skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Value of Probabilistic Weather Forecasts: Assessment by Real-Time Optimization of Irrigation Scheduling

Abstract

This paper presents a modeling framework for real-time decision support for irrigation scheduling using the National Oceanic and Atmospheric Administration's (NOAA's) probabilistic rainfall forecasts. The forecasts and their probability distributions are incorporated into a simulation-optimization modeling framework. In this study, modeling irrigation is determined by a stochastic optimization program based on the simulated soil moisture and crop water-stress status and the forecasted rainfall for the next 1-7 days. The modeling framework is applied to irrigated corn in Mason County, Illinois. It is found that there is ample potential to improve current farmers practices by simply using the proposed simulation-optimization framework, which uses the present soil moisture and crop evapotranspiration information even without any forecasts. It is found that the values of the forecasts vary across dry, normal, and wet years. More significant economic gains are found in normal and wet years than in dry years under the various forecast horizons. To mitigate drought effect on crop yield through irrigation, medium- or long-term climate predictions likely play a more important role than short-term forecasts. NOAA's imperfect 1-week forecast is still valuable in terms of both profit gain and water saving. Compared with the no-rain forecast case, the short-term imperfect forecasts couldmore » lead to additional 2.4-8.5% gain in profit and 11.0-26.9% water saving. However, the performance of the imperfect forecast is only slightly better than the ensemble weather forecast based on historical data and slightly inferior to the perfect forecast. It seems that the 1-week forecast horizon is too limited to evaluate the role of the various forecast scenarios for irrigation scheduling, which is actually a seasonal decision issue. For irrigation scheduling, both the forecast quality and the length of forecast time horizon matter. Thus, longer forecasts might be necessary to evaluate the role of forecasts for irrigation scheduling in a more effective way.« less

Authors:
; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1029038
Report Number(s):
PNNL-SA-83186
TRN: US201122%%669
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
Journal of Water Resources Planning and Management
Additional Journal Information:
Journal Volume: 137; Journal Issue: 5
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; CLIMATES; CROPS; DROUGHTS; ECONOMICS; ILLINOIS; IRRIGATION; MOISTURE; OPTIMIZATION; PERFORMANCE; PROBABILITY; PROFITS; SIMULATION; SOILS; WATER; WEATHER

Citation Formats

Cai, Ximing, Hejazi, Mohamad I., and Wang, Dingbao. Value of Probabilistic Weather Forecasts: Assessment by Real-Time Optimization of Irrigation Scheduling. United States: N. p., 2011. Web. doi:10.1061/(ASCE)WR.1943-5452.0000126.
Cai, Ximing, Hejazi, Mohamad I., & Wang, Dingbao. Value of Probabilistic Weather Forecasts: Assessment by Real-Time Optimization of Irrigation Scheduling. United States. doi:10.1061/(ASCE)WR.1943-5452.0000126.
Cai, Ximing, Hejazi, Mohamad I., and Wang, Dingbao. Thu . "Value of Probabilistic Weather Forecasts: Assessment by Real-Time Optimization of Irrigation Scheduling". United States. doi:10.1061/(ASCE)WR.1943-5452.0000126.
@article{osti_1029038,
title = {Value of Probabilistic Weather Forecasts: Assessment by Real-Time Optimization of Irrigation Scheduling},
author = {Cai, Ximing and Hejazi, Mohamad I. and Wang, Dingbao},
abstractNote = {This paper presents a modeling framework for real-time decision support for irrigation scheduling using the National Oceanic and Atmospheric Administration's (NOAA's) probabilistic rainfall forecasts. The forecasts and their probability distributions are incorporated into a simulation-optimization modeling framework. In this study, modeling irrigation is determined by a stochastic optimization program based on the simulated soil moisture and crop water-stress status and the forecasted rainfall for the next 1-7 days. The modeling framework is applied to irrigated corn in Mason County, Illinois. It is found that there is ample potential to improve current farmers practices by simply using the proposed simulation-optimization framework, which uses the present soil moisture and crop evapotranspiration information even without any forecasts. It is found that the values of the forecasts vary across dry, normal, and wet years. More significant economic gains are found in normal and wet years than in dry years under the various forecast horizons. To mitigate drought effect on crop yield through irrigation, medium- or long-term climate predictions likely play a more important role than short-term forecasts. NOAA's imperfect 1-week forecast is still valuable in terms of both profit gain and water saving. Compared with the no-rain forecast case, the short-term imperfect forecasts could lead to additional 2.4-8.5% gain in profit and 11.0-26.9% water saving. However, the performance of the imperfect forecast is only slightly better than the ensemble weather forecast based on historical data and slightly inferior to the perfect forecast. It seems that the 1-week forecast horizon is too limited to evaluate the role of the various forecast scenarios for irrigation scheduling, which is actually a seasonal decision issue. For irrigation scheduling, both the forecast quality and the length of forecast time horizon matter. Thus, longer forecasts might be necessary to evaluate the role of forecasts for irrigation scheduling in a more effective way.},
doi = {10.1061/(ASCE)WR.1943-5452.0000126},
journal = {Journal of Water Resources Planning and Management},
number = 5,
volume = 137,
place = {United States},
year = {2011},
month = {9}
}