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The prediction of evapotranspiration (ET0) is crucial for agricultural ecosystems, irrigation management, and environmental climate regulation. Traditional methods for predicting ET0 require a variety of meteorological parameters. However, obtaining data for these multiple parameters can be challenging, leading to inaccuracies or inability to predict ET0 using traditional methods. This affects decision-making in critical applications such as agricultural irrigation scheduling and water management, consequently impacting the development of agricultural ecosystems. This issue is particularly pronounced in economically underdeveloped regions. Therefore, this paper proposes a machine learning-based evapotranspiration estimation method adapted to evapotranspiration conditions. Compared to traditional methods, our approach relies less on the variety of meteorological parameters and yields higher prediction accuracy. Additionally, we introduce a 'region of evapotranspiration adaptability' division method, which takes into account geographical differences in ET0 prediction. This effectively mitigates the negative impact of anomalies or missing data from individual meteorological stations, making our method more suitable for practical agricultural irrigation and ecosystem water resource management. We validated our approach using meteorological data from 25 stations in Heilongjiang, China. Our results indicate that non-adjacent geographical areas, despite different climatic conditions, can have similar impacts on ET0 prediction. In summary, our method facilitates accurate ET0 prediction, offering new insights for the development of agricultural irrigation and ecosystems, and further contributes to agricultural food supply.

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Wheat (Triticum aestivum L.) is a staple food crop that plays a crucial role in global food security. A suitable planting pattern and optimum nitrogen (N) split management are efficient practices for improving wheat production. Therefore, an experiment was performed to explore the effect of N split management and sowing patterns on wheat at the Agronomy Research Farm, The University of Agriculture Peshawar, during rabi season 2020-21 and 2021-22. The treatments consisted of different nitrogen rates of 0, 80, 120, and 160 kg ha- 1 and planting patterns of W, M, broadcast and line sowing. The pooled analysis of both cropping seasons showed that application of 120 kg N ha- 1 increased spikelets spike- 1, grains spike- 1, 1000 grains weight, grain yield, grain N content, evapotranspiration and water use efficiency by 21.9, 16.7, 21.8, 70, 13, 19.9 and 40% as compared to control, respectively. In addition, W and M were observed the best management practices among all planting patterns. The M planting pattern enhanced chlorophyll a, b, carotenoids and evapotranspiration while W plating pattern improved yield components and yield of wheat as compared to broadcast planting patterns. The principal component analysis biplot showed a close association of M and W planting patterns with 120 kg N ha- 1 in most of the studied traits. Hence, it is concluded that split application of 120 kg N ha- 1 in W and M sowing patterns enhanced growth, biochemical traits and water use efficiency, reducing N fertilization from 160 to 120 kg ha- 1 while increasing grain yield of wheat. Hence, it is recommended that application of 120 kg N ha⁻¹ in combination with W and M planting patterns offer a sustainable approach to enhancing wheat production in the alkaline soil conditions of the Peshawar valley.

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In view of the changing climate and growing global food demand, efficient water management is crucial for sustainable agriculture. Accurate measurement of evapotranspiration is essential for determining crop water demand and consequently for designing and managing irrigation systems. This study, conducted at Melkassa Agricultural Research Center in Ethiopia, utilized a drainage lysimeter to investigate the water requirements and crop coefficients of the Kingbird wheat variety during the December to March cropping season in 2021/22 and 2022/23. The experiment involved planting Kingbird wheat both inside and outside the lysimeter and irrigating using a watering can. Neutron probe measurement monitored the water balance in the soil. The study determined an average crop evapotranspiration of 427.28 mm and a reference evapotranspiration of 471.30 mm indicating a water requirement of 4273 m³ to fully grow wheat on a hectare of land. The derived average crop coefficient values were 0.43, 0.93, 1.15, and 0.30 for the initial, mid-season, and end growth stages, respectively. Furthermore, a fifth-order polynomial function was developed to predict crop coefficient values based on days after sowing. The findings provide valuable insights for enhancing the design and management of irrigated wheat production in the region. The specific crop coefficient values determined for different growth stages are crucial for optimizing irrigation scheduling and improving water-use efficiency, contributing to sustainable wheat production in semi-arid environment.

