Assessing the cooling potential of climate change adaptation measures in rural areas.

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.

An improved remote sensing reference evapotranspiration estimation by simple satellite data and machine learning.

Reference evapotranspiration (ET0) estimation is crucial for efficient irrigation planning, optimized water management and ecosystem modeling, yet it presents significant challenges, particularly when meteorological data availability is limited. This study utilized remote sensing data of land surface temperature (LST), day of year, and latitude, and employed a machine learning approach (e.g., random forest) to develop an improved remote sensing ET0 model. The model performed excellently in 567 meteorological stations in China with an R2 of 0.97, RMSE of 0.40, MBE of 0.00, and MAPE of 0.11 compared to the FAO-PM ET0; it also performed well globally, yielding an average R2 of 0.97 and RMSE of 0.43 across 120 sites in mid-latitude (20°-50°) regions. This model demonstrates simplicity, accuracy, robust and generalization, holding great potential for widespread application, especially in the large-scale, high-resolution estimation of ET0. This study will contribute to advancements in water resources management, agricultural planning, and climate change studies.

Relationships among soil moisture at various depths under diverse climate, land cover and soil texture.

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 vegetation 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 <40 cm based on surface soil moisture is most promising. However, the influence of climate, land cover, and soil textures 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.

Tomato yield, and water use efficiency as affected by nitrogen rate and irrigation regime in the central low lands of Ethiopia.

Tomato yield can be increased by the application of optimum water and fertilizer. A field experiment was conducted in Efratana Gidim district, North Shewa, Amhara, Ethiopia, during 2019 and 2020. The objective was to determine the nitrogen (N) rate and irrigation regime for optimum tomato yield and water use efficiency (WUE). The experiment consisted of three-irrigation regimes (75% ETc (Evapotranspiration from the crop), 100% ETc, and 125% ETc) and four nitrogen (N) rates (control; i.e. without N application1, 46 kg N ha-1, 92 kg N ha-1, and 138 kg N ha-1). The treatments were laid out in a split-plot design with four replications. The Irrigation regime were assigned to the main plot, while the N rate were assigned to the subplot. Data on growth, yield, and yield-related traits of tomatoes, include; plant height, number of fruit clusters per plant, fruit length, fruit diameter, number of marketable fruits, number of un-marketable fruits, the total number of fruits, marketable fruit yield, un-marketable fruit yield, total yield were collected. The data were subjected to analysis of variance using R studio. The results indicated that the experimental site had low total N content, and the application of N fertilizer significantly improved tomato yield. Increasing irrigation depth also significantly increased tomato yield. The result indicated that the highest mean marketable fruit yield (35,903 kg ha-1) was obtained from the combined application of 125% ETc with 92 kg N ha-1, while the lowest (13,655 kg ha-1) marketable fruit yield was obtained from 75% ETc with 92 kg N ha-1. The analysis of variance showed that the highest (5.4 kg m-3) WUE recorded from 75% ETc with 46 kg N ha-1 increased WUE by 77% (2.4 kg m-3) compared with the lowest (2.3 kg m-3) WUE recorded from 125% ETc with 0 kg N ha-1. The partial budget analysis also indicated that the highest net benefit (266,272 ETB (Ethiopian Birr) ha-1) and an acceptable marginal rate of return (1240%) for the invested capital was recorded from the combined application of 125% ETc with 92 kg N ha-1. Therefore, the application of 125% ETc with 92 kg N ha-1 resulted in the highest net benefit.

[Assessment of the lagging effect of vegetation response and loss probability in the Pearl River basin under drought stress]. / å¹²æ—±èƒè¿«ä¸‹ç æ±ŸæµåŸŸæ¤è¢«å“åº”çš„æ»žåŽæ•ˆåº”åŠæŸå¤±æ¦‚çŽ‡è¯„ä¼°.

