Evapotranspiration Datasets Research Articles

Uncertainty Analysis and Data Fusion of Multi-Source Land Evapotranspiration Products Based on the TCH Method

Evapotranspiration (ET) is a very important variable in the global water cycle, carbon cycle, and energy cycle. However, there are still some uncertainties in existing ET products. Therefore, this paper evaluates the uncertainty of three widely used global ET products (ERA5-Land, GLDAS-Noah, and MERRA-2) based on the three-cornered hat (TCH) method, and generates a new ET product based on this. The new product is a long-series global monthly ET dataset with a spatial resolution of 0.25° × 0.25° and a time span of 21 years. The results show that ERA5-Land (8.46 mm/month) has the lowest uncertainty among the three ET products, followed by GLDAS-Noah (8.81 mm/month) and MERRA-2 (11.78 mm/month). The new product (TCH) captures ET trends in different regions as well as validating against in situ flux observations, and it exhibits better performance than the re-analysis dataset (ERA5-Land) in vegetation classifications such as evergreen needle-leaf forest, grassland, open shrubland, savanna, and woody savanna. The linear trend analysis of the new product shows a significant decreasing trend in south-eastern South America and southwestern parts of Africa, and an increasing trend in almost all other regions, including eastern North America, north-eastern South America, western Europe, north-central Africa, southern Asia, and south-eastern Oceania.

Long-term gridded land evapotranspiration reconstruction using Deep Forest with high generalizability

Previous datasets have limitations in generalizing evapotranspiration (ET) across various land cover types due to the scarcity and spatial heterogeneity of observations, along with the incomplete understanding of underlying physical mechanisms as a deeper contributing factor. To fill in these gaps, here we developed a global Highly Generalized Land (HG-Land) ET dataset at 0.5° spatial resolution with monthly values covering the satellite era (1982–2018). Our approach leverages the power of a Deep Forest machine-learning algorithm, which ensures good generalizability and mitigates overfitting by minimizing hyper-parameterization. Model explanations are further provided to enhance model transparency and gain new insights into the ET process. Validation conducted at both the site and basin scales attests to the dataset’s satisfactory accuracy, with a pronounced emphasis on the Northern Hemisphere. Furthermore, we find that the primary driver of ET predictions varies across different climatic regions. Overall, the HG-Land ET, underpinned by the interpretability of the machine-learning model, emerges as a validated and generalized resource catering to scientific research and various applications.

GeeSEBAL-MODIS: Continental-scale evapotranspiration based on the surface energy balance for South America

Monitoring actual evapotranspiration (ET) is critical for the accurate assessment of water availability and water resources management, especially in areas with dry climates and frequent droughts. The Surface Energy Balance Algorithm for Land (SEBAL) has been used over several land and climate conditions, and is able to estimate ET at field scale with high accuracy. However, model complexity and subjective parameterization have hindered its operationalization, until the recent development of the geeSEBAL model, which implements the SEBAL model on the Google Earth Engine platform. Here, we present a unique methodology for a continental-scale application of SEBAL, called geeSEBAL-MODIS, that employs novel land surface temperature normalization techniques, enabling the application of contextual ET models to very large scales. We introduce a dynamic ET dataset for the entire South American continent, between 2002 and 2021, at 500 m spatial and 8 days temporal resolution. The satellite-based data were compared against daily ET measured at 27 flux towers as well as water balance-based annual ET from 29 large river basins. geeSEBAL-MODIS data were also compared to eight state-of-the-art global ET datasets. At local scale, geeSEBAL-MODIS demonstrated a satisfactory performance (correlation (r) = 0.65, Kling-Gupta Efficiency (KGE) = 0.64, Mean Absolute Error (MAE) = 0.83 mm day−1 (24.7 %) and Root Mean Squared Error (RMSE) = 1.07 mm day−1 (31.8 %)), with negligible bias. At basin scale, geeSEBAL-MODIS generally underestimated ET (bias = -85 mm year−1, r = 0.65, KGE = 0.47, MAE = 107 mm year−1 (10.1 %) and RMSE = 137 mm year−1 (12.9 %)). Compared to other global datasets, geeSEBAL-MODIS demonstrated better performance over multiple South American biomes, climates and land cover types. The developed dataset also provides lower errors (local monthly RMSE = 23.0 mm month−1 and basin annual RMSE = 138 mm year−1) and when compared to the performance of the global datasets (local monthly RMSE between 23.9 and 30.1 mm month−1 and basin annual RMSE between 161 and 308 mm year−1). The analyses demonstrate that geeSEBAL-MODIS can be used as a tool for monitoring climate change and human-related impacts on ET. The geeSEBAL-MODIS model opens the path for high accuracy global ET monitoring at moderate to high resolution, supporting advances in water resources management around the globe.