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The main challenges for utilizing daily evapotranspiration (ET) estimation in the study area revolve around the need for accurate and reliable data inputs, as well as the interpretation of ET dynamics within the context of local agricultural practices and environmental conditions. Factors such as cloud cover, atmospheric aerosols, and variations in land cover pose challenges to the precise estimation of ET from remote sensing data. This research aimed to utilize Landsat 8 and 9 datasets from the 2022-23 period in the Udham Singh Nagar district to apply the modified Priestley-Taylor (MPT) model for estimating ET. An average ET was estimated 1.33, 1.57, 1.70, 2.99, and 3.20 mm day-1 with 0.29, 0.33, 0.41, 0.69, and 1.03 standard deviation for December, January, February, March, and April month, respectively. In the validation phase, a strong correlation was found between the evaporative fraction derived from MPT and that observed by lysimeter, with R2 = 0.71, mean biased error = 0.04 mm day-1, root mean squared error = 0.62 mm day-1 and agreement index of 0.914. These results collectively support the effectiveness of the MPT model in accurately estimating ET across Udham Singh Nagar district. In essence, this research not only confirms the MPT model's capability in ET estimation but also offers detailed insights into the spatial and temporal fluctuations of energy fluxes and daily ET rates.

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Nutrients and water are important ecophysiological components for apples' development and productivity. The combination of high-density plantation, drip irrigation, and weekly fertigation not only conserves irrigation water, but also reduces cultivation costs compared to conventional methods. Leaf nutrient analysis provides insight into nutrient levels and assists in determining irrigation and fertigation schedules. We conducted the current research over two years (2021-22 and 2022-23) to evaluate different drip-fertigation effects on leaf nutrient status, vegetative growth, and yield of high-density apples. The experimental study employed a factorial randomised block design, replicating 16 different treatment combinations three times each. Each replication consisted of three plants, and the treatments included four irrigation levels (100 %, 80 %, 60 %, and control) and four fertigation levels (absolute control, 100 %, 75 %, and 50 % of the recommended NPK dosage). Analysis of the leaves indicated that IR1 (Drip irrigation at 100 % ETc) showed notably higher levels of nitrogen at (3.06 %), phosphorus at (0.48 %) and potassium at (2.07 %) compared to other treatments. Regarding fertigation levels, FN1 [100 % (AD) NPK] showed the highest nitrogen (3.12 %), phosphorus (0.50 %), and potassium (2.09 %) content. Parameters related to vegetative growth, including tree height, plant spread in both east-west (EW) and north-south (NS) directions, trunk girth, annual extension growth, and leaf area showed significant increases with higher irrigation and fertigation levels, surpassing conventional irrigation (IR4) by 6.17 percent, 7.78 percent (EW), 8.62 percent (NS), 10.49 percent, 4.53 percent and 1.96 percent, respectively. Among fertigation, FN1- 100 % AD (NPK) registered a maximum increase in growth parameters. Our analysis demonstrated that combining irrigation and fertigation improved leaf nutrient status and vegetative growth characteristics, which are critical determinants of fruit yield.

