We apply predictive weather metrics and land model sensitivities to improve the Colorado State University Water Irrigation Scheduler for Efficient Application (WISE). WISE is an irrigation decision aid that integrates environmental and user information for optimizing water use. Rainfall forecasts and verification performance metrics are used to estimate predictive rainfall probabilities that are used as input data within the irrigation decision aid. These input data errors are also used within a land model sensitivity study to diagnose important prognostic water movement behaviors for irrigation tool development purposes simultaneously performing the analysis in space and time. Thus, important questions such as “how long can a crop water application be delayed while maintaining crop yield production?” are addressed by evaluating crop growth stage interactions as a function of soil depth (i.e., space), rainfall events (i.e., time), and their probabilistic uncertainties. Editor’s note: This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.

中文翻译：

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灌溉计划工具中预测性天气不确定性的使用第二部分：度量标准和伴随变量的应用

我们应用预测性天气指标和土地模型敏感性来改善科罗拉多州立大学有效应用的水灌溉计划。WISE是一种灌溉决策辅助工具，它整合了环境和用户信息，以优化用水。降雨预报和验证性能指标用于估算预报降雨概率，这些概率用作灌溉决策辅助工具中的输入数据。这些输入数据错误还用于土地模型敏感性研究中，以诊断重要的水运预测行为，以开发灌溉工具，同时进行时空分析。从而，重要的问题，例如“在维持作物产量的同时，作物水分施用可以延迟多长时间？” 通过评估作为土壤深度（即空间），降雨事件（即时间）及其概率不确定性的函数的作物生长期相互作用来解决这些问题。编者注：本文是“优化Ogallala含水层用水以维持食品系统”专题系列的一部分。有关该系列的简介和背景信息，请参见2019年2月号。