Development of web-based WERM-S module for estimating spatially distributed rainfall erosivity index (EI30) using RADAR rainfall data

Risal, Avay; Lim, Kyoung Jae; Bhattarai, Rabin; Yang, Jae E.; Noh, Huiseong; Pathak, Rohit; Kim, Jonggun

doi:10.1016/j.catena.2017.10.015

Cited by 22 publications

(8 citation statements)

References 31 publications

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“…The EI30 index is commonly used in RUSLE to predict the impact of rainfall events on soil loss [5,18]. For a single storm event, the EI30 is the value of kinetic energy, E in MJ ha −1 , multiplied by the peak 30-min rainfall intensity I 30 (mm hr −1 ).…”

Section: Rapid Radar Rainfall Data Processing and Storm Event-based Rainfall Erosivity Estimationmentioning

confidence: 99%

“…Weather radar data have very high temporal resolution (15 min or less) and spatial resolution (1 km or less) with the potential for estimating event-based rainfall erosivity or the 30-min rainfall erosivity index (EI30). Such data sets have recently been applied in event-based erosion modeling to compute a spatial EI30 index [18] and to monitor erosion after wildfires at Warrumbungle National Park in NSW, Australia [5,6]. Its application over a large area or catchment at near real-time of rainfall events is still a research and implementation challenge.…”

Section: Introductionmentioning

confidence: 99%

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Rapid Assessment of Hillslope Erosion Risk after the 2019–2020 Wildfires and Storm Events in Sydney Drinking Water Catchment

et al. 2020

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The Australian Black Summer wildfires between September 2019 and January 2020 burnt many parts of eastern Australia including major forests within the Sydney drinking water catchment (SDWC) area, almost 16.000 km2. There was great concern on post-fire erosion and water quality hazards to Sydney’s drinking water supply, especially after the heavy rainfall events in February 2020. We developed a rapid and innovative approach to estimate post-fire hillslope erosion using weather radar, remote sensing, Google Earth Engine (GEE), Geographical Information Systems (GIS), and the Revised Universal Soil Loss Equation (RUSLE). The event-based rainfall erosivity was estimated from radar-derived rainfall accumulations for all storm events after the wildfires. Satellite data including Sentinel-2, Landsat-8, and Moderate Resolution Imaging Spectroradiometer (MODIS) were used to estimate the fractional vegetation covers and the RUSLE cover-management factor. The study reveals that the average post-fire erosion rate over SDWC in February 2020 was 4.9 Mg ha−1 month−1, about 30 times higher than the pre-fire erosion and 10 times higher than the average erosion rate at the same period because of the intense storm events and rainfall erosivity with a return period over 40 years. The high post-fire erosion risk areas (up to 23.8 Mg ha−1 month−1) were at sub-catchments near Warragamba Dam which forms Lake Burragorang and supplies drinking water to more than four million people in Sydney. These findings assist in the timely assessment of post-fire erosion and water quality risks and help develop cost-effective fire incident management and mitigation actions for such an area with both significant ecological and drinking water assets. The methodology developed from this study is potentially applicable elsewhere for similar studies as the input datasets (satellite and radar data) and computing platforms (GEE, GIS) are available and accessible worldwide.

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Section: Rapid Radar Rainfall Data Processing and Storm Event-based Rainfall Erosivity Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rapid Assessment of Hillslope Erosion Risk after the 2019–2020 Wildfires and Storm Events in Sydney Drinking Water Catchment

et al. 2020

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“…50 Compared with station-based observations, gridded precipitation data from radar-based and satellite-based datasets cover larger areas for longer periods. These gridded data have been widely used to estimate the rainfall erosivity in China (Teng et al, 2018), Germany (Risal et al, 2018), Africa (Vrieling et al, 2010), the United States (Kim et al, 2020), and other regions. They have contributed greatly to our knowledge of the spatiotemporal patterns of rainfall erosivity; however, the uncertainties 55 in rainfall erosivity obtained using gridded data have not been quantified, although obvious biases between gridded and observed precipitation values have been demonstrated (Freitas et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