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Reference crop evapotranspiration (ET0) is a crucial variable for estimating the ecological water demand of vegetation. Under climate change, the trends of ET0 change vary in different regions. The study of spatial and temporal variations in ET0 and attribution analysis at the regional scale is more conducive to the regional agricultural water management and ecological water demand estimation under the changing environment. We analyzed the change trend, spatial distribution and the contribution of meteorological factors to annual ET0 change of the Fenwei Plain during a historical period (1985-2015) and a future period (2030-2060) based on the latest climate data and high-precision grid data from the Sixth International Coupled Model Intercomparison Project (CMIP6). The results showed that the meteorological data from CMIP6 could be used for the prediction of ET0 after bias correction, and that the prediction accuracy of the multi-model ensemble approach (R2 of 82.9%, RMSE of 14.9 mm) was higher than that of a single climate model. ET0 in the Fenwei Plain showed a significant decreasing trend in the historical period, but a non-significant increasing and significant increasing trend in the future period under the SSP245 and SSP585 scenarios, respectively. The vapor pressure deficit had the largest contribution to the ET0 change in both the historical and future periods, and was the primary meteorological factor affecting the ET0 change in the Fenwei Plain under the climate change. Solar radiation and wind speed were important meteorological factors affecting the ET0 change in the historical period, while temperature and wind speed were the important meteorological factors affecting the ET0 change in the future period. The meteorological factors that had great contribution to ET0 change were due to the larger multi-year relative change rates, rather than the high sensitivity of these meteorological factors to ET0. The ET0 of the plain under the SSP245 and SSP585 scenarios increased by 4.2% and 3.1% in the future period, respectively, compared with the historical period. The differences in the spatial distribution of the result were mainly from the eastern and western regions of the plain. Based on the high-precision spatial and temporal distribution of ET0, the spatial and temporal data could be used as a reference for the development of various adaptation for climate change in the Fenwei Plain.

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Numerous harmful contaminants (i.e. salt and heavy metals) have become major threats to soil and are being introduced into the soil through human and geological activities. These contaminants are raising global concerns about their toxic effects on food safety, human health and reclamation mechanisms. Microbial-inoculated biochar can improve soil environment by immobilizing and transforming contaminants in soil and altering the physico-chemical and biochemical properties of soil. In this review we will discuss the positive effects of microbial-modified biochar on physicochemical properties of contaminated soil. It can decrease the pH, EC while increase CEC, OM and other biochemical properties of soil. Additionally, we discuss the efficacy of biochar as a microbial carrier for salt and heavy metals-contaminated soil and plant growth in those soils. This review provides a better understanding of the potential of microbial biochar can be used for bioremediation of contaminated soil, which will help the researcher to modify biochar in a targeted way for specific applications.

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A semi-mechanistic oil palm growth and yield model called Sawit.jl was developed to account for a wide range of planting densities and soil textures under Malaysia's climate conditions. The model comprises components related to meteorology, photosynthesis, energy balance, soil water content, and crop growth. The model simulates instantaneous meteorological properties using daily weather data, calculates simultaneous evaporation from crop and soil with the Shuttleworth-Wallace model, determines soil water content through Darcy's law, and adapts a biochemical C3 model for photosynthesis. The model is also parameterized using updated measurements from the newer tenera oil palm, including temperature-dependent Rubisco kinetics, specific leaf area, and the partitioning of nutrients and dry matter between various tree parts. Sawit.jl was validated using historical field measurement data from seven Malaysian oil palm sites, encompassing palm ages spanning 1-23 years. These seven sites differed in soil type (Inceptisols and Ultisols), planting density (82-299 palms ha-1), soil texture (27-59 % clay and 7-67 % sand), and rainfall (1800-2800 mm yr-1). The model showed overall good accuracy in simulating oil palm parameters (except for trunk weight) across diverse conditions, with model agreement metrics ranging from 6 to 27 % for model absolute errors, -22 to +17 % for model bias, and 0.38 to 0.98 for the Kling-Gupta Efficiency index. The model also predicted the response of oil palm yield to abrupt rainfall changes, such as those during El Niño and La Niña events, while accounting for how soil texture, rainfall, and other meteorological factors influence water deficits and crop photosynthesis. However, model accuracy varied by site, planting density, and oil palm parameter. Model accuracy can be increased by more accurately representing the oil palm microclimate, incorporating fruiting activity, and refining the dry matter partitioning mechanism for the trunk.

