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Rivers draining the southern Himalaya provide most of the water supply for the densely populated Indo-Gangetic plains. Despite the importance of water resources in light of climate change, the relative contributions of rainfall, snow and glacier melt to discharge are not well understood, due to the scarcity of ground-based data in this complex terrain. Here, we quantify discharge sources in the Sutlej Valley, western Himalaya, from 2000 to 2012 with a distributed hydrological model that is based on daily, ground-calibrated remote-sensing observation. Based on the consistently good model performance, we analyzed the spatiotemporal distribution of hydrologic components and quantified their contribution to river discharge. Our results indicate that the Sutlej River's annual discharge at the mountain front is sourced to 55% by effective rainfall (rainfall reduced by evapotranspiration), 35% by snow melt and 10% by glacier melt. In the high-elevation orogenic interior glacial runoff contributes ∼30% to annual river discharge. These glacier melt contributions are especially important during years with substantially reduced rainfall and snowmelt runoff, as during 2004, to compensate for low river discharge and ensure sustained water supply and hydropower generation. In 2004, discharge of the Sutlej River totaled only half the maximum annual discharge; with 17.3% being sourced by glacier melt. Our findings underscore the importance of calibrating remote-sensing data with ground-based data to constrain hydrological models with reasonable accuracy. For instance, we found that TRMM (Tropical Rainfall Measuring Mission) product 3B42 V7 systematically overestimates rainfall in arid regions of our study area by a factor of up to 5. By quantifying the spatiotemporal distribution of water resources we provide an important assessment of the potential impact of global warming on river discharge in the western Himalaya. Given the near-global coverage of the utilized remote-sensing datasets this hydrological modeling approach can be readily transferred to other data-sparse regions.
The Lena Delta in Siberia is the largest delta in the Arctic and as a snow-dominated ecosystem particularly vulnerable to climate change.
Using the two decades of MODerate resolution Imaging Spectroradiometer satellite acquisitions, this study investigates interannual and spatial variability of snow-cover duration and summer vegetation vitality in the Lena Delta.
We approximated snow by the application of the normalized difference snow index and vegetation greenness by the normalized difference vegetation index (NDVI). We consolidated the analyses by integrating reanalysis products on air temperature from 2001 to 2021, and air temperature, ground temperature, and the date of snow-melt from time-lapse camera (TLC) observations from the Samoylov observatory located in the central delta.
We extracted spring snow-cover duration determined by a latitudinal gradient. The 'regular year' snow-melt is transgressing from mid-May to late May within a time window of 10 days across the delta.
We calculated yearly deviations per grid cell for two defined regions, one for the delta, and one focusing on the central delta. We identified an ensemble of early snow-melt years from 2012 to 2014, with snow-melt already starting in early May, and two late snow-melt years in 2004 and 2017, with snow-melt starting in June. In the times of TLC recording, the years of early and late snow-melt were confirmed.
In the three summers after early snow-melt, summer vegetation greenness showed neither positive nor negative deviations. Whereas, vegetation greenness was reduced in 2004 after late snow-melt together with the lowest June monthly air temperature of the time series record. Since 2005, vegetation greenness is rising, with maxima in 2018 and 2021.
The NDVI rise since 2018 is preceded by up to 4 degrees C warmer than average June air temperature. The ongoing operation of satellite missions allows to monitor a wide range of land surface properties and processes that will provide urgently needed data in times when logistical challenges lead to data gaps in land-based observations in the rapidly changing Arctic.