Distribution Of Evapotranspiration Research Articles

Quantifying the Distribution of Evapotranspiration at PV and APV Sites Using Soil Moisture

Solar panels affect the distribution of water and energy reaching the ground causing changes in soil moisture, evapotranspiration and percolation. In the context of Agri-Photovoltaics those changes influence plant growth and yield as well as irrigation demands while large Photovoltaic installations could potentially lead to changes in the water balance of the catchment. In either case, evapotranspiration plays an important role as the installation of panels of any design leads to shading thereby reducing the water loss to the soil through evapotranspiration. As it is difficult to measure evapotranspiration, the authors proceeded using soil moisture observations to quantify evapotranspiration pattern in dry periods. They found on average a 44 % higher evapotranspiration rate over 12 dry periods of varying conditions under the panels compared to a reference area at the research site Pillnitz. However, similar observations at the second site, Weesow show also a reversed behaviour due to reduced soil water availability as a result of the higher evapotranspiration at the reference area.

Main influencing factors of terrestrial evapotranspiration for different land cover types over the Tibetan Plateau in 1982–2014

Introduction: Terrestrial evapotranspiration (ET) over the Tibetan Plateau (TP) has important implications for the global water cycle, climate change, and ecosystem, and its changes and driving factors have drawn increasing attention. Previous research studies have minimally quantified the effects and identified the pathways of the influencing factors on ET over different land surface types.Methods: In this study, we analyze the spatiotemporal distribution and variation of ET over the TP in 1982–2014 based on multiple datasets. Furthermore, the effects of each influencing factor on ET are quantified over different land surface types, and the major influencing factors and their affecting pathways are identified using structure equation modeling (SEM), which is a statistical method used to analyze relationships among multiple variables.Results: The results show that the climatology of ET decreases gradually from southeastern to northwestern TP, with the maximum spatial averaged value of 379.979 ± 0.417 mm a−1 for the fifth generation of European Reanalysis (ERA5) and the minimum of 249.899 ± 0.469 mm a−1 for the Global Land Data Assimilation System (GLDAS). The most significant differences among the ET datasets mainly occur in the summer. The annual ET averaged over the TP presents an increased trend from 1982 to 2014, as shown by all of the ET datasets. However, there are larger discrepancies in the spatial distribution of the increased trend for these datasets. The assessment result shows that the 0.05° land evapotranspiration dataset for the Qinghai–Tibet Plateau (LEDQTP) has the highest temporal correlation coefficient (0.80) and the smallest root-mean-square error (23.50 mm) compared to the observations. Based on LEDQTP, we find that precipitation is the main influencing factor of ET, which primarily affects ET through direct pathways in bare soil and grassland regions, with standardized estimates of 0.521 and 0.606, respectively. However, in meadow and shrub and forest regions, the primary factor influencing ET is air temperature, which is primarily affected by an indirect pathway through a vapor pressure deficit. Air temperature is also the controlling factor in sparse vegetation regions, but it affects ET through a direct pathway.Discussion: This study may provide some new useful information on the effects of climate change on ET in different land cover types over the TP.

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.

Spatiotemporal Variations in Evapotranspiration and Their Driving Factors in Southwest China between 2003 and 2020

The widespread distribution of karst landforms has led to a shortage of water resources in Southwest China. Understanding the spatiotemporal variations in and driving factors of evapotranspiration (ET) in this area is crucial for understanding and predicting severe water resource shortage. This study conducted trend analysis using meteorological data from 2003 to 2020 as well as remote sensing products such as Penman–Monteith–Leuning equation version 2 (PML-V2) ET. The factors influencing the spatial distribution pattern of average ET were identified using a geographical detector. Partial correlation analysis was performed to characterize the relationships between ET and the factors governing its variations, determined using the random forest model. The results demonstrated the following: (1) The average ET decreased with increasing latitude and altitude, primarily affected by the landform type in terms of longitude and displaying “W”-shaped fluctuations. Overall, the annual ET exhibited a significant (p < 0.05) increasing trend, with 72.63% of its area under the increasing trend. (2) The results of the geographic detector indicated sunshine duration as the strongest explanatory factor of the spatial distribution of ET, followed by enhanced vegetation index (EVI), landform type, precipitation, elevation, slope, and aspect. Instead of an individual factor, the interplay between multiple factors more considerably influenced the spatial distribution pattern of ET. (3) The EVI exhibited an overall increasing trend, with a significant increase over 73.59% of the study area and a positive correlation with ET. Thus, the increase in EVI had the strongest impact on ET in the study area, which was further confirmed by the results of the random forest model for 42.92% of the study area. Thus, the present findings clarify the spatiotemporal variations in and driving factors of ET in Southwest China and can serve as a benchmark for policies aiming to develop and manage water resources in this region.

