Energy Balance Algorithm Research Articles

Assessment of Spatiotemporal Variations in Actual Evapotranspiration using the pySEBAL Model and Remote Sensing Data in Navsari, Gujarat, India

Evapotranspiration (ET), encompassing both transpiration from plants and evaporation from the Earth's surface, is a vital component of the hydrological cycle and influences water and energy exchanges between the surface and the atmosphere. This study investigates the application of the Surface Energy Balance Algorithm for Land (SEBAL) to estimate actual evapotranspiration (AET) over a selected region. The study area, located in Navsari district of Gujarat, India, was characterized by varying climates and land cover conditions. Cloud-free LANDSAT 8 satellite images were utilized at a spatial resolution of 30 x 30 meters to execute the SEBAL model within the Python-built GRASSGIS software. The SEBAL algorithm, based on the principles of energy balance, calculates AET by considering net radiation, sensible heat flux, and ground heat flux. The results showed that AET correlated with factors such as vegetation cover, land surface temperature, and net radiation. The AET rates exhibited significant spatial and temporal variations, with the highest rates observed in May and the lowest in months with reduced net radiation. Across the study area, the dominant AET rate ranges from 3 to 6 mm/day during the summer months and from 2 to 3 mm/day during the winter months. This pattern encompasses more than 65% of the total study area. Moreover, the highest AET values derived from the SEBAL model are contrasted with potential ET (PET) estimates obtained through diverse empirical techniques. This analysis demonstrates a robust alignment between SEBAL-based AET and PET calculations. Notably, in the months of April and May, when the peak AET closely corresponds to PET, there is a potential indication of impending water scarcity concerns.

Intercomparison of citrus evapotranspiration among eddy covariance, OpenET ensemble models, and the Water and Energy Balance Model (BAITSSS)

Remote sensing-based surface energy balance algorithms have been used to estimate water use of various crops. However, citrus evapotranspiration (ET) estimation is challenging mainly due to evergreen leaves and a clumped canopy structure. In this study, we evaluated the performance of two methods for calculating ET: the ensemble of OpenET models, which are mostly satellite thermal-based models, and the BAITSSS water and energy balance model. Calculated ET was compared with (i.) eddy covariance (EC) ET measurements and (ii.) water received (irrigation plus precipitation) data for two citrus orchards in San Joaquin Valley, California. Polaris-based soil hydraulic properties and measured volumetric water content were used for BAITSSS parameterization and initialization, respectively. Sentinel-2 based NDVI was used for BAITSSS simulation. Results showed that annual ET based on the OpenET ensemble model (1169 mm) was on average 30 % larger (r2 ∼ 0.71, RMSE ∼ 1.16 mm) than both EC ET (908 mm) and water received (886 mm). The disparity mostly occurred in spring. BAITSSS, on the other hand, showed mixed results compared to observations (r2 ∼ 0.77, RMSE ∼ 0.94 mm). Both measured from EC and modeled ET from BAITSSS and ensemble OpenET values were below grass reference ET (ETo) for the majority of the simulation period. Soil moisture and water received data indicated the orchards may have been deficit irrigated. Overall, this study highlights the challenges of ET modeling in citrus orchards and the need for improved estimation of ET for this specialty crop.

Tailoring the surface energy balance algorithm for land-improved (SEBALI) model using high-resolution land/use land cover for monitoring actual evapotranspiration

Access to accurate near real-time actual evapotranspiration (ETa) data is crucial for monitoring and planning water uses, contributing to the sustainable management and development of water resources. However, ongoing ETa modeling efforts need to be improved when applied to small-sized farmlands in rugged landscapes like in the upper Blue Nile Basin. This study aimed to customize the Google Earth Engine-based Surface Energy Balance for the Algorithm Land–Improved (SEBALIGEE) model using high-resolution land use and land cover data. Additionally, the model, originally based on JavaScript, was updated to Python (SEBALIGEEpy), thereby enhancing its accessibility to a broader range of users and modelers. The ETa simulated with an updated version of the model was validated over croplands using publicly available AmeriFlux eddy covariance data. Furthermore, the SEBALIGEEpy-based ETa was evaluated over the Koga smallholder irrigation scheme in the Upper Blue Nile Basin, Ethiopia, by comparing it with the ETa obtained from the remote sensing-based Water Productivity Open-Access Portal (WaPOR) over seven seasons (2015/16–2022/23). The validation results show that the updated model provides more accurate ETa estimates with fewer missing records, with 9–11 % using the updated model compared to 14–29 % using the original SEBALIGEE model. The customized model has the potential to be used for agricultural water management in areas with fragmented land parcels like the Upper Blue Nile basin and beyond.