We quantified the lag time of vegetation response to drought in the Pearl River basin (PRB) based on the standardized precipitation evapotranspiration index (SPEI) and normalized difference vegetation index (NDVI), and constructed a vegetation loss probability model under drought stress based on the Bayesian theory and two-dimensional joint distribution. We further quantitatively evaluated the spatial variations of loss probability of four vegetation types (evergreen broadleaf forest, mixed forest, grassland, and cropland) under different drought intensities. The results showed that the drought risk in eastern West River, the upper reaches of North River and East River, and southern Pearl River Delta was obviously higher than that in other regions during 1982-2020. The response time of vegetation to drought in high-altitude areas in the upper reaches of PRB (mostly<3 month) was generally shorter than that in low altitude areas (>8 month). Drought exacerbated the probability of vegetation loss, with higher vulnerability of mixed forest than the other three vegetation types. The loss probability of vegetation was lower in northwestern PRB than that in central PRB.

Lanthanum interferes with the fundamental rhythms of stomatal opening, expression of related genes, and evapotranspiration in Arabidopsis thaliana.

The accumulation of rare earth elements (REEs) in the global environment poses a threat to plant health and ecosystem stability. Stomata located on leaves serve as the primary site for plant responses to REE-related threats. This study focused on lanthanum [La(III)], a prevalent REE in the atmospheric environment. Using interdisciplinary techniques, it was found that La(III) (≤80 µM) interfered with the fundamental rhythms of stomatal opening, related gene expression, and evapotranspiration in plants. Specifically, when exposed to low concentrations of La(III) (15 and 30 µM), the expression levels of six genes were increased, stomatal opening was enhanced, and the evapotranspiration rate was accelerated. The interference on stomatal rhythms was enhanced with higher concentrations of La(III) (60 and 80 µM), increasing the expression levels of six genes, stomatal opening, and evapotranspiration rate. To counter the interference of low concentrations of La(III) (15 and 30 µM), plants accelerated nutrient replenishment through La(III)-induced endocytosis, which the redundant nutrients enhanced photosynthesis. However, replenished nutrients failed to counter the disruption of plant biological rhythms at higher concentrations of La(III) (60 and 80 µM), thus inhibiting photosynthesis due to nutrient deficit. The interference of La(III) on these biological rhythms negatively affected plant health and ecosystem stability.

Ecohydrological assessment of the water balance of the world's highest elevation tropical forest (Polylepis).

Polylepis trees grow at elevations above the continuous tree line (3000-5000 m a.s.l.) across the Andes. They tolerate extreme environmental conditions, making them sensitive bioindicators of global climate change. Therefore, investigating their ecohydrological role is key to understanding how the water cycle of Andean headwaters could be affected by predicted changes in environmental conditions, as well as ongoing Polylepis reforestation initiatives in the region. We estimate, for the first time, the annual water balance of a mature Polylepis forest (Polylepis reticulata) catchment (3780 m a.s.l.) located in the south Ecuadorian páramo using a unique set of field ecohydrological measurements including gross rainfall, throughfall, streamflow, and xylem sap flow in combination with the characterization of forest and soil features. We also compare the forest water balance with that of a tussock grass (Calamagrostis intermedia) catchment, the dominant páramo vegetation. Annual gross rainfall during the study period (April 2019-March 2020) was 1290.6 mm yr-1. Throughfall in the Polylepis forest represented 61.2 % of annual gross rainfall. Streamflow was the main component of the water balance of the forested site (59.6 %), while its change in soil water storage was negligible (<1 %). Forest evapotranspiration was 54.0 %, with evaporation from canopy interception (38.8 %) more than twice as high as transpiration (15.1 %). The error in the annual water balance of the Polylepis catchment was small (<15 %), providing confidence in the measurements and assumptions used to estimate its components. In comparison, streamflow and evapotranspiration at the grassland site accounted for 63.7 and 36.0 % of the water balance, respectively. Although evapotranspiration was larger in the forest catchment, its water yield was only marginally reduced (<4 %) in relation to the grassland catchment. The substantially higher soil organic matter content in the forest site (47.6 %) compared to the grassland site (31.8 %) suggests that even though Polylepis forests do not impair the hydrological function of high-Andean catchments, their presence contributes to carbon storage in the litter layer of the forest and the underlying soil. These findings provide key insights into the vegetation-water­carbon nexus in high Andean ecosystems, which can serve as a basis for future ecohydrological studies and improved management of páramo natural resources considering changes in land use and global climate.