Vegetation as the catalyst for water circulation on global terrestrial ecosystem

Global climate change is expected to further intensify the global water cycle, leading to more rapid evaporation and more intense precipitation. At the same time, the growth and expansion of natural vegetation caused by climate change and human activities create potential conflicts between ecosystems and humans over available water resources. Clarifying how terrestrial ecosystem evapotranspiration responds to global precipitation and vegetation facilitates a better understanding of and prediction for the responses of global ecosystem energy, water, and carbon budgets under climate change. Relying on the spatial and temporal distribution of evapotranspiration, precipitation, and solar-induced chlorophyll fluorescence (SIF) from remote sensing platforms, we decouple the interaction mechanism of evapotranspiration, precipitation, and vegetation in linear and nonlinear scenarios using correlation and partial correlation analysis, multiple linear regression analysis, and binning. Major conclusions are as follows: (1) As a natural catalyst of the global water cycle, vegetation plays a crucial role in regulating the relationship between climate change and the water‑carbon-energy cycle. (2) Vegetation, a key parameter affecting the water cycle, participates in the entire water cycle process. (3) The increase in vegetation productivity and photosynthesis plays a dominant role in promoting evapotranspiration in vegetated areas, while the increase in precipitation dominates the promotion of evapotranspiration in non-vegetated areas.

The effect of time scales on the distribution of evapotranspiration and driving factors in desert grasslands

Promoting the reasonable allocation of evaporation (E) and transpiration (T) at a certain time scale is important for controlling the grassland desertification. Nevertheless, evapotranspiration (ET) and its components on a single time scale often cannot guarantee the dynamic and reasonable distribution of E and T. This study quantified ET and its component distribution in different time scales of the plant-growing season in desert grassland ecosystems to address this contradiction. Further the main driving factors of ET and its components at different time scales were clarified under multi-factor coupling. Results indicated that E was the leading component of ET in the ecosystem, with an average T/ET of 39.64%. The dominant factors of T/ET on different time scales were air temperature (Ta) and surface temperature (Ts), which showed significant negative correlations (p < 0.05). ET and its components showed obvious unimodal changes at the hourly scale. The peaks occurred at 12:00–14:00, and they all increased with increasing radiation (Ra), Ts, Ta, and saturated water vapor pressure difference (VPD). Ta was the dominant factor with a correlation coefficient of 0.76, followed by VPD> Ra>Ts. At the daily scale, ET and its components showed clear seasonal changes, ET was 1.94 mm d-1and T was 0.77 mm d-1. The dominant factors were soil moisture content (SWC) of the 0–10 cm soil layer and Ts. The dominant factors affecting ET and its components in the early, middle, and late stages of growth were relative humidity (RH), SWC, and Ts. At the monthly scale, the cumulative values of ET and its components were the largest in August and the lowest in October, mainly regulated by precipitation (P), whereas, T/ET had no significant correlation with P. The results from this study can provide a scientific basis for vegetation restoration and environmental protection in desert grassland ecological function areas.

Coupled vegetation-climate dominates the evapotranspiration variability of Forest Protection Project

Study regionThe Natural Forest Protection Project area in China (NFPP). Study focusApplying the Pattern-Process-Mechanism theory, we analyzed the spatiotemporal differentiation pattern and change trend of evapotranspiration (ET) in NFPP from 2000 to 2018. We employed methods such as geographical detectors and path analysis to identify the dominant factors and response mechanisms of ET changes in NFPP, specifically those resulting from vegetation recovery and climate change. New hydrological insights for the regionOur study reveals that ET in NFPP exhibits a fluctuating increasing trend, rising from 299.155 mm/yr in 2000–336.805 mm/yr in 2018, with an annual average of 316.486 mm/yr and a change rate of 2.092 mm/yr. The proportion of areas with decreasing ET trend in NFPP (50.944%) is approximately equal to that of areas with increasing ET trend (49.056%). Vegetation restoration (18.959%), water conditions (18.460%), and thermal conditions (56.526%) are the predominant factors that influence the changes and distribution of ET in NFPP. However, the trend of ET and its dominant factors vary by region. We emphasize that, given the circumstances of climate change and the continued increase in ET, future vegetation recovery strategies must consider water usage, as this has critical practical implications for the sustained maintenance of regional ecological engineering outcomes.