Estimation of Actual Evapotranspiration and Water Stress in Typical Irrigation Areas in Xinjiang, Northwest China

The increasing water demand and the disparities in the spatiotemporal distribution of water resources will lead to increasingly severe water shortages in arid areas. Accurate evapotranspiration estimation is the basis for evaluating water stress and informing sustainable water resource management. In this study, we constructed a surface energy balance algorithm for land (SEBAL) model based on the Google Earth Engine platform to invert the actual evapotranspiration (ETa) in typical irrigation areas in Xinjiang, northwest China, during the growing season from 2005 to 2021. The inversion results were evaluated using the observed evaporation data and crop evapotranspiration estimated by the FAO Penman–Monteith method. The water stress index (WSI) was then calculated based on the simulated ETa. The impacts of climatic factors, hydrological conditions, land-use change, and irrigation patterns on ETa and WSI were analyzed. The results indicated the following: (1) The ETa simulated by the SEBAL model matched well with the observed data and the evapotranspiration estimated using the FAO Penman–Monteith approach, with correlation coefficients greater than 0.7. (2) The average ETa was 704 mm during the growing season, showing an increasing trend in the irrigation area of the Yanqi Basin (IAY), whereas for the irrigation area of Burqin (IAB) the average ETa was 677 mm during the growing season, showing an increasing trend. The land cover type mainly influenced the spatial distribution of ETa in the two study areas. (3) The WSI in both irrigation areas exhibited a decreasing trend, with the WSI in the IAY lower than that in the IAB. (4) Climate warming, increases in irrigation areas, and changes in cropping patterns led to increased ETa in the IAY and IAB; the overall decreasing trend in the WSI derived from the popularization of agricultural water-saving irrigation patterns in both regions, which reduces ineffective evapotranspiration and contributes positively to solving the water shortage problem in the basins. This study provides insight into water resource management in the Xinjiang irrigation areas.

Evapotranspiration estimation using high-resolution aerial imagery and pySEBAL for processing tomatoes

AbstractThe use of high-resolution aerial imagery for assessing actual crop evapotranspiration $$ \left({ET}_{a}\right)$$ holds the potential to optimize the use of limited water resources in agriculture. Despite this potential, there is a shortage of information regarding the effectiveness of energy balance algorithms, initially designed for satellite remote sensing in estimating $$ {ET}_{a}$$ using aerial imagery. This study addresses this gap by employing the remote sensing model pySEBAL (Surface Energy Balance Algorithm for Land) in conjunction with high-resolution aerial imagery to estimate $$ {ET}_{a}$$ for processing tomatoes. Throughout the 2021 growing season, an aircraft captured multispectral and thermal imagery over a processing tomato field near Esparto, California, USA. Simultaneously, an eddy covariance flux tower within the field measured high-frequency turbulent fluxes and low-frequency biometeorology variables essential for evaluating the energy balance. The comprehensive assessment of energy balance components, including $$ {ET}_{a}$$, yielded compelling evidence that pySEBAL accurately estimated $$ {ET}_{a}$$ at high spatial resolution. The root mean square error (RMSE) and normalized RMSE for various energy balance components were as follows: 33 W m− 2 (12%) for latent heat flux, 29 W m− 2 (35%) for sensible heat flux, 24 W m− 2 (4%) for net radiation, and 10 W m− 2 (15%) for soil heat flux. Additionally, $$ {ET}_{a}$$ exhibited an RMSE and NRMSE of 0.26 mm d− 1 (6%). Moreover, the spatial mapping of $$ {ET}_{a}$$ across the processing tomato field visually depicted the spatial variability associated with irrigation scheduling, crop development, areas affected by disease, and soil heterogeneity. This research underscores the value of high resolution spatial aerial imagery and pySEBAL algorithm for estimating $$ {ET}_{a}$$ variability in the field, a crucial aspect for guiding precision irrigation management and ensuring the optimal use of limited water resources in agriculture.

