On 7 Feb 2021, a catastrophic mass flow descended the Ronti Gad, Rishiganga, and Dhauliganga valleys in Chamoli, Uttarakhand, India, causing widespread devastation and severely damaging two hydropower projects. Over 200 people were killed or are missing. Our analysis of satellite imagery, seismic records, numerical model results, and eyewitness videos reveals that ~27x106 m3 of rock and glacier ice collapsed from the steep north face of Ronti Peak. The rock and ice avalanche rapidly transformed into an extraordinarily large and mobile debris flow that transported boulders >20 m in diameter, and scoured the valley walls up to 220 m above the valley floor. The intersection of the hazard cascade with downvalley infrastructure resulted in a disaster, which highlights key questions about adequate monitoring and sustainable development in the Himalaya as well as other remote, high-mountain environments.

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The pristine waters of Kashmir Himalaya are showing signs of deterioration due to multiple reasons. This study researches the causes of deteriorating water quality in the Lidder River, one of the main tributaries of Jhelum River in Kashmir Himalaya. The land use and land cover of the Lidder catchment were generated using multi-spectral, bi-seasonal IRS LISS III (October 2005 and May 2006) satellite data to identify the extent of agriculture and horticulture lands that are the main non-point sources of pollution at the catchment scale. A total of 12 water quality parameters were analyzed over a period of 1 year. Water sampling was done at eight different sampling sites, each with a varied topography and distinct land use/land cover, along the length of Lidder River. It was observed that water quality deteriorated during the months of June-August that coincides with the peak tourist flow and maximal agricultural/horticultural activity. Total phosphorus, orthophosphate phosphorus, nitrate nitrogen, and ammoniacal nitrogen showed higher concentration in the months of July and August, while the concentration of dissolved oxygen decreased in the same period, resulting in deterioration in water quality. Moreover, tourism influx in the Lidder Valley shows a drastic increase through the years, and particularly, the number of tourists visiting the valley has increased in the summer months from June to September, which is also responsible for deteriorating the water quality of Lidder River. In addition to this, the extensive use of fertilizers and pesticides in the agriculture and horticulture lands during the growing season (June-August) is also responsible for the deteriorating water quality of Lidder River.

Five watersheds (W1, W2, W3, W4 and W5) in the upper Indus basin were chosen for detailed studies to understand the influences of geomorphology, drainage basin morphometry and vegetation patterns on hydrology. From the morphometric analysis, it is evident that the hydrologic response of these watersheds changes significantly in response to spatial variations in morphometric parameters. Results indicate that W1, W2 and W5 contribute higher surface runoff than W3 and W4. Further, the topographic and land cover analyses reveal that W1, W2 and W5 generate quick runoff that may result in flooding over prolonged rainy spells. A physically based semi-distributed hydrologic model (soil and water assessment tool, SWAT) was used for simulating the hydrological response from the watersheds. As per the simulations, W5 watershed produces the highest runoff of 11.17 mm/year followed by W1 (7.9 mm/year), W2 (6.6 mm/year), W4 (5.33 mm/year) and W3 (4.29 mm/year). Thus, W5 is particularly more vulnerable to flooding during high rain spells followed by W1, W2, W4 and W3, respectively. Synthetic unit hydrograph analysis of the five watersheds also reveals high peak discharge for W5. The simulated results on the hydrological response from the five watersheds are quite in agreement with those of the morphometric, topographic, vegetation and unit hydrograph analyses. Therefore, it is quite evident that these factors have significant impact on the hydrological response from the watersheds and can be used to predict flood peaks, sediment yield and water discharge from the ungauged watersheds.

AcknowledgmentIt has taken the efforts of many people and the support of their organization during the last several years to allow us to reach this new milestone in snowmelt runoff modeling. The following organizations and people were particularly helpful and supportive: It is now possible to divide a basin into as many as 16 elevation or other zones in order to refine the modeling, while Version 4 only allowed 8. These improvements facilitate new developments in SRM applications which are already taking place: runoff modeling by using different land use zones, separating satellite mapping of snow and glaciers, runoff modeling in very large basins with an extreme elevation range, and others. The specific features of WinSRM Version 1.11 are explained in detail in this document in Sections 8.5, 8.6, 9, and 10.WinSRM Version 1.11 has been developed without sacrificing the advantages of the SRM Version 4, in particular the speed of getting results. Both versions are available on the Internet by accessing http://www.ars.usda.gov/Services/docs.htm?docid=8872. Should this link not be "current" for the reader, one can "search" on "SRM home" or "WinSRM" to locate a "current site".So far, four SRM workshops (in 1992, 1994, 1996, and 1998) have been organized at the University of Bern, Switzerland, with about 130 participants from 20 countries taking part. A fifth SRM workshop was organized in 2005 at New Mexico State University. In addition, the authors are available to assist users in overcoming special problems which may be encountered. INTRODUCTIONThe Snowmelt-Runoff Model (SRM) is designed to simulate and forecast daily streamflow in mountain basins where snowmelt is a major runoff factor. Most recently, it has also been applied to evaluate the effect of a changed climate on seasonal snow cover and runoff. SRM was developed by Martinec (1975) in small European basins. Thanks to the progress of satellite remote sensing of snow cover, SRM has been applied to larger and larger basins. Recently, the runoff was modelled in the basin of the Ganges River, which has an area of 917,444 km 2 and an elevation range from 0 to 8,840 m a.s.l. Contrary to the original assumptions, there appear to be no limits for application with regard to the basin size and the elevation range. Also, a dominant role of snowmelt does not seem to be a necessary condition. It is, however, advisable to carefully evaluate the formula for the recession coefficient.Runoff computations by SRM appear to be relatively easily understood. To date the model has been applied by various agencies, institutes and universities in over 100 basins, situated in 29 different countries as listed in Table 1. More than 80% of these applications have been performed by independent users, as is evident from 80 references to pertinent publications. Some of the localities are shown in Figure 1. SRM also successfully underwent tests by the World Meteorological Organization with regard to runoff simulations (WMO, 1986) and to partially simulated conditions of real ti...