Spatiotemporal Variation Characteristics of Reference Evapotranspiration and Relative Moisture Index in Heilongjiang Investigated through Remote Sensing Tools

Reference evapotranspiration (ET0) is one of the significant parameters in agricultural irrigation, especially in Heilongjiang, a big agricultural province in China. In this research, the spatiotemporal variation characteristics of evapotranspiration (ET), relative moisture index (MI) and influencing factors of ET0 in Heilongjiang, which was divided into six ecology districts according to landforms, were analyzed with meteorological data observed over 40 years from 1980 and MOD16 products from 2000 to 2017 using Morlet wavelet analysis and partial correlation analysis. The results indicated that (1) the spatial distribution of ET and PET in Heilongjiang in humid, normal and arid years showed a distribution of being higher in the southwest and lower in the northwest, and higher in the south and lower in the north. The PET was higher than ET from 2002 to 2017, and the difference was small, indicating that the overall moisture in Heilongjiang was sufficient in these years. (2) In the last 40 years, the ET0 increased while the annual MI decreased. The annual minimum of MI in the six regions of Heilongjiang was −0.25, showing that all six regions were drought free. (3) The importance of the meteorological factors affecting ET0 was ranked as average relative humidity &gt; average wind speed &gt; sunshine duration. This research provides scientific guidance for the study of using remote sensing to reverse ET.

Spatio-temporal changes of evapotranspiration over the Zoige Wetland Basin in China using TSEB and GF satellites

ABSTRACT The Zoige wetland located in the eastern margin of the Tibet Plateau, is very important for the runoff regulation and biodiversity in the Yellow River basin. Due to the large non-uniformity of the alpine wetland surface, the use of higher spatial resolution satellite remote sensing data is essential to match or validate the results of ground observation. The land evapotranspiration in this survey was estimated by using the GF-4 satellite data from 2016 to 2020 into the Two Source Energy Balance (TSEB) model, and the estimated evapotranspiration is compared to the ground observation. The results show that: (1) the regression analysis of the actual observed evapotranspiration and the evapotranspiration estimated by the TSEB model shows that the actual evapotranspiration and the evapotranspiration calculated by the TSEB model have a good positive linear correlation (R2 > 0.8) at the Maqu observation site and the Huahu wetland observation site. Therefore, the accuracy of the evapotranspiration calculated by the TSEB model is high. (2) The spatio-temporal distribution of evapotranspiration and relevant land surface variables is generally characterized by high in the southeast and low in the northwest over the Zoige wetland basin. (3) The mean daily evapotranspiration for different GF-4 observation over the Zoige area is as follows: 1.8 mm on 8 April 2018, 3.6 mm on 20 July 2016, 1.4 mm on 29 November 2020 and 0.9 mm on 15 December 2017, and the magnitudes of evapotranspiration of the different surface are as follows: waterbody > grassland > bare land. (4) The histogram of the frequency distribution of evapotranspiration show that 3.2–4.0 mm on 20 July 2016, 0–0.8 mm on 15 December 2017, 0.8–1.6 mm on 8 April 2018 and 1.6–2.4 mm on 29 November 2020 are the maximum evapotranspiration range over the Zoige wetlands area.