Generating a monthly variability of sea surface salinity based on source tracing of salt concentration and the estimated SEBAL-evaporation

Abstract The variation and spatial distribution of sea surface salinity (SSS) depend on the geographic condition of the water surfaces and the temporal variation of atmospheric conditions. The SSS might differ in a local coastal area compared to similar situations in global and regional oceans. The SSS values have been estimated based on spatial regression of extracted water-salt concentration as a source tracing of salt against corrected Landsat 8 satellite data during the drought season of April 2023. Here, the electrical conductivity (EC) from the Cimanuk River can be used as primary data. This result, paired with the evaporation-derived surface energy balance algorithm for land (SEBAL) algorithm, explains a monthly SSS variability after the validation using pre-defined resampled regional SSS and evaporation data. The result shows variations in estimated SSS values along with fluctuated SEBAL evaporation ranging from 1.64 to 1.62 dS/m and 1.04 to 0.41 W/m2, respectively. It describes monthly variability and their relationship in a local coastal area limited to the condition of a drought season. However, the validation shows that the root means square error (RMSE) of 1.00 from the SSS map, produced by the regression model involving band 7 of Landsat 8 and 9, has satisfied the reasonable SSS value ranges besides the best accuracy.

Estimation of Evaporation and Drought Stress of Pistachio Plant Using UAV Multispectral Images and a Surface Energy Balance Approach

Water scarcity is a critical abiotic stress factor for plants in arid and semi-arid regions, impacting crop development and production yield and quality. Monitoring water stress at finer scales (e.g., farm and plant), requires multispectral imagery with thermal capabilities at centimeter resolution. This study investigates drought stress in pistachio trees in a farm located in Yazd province, Iran, by using Unmanned Aerial Vehicle (UAV) images to quantify evapotranspiration and assess drought stress in individual trees. Images were captured on 10 July 2022, using a Matrix 300 UAV with a MicaSense Altum multispectral sensor. By employing the Surface Energy Balance Algorithm for Land (SEBAL), actual field evapotranspiration was accurately calculated (10 cm spatial resolution). Maps of the optimum crop coefficient (Kc) were developed from the Normalized Difference Vegetation Index (NDVI) based on standard evapotranspiration using the Food and Agriculture Organization (FAO) 56 methodology. The comparison between actual and standard evapotranspiration allowed us to identify drought-stressed trees. Results showed an average and maximum daily evaporation of 4.3 and 8.0 mm/day, respectively, in pistachio trees. The real crop coefficient (Kc) for pistachio was 0.66, contrasting with the FAO 56 standard of 1.17 due to the stress factor (Ks). A significant correlation was found between Kc and NDVI (R2 = 0.67, p < 0.01). The regression model produced a crop coefficient map, valuable to support precise irrigation management and drought prevention, considering the heterogeneity at the farm scale.

A review on models, products and techniques for evapotranspiration measurement, estimation, and validation

AbstractIn this review study, the major available methods for measurement and estimation of evapotranspiration (ET) are discussed briefly while explaining the latest developments. The best available validation methods are also reviewed and explained. It highlights the importance of accurate ET quantification in managing water resources, evaluating climate change impacts, and supporting crop water requirement management. Measurement methods such as scintillometry, lysimetry, and the eddy covariance (EC) flux method are presented. Additionally, hydrological models are discussed as estimation approaches for actual and potential ET. The paper explores various ET estimation products, particularly those based on remote sensing techniques. Specifically, methods like Mapping EvapoTranspiration at high Resolution with Internalized Calibration (METRIC), Simplified Surface Energy Balance Operational (SSEBop ET), Moderate Resolution Imaging Spectroradiometer (MOD16), Surface Energy Balance Algorithm for Land (SEBAL), Global Land Surface Evaporation: Amsterdam Methodology (GLEAM), Satellite Application Facility on Land Surface Analysis (LSA‐SAF), and Global Land Data Assimilation System (GLDAS) are described. The integration of machine learning (ML) with EC and remote sensing is investigated, with a comprehensive discussion of different ML approaches. Validation methods including the EC method, water balance method‐derived ET (WBET), and statistical techniques are explained. Overall, this review paper provides a comprehensive overview of ET quantification, covering measurement techniques, estimation approaches, remote sensing methods, and the integration of ML. The insights gained from this review contribute to a profound knowledge of ET dynamics and helps those sectors dealing with drought monitoring, water resource management and climate change assessments.

