Runoff Projection from an Alpine Watershed in Western Canada: Application of a Snowmelt Runoff Model

The rising global temperature is shifting the runoff patterns of snowmelt-dominated alpine watersheds, resulting in increased cold season flows, earlier spring peak flows, and reduced summer runoff. Projections of future runoff are beneficial in preparing for the anticipated changes in streamflow regimes. This study applied the degree–day Snowmelt Runoff Model (SRM) in combination with the MODIS to remotely sense snow cover observations for modeling the snowmelt runoff response of the Upper Athabasca River Basin in western Canada. After assessing its ability to simulate the observed historical flows, the SRM was applied for projecting future runoff in the basin. The inclusion of a spatial and temporal variation in the degree–day factor (DDF) and separation of the DDF for glaciated and non-glaciated areas were found to be important for improved simulation of varying snow conditions over multiple years. The SRM simulations, driven by an ensemble of six statistically downscaled GCM runs under the RCP8.5 scenario for the future period (2070–2080), show a consistent pattern in projected runoff change, with substantial increases in May runoff, smaller increases over the winter months, and decreased runoff in the summer months (June–August). Despite the SRM’s relative simplicity and requirement of only a few input variables, the model performed well in simulating historical flows, and provides runoff projections consistent with historical trends and previous modeling studies.

Download Full-text

Model Sophistication in Relation to Scales in Snowmelt Runoff Modeling

Hydrology Research ◽

10.2166/nh.2000.0016 ◽

2000 ◽

Vol 31 (4-5) ◽

pp. 267-286 ◽

Cited By ~ 6

Author(s):

Lars Bengtsson ◽

Vijay P. Singh

Keyword(s):

Stream Flow ◽

Overland Flow ◽

River Basins ◽

Rainfall Runoff ◽

Snowmelt Runoff ◽

Degree Day ◽

Groundwater Origin ◽

Runoff Modeling ◽

Snow Model ◽

Runoff Model

Snowmelt induced runoff from river basins is usually successfully simulated using a simple degree-day approach and conceptual rainfall-runoff models. Fluctuations within the day can not be described by such crude approaches. In the present paper, it is investigated which degree of sophistication is required in snow models and runoff models to resolve the basin runoff from basins of different character, and also how snow models and runoff models must adapt to each other. Models of different degree of sophistication are tested on basins ranging from 6,000 km2 down to less than 1 km2. It is found that for large basins it is sufficient to use a very simple runoff module and a degree day approach, but that the snow model has to be distributed related to land cover and topography. Also for small forested basins, where most of the stream flow is of groundwater origin, the degree-day method combined with a conceptual runoff model reproduces the snowmelt induced runoff well. Where overland flow takes place, a high resolution snow model is required for resolving the runoff fluctuations at the basin outlet.

Download Full-text

SNOWMELT - RUNOFF MODEL FOR STREAM FLOW FORECASTS

Hydrology Research ◽

10.2166/nh.1975.0010 ◽

1975 ◽

Vol 6 (3) ◽

pp. 145-154 ◽

Cited By ~ 162

Author(s):

J. MARTINEC

Keyword(s):

Simple Model ◽

Stream Flow ◽

Snow Accumulation ◽

Rainfall Runoff ◽

Snowmelt Runoff ◽

Degree Day ◽

Recession Coefficient ◽

Runoff Model

Following the development of rainfall-runoff models the attention of the international hydrologic program is now increasingly focused on the snowmelt-runoff. The present simple model is based on taking into account the variability of the degree-day factor, recession coefficient and snow coverage. It can be adapted to heterogenous conditions of snow accumulation and temperature in mountainous basins.

Download Full-text

Application of the Snowmelt Runoff Model in the Salang River Basin, Afghanistan Using MODIS Satellite Data

Journal of Hydrology and Meteorology ◽

10.3126/jhm.v9i1.15586 ◽

2016 ◽

Vol 9 (1) ◽

pp. 109-118

Author(s):

Hedayatullah Arian ◽

Rijan B. Kayastha ◽

Bikas C. Bhattarai ◽

Ahuti Shresta ◽

Hafizullah Rasouli ◽

...