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

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

A Hybrid Framework for Simulating Actual Evapotranspiration in Data-Deficient Areas: A Case Study of the Inner Mongolia Section of the Yellow River Basin

Evapotranspiration (ET) plays an important role in transferring water and converting energy in the land–atmosphere system. Accurately estimating ET is crucial for understanding global climate change, ecological environmental problems, the water cycle, and hydrological processes. Machine learning (ML) algorithms have been considered as a promising method for estimating ET in recent years. However, due to the limitations associated with the spatial–temporal resolution of the flux tower data commonly used as the target set in ML algorithms, the ability of ML to discover the inherent laws within the data is reduced. In this study, a hybrid framework was established to simulate ET in data-deficient areas. ET simulation results of a coupled model comprising the Budyko function and complementary principle (BC2021) were used as the target set of the random forest model, instead of using the flux station observation data. By combining meteorological and hydrological data, the monthly ET of the Inner Mongolia section of the Yellow River Basin (IMSYRB) was simulated from 1982 to 2020, and good results were obtained (R2 = 0.94, MAE = 3.82 mm/mon, RMSE = 5.07 mm/mon). Furthermore, the temporal and spatial variations in ET and the influencing factors were analysed. In the past 40 years, annual ET in the IMSYRB ranged between 241.38 mm and 326.37 mm, showing a fluctuating growth trend (slope = 0.80 mm/yr), and the summer ET accounted for the highest proportion in the year. Spatially, ET in the IMSYRB showed a regular distribution of high ET in the eastern region and low ET in the western area. The high ET value areas gradually expanded from east to west over time, and the area increased continuously, with the largest increase observed in the 1980s. Temperature, precipitation, and normalized difference vegetation index (NDVI) were found to be the most important factors affecting ET in the region and play a positive role in promoting ET changes. These results provide an excellent example of long-term and large-scale accurate ET simulations in an area with sparse flux stations.

The Ratio Distribution of Evapotranspiration to Precipitation well Related With Soil Thickness in Karst Watershed of Southwest China

AbstractEvapotranspiration (ET) is predominant variable for water management in various types of ecosystems, and it has been intensively studied through in situ measuring and modeling methods. However, it is challenging to measure actual ET and upscale it to regional level for karst landscapes, where aquifers typically show remarkable and rapid responses to precipitation events. There is an urgent need to quantify water fluxes to provide reliable evidence for the protection and sustainable management of karst water resources. Therefore, in this study, five plots were built to observe actual ET based on Thermal Dissipation Probes, redesigned ventilated chamber and in‐site microlysimeters in a karst watershed in southwest China. Then, three models (Penman‐Monteith‐Leurning, PML; Remote Sensing‐Priestley and Taylor; and Hargreaves) were constructed to upscale ET estimation to the regional level based on Landsat‐8 images and MODIS data. The results showed that: (a) The PML model performed better than other models (p &lt; 0.01) with higher R2 values (0.72 for MODIS images and 0.87 for Landsat‐8 images); (b) Daily ET exhibited significant seasonal variability and different spatial distribution, and yearly ET exhibited well related with soil thickness (R2 = 0.58); (c) Daily ET had a slightly positive correlation with DEM, however, had a negative correlation with ground temperature. This study is helpful to improve accuracy of measured and estimated ET by combining remote sensing data, and it has important implications for upscaling it to regional level. It suggests that ET is strongly regulated by constant supply of water, vegetation coverage, and available energy in karst catchments.

Triple collocation-based multi-source evaporation and transpiration merging

The main objective of this study is to combine three reanalysis-based evaporation (E) and transpiration (T) estimates (i.e., ERA5-Land, GLDAS, and MERRA2) to improve the accuracy of evapotranspiration (ET) over East Asia. Here, we calculate the uncertainty of E and T separately through triple collocation (TC), which then used as a weighting factor to produce merged estimates of E and T. The combination of the two was quantitatively evaluated by comparing it with ground-based measurements. Before assessing the merged ET estimates, the error standard deviation (σmean) of TC was analyzed. ERA5-Land showed the smallest σmean for E for most of the study region, although MERRA2 achieved superior performance for T compared with ERA5-Land. This behavior further resulted in the highest weighting factors of ERA5-Land (59%) and MERRA2 (49%) for E and T, respectively. An evaluation of merged ET estimates by comparison with flux tower measurements revealed the close agreement of the two datasets in crop and forest regions. For croplands, the merged ET estimates showed better statistics compared with the two worst original products. However, the merged ET showed limitation in reflecting crop rotation due to the uncertainty of ERA5-Land E, which yielded the largest weighting factor over cropland. At forest sites, merged ET estimates yielded better statistical performance than the three individual ET products as well as the ET estimated through TC without separating E and T. Detailed analysis of the ratio of T and ET revealed that the merged ET was similar in magnitude to ground-based measurements. Spatial distribution of annual and seasonal ET indicated that the overall improvement of merged ET resulted from superior estimates of T during the spring and summer. These results confirm that separate error characterization of E and T through TC can enhance the accuracy of terrestrial ET estimates.