Actual evapotranspiration and energy balance estimation from vineyards using micro-meteorological data and machine learning modeling

Actual evapotranspiration (ETa) can be commonly estimated using numerical models based on i) weather and plant-based parameters, ii) from remotely sensed data and energy balance algorithms, and lately, iii) through the development and implementation of machine learning (ML) modeling techniques. In this work, supervised ML models were developed from a vineyard located in Talca, Chile, (i) to estimate actual evapotranspiration (ETa) (Model 1; M1) using the micrometeorological approach [Eddy Covariance; EC; sensible (H), latent (LE), soil heat fluxes (G) and net radiation (Rn)] and data from an automatic meteorological station (AMS) in reference conditions as ground-truth (inputs); (ii) to estimate energy balance components (Model 2; M2) from AMS data (inputs) and EC energy balance data as targets; (iii) to estimate ETa from the EC’s measured ETa data as target and thermal time data (degree hours; DH) calculated from air temperature with a base of 5 °C increments from 5 – 45 °C as inputs (Model 3; M3) and iv) to estimate energy balance components (targets from EC) from the same inputs of Model 3 (Model 4; M4). Results showed that the developed ML models had high accuracy and performance with no signs of over or under-fitting with a high correlation (R) and slope (b) close to unity (M1; R=0.94; b=0.89; M2; R=0.97; b=0.93; M3; R=0.97; b=0.89–0.95; M4; R=0.98; b=0.97). Furthermore, models were directly deployed over another vineyard located 22 km West of the modeled vineyard at 60 m lower over the sea level with significant performances and R values (R = 0.64–0.87; b = 0.66–1.00 for M1 to M4, respectively). These models could be used for precision irrigation to increase water use efficiency and better control canopy vigor, balance fruit and vegetative components, and ultimately improve berry and wine quality traits.

How much evaporation occurs in Brazilian reservoirs? A multi-model perspective

Estimating evaporative losses from reservoirs is essential for water resource management. While remote sensing provides great opportunities for better constrain this process, there are still large uncertainties related to the behavior of evaporative losses across large domains. Here, we present a multi-model approach combined with remote sensing data to estimate evaporation losses at a national scale for dozens of reservoirs in Brazil. Open water evaporation (Ew) rates were obtained using two surface energy balance (SEB) models (the Google Earth Engine implementation of the Surface Energy Balance Algorithm for Land (geeSEBAL) and the Simplified Surface Energy Balance (SSEBop)). Due to the lack of in situ evaporation data, the estimations were cross-validated with results from the validated product Moderate-Resolution Imaging Spectroradiometer (MODIS) Water Reservoir Monthly Level 3 Global (MOD28C3) and the widely used open water evaporation equation described by Penman. Ew estimates were obtained for 74 large reservoirs, representing almost 70% of the area occupied by artificial water bodies in Brazil. Monthly estimates of Ew showed satisfactory agreement among the models, especially for geeSEBAL, SSEBop, and MOD28C3. Reservoirs with higher Ew were located in the semi-arid Caatinga Biome. The total annual evaporation and net evaporation (difference between lake Ew and surrounding evapotranspiration) for the assessed reservoirs were 46.6 ± 1.0 km³ and 16.4 ± 0.5 km³, respectively, for the period 2003–2021, based on an average of geeSEBAL and SSEBop. We also found that net evaporation is very high in dry climate zones and lower in tropical and humid subtropical climate zones. Our results show the advantage of integrating remote sensing datasets and evaporation models with cloud computing to obtain evaporation estimates in a tropical region and over a large domain. These multi-model evaporation estimates can improve our understanding of water resources in ungauged regions at a large spatial scale.

