Snowmelt Runoff Forecasting in Himalayan Basins Using Remote Sensing Inputs

2021 ◽  
pp. 19-37
Author(s):  
E. Siva Sankar ◽  
B. Simhadri Rao ◽  
K. Abdul Hakeem
Keyword(s):  
Remote Sensing ◽  
Snowmelt Runoff ◽  
Runoff Forecasting

Assimilation of meteorological and remote sensing data for snowmelt runoff forecasting

2008 ◽  
Vol 112 (4) ◽  
pp. 1408-1420 ◽  
Author(s):  
T NAGLER ◽  
H ROTT ◽  
P MALCHER ◽  
F MULLER
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Snowmelt Runoff ◽  
Sensing Data ◽  
Runoff Forecasting

scholarly journals Remote Sensing of Snow Cover Variability and Its Influence on the Runoff of Sápmi’s Rivers

Geosciences ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 130
Author(s):  
Sebastian Rößler ◽  
Marius S. Witt ◽  
Jaakko Ikonen ◽  
Ian A. Brown ◽  
Andreas J. Dietz
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Air Temperature ◽  
Kendall Test ◽  
The Arctic ◽  
Boreal Winter ◽  
Snowmelt Runoff ◽  
River Catchment ◽  
Catchment Areas ◽  
Runoff Model

The boreal winter 2019/2020 was very irregular in Europe. While there was very little snow in Central Europe, the opposite was the case in northern Fenno-Scandia, particularly in the Arctic. The snow cover was more persistent here and its rapid melting led to flooding in many places. Since the last severe spring floods occurred in the region in 2018, this raises the question of whether more frequent occurrences can be expected in the future. To assess the variability of snowmelt related flooding we used snow cover maps (derived from the DLR’s Global SnowPack MODIS snow product) and freely available data on runoff, precipitation, and air temperature in eight unregulated river catchment areas. A trend analysis (Mann-Kendall test) was carried out to assess the development of the parameters, and the interdependencies of the parameters were examined with a correlation analysis. Finally, a simple snowmelt runoff model was tested for its applicability to this region. We noticed an extraordinary variability in the duration of snow cover. If this extends well into spring, rapid air temperature increases leads to enhanced thawing. According to the last flood years 2005, 2010, 2018, and 2020, we were able to differentiate between four synoptic flood types based on their special hydrometeorological and snow situation and simulate them with the snowmelt runoff model (SRM).

Exploring the use of gpr and satellite-based snow data for snowmelt runoff predictions

2021 ◽  
Author(s):  
Ilaria Clemenzi ◽  
David Gustafsson ◽  
Jie Zhang ◽  
Björn Norell ◽  
Wolf Marchand ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Data Assimilation ◽  
Snow Water Equivalent ◽  
Distribution Model ◽  
Accurate Estimation ◽  
Snowmelt Runoff ◽  
Snow Water ◽  
Snow Distribution ◽  
The Impact

<p>Snow in the mountains is the result of the interplay between meteorological conditions, e.g., precipitation, wind and solar radiation, and landscape features, e.g., vegetation and topography. For this reason, it is highly variable in time and space. It represents an important water storage for several sectors of the society including tourism, ecology and hydropower. The estimation of the amount of snow stored in winter and available in the form of snowmelt runoff can be strategic for their sustainability. In the hydropower sector, for example, the occurrence of higher snow and snowmelt runoff volumes at the end of the spring and in the early summer compared to the estimated one can substantially impact reservoir regulation with energy and economical losses. An accurate estimation of the snow volumes and their spatial and temporal distribution is thus essential for spring flood runoff prediction. Despite the increasing effort in the development of new acquisition techniques, the availability of extensive and representative snow and density measurements for snow water equivalent estimations is still limited. Hydrological models in combination with data assimilation of ground or remote sensing observations is a way to overcome these limitations. However, the impact of using different types of snow observations on snowmelt runoff predictions is, little understood. In this study we investigated the potential of assimilating in situ and remote sensing snow observations to improve snow water equivalent estimates and snowmelt runoff predictions. We modelled the seasonal snow water equivalent distribution in the Lake Överuman catchment, Northern Sweden, which is used for hydropower production. Simulations were performed using the semi-distributed hydrological model HYPE for the snow seasons 2017-2020. For this purpose, a snowfall distribution model based on wind-shelter factors was included to represent snow spatial distribution within model units. The units consist of 2.5x2.5 km<sup>2</sup> grid cells, which were further divided into hydrological response units based on elevation, vegetation and aspect. The impact on the estimation of the total catchment mean snow water equivalent and snowmelt runoff volume were evaluated using for data assimilation, gpr-based snow water equivalent data acquired along survey lines in the catchment in the early spring of the four years, snow water equivalent data obtained by a machine learning algorithm and satellite-based fractional snow cover data. Results show that the wind-shelter based snow distribution model was able to represent a similar spatial distribution as the gpr survey lines, when assessed on the catchment level. Deviations in the model performance within and between specific gpr survey lines indicate issues with the spatial distribution of input precipitation, and/or need to include explicit representation of snow drift between model units. The explicit snow distribution model also improved runoff simulations, and the ability of the model to improve forecast through data assimilation.</p>

scholarly journals REMOTE SENSING BASED SNOWMELT RUNOFF MODEL COUPLED WITH DISTRIBUTED HYDROLOGICAL MODEL IN UPPER YELLOW RIVER BASIN