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Evaluating the differences in evapotranspiration between urban and surrounding non-urban areas (i.e., ∆ET) has critical implications for urban ecological planning and water resources management. However, it is unclear how the magnitude of changes in ∆ET caused by urbanization varies under different climatic conditions in China. Here, using the remotely ET estimates at 1 km spatial resolution, we firstly estimated the magnitude of changes in ∆ET and then quantified the main driving factors influencing variations in ∆ET of 7 national-level urban agglomerations (UAs) across China during 2003-2020. Results showed that all annual ETurban values were smaller than ETnon-urban of 7 UAs, and the absolute ∆ET values of cities in South China were generally higher than those in North China. There is an apparent effect of urbanization on ∆ET increase in Guanzhong Plain City Group, Central Plain UA and Guangdong-Hong Kong-Macao Greater Bay Area (GHKMGBA), while ∆ET decrease in Chengdu-Chongqing City Group and Yangtze River Delta (YRDUA) were primarily due to the climate change. The suppressing effects of temperature and NDVI on ∆ET decrease in YRDUA were enhanced, and the promoting effect of GDP on ∆ET increase in GHKMGBA was weakened. Considering nonstationary features, urbanization appears to heighten extreme ∆ET by 0.83 %, 4.83 % and 10.39 % under 5-year, 20-year, and 50-year return periods over all the 7 UAs, respectively. Collectively, our findings confirm that urbanization is a significant factor that leads to ∆ET increase, and the factors affecting the response of urban water circulation system need to be deeply decomposed.

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This study analyzes the influences of evapotranspiration or substrate moisture variation on the indoor-temperature reduction of green roofs compared to the control group. A multiple linear regression (MLR) model for the operation stage based on observation and an integrated MLR model for the planning stage based on simulation are verified. The MLR model shows 0.64 °C of the Root Mean Square Error (RMSE) in predicting the hourly difference of temperature reduction based on the measured change in evapotranspiration and air temperature. The contributions of the hourly increment of air temperature (ΔTa) and increment of evapotranspiration (ΔET) are similar to the hourly increment of temperature reduction (ΔTdif). Then, the feasibility of the integrated MLR model is demonstrated based on the evapotranspiration and substrate moisture of a green roof simulated by a hydrological model as well as the indoor-temperature reduction simulated by a building energy model, which has fair performances in capturing the heat-transfer and water-balance physical process within a green roof. The integrated MLR model shows that evapotranspiration is relatively essential, followed by substrate moisture, air temperature, and vapor pressure. Despite the modeling bias, the integrated model quantitatively relates the influential factors to temperature reduction and predicts temperature reduction with an RMSE of 1.02 °C. The integrated model can quantify the influence of irrigation on temperature reduction under various climate conditions and green roof structures. This study demonstrates the procedure of establishing the integrated model. It shows the potential of the integrated model to provide decision support on irrigation for multi-purpose optimization of green roof performances.

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Crop yields are affected by hydroclimatic and edaphic conditions, but their interacting roles are often neglected when assessing crop yields at the regional scale. Moreover, often used hydroclimatic conditions such as precipitation and temperature are not as physiologically linked to primary production and yields as actual evapotranspiration. Using statistical models, we quantified the combined effects of edaphic and hydroclimatic conditions on county yields of irrigated rice and rainfed corn, soybean, and spring and winter wheat in the USA (2000-2019). Precipitation and temperature, or actual evapotranspiration, aggregated during the growing season or before and after flowering/silk emergence, in interaction with soil sand content or bulk density, explained up to 87 % of the yield variability. However, actual evapotranspiration explained yields better than precipitation and temperature and their interactions for most combinations of crops and growth periods. At high actual evapotranspiration, yield plateaued or, for spring wheat, decreased. Yields were generally most sensitive to changes in hydroclimatic conditions during part of rather than the entire growing season, and most often after flowering. Soil texture and bulk density modulated the impacts of hydroclimatic conditions: corn and soybean yields were higher in finer soils compared with sandy soils under high evapotranspiration, but lower at low evapotranspiration. Additionally, the yield-maximizing precipitation decreased with sand content and increased with bulk density for most crops. Increasingly available actual evapotranspiration estimates, combined with soil properties, offer an alternative, and more physiologically-based, yield predictor over large climatic gradients to the more widely used precipitation and temperature.

