Snow- and glacier-fed river basins
Last updated
Last updated
SPHY is being used in large Asian river basins with significant contribution of glacier melt and snowmelt to the total flow ((Khanal et al., 2021)Immerzeel et al., 2012, Lutz et al., 2012, 2014a, 2016). The major goals of these applications are two-fold:
Assess the current hydrological regimes at high resolution; e.g., assess spatial differences in the contributions of glacier melt, snowmelt and rainfall–runoff to the total flow.
Quantify the effects of climate change on the hydrological regimes in the future and how these affect the water availability.
Rivers originating in the high mountains of Asia are considered to be the most meltwater-dependent river systems on Earth (Schaner et al. 2012). In the regions surrounding the Himalayas and the Tibetan Plateau, large human populations depend on the water supplied by these rivers (Immerzeel et al., 2010). However, the dependency on meltwater differs strongly between river basins as a result of differences in climate and differences in basin hypsometry (Immerzeel and Bierkens 2012). Only by using a distributed hydrological modeling approach that includes the simulation of key hydrological and cryospheric processes, and inclusion of transient changes in climate, snow cover, glaciers and runoff, can appropriate adaptation and mitigation options be developed for this region (Sorg et al. 2012). The SPHY model is very suitable for such goals, and has therefore been widely applied in the region (Khanal et al., 2021).
For application in this region, SPHY was set up at a 1km spatial resolution using a daily time step, and forced with historical air temperature (Tavg, Tmax, Tmin) and precipitation data, obtained from global and regional datasets (e.g., APHRODITE, (Yatagai et al. 2012); Princeton, (Sheffield, Goteti and Wood 2006); TRMM, (Gopalan et al. 2010)) or interpolated WMO station data from a historical reference period. For this historical reference period, SPHY was calibrated and validated using observed streamflow. For the future period, SPHY was forced with downscaled climate change projections obtained from general circulation models (GCMs), as available through the Climate Model Intercomparison Projects (e.g., CMIP3, (Meehl et al. 2007); CMIP5, (Taylor et al., 2012)), which were used as a basis for the Assessment Reports prepared by the Intergovernmental Panel on Climate Change (IPCC).
In central Asia, SPHY was applied in a study (ADB 2012; Immerzeel et al., 2012; Lutz et al., 2012) that focused on the impacts of climate change on water resources in the Amu Darya and Syr Darya river basins. SPHY was used to quantify the hydrological regimes in both basins, and subsequently to project the outflow from the upstream basins to the downstream areas by forcing the model with an ensemble of five CMIP3 GCMs. The SPHY model output fed into a water allocation model that was set up for the downstream parts of the Amu Darya and Syr Darya river basins.
In the Himalayan Climate Change Adaptation Programme (HICAP), led by the International Centre for Integrated Mountain Development (ICIMOD), SPHY has been successfully applied in the upstream basins of the Indus, Ganges, Brahmaputra, Salween and Mekong rivers (Lutz et al. 2013; Lutz et al. 2014a). In this study the hydrological regimes of these five basins have been quantified and the calibrated and validated model (Figure 8) was forced with an ensemble of eight GCMs to create water availability scenarios until 2050. Table 7 lists the calibration and validation results. Based on the validation results, we concluded that the model performs satisfactorily given the large scale, complexity and heterogeneity of the modeled region and data scarcity (Lutz et al. 2014a). We use one parameter set for the entire domain, which inherently means some stations perform better than others. In the particular case of the upper Indus, another possible explanation could be uncertainty in air temperature forcing in the highest parts of the upper Indus basin (locations Dainyor bridge, Besham Qila and Tarbela inflow in Table 7), since especially in this area, the used forcing data sets are based on very sparse observations. SPHY allowed the assessment of the current contribution of glacier melt and snowmelt to total flow (Figure 9), and how total flow volumes and the intra-annual distribution of river flow will change in the future (Lutz et al. 2014a).
Table 7: Station locations used for calibration and validation of the SPHY model in HICAP (Lutz et al., 2014a). Three stations were used for calibration for 1998–2007. Five stations were used for an independent validation for the same period. The Nash–Sutcliffe efficiency (NS) and bias metrics were calculated at a monthly time step.
For basins with snowmelt being an important contributor to the flow, besides calibration to observed flow, the snow-related parameters in the SPHY model can also be calibrated to observed snow cover. For the Upper Indus basin, the snow-related parameters degree-day factor for snow () and snow water storage capacity (SSC) were calibrated independently using MODIS snow cover imagery (Lutz et al., 2016). The same MODIS data set was used as in Immerzeel et al. (2009). From the beginning of 2000 until halfway through 2008, the snow cover imagery was averaged for 46 different periods of 8 days (5 days for the last period) to generate 46 different average snow cover maps. For example, period 1 is the average snow cover for 01–08 January for 2000 until 2008, whereas period 2 is the average snow cover for 09–16 January for 2000 until 2008, etc. The SPHY model was run for 2000–2007 at a daily time step and, for each km grid cell, the average snow cover was calculated for the same 46 periods as in the MODIS observed snow cover data set. Subsequently, these simulated snow cover maps were resampled to 0.05 spatial resolution, which is the native resolution of the MODIS product. Figure 10 shows the basin-average observed and simulated fractional snow cover for the 46 periods during 2000–2007 and Figure 11 shows the same at the 0.05 grid-cell level. As a final step, the baseflow recession coefficient () and routing coefficient (kx) were calibrated to match the simulated streamflow with the observed streamflow.
In the Pan-Third Pole Environment study for a Green Silk Road (Pan-TPE), SPHY has been successfully applied in the 15 major river basins of the High Mountains of Asia (HMA) (Khanal et al., 2021)This study explores changes in climate, water supply and demand, and suitable adaptation measures for green development of the Silk Road Economic Belt (SREB) in the river basins crossed by the SREB transect. To robustly assess the 21st-century climate change impact on hydrology in the entire HMA at a wide range of scales (annual, decadal and multi-decadal), this study uses a 5km spatial and daily time step temporal resolution SPHYv3.0 model. The SPHYv3.0 model results are then used to understand the regional hydrological patterns (Figure 12) and then quantify the compound effects of future changes in precipitation and temperature based on the range of climate change projections in the CMIP6 climate model ensemble. The SPHYv3.0 model in this study uses ERA5 (Hersbach et al., 2020) as input meteorological forcings. The SPHYv3.0 uses dynamic glacier module as described in section 2.6.4. For more details regarding the study readers are referred to (Khanal et al., 2021).
