Evapotranspiration Model Research Articles

Early season irrigation detection and evapotranspiration modeling of winter vegetables based on Planet satellite using water and energy balance algorithm in lower Colorado basin

Early season irrigation detection and evapotranspiration modeling of winter vegetables based on Planet satellite using water and energy balance algorithm in lower Colorado basin

Estimating daily potential evapotranspiration using GNSS-based precipitable water vapor

Potential evapotranspiration (PET) is a crucial component of the hydrological cycle and energy balance. Although the Penman-Monteith (PM) model is the most widely used method to estimate daily PET, it requires temperature, relative humidity, solar radiation, and wind speed. In Thailand, the number of potential weather stations to provide the required data is limited, which resulted in the absence of some input variables in many locations. The objective of this study is to develop the revised potential evapotranspiration (RPET) model to estimate daily PET using Global Navigation Satellite System-derived Precipitable Water Vapor (GNSS-PWV) and temperature data. The multiple linear regression analysis was used to develop and validate the RPET model. The performance of the RPET model along with the Global Land Evaporation Amsterdam Model (GLEAM v3.2 b) and the European Centre for Medium-Range Weather Forecasts Reanalysis-5 (ERA5-Land) products was investigated using the PM model. The results revealed that the RPET model showed a strong correlation with the PM model (r = 0.85, RMSE = 0.97 mm day−1, RSR = 0.53, NSE = 0.72) under limited meteorological inputs. The RPET model performance was superior when compared to GLEAM and ERA5-Land (r = 0.80, RMSE = 1.06 mm day−1). Therefore, the proposed model is greatly suitable for daily PET estimation with only required GNSS-PWV and temperature data, and this can be implemented for drought assessment and water resources management.

Impact of aerodynamic temperature on ET estimates of Mediterranean irrigated crops from an energy-water balance model

The aerodynamic temperature identifies the temperature at an effective depth within the vegetation canopy, where sensible heat flux is originated, thus regulating the surface energy balance partitioning between latent and sensible heat. As it cannot be directly measured, a common practice in thermal infrared based retrieval of evapotranspiration is to substitute it with the radiometric temperature. However, numerous works have proven the two temperatures to be quite different, with peak differences as high as ±10 °C, with direct consequences in evapotranspiration modelling over complex landscapes. In this work, the main focus is on the integration of aerodynamic temperature within a hydrological modelling scheme, in order to identify possible improvements in the latent heat (and, thus, evapotranspiration) estimation. A first part of the analysis centres on comparing the differences between indirect estimates of aerodynamic temperature and measurements of radiometric surface temperature, showing divergences sometimes exceeding 10–15 °C. In a second step, a sensitivity analysis of the variable with respect to some environmental parameters was conducted. Meteorological parameters were found to be influential, whereas vegetation parameters played a relatively lesser role. In order to plug the aerodynamic temperature within the selected hydrological model (FEST-EWB), a test models was developed, testing the impact of the (measured) variable within the different turbulent fluxes. As the final aim was to continuously integrate the aerodynamic temperature within the hydrological model, possible parametrizations were scrutinised, using the meteorological dependencies already investigated in the first part of the study. The newly-developed FEST-AeroT model, with a continuous parametrization of aerodynamic temperature, produced mild improvements in sensible and latent heat estimation and virtually no gain in net radiation. The model and parametrization uncertainties may be the reason why such a high a priori temperature divergence produced so small differences in final energy flux modelling.

