Evapotranspiration Forecasts Research Articles

Evaluation of Five Equations for Short-Term Reference Evapotranspiration Forecasting Using Public Temperature Forecasts for North China Plain

Accurate short-term forecasts of daily reference evapotranspiration (ET0) are essential for real-time irrigation scheduling. Many models rely on current and historical temperature data to estimate daily ET0. However, easily accessible temperature forecasts are relatively less reported in short-term ET0 forecasting. Furthermore, the accuracy of ET0 forecasting from different models varies locally and also across regions. We used five temperature-dependent models to forecast daily ET0 for a 7-day horizon in the North China Plain (NCP): the McCloud (MC), Hargreaves-Samani (HS), Blaney-Criddle (BC), Thornthwaite (TH), and reduced-set Penman–Monteith (RPM) models. Daily meteorological data collected between 1 January 2000 and 31 December 2014 at 17 weather stations in NCP to calibrate and validate the five ET0 models against the ASCE Penman–Monteith (ASCE-PM). Forecast temperatures for up to 7 d ahead for 1 January 2015–19 June 2021 were input to the five calibrated models to forecast ET0. The performance of the five models improved for forecasts at all stations after calibration. The calibrated RPM is the preferred choice for forecasting ET0 in NCP. In descending order of preference, the remaining models were ranked as HS, TH, BC, and MC. Sensitivity analysis showed that a change in maximum temperature influenced the accuracy of ET0 forecasting by the five models, especially RPM, HS, and TH, more than other variables. Meanwhile, the calibrated RPM and HS equations were better than the other models, and thus, these two equations were recommended for short-term ET0 forecasting in NCP.

Short- and mid-term forecasts of actual evapotranspiration with deep learning

• Deep learning is applied for nowcasting and forecasting of evapotranspiration. • Weather variables and soil moisture are used as input for the deep learning models. • The models were applied to seven distinct climate zones within the continental U.S. • Results reveal great potential for evapotranspiration forecasting. Evapotranspiration is a key component of the hydrologic cycle. Accurate short-, medium-, and long-term forecasts of actual evapotranspiration (ET a ) are crucial not only for quantifying the impacts of climate change on the water and energy balance, but also for real-time estimation of crop water demand and irrigation water allocation in agriculture. Despite considerable advances in satellite remote sensing technology and the availability of long ground-measured and remotely sensed ET a timeseries, real-time ET a forecasts are deficient. Applying a state-of-the-art deep learning (DL) approach, Long Short-Term Memory (LSTM) models were employed to nowcast (real-time) and forecast (ahead of time) ET a based on (1) major meteorological and ground-measured (i.e., soil moisture) input variables and (2) long ET a timeseries from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard of the NASA Aqua satellite. The conventional LSTM and convolutional LSTM (ConvLSTM) DL models were evaluated for seven distinct climatic zones across the contiguous United States. The employed LSTM and ConvLSTM models were trained and evaluated with data from the National Climate Assessment-Land Data Assimilation System (NCA-LDAS) and with MODIS/Aqua Net Evapotranspiration MYD16A2 product data. The obtained results indicate that when major atmospheric and soil moisture input variables are used for the conventional LSTM models, they yield accurate daily ET a forecasts for short (1, 3, and 7 days) and medium (30 days) time scales, with normalized root mean squared errors (NRMSE) and Nash-Sutcliffe efficiencies (NSE) of less than 10% and greater than 0.77, respectively. At the watershed scale, the univariate ConvLSTM models yielded accurate weekly spatiotemporal ET a forecasts (mean NRMSE less than 6.4% and NSE greater than 0.66) with higher computational efficiency for various climatic conditions. The employed models enable precise forecasts of both the current and future states of ET a , which is crucial for understanding the impact of climate change on rapidly depleting water resources.