Parameterization of Four Models to Estimate Crop Evapotranspiration in a Solar Greenhouse.

Working to simplify mechanistic models on the basis of reliability for estimating crop evapotranspiration (ET) in a greenhouse is still worthwhile for horticulturists. In this study, four ET models (Penman-Monteith, Priestley-Taylor, and Shuttleworth-Wallace models, and the Crop coefficient method) were parameterized after taking the restriction effect of resistance parameters in these models on ET into account, named as PA-PM, PA-PT, PA-CC, and PA-SW, respectively. The performance of these four parameterized models was compared at different growth stages, as well as the entire growing season. Tomatoes that were ET-grown in a solar greenhouse without a heating device were measured using weighting lysimeters during 2016-2017 and 2019-2021, in which data from 2016 were used to adjust the model parameters, and data from the other four study years were used to examine the model performance. The results indicated that the PA-PT and PA-CC models have a better performance in estimating tomato ET at four growth stages, while the PA-PM and PA-SW performed well only at the development and middle stages. Compared to the ET that was measured with the weighting lysimeters, the ET that was predicted using the PA-PM model was 27.0% lower at the initial stage, and 8.7% higher at the late stage; the ET that was computed using the PA-SW model was 19.5% and 13.6% higher at the initial and late stages, respectively. The PA-PT model yielded the lowest root mean square error and the highest index of agreement against the other models over the entire growing season, indicating that the PA-PT model is the best recommended model for estimating tomato ET in a solar greenhouse.

EvatCrop: a novel hybrid quasi-fuzzy artificial neural network (ANN) model for estimation of reference evapotranspiration.

Reference evapotranspiration (ET0 ) is a significant parameter for efficient irrigation scheduling and groundwater conservation. Different machine learning models have been designed for ET0 estimation for specific combinations of available meteorological parameters. However, no single model has been suggested so far that can handle diverse combinations of available meteorological parameters for the estimation of ET0. This article suggests a novel architecture of an improved hybrid quasi-fuzzy artificial neural network (ANN) model (EvatCrop) for this purpose. EvatCrop yielded superior results when compared with the other three popular models, decision trees, artificial neural networks, and adaptive neuro-fuzzy inference systems, irrespective of study locations and the combinations of input parameters. For real-field case studies, it was applied in the groundwater-stressed area of the Terai agro-climatic region of North Bengal, India, and trained and tested with the daily meteorological data available from the National Centres for Environmental Prediction from 2000 to 2014. The precision of the model was compared with the standard Penman-Monteith model (FAO56PM). Empirical results depicted that the model performances remarkably varied under different data-limited situations. When the complete set of input parameters was available, EvatCrop resulted in the best values of coefficient of determination (R2 = 0.988), degree of agreement (d = 0.997), root mean square error (RMSE = 0.183), and root mean square relative error (RMSRE = 0.034).

Maize yield prediction and condition monitoring at the sub-county scale in Kenya: synthesis of remote sensing information and crop modeling.

