30% of basin-average annual precipitation. Snow sublimation accounts for 11% of the mean annual snow ablation, but with a large spatial variability across the basin.