Investigating the Accuracies in Short-Term Weather Forecasts and Its Impact on Irrigation Practices
Publication: Journal of Water Resources Planning and Management
Volume 149, Issue 2
Abstract
The effectiveness of irrigation schedules and crop growth models is largely dependent on the accuracies associated with the most uncertain input parameter, viz. weather forecasts. This research is focused on quantifying the inaccuracies associated with the India Meteorological Department (IMD) issuing short-term weather forecasts (with 1–5 days lead time), and their propagation into irrigation models, with an objective to select the optimal parameters for use with simulation. While precipitation (P) forecasts were directly used, remaining meteorological forecasts were converted to reference evapotranspiration () forecasts. The effectiveness of the ‘P’ and ‘’ forecasts was found to be low at all lead times. We applied two popular bias correction methods: linear scaling (LS) and empirical quantile mapping (EQM), and observed a marginal improvement in forecast skill. Bias-corrected, forecast-driven irrigation scenarios, along with conventional irrigation (that ignores weather forecast), and a hypothetical perfect 5-day forecast-based irrigation (as reference) system were tested on a water-intensive paddy crop for two growing seasons (S1: monsoon and S2: winter). Conventional irrigation resulted in the highest use of irrigation water (820 mm in S1, 880 mm in S2) and percolation loss (1,140 mm in S1, 680 mm in S2), while achieving a low relative yield (0.88 in S1, 0.87 in S2). LS-corrected forecasts outperformed other scenarios with and savings in irrigation costs for S1 and S2, respectively. While IMD forecasts greatly improved irrigation schedules in the monsoon season, their usage for winter crops was found to be trivial. Our findings conclude that LS-corrected IMD forecasts were moderately reliable over multiple lead times and can serve as a valuable addition to irrigation scheduling, provided the contribution of ‘P’ to the total water balance is significant.
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Data Availability Statement
Some or all the data along with the model code used in this study can be obtained from the corresponding author upon reasonable request. Following datasets/simulation files are available in the online repository (https://github.com/ShubhamGedam/AgriIITH).
1.
Daily observations of meteorological datasets.
2.
IMD-published rainfall with 1 to 5 day lead times (raw, bias-corrected).
3.
Penman–Monteith Estimates of ET for 1 to 5 day lead times (raw, bias-corrected).
4.
Daily water balance fluxes for each irrigation scenario (CI, F-RAW, F-LS, F-EQM, HF).
Acknowledgments
The authors acknowledge the services of Dr. Sarah Kamala Kumari Mylabathula, Department of English and Humanities, MVGR College of Engineering, Vizianagaram for proofreading the manuscript. The authors also acknowledge the anonymous reviewers for their insightful comments. This research evolved as an extension of a term project in CE6520-Irrigation Water Management course at IIT Hyderabad.
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© 2022 American Society of Civil Engineers.
History
Received: Nov 22, 2021
Accepted: Sep 14, 2022
Published online: Nov 23, 2022
Published in print: Feb 1, 2023
Discussion open until: Apr 23, 2023
ASCE Technical Topics:
- Agriculture
- Business management
- Climates
- Crops
- Developing countries
- Engineering fundamentals
- Environmental engineering
- Forecasting
- Irrigation
- Irrigation engineering
- Irrigation systems
- Mathematics
- Meteorology
- Model accuracy
- Models (by type)
- Parameters (statistics)
- Practice and Profession
- Statistics
- Water and water resources
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