Global Land Evaporation Amsterdam Model Research Articles

Optimal combinations of global evapotranspiration and terrestrial water storage products for catchment water balance

ABSTRACT During the last two decades, a great number of global products about evapotranspiration ( E ) and terrestrial water storage ( S ) have been released. This has led to numerous combinations for describing catchment water balance, and hence determining an appropriate combination has become an important issue. The main objective of this study is to evaluate various global products of E and S at the catchment scale and determine the most appropriate combination, taking two Korean catchments as examples. For E , we evaluated global evapotranspiration (Global ET), Global Land Evaporation Amsterdam Model (GLEAM), and Penman–Monteith–Leuning (PML). For S , six individual datasets of Gravity Recovery and Climate Experiment (GRACE) were evaluated, with Global Land Data Assimilation System (GLDAS) products used for comparison. We found that the performance is sensitive to the choice of E product while various S products displayed minimal differences in results. Based on four evaluation criteria, the combination of PML and GRACE-SH-GFZ is suggested as the most suitable pair. For the period between 2003 and 2013, these data exhibit noteworthy trends of increasing E , offset by decreasing S . Warming climate is suspected to be behind these trends. Our research presents an approach that allows for the estimation of monthly streamflow exclusively with global data products, which is an advancement in hydrological analysis and particularly useful for regions that lack in-situ data networks. This approach provides a new perspective in the application of global datasets for the assessment of water balance and could significantly improve predictions in ungauged basins.

A comprehensive evaluation of hydrological processes in a second‐generation dynamic vegetation model

AbstractThe global water and carbon cycles are greatly influenced by terrestrial vegetation, making trustworthy representations of dynamic biosphere–hydrosphere interactions a crucial component of both ecosystem and climate models. This paper comprehensively evaluates the hydrological performance of a leading dynamic global vegetation model Lund‐Potsdam‐Jena General Ecosystem Simulator (LPJ‐GUESS), using a broad range of the latest available global observation‐based gridded datasets that cover the main components of the hydrological cycle. Overall, we find that the hydrological components modelled by LPJ‐GUESS agree well with global gridded datasets of runoff, evapotranspiration and surface soil moisture, though there are discrepancies in some regions and periods. Furthermore, LPJ‐GUESS accurately captures both inter‐ and intra‐annual variations of runoff in most regions and catchment areas, including the Danube, Murray, Yangtze, Yenisei and Nile basins. Total evapotranspiration modelled by LPJ‐GUESS agrees closely with the evapotranspiration estimates of the Global Land Evaporation Amsterdam Model and PML‐V2 datasets, but with some disagreement in the individual components, especially for evaporation. The surface soil moisture simulated by LPJ‐GUESS aligns with ESA‐CCI (v5.3) surface soil moisture datasets in most regions, with greatest discrepancies in subarctic areas. We attribute these discrepancies to two main sources: (1) absent or poor representation of processes such as river routing, storage and supply of water bodies, and cropland irrigation; and (2) uncertainties in both reference datasets and input to the model, including precipitation, soil texture, and land use.

Terrestrial Evapotranspiration Over China From 1982 to 2020: Consistency of Multiple Data Sets and Impact of Input Data

AbstractDue to limited direct measurements, regional or global terrestrial evapotranspiration (ET) is generally derived from a combination of meteorological and satellite observations. Although the inhomogeneity of the observed climate data has been widely reported, its impact on the calculated ET has not been adequately quantified. This study aimed to calculate ET using the modified Penman‐Monteith (MPM) model with raw and homogenized meteorological data. Additionally, we compared the calculated ET with those estimates from variable methods (water balance, satellite‐based, and reanalysis) in China and its six major river basins from 1982 to 2020. During the overlapping period of 1997–2018, ET calculated from raw input data decreased slightly at −0.39 mm yr−2 (p = 0.64) in China, whereas homogenized ET showed a significant increasing trend of 0.93 mm yr−2 (p = 0.02), with a better agreement with water balance ET (1.93 mm yr−2, p = 0). Global Land Evaporation Amsterdam Model (GLEAM) and Modern‐Era Retrospective Analysis for Research and Applications, version 2 (MERRA2) could reproduce the increasing trends with 2.08 mm yr−2 (p = 0) and 2.59 mm yr−2 (p = 0). The intercomparison of input variables (solar radiation, relative humidity, wind speed, precipitation, and air temperature) among ET products revealed substantial differences, which can account for the discrepancies in ET estimates. Homogenized ET, GLEAM and MERRA2 exhibited significant increasing trends in China and most river basins from 1982 to 2020. Our findings underscore the importance of utilizing homogenized input data for more accurate ET estimation.