Multi-step daily forecasting of reference evapotranspiration for different climates of India: A modern multivariate complementary technique reinforced with ridge regression feature selection

Accurate ahead forecasting of reference evapotranspiration (ETo) is crucial for effective irrigation scheduling and management of water resources on a regional scale. A variety of methods are available for ETo simulation, but the most trending is complementary artificial intelligence (AI) paradigms. In this research, a novel Multivariate Variational Mode Decomposition technique (MVMD) integrated with the Ridge Regression (RR) feature selection algorithm and Kernel Extreme Learning Machine (KELM) model (i.e., MVMD-RR-KELM) was adopted to multi-step ahead (t + 3, and t + 7) forecasting of daily ETo in different climate of India. Here, the complementary expert system hybridized with the Boosted Regression Tree (BRT) and Extreme Gradient Boosted (XGBoost) along with the standalone counterpart models (KELM, BRT, and XGBoost) were examined to validate the robustness of the primary model. The complementary (i.e., MVMD-RR-KELM, MVMD-RR-BRT, & MVMD-RR-XGBoost) and their standalone counterpart models were trained and tested using daily climatic data of Hisar (located in Haryana State), Bathinda, and Ludhiana (located in Punjab State) sites. The forecasting accuracy of standalone and hybrid AI models was assessed using six goodness-of-fit metrics, i.e., R (Correlation Coefficient), RMSE (root mean square error), MAPE (mean absolute percentage error), NSE (Nash-Sutcliffe Efficiency), IA (Index of Agreement), U95% (Uncertainty Coefficient with 95% level) along with visual interpretation. According to the testing results, the hybrid MVMD-RR-KELM models had superior performance than other AI models for forecasting three and seven days ahead ETo. The KELM model optimized using the MVMD-RR technique provides promising and robust results with higher forecasting accuracy and minimum error for multi-step ahead forecasting of ETo in semi-arid and sub-humid regions.

An analysis framework to evaluate irrigation decisions using short-term ensemble weather forecasts

Irrigation water is an expensive and limited resource and optimal scheduling can boost water efficiency. Scheduling decisions often need to be made several days prior to an irrigation event, so a key aspect of irrigation scheduling is the accurate prediction of crop water use and soil water status ahead of time. This prediction relies on several key inputs including initial soil water status, crop conditions and weather. Since each input is subject to uncertainty, it is important to understand how these uncertainties impact soil water prediction and subsequent irrigation scheduling decisions. This study aims to develop an uncertainty-based analysis framework for evaluating irrigation scheduling decisions under uncertainty, with a focus on the uncertainty arising from short-term rainfall forecasts. To achieve this, a biophysical process-based crop model, APSIM (The Agricultural Production Systems sIMulator), was used to simulate root-zone soil water content for a study field in south-eastern Australia. Through the simulation, we evaluated different irrigation scheduling decisions using ensemble short-term rainfall forecasts. This modelling produced an ensemble of simulations of soil water content, as well as ensemble simulations of irrigation runoff and drainage. This enabled quantification of risks of over- and under-irrigation. These ensemble estimates were interpreted to inform the timing of the next irrigation event to minimize both the risks of stressing the crop and/or wasting water under uncertain future weather. With extension to include other sources of uncertainty (e.g., evapotranspiration forecasts, crop coefficient), we plan to build a comprehensive uncertainty framework to support on-farm irrigation decision-making.

Reconstructing climate trends adds skills to seasonal reference crop evapotranspiration forecasting

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.

Bias-correcting input variables enhances forecasting of reference crop evapotranspiration

Abstract. Reference crop evapotranspiration (ETo) is calculated using a standard formula with temperature, vapor pressure, solar radiation, and wind speed as input variables. ETo forecasts can be produced when forecasts of these input variables from numerical weather prediction (NWP) models are available. As raw ETo forecasts are often subject to systematic errors, statistical calibration is needed for improving forecast quality. The most straightforward and widely used approach is to directly calibrate raw ETo forecasts constructed with the raw forecasts of input variables. However, the predictable signal in ETo forecasts may not be fully implemented by this approach, which does not deal with error propagation from input variables to ETo forecasts. We hypothesize that correcting errors in input variables as a precursor to forecast calibration will lead to more skillful ETo forecasts. To test this hypothesis, we evaluate two calibration strategies that construct raw ETo forecasts with the raw (strategy i) or bias-corrected (strategy ii) input variables in ETo forecast calibration across Australia. Calibrated ETo forecasts based on bias-corrected input variables (strategy ii) demonstrate lower biases, higher correlation coefficients, and higher skills than forecasts produced by the calibration using raw input variables (strategy i). This investigation indicates that improving raw forecasts of input variables could effectively reduce error propagation and enhance ETo forecast calibration. We anticipate that future NWP-based ETo forecasting will benefit from adopting the calibration strategy developed in this study to produce more skillful ETo forecasts.