Applicability evaluation of soil moisture constraint algorithms in remote sensing evapotranspiration models

Remotely sensed (RS) evapotranspiration (ET) models have been widely used to estimate ET over large areas. However, a common challenge for these models is the lack of reliable soil moisture (SM) constraints. Due to the lack of reliable, spatiotemporal continuous SM profile data, existing RS-ET models tend to use atmospheric or land surface variables as proxies for SM constraints. Although several proxy algorithms for SM constraints have been developed, few studies have evaluated their performance in ET simulations. To address this gap, we evaluated the applicability of five proxy algorithms for SM constraints (namely, fVPD, fLST, fDT, fZhang, and fdrying) to ET simulations in China using the Penman-Monteith-Leuning (PML) model. These algorithms were evaluated at 14 ChinaFlux sites and 286 basins using flux tower measurements and water balance-based ET estimates, respectively. The results show that among the five algorithms, fdrying performs test at the flux sites, with a median of Kling-Gupta efficiency (KGE) of 0.75. The second-best algorithm is fZhang (KGE = 0.73), followed by fDT (KGE = 0.70), fLST (KGE = 0.68), and fVPD (KGE = 0.65). The performance ranking of the five algorithms at the basin scale is consistent with that at the flux sites. Using the flux site SM measurements as a reference, we further found that the algorithms with better performance in ET simulations also have better SM simulation capabilities. This study highlights the importance of reliable SM constraints in the RS-ET models.

Evaluation the Performance of Three Types of Two-Source Evapotranspiration Models in Urban Woodland Areas

The determination of the evapotranspiration (ET) and its components in urban woodlands is crucial to mitigate the urban heat island effect and improve sustainable urban development. However, accurately estimating ET in urban areas is more difficult and challenging due to the heterogeneity of the underlying surface and the impact of human activities. In this study, we compared the performance of three types of classic two-source ET models on urban woodlands in Shenzhen, China. The three ET models include a pure physical and process-based ET model (Shuttleworth–Wallace model), a semi-empirical and physical process-based ET model (FAO dual-Kc model), and a purely statistical and process-based ET model (deep neural network). The performance of the three models was validated using an eddy correlation and stable hydrogen and oxygen isotope observations. The verification results suggested that the Shuttleworth–Wallace model achieved the best performance in the ET simulation at main urban area site (coefficient of determination (R2) of 0.75). The FAO-56 dual Kc model performed best in the ET simulation at the suburb area site (R2 of 0.77). The deep neural network could better capture the nonlinear relationship between ET and various environmental variables and achieved the best simulation performance in both of the main urban and suburb sites (R2 of 0.73 for the main urban and suburb sites, respectively). A correlation analysis showed that the simulation of urban ET is most sensitive to temperature and least sensitive to wind speed. This study further analyzed the causes for the varying performance of the three classic ET models from the model mechanism. The results of the study are of great significance for urban temperature cooling and sustainable urban development.

A physically-based potential evapotranspiration model for global water availability projections

When the projections from the hydrological models conflict with those from the fully-coupled climate model, the vegetation appears to be responsible for the divergence. In the presence of an elevated concentration of atmospheric CO2 (Ca), the leaf-level stomatal conductance (gsl) tends to decrease thereby reducing water demand, and such effects can be partly offset by the increased leaf area (generally represented by leaf area index, LAI) simultaneously. Nonetheless, the impacts of vegetation are rarely explicitly quantified in existing potential evapotranspiration (EP) models. In this study, we proposed a new EP equation that combines a well-established gsl model with a simple bulk upscale model. The new model allows us to investigate the effects of elevated Ca-induced physiological and structural changes in vegetation on EP estimations. The results show that, compared to conventional EP models (e.g., the Penman-Monteith reference crop model), the new EP equation performs better in capturing long-term EP changes. Furthermore, we embedded the new EP equation in a simple hydrologic model, the Budyko framework, to estimate the changes in long-term runoff. By accounting for both physiological and structural responses of vegetation, estimated runoff by the Budyko framework shows a significant increase of ∼ 30 mm/year in 2071–2100 compared to 2006–2035, consistent with projections from fully-coupled climate models. The increase in LAI offset ∼ 30% of the water-saving caused by stomatal closure on a global average. The newly developed EP model provides a fully transparent and easy-to-use way for describing the impacts of climate change-induced vegetation responses on EP and can be effectively employed to detect long-term hydrological changes.