Hydrological digital twin model of a large anthropized italian alpine catchment: The Adige river basin

Understanding and simulating the hydrological cycle, especially in a context of climate change, is crucial for quantitative water risk assessment and basin management. The hydrological cycle is complex as it is a combination of non-linear natural processes and anthropogenic influences that alter landforms and water flows. Human-induced changes of relevance, including changes in land uses, construction of dams and artificial reservoirs, and diversion of the river course, lead to changes in water flows throughout the basin. These should be explicitly accounted for a realistic representation of the anthropogenically altered hydrological cycle. Such a realistic representation of the hydrological cycle is a necessary input for the water risk assessment in a particular region. In this paper, we present a hydrological digital twin (HDT) model of a large anthropized alpine basin: the Adige basin located in the northeast of Italy.Most catchments model often overlook land-uses changes over time and forget to model reservoir operation and their influence over time on water flow. Yet, for example, the Adige basin has>30 reservoirs affecting the water flow. We therefore use the GEOframe modeling framework to demonstrate the ability to create a hydrological twin model accounting for these anthropogenic changes.Specifically, we model each component of the water cycle over 39 years (1980–2018) at daily timescale through calibration of the Adige HDT with a multi-site approach using discharge data of 33 stations, based on a high-resolution (1 km) temperature and precipitation dataset and a calculated crop potential evapotranspiration (PETc) dataset, which accounts for human-induced change of the land cover over time. The modeling system also includes the simulation of artificial reservoirs and dams by the dynamically zoned target release (DZTR) reservoir model.The Adige HDT is assessed/validated/compared through a variety of hydrological processes (i.e., river and reservoir discharges, PETc and actual evapotranspiration, snow, and soil moisture) and data sources (i.e., observations and remote sensing data).Overall, the HDT reproduces well the measured discharge in space and time with a Kling Gupta Efficiency (KGE) above 0.7 (0.8) for 30 (23) of the 33 gauge-stations. For 7 artificial reservoirs with available data, the reservoir turbinated discharges are successfully reproduced with an average KGE of 0.92. A comparison between modeled and MODIS remote sensing snow data showed an average error of < 10% across the entire basin; the model also presented a good spatio-temporal agreement both with GLEAMS potential (and actual evapotranspiration) with an average KGE of 0.63 (0.60) and a high-level of correlation (0.5 on average) with the ASCAT satellite retrieved soil moisture.The findings of this paper demonstrate the potential of the open-source, component-based, GEOframe system to build a HDT, to provide a reliable and long term (39 years) estimation of all the water cycle components in a complex anthropized river basin at high spatial resolution. Spatially detailed HDT models results of this type can be used to inform basin-wise adaptation policy decisions and better water management practices in a time of changing climate.

A global 5 km monthly potential evapotranspiration dataset (1982–2015) estimated by the Shuttleworth–Wallace model