2005 ◽  
Vol 49 ◽  
pp. 331-336
Author(s):  
Bashir AHMAD ◽  
Dawen YANG ◽  
Toshio KOIKE ◽  
Hiroshi Ishidaira ◽  
Li Chong ◽  
...  
Keyword(s):  
Remote Sensing ◽  
River Basin ◽  
Hydrological Model ◽  
Yellow River ◽  
Yellow River Basin ◽  
Distributed Hydrological Model ◽  
Snowmelt Runoff ◽  
Upper Yellow River ◽  
Runoff Model

THE APPLICATION OF SNOWCOVER AREA EVALUATION FROM REMOTE SENSING DATA TO SNOWMELT RUNOFF TANK ANALYSIS

1991 ◽  
Vol 35 ◽  
pp. 57-62
Author(s):  
Kittipong JIRAYOOT ◽  
So KAZAMA ◽  
Masaki SAWAMOTO
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Snowmelt Runoff ◽  
Sensing Data

scholarly journals A snowmelt runoff forecasting model coupling WRF and DHSVM

2009 ◽  
Vol 13 (10) ◽  
pp. 1897-1906 ◽  
Author(s):  
Q. Zhao ◽  
Z. Liu ◽  
B. Ye ◽  
Y. Qin ◽  
Z. Wei ◽  
...  
Keyword(s):  
Weather Forecasting ◽  
Atmospheric Model ◽  
Forecasting Model ◽  
Tianshan Mountain ◽  
Mountain Range ◽  
Snowmelt Runoff ◽  
Runoff Simulation ◽  
Limited Region ◽  
Runoff Forecasting ◽  
Forecasting System

Abstract. This study linked the Weather Research and Forecasting (WRF) modelling system and the Distributed Hydrology Soil Vegetation Model (DHSVM) to forecast snowmelt runoff. The study area was the 800 km2 Juntanghu watershed of the northern slopes of Tianshan Mountain Range. This paper investigated snowmelt runoff forecasting models suitable for meso-microscale application. In this study, a limited-region 24-h Numeric Weather Forecasting System was formulated using the new generation atmospheric model system WRF with the initial fields and lateral boundaries forced by Chinese T213L31 model. Using the WRF forecasts, the DHSVM hydrological model was used to predict 24 h snowmelt runoff at the outlet of the Juntanghu watershed. Forecasted results showed a good similarity to the observed data, and the average relative error of maximum runoff simulation was less than 15%. The results demonstrate the potential of using a meso-microscale snowmelt runoff forecasting model for forecasting floods. The model provides a longer forecast period compared with traditional models such as those based on rain gauges or statistical forecasting.

scholarly journals Assimilation of Snowmelt Runoff Model (SRM) Using Satellite Remote Sensing Data in Budhi Gandaki River Basin, Nepal

2020 ◽  
Vol 12 (12) ◽  
pp. 1951 ◽  
Author(s):  
Til Prasad Pangali Sharma ◽  
Jiahua Zhang ◽  
Narendra Raj Khanal ◽  
Foyez Ahmed Prodhan ◽  
Basanta Paudel ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
River Basin ◽  
Satellite Remote Sensing ◽  
River Discharge ◽  
Remote Sensing Data ◽  
Himalayan Region ◽  
Snowmelt Runoff ◽  
Sensing Data ◽  
Runoff Model

The Himalayan region, a major source of fresh water, is recognized as a water tower of the world. Many perennial rivers originate from Nepal Himalaya, located in the central part of the Himalayan region. Snowmelt water is essential freshwater for living, whereas it poses flood disaster potential, which is a major challenge for sustainable development. Climate change also largely affects snowmelt hydrology. Therefore, river discharge measurement requires crucial attention in the face of climate change, particularly in the Himalayan region. The snowmelt runoff model (SRM) is a frequently used method to measure river discharge in snow-fed mountain river basins. This study attempts to investigate snowmelt contribution in the overall discharge of the Budhi Gandaki River Basin (BGRB) using satellite remote sensing data products through the application of the SRM model. The model outputs were validated based on station measured river discharge data. The results show that SRM performed well in the study basin with a coefficient of determination (R2) >0.880. Moreover, this study found that the moderate resolution imaging spectroradiometer (MODIS) snow cover data and European Centre for Medium-Range Weather Forecasts (ECMWF) meteorological datasets are highly applicable to the SRM in the Himalayan region. The study also shows that snow days have slightly decreased in the last three years, hence snowmelt contribution in overall discharge has decreased slightly in the study area. Finally, this study concludes that MOD10A2 and ECMWF precipitation and two-meter temperature products are highly applicable to measure snowmelt and associated discharge through SRM in the BGRB. Moreover, it also helps with proper freshwater planning, efficient use of winter water flow, and mitigating and preventive measures for the flood disaster.

Snow cover monitoring by remote sensing and snowmelt runoff calculation in the upper Huanghe River basin

2002 ◽  
Vol 12 (2) ◽  
pp. 120-125 ◽  
Author(s):  
Yong-chao Lan ◽  
Jian Wang ◽  
Er-si Kang ◽  
Quan-jie Ma ◽  
Ji-shi Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
River Basin ◽  
Snowmelt Runoff ◽  
Huanghe River

Toward Snowmelt Runoff Forecast Based on Multisensor Remote-Sensing Informnation

1987 ◽  
Vol GE-25 (6) ◽  
pp. 746-750 ◽  
Author(s):  
Michael Baumgartner ◽  
Klaus Seidel ◽  
Jaroslav Martinec
Keyword(s):  
Remote Sensing ◽  
Snowmelt Runoff ◽  
Runoff Forecast
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