New gridded dataset of rainfall erosivity (1950–2020) on the Tibetan Plateau

Chen

Duan

Ding

et al. 2022

Preprint

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Abstract. The risk of water erosion on the Tibetan Plateau (TP), a typical fragile ecological area, is increasing with climate change. Rainfall erosivity maps are useful for understanding the spatiotemporal patterns of rainfall erosivity and identifying vulnerable regions. This study generated a gridded annual rainfall erosivity dataset of the TP for 1950–2020 using a new approach based on 1-min precipitation observations at 1787 weather stations and 0.25° hourly European Center for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) precipitation data. We conclude that ERA5 is generally useful for mapping annual rainfall erosivity on the TP, considering the high correlation coefficient and consistent spatiotemporal patterns between the ERA5-based and observed annual rainfall erosivity. In addition, obvious underestimation of the ERA5-based annual rainfall erosivity was found. After correction by a multiplier factor map, the annual rainfall erosivity values for 2013–2020 are in good agreement with the observed values in terms of the correction coefficient and probability density. Finally, a new annual rainfall erosivity dataset for 1950–2020 was produced after the ERA5-based annual rainfall erosivity values were corrected. We found that the area-averaged mean annual rainfall erosivity on the TP is 307 MJ·mm·ha−1·h−1 and tends to decrease from southeast to northwest. Key regions with large rainfall erosivity potential are concentrated in the Bomi–West Sichuan and Dawang–Chayu areas. This new annual rainfall erosivity dataset could extend our knowledge of rainfall erosivity patterns and provide fundamental data for quantifying soil erosion in the TP.

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“…However, the process of calculation of R-factor from rainfall data is time-consuming, although the Web ERosivity Module (WERM) software can calculate rainfall erosivity factor [27]. Furthermore, the radar rainfall dataset can be used to calculate spatial USLE R raster values using Web Erosivity Model-Spatial (WERM-S) [28]. These days, Machine Learning/Deep Learning (ML/DL) has been suggested as an alternative to predict and simulate natural phenomena [29].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Rainfall Erosivity Factor Estimation Using Machine and Deep Learning Models

Lee

Hong

et al. 2021

Water

Self Cite

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Rainfall erosivity factor (R-factor) is one of the Universal Soil Loss Equation (USLE) input parameters that account for impacts of rainfall intensity in estimating soil loss. Although many studies have calculated the R-factor using various empirical methods or the USLE method, these methods are time-consuming and require specialized knowledge for the user. The purpose of this study is to develop machine learning models to predict the R-factor faster and more accurately than the previous methods. For this, this study calculated R-factor using 1-min interval rainfall data for improved accuracy of the target value. First, the monthly R-factors were calculated using the USLE calculation method to identify the characteristics of monthly rainfall-runoff induced erosion. In turn, machine learning models were developed to predict the R-factor using the monthly R-factors calculated at 50 sites in Korea as target values. The machine learning algorithms used for this study were Decision Tree, K-Nearest Neighbors, Multilayer Perceptron, Random Forest, Gradient Boosting, eXtreme Gradient Boost, and Deep Neural Network. As a result of the validation with 20% randomly selected data, the Deep Neural Network (DNN), among seven models, showed the greatest prediction accuracy results. The DNN developed in this study was tested for six sites in Korea to demonstrate trained model performance with Nash–Sutcliffe Efficiency (NSE) and the coefficient of determination (R2) of 0.87. This means that our findings show that DNN can be efficiently used to estimate monthly R-factor at the desired site with much less effort and time with total monthly precipitation, maximum daily precipitation, and maximum hourly precipitation data. It will be used not only to calculate soil erosion risk but also to establish soil conservation plans and identify areas at risk of soil disasters by calculating rainfall erosivity factors.

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Development of web-based WERM-S module for estimating spatially distributed rainfall erosivity index (EI30) using RADAR rainfall data

Cited by 22 publications

References 31 publications

Rapid Assessment of Hillslope Erosion Risk after the 2019–2020 Wildfires and Storm Events in Sydney Drinking Water Catchment

Rapid Assessment of Hillslope Erosion Risk after the 2019–2020 Wildfires and Storm Events in Sydney Drinking Water Catchment

New gridded dataset of rainfall erosivity (1950–2020) on the Tibetan Plateau

Evaluation of Rainfall Erosivity Factor Estimation Using Machine and Deep Learning Models

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