Evapotranspiration modeling – off-season table grape vines grown in bi-annual cycles

Evapotranspiration modeling – off-season table grape vines grown in bi-annual cycles

The Potential Use of Global Evapotranspiration Products and Models to Estimate Lake Residence Time at Large Scale

AbstractWater residence time (WRT) is an important lake attribute that influences almost all in‐lake nutrient processes. However, information on WRT is often missing or outdated, especially in regions lacking lake inflow or outflow observations. Recent advances in global precipitation and evapotranspiration (ET) products and calibration‐free ET models provide the possibility of estimating lake WRT at large scales using estimated runoff from the water balance over the lake catchment. Here, we tested four global ET products and two calibration‐free ET models for estimating the mean annual lake outflow and WRT of 67 globally distributed lakes using precipitation inputs from four global precipitation products. The results show that the WRT estimated using the water balance method and global ET estimates generally correlates well with reference WRT (R = 0.90 ∼ 0.99) when using reliable precipitation inputs and is slightly more accurate than the estimates from the existing global WRT dataset and methods (R = 0.79 ∼ 0.94). Despite the large relative error (RE) in the estimates of the WRT, the RE of the phosphorus retention derived from the estimated WRT is acceptable (RE < ±30%). The simplicity and versatility of this approach underline its potential applications to large‐scale studies that require a fair estimation of WRT.

ANN-based PCA to predict evapotranspiration: a case study in India

Abstract The Penman–Monteith evapotranspiration (ET) model has superior predictive ability to other methods, but it is challenging to apply in several Indian stations, owing to the need for a large number of climatic variables. The study investigated an artificial neural network (ANN) model for calculating ET for various agro-climatic regions of India. Sensitivity analysis showed that the overall average changes in ET0 values for 25% change in the climatic variables were 18, 16, 14, 7, 5, and 4%, respectively, for Tmax, RHmean, Rn, wind speed, Tmin, and sunshine hours. The dominant climatic variables were identified from the principal component analysis (PCA) and ET0 was computed using an ANN with dominant climatic variables. The ANN architecture with backpropagation technique had one hidden layer and neurons ranging from 10 to 30 for all climatic variables and from 5 to 10 for PCA variables. The new ET models were statistically compared with Penman–Monteith ET estimate, and found reliable. PCA variables guaranteed an estimate of ET0 accounting for 98% of the variability. The average values of coefficient of determination, standard error of estimate, and percentage efficiency were observed as 0.96, 0.24, and 94%, respectively.

Two decades of epidemiological surveillance of the pine wood nematode in France reveal its absence despite suitable conditions for its establishment

Key messageThis study takes stock of the first 20 years (2000–2019) of monitoring the pine wood nematode (PWN) in metropolitan France. While PWN was never found in the wild during this period, it was reported in some wood-based commodities entering or circulating on French territory. This stresses the importance of remaining extremely vigilant, as the conditions found in France, especially weather conditions, could be particularly suitable for the pest’s establishment.ContextThe pine wood nematode (PWN) Bursaphelenchus xylophilus, responsible for pine wilt disease (PWD), is one of the most important forest tree pests worldwide. It is thus the focus of many monitoring programmes. In the European Union, for example, it is categorised as a priority quarantine pest, so each member state is obliged to monitor the PWN on its territory.AimsThe first objective of this paper was to describe PWN monitoring in metropolitan France, namely how it is organised and whether it has led to the nematode’s detection. Secondly, we wished to investigate what the levels of PWD expression for host pines infected by B. xylophilus would be in France. Thirdly, we wanted to find out whether other Bursaphelenchus species had been found on French territory during these two decades of PWN monitoring.MethodsWe analysed data from samples collected in the framework of the monitoring programme between 2000 and 2019 to track the PWN in its host pines, its insect vector (Monochamus spp.) and in wood-based commodities imported into or circulating in metropolitan France. We also generated risk maps of PWD expression based on an evapo-transpiration model using climate data for the period 2000–2019.ResultsThis monitoring, which was regularly reinforced from 2000 to 2019, consisted of sampling and analysing around 18,000 wood samples and 66,000 insects over this period. Although the PWN was not detected in pine stands or within its insect vector, some wood-based commodities were found to be contaminated. Risk maps of PWD expression show that in the most recent years (2015–2019), the weather conditions in a large fraction of metropolitan France were suited to PWD expression, mostly with a delay (i.e., latency) between infection and observable wilt symptoms. PWN monitoring has also revealed the presence of other Bursaphelenchus species, most of which were discovered for the first time in metropolitan France and are described herein.ConclusionWhile metropolitan France is still free of the PWN, this study emphasises the need to remain cautious as the French territory appears particularly suitable for this pest’s establishment. Furthermore, our research has led us to propose some ideas on how to improve PWN monitoring.