Seasonal ensemble forecasts for soil moisture, evapotranspiration and runoff across Australia

The seasonal predictability of hydrological variables at high resolution across Australia was assessed using a new national seasonal ensemble forecasting system for soil moisture, evapotranspiration and runoff. The system consists of a gridded water balance model (AWRA-L) forced with downscaled and calibrated seasonal climate forecasts from a climate modelling system, ACCESS-S1. Here, we evaluate the hydrological forecasts at one- to six-months lead time relative to a historical reference simulation forced with observed climate inputs. Deterministic and probabilistic skill, accuracy and reliability of the forecast ensemble were assessed, with a specific focus on forecasts of hydrological extremes. Additionally, we assess the performance of the hindcast for selected use cases, particularly focusing on agriculture and water management.Nationally, our assessment of the hydrological forecasts indicated good skill for all variables at one-month lead time, and up to three-months lead time for soil moisture and actual evapotranspiration in many key regions in Australia. Regarding hydrological extremes, we find particularly high skill at longer lead times for low soil moisture and actual evapotranspiration events, highlighting the potential value for applications such as hydrological drought prediction or bushfire risk assessments. Bias of the hydrological forecasts based on calibrated seasonal climate forecasts was shown to be close to zero for all variables, regions and lead times and insignificant, with a minor exception for potential evapotranspiration.More detailed evaluation across economically important regions and river basins, in particular, the Australian Wheatbelt and the Murray-Darling Basin (the largest river basin in Australia and an economically and environmentally critical region), highlights promising potential for the soil moisture and evapotranspiration forecast to be used in applications in agriculture, such as for optimising irrigation or scheduling sowing and harvesting dates. For hydrologically important headwater regions across the Great Dividing Range and the alpine regions of Victoria, the forecasts of runoff show good skill at longer lead times indicating the value of the forecasts for reservoir management.Overall, we conclude that the forecasting system shows sufficient skill for a wide range of applications and regions up to a lead time of three months. We outline limitations of the presented system and highlight potential future research directions.

Forecasting evapotranspiration in different climates using ensembles of recurrent neural networks

Accurate ahead evapotranspiration forecasting is crucial for irrigation planning, for wetlands, agricultural and forest habitats preservation, and for water resource management. Deep learning algorithms can be used to develop effective forecasting models of ahead evapotranspiration. In this study, three Recurrent Neural Network-based models were built for the prediction of short term ahead actual evapotranspiration. Two variants of each model were developed changing the employed algorithm, selecting between long short-term memory (LSTM) and nonlinear autoregressive network with exogenous inputs (NARX), while the modeling was performed in the context of an ensemble approach. The prediction models were trained and tested using data from two sites with different climates: Cypress Swamp, southern Florida, and Kobeh Valley, central Nevada. With reference to the subtropical climatic conditions of South Florida, LSTM models proved to be more accurate than NARX models, while some exogenous variables such as sensible heat flux and relative humidity did not affect the results significantly. An increase of the forecast horizon from 1 to 7 days resulted in a slight reduction in the accuracy of both the LSTM- and NARX based models. Considering instead the semi-arid climate of Central Nevada, NARX models generally provided more accurate results, which were only slightly affected by relative humidity, sensible heat flux, and forecast horizon. On the other hand, LSTM models performance decayed if sensible heat flux and relative humidity were neglected, and if the forecast horizon was increased from 1 to 7 days. Deep learning-based models can provide very accurate predictions of actual evapotranspiration, but the performance of the models can be significantly affected by local climatic conditions.

Evaluation of prediction and forecasting models for evapotranspiration of agricultural lands in the Midwest U.S