Assessing Satellite-Derived OpenET Platform Evapotranspiration of Mature Pecan Orchard in the Mesilla Valley, New Mexico

Pecan is a major crop in the Mesilla Valley, New Mexico. Due to prolonged droughts, growers face challenges related to water shortages. Therefore, irrigation management is crucial for farmers. Advancements in satellite-derived evapotranspiration (ET) models and accessibility to data from web-based platforms like OpenET provide farmers with new tools to improve crop irrigation management. This study evaluates the evapotranspiration (ET) of a mature pecan orchard using OpenET platform data generated by six satellite-based models and their ensemble. The ET values obtained from the platform were compared with the ET values obtained from the eddy covariance (ETec) method from 2017 to 2021. The six models assessed included Google Earth Engine implementation of the Surface Energy Balance Algorithm for Land (geeSEBAL), Google Earth Engine implemonthsmentation of the Mapping Evapotranspiration at High Resolution with Internalized Calibration (eeMETRIC) model, Operational Simplified Surface Energy Balance (SSEBop), Satellite Irrigation Management Support (SIMS), Priestley–Taylor Jet Propulsion Laboratory (PT-JPL), and Atmosphere–Land Exchange Inverse and associated flux disaggregation technique (ALEXI/DisALEXI). The average growing season ET of mature pecan estimated from April to October of 2017 to 2021 by geeSEBAL, eeMETRIC, SSEBop, SIMS, PT-JPL, ALEXI/DisALEXI, and the ensemble were 1061, 1230, 1232, 1176, 1040, 1016, and 1130 mm, respectively, and 1108 mm by ETec. Overall, the ensemble model-based monthly ET of mature pecan during the growing season was relatively close to the ETec (R2 of 0.9477) with a 2% mean relative difference (MRD) and standard error of estimate (SEE) of 15 mm/month for the five years (N = 60 months). The high agreement of the OpenET ensemble of the six satellite-derived models’ estimates of mature pecan ET with the ETec demonstrates the utility of this promising approach to enhance the reliability of remote sensing-based ET data for agricultural and water resource management.

Application of surface energy balance Algorithm for land (SEBAL) for mapping spatiotemporal evapotranspiration over various climatic zones of Pakistan

To address the emerging issues of water scarcity in a hydrological realm, estimation of evapotranspiration (ET) at spatiotemporal scale is essential. It is crucial for understanding climate change, particularly for Pakistan's ongoing water resource management policy under the United Nations' Sustainable Development Goals (SDGs), where water is given top priority. In situ techniques can quantify ET, but with a small spatial footprint, it cannot accurately depict its regional distribution. Therefore, geospatial techniques and data, especially high resolution satellite imagery, estimate ET for large heterogeneous surfaces. The Surface Energy Balance Algorithm for Land (SEBAL) was employed in this study to estimate the spatiotemporal distribution of ET in various climatic zones of Pakistan using Landsat 8 images. A total of 48 satellite images were processed for six study sites (Skardu, Kalam, Peshawar, D.I. Khan, Quetta and Tando Jam) for the summer months from 2013 to 2020. Accuracy of SEBAL-based ET was evaluated with ground-based ET. The model performs well with daily rate of ET under southern desert hot climate of Tando Jam with highest pearson’s correlation coefficient (r) of 0.89, lowest root mean squared difference (RMSD) and mean bias error (MBE) of 0.42 mm/day and 0.26 mm/day, respectively. The steppe-hot and extremely humid climate of Peshawar follow Tando Jam, with r value of 0.82, RMSD value of 0.76 mm/day, and MBE value of 0.40 mm/day. Central desert hot climate of Dera Ismail Khan(D.I. Khan) has the lowest (though still better) r value at 0.74, while the other two indices (RMSD and MBE) do not differ significantly from other sites. A high RMSD (0.84 mm/day) for the daily ET rate is recorded in Skardu's northern, cold, snowy climate; the rest of the indices are comparable to D.I. Khan region. In Kalam, the ET rate for various land uses was calculated and was found highest for snow cover (4.66 to 6.45 mm/day), followed by forest cover (3.69 to 5.79 mm/day), and open water body (3.25 to 5.25 mm/day). The SEBAL model exhibitedtremendous potential to be applied under a range of climatic conditionsin this study. Evapotranspiration can provide vital information for monitoring hydrological extremes (droughts and floods), environmental protection, planning climate-resilient agriculture, combating climate change, and sustainable management of water resources.