Using artificial neural networks to predict the reference evapotranspiration

Artificial neural network models (ANNs) were used in this study to predict reference evapotranspiration ( ETo) using climatic data from the meteorological station at the test station in Kafr El-Sheikh Governorate as inputs and reference evaporation values computed using the Penman–Monteith (PM) equation. These datasets were used to train and test seven different ANN models that included different combinations of the five diurnal meteorological variables used in this study, namely, maximum and minimum air temperature ( Tmax and Tmin), dew point temperature ( Tdw), wind speed ( u), and precipitation (P), how well artificial neural networks could predict ETo values. A feed- forward multi-layer artificial neural network was used as the optimization algorithm. Using the tansig transfer function, the final architected has a 6-5-1 structure with 6 neurons in the input layer, 5 neurons in the hidden layer, and 1 neuron in the output layer that corresponds to the reference evapotranspiration. The root mean square error ( RMSE) of 0.1295 mm∙day –1 and the correlation coefficient ( r) of 0.996 are estimated by artificial neural network ETo models. When fewer inputs are used, ETo values are affected. When three separate variables were employed, the RMSE test values were 0.379 and 0.411 mm∙day –1 and r values of 0.971 and 0.966, respectively, and when two input variables were used, the RMSE test was 0.595 mm∙day –1 and the r of 0.927. The study found that including the time indicator as an input to all groups increases the prediction of ETo values significantly, and that including the rain factor has no effect on network performance. Then, using the Penman–Monteith method to estimate the missing variables by using the ETo calculator the normalised root mean squared error ( NRMSE) reached about 30% to predict ETo if all data except temperature is calculated, while the NRMSE reached about of 13.6% when used ANN to predict ETo using variables of temperature only.

Development of machine learning-based reference evapotranspiration model for the semi-arid region of Punjab, India

Evapotranspiration (ET) is a critical element of the hydrological cycle, and its proper assessment is essential for irrigation scheduling, agricultural and hydro-meteorological studies, and water budget estimation. It is computed for most applications as a product of reference crop evapotranspiration (ET0) and crop coefficient, notably using the well-known two-step method. Accurate predictions of reference evapotranspiration (ET0) using limited meteorological inputs are critical in data-constrained circumstances. Due to the unavailability and heterogeneity of broad parameters of the FAO PM method, it becomes a major constraint for accurately estimating ET0. To overcome the complexity of calculation, the present study was focused on developing a Random Forest-based (RF) ET0 model to estimate the crop ET for the semi-arid region of northwest India. The RF-based model was developed by focusing on the easily available data at the farm level. For comparative study existing models like Hargreaves–Samani, Modified Penman and modified Hargreaves–Samani were used to estimate the ET0. The models' calibration and validation were done using meteorological data collected from the weather station of Punjab Agricultural University for 21 years (1990–2010) and nine years (2011–2019), respectively, and the FAO PM model was taken as a standard. The mean absolute error (MAE) and root-mean-square error (RMSE) were found to be least as 0.95 mm/d and 1.32 mm/d, respectively for the developed RF model, with an r2 value of 0.92. The seasonal ET0 estimated by modified Hargreaves–Samani (MHS) and RF were found as 498.3, 482.1 mm in rabi season and 755, 744.8 mm in kharif season respectively, whereas the annual ET0 was 1380.2 and 1355.7 mm respectively. The predicted ET0 values by RF-based model were used for irrigation scheduling of two growing seasons (2020–2021) of maize and wheat crops. The outcome of the field trial also demonstrates that there was no appreciable yield drop in the crop when compared to irrigation scheduling by the FAO PM model, demonstrating the applicability of the developed model for irrigation in the semiarid region of the Punjab in India.