Evapotranspiration (ET) prediction and forecasting play a vital role in improving water use in agriculturally intensive areas. Metrological and biophysical predictors that drive ET in managed landscapes have complex nonlinear relationships. Deep learning and data-driven methods have shown promising performance for identifying the dependencies among variables. Here, we evaluated the potentials of random forest (RF) and long short-term memory (LSTM) neural networks to estimate and forecast daily ET for corn, soybeans, and potatoes in diverse agricultural farms during 2003–2019. The modeling framework was applied for nineteen fields where eddy covariance ET and meteorological observations in the Midwest USA for growing season (April-October) is available. In this study, we applied data-driven models (RF and LSTM) with 3 sets of predictors (5, 11, and 16 predictors). Results show that a 16 predictor RF model (RF_16 R2 = 0.7, Willmott’s skill score = 0.90) outperformed a process-based land surface model (LSM R2 = 0.57, Willmott’s skill score = 0.86) for predicting daily ET, while LSTM performance was lower (LSTM_16 R2 = 0.65, Willmott’s skill score = 0.89 and LSTM_11 R2 = 0.62, Willmott’s skill score = 0.86) than RF using the same sets of predictors. Vapor pressure and crop coefficients were identified as the most important predictors for irrigated crops, while short wave radiation and enhanced vegetation index were key predictors for non-irrigated crops. For certain crop types, such as corn and soybeans on fine-grained soils (silt loam), a simpler version RF, using only 11 drivers, can provide comparable results (R2 = 0.70 vs 0.69 and Willmott’s skill score = 0.90 vs 0.88). For short-term 3-day ET forecasting, LSTM is more sensitive to uncertainty in ensemble forecast meteorology than RF. ET forecasts were strongly sensitive to forecast uncertainty of vapor pressure. The proposed modeling architecture provides a field-scale, locally calibrated tool for accurate prediction and short-term forecasting of daily ET in areas where in situ ET, metrological, and biophysical data are lacking.

Role of Short-Term Weather Forecast Horizon in Irrigation Scheduling and Crop Water Productivity of Rice

Crop simulation models in conjunction with weather forecasts help in understanding soil-water-plant interactions in real time, and thus in effective management of irrigation water. However, selection of the optimal forecast horizon for use with irrigation scheduling is challenging in the context of uncertainties associated with weather forecasts. This study is aimed at evaluating the effectiveness of the Indian Meteorological Department (IMD) short-term weather forecasts with different forecast horizons (1, 3, and 5 days) in simulating crop water and yield dynamics of rice. Rule-based irrigation is triggered by specifying thresholds on the current day’s ponding depth (do), precipitation (Po), and evapotranspiration (ETo) as well as their forecast values (∑i=1nPi; ∑i=1nETi; n=1,3,5). The IMD forecasts of temperature, humidity, wind speed, and cloud cover were translated into ETo forecasts using the Penman-Monteith equation and region-specific crop coefficients. The Soil–Water–Atmosphere–Plant (SWAP) model was modified to simulate soil–water and plant growth conditions by considering the three forecast scenarios along with conventional irrigation (ignoring weather forecast), and a hypothetical perfect 5-day forecast (reference). Experiments were conducted in four paddy fields in a command area of south India for two monsoon seasons (2018 and 2019) to calibrate and parameterize the SWAP model. The accuracy of forecast variables is increased with increase in length of forecast horizon (root-mean-square error = 12.5±0.98, skill score = 0.6%±0.03% at 1-day lead to 5.9±2.5, 22.4%±7.8% at 5-day lead) due to averaging. Conventional irrigation has resulted in the highest use of irrigation water (912±12.5 mm), percolation losses (1,245±19.5 mm), and electricity (806.5±100.5 kW), achieving a low yield (2,331±138 kg ha−1). Irrigation scheduling with 5-day forecast horizon outperformed other scenarios (69% water saving and 23% higher yield), and is slightly inferior to the hypothetical perfect forecast. Our results conclude that IMD forecasts, though moderately reliable at multiple lead times, can serve as a valuable tool in scheduling irrigation activities for sustainable management.

Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review

Water stress is one of the major challenges to food security, causing a significant economic loss for the nation as well for growers. Accurate assessment of water stress will enhance agricultural productivity through optimization of plant water usage, maximizing plant breeding strategies, and preventing forest wildfire for better ecosystem management. Recent advancements in sensor technologies have enabled high-throughput, non-contact, and cost-efficient plant water stress assessment through intelligence system modeling. The advanced deep learning sensor fusion technique has been reported to improve the performance of the machine learning application for processing the collected sensory data. This paper extensively reviews the state-of-the-art methods for plant water stress assessment that utilized the deep learning sensor fusion approach in their application, together with future prospects and challenges of the application domain. Notably, 37 deep learning solutions fell under six main areas, namely soil moisture estimation, soil water modelling, evapotranspiration estimation, evapotranspiration forecasting, plant water status estimation and plant water stress identification. Basically, there are eight deep learning solutions compiled for the 3D-dimensional data and plant varieties challenge, including unbalanced data that occurred due to isohydric plants, and the effect of variations that occur within the same species but cultivated from different locations.