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Evapotranspiration (ET) is an important water budget term for understanding the recovery of stormwater retention in green roof systems (GRs). However, ET evaluations, particularly in full-scale GRs, remain challenging. This study investigated ET dynamics within a GR in the City of Pittsburgh, USA, using a water balance based on continuously monitored soil moisture from moisture sensors over 15 months. Results suggest under well-watered soil conditions, daily moisture loss correlated with solar radiation, temperature, and humidity, in decreasing order of correlation strength, while wind speed had limited effects. Compared to sensor-informed moisture loss (using moisture-based water balance), the Hargreaves and FAO-56 Penman-Monteith equations predicted cumulative ET that was 1.8 and 2.1 times higher, respectively. When soil moisture declined and approached the temporary wilting points, a noticeable reduction in daily moisture loss was observed. This suggests the necessity of using a water stress coefficient alongside a crop coefficient to represent actual ET based on FAO-56 Penman-Monteith estimates. Seasonal crop coefficients from dominant native plant species present at our monitored location, eastern bluestar (Amsonia tabernaemontana) and creeping woodsorrel (Oxalis corniculata), had mean values of 0.48, 0.62, and 0.65 for fall, spring, and summer, respectively. The impact of water stress on ET could be characterized by a linear relationship with moisture content. Our results highlight the importance of soil moisture in regulating ET processes and demonstrate the utility of soil moisture data for evaluating ET in GRs and informing irrigation practices.

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Rising atmospheric carbon dioxide concentrations ([CO2]) affect crop growth and the associated hydrological cycle through physiological forcing, which is mainly regulated by reducing stomatal conductance (gs) and increasing leaf area index (LAI). However, reduced gs and increased LAI can affect crop water consumption, and the overall effects need to be quantified under elevated [CO2]. Here we develop a SWAT-gs-LAI model by incorporating a nonlinear gs-CO2 equation and a missing LAI-CO2 relationship to investigate the responses of water consumption of grain maize, maize yield, and losses of water and soil to elevated [CO2] in the Upper Mississippi River Basin (UMRB; 492,000 km2). Results exhibited enhanced maize yield with decreased water consumption for increases in [CO2] from 495 ppm to 825 ppm during the historical period (1985-2014). Elevated [CO2] promoted surface runoff but suppressed sediment loss as the predominant impact of LAI-CO2 leading to enhanced surface cover. A comprehensive analysis of future climate change showed increased maize water consumption in comparison to the historical period, driven by the more pronounced effects of overall climate change rather than solely elevated [CO2]. Generally, future climate change promoted maize yield in most regions of the UMRB for three Shared Socioeconomic Pathway (SSP) scenarios. Surface runoff was shown to increase generally in the future with sediment loss increasing by an average of 0.39, 0.42, and 0.66 ton ha-1 for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. This was due to negative climatic change effects largely surpassing the positive effect of elevated [CO2], particularly in zones near the middle and lower stream. Our results underscore the crucial role of employing a physically-based model to represent crop physiological processes under elevated [CO2] conditions, improving the reliability of predictions related to crop growth and the hydrological cycle.

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Drought is one of the vital meteorological disasters that influence crop growth. Timely and accurately estimating the drought dynamics of crops is valuable for decision-maker to formulate scientific management measures of agricultural drought risk. In this study, the evapotranspiration and drought dynamics of winter wheat from 1981 to 2020 in the Huang-Huai-Hai (HHH) region of China were evaluated based on long-term multi-source observation data. Four key developmental stages of winter wheat were given attentions: growth before winter stage, overwintering stage, stage of greening-heading, and stage of filling-maturity. The crop water deficit index (CWDI) on a daily scale was established for quantitatively appraising the impacts of drought on winter wheat. Our results indicated that interannual variation in reference crop evapotranspiration (ET0) during the growth season of winter wheat from 1981 to 2020 in the HHH region showed a slight increase trend, with an average of 602.4 mm and obvious spatial differences of decreasing from the Northeast to the Southwest. Over the past forty years, the winter wheat in the HHH region was most severely affected by severe drought, followed by moderate drought, and finally mild drought. In addition, the impacts of drought on winter wheat at different critical growth stages varied greatly. For the growth before winter stage, the winter wheat was mainly threatened by mild, moderate, and severe droughts. For the overwintering stage, the winter wheat was mainly threatened by moderate, severe, and extreme droughts. For the greening-heading stage, the winter wheat was mainly threatened by mild, moderate, severe, and extreme droughts. For the filling-maturity stage, the winter wheat was mainly threatened by mild and moderate droughts. Finally, the impacts of drought on winter wheat during 1981-2020 in the HHH region were revealed to differ extraordinarily in space. In particular, the areas of winter wheat affected by severe drought significantly decreased. However, the areas of winter wheat affected by moderate drought clearly expanded. Our findings provide new insights for further improving climate change impact studies and agricultural drought defense capabilities adapting to continuous environmental change.

