Ensemble forecasting of short‐term system scale irrigation demands using real‐time flow data and numerical weather predictions

Abstract

Irrigation demands fluctuate in response to weather variations and a range of irrigation management decisions, which creates challenges for water supply system operators. This paper develops a method for real‐time ensemble forecasting of irrigation demand and applies it to irrigation command areas of various sizes for lead times of 1 to 5 days. The ensemble forecasts are based on a deterministic time series model coupled with ensemble representations of the various inputs to that model. Forecast inputs include past flow, precipitation, and potential evapotranspiration. These inputs are variously derived from flow observations from a modernized irrigation delivery system; short‐term weather forecasts derived from numerical weather prediction models and observed weather data available from automatic weather stations. The predictive performance for the ensemble spread of irrigation demand was quantified using rank histograms, the mean continuous rank probability score (CRPS), the mean CRPS reliability and the temporal mean of the ensemble root mean squared error (MRMSE). The mean forecast was evaluated using root mean squared error (RMSE), Nash–Sutcliffe model efficiency (NSE) and bias. The NSE values for evaluation periods ranged between 0.96 (1 day lead time, whole study area) and 0.42 (5 days lead time, smallest command area). Rank histograms and comparison of MRMSE, mean CRPS, mean CRPS reliability and RMSE indicated that the ensemble spread is generally a reliable representation of the forecast uncertainty for short lead times but underestimates the uncertainty for long lead times.

Comment on hess-2021-69

“…Forecasting of future ETo based on NWP outputs is gaining popularity as a result of high data availability, flexible forecast horizons, and timeliness (Perera et al, 2014(Perera et al, , 2016Silva et al, 2010;Yang et al, 2019). As for forecasts of other weather variables, statistical calibration plays an important role in improving the quality of raw ETo forecasts (Medina and Tian, 2019).…”

“…As a variable measuring the evaporative demand of the atmosphere, reference crop evapotranspiration (ETo) has been widely used to estimate potential water loss from the land surface to the atmosphere (Hopson and Webster, 2009;Liu et al, 2019;Renard et al, 2010). Quantification of ETo has been increasingly performed to support efficient water use and water management (Mushtaq et al, 2019;Perera et al, 2016). Forecasts of short-term ETo (days to weeks) are highly valuable for real-time decision-making on farming activities and water allocation to competing users (Djaman et al, 2018;Kumar et al, 2012).…”

Improving Irrigation Scheduling Using MOSES Short-Term Irrigation Forecasts and In Situ Water Resources Measurements on Alluvial Soils of Lower Danube Floodplain, Romania

“…Additionally, at the field scale, between 60 and 120 mm of water can be saved for autumn wheat, whose roots can be supplied with water from the shallow aquifer during the spring season. According to previous studies [14][15][16], the use of weather forecast should be considered for improving irrigation scheduling and for saving water in the irrigated agriculture. Our proposed approach is based on both weather and water table forecasts for providing irrigation forecast adapted to the alluvial soils specific to the Embanked Great Island of Danube River, Brăila, Romania.…”

“…In the last two decades, several Decision Support Systems for irrigation scheduling (IS-DSS) have been designed for supporting irrigation decision makers [7][8][9]: PlanteInfo (Denmark), WISE (Washington Irrigation Scheduling Expert), IRRINET (Italy), IrriSAT (Australia), ISS-ITAP (Spain), BEWARE (Greece), Anglian river Basin (UK), and IRRISA (France). Modern IS DSS can be designed using different approaches such as Earth Observation (EO) techniques for crop monitoring [10][11][12][13], short and long-term weather forecasts [14][15][16], soil water balance numerical simulations [17,18], machine learning techniques [6], etc. Each of these approaches presents advantages and disadvantages, as follows: EO techniques are suitable for crop monitoring over large areas but in the case of optical sensors the presence of clouds results in gaps in observations; short and long-term weather forecasts provide anticipated meteorological conditions over a specific area and period of time but their performance decreases as the forecast period increases; soil water balance models are able to simulate soil water movement in the root zone but require high spatial resolution data.…”

Bias-correcting input variables enhances forecasting of reference crop evapotranspiration

“…As a variable measuring the evaporative demand of the atmosphere, reference crop evapotranspiration (ETo) has been widely used to estimate potential water loss from the land surface to the atmosphere (Hopson and Webster, 2009;Liu et al, 2019;Renard et al, 2010). Quantification of ETo has been increasingly performed to support efficient water use and water management (Mushtaq et al, 2019;Perera et al, 2016). Forecasts of short-term ETo (days to weeks) are highly valuable for real-time decision-making on farming activities and water allocation to competing users (Djaman et al, 2018;Kumar et al, 2012).…”