Abstract. As the theoretical upper bound of evapotranspiration (ET) or water use by ecosystems, potential ET (PET) has always been widely used as a variable linking a variety of disciplines, such as climatology, ecology, hydrology, and agronomy. However, substantial uncertainties exist in the current PET methods (e.g., empiric models and single-layer models) and datasets because of unrealistic configurations of land surface and unreasonable parameterizations. Therefore, this study comprehensively considered interspecific differences in various vegetation-related parameters (e.g., plant stomatal resistance and CO2 effects on stomatal resistance) to calibrate and parametrize the Shuttleworth–Wallace (SW) model for forests, shrubland, grassland, and cropland. We derived the parameters using identified daily ET observations with no water stress (i.e., PET) at 96 eddy covariance (EC) sites across the globe. Model validations suggest that the calibrated model could be transferable from known observations to any location. Based on four popular meteorological datasets, relatively realistic canopy height, time-varying land use or land cover, and the leaf area index, we generated a global 5 km ensemble mean monthly PET dataset that includes two components of potential transpiration (PT) and soil evaporation (PE) for the 1982–2015 time period. Using this new dataset, the climatological characteristics of PET partitioning and the spatiotemporal changes in PET, PE, and PT were investigated. The global mean annual PET was 1198.96 mm with PT/PET of 41 % and PE/PET of 59 %, controlled moreover by PT and PE of over 41 % and 59 % of the globe, respectively. Globally, the annual PET and PT significantly (p&lt;0.05) increase by 1.26 and 1.27 mm yr−1 over the last 34 years, followed by a slight decrease in the annual PE. Overall, the annual PET changes over 53 % of the globe could be attributed to PT, and the rest to PE. The new PET dataset may be used by academic communities and various agencies to conduct climatological analyses, hydrological modeling, drought studies, agricultural water management, and biodiversity conservation. The dataset is available at https://doi.org/10.11888/Terre.tpdc.300193 (Sun et al., 2023).

Spatial and temporal variations of vegetation water use efficiency and its response to climate change and human activities in the West Liao River Plain, China

Water use efficiency [WUE = gross primary production (GPP)/evapotranspiration (ET)] is an important indicator of the degree of coupling between carbon and water cycles in ecosystems. However, the response of the carbon and water cycles to climate change and human activities,as well as the underlying driving mechanisms in the West Liao River Plain (WLRP), a typical farming–pasturing ecotone in northern China, remain unclear. This study examined the temporal and spatial variation characteristics of WUE in the WLRP from 2000 to 2020 using linear regression and the coefficient of variation (CV) method based on MODIS GPP and ET datasets. The relationships between WUE, meteorological factors, and human activities as well as the mechanism driving WUE changes were revealed through correlation analyses, residual analysis, and the grey correlation model. The interannual change of WUE from 2000 to 2020 showed a fluctuating but weakly upward trend. The intra-annual change in WUE followed an M-type bimodal trend, with two peaks from May to June and August to September. Areas with increased WUE accounted for 50.82% of the study area, and 11.11% of these showed a significant increasing trend. WUE was mainly positively correlated with temperature and solar radiation and negatively correlated with precipitation and VPD and presented obvious regional differences. Solar radiation had the most significant impact on WUE. WUE change is not entirely driven by climate change, and human activities have also played an important role. In areas where WUE increased, The average contribution rate of climate change was 72.4%, and that of human activities was 27.6%. This study reveals the temporal and spatial dynamics of WUE in the WLRP and highlights the influence of human activities on WUE changes.

Tackling the problem of noisy IoT sensor data in smart agriculture: Regression noise filters for enhanced evapotranspiration prediction

In smart agriculture, the accurate prediction of evapotranspiration plays a crucial role in optimizing water usage and maximizing crop yield. However, the increasing adoption of IoT sensor technologies has resulted in the accumulation of large amounts of data, which are frequently contaminated by noise and pose a significant challenge to extract reliable knowledge through data modeling. This research addresses the problem of noisy IoT sensor data and its impact on evapotranspiration prediction, an essential aspect of agricultural practices. The effect of noise on sensor variables and evapotranspiration is extensively analyzed by simulating different noise levels in evapotranspiration datasets collected from various agricultural areas in Spain, enabling a comprehensive evaluation of its impact on the performance of data science models. Despite the potential consequences of this type of errors, a noise preprocessing stage is often overlooked in existing literature in this field, which is necessary to improve data quality prior to modeling. In order to address this challenge, this paper proposes the usage of regression noise filters as approach to mitigate the detrimental effects of noisy IoT sensor data on evapotranspiration prediction. Additionally, we introduce the rgnoisefilt R package, which offers a practical and efficient implementation of noise filtering techniques for regression datasets, providing a valuable solution for handling noisy data in smart agriculture applications. The experimental results obtained emphasize the negative impacts of noise on evapotranspiration prediction performance and highlight the importance of an appropriate data treatment to mitigate system deterioration. Furthermore, the findings of this research emphasize the efficacy of the regression noise filters implemented in the rgnoisefilt software, enhancing the performance of the models built and providing a valuable tool for improving data quality in smart agriculture.