Mid-season lodging modulates photosynthesis, evapotranspiration, and dry matter accumulation and distribution simulated by the optimized model in maize

IntroductionMid-season lodging in maize (Zea mays L.) often restricts grain yield potential and is a great hurdle in improving production efficiency. The aim of this research was to study the effects of lodging on photosynthesis, evapotranspiration, dry matter accumulation, and distribution in a maize population.MethodsWe examined the effects of lodging on photosynthesis, dry matter accumulation, and distribution of maize in Zhengzhou Agro-meteorological station in August 2016, following a strong wind lodging process. Based on observational data of crops, meteorology, and CO2/H2O flux from milk maturity to maturity of maize in a normal growth year (2017), a model of population photosynthesis and evapotranspiration of maize under normal growth conditions was constructed. The validated model was used to simulate the theoretical value of photosynthesis and evapotranspiration in lodging years (2016), then the measured value of population photosynthesis and evapotranspiration after lodging was calculated based on the measured data of CO2/H2O flux from milk maturity to maturity of maize, and the difference between the simulated value and the measured value of population photosynthesis and evapotranspiration in lodging years (2016) was compared and analyzed. The correlation between dry matter accumulation and population photosynthetic accumulation was examined in order to estimate the reduction of dry matter accumulation after lodging. The effect of lodging on dry matter accumulation, distribution, and yield was analyzed using field biomass data. The population photosynthesis model and evapotranspiration model could accurately simulate the characteristics of normal growth conditions of summer maize.ResultsThe population photosynthesis model absolute error between the simulated value and the measured values in 2017 was −0.43 mg m−2 s−1, and the relative error was −3.3%; the evapotranspiration model absolute error between the simulated value and the measured values in 2017 was −0.005 mm·30 min−1, and the relative error was −10.7%. In 2016, the measured value of photosynthesis after lodging was significantly lower than the simulated value, and the daily average population photosynthesis rate decreased by 13.99 mg m−2 s−1 or 53%. Under the same condition, the daily average evapotranspiration decreased by 1.03 mm d−1 or 28%. The lodging process altered the accumulation and distribution of dry matter in maize. The dry weight of the stem and sheath increased by 5.5% and the ear weight decreased by 10.9% after lodging, compared to without lodging but there was no significant effect on leaf dry weight. After lodging, the proportion of stem sheath distribution increased by 3.0%, while the proportion of ear distribution decreased by 3.0%. After lodging, 100-grain weight and plant grain weight decreased by 2.8 and 10.8%, respectively. According to the lodging rate and density theory of computation yield, the yield of the entire field was reduced by 5.0%.

Assessment of data intelligence algorithms in modeling daily reference evapotranspiration under input data limitation scenarios in semi-arid climatic condition.

Crop evapotranspiration is essential for planning and designing an efficient irrigation system. The present investigation assessed the capability of four machine learning algorithms, namely, XGBoost linear regression (XGBoost Linear), XGBoost Ensemble Tree, Polynomial Regression (Polynomial Regr), and Isotonic Regression (Isotonic Regr) in modeling daily reference evapotranspiration (ETo) at IARI, New Delhi. The models were developed considering full and limited dataset scenarios. The efficacy of the constructed models was assessed against the Penman-Monteith (PM56) model estimated daily ETo. Results revealed the under full and limited dataset conditions, XGBoost Ensemble Tree gave the best results for daily ETo modeling during the model training period, while in the testing period under scenarios S1(Tmax) and S2 (Tmax, and Tmin), the Isotonic Regr models yielded superior results over other models. In addition, the XGBoost Ensemble Tree models outperformed others for the rest of the input data scenarios. The XGBoost Ensemble Tree algorithms reported the best values of correlation coefficient (r), mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE). Thus, we recommend applying the XGBoost Ensemble Tree algorithm for precisely modeling daily ETo in semi-arid climatic conditions.

