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Remote sensing technology is widely used to obtain evapotranspiration (ETa), but whether it can distinguish the differences in farmland energy balance components and ETa under different irrigation methods has not been studied. We used Landsat 8 data as the primary dataset to drive the METRIC model and inverted the surface parameters and ETa of the Shiyang River Basin from 2014 to 2018. After improving the METRIC model using Ta obtained by the regression method instead of interpolation to calculate the net radiation flux (Rn), R2 was improved from 0.45 to 0.53, and the RMSE was reduced from 61 W/m2 to 51 W/m2. The ETa estimation results on satellite overpass days performed well, with R2 equal to 0.93 and RMSE equal to 0.48 mm when compared with the Eddy covariance method (EC) observations. Subsequently, the different growth stages and daily average ETa estimates of maize were compared with three observations (water balance, WB; Bowen ratio and energy balance method, BREB; and EC). The daily estimates of ETa correlate well with the observations of BREB (R2BI = 0.82, R2DI = 0.92; RMSEBI = 0.46 mm/day, RMSEDI = 0.32 mm/day) and EC (R2BI = 0.85, R2DI = 0.92; RMSEBI = 0.45 mm/day, RMSEDI = 0.34 mm/day), and the estimation for drip irrigation was found to be better than for border irrigation. The total accuracy of the ETa estimation on the five-year overpass day of maize farmland reached R2 = 0.93 and RMSE = 0.48 mm. With sufficient remote sensing data, the 4-year average ETa of maize was 31 mm lower for DI than for BI, and the mean value of ETa obtained from the three observation methods was 40 mm. The METRIC model can be used to distinguish ETa differences between the two irrigation methods in maize farmlands. [ABSTRACT FROM AUTHOR]

Modelling crop evapotranspiration (ET) response to different planting scenarios in an irrigation district plays a significant role in optimizing crop planting patterns, resolving agricultural water scarcity and facilitating the sustainable use of water resources. In this study, the SWAT model was improved by transforming the evapotranspiration module. Then, the improved model was applied in Qingyuan Irrigation District of northwest China as a case study. Land use, soil, meteorology, irrigation scheduling and crop coefficient were considered as input data, and the irrigation district was divided into subdivisions based on the DEM and local canal systems. On the basis of model calibration and verification, the improved model showed better simulation efficiency than did the original model. Therefore, the improved model was used to simulate the crop evapotranspiration response under different planting scenarios in the irrigation district. Results indicated that crop evapotranspiration decreased by 2.94% and 6.01% under the scenarios of reducing the planting proportion of spring wheat (scenario 1) and summer maize (scenario 2) by keeping the total cultivated area unchanged. However, the total net output values presented an opposite trend under different scenarios. The values decreased by 3.28% under scenario 1, while it increased by 7.79% under scenario 2, compared with the current situation. This study presents a novel method to estimate crop evapotranspiration response under different planting scenarios using the SWAT model, and makes recommendations for strategic agricultural water management planning for the rational utilization of water resources and development of local economy by studying the impact of planting scenario changes on crop evapotranspiration and output values in the irrigation district of northwest China. [ABSTRACT FROM AUTHOR]

• PET and ground-based ET 0 estimation are integrated to reflect the spatiotemporal variability and improve ET 0 accuracy. • A distributed interval nonlinear multiobjective programming (DINMP) model is proposed. • Uncertainties, conflicting objectives, spatial information, and marginal benefit are fully considered in DINMP model. • DINMP is applied to a real-world case and the results can contribute to the sustainable development of agriculture. Considering the uncertainties in agricultural system and spatiotemporal variability in evapotranspiration and precipitation, a distributed interval nonlinear multiobjective programming (DINMP) model was developed for optimal allocation of limited irrigation water resources in the middle reaches of Heihe River basin. The meteorological data from meteorological stations were used to estimate reference crop evapotranspiration (ET 0) through FAO56 Penman-Monteith (PM) equation, and then the remote sensing MOD16/PET data were fitted by linear regression model according to the FAO56 PM results. The 95% confidence interval was used to further improve the accuracy of the fitting results. Thus, satellite-based potential evapotranspiration (PET) and ground-based ET 0 estimation were integrated to not only reflect the spatial and temporal variability but also guarantee the accuracy of the ET 0. In the terms of precipitation, spatial interpolation was used to spatial information of precipitation. Based on these spatiotemporal data, a DINMP with three objectives, including maximizing economic benefits and water saving as well as minimizing water shortage of critical growth periods, was formulated, and further solved by fuzzy coordination method. The optimal allocation scheme improves the irrigation water productivity by [0.50, 0.66] kg/m3, and decreases net irrigation water allocation by [0.33, 1.01] ×108 m3. These results show that DINMP can not only consider the uncertainties and multiple objectives in agricultural water management, but also improve the spatial resolution of optimal water allocation strategies. The framework of this study can provide a reference for agricultural water managers in similar areas to obtain more reasonable water allocation schemes. [ABSTRACT FROM AUTHOR]

Crop models are widely used to assist in agricultural management decision-making and water productivity optimization. However, traditional crop models often depend on artificial and specific field management inputs, posing challenges in maintaining crops within a desired stress range. Consequently, the derived optimization schemes from these models become highly uncertain. Moreover, the complexity of the mechanisms involved and the multitude of parameters make it challenging to apply traditional crop models uniformly across various crops and regions. In this study, we have developed a novel crop model called WHCrop (Water-Heat Driven Crop model) that effectively captures, reflects, and controls the impact of various environmental factors (meteorology, topography, soil, and management) on crop growth process. The WHCrop model combines the simulation principles of biomass and yield from the CERES module in the DSSAT model, along with the soil water balance from the AquaCrop model, to estimate the dynamics of crop growth and production processes. Results indicated that WHCrop-based simulations, including canopy cover (CC), daily evapotranspiration (ET), and yield, matched well with ground-based measurements, and were better than the traditional crop model (DSSAT and AquaCrop) at both field and regional scales, especially under deficient irrigation conditions. Besides capturing the key variables associated with crop growth, WHCrop model could reproduce the adaptive response of these various to regional-scale temperature changes. Notably, the WHCrop model could effectively minimize uncertainties resulting from individual environmental change, thanks its incorporation of dynamic response mechanisms for crop growth under stress factors. Overall, the novel and informative WHCrop model offers some advantages over traditional crop models since it allows for optimal decision making to be derived from the randomly different inputs. As a result, the WHCrop model proves instrumental in assisting decision-makers in formulating critical water allocation strategies and developing effective management recommendations to enhance regional agricultural water productivity. • WHCrop model simulates crop growth dynamics using water and heat drive. • WHCrop model enables randomly varied inputs for optimal decision-making. • WHCrop model simulates daily crop growth responses to water stress and climate changes. • WHCrop model needs fewer and readily available input data than traditional crop model. • WHCrop outperformed traditional crop models in simulating crop growth across scales. [ABSTRACT FROM AUTHOR]

Highlights: [•] An adjusted SW (MSW) model was proposed after considering mulching effect on ET. [•] The original SW model overestimated maize ET significantly over the whole season. [•] PM model performed well when canopy fully covered the substrate. [•] MSW model can simulate ET and Es well under film mulch without non-water stress. [ABSTRACT FROM AUTHOR]