Estimation of ecosystem water consumption and the suitable scale of cultivated land in the Karamay region and Muzat River basin based on the optimized SEBAL energy balance model and water resource constraint model

Food security is closely related to the development of human society, and the root of food production lies in cultivated land, with water conservancy as its lifeline. This study estimates the ecological water consumption of located in the arid region of Northwest China (the Karamay region and Muzat River basin) from 1990 to 2020 based on the optimized Land Surface Energy Balance Algorithm. The verification accuracy of SEBAL energy balance model is greatly improved after optimization. It was showed an increasing trend in the Karamay region and Muzat River basin, increasing at the rates of 2.84 mm/year and 2.86 mm/year respectively. The suitability of cultivated land was evaluated by combining four periods of 30 m spatial resolution land use/land cover data from 1990, 2000, 2010, and 2020, as well as the Hydrological Statistical Yearbook and Xinjiang Statistical Yearbook. Through the analysis of spatial optimization for cultivated land, it can be inferred that the primary limiting factors affecting cultivated land suitability in the Karamay region are irrigation guarantee rate (54.03%) and soil salinity (11.98%). Muzat River region are irrigation guarantee rate (32.19%) and soil salinity (18.62%). By comparing the scenarios of setting ecological priority and cultivated land priority, it is concluded that under the conditions of water resource constraints and 50%, 75% and 90% design irrigation assurance rates, Karamay still has cultivated land expansion potential, which can be used as the main preparatory reclamation area. In addition to the traditional agriculture irrigation area, the Muzat River basin still has development potential under the condition of ecological priority and no more than 75% irrigation design assurance rate. The study on the cultivated land suitability under the condition of water resource constraints can provide new ideas for food security.

Multi-datasets to monitor and assess meteorological and hydrological droughts in a typical basin of the Brazilian semiarid region.

This study analyzed the meteorological and hydrological droughts in a typical basin of the Brazilian semiarid region from 1994 to 2016. In recent decades, this region has faced prolonged and severe droughts, leading to marked reductions in agricultural productivity and significant challenges to food security and water availability. The datasets employed included a digital elevation model, land use and cover data, soil characteristics, climatic data (temperature, wind speed, solar radiation, humidity, and precipitation), runoff data, images from the MODIS/TERRA and AQUA sensors (MOD09A1 and MODY09A1 products), and soil water content. A variety of methods and products were used to study these droughts: the meteorological drought was analyzed using the Standardized Precipitation Index (SPI) derived from observed precipitation data, while the hydrological drought was assessed using the Standardized Soil Index (SSI), the Nonparametric Multivariate Standardized Drought Index (NMSDI), and the Parametric Multivariate Standardized Drought Index (PMSDI). These indices were determined using water balance components, including streamflow and soil water content, from the Soil Water Assessment Tool (SWAT) model, and evapotranspiration data from the Surface Energy Balance Algorithm for Land (SEBAL). The findings indicate that the methodology effectively identified variations in water dynamics and drought periods in a headwater basin within Brazil's semiarid region, suggesting potential applicability in other semiarid areas. This study provides essential insights for water resource management and resilience building in the face of adverse climatic events, offering a valuable guide for decision-making processes.

