Analysis of Snow Dynamics in Beas River Basin, Western Himalaya Using Combined Terra–Aqua MODIS Improved Snow Product and in Situ Data During Twenty-First Century

2021 ◽  
pp. 115-128
Author(s):  
Dhiraj Kumar Singh ◽  
Hemendra Singh Gusain ◽  
Sanjay Kumar Dewali ◽  
Reet Kamal Tiwari ◽  
Ajay Kumar Taloor
Keyword(s):  
River Basin ◽  
Western Himalaya ◽  
First Century ◽  
In Situ Data ◽  
Twenty First Century

Spatio-Temporal Analysis of Early Twenty-First Century Areal Changes in the Kabul River Basin Cryosphere

2018 ◽  
Vol 2 (3) ◽  
pp. 563-571 ◽  
Author(s):  
Amjad Masood ◽  
Muhammad Zia ur Rahman Hashmi ◽  
Haris Mushtaq
Keyword(s):  
River Basin ◽  
Temporal Analysis ◽  
First Century ◽  
Early Twenty ◽  
Twenty First Century ◽  
Spatio Temporal ◽  
Kabul River

Assessing the climate change impacts on the rainfall erosivity throughout the twenty-first century in the Grande River Basin (GRB) headwaters, Southeastern Brazil

2015 ◽  
Vol 73 (12) ◽  
pp. 8683-8698 ◽  
Author(s):  
Carlos Rogério de Mello ◽  
Léo Fernandes Ávila ◽  
Marcelo Ribeiro Viola ◽  
Nilton Curi ◽  
Lloyd Darrell Norton
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Climate Change Impacts ◽  
First Century ◽  
Southeastern Brazil ◽  
Rainfall Erosivity ◽  
Twenty First Century

scholarly journals Downscaling of SMAP Soil Moisture in the Lower Mekong River Basin

Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 56 ◽  
Author(s):  
Chelsea Dandridge ◽  
Bin Fang ◽  
Venkat Lakshmi
Keyword(s):  
Soil Moisture ◽  
Surface Temperature ◽  
River Basin ◽  
River Basins ◽  
Large River ◽  
Mekong River ◽  
Mekong River Basin ◽  
In Situ Data ◽  
Large River Basins

In large river basins where in situ data were limited or absent, satellite-based soil moisture estimates can be used to supplement ground measurements for land and water resource management solutions. Consistent soil moisture estimation can aid in monitoring droughts, forecasting floods, monitoring crop productivity, and assisting weather forecasting. Satellite-based soil moisture estimates are readily available at the global scale but are provided at spatial scales that are relatively coarse for many hydrological modeling and decision-making purposes. Soil moisture data are obtained from NASA’s soil moisture active passive (SMAP) mission radiometer as an interpolated product at 9 km gridded resolution. This study implements a soil moisture downscaling algorithm that was developed based on the relationship between daily temperature change and average soil moisture under varying vegetation conditions. It applies a look-up table using global land data assimilation system (GLDAS) soil moisture and surface temperature data, and advanced very high resolution radiometer (AVHRR) and moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and land surface temperature (LST). MODIS LST and NDVI are used to obtain downscaled soil moisture estimates. These estimates are then used to enhance the spatial resolution of soil moisture estimates from SMAP 9 km to 1 km. Soil moisture estimates at 1 km resolution are able to provide detailed information on the spatial distribution and pattern over the regions being analyzed. Higher resolution soil moisture data are needed for practical applications and modelling in large watersheds with limited in situ data, like in the Lower Mekong River Basin (LMB) in Southeast Asia. The 1 km soil moisture estimates can be applied directly to improve flood prediction and assessment as well as drought monitoring and agricultural productivity predictions for large river basins.

scholarly journals Climate Change Induced Trends and Uncertainties in Phytoplankton Spring Bloom Dynamics

2021 ◽  
Vol 8 ◽  
Author(s):  
Lőrinc Mészáros ◽  
Frank van der Meulen ◽  
Geurt Jongbloed ◽  
Ghada El Serafy
Keyword(s):  
Time Series ◽  
Chlorophyll A ◽  
Spring Bloom ◽  
First Century ◽  
Fusion Model ◽  
Feature Extraction Method ◽  
Structural Time Series ◽  
Twenty First Century

Spring phytoplankton blooms in the southern North Sea substantially contribute to annual primary production and largely influence food web dynamics. Studying long-term changes in spring bloom dynamics is therefore crucial for understanding future climate responses and predicting implications on the marine ecosystem. This paper aims to study long term changes in spring bloom dynamics in the Dutch coastal waters, using historical coastal in-situ data and satellite observations as well as projected future solar radiation and air temperature trajectories from regional climate models as driving forces covering the twenty-first century. The main objective is to derive long-term trends and quantify climate induced uncertainties in future coastal phytoplankton phenology. The three main methodological steps to achieve this goal include (1) developing a data fusion model to interlace coastal in-situ measurements and satellite chlorophyll-a observations into a single multi-decadal signal; (2) applying a Bayesian structural time series model to produce long-term projections of chlorophyll-a concentrations over the twenty-first century; and (3) developing a feature extraction method to derive the cardinal dates (beginning, peak, end) of the spring bloom to track the historical and the projected changes in its dynamics. The data fusion model produced an enhanced chlorophyll-a time series with improved accuracy by correcting the satellite observed signal with in-situ observations. The applied structural time series model proved to have sufficient goodness-of-fit to produce long term chlorophyll-a projections, and the feature extraction method was found to be robust in detecting cardinal dates when spring blooms were present. The main research findings indicate that at the study site location the spring bloom characteristics are impacted by the changing climatic conditions. Our results suggest that toward the end of the twenty-first century spring blooms will steadily shift earlier, resulting in longer spring bloom duration. Spring bloom magnitudes are also projected to increase with a 0.4% year−1 trend. Based on the ensemble simulation the largest uncertainty lies in the timing of the spring bloom beginning and -end timing, while the peak timing has less variation. Further studies would be required to link the findings of this paper and ecosystem behavior to better understand possible consequences to the ecosystem.