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Recycled wastewater effluent irrigation and implementing limited irrigation rates are two promising strategies for water conservation in agriculture. However, one major challenge is the accumulation and translocation of Pharmaceutical and Personal Care Products (PPCPs) from recycled water to crops. This study investigated the effects of UV persulfate (UV/PS) treatment of recycled water and limited irrigation rate on PPCPs accumulation and physiological responses of St. Augustine turfgrass via a 14-week field trial. Carbamazepine (CBZ), sulfamethoxazole (SMX), triclosan (TCS), fluoxetine (FLX) and diclofenac (DCF) were spiked at 0.1-1.5 µg/L into recycled water and two limited irrigation rates corresponding to 60 % and 80 % of reference Evapotranspiration (ETo) were applied. Results showed that UV/PS removed 60 % of CBZ and > 99 % of other PPCPs from recycled water. Irrigation with UV/PS treated recycled water resulted in approximately a 60 % reduction in CBZ accumulation and complete removal of SMX, DCF, FLX and TCS in both turfgrass leaves and roots. A more limited irrigation rate at 60 % ETo resulted in a higher accumulation of CBZ accumulation compared to 80 % ETo. Similarly, the canopy temperature increased under 60 % ETo irrigation rate compared to 80 % ETo, suggesting that turfgrass under 60 % ETo was more prone to water stress. Applying a 60 % ETo irrigation rate was not sufficient to maintain the turfgrass quality in the acceptable range. A negative correlation between the visual quality and cumulative mass of PPCPs in turfgrass leaves at different irrigation rates was observed, yet irrigation rate was the major driver of turfgrass overall quality and health. Insights from this study will help to integrate recycled water with treatment and limited irrigation, thereby enhancing agricultural water reuse practices.

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Atmospheric heat has become a major public concern in a rapidly warming world. Evapotranspiration, however, provides effective land surface cooling during the vegetation period. Adversely, modern cultural landscapes - due to both water and potential evapotranspiration pathways lacking - are increasingly incapable of offering this important benefit. We hypothesised that concerted measures for a revived landscape water retention can fuel plant transpiration, especially during dry periods, and thus contribute to climate change adaptation by stabilising the regional climate. Seeking nature-based ways to an improved landscape water retention, we used the land surface temperature (LST) as a proxy for landscape mesoclimate. For our drought-prone rural study area, we identified potential candidate environmental predictors for which we established statistical relationships to LST. We then, from a set of potential climate change adaptation measures, mapped selected items to potential locations of implementation. Building on that, we evaluated a certain measures' probable cooling effect using (i) the fitted model and (ii) the expected expression of predictors before and after a hypothetical measure implementation. In the modelling, we took into account the spatial and temporal autocorrelation of the LST data and thus achieved realistic parameter estimates. Using the candidate predictor set and the model, we were able to establish a ranking of the effectiveness of climate adaptation measures. However, due to the spatial variability of the predictors, the modelled LST is site-specific. This results in a spatial differentiation of a measure's benefit. Furthermore, seasonal variations occur, such as those caused by plant growth. On average, the afforestation of arable land or urban brownfields, and the rewetting of former wet meadows have the largest cooling capacities of up to 3.5 K. We conclude that heat countermeasures based on fostering both evapotranspiration and landscape water retention, even in rural regions, offer promising adaptation ways to atmospheric warming.