The influence of variations in actual evapotranspiration on drought in China's Southeast River basin

Revealing changes in actual evapotranspiration is essential to understanding regional extreme hydrological events (e.g., droughts). This study utilized the Global Land Evaporation Amsterdam Model (GLEAM) to analyse the spatial and temporal characteristics of actual evapotranspiration over 40 years in the Southeast River basin of China. The relationship between changes in actual evapotranspiration and the drought index was quantified. The results indicated a significant increase in actual evapotranspiration in the Southeast River basin from 1981 to 2020 (2.51 mm/year, p < 0.01). The actual evapotranspiration components were dominated by vegetation transpiration (73.45%) and canopy interception (18.26%). The actual evapotranspiration was closely related to the normalised difference vegetation index (r = 0.78, p < 0.01), and vegetation changes could explain 10.66% of the increase of actual evapotranspiration in the Southeast River basin since 2000. Meanwhile, actual evapotranspiration and standardised precipitation evapotranspiration index (SPEI) showed a highly significant negative spatial correlation, with a Moran's I index of − 0.513. The rise in actual evapotranspiration is an important trigger factor for seasonal droughts in the region. Therefore, these results help deepen the understanding of hydro-climatic process changes in the southeastern coastal region of China.

Global high-resolution drought indices for 1981–2022

Abstract. Droughts are among the most complex and devastating natural hazards globally. High-resolution datasets of drought metrics are essential for monitoring and quantifying the severity, duration, frequency, and spatial extent of droughts at regional and particularly local scales. However, current global drought indices are available only at a coarser spatial resolution (&gt;50 km). To fill this gap, we developed four high-resolution (5 km) gridded drought records based on the standardized precipitation evaporation index (SPEI) covering the period 1981–2022. These multi-scale (1–48 months) SPEI indices are computed based on monthly precipitation (P) from the Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS, version 2) and Multi-Source Weighted-Ensemble Precipitation (MSWEP, version 2.8), and potential evapotranspiration (PET) from the Global Land Evaporation Amsterdam Model (GLEAM, version 3.7a) and hourly Potential Evapotranspiration (hPET). We generated four SPEI records based on all possible combinations of P and PET datasets: CHIRPS_GLEAM, CHIRPS_hPET, MSWEP_GLEAM, and MSWEP_hPET. These drought records were evaluated globally and exhibited excellent agreement with observation-based estimates of SPEI, root zone soil moisture, and vegetation health indices. The newly developed high-resolution datasets provide more detailed local information and can be used to assess drought severity for particular periods and regions and to determine global, regional, and local trends, thereby supporting the development of site-specific adaptation measures. These datasets are publicly available at the Centre for Environmental Data Analysis (CEDA; https://doi.org/10.5285/ac43da11867243a1bb414e1637802dec) (Gebrechorkos et al., 2023).

Geospatial dataset for hydrologic analyses in India (GHI): a quality-controlled dataset on river gauges, catchment boundaries and hydrometeorological time series

Abstract. Streamflow gauging stations not only track the pulse of rivers but also act as common reference points for hydrologic and other environmental analyses. As such, streamflow data and metadata on gauging stations – Geographic Information System (GIS) data on station locations, their upstream catchment boundaries and river flow networks – are critical for analyses. However, for India's river basins, the availability of such data is limited; when available, data are not in an analysis-ready format and can have substantial errors. Studies often use available information from India's water agencies as is, without checking its validity. This study addresses the above limitations by building a new dataset using existing metadata (from the Central Water Commission, CWC, and the Water Resources Information System, WRIS) and checking it against publicly available information from global data sources (e.g., World Wildlife Fund, Multi-Error-Removed Improved-Terrain Hydro and Copernicus) and online maps (e.g., Google Maps). The quality control process categorizes existing metadata based on their consistency with these sources; also, existing metadata are supplemented with additional information where needed. The new dataset developed here is called the “Geospatial dataset for Hydrologic analyses in India” (GHI) and uses Hydrological data and maps based on Shuttle Elevation Derivatives at multiple Scales (HydroSHEDS) data as the underlying template. GHI has both geospatial and time series information. In this initial version of GHI, the spatial domain includes only the river basins of Peninsular India where daily streamflow data are publicly available. Following the quality control process, the CWC's 645 stations in Peninsular India were categorized into three groups: Group 1 (reliable metadata and adequate daily streamflow data; 213 stations), Group 2 (reliable metadata and inadequate or no daily streamflow data; 259 stations) and Group 3 (missing or unreliable metadata; 173 stations). For each of the 472 stations falling into groups 1 and 2, catchment-specific annual and monthly time series spanning 71 water years (1950–2020) of the following were compiled: observed precipitation from the Indian Meteorological Department (IMD); observed streamflow from WRIS; estimated precipitation, evapotranspiration (ET) and streamflow from ERA5-Land; and ET from the Global Land Evaporation Amsterdam Model (GLEAM). A preliminary analysis of catchment-scale time series of data indicates that, while the compiled data appear reasonable over most of the study domain, spurious runoff–precipitation ratios were observed in the hilly coastal regions of Western India. This adds to yet another data-related obstacle faced by the hydrologic community. In order to quantify historical changes and reconcile them with anticipated future changes, the community needs robust and reliable hydrographic and hydrometeorological datasets as well as unrestricted access to such datasets. The goal of this study is to highlight the limitations of existing datasets and pave the way for a community-led effort towards building the needed datasets. GHI serves as a placeholder until such datasets become available. Potential improvements to GHI are discussed. GHI is publicly available at https://doi.org/10.5281/zenodo.7563599 (Goteti, 2023).