Towards a universal evapotranspiration model based on optimality principles

Natural resource management requires knowledge of terrestrial evapotranspiration (ET). Most existing numeric models for ET include multiple plant- or ecosystem-type specific parameters that require calibration. This is a significant source of uncertainty under changing environmental conditions. A novel ET model with no type−specific parameters was developed recently. Based on the coupling the diffusion (via stomata) of water and carbon dioxide (CO2), this model predicts canopy conductance based on environmental conditions using eco-evolutionary optimality principles that apply to all plant types. Transpiration (T) and ET are calculated from canopy conductance using the Penman-Monteith equation for T and a universal empirical function for the T:ET ratio. Here, the model is systematically evaluated at globally distributed eddy-covariance sites and river basins. Site-scale modelled ET agrees well with flux data (r = 0.81, root mean square error = 0.73 mm day–1 in 23,623 records) and modelled ET in 39 river basins agrees well with the ET estimated by monthly water budget using two runoff datasets (r = 0.62 and 0.66, respectively). Modelled global patterns of ET are consistent with existing global ET products. The model's universality, parsimony and accuracy combine to indicate a broad potential field of application in resource management and global change science.

Spatiotemporal patterns and driving factors of evapotranspiration (natural and artificial) in the Ziya River Basin, China

Evapotranspiration (ET) accounts for 60%-70% of terrestrial precipitation. Accurately calculating ET has always been one of the hot issues in ecohydrological research. Watershed ET includes natural ET and artificial ET. Natural ET includes vegetation transpiration, bare land evaporation (the two called land ET) and water surface evaporation. Artificial ET mainly refers to irrigation ET, that is, ET generated by human activities. Estimating total ET is the premise of watershed ET management. In this paper, we used the Penman‒Monteith model to estimate land ET, the Dalton model to estimate water surface evaporation, and the AquaCrop model to estimate ET generated by agricultural irrigation water in the Ziya River Basin from 2005 to 2015. Based on the integrated watershed evapotranspiration model, the effects of driving factors on ET were quantitatively analysed. The results showed that the Penman‒Monteith model, Dalton model and AquaCrop model had high simulation accuracy. The total ET fluctuated between 418.4 and 452.3 mm/yr, and the annual average ET was 437.5 mm/yr. The ET of the main land use types was ranked as woodland (496.7 mm/yr) > arable land (468.9 mm/yr) > grassland (320.0 mm/yr). Spatially, the ET in mountainous areas was lower than that in plain areas. The distribution of ET in the total amount, mountainous and plain areas, woodland, grassland and arable land showed a significant Gaussian distribution. Temporally, the interannual variation was small, and the intra-annual difference was mainly caused by climate factors. The conversion of grassland and woodland into arable land had the greatest impact on the total ET. Temperature, precipitation and leaf area index had a positive impact on ET. The impact of land cover change on ET was more significant than that of climate factors. This study can provide an integrated approach for ET estimation in water resource management and land planning.

Overestimated global dryland expansion with substantial increases in vegetation productivity under climate warming

Drylands are serviced as an essential component of the earth’s ecosystem. The potential changes in dryland areas are of great importance to the environment, but various debates remain as to whether and to what extent drylands are expected to expand. Here we employ a physically-based potential evapotranspiration (E P) model accounting for vegetation response to climate change to quantify potential changes in dryland areas, on the basis of a commonly used indicator, aridity index (multiyear mean E P over precipitation). Results show that by the end of this century, drylands will expand slightly by ∼5%, while vegetation productivity will increase by ∼50%. Elevated CO2 slows down the increase rate of E P that impedes the expansion of drylands, but greatly promotes vegetation growth with increases in both leaf assimilation and canopy foliage. These findings improve our understanding of the potential changes in dryland and their ecological impacts in a warmer climate.