Energy Balanced Self-Organizing Networks Algorithm for Three-Dimensional Internet of Things

Energy Balanced Self-Organizing Networks Algorithm for Three-Dimensional Internet of Things

Spatiotemporal trends and evapotranspiration estimation using an improvised SEBAL convergence method for the semi-arid region of Western Rajasthan, India

Abstract The study demonstrates how to estimate evapotranspiration (ET) for the Western Rajasthan region of India utilizing remotely sensed images with the Surface Energy Balance Algorithm for Land (SEBAL). Landsat 8 and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite inputs were used to compute seasonal and annual ET on the Google Earth Engine platform. The assessment utilizing the SEBAL algorithm, in combination with the Food and Agriculture Organization (FAO) Penman–Monteith (PM) and Hargreaves methods, demonstrates that SEBAL has adequate reliability for estimating ET for a spatially large extent in semi-arid regions when evaluated with the Hargreaves method. The values of R2, root-mean-square error (RMSE), and mean bias error (MBE) for FAO-PM were 0.63, 1.65 mm/day, and 1.28 mm/day, respectively. For the Hargreaves method, the values of R2, RMSE, and MBE were 0.96, 0.41 mm/day, and −0.31 mm/day, respectively. The study showed that the northern region witnessed the highest ET due to the availability of abundant surface water for irrigation. Overall, the results demonstrate the SEBAL model's effectiveness in estimating evapotranspiration. A downward trend in ET is observed in the region, mainly due to changing climatic conditions.

Simulation and Assessment of Daily Evapotranspiration in the Heihe River Basin over a Long Time Series Based on TSEB-SM

The high spatial and temporal resolution of recently developed evapotranspiration (ET) products facilitates agricultural water-savings in irrigated areas as well as improved estimates of crop yield, especially in arid and semi-arid regions. However, cloud cover interferes with ET estimates, in particular when using thermal-infrared-based models in temperate and tropical regions. Previous studies have shown that the two-source energy balance (TSEB) model coupled with soil moisture (TSEB-SM) has great potential for estimating surface ET by overcoming this issue. In this study, the TSEB-SM model was first used to generate a spatiotemporally continuous 1 km daily ET dataset across the Heihe River Basin in China from 2000 to 2020, which was then evaluated against four spatially distributed sites (Arou, Huazhaizi, Daman, and Sidaoqiao) and further compared with the two most widely used daily ET datasets (PML-V2 (Penman–Monteith–Leuning) and SEBAL (surface energy balance algorithm for land)). The results showed that the newly developed ET dataset agrees well with ground-based observations and outperforms the PML-V2 and SEBAL products in precisely characterizing the seasonal fluctuations and spatial distribution as well as the spatiotemporal trends of ET. In particular, ET in the Heihe River Basin exhibits clear regional differences. The upstream and midstream grassland and irrigated oasis areas provide much higher annual ET than the downstream desert areas, with a difference of up to 600 mm/year. A three-cornered hat (TCH)-based pixel-by-pixel analysis further demonstrated that the TSEB-SM and PML-V2 products have substantially smaller relative uncertainties as compared to SEBAL ET. In general, the proposed ET datasets are expected to be more beneficial for irrigation scheduling and to provide more efficient water management across the Heihe River Basin.

Corn, soybeans and winter wheat water requirements over the contiguous United States between 2013 and 2021: The application of the SEBALIGEE v2 global model

The open-source fully-automated Surface Energy Balance Algorithm for Land-Improved (SEBALI) Google Earth Engine (SEBALIGEE) estimates 30-m actual evapotranspiration (ET) at a monthly rate, a much needed parameter in many hydrological and agricultural applications. An improved version of the basin-based SEBALIGEE v1 is proposed in this paper. The improvement of SEBALIGEE v1, designated as v2, focuses primarily on incorporating advanced machine learning approaches which have enabled for the first time to implement SEBALIGEE over any scale application and enhance its overall performance. More particularly, an evaluation of the monthly ET estimated from the new algorithm across several fluxnet sites in the US, China, Italy, Belgium, Germany, and France, yielded a Mean Absolute Error (MAE) of 12.22 mm/month versus 14.54 mm/month in the original SEBALIGEE v1. Furthermore, we applied the new any-scale capability of SEBALIGEE v2 over the contiguous United States (CONUS) while emphasizing on the three main crops, including corn, soybeans and winter wheat. Our analysis indicated that all three crops presented two similar ET seasonal cycles with peaks occurring in late spring to the summer (May-Aug) and between October and January, corresponding well to the key stages of crop life cycle. Moreover, corn and soybeans exhibited similar magnitudes of ET (36 ∼ 168 mm/month) and higher than winter wheat (33 ∼ 122 mm/month), with large standard deviations observed in the ET estimates amogst all the crops. On interannual comparisons, the corn and soybeans ET and aKc showed higher values than winter wheat, with the highest and lowest years identified and discussed. Following an exploratory analysis against some of the most common interfering variables such as air temperature, dewpoint temperature, surface net solar radiation, wind speed, SPEI drought index calculated on 14 days and 30-days, it was noted that the surface net solar radiation had the most influencing factor on ET in corn and soybeans plantations with R2 values of ∼0.72. The SPEI-30 stands out for winter wheat, showing a water scarcity tolerance up to a month in most of its developing stages. Different management practices are then recommended in each of these two crops’ categories (corn and soybeans vs. winter wheat).