Integration of flux footprint and physical mechanism into convolutional neural network model for enhanced simulation of urban evapotranspiration

Estimating urban evapotranspiration (ET) is of great significance for urban water resource allocation and assessing the urban heat island effect. However, most current urban ET models are based on the energy balance theory to estimate urban ET. These models lead to significant errors in urban ET simulation due to the surface heterogeneity and the existence of anthropogenic heat fluxes in urban areas. To solve this issue, this study proposes a modified machine learning-based urban ET method that can estimate urban ET at the site and regional scales. To better characterize the heterogeneity of urban surfaces, the flux footprint of in-situ ET and physical mechanism of ET process are integrated into the convolutional neural network (CNN) model. The modified CNN model is tested in a fast-developing city: Shenzhen, China based on two Eddy Correlation (EC) observations. The verification results indicated that coupling flux footprint and physical mechanism into the CNN model could effectively improve the accuracy of urban ET simulation at the site scale. The modified CNN model significantly reduced the root-mean-square-error (RMSE) of 25.8 W/m2 and increased the determination coefficient (R2) of 0.17 compared to the CNN-O model (The CNN-O model is defined as the CNN model do not integrate flux footprint and physical mechanism of ET). Further analyses suggested that fusing flux footprint data into a machine learning model helps enhance ET estimation in regions with high heterogeneity and highly variable wind directions. Moreover, the integration of physical mechanisms significantly enhanced the model capability to simulate extreme ET events. The modified CNN model is further applied to map the spatial distribution of urban ET and reconstruct long-term urban ET changes. The spatial pattern of urban ET exhibited large spatial variability, where the urban ET in water bodies (mean λET larger than 480 W/m2) and vegetation-covered areas (mean λET larger than 260 W/m2) are substantially higher than the impervious surfaces (mean λET less than 30 W/m2). Long-term trend analyses demonstrated that urbanization resulted in decline in urban ET. The average decreasing rate of urban ET is 1.61 mm/yr (P < 0.05), with a 18 % decrease relative to the long-term ET average. The leading causes for the decline of urban ET are the increased impervious surfaces and the decreased radiation. This study improved the simulation accuracy of urban ET and revealed the response of urban ET to urbanization.

Performance evaluation of Satellite-based actual evapotranspiration technique

Estimating crop evapotranspiration is vital for the calculation of irrigation water requirements. Remote sensing data have proven to be a valuable and efficient tool for estimating evapotranspiration. It has been used intensively over the past decade due to free, high temporal and spectral resolution data availability. The main aim of this study was to estimate the evapotranspiration (ET) over a selected area in El-Beheira governorate, Egypt based on the Simplified Surface Energy Balance Index (S-SEBI) using nine Landsat-8 images acquired from January to December 2020. The performance of the studied method was compared with the CROPWAT-8 model. The results revealed the acceptable accuracy of the ET estimated from S-SEBI algorithms with Landsat 8 images according to the coefficient of determination (r2 = 0.82) and root mean square error (RMSE = 0.53 mm/day). Therefore, it is recommended to use the S-SEBI to calculate the spatial evapotranspiration distribution using Landsat-8 images to provide the required information for determining irrigation water requirements and suggesting an efficient water management strategy.

Evaluation of remotely sensed global evapotranspiration data from inland river basins

