Rainfall-runoff modeling for storm events in a coastal forest catchment using neural networks

The process of transformation of rainfall into runoff over a catchment is very complex and highly nonlinear and exhibits both temporal and spatial ＂variabilities, In this article, a rainfall-runoff model using the artificial neural networks （ANN） is proposed for simulating the runoff in storm events. The study uses the data from a coastal forest catchment, located in Seto Inland Sea, Japan, This article studies the accuracy of the short-term rainfall forecast obtained by ANN time-series analysis techniques and using antecedent rainfall depths and stream flow as the input information. The verification results from the proposed model indicate that the approach of ANN rainfall-runoff model presented in this paper shows a reasonable agreement in rainfall-runoff modeling with high accuracy,     

EVALUATION OF EROSION PRODUCTIVITY IMPACT CALCULATOR （EPIC） MODEL FOR MIDDLE MOUNTAIN REGION OF NEPAL

This study verifies the applicability of EPIC model for an erosion plot (61 .2 m~2) and an uplandterraced watershed (72 ha) using a total of 94 rainfall events over a study period of two years. Inorder to analyze the effect of storm size on runoff and soil loss processes, rainfall events aredivided into three groups: small (<25mm), moderate (25--50mm) and large (>50mm). Resultsindicate that the model could predict reasonably well the runoff and soil loss from the erosion plotand the watershed for the moderate and large rainfall events. However, the runoff and soil lossprediction for the small rainfall events is found to be poor. On annual basis, both surface runoff andsoil loss predictions match well the observations. In ligh of the importance of the moderate andlarge rainfall events in producing most of the annual runoff and soil loss in the study area, the EPICmodel is applied to assess the impacts of erosion on agricultural productivity and to evaluatemanagement practices to protect watersheds in the     

本世纪末将出现更多极端天气

美国大气研究中心预测，本世纪末全球将发生更多的炎热、暴风雨、干旱等极端天气。模拟显示，2080年至2099年，全球高温期会增加；南北半球纬度高于40度的大部分地区发生暴雨或暴雪频率大大增加；美国西部、欧洲南部和巴西东部等地区，干旱期会显延长。     

A mesoscale analysis of heavy rain caused by frontal and topographical heterogeneities on Taiwan Island

The prevailing mesoscale model MM5 (V3) is used to simulate a heavy rain case caused by interaction between a move-in front and topographical heterogeneities on Taiwan Island. It is found that both thermodynamic and dynamic fields along the front are heterogeneous in time and space. The heterogeneity becomes more significant as the effect of topography is added on. The heterogeneous distribution of physical variables along the front is the main reason for the heterogeneous frontal rain band; the optimum cooperation of the low level and upper level jet is another reason for the development of the rain band.Topography can strengthen the rainfall and causes extremely heavy rain cells. Updraft induced by topography extends to a rather low level, while the uplifted air by frontal circulation can reach to higher levels.The quasi-steady topographic circulation overlaps the frontal circulation when the front moves over Taiwan Island; the advantageous cooperation of various mesoscale conditions causes the large upward velocity on the windward side of the island.