Soil Moisture–Evapotranspiration Overcoupling and L-Band Brightness Temperature Assimilation: Sources and Forecast Implications

AbstractThe assimilation of L-band surface brightness temperature (Tb) into the land surface model (LSM) component of a numerical weather prediction (NWP) system is generally expected to improve the quality of summertime 2-m air temperature (T2m) forecasts during water-limited surface conditions. However, recent retrospective results from the European Centre for Medium-Range Weather Forecasts (ECMWF) suggest that the assimilation of L-band Tb from the European Space Agency’s (ESA) Soil Moisture Ocean Salinity (SMOS) mission may, under certain circumstances, degrade the accuracy of growing-season 24-h T2m forecasts within the central United States. To diagnose the source of this degradation, we evaluate ECMWF soil moisture (SM) and evapotranspiration (ET) forecasts using both in situ and remote sensing resources. Results demonstrate that the assimilation of SMOS Tb broadly improves the ECMWF SM analysis in the central United States while simultaneously degrading the quality of 24-h ET forecasts. Based on a recently derived map of true global SM–ET coupling and a synthetic fraternal twin data assimilation experiment, we argue that the spatial and temporal characteristics of ECMWF SM analyses and ET forecast errors are consistent with the hypothesis that the ECMWF LSM overcouples SM and ET and, as a result, is unable to effectively convert an improved SM analysis into enhanced ET and T2m forecasts. We demonstrate that this overcoupling is likely linked to the systematic underestimation of root-zone soil water storage capacity by LSMs within the U.S. Corn Belt region.

Comparison of probabilistic post-processing approaches for improving numerical weather prediction-based daily and weekly reference evapotranspiration forecasts

Abstract. Reference evapotranspiration (ET0) forecasts play an important role in agricultural, environmental, and water management. This study evaluated probabilistic post-processing approaches, including the nonhomogeneous Gaussian regression (NGR), affine kernel dressing (AKD), and Bayesian model averaging (BMA) techniques, for improving daily and weekly ET0 forecasting based on single or multiple numerical weather predictions (NWPs) from the THORPEX Interactive Grand Global Ensemble (TIGGE), which includes the European Centre for Medium-Range Weather Forecasts (ECMWF), the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS), and the United Kingdom Meteorological Office (UKMO) forecasts. The approaches were examined for the forecasting of summer ET0 at 101 US Regional Climate Reference Network stations distributed all over the contiguous United States (CONUS). We found that the NGR, AKD, and BMA methods greatly improved the skill and reliability of the ET0 forecasts compared with a linear regression bias correction method, due to the considerable adjustments in the spread of ensemble forecasts. The methods were especially effective when applied over the raw NCEP forecasts, followed by the raw UKMO forecasts, because of their low skill compared with that of the raw ECMWF forecasts. The post-processed weekly forecasts had much lower rRMSE values (between 8 % and 11 %) than the persistence-based weekly forecasts (22 %) and the post-processed daily forecasts (between 13 % and 20 %). Compared with the single-model ensemble, ET0 forecasts based on ECMWF multi-model ensemble ET0 forecasts showed higher skill at shorter lead times (1 or 2 d) and over the southern and western regions of the US. The improvement was higher at a daily timescale than at a weekly timescale. The NGR and AKD methods showed the best performance; however, unlike the AKD method, the NGR method can post-process multi-model forecasts and is easier to interpret than the other methods. In summary, this study demonstrated that the three probabilistic approaches generally outperform conventional procedures based on the simple bias correction of single-model forecasts, with the NGR post-processing of the ECMWF and ECMWF–UKMO forecasts providing the most cost-effective ET0 forecasting.

Calibration of GFS model by using real time weather data for water requirement forecasting of vegetables in Chtouka region

In the ultimate goal to forecast weather parameters in the agriculture field and precisely in predicting water requirements, a software was developed based on Global forecasting system. To assess its reliability, data of six weather stations were used in the Chtouka region to provide measured weather parameters and allow, thus, software calibration. Air temperature, air humidity, global radiation, evapotranspiration, precipitation and wind speed/direction are studied as weather parameters. Based on statistical comparisons using normalized root mean square error (NRSE), absolute percentage error (MAPE) and mean bias error (MBE), the software was revealed efficient to provide reliable temperature, global radiation and evapotranspiration forecasting but remains unable to predict relative humidity, wind speed/direction and precipitation in an accurate way.