AbstractRemotely sensed (RS) evapotranspiration (ET) data sets have been widely evaluated and applied at regional scale. However, the research is relatively insufficient on inland basins. RS global ET data sets were evaluated in 19 major inland basins around the world in this study. Monthly ET was estimated by using water balance (ET_WB), as well as multi‐source precipitation and total water storage anomaly data sets from each basin. The main attributes of the ET changes were identified in each basin. The accuracy of 10 ET products was evaluated by monthly ET_WB series over the entire study region and at the basin scales. Results showed that precipitation changes were the main cause of ET changes in most basins (i.e., 11/19). The impacts of human activities, mainly because of increased irrigation diversion, dominated the ET changes in the five basins located in the western and northern regions of the Eurasian inland basin. Accurate estimated ET series in inland basins with a regular unimodal or U distribution of mean monthly ET were also obtained; this was not the case for basins with irregular seasonal ET variations. In general, ET products exhibited accurate monthly ET estimates in most inland basins, but their accuracy was reduced at the Helmand River, Iranian inland rivers, Qaidam, and Turpan Basins. All products tended to underestimate ET, except for the Penman–Monteith–Leuning product. Overall, the global land surface satellite and global land evaporation Amsterdam model products performed the best in inland basins, while the moderate resolution imaging spectroradiometer (MODIS; MOD16A2) and surface energy balance system (SEBS) products performed relatively poorly. In addition, all products except SEBS and MOD16A2 could capture the spatial ET distribution in inland basins, and the spatial correlation coefficient ranged between 0.83 and 0.93. In general, an ensemble of optimized ET products could improve the accuracy of simulated monthly ET and reduce the uncertainty of the simulated series over the entire study region and at the basin scales.

A remote sensing-based method for daily evapotranspiration mapping and partitioning in a poorly gauged basin with arid ecosystems in the Qinghai-Tibet Plateau

Accurate estimation and partitioning of terrestrial evapotranspiration (ET) is essential for understanding the hydrological cycle and water use efficiency at different scales. Such knowledge is particularly important for sustainable water resources management in arid ecosystems. A daily scale framework for continuous ET mapping and partitioning was proposed in this study based on a universal solution of the traditional Ts-VI models. Its essence is to transform the Ts-VI feature space from regional to pixel scale on the basis of surface energy balance principle. Consequently, the ET estimation and partitioning was only related to the boundary conditions at pixel scale, regardless of the Ts-VI configuration over the spatial domain. We demonstrated the framework with MODIS (Moderate Resolution Imaging Spectroradiometer) products in Qaidam Basin, a poorly gauged basin with arid ecosystems in the Qinghai-Tibet Plateau in China. The scheme was performed pixel by pixel using as much of the remote sensing observations as possible, aiming at that it could be applied to other poorly gauged regions. The comparative analysis indicates that our ET estimates agreed well with three readily available ET products and those retrieved from water balance analysis. The advantages of our universal Ts-VI model mainly lie in its high spatial resolution and daily continuity, which enables it to capture the spatial details and temporal variations more accurately. The results show that our annual average ET estimation from 2001 to 2020 was around 179.20 mm/year, which was very close to its annual average precipitation. The annual average soil evaporation (E) and plant transpiration (T) was 168.77 and 10.43 mm/year, accounting for 94 % and 6 % of the total ET, respectively. ET in Qaidam Basin decreased generally from southeast to northwest and from surrounding mountains to the interior with significant vertical zonality. Consequently, ET of different landscapes followed the order: woodland > shrubland > alpine vegetation > alpine meadow > bare rock > alpine grassland > salt crust > desert grassland > bare soil > gobi > sandy land. As an arid basin, the spatial distribution of ET in Qaidam Basin was mainly controlled by precipitation. However, with the further decrease of temperature in high mountains, ET started to be controlled by energy availability rather than water availability. Accordingly, the variations of our ET estimates with altitude increased first and then decreased, with a peak value of 287.67 mm observed at altitudes of 4600–4800 m.

Spatial distribution of evapotranspiration by fractional vegetation cover index on irrigated cropland banana (Musa Spp.) in the semiarid

Water management can be done in many ways, but the methods that use evapotranspiration (ET) are the most common. The Penman-Monteith FAO56 (PM) standard method is effectively used to estimate evapotranspiration and, in this way, ensure proper water application. In addition, remote sensing is also used for water management. The free availability of software and high-resolution satellite imagery opens many possibilities. Methods such as spatial distribution of ET provide an overall view that contributes to better use of water resources. In this sense, the objective of this study is to demonstrate the spatial distribution of ET determined by the PM method by using an index that measures fractional vegetation cover (Fc). For this purpose, data from weather stations and Landsat 8 collection 2 level 1 image from the OLI/TIRS sensor in spectral bands 4 and 5 were used. The study area is a dwarf silver banana plantation located in the semiarid region of northeastern Brazil, in the Missão Velha city area. The spatialization starts with the determination of the normalized difference vegetation index (NDVI), followed by the determination of the Fc, using the ET from the PM method together with the crop coefficient (Kc). Evapotranspiration mapping images with internalized calibration (METRIC) corresponding to the same location and date were also used to compare and analyze the results. The novelty of this study is utilizing the ET determinate with weather station data and make the spatial distribution of ET (SPD-ET) in satellite images, from a practical and easy way, which can be quickly replicated and used by anyone. The linear regression between SPD-ET and METRIC-ET gives an R2 above 0.80; similar results were obtained for the linear regression between EToF and Fc, with an R2, also above 0.80. The mean values between METRIC-ET and SPD-ET have a deviation of 1.62 mm d-1. The mean absolute percent error between METRIC-ET and SPD-ET was 9.75%, while between EToF and Fc 5.28%. The mean absolute percent error between METRIC-ET and SPD-ET was 9.75%, whereas between EToF and Fc it was 5.28%. Since the difference between the methods is just under 10%, we can conclude that spatializing ET with Fc is as accurate as ET, which was obtained from METRIC.