Agricultural production assessments are crucial for formulating strategies for closing yield gaps and enhancing production efficiencies. While in situ crop yield measurements can provide valuable and accurate information, such approaches are costly and lack scalability for large-scale assessments. Therefore, crop modeling and remote sensing (RS) technologies are essential for assessing crop conditions and predicting yields at larger scales. In this study, we combined RS and a crop growth model to assess phenology, evapotranspiration (ET), and yield dynamics at grid and sub-county scales in Kenya. We synthesized RS information from the Food and Agriculture Organization (FAO) Water Productivity Open-access portal (WaPOR) to retrieve sowing date information for driving the model simulations. The findings showed that grid-scale management information and progressive crop growth could be accurately derived, reducing the model output uncertainties. Performance assessment of the modeled phenology yielded satisfactory accuracies at the sub-county scale during two representative seasons. The agreement between the simulated ET and yield was improved with the combined RS-crop model approach relative to the crop model only, demonstrating the value of additional large-scale RS information. The proposed approach supports crop yield estimation in data-scarce environments and provides valuable insights for agricultural resource management enabling countermeasures, especially when shortages are perceived in advance, thus enhancing agricultural production.

The simultaneous prediction of yield and maturity date for wheat-maize by combining satellite images with crop model.

BACKGROUND: The simultaneous prediction of yield and maturity date has an important impact on ensuring food security. However, few studies have focused on simultaneous prediction of yield and maturity date for wheat-maize in the North China Plain (NCP). In this study, we developed the prediction model of maturity date and yield (PMMY) for wheat-maize using multi-source satellite images, an Agricultural Production Systems sIMulator (APSIM) model and a random forest (RF) algorithm. RESULTS: The results showed that the PMMY model using peak leaf area index (LAI) and accumulated evapotranspiration (ET) has the optimal performance in the prediction of maturity date and yield. The accuracy of the PMMY model using peak LAI and accumulated ET was higher than that of the PMMY model using only peak LAI or accumulated ET. In a single year, the PMMY model had good performance in the prediction of maturity date and yield. The latitude variation in spatial distribution of maturity date for WM was obvious. The spatial heterogeneity for yield of wheat-maize was not prominent. Compared with 2001-2005, the maturity date of the two crops in 2016-2020 advanced 1-2 days, while yield increased 659-706 kg ha-1. The increase in minimum temperature was the main meteorological factor for advance in the maturity date for wheat-maize. Precipitation was mainly positively correlated with maize yield, while the increase in minimum temperature and solar radiation was crucial to the increase in yield. CONCLUSION: The simultaneous prediction of yield and maturity can be used to guide agricultural production and ensure food security. © 2024 Society of Chemical Industry.

Comparative assessment of deep belief network and hybrid adaptive neuro-fuzzy inference system model based on a meta-heuristic optimization algorithm for precise predictions of the potential evapotranspiration.

Accurately predicting potential evapotranspiration (PET) is crucial in water resource management, agricultural planning, and climate change studies. This research aims to investigate the performance of two machine learning methods, the adaptive network-based fuzzy inference system (ANFIS) and the deep belief network (DBN), in forecasting PET, as well as to explore the potential of hybridizing the ANFIS approach with the Snake Optimizer (ANFIS-SO) algorithm. The study utilized a comprehensive dataset spanning the period from 1983 to 2020. The ANFIS, ANFIS-SO, and DBN models were developed, and their performances were evaluated using statistical metrics, including R2, R adj 2 , NSE, WI, STD, and RMSE. The results showcase the exceptional performance of the DBN model, which achieved R2 and R adj 2 values of 0.99 and NSE and WI scores of 0.99 across the nine stations analyzed. In contrast, the standard ANFIS method exhibited relatively weaker performance, with R2 and R adj 2 values ranging from 0.52 to 0.88. However, the ANFIS-SO approach demonstrated a substantial improvement, with R2 and R adj 2 values ranging from 0.94 to 0.99, suggesting the value of optimization techniques in enhancing the model's capabilities. The Taylor diagram and violin plots with box plots further corroborated the comparative analysis, highlighting the DBN model's superior ability to closely match the observed standard deviation and correlation and its consistent and low-variance predictions. The ANFIS-SO method also exhibited enhanced performance in these visual representations, with an RMSE of 0.86, compared to 0.95 for the standard ANFIS. The insights gained from this study can inform the selection of the most appropriate modeling technique, whether it be the high-precision DBN, the flexible ANFIS, or the optimized ANFIS-SO approach, based on the specific requirements and constraints of the application.