An Automatic Cascaded Movement Approach to Solve the Energy Replenishment Problem in WPT-Based Mobile WRSNs

In Wireless Rechargeable Sensor Networks (WRSNs), prolonging the sensors’ lifetime is a crucial issue that requires attention. Most studies in the field have focused on path planning for mobile chargers to replenish energy supply for static sensors. Different from the most existing studies, this paper explores the energy replenishment problem in WRSNs with mobile sensors. The paper addresses two issues to extend the network’s lifetime: <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1$</tex-math> </inline-formula> ) how to balance energy consumption between mobile sensors using their mobility capabilities; and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2$</tex-math> </inline-formula> ) how to dispatch redundant mobile sensors, charged and calibrated, to replace low-energy sensors. For the first issue, an energy balancing algorithm is proposed that uses cascaded movement to better the cascading schedule. For the second issue, a redundant mobile sensor dispatch algorithm is proposed that prioritizes mobile sensors most in need of energy replenishment for replacement by a charged and calibrated redundant mobile sensor. The proposed algorithms are extensively evaluated through simulation and have demonstrated their effectiveness in achieving energy balance and prolonging the network lifetime. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Industrial automation applications often involve harsh and hazardous environments with high temperatures, dirt, dust, corrosive materials, and chemical risks. To ensure the longevity of sensors in such conditions, in addition to considering charging, sensors must also be regularly maintained and calibrated for accurate data sensing. However, maintaining and calibrating sensors requires the use of precise instruments that cannot be handled by mobile chargers. To address this issue, we propose studying how mobile sensors can cooperate with each other to complete monitoring tasks and return to the base station for charging, maintenance, and calibration before they run out of energy. The proposed algorithms are applicable to real-life precision monitoring applications in harsh or hazardous environments. This paper provides pseudo code, mathematical expressions, performance evaluation, and results, which can serve as a practical implementation guide for practitioners.

Land use and land cover change impact on characteristics of surface evapotranspiration in semi-arid environment of Western Rajasthan, India

Abstract The development of the Indira Gandhi Nahar Pariyojana (IGNP) in western Rajasthan has significantly impacted land use and land cover (LULC) changes, which have hugely transformed the hydrologic cycle in recent decades. Hence, we investigate the surface evapotranspiration (ET) response to LULC changes in the IGNP for the period from 1990 to 2020. The Surface Energy Balance algorithm (Surface Energy Balance Algorithm for Land) was applied to the Landsat dataset along with local weather data obtained from the Indian Meteorological Department. A machine learning-based random forest classifier was applied to develop the LULC maps. The higher values of ET are mainly distributed in the northwest. Area covered by croplands has shown an increasing trend over the decades at a rate of 734.76 km2·a−1, urban settlements/ built-up areas increased at a rate of 14.67 km2·a−1, barren area decreased gradually at a rate of 751.67 km2·a−1, and area covered by the few water bodies showed minor fluctuations. Overall, the LULC changes have a smaller influence on ET rates but has a considerable impact on the total ET in the IGNP region due to an increase in the area covered by vegetation as a result of increased water availability.