A Satellite-Based Approach for Quantifying Terrestrial Water Cycle Intensity

The terrestrial water cycle intensity (WCI) is a widely used tool to quantify the impact of climate change on the distribution of global water resources. In this study, a satellite-based WCI was tested by comparing the parameter-elevation regressions on independent slopes model (PRISM) precipitation estimates with those of the Global Precipitation Measurement (GPM) satellite mission across the contiguous United States (CONUS), based on an existing Köppen–Geiger climate classification for the CONUS. Both precipitation products were not statistically different across all climate classes. Consequently, satellite-based WCI changes between two multiannual periods (2001 to 2009 and 2010 to 2019) were calculated at a 0.1-degree spatial resolution using the GPM and a validated global evapotranspiration dataset. This study showed that: (1) The water cycle is speeding up in many parts of the CONUS, particularly the West, driven by recent increases in both precipitation and evapotranspiration through much of the region. (2) The El Niño-Southern Oscillation (ENSO) may be influencing the WCI of the CONUS by driving precipitation in the west, southeast, and parts of the north, and dryness in the northeast regions. The hydrological impacts of these results cannot be generalized. However, flood and drought risks, water availability and quality issues remain key primary concerns.

Dense flux observations reveal the incapability of evapotranspiration products to capture the heterogeneity of evapotranspiration

Accurate terrestrial evapotranspiration (ET) estimation over complex surfaces with spatial heterogeneity is crucial for local, regional, and global applications. Currently, a variety of global ET products with different spatiotemporal resolutions have been developed and evaluated. However, little is known about their performance in capturing the heterogeneity of ET over complex surfaces. Focusing on the Heihe River Basin (HRB), a typical arid and semi-arid region that is hydrologically vulnerable, this study compared ET from eleven global ET products (six remotely- sensed ET, two land surface model ET, one hydrological model-based ET, one reanalysis ET, and one synthesis ET) and one regional ET dataset against dense eddy covariance observations in terms of the magnitude, seasonal cycle, and spatial pattern. In general, the remotely-sensed ET and synthesis ET outperformed other categories, with the operational Simplified Surface Energy Balance (SSEBop), Penman-Monteith–Leuning (PML), Moderate Resolution Imaging Spectroradiometer (MOD16) and Global LAnd Surface Satellite (GLASS) performing relatively better (root mean square error ranging from 1.22 to 1.57 mm d-1). Across all the land cover types, ET products reproduced a relatively feasible ET over the desert steppe, meadow, and barren, but substantially underestimated ET over ecosystems with high ET values but a low land area fraction over HRB (cropland, wetland, and forest). This highlights the importance of accurately representing the heterogeneity and local climates in the complex land surfaces for ET quantification in regional water resource management.

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

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

Triple collocation-based merging of multi-source gridded evapotranspiration data in the Nordic Region

Accurate evapotranspiration (ET) data are required for many hydro-meteorological applications. Compared with the traditional evaluation that requires in-situ measurements, the triple collocation (TC) technique estimates geophysical product errors without the need for ground truth, which is especially suitable over large areas lacking a dense in-situ network. However, violations of the zero-error cross-correlation (ECC) assumption are found to be the dominant sources of impairing the TC robustness. This study presents the first application of a TC-based merging framework that optimally considers ECC to merge multi-source gridded ET products in the Nordic Region during 2003–2018. The ECC estimates of each ET dataset pair calculated by the quadruple collocation approach are used to select the qualified triplets from four products, including FLUXCOM, Global Land Surface Satellite (GLASS), Global Land Evaporation and Amsterdam Model (GLEAM), and Penman-Monteith-Leuning Version 2 (PML-V2). Then the ET merged datasets are generated by weighting TC-based rescaled error variances of the parent datasets through least square merging. Finally, the accuracy of both the parent and the merged datasets are assessed with the Integrated Carbon Observation System (ICOS) flux data in the Nordic Region based on multiple statistical metrics. Results demonstrate that the ECC values provide intuitive evidence for filtering unqualified TC triplets. Both the absolute and relative error variances (signal-to-noise ratio) are considered for ET dataset evaluation. Overall PML-V2 has the best performance among the evaluated four products. Two merged ET datasets with the reference climatology of FLUXCOM outperform all parent products with the lowest errors by using ICOS data as reference among all sites – indicating the feasibility of TC technique for improving ET accuracy in the Nordic Region. This study also analyses the impacts of reference climatology selection on the TC merged results.