scholarly journals Assessment of the accuracy of snow water equivalent calculation with the use of global numerical weather prediction models and SnoWE snowpack model (by the example of the Kama River basin)

2021 ◽  
Vol 66 (1) ◽  
Author(s):  
Andrey N. Shikhov ◽  
◽  
Evgenii V. Churiulin ◽  
Rinat K. Abdullin ◽  
◽  
...  
Keyword(s):  
River Basin ◽  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Snow Water Equivalent ◽  
Weather Prediction ◽  
Entire Area ◽  
Numerical Weather ◽  
In Situ Data ◽  
Snow Water

The paper discusses the results of snow cover formation and snowmelt modeling in the Kama river basin (S = 507 km2) using two approaches previously developed by the authors. The first one is the SnoWE snowpack model developed at the Hydrometeorological Center of the Russian Federation and used in quasi-operational mode since 2015, and the second is GIS-based empirical technique which was previously implemented for the Kama river basin. Both methods are based on a combination of numerical weather prediction (NWP) models data with operational synoptic observations at the weather stations. The study was performed for the winter seasons 2018/19 and 2019/20. To assess the reliability of simulated snow water equivalent (SWE), we obtained in-situ data from 68 locations (snow survey routes) distributed over the entire area of ​​the river basin. As a result of the study, the main advantages and limitations of two methods for SWE calculation were identified. As for the maximum values of SWE, the root mean square error (RMSE) of simulated SWE ranges from 14% to 28% of the average observed SWE according to in-situ data. It was found, that the SnoWE model more reliably reproduces SWE in the lowland part of the river basin. Simultaneously, SWE was substantially underestimated according to the SnoWE model in the northern and mountainous parts of the basin,. The second method provides a more realistic estimate of the spatial distribution of SWE over the area, as well as a higher accuracy of calculation for its northern part of the river basin. The main drawback of the method is the substantial overestimation of the intensity of snowmelt and snow sublimation. Consequently, the accuracy of SWE calculations sharply decreases in the spring season. Wherein, SWE calculation accuracy in the winter season 2019/20 was substantially lower than in 2018/19 due to frequent thaws.

scholarly journals Assessment of projected agro-climatic indices over Awun river basin, Nigeria for the late twenty-first century

2018 ◽  
Vol 151 (3-4) ◽  
pp. 445-462 ◽  
Author(s):  
Talardia Gbangou ◽  
Mouhamadou Bamba Sylla ◽  
Onemayin David Jimoh ◽  
Appollonia Aimiosino Okhimamhe
Keyword(s):  
River Basin ◽  
First Century ◽  
Climatic Indices ◽  
Twenty First Century

scholarly journals Evaluation of Satellite-Based Rainfall Estimates in the Lower Mekong River Basin (Southeast Asia)

2019 ◽  
Vol 11 (22) ◽  
pp. 2709 ◽  
Author(s):  
Chelsea Dandridge ◽  
Venkat Lakshmi ◽  
John Bolten ◽  
Raghavan Srinivasan
Keyword(s):  
Southeast Asia ◽  
River Basin ◽  
Watershed Modeling ◽  
Rain Gauge ◽  
Mekong River ◽  
Coefficient Of Determination ◽  
Mekong River Basin ◽  
In Situ Data ◽  
Rainfall Estimates

Satellite-based precipitation is an essential tool for regional water resource applications that requires frequent observations of meteorological forcing, particularly in areas that have sparse rain gauge networks. To fully realize the utility of remotely sensed precipitation products in watershed modeling and decision-making, a thorough evaluation of the accuracy of satellite-based rainfall and regional gauge network estimates is needed. In this study, Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42 v.7 and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) daily rainfall estimates were compared with daily rain gauge observations from 2000 to 2014 in the Lower Mekong River Basin (LMRB) in Southeast Asia. Monthly, seasonal, and annual comparisons were performed, which included the calculations of correlation coefficient, coefficient of determination, bias, root mean square error (RMSE), and mean absolute error (MAE). Our validation test showed TMPA to correctly detect precipitation or no-precipitation 64.9% of all days and CHIRPS 66.8% of all days, compared to daily in-situ rainfall measurements. The accuracy of the satellite-based products varied greatly between the wet and dry seasons. Both TMPA and CHIRPS showed higher correlation with in-situ data during the wet season (June–September) as compared to the dry season (November–January). Additionally, both performed better on a monthly than an annual time-scale when compared to in-situ data. The satellite-based products showed wet biases during months that received higher cumulative precipitation. Based on a spatial correlation analysis, the average r-value of CHIRPS was much higher than TMPA across the basin. CHIRPS correlated better than TMPA at lower elevations and for monthly rainfall accumulation less than 500 mm. While both satellite-based products performed well, as compared to rain gauge measurements, the present research shows that CHIRPS might be better at representing precipitation over the LMRB than TMPA.

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