Modification of the radiation-based Abtew reference evapotranspiration model under humid climate, Northeast India

Abstract This study aimed to improve the Abtew model for reference evapotranspiration (ET0) calculation in Northeast India using seven temperature-based solar radiation models. The temperature-based models require only air temperature as input data, which can be easily measured in most locations worldwide. The performance of the improved Abtew models (A1-A7), along with the Stephen Stewart model (SS), the Irmak model (Ir), and the modified Turc model (MT), was evaluated under the climatic conditions of Dibrugarh, Northeast India, using statistical indices such as mean absolute error (MAE), root mean square error (RMSE), standard error (SE), coefficient of correlation (r), coefficient of determination (R2), and index of agreement (D). The results showed that the seven improved Abtew models (RMSE = 0.40–0.53 mm/day; D = 0.81–0.93) outperformed the four physical models (RMSE = 0.43–2.77 mm/day; D = 0.49–0.91) for the ET0 estimate at Dibrugarh. The statistical analysis identified that the A6 model ranked highest for Dibrugarh. This study highlights the significant improvement in ET0 estimation accuracy by utilizing temperature-based solar radiation models in the Abtew model. Therefore, we strongly recommend using the A6 model to estimate ET0, which requires only temperature data as input, for estimating ET0 under the climatic conditions of Dibrugarh, Northeast India.

Increasing Cloud Coverage Deteriorates Evapotranspiration Estimating Accuracy From Satellite, Reanalysis and Land Surface Models Over East Asia

AbstractAccurate estimation of evapotranspiration (ET) can be affected by cloud change, while cloud‐induced bias errors in ET change remain poorly understood. Here we evaluated seven ET data sets from satellite microwave and optical models, atmospheric reanalysis and land surface models over East Asia. Results showed that although the data sets estimated ET changes overall well under mild cloud increase, all of them had a systematic overestimation (by 73.8%–159.8%) and deteriorated capability of capturing ET change under heavy cloud increase, especially over dense vegetation with large cloud cover. Overestimated incident solar radiation under clouds contributed to the deteriorated performances of ET. Cloud‐induced change in radiation and its errors played larger role in summer ET change over densely vegetated lands, while those in air temperature and relative humidity were dominant jointly over grass and barren lands. These results can serve as a reference for improving ET models and their forcing inputs under clouds.

The conflicts of agricultural water supply and demand under climate change in a typical arid land watershed of Central Asia

Study Region. The Bosten Lake basin. Study Focus. The irrigated agriculture distributed in arid/semi-arid areas is of great significance for. food security and sustainable development. However, the shortage of water resources. limits agricultural development in these areas, and the water distribution pattern under. climate change is also uncertain. In this research, the Bosten Lake basin was selected. as the study area, and the monthly agricultural water supply (AWS) and demand. (AWD) in the historical and future periods were evaluated. New Hydrological Insights for the Region. Supported by the hydrological model and evapotranspiration model, the AWS and. AWD of the watershed were first evaluated, and 37 GCMs under CMIP6 were used to. expand the study to future periods, a novel downscaling scheme consisting of IDW and. BMA is used to increase the reliability of the results. The supply and demand of. agricultural water in the future scenarios within the region are revealed. In the. forthcoming future (the 2030 s), the watershed will maintain the warming and wetting. trend in the historical period. In the long-term future (the 2060 s), agricultural water. scarcity will become more severe, especially under the high emission scenario. (ssp585). The adaption strategies to address climate change have also been. proposed, and efficient water conveyance is highly recommended. This study is. expected to provide a reference for water resources management in arid/semi-arid. watersheds.