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Background: Accurate estimation of reference crop evapotranspiration (ET0) is crucial for farmland hydrology, crop water requirements, and precision irrigation decisions. The Penman-Monteith (PM) model has high accuracy in estimating ET0, but it requires many uncommon meteorological data inputs. Therefore, an ideal method is needed that minimizes the number of input data variables without compromising estimation accuracy. This study aims to analyze the performance of various methods for estimating ET0 in the absence of some meteorological indicators. The Penman-Monteith (PM) model, known for its high accuracy in ET0 estimation, served as the standard value under conditions of adequate meteorological indicators. Comparative analyses were conducted for the Priestley-Taylor (PT), Hargreaves (H-A), McCloud (M-C), and FAO-24 Radiation (F-R) models. The Bayesian estimation method was used to improve the ET estimation model. Results: Results indicate that, compared to the PM model, the F-R model performed best with inadequate meteorological indicators. It demonstrates higher average correlation coefficients (R2) at daily, monthly, and 10-day scales: 0.841, 0.937, and 0.914, respectively. The corresponding root mean square errors (RMSE) are 1.745, 1.329, and 1.423, and mean absolute errors (MAE) are 1.340, 1.159, and 1.196, with Willmott's Index (WI) values of 0.843, 0.862, and 0.859. Following Bayesian correction, R2 values remained unchanged, but significant reductions in RMSE were observed, with average reductions of 15.81%, 29.51%, and 24.66% at daily, monthly, and 10-day scales, respectively. Likewise, MAE decreased significantly, with average reductions of 19.04%, 34.47%, and 28.52%, respectively, and WI showed improvement, with average increases of 5.49%, 8.48%, and 10.78%, respectively. Conclusion: Therefore, the F-R model, enhanced by the Bayesian estimation method, significantly enhances the estimation accuracy of ET0 in the absence of some meteorological indicators.

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Background: Reference evapotranspiration (ETo), which is used as the basic data in many studies within the scope of hydrology, meteorology, irrigation and soil sciences, can be estimated by using the evaporation (Epan) measured from the class-A pan evaporimeter. However, this method requires reliable pan coefficients (Kp). Many empirical models are used to estimate Kp coefficients. The reliability of these models varies depending on climatic and environmental conditions. Therefore, they need to be tested in the local conditions where they will be used. In this study, conducted in Kahramanmaras, which has a semi-arid Mediterranean climate in Turkey during the July-October periods of 2020 and 2021, aimed to determine the usability levels of six Kp models in estimating daily and monthly average ETo. Methods: The Kp coefficients estimated by the models were multiplied with the daily Epan values, and the daily average ETo values were estimated on the basis of the model. The daily Epan values were measured using an ultrasonic sensor sensitive to the water surface placed on the class-A pan evaporimeter. The ultrasonic sensor was managed by a programmable logic controller (PLC). To enable the sensor to be managed by PLC, a software was prepared using the CODESYS programming language and uploaded to the PLC. The daily average ETo values determined by the FAO-56 Penman-Monteith equation were accepted as actual values. The ETo values estimated by the Kp models were compared with the actual ETo values using the mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE) and determination coefficient (R2) statistical approaches. Results: The Wahed & Snyder outperformed the other models in estimating daily (MAE = 0.78 mm day-1, MAPE = 14.40%, RMSE = 0.97 mm day-1, R2 = 0.82) and monthly (MAE = 0.32 mm day-1, MAPE = 5.88%, RMSE = 0.32 mm day-1, R2 = 0.99) average ETo. FAO-56 showed the nearest performance to Wahed & Snyder. The Snyder model presented the worst performance in estimating daily (MAE = 2.09 mm day-1, MAPE = 37.53%, RMSE = 2.36 mm day-1, R2 = 0.82) and monthly (MAE = 1.83 mm day-1, MAPE = 31.82%, RMSE = 1.87 mm day-1, R2 = 0.99) average ETo. It has been concluded that none of the six Kp models can be used to estimate the daily ETo in Kahramanmaras located in the Mediterranean-Southeastern Anatolian transitional zone, and only Wahed & Snyder and FAO-56 can be used to estimate the monthly ETo without calibration.