Analysis of factors affecting evapotranspiration zoning.

Reference evapotranspiration (ETo) has a significant role in water resource planning and management as well as analysis of crop production and other agricultural tasks. Methods for estimating ETo may require diurnal/monthly assessments to perceive the consequences of climatic changes on local regions. The spatial and temporal patterns of ETo were analyzed in the current work using data from 340 weather stations in Iran. The entropy theory was used to assess the uncertainty of the utilized variables and the modified Kendall test was applied for temporal trend analysis. The interpolation (e.g., kriging) and ordinary least squares (OLS) methods were used for spatio-temporal ETo classification/modeling. The spatial analysis demonstrated that the OLS method with a good fit measure (R2 = 0.985) successfully simulated the spatial relationships of ETo with climatic parameters. After examining error indices, the cokriging method with an exponential variogram was introduced as the best method of seasonal and annual ETo classification in Iran. Spatially and temporally calculated ETo patterns using modified Hargreaves (MHGR) and MODIS methods closely resembled the standard FAO Penman-Monteith (FPM-56) method, all indicating a gradual increase in ETo. MHGR and MODIS methods serve as suitable alternatives for estimating ETo in various climatic regions of Iran, provided data availability.

Groundwater recharge estimation using WetSpass-M and MTBS leveraging from HydroOffice and WHAT tools for baseflow in Weyib watershed, Ethiopia.

WetSpass-M model and multi-technique baseflow separation (MTBS) were applied to estimate spatio-temporal groundwater recharge (GWR) to be used to comprehend and enhance sustainable water resource development in the data-scarce region. Identification of unit Hydrographs And Component flows from Rainfall, Evaporation, and Streamflow (IHACRES) techniques outperform the existing 13 MTBS techniques to separate baseflow depending on the correlation matrix; mean baseflow was 5.128 m3/s. The WetSpass-M model performance evaluated by Nash-Sutcliff Efficiency (NSE) was 0.95 and 0.89; R2 was 0.90 and 0.85 in comparison to observed and simulated mean monthly baseflow and runoff (m3/s), respectively. The estimated mean annual water balance was 608.2 mm for actual evapotranspiration, 221.42 mm for the surface runoff, 87.42 mm for interception rate, and 177.66 mm for GWR, with an error of - 3.29 mm/year. The highest annual actual evapotranspiration was depicted in areas covered by vegetation, whereas lower in the settlement. The peak annual interception rates have been noticed in areas covered with forests and shrublands, whereas the lowest in settlement and bare land. The maximum annual runoff was depicted in settlement and bare land, while the lowest was in forest-covered areas. The annual recharge rates were low in bare land due to high runoff and maximum in forest-covered areas due to low surface runoff. The watershed's downstream areas receive scanty annual rainfall, which causes low recharge and drought. The findings point the way ahead in terms of selecting the best approach across multi-technique baseflow separations.

Performance evaluation of different empirical models for reference evapotranspiration estimation over Udhagamandalm, The Nilgiris, India.

Evapotranspiration (ETo) is an important component of the hydrological cycle and reliable estimates of ETo are essential for assessing crop water requirements and irrigation management. Direct measurement of evapotranspiration is both costly and involves complex and intricate procedures. Hence, empirical models are commonly utilized to estimate ETo using accessible meteorological data. Given that empirical methods operate on various assumptions, it is essential to assess their performance to pinpoint the most suitable methods for ETo calculation based on the availability of input data and the specific climatic conditions of a region. This study aims to evaluate different empirical methods of ETo in the tropical highland Udhagamandalam region of Tamil Nadu, India, utilizing sixty years of meteorological data from 1960-2020. In this study, 8 temperature-based and 10 radiation-based empirical models are evaluated against ETo estimates derived from pan evaporation observation and the FAO Penman-Monteith method (FAO-PM), respectively. Statistical error metrics indicate that both temperature and radiation-based models perform better for the Udhagamandalam region. However, radiation-based models performed better than the temperature based models. This is possibly due to the high humidity of the study region throughout the year. The results suggest that simple temperature and radiation-based models using minimum meteorological information are adequate to estimate ETo and thus find potential application in agricultural water practices, hydrological processes, and irrigation management.