State-of-art of genetic programming applications in water-resources systems analysis.

Evolutionary algorithms (EAs) have become competitive solvers of a wide variety of water-resources optimization problems. Genetic programming (GP) has become a leading EA since its inception in 1985. This paper reviews the state-of-the-art of GP and its applications in water-resources systems analysis. A comprehensive knowledge about GP's theory and modeling approach is essential for its successful application in water-resources systems analysis. This review presents variants of GP that have been proven useful in various applications to water resources problems. Several examples of applications of GP in water-resources systems analysis are herein presented. This review reveals GP's capability and superiority compared to other conventional methods, which makes it suitable for solving a wide variety of water-related problems including rainfall-runoff modeling, streamflow sediment prediction, flood prediction and routing, evaporation and evapotranspiration forecasting, reservoir operation, groundwater modeling, water quality modeling, water demand forecasting, and water distribution systems.

Multi-year prediction of European summer drought conditions for the agricultural sector

Decadal climate prediction, where climate models are initialized with the contemporaneous state of the Earth system and run for a decade into the future, represents a new source of near-term climate information to better inform decisions and policies across key climate-sensitive sectors. This paper illustrates the potential usefulness of such predictions for building a climate service for agricultural needs. In particular, we assess the forecast quality of multi-model climate predictions in estimating two user-relevant drought indices, Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Precipitation Index (SPI), at multi-annual timescales during European summer. We obtain high skill for predicting five-year average (forecast years 1–5) SPEI across Southern Europe, while for the same forecast period SPI exhibits high and significant skill over Scandinavia and its surrounding regions. In addition, an assessment of the added value of initialized decadal climate information with respect to standard uninitialized climate projections is presented. The model initialization improves the forecast skill over Central Europe, the Balkan region and Southern Scandinavia. Most of the increased skill found with initialization seems to be due to the climate forecast systems ability to improve the extended summer precipitation and potential evapotranspiration forecast, as well as their ability to adequately represent the observed effects of these climate variables on the drought indices.

A Bayesian modelling approach to forecasting short-term reference crop evapotranspiration from GCM outputs

Evapotranspiration is one of the most important climate variables for agricultural and environmental management. We investigate the characteristics of daily crop evapotranspiration (ETo) forecasts up to 16 days ahead derived from GCM outputs using the Penman-Monteith Equation. Furthermore, we propose a post-processing method to calibrate the raw ETo forecasts to observations and improve overall reliability and skill. The post-processing method comprises: (1) the Yeo-Johnson transformation to handle the non-normal distribution of daily ETo, (2) the bi-variate normal distribution to formulate the relationship between transformed raw forecasts and observations, and (3) the Schaake shuffle to establish realistic temporal behaviour in each ensemble member. For three distinct Australian case-study locations, ETo forecasts derived from raw ACCESS-S1 outputs are positively correlated with observations up to 16 days ahead. However, raw forecasts verify more poorly than naïve climatology forecasts because of significant bias and poor ensemble spreads. Post-processing the raw ACCESS-S1 ETo improves skill and reliability markedly. Skilful daily ETo forecasts are achieved in the first week, and the skill of cumulative ETo forecasts extends beyond two weeks into the future. The proposed ETo post-processing method could be applied to support operational, multi-week forecasting of short-term ETo in support of the day-to-day decisions of irrigators in Australia and elsewhere.

Ensemble forecasting of monthly and seasonal reference crop evapotranspiration based on global climate model outputs