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Groundwater resources serve as the primary source of water in the agro-pastoral ecotone of northern China, where scarcity of water resources constrains the development of agriculture and animal husbandry. As a typical rainfed agricultural area, the agro-pastoral ecotone in Inner Mongolia is entirely dependent on groundwater for agricultural irrigation. Due to the substantial groundwater consumption of irrigated farmland, groundwater levels have been progressively declining. To obtain a sustainable irrigation pattern that significantly conserves water, this study faces the challenge of unclear water transport relationships among water, soil, and crops, undefined water cycle mechanism in typical irrigation units, and water use efficiency, which was not assessed. Therefore, this paper, based on in situ experimental observations and daily meteorological data in 2022-2023, utilized the DSSAT model to explore the growth processes of potato, oat, alfalfa, and sunflower, the soil water dynamics, the water balance, and water use efficiency, analyzed over a typical irrigation area. The results indicated that the simulation accuracy of the DSSAT model was ARE < 10%, nRMSE/% < 10%, and R2 ≥ 0.85. The consumption of the soil moisture during the rapid growth stage for the potatoes, oats, alfalfa, and sunflower was 7-13% more than that during the other periods, and the yield was 67,170, 3345, 6529, and 4020 kg/ha, respectively. The soil evaporation of oat, potato, alfalfa, and sunflower accounted for 18-22%, 78-82%; 57-68%, and 32-43%, and transpiration accounted for 40-44%, 56-60%, 45-47%, and 53-55% of ETa (333.8 mm-369.2 mm, 375.2 mm-414.2 mm, 415.7 mm-453.7 mm, and 355.0 mm-385.6 mm), respectively. It was advised that irrigation water could be appropriately reduced to decrease ineffective water consumption. The water use efficiency and irrigation water use efficiency for potatoes was at the maximum amount, ranging from 16.22 to 16.62 kg/m3 and 8.61 to 10.81 kg/m3, respectively, followed by alfalfa, sunflowers, and oats. For the perspective of water productivity, it was recommended that potatoes could be extensively cultivated, alfalfa planted appropriately, and oats and sunflowers planted less. The findings of this study provided a theoretical basis for efficient water resource use in the agro-pastoral ecotone of Northern China.

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Soil moisture is an important component of the hydrological cycle and a key mediator between land surface and atmospheric interactions. Although substantial progress has been made in remote sensing of soil moisture at different spatial scales, the shallow penetration depth of remote sensors greatly limits their utility for applications in meteorological modelling and hydrological studies where the critical variable of interest is the root-zone soil moisture content. Therefore, this study assesses the relationship between soil moisture at the surface (10 cm) and in lower soil layers (20, 40, 60, 80, 100, and 120 cm) under varying climates, soils, and crop types. Cross-correlation analysis is applied to daily in-situ soil moisture measurements from 4712 locations in agricultural lands across the contiguous United States. Our analysis demonstrates that zero-day lag always produced the highest correlation between 10 cm soil moisture and soil moisture in the lower layers. In addition, a positive and strong relationship between 10 and 20 cm soil moisture (r = 0.84) was observed, while the relationships between 10 and 40 cm soil moisture were moderate (r = 0.52). The decline in cross-correlation continued to the deeper soil layers, which indicated that, on a daily timescale, the surface soil moisture gradually becomes decoupled with soil moisture at greater depths. Therefore, our research suggests that the estimation of soil moisture in the soil layers up to 40 cm based on surface soil moisture is most promising. However, the influence of climate, crop type, and soil texture on the strength of relationships between surface and lower layers makes the prediction difficult. The comparatively weak relationship between precipitation and soil moisture (0.09-0.32), as well as the relationship between reference evapotranspiration (ETo) and soil moisture (-0.19-0.18), in this study can be attributed to scale mismatching from different data sources.