Accurate estimation of evapotranspiration (ET) is vital for water resource development, planning and management, particularly in the present global warming context. A large number of empirical ET models have been developed for estimating ET. The main limitations of this method are that it requires several meteorological variables and an extensive data span to comprehend the ET pattern accurately, which is not available in most developing countries. The efficiency of 30 empirical ET models has been evaluated in this study to rank them for Pakistan to facilitate the selection of suitable models according to data availability. Princeton Global Meteorological Forcing daily climate data with a 0.25° × 0.25° resolution for 1948–2016 were utilized. The ET estimated using Penman–Monteith (PM) was considered as the reference. Multi-criteria group decision making (MCGDM) was used to rank the models for Pakistan. The results showed the temperature-based Hamon as the best model for most of Pakistan, followed by Hargreaves–Samani and Penman models. Hamon also showed the best performance in terms of different statistical metrics used in the study with a mean bias (PBias) of −50.2%, mean error (ME) of −1.62 mm and correlation coefficient (R2) of 0.65. Ivan showed the best performance among the humidity-based models, Irmak-RS and Ritch among the radiation-based models and Penman among the mass transfer-based models. Northern Pakistan was the most heterogeneous region in the relative performance of different ET models.

/pdf/ranking-of-empirical-evapotranspiration-models-in-different-3sd5pthe.pdf

Ranking of Empirical Evapotranspiration Models in Different Climate Zones of Pakistan

Refined spatiotemporal analysis of drought characteristics under different characteristic variable matchings: a case study of the middle reaches of the Yellow River basin, China

The global food and water crises caused by climate warming are becoming increasingly significant. One of the important aspects of surface water transfer is evapotranspiration. The accurate estimation of evapotranspiration (ET) is of great significance in water resource management. However, accurate estimation of large-scale ET is challenging due to the adequate selection of the hot and cold pixels, the heterogeneity of the subsurface and the instability of the lower atmosphere. Therefore, in this paper, a surface energy balance model (Y-SEBAL) was proposed based on the SEBAL model and improved sensible heat flux calculation, and used to simulate large-scale actual evapotranspiration. Based on the principle of surface energy balance, this model introduced the empirical formula of sensible heat flux and simplifies the selection of limit to address the challenge of calculating the sensible heat flux of the crucial variable in the SEBAL model. The study simulated the spatial and temporal evolution of regional evapotranspiration with the model and verified the model simulation performance. In addition, the sensitivity of the model-driven data was quantified. The results showed that the simulation value of the Y-SEBAL model was highly consistent with the eddy covariance measurement data. In the verification results, the R value was 0.82, the index of agreement was 0.90, and the root mean square error was 0.81 mm·d−1. The performance verification indices were better than the SEBAL model, MOD16 model, and SSEBop model. And the model had the highest sensitivity to wind speed, reaching 0.714. The Y-SEBAL model improved the simulation performance of the original model and simulated the actual evapotranspiration. The study can provide a new solution for improving the retrieval of evapotranspiration by remote sensing at the regional scale. 

Estimation of regional actual evapotranspiration based on the improved SEBAL model

Climate adaptation through crop migration requires a nexus perspective for environmental sustainability in the North China Plain

Accurate reference crop evapotranspiration (ET0) estimation is essential for agricultural water management, crop productivity, and irrigation systems. As the standard ET0 estimation method, the Penman-Monteith equation has been widely recommended worldwide. However, its application is still restricted to comprehensive meteorological data deficiency, making the exploration of alternative simpler models for acceptable ET0 estimation highly meaningful. Concerning the aforementioned requirement, this study developed the novel deep learning model (MA-CNN-BiLSTM), which incorporates Multi-Head Attention mechanism (MA), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory network (BiLSTM) as intricate relationship processor, feature extractor, and regression component, to estimate ET0 based on radiation-based (Rn-based), humidity-based (RH-based), and temperature-based (T-based) input combinations at 600 stations during 1961–2020 throughout China under internal and external cross-validation strategies. Besides, through a comparative evaluation among MA-CNN-BiLSTM, CNN-BiLSTM, BiLSTM, LSTM, Multivariate Adaptive Regression Splines (MARS), and empirical models, the result indicated that MA-CNN-BiLSTM achieved superior precision, with values of Determination Coefficient (R2), Nash–Sutcliffe efficiency coefficient (NSE), Relative Root Mean Square Error (RRMSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) ranging 0.877–0.972, 0.844–0.962, 0.129–0.292, 0.294–0.644 mm d−1, 0.244–0.566 mm d−1 for internal strategy and 0.797–0.927, 0.786–0.920, 0.162–0.335, 0.409–0.969 mm d−1, 0.294–0.699 mm d−1 for external strategy. Specifically, Rn-based MA-CNN-BiLSTM excelled in the temperate continental zone (TCZ) and mountain plateau zone (MPZ), while RH-based MA-CNN-BiLSTM yielded best precision in others. Furthermore, the internal strategy was superior to external strategy by 2.74–106.04% for R2, 1.11–120.49% for NSE, 1.41–40.27% for RRMSE, 1.68–45.53% for RMSE, and 1.21–38.87% for MAE, respectively. In summary, the main contribution of the present study is the proposal of a novel LSTM-type ET0 model (MA-CNN-BiLSTM) to cope with various data-missing scenarios throughout China, which can provide effective support for decision-making in regional agriculture water management. 

Estimating reference crop evapotranspiration using improved convolutional bidirectional long short-term memory network by multi-head attention mechanism in the four climatic zones of China

Vulnerability of crop water footprint in rain-fed and irrigation agricultural production system under future climate scenarios

Water scarcity in agricultural production has emerged as a significant constraint to food security in China. To improve agricultural output and ensure its stability, it is imperative to assess the agricultural water use efficiency. The crop water footprint (WF) is an effective tool to assess the type, amount, and efficiency of agricultural water usage. However, existing quantitative studies on the crop water footprint within regions exhibit limitations in terms of their low spatial resolution and deficient spatial heterogeneity. In this study, the World Food Studies (WOFOST) model was combined with the Ensemble Kalman filter (EnKF) assimilation algorithm, with the leaf area index (LAI) and soil moisture (SM) obtained by remote sensing as state variables. An improved method for quantifying the WF of wheat based on crop model-remote sensing information data assimilation was proposed. Regarding the results, the average blue water footprint (WFblue), green water footprint (WFgreen), and total water footprint (WF) of wheat were 363 m3/t, 551 m3/t, and 914 m3/t, respectively. In the southern of the study area, WFgreen exhibits higher values, with lower levels in the north. WFblue and WF demonstrated inverse spatial patterns, with higher values observed in the northern areas and lower values in the southern regions. The spatial heterogeneity of WFblue and WF was more significant than that of WFgreen. The WF of wheat quantified by the data assimilation method had a high spatial resolution, and could be effectively used to explore the spatial heterogeneity of WF. This study can provide a reliable reference for the effective use of water resources. 

Quantification of wheat water footprint based on data assimilation of remote sensing and WOFOST model

Advancing Evapotranspiration Modeling With Optimized Soil and Canopy Resistance Combinations

Actual evapotranspiration estimation and applicability Assessment based on the Y-SEBAL model in China

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