Long-range forecasts of climatic variables are generated by climate centres around the world using global climate models (GCMs). This paper investigates ensemble forecasting of reference crop evapotranspiration (ETo) based on GCM outputs. The Penman-Monteith formula is used to calculate raw forecasts of ETo from GCM forecasts of solar radiation, temperature, wind speed, and vapor pressure. The Bayesian joint probability (BJP) modelling approach is applied to post-process raw monthly forecasts, separately for different lead times (month 1, 2 and 3 ahead). The Schaake shuffle is then employed to link the ensemble members of post-processed forecasts for all lead times to give a temporal structure. Forecasts of seasonal ETo total are obtained by aggregating the monthly forecasts. For comparison purposes, seasonal forecasts are also derived directly by post-processing raw seasonal forecasts without going through the monthly steps. Three case studies are presented for post-processing forecasts from the Australian Community Climate and Earth System Simulator-Seasonal (ACCESS-S1). Both raw forecasts and observations of monthly and seasonal ETo are found to be reasonably normally distributed. The post-processed forecasts of monthly and seasonal ETo are skilful in reference to climatology forecasts and statistically reliable in ensemble spread. The indirect and direct ways of generating forecasts of seasonal ETo total show similar skill and reliability, demonstrating the effectiveness of the Schaake shuffle. In this paper, the proposed post-processing method is evaluated through leave-one-out cross validation. The method can be easily adapted for post-processing raw GCM forecasts in real-time to produce ensemble forecasts of monthly and seasonal ETo.

Short-term forecasting of daily reference evapotranspiration using the reduced-set Penman-Monteith model and public weather forecasts

Farm irrigation management and real-time irrigation decisions are based on a series of parameters including irrigation water demand predictions which estimated by the short-term daily reference evapotranspiration (ETo). Based on the temperature data, a simplest empirical model called the temperature-based method, was developed to forecast near-future ETo. Although temperature is one of the most influential weather variables in ETo forecasting, wind velocity also has non-ignorable impact on the forecast performance of ETo. Therefore, we employed the Reduced-set Penman-Monteith (RPM) model for short-term ETo forecasting using temperature data derived from public weather forecasts and wind speed data. Four different types of wind speed data were adopted as model inputs, including wind speed assumed as a default value, i.e., u2 = 2.0 m s−1 (W1), forecasted wind speed (W2), long-term daily average wind speed (W3) and annual average wind speed (W4). Based on the above-mentioned wind speed inputs, we presented four types of RPM models (RC, RF, RD and RM models respectively corresponding to W1, W2, W3 and W4) to forecast daily ETo for lead times up to 7 days for eight weather stations in China. The results confirmed that the highest precision wind speed input was W3, followed by W4, W1 and W2, and the most accurate ETo forecasts of four RPM models was provided by RC, followed with RD, RM and RF. By comparing with one of the most common used temperature-based models, the Hargreaves-Samani (HS), it was found that the performance of RPM approaches were better than that of HS model for arid and sub-arid regions whereas opposite results were found in sub-tropical area, suggesting inclusion of wind speed parameter makes positive impact on ETo forecast for arid and semi-arid regions. Further, it was found that the greatest type of RPM model, the RC model, was superior to HS method for most climate regions, including arid, humid, sub-arid and sub-humid areas, thus, the W1 wind speed type could be highly recommended for ETo forecasting for most climate regions.

Medium-range reference evapotranspiration forecasts for the contiguous United States based on multi-model numerical weather predictions

Reference evapotranspiration (ET0) plays a fundamental role in agronomic, forestry, and water resources management. Estimating and forecasting ET0 have long been recognized as a major challenge for researchers and practitioners in these communities. This work explored the potential of multiple leading numerical weather predictions (NWPs) for estimating and forecasting summer ET0 at 101 U.S. Regional Climate Reference Network stations over nine climate regions across the contiguous United States (CONUS). Three leading global NWP model forecasts from THORPEX Interactive Grand Global Ensemble (TIGGE) dataset were used in this study, including the single model ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (EC), the National Centers for Environmental Prediction Global Forecast System (NCEP), and the United Kingdom Meteorological Office forecasts (MO), as well as multi-model ensemble forecasts from the combinations of these NWP models. A regression calibration was employed to bias correct the ET0 forecasts. Impact of individual forecast variables on ET0 forecasts were also evaluated. The results showed that the EC forecasts provided the least error and highest skill and reliability, followed by the MO and NCEP forecasts. The multi-model ensembles constructed from the combination of EC and MO forecasts provided slightly better performance than the single model EC forecasts. The regression process greatly improved ET0 forecast performances, particularly for the regions involving stations near the coast, or with a complex orography. The performance of EC forecasts was only slightly influenced by the size of the ensemble members, particularly at short lead times. Even with less ensemble members, EC still performed better than the other two NWPs. Errors in the radiation forecasts, followed by those in the wind, had the most detrimental effects on the ET0 forecast performances.