Bark beetle impacts on forest evapotranspiration and its partitioning

Insect outbreaks affect forest structure and function and represent a major category of forest disturbance globally. However, the resulting impacts on evapotranspiration (ET), and especially hydrological partitioning between the abiotic (evaporation) and biotic (transpiration) components of total ET, are not well constrained. As a result, we combined remote sensing, eddy covariance, and hydrological modeling approaches to determine the effects of bark beetle outbreak on ET and its partitioning at multiple scales throughout the Southern Rocky Mountain Ecoregion (SRME), USA. At the eddy covariance measurement scale, 85 % of the forest was affected by beetles, and water year ET as a fraction of precipitation (P) decreased by 30 % relative to a control site, with 31 % greater reductions in growing season transpiration relative to total ET. At the ecoregion scale, satellite remote sensing masked to areas of >80 % tree mortality showed corresponding ET/P reductions of 9–15 % that occurred 6–8 years post-disturbance, and indicated that the majority of the total reduction occurred during the growing season; the Variable Infiltration Capacity hydrological model showed an associated 9–18 % increase in the ecoregion runoff ratio. Long-term (16–18 year) ET and vegetation mortality datasets extend the length of previously published analyses and allowed for clear characterization of the forest recovery period. During that time, transpiration recovery outpaced total ET recovery, which was lagged in part due to persistently reduced winter sublimation, and there was associated evidence of increasing late summer vegetation moisture stress. Overall, comparison of three independent methods and two partitioning approaches demonstrated a net negative impact of bark beetles on ET, and a relatively greater negative impact on transpiration, following bark beetle outbreak in the SRME.

Revisiting the role of transpiration in the variation of ecosystem water use efficiency in China

Efforts to develop effective climate strategies necessitate a better understanding of the relationship between terrestrial water and carbon cycles. Water use efficiency (WUE) has been often used to characterize this relationship, while the role of transpiration (T) in the variation of ecosystem WUE has been less investigated. Here, we partitioned WUEET (the ratio of gross primary productivity (GPP) to evapotranspiration (ET)) into a two-component process, i.e., the ratio of gross primary productivity to plant transpiration, GPP/T, that is WUET, and the ratio of plant transpiration to evapotranspiration, T/ET. Based on two GPP datasets (i.e., GPP based on the light use efficiency model or the vegetation index- NIRv) and the GLEAM ET dataset, this study investigated the role of T in the variation of WUE in the ecosystem level and how the role is affected by drought. We found that drought can lead to the change of ET partitioning, thus affecting the variability of WUE. The variability of WUEET was dominated by WUET. In general, the proportion of T increased gradually from humid to arid areas. To adapt to drought conditions, vegetation in arid areas tend to have a high stress resistance by increasing their WUE. We further found that WUET has stronger seasonal stability than WUEET. GPP dominated WUEET variability in humid/sub humid areas, while ET and GPP jointly dominated WUEET variability in semi-arid/arid areas. GPP dataset based on light use efficiency (LUE) could better reflect the impact of drought on vegetation. This study contributes to a better understanding of the change mechanism of ecosystem WUE and emphasizes the critical role of physiological process components in water-carbon cycling.