Keyword(s):

River Basin ◽

River Flow ◽

Snowmelt Runoff ◽

Annual Average ◽

Temperature And Precipitation ◽

Promising Tool ◽

Daily Discharge ◽

Input Variables ◽

The Impact ◽

Runoff Model

This study is carried out on the Salang River basin, which is located at the northern part of the Kabul River basin, and in the south facing slope of the Hindu Kush Mountains. The basin drains through the Salang River, which is one of the tributaries of the Panjshir River. The basin covers an area of 485.9km2 with a minimum elevation of 1653 m a.s.l. and a maximum elevation of 4770 m a.s.l. The Salang River sustains a substantial flow of water in summer months due to the melting of snow. In this study, we estimate daily discharge of Salang River from 2009 to 2011 using the Snowmelt Runoff Model (SRM, Version 1.12, 2009), originally developed by J. Martinec in 1975. The model uses daily observed precipitation, air temperature and snow cover data as input variables from which discharge is computed. The model is calibrated for the year 2009 and validated for 2010 and 2011. The observed and calculated annual average discharges for the calibration year 2009 are 11.57m3s-1 and 10.73m3s-1, respectively. Similarly, the observed and calculated annual average discharges for the validation year 2010 are 11.55m3s-1 and 10.07m3s-1, respectively and for 2011, the discharges are 9.05 m3s-1 and 9.6m3s-1, respectively. The model is also tested by changing temperature and precipitation for the year 2009. With an increase of 1°C in temperature and 10% in precipitation, the increases in discharge for winter, summer and annually are 21.8%, 13.5% and 14.8%, respectively. With an increase of 2°C in temperature and 20% in precipitation, the increases are 48.5%, 43.3% and 44.1%, respectively. The results obtained suggest that the SRM can be used as a promising tool to estimate the river discharge of the snow fed mountainous river basins of Afghanistan and to study the impact of climate change on river flow pattern of such basins.Journal of Hydrology and Meteorology, Vol. 9(1) 2015, p.109-118

Download Full-text

Estimation of Discharge From Upper Kabul River Basin, Afghanistan Using the Snowmelt Runoff Model

Journal of Hydrology and Meteorology ◽

10.3126/jhm.v9i1.15584 ◽

2016 ◽

Vol 9 (1) ◽

pp. 85-94 ◽

Cited By ~ 1

Author(s):

Hafizullah Rasouli ◽

Rijan B. Kayastha ◽

Bikas C. Bhattarai ◽

Ahuti Shrestha ◽

Hedayatullah Arian ◽

...

Keyword(s):

River Basin ◽

Daily Precipitation ◽

Snowmelt Runoff ◽

Maximum Elevation ◽

Hindu Kush ◽

Daily Discharge ◽

Input Variables ◽

Runoff Model ◽

Kabul River ◽

Good Agreement

In this study, we estimated discharge from Upper Kabul River basin in the Hindu Kush Mountain (Paghman range) in Afghanistan. The Upper Kabul River basin covers an area of 1633.8km2 with a maximum elevation of 4522 m and minimum elevation of 1877 m. The Kabul River is one of the main rivers in Afghanistan and sustains a significant flow of water in summer months due to the melting of snow. In this study, daily discharge from Upper Kabul River basin, west of Kabul basin, for 2009 and 2011 is estimated by using Snowmelt Runoff Model (SRM) (Version 1.12, 2009), originally developed my J. Martinec in 1975. Daily precipitation, air temperature, discharge and snow cover data are used in the model as input variables. We calibrated the model for 2009 and validated in 2011. The observed and calculated annual average discharges in 2009 are 5.7m3/s and 5.6m3/s, respectively; and in 2011 are 1.33m3/s and 1.31m3/s, respectively. The model results are in good agreement with the measured daily discharges. With an increase of 1°C in temperature and 10% precipitation, the increase in discharge in winter, summer and annually relative to 2009 discharge are 39%, 18.5% and 17.9%, respectively. Similarly, with an increase of 2°C in temperature and 20% in precipitation, modeled discharge increases by 51.2%, 40.8% and 47.3%, respectively. The results obtained suggest that the SRM can be used efficiently for estimating discharge in the snow fed sub-catchment of the Upper Kabul River basin and other mountain basins in Afghanistan.Journal of Hydrology and Meteorology, Vol. 9(1) 2015, p.85-94

Download Full-text

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

Geosciences ◽

10.3390/geosciences11030130 ◽

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).

