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首页> 外文期刊>Stochastic environmental research and risk assessment >Let-It-Rain: a web application for stochastic point rainfall generation at ungaged basins and its applicability in runoff and flood modeling
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Let-It-Rain: a web application for stochastic point rainfall generation at ungaged basins and its applicability in runoff and flood modeling

机译:Let-It-Rain:用于非流域盆地随机点降雨产生的Web应用程序及其在径流和洪水建模中的适用性

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摘要

We present a web application named Let-ItRain that is able to generate a 1-h temporal resolution synthetic rainfall time series using the modified Bartlett-Lewis rectangular pulse (MBLRP) model, a type of Poisson stochastic rainfall generator. Let-It-Rain, which can be accessed through the web address http://www.LetItRain. info, adopts a web-based framework combining ArcGIS Server from server side for parameter value dissemination and JavaScript from client side to implement the MBLRP model. This enables any desktop and mobile end users with internet access and web browser to obtain the synthetic rainfall time series at any given location at which the parameter regionalization work has been completed currently the contiguous United States and Republic of Korea) with only a few mouse clicks. Let-It-Rain shows satisfactory performance in its ability to reproduce observed rainfall mean, variance, auto-correlation, and probability of zero rainfall at hourly through daily accumulation levels. It also shows a reasonably good performance in reproducing watershed runoff depth and peak flow. We expect that LetIt-Rain can stimulate the uncertainty analysis of hydrologic variables across the world.
机译:我们介绍了一个名为Let-ItRain的Web应用程序,该应用程序可以使用改进的Bartlett-Lewis矩形脉冲(MBLRP)模型(一种Poisson随机降雨发生器)来生成1小时时间分辨率的合成降雨时间序列。可以通过网址http://www.LetItRain访问的Let-It-Rain。 info,采用了一个基于Web的框架,该框架结合了服务器端的ArcGIS Server进行参数值分发和客户端的JavaScript,以实现MBLRP模型。这使任何具有互联网访问权限和网络浏览器的台式机和移动终端用户,只需单击几下鼠标,就可以在任何给定位置(当前为美国和大韩民国(目前为美国和大韩民国)完成参数分区工作)的任何给定位置获取合成降雨时间序列。 。 Let-It-Rain在每小时观测到的每日累积水平上再现观测到的降雨平均值,方差,自相关和零降雨的概率方面显示出令人满意的性能。在再现流域径流深度和峰值流量方面也显示出相当好的性能。我们希望,LetIt-Rain可以刺激全球水文变量的不确定性分析。

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