Development of a Benchmark Eddy Flux Evapotranspiration Dataset for Evaluation of Satellite-Driven Evapotranspiration Models Over the CONUS

A large sample of ground-based evapotranspiration (ET) measurements made in the United States, primarily from eddy covariance systems, were post-processed to produce a benchmark ET dataset. The dataset was produced primarily to support the intercomparison and evaluation of the OpenET satellite-based remote sensing ET (RSET) models and could also be used to evaluate ET data from other models and approaches. OpenET is a web-based service that makes field-delineated and pixel-level ET estimates from well-established RSET models readily available to water managers, agricultural producers, and the public. The benchmark dataset is composed of flux and meteorological data from a variety of providers covering native vegetation and agricultural settings. Flux footprint predictions were developed for each station and included static flux footprints developed based on average wind direction and speed, as well as dynamic hourly footprints that were generated with a physically based model of upwind source area. The two footprint prediction methods were rigorously compared to evaluate their relative spatial coverage. Data from all sources were post-processed in a consistent and reproducible manner including data handling, gap-filling, temporal aggregation, and energy balance closure correction. The resulting dataset included 243,048 daily and 5,284 monthly ET values from 194 stations, with all data falling between 1995 and 2021. We assessed average daily energy imbalance using 172 flux sites with a total of 193,021 days of data, finding that overall turbulent fluxes were understated by about 12% on average relative to available energy. Multiple linear regression analyses indicated that daily average latent energy flux may be typically understated slightly more than sensible heat flux. This dataset was developed to provide a consistent reference to support evaluation of RSET data being developed for a wide range of applications related to water accounting and water resources management at field to watershed scales.

Optimization of Irrigation Water to Maximize Transplanted Aman Rice Production in Selected Areas of Bangladesh

In rainfed and irrigated agriculture, optimization of irrigation scheduling is very essential to maximize the crop production using the limited of agricultural water resources. A study was conducted to estimate requirement of irrigation and its scheduling of T. Aman (wet season) rice in the Northwest and Southeast region of Bangladesh using CROPWAT model. This paper utilized the daily rainfall and reference evapotranspiration data sets, obtained from seven weather stations for the period of 1981 to 2015. Effective rainfall and reference crop evapotranspiration (ETo) were calculated using the USDA Soil Conservation method and Penman-Monteith method, respectively. The simulation model of this explored the appropriate transplanting date to utilize maximum rainwater and to avoid supplemental irrigation. Four transplanting dates 15 July, 30 July, 15 August and 30 August were considered for transplanting T. Aman rice in the study areas. The optimization results showed that the water requirement was lower of the rice crop transplanting on 15 July in all locations due to higher effective rainfall. No supplemental irrigation was required for T. Aman cultivation transplanted on 30 July in Rangpur, Dinajpur, Bogura and Mymensingh regions since rainfall was sufficient to meet the demand of the crop. Therefore, this period was found for suitable and recommended for transplanting of T. Aman rice in these four locations. Transplanting after 30 July in these four locations needed supplemental irrigations. This study also revealed that 30 July transplanting required one supplemental irrigation in Jashore and Rajshahi regions due to insufficient rainfall. This period was also considered as the appropriate transplanting time in these two locations. However, in Pabna, one supplemental irrigation must be applied for T. Aman even transplanting on 15 July owing to in adequate rainfall. Irrigation requirement and number of irrigations were gradually increased with delaying the transplanting time. Early transplanting demanded less irrigation than late transplanting. Bangladesh Rice J. 25 (2) : 11-20, 2021

A daily and 500 m coupled evapotranspiration and gross primary production product across China during 2000–2020

Abstract. Accurate high-resolution actual evapotranspiration (ET) and gross primary production (GPP) information is essential for understanding the large-scale water and carbon dynamics. However, substantial uncertainties exist in the current ET and GPP datasets in China because of insufficient local ground measurements used for model constraint. This study utilizes a water–carbon coupled model, Penman–Monteith–Leuning Version 2 (PML-V2), to estimate 500 m ET and GPP at a daily scale. The parameters of PML-V2(China) were well calibrated against observations of 26 eddy covariance flux towers across nine plant functional types in China, indicated by a Nash–Sutcliffe efficiency (NSE) of 0.75 and a root mean square error (RMSE) of 0.69 mm d−1 for daily ET, respectively, and a NSE of 0.82 and a RMSE of 1.71 g C m−2 d−1 for daily GPP. The model estimates get a small Bias of 6.28 % and a high NSE of 0.82 against water-balance annual ET estimates across 10 major river basins in China. Further evaluations suggest that the newly developed product is better than other typical products (MOD16A2, SEBAL, GLEAM, MOD17A2H, VPM, and EC-LUE) in estimating both ET and GPP. Moreover, PML-V2(China) accurately monitors the intra-annual variations in ET and GPP in the croplands with a dual-cropping system. The new data showed that, during 2001–2018, the annual GPP and water use efficiency experienced a significant (p&lt;0.001) increase (8.99 g C m−2 yr−2 and 0.02 g C mm−1 H2O yr−1, respectively), but annual ET showed a non-significant (p&gt;0.05) increase (0.43 mm yr−2). This indicates that vegetation in China exhibits a huge potential for carbon sequestration with little cost in water resources. The PML-V2(China) product provides a great opportunity for academic communities and various agencies for scientific studies and applications, freely available at https://doi.org/10.11888/Terre.tpdc.272389 (Zhang and He, 2022).

