Abstract
Abstract. Evapotranspiration plays an important role in the terrestrial water cycle. Reference crop evapotranspiration (ETo) has been widely used to estimate water transfer from vegetation surface to the atmosphere. Seasonal ETo forecasting provides valuable information for effective water resource management and planning. Climate forecasts from general circulation models (GCMs) have been increasingly used to produce seasonal ETo forecasts. Statistical calibration plays a critical role in correcting bias and dispersion errors in GCM-based ETo forecasts. However, time-dependent errors resulting from GCM misrepresentations of climate trends have not been explicitly corrected in ETo forecast calibrations. We hypothesize that reconstructing climate trends through statistical calibration will add extra skills to seasonal ETo forecasts. To test this hypothesis, we calibrate raw seasonal ETo forecasts constructed with climate forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5 model across Australia, using the recently developed Bayesian joint probability trend-aware (BJP-ti) model. Raw ETo forecasts demonstrate significant inconsistencies with observations in both magnitudes and spatial patterns of temporal trends, particularly at long lead times. The BJP-ti model effectively corrects misrepresented trends and reconstructs the observed trends in calibrated forecasts. Improving trends through statistical calibration increases the correlation coefficient between calibrated forecasts and observations (r) by up to 0.25 and improves the continuous ranked probability score (CRPS) skill score by up to 15 (%) in regions where climate trends are misrepresented by raw forecasts. Skillful ETo forecasts produced in this study could be used for streamflow forecasting, modeling of soil moisture dynamics, and irrigation water management. This investigation confirms the necessity of reconstructing climate trends in GCM-based seasonal ETo forecasting and provides an effective tool for addressing this need. We anticipate that future GCM-based seasonal ETo forecasting will benefit from correcting time-dependent errors through trend reconstruction.
Highlights
As a critical process in the terrestrial water cycle, evapotranspiration transfers a large amount of water from the land surface to the atmosphere
We evaluate the capability of Bayesian joint probability (BJP)-ti in reconstructing temporal trends for months with large areas of statistically significant trends in observed ETo
We further evaluate the overall performance of the calibration over the whole study period by comparing the continuous ranked probability score (CRPS) skill scores of the raw and BJP-ti calibrated forecasts (Fig. 5)
Summary
As a critical process in the terrestrial water cycle, evapotranspiration transfers a large amount of water from the land surface to the atmosphere. Reference crop evapotranspiration (ETo) measures the evaporative demand of the atmosphere for a hypothetical crop of a given height with defined surface resistance factor and albedo. It is generally computed using the Penman–Monteith equation, following Allen et al Forecasting of ETo has been used to support water resource management (Anderson et al, 2015; Le Page et al, 2021) and improve soil moisture modeling (Yu et al, 2016). Seasonal ETo forecasts have been used to support water allocation among competing users (Chauhan and Shrivastava, 2009) and in planning farming activities (Zinyengere et al, 2011). Climate forecasts produced by general circulation models (GCMs) have been increasingly used for seasonal ETo forecasting, since GCMs often produce forecasts of all climate variables needed to estimate future ETo (Tian et al, 2014; Zhao et al, 2019a)
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