Partitioning urban forest evapotranspiration based on integrating eddy covariance of water vapor and carbon dioxide fluxes.

Partitioning of evapotranspiration (ET) in urban forest lands plays a vital role in mitigating ambient temperature and evaluating the effects of urbanization on the urban hydrological cycle. While ET partitioning has been extensively studied in diverse natural ecosystems, there remains a significant paucity of research on urban ecosystems. The flux variance similarity (FVS) theory is used to partition urban forest ET into soil evaporation (E) and vegetation transpiration (T). This involves measurements from eddy covariance of water vapor and carbon dioxide fluxes, along with an estimated leaf-level water use efficiency (WUE) algorithm. The study compares five WUE algorithms in partitioning the average transpiration fraction (T/ET) and validates the results using two years of oxygen isotope observations. Although all five FVS-based WUE algorithms effectively capture the dynamic changes in hourly scale T and E across the four seasons, the algorithm that assumes a constant ratio of intercellular CO2 concentration (ci) to ambient CO2 concentration (ca) provides the most accurate simulation results for the ratio of T/ET. The performance metrics for this specific algorithm include the RMSE of 0.06, R2 of 0.88, the bias of 0.02, and MAPE of 8.9 %, respectively. Comparing urban forests to natural forests, the T/ET in urban areas is approximately 2.4-25.3 % higher, possibly due to the elevated air temperature (Ta), greater leaf area index (LAI), and increased soil water availability. Correlation analysis reveals that the T/ET dynamic is primarily controlled by Ta, LAI, net radiation, ca, and soil water content at half-hourly, daily, and monthly scales. This research provides valuable insights into the performance and applicability of various WUE algorithms in urban forests, contributing significantly to understanding the impact of urbanization on energy, water, and carbon cycles within ecosystems.

Non-linear response of Norway spruce to climate variation along elevational and age gradients in the Carpathians.

Climate change, namely increased warming coupled with a rise in extreme events (e.g., droughts, storms, heatwaves), is negatively affecting forest ecosystems worldwide. In these ecosystems, growth dynamics and biomass accumulation are driven mainly by environmental constraints, inter-tree competition, and disturbance regimes. Usually, climate-growth relationships are assessed by linear correlation due to the simplicity and straightforwardness of modeling. However, applying this method may bias results, since the ecological and physiological responses of trees to environmental factors are non-linear, and usually bell-shaped. In the Eastern Carpathian, Norway spruce is at the southeasternmost edge of its natural occurrence; this region is thus potentially vulnerable to climate change. A non-linear assessment of climate-growth relationships using machine-learning techniques for Norway spruce in this area had not been conducted prior to this study. To address this knowledge gap, we analyzed a large tree-ring network from 158 stands, with over 3000 trees of varying age distributed along an elevational gradient. Our results showed that non-linearity in the growth-climate response of spruce was season-specific: temperatures from the previous autumn and current growing season, along with water availability during winter, induced a bell-shaped response. Moreover, we found that at low elevations, spruce growth was mainly limited by water availability in the growing season, while winter temperatures are likely to have had a slight influence along the entire elevational gradient. Furthermore, at elevations lower than 1400 m, spruce trees were also found to be sensitive to previous autumn water availability. Overall, our results shed new light on the response of Norway spruce to climate in the Carpathians, which may aid in management decisions.

A doubled increasing trend of evapotranspiration on the Tibetan Plateau.