Variation in the hydrological cycle in the Three-River Headwaters Region based on multi-source data

The hydrological processes in the Three-River Headwaters Region (TRHR), which is located in the Qinghai-Tibetan Plateau and includes the Yangtze River Headwater Region (YARHR), the Yellow River Headwater Region (YERHR), and the Lantsang River Headwater Region (LARHR), have changed under climate warming. Based on multi-source data, the spatial and temporal changes in precipitation, evapotranspiration, soil water storage, glacier melt, snowmelt and runoff in the Three-River Headwaters Region from 1982 to 2014 were comprehensively analysed. The annual precipitation data for the Three-River Headwaters Region from ERA5-Land, the Climatic Research Unit, the China Meteorological Forcing Dataset and the Global Land Data Assimilation System (GLDAS) all showed an increasing trend; the annual evapotranspiration data from ERA5-Land, Global Land Data Assimilation System, Global Land Evaporation Amsterdam Model (GLEAM) and Terrestrial Evapotranspiration Dataset across China (TEDC) all showed an increasing trend; and the annual soil water storage data from ERA5-Land, Global Land Data Assimilation System and Global Land Evaporation Amsterdam Model all showed an increasing trend. The annual snowmelt data from ERA5-Land, Global Land Data Assimilation System and SMT-Y datasets all showed a decreasing trend. The annual glacier melt increased in the Yangtze River Headwater Region and Yellow River Headwater Region and decreased in the Lantsang River Headwater Region. The increases in precipitation, evapotranspiration, soil water content and glacial melt, and the decreases in snowfall and snowmelt indicate an accelerated hydrological cycle in the Three-River Headwaters Region over the 1982 to 2014 period. The significant increase in precipitation is the main reason for the significant increase in runoff in the Yangtze River Headwater Region. The increase in precipitation in the Yellow River Headwater Region was less than the sum of the increase in evapotranspiration and soil water storage, resulting in a decreasing trend of runoff in the Yellow River Headwater Region. The increase in precipitation in the Lantsang River Headwater Region was slightly larger than the sum of that in evapotranspiration and soil water storage, and there was an insignificant increase in the runoff in the Lantsang River Headwater Region.

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.

Forecasting Potential Evapotranspiration Using Seasonal ARIMA Model for Northern Telangana Zone, India

Present study aims to predict the evapotranspiration values over the Northern Telangana Zone through the identification or patterns in correlated data trends and seasonal variation and to assess the accuracy of the forecasting model. Plans for managing crop water consumption include potential evapotranspiration heavily. As a result, in a semi-arid environment, forecasting of the potential evapotranspiration is the foundation of any successful water resources management plans. The Thronthwaite method was used to estimate daily evapotranspiration, and a Seasonal Auto Regressive Integrated Moving Average was used to forecast potential evapotranspiration. Time series analysis of evapotranspiration data set showed a seasonality behaviour and thus Seasonal ARIMA model with the least Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC) values were selected. The Seasonal Arima model selected for the districts Adilabad, Jagtial, Karimnagar, KumuramBheem, Nirmal, and Peddapalli was (2,0,2)(2,1,0)12 and for Nizamabad district (2,0,2)(1,1,0)12. Basic statistical properties are used to compare the observed and forecasted data which shown that that there is no significant difference between the mean values of the observed and predicted data at a 5% significance level. Hence the developed model was optimum to forecast the evapotranspiration over the study area and to sustain the forecasting accuracy.

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.