A novel multi-scale parameter estimation approach to the Hargreaves-Samani equation for estimation of Penman-Monteith reference evapotranspiration

The main focus of this study is to develop a multi-scale surrogate model for the FAO-56 Penman-Monteith (PM) evapotranspiration (ETo) using Hargreaves-Samani (HS) equation, which uses only temperature as a hydrometeorological variable to estimate ET. This feature is particularly useful for scarce data regions and climate change impact assessment studies, where the direct estimation of ETo from the PM equation can be problematic. As the parameters of the HS equation may vary across space, a Bayesian approach was adopted to estimate (or recalibrate) them rather than relying on the fixed values as suggested in the traditional model. The Bayesian approach allows a sound development of our model in a multi-scale temporal framework, where the ETo at daily, monthly and annual scales are jointly used to estimate the HS equation parameters. The proposed and reference models are applied and tested using meteorological data from 17 stations located across the Han river basin in South Korea. The results indicate that the traditional HS equation with fixed parameters and without recalibration tends to overestimate the reference ET for all stations. The locally recalibrated approach to the HS equation at a daily temporal scale can effectively reduce the systematic bias associated with the use of the traditional HS equation but fails to reproduce the underlying distribution of ETo at different temporal scales (e.g., monthly and annual). This leads to a large systematic bias in ETo at these scales. In contrast, the proposed multi-scale surrogate model offers a more precise estimation of the reference ET at a daily timescale as well as at the aggregated monthly and annual temporal scales. This is particularly useful to minimize the systematic bias often observed when using traditional surrogate models for the reference ET in hydrological studies such as rainfall-runoff modeling and assessment of climate change impact on water resources.

Climate and management impacts on the spatiotemporal dynamics of water-carbon fluxes in the North China Plain

Evapotranspiration (ET) and gross primary production (GPP) over agroecosystems could be noticeably altered by climate change and human activities. A better understanding of the impacts on water-carbon interactions and the underlying mechanisms, are crucial to improve water use efficiency (WUE). Comparing to 23 site-years of observations, regional ET dataset and statistical yields, the Remote Sensing-based cropland Ecohydrological Model (ReSEM) was validated to estimate the spatiotemporal dynamics of ET, GPP and WUE for dryland agroecosystems in the North China Plain (NCP) from 2001 to 2018. Spatially, the distributions of mean annual ET, GPP and WUE were comparable which changed obviously in different climatic conditions while varied slightly between various irrigation areas. The significantly increased WUE occurred in 38.6 % cropland, mainly located in the humid south regions with less irrigation requirement and the central sub-humid areas along the Yellow River with access to surface irrigation. The averaged increasing trend of WUE in the heavily irrigated semi-arid areas was relatively low. The temporal dynamic of regional mean WUE indicated a significantly increased rate of 0.026 gC kg−1H2O per year during 2001–2018. The spatiotemporal trends of WUE were mainly resulted from the significantly increased GPP rather than ET. The experimental simulations demonstrated that agricultural management dominated the interannual trend of WUE, whose relative contribution (79.5 %) was obviously larger than that of air CO2 concentration (40.2 %) and climate variable change (−19.7 %). Besides, the effects of agricultural management on WUE were further divided based on the classified six cropping systems, 82.4 % of which was attributed to the management of winter wheat-summer maize rotation system. This study sheds light on the improvement of WUE for dryland agroecosystem under changing environments and highlights the dominant impact of agricultural management.