Download Full-text

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

PROCEEDINGS OF HYDRAULIC ENGINEERING ◽

10.2208/prohe.49.331 ◽

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

Download Full-text

IMPACTS OF SNOWMELT AND CLIMATE CHANGE ON HIMALAYAN RIVERS: THE CASE OF DATA-SCARCE DUDHKOSHI RIVER IN EASTERN NEPAL

International Journal of Engineering Applied Sciences and Technology ◽

10.33564/ijeast.2021.v05i12.002 ◽

2021 ◽

Vol 5 (12) ◽

Author(s):

Sudeep Pokhrel ◽

Saraswati Thapa

Keyword(s):

Maximum Temperature ◽

Climate Projections ◽

Snow Melt ◽

Snow Hydrology ◽

Statistical Downscaling Model ◽

Snowmelt Runoff ◽

Annual Average ◽

Temperature And Precipitation ◽

Increasing Trends ◽

Runoff Model

Water from snow-melt is crucial to provide ecosystem services in downstream of the Himalayas. To study the fate of snow hydrology, an integrated modeling system has been developed coupling Statistical Downscaling Model (SDSM) outputs with Snowmelt Runoff Model (SRM) in the Dudhkoshi Basin, Nepal. The SRM model is well-calibrated in 2011 and validated in 2012 and 2014 using MODIS satellite data. The annual average observed and simulated discharges for the calibration year are 177.89 m3 /s and 181.47 m3 /s respectively. To assess future climate projections for the periods 2020s, 2050s, and 2080s, the SDSM model is used for downscaling precipitation, maximum temperature, and minimum temperature from the Canadian GCM model (CanESM2) under three different scenarios RCP2.6, RCP4.5 and RCP8.5. All considered scenarios are significant in predicting increasing trends of maximumminimum temperature and precipitation and the storehouse of freshwater in the mountains is expected to deplete rapidly if global warming continues.

Download Full-text

Effect of input variables on rainfall-runoff modeling using a deep learning method

10.5194/egusphere-egu21-4398 ◽

2021 ◽

Author(s):

Kazuki yokoo ◽

Kei ishida ◽

Takeyoshi nagasato ◽

Ali Ercan

Keyword(s):

Deep Learning ◽

Air Temperature ◽

Meteorological Variables ◽

Learning Method ◽

Rainfall Runoff ◽

Model Accuracy ◽

Runoff Modeling ◽

Input Variables ◽

Rainfall Runoff Model ◽

Runoff Model

<p>In recent years, deep learning has been applied to various issues in natural science, including hydrology. These application results show its high applicability. There are some studies that performed rainfall-runoff modeling by means of a deep learning method, LSTM (Long Short-Term Memory). LSTM is a kind of RNN (Recurrent Neural Networks) that is suitable for modeling time series data with long-term dependence. These studies showed the capability of LSTM for rainfall-runoff modeling. However, there are few studies that investigate the effects of input variables on the estimation accuracy. Therefore, this study, investigated the effects of the selection of input variables on the accuracy of a rainfall-runoff model by means of LSTM. As the study watershed, this study selected a snow-dominated watershed, the Ishikari River basin, which is in the Hokkaido region of Japan. The flow discharge was obtained at a gauging station near the outlet of the river as the target data. For the input data to the model, Meteorological variables were obtained from an atmospheric reanalysis dataset, ERA5, in addition to the gridded precipitation dataset. The selected meteorological variables were air temperature, evaporation, longwave radiation, shortwave radiation, and mean sea level pressure. Then, the rainfall-runoff model was trained with several combinations of the input variables. After the training, the model accuracy was compared among the combinations. The use of meteorological variables in addition to precipitation and air temperature as input improved the model accuracy. In some cases, however, the model accuracy was worsened by using more variables as input. The results indicate the importance to select adequate variables as input for rainfall-runoff modeling by LSTM.</p>

Download Full-text