Estimation of evapotranspiration (ETa) change on the Tibetan Plateau (TP) is essential to address the water requirement of billions of people surrounding the TP. Existing studies have shown that ETa estimations on the TP have a very large uncertainty. In this article, we discuss how to more accurately quantify ETa amount and explain its change on the TP. ETa change on the TP can be quantified and explained based on an ensemble mean product from climate model simulations, reanalysis, as well as ground-based and satellite observations. ETa on the TP experienced a significant increasing trend of around 8.4 ± 2.2 mm (10 a)-1 (mean ± one standard deviation) during 1982-2018, approximately twice the rate of the global land ETa (4.3 ± 2.1 mm (10 a)-1). Numerical attribution analysis revealed that a 53.8% TP area with the increased ETa was caused by increased temperature and 23.1% part was due to soil moisture rising, because of the warming, melting cryosphere, and increased precipitation. The projected future increase in ETa is expected to cause a continued acceleration of the water cycle until 2100.

Precision Estimation of Crop Coefficient for Maize Cultivation Using High-Resolution Satellite Imagery to Enhance Evapotranspiration Assessment in Agriculture.

The estimation of crop evapotranspiration (ETc) is crucial for irrigation water management, especially in arid regions. This can be particularly relevant in the Po Valley (Italy), where arable lands suffer from drought damages on an annual basis, causing drastic crop yield losses. This study presents a novel approach for vegetation-based estimation of crop evapotranspiration (ETc) for maize. Three years of high-resolution multispectral satellite (Sentinel-2)-based Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Red Edge Index (NDRE), and Leaf Area Index (LAI) time series data were used to derive crop coefficients of maize in nine plots at the Acqua Campus experimental farm of Irrigation Consortium for the Emilia Romagna Canal (CER), Italy. Since certain vegetation indices (VIs) (such as NDVI) have an exponential nature compared to the other indices, both linear and power regression models were evaluated to estimate the crop coefficient (Kc). In the context of linear regression, the correlations between Food and Agriculture Organization (FAO)-based Kc and NDWI, NDRE, NDVI, and LAI-based Kc were 0.833, 0.870, 0.886, and 0.771, respectively. Strong correlation values in the case of power regression (NDWI: 0.876, NDRE: 0.872, NDVI: 0.888, LAI: 0.746) indicated an alternative approach to provide crop coefficients for the vegetation period. The VI-based ETc values were calculated using reference evapotranspiration (ET0) and VI-based Kc. The weather station data of CER were used to calculate ET0 based on Penman-Monteith estimation. Out of the Vis, NDWI and NDVI-based ETc performed the best both in the cases of linear (NDWI RMSE: 0.43 ± 0.12; NDVI RMSE: 0.43 ± 0.095) and power (NDWI RMSE: 0.44 ± 0.116; NDVI RMSE: 0.44 ± 0.103) approaches. The findings affirm the efficacy of the developed methodology in accurately assessing the evapotranspiration rate. Consequently, it offers a more refined temporal estimation of water requirements for maize cultivation in the region.

Assessment of satellite-based water requirements for a drip-irrigated apple orchard in Mediterranean agroclimatic conditions.

Accurate assessment of evapotranspiration (ETa) and crop coefficient (Kc) is crucial for optimizing irrigation practices in water-scarce regions. While satellite-based surface energy balance models offer a promising solution, their application to sparse canopies like apple orchards requires specific validation. This study investigated the spatial and temporal dynamics of ETa and Kc in a drip-irrigated 'Pink Lady' apple orchard under Mediterranean conditions over three growing seasons (2012/13, 2013/14, 2014/15). The METRIC model, incorporating calibrated sub-models for leaf area index (LAI), surface roughness (Zom), and soil heat flux (G), was employed to estimate ETa and Kc. These estimates were validated against field-scale Eddy Covariance data. Results indicated that METRIC overpredicted Kc and ETa with errors less than 10 %. These findings highlight the potential of the calibrated METRIC model as a valuable decision-making tool for irrigation management in apple orchards.