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Application of Web ERosivity Module (WERM) for estimation of annual and monthly R factor in Korea
Risal, Avay,Bhattarai, Rabin,Kum, Donghyuk,Park, Youn Shik,Yang, Jae E.,Lim, Kyoung Jae Catena Verlag 2016 Catena Vol.147 No.-
<P><B>Abstract</B></P> <P>Soil erosion is a very serious problem from agricultural as well as environmental point of view. Various computer models have been used to estimate soil erosion and assess erosion control practice. Universal Soil Loss Equation (USLE) is one of the most frequently used soil loss estimation models which have been used in many countries around the world. Erosivity (USLE R-factor) is one of the USLE input parameters to reflect impacts of rainfall in computing soil loss. R factor for a specific rainfall event depends upon maximum rainfall intensity of specific period and kinetic energy of that event. Annual R factor is calculated as the sum of erosivities of such rainfall events that occurred. It is usually calculated from rainfall data having higher temporal resolution but the process of calculation is very tedious and also the higher temporal resolution data are not readily available in many parts of the world. Various regression models have been developed to estimate monthly R factor as well as annual R factor using monthly/yearly rainfall amount. However, it is rarely allowed to estimate R factor with higher accuracy using these models since they were developed from obsolete dataset and also only the rainfall amount was used for an input parameter without rainfall intensity. In this study, a web-based Erosivity estimation system (Web ERosivity Module-WERM) was developed to compute R factor using 10min interval rainfall data. The model was then tested for 75 different cities in Korea using the rainfall data of 15 to 18years from 1997 to 2014 obtained from Korea Meteorological Administration (KMA). Using the monthly rainfall data and R factor values obtained from the model, regression equation for 25 cities was developed to estimate monthly R factor from the monthly rainfall with amount and intensity of rainfall considered. The coefficient of determination (R<SUP>2</SUP>) of the regression equation ranged from 0.75 to 0.92. This indicated that these regression equations can be used to estimate the value of R-factor from the monthly rainfall data with more than 75% accuracy. The WERM is very simple to use and it can be a very effective tool to compute R factor using higher temporal resolution rainfall data. Along with this, it is possible to calculate R factor using local daily rainfall with the help of regression equations which are available for 25 cities in South Korea till now.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Yearly, monthly, event-based and average annual R factor can be obtained using the WERM. </LI> <LI> Average annual R factor value of South Korea was calculated as 6189MJmm/ha/h/year. </LI> <LI> R factors for particular 3months contributed more than 75% of average annual R factor. </LI> <LI> The regression model provided more than 75% accuracy in R factor estimates. </LI> </UL> </P>
기후변화 시나리오 이용에 따른 농업시스템의 미래예측 영향 분석
정한석 ( Hanseok Jeong ),라빈바타라이 ( Rabin Bhattarai ),황세운 ( Syewoon Hwang ) 한국농공학회 2019 한국농공학회 학술대회초록집 Vol.2019 No.-
Climate change scenarios are widely used for exploring future changes in environmental systems. However, many aspects of the uncertainties associated with the use of climate change scenarios in environmental systems modeling have not yet been studied sufficiently. We explore how the way that baseline scenarios are defined and general circulation model (GCM) outputs are used effects climate change impact assessments of agricultural systems. Our study builds on a previously validated agricultural systems model, the Root Zone Water Quality Model (RZWQM), coupled with the Decision Support System for Agrotechnology Transfer (DSSAT), which models a tiled-drained field in central Illinois of the United States and uses nine GCM outputs to investigate the effects. Our model simulations demonstrated the following three results. Firstly, the evaluation of climate change impacts presented a significant difference between the types of baseline used. The baseline scenario should be defined using the bias-corrected retrospective GCM outputs. Secondly, once GCM outputs are bias-corrected, the selective use of GCM outputs did not add significant value over using all available GCM outputs to provide more plausible future predictions of agricultural systems’ responses. Notably, however, selective use may have impacts comparable to the carbon dioxide emission scenarios in the field-scale agricultural climate change impact assessments. Thirdly, raw GCM outputs should be avoided for the predictions of field-scale agricultural systems’ responses to climate change. Our findings can help provide a clearer picture of how GCM outputs should be used in agricultural systems modeling.