Validation and Bias Correction of Forecast Reference Evapotranspiration for Agricultural Applications in Nevada

Abstract

Accurate estimates of reference evapotranspiration (ET 0 ) are critical for estimating actual crop evapotranspiration and agricultural water use. This study uses observations from the Nevada Integrated Climate and Evapotranspiration Network (NICE Net) to validate forecasts of ET 0 and its driving variables from the National Weather Service's National Digital Forecast Database (NDFD). Daily NDFD ET 0 at lead times of 1 to 6 days were compared against 18 NICE Net stations. Correlations between NDFD and observations generally ranged between 0.4 and 0.9, with lower correlations at longer leads and a notable drop in skill during July and August. Systematic arid biases (high bias for temperatures and low bias for humidity) were found in NDFD with a strong warm minimum temperature bias and low vapor pressure bias most prominent during the growing season. Some of the largest relative biases were found in wind speed, although they were systematic and varied greatly by location. A case study revealed that NDFD consistently underestimates the variability found in observed minimum temperature, solar radiation, wind speed, and ET 0 . Cloudy days during summer were not well represented in the NDFD estimated solar radiation, which had a cascading impact on temperature, vapor pressure, and ET 0 estimates. A monthly ratio-based bias-correction was applied to NDFD ET 0 , which reduced the root-mean squared error by 5%-30% for most locations. Bias-corrected ET 0 forecasts from NDFD or other forecast systems show potential as a guide to develop weekly irrigation schedules for agricultural producers, with the ultimate goal of reducing applications of excess irrigation water.

agweather-qaqc: An Interactive Python Package for Quality Assurance and Quality Control of Daily Agricultural Weather Data and Calculation of Reference Evapotranspiration

“…This software was used to generate a benchmark agricultural weather station reference ET dataset for intercomparison and bias correction of a gridded weather dataset, gridMET (Abatzoglou, 2011) as part of OpenET (Melton et al, 2021), an online platform that provides satellite-based actual ET estimates at field scale across the western United States. This software has also been used to generate datasets of reference ET for weather stations in the Upper Colorado River Basin (UCRB) as part of a yearly Bureau of Reclamation publication of agricultural consumptive uses and losses (Pearson et al, 2019(Pearson et al, , 2020(Pearson et al, , 2021, intercomparison of OpenET models for the UCRB (Huntington et al, 2022), and was used in a skill analysis of NOAA's Forecast Reference Evapotranspiration (McEvoy et al, 2022).…”

“…As a result, poor quality agricultural weather station data are common, and, unless flagged for removal or corrected, will impact the accuracy of reference ET calculations (Allen, 1996). Ensuring that agricultural station data are high quality is especially important for studies where station data are considered to be 'truth' when comparing to related model predictions and forecasts (Blankenau et al, 2020;McEvoy et al, 2022). Similarly, reference ET station data need to represent sufficiently watered environments if they are to be used to validate or to bias-correct gridded reference ET datasets (Allen et al, 2021;Huntington et al, 2018).…”

Estimation of Reference Evapotranspiration in a Semi-Arid Region of Mexico

“…It is available on different platforms: National Aeronautics and Space Administration—Prediction of Worldwide Energy Resource (NASA-POWER) [ 27 , 45 ], Global Land Data Assimilation System (GLDAS) [ 46 ], Climate Forecast System ver. 2 (CFSv2) [ 47 ], North American Land Data Assimilation System (NLDAS) [ 48 ], and National Digital Forecast Database (NDFD) [ 49 ]. These global or regional platforms provide data with higher spatial and temporal resolution [ 27 ].…”