Exploring the contribution of environmental factors to evapotranspiration dynamics in the Three-River-Source region, China

Evapotranspiration (ET) exerts substantial effects on terrestrial ecosystem carbon and water cycles. A deep understanding of the spatial and temporal variability in ET and its dominant controls has crucial implications for future ecological management, especially for the ecologically-fragile Three-River-Source region (TRSR) in China. The study examined the performance of three ET products including Global Land Evaporation Amsterdam Model (GLEAM), Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) and Global Land Surface Satellite (GLASS) based on eddy covariance-based flux observations. On this basis, ET dynamics in the TRSR from 1982 to 2018 were analyzed at two time scales using the GLASS ET product. Spatially, the pattern in ET showed a gradual upward trend from northwest to southeast. Meanwhile, ET in the majority of regions significantly enhanced, with multiannual mean ET and summer ET (June to August) increasing by 0.609 mm/yr and 0.765 mm/yr, respectively. Especially, ET in summer dominated the interannual dynamics of ET in the TRSR. The individual contributions of five environmental factors including Leaf Area Index (LAI), solar radiation (Rs), air temperature (Ta), precipitation (PRE) and vapour pressure deficit (VPD), to the variability in ET were isolated using ridge regression. Significant differences appeared when explaining ET over different time scales. Rs (33.05 %) and LAI (25.29 %) were the dominant control variables in shaping the interannual ET, with VPD contributing the least by 7.19 %. However, the long-term dynamics in summer ET were mainly controlled by LAI (35.73 %) and PRE (31.19 %), and Ta exerted the least effect by 5.73 %. All analyses will strengthen our understanding of the relationships between climate, vegetation and ET in the TRSR area, China.

Spatial patterns and recent temporal trends in global transpiration modelled using eco-evolutionary optimality

Transpiration from vegetation accounts for about two thirds of land evapotranspiration (ET), and exerts important effects on of global water, energy, and carbon cycles. Resistance-based ET partitioning models using remote sensing data are one of the main methods to estimate global land transpiration, overcoming the limitation by the sparse distribution and short observation periods of site-level measurements. However, the uncertainties of estimated transpiration for these models mainly come from the resistance parameterization based on specific empirical parameters across different plant functional types (PFT). A model based on eco-evolutionary optimization (P model) has recently been proposed to simulate stomatal conductance without the need of calibrated parameters. Here, we calculated global long-term (1982–2018) monthly transpiration with the Penman-Monteith (PM) equation using canopy conductance estimated by the P model (PM-P) and Ball-Berry-Leuning model (PM-BBL). Using the observations of SAPFLUXNET and FLUXNET sites as reference, the performance of PM-P was comparable with that of PM-BBL and Global Land Evaporation Amsterdam model (GLEAM). Multi-year mean and trends in growing season transpiration estimated by GLEAM and the PM-P model revealed a similar spatial distribution globally. Both GLEAM and the PM-P model showed a widespread increasing trend of growing season transpiration over 72.06%∼80.38% of global land, especially for some main greening hotspots with >3.0 mm/year. The good performance of the P model indicated that it could avoid the uncertainties emerging from the resistance parameterization with too many empirical parameters and had the potential to accurately estimate global transpiration.

Evaluation of flash drought under the impact of heat wave events in southwestern Germany

Flash droughts are a recently recognised type of extreme drought defined by the rapid onset and strong intensification of drought conditions. Our understanding of flash drought processes under the influence of heat waves needs to be improved in the context of global warming. Here, we applied a physically based hydrological model, i.e., TRAnspiration and INterception (TRAIN) model to simulate root zone soil moisture (RZSM) and evapotranspiration (ET) with daily time steps and at a 1 × 1 km resolution to identify and assess flash droughts. Two states, Baden-Württemberg (BW) and Rhineland-Palatinate (RP), located in southwestern Germany, were selected as the study areas. Three datasets, the Global Land Evaporation Amsterdam Model (GLEAM) dataset, ERA5-Land (land component of the fifth generation of European ReAnalysis) dataset, and SMAP-L4 (Soil Moisture Active Passive Level-4) dataset, were selected to evaluate the TRAIN simulated RZSM and ET from 1961 to 2016. The results show that the simulated RZSM had the highest correlation with the ERA5-Land products, followed by SMAP-L4 and GLEAM, with regional average correlation coefficients (CC) of 0.765, 0.762, and 0.746, respectively. The CC of the TRAIN simulated ET with ERA5-Land and GLEAM ET were 0.828 and 0.803, respectively. The results of the trend analyses showed a significant increase (p < 0.05) in the number of flash droughts and heat waves in both the BW and RP states. A comparative analysis revealed that the mean duration and onset speed of flash droughts in BW (RP) without heat waves were 10.42 (10.67) pentads and 19.69th percentile/pentad (17.16th percentile/pentad), respectively, while associated with heat waves they were 8.95 (9.53) pentads and 21.77th percentile/pentad (19.91th percentile/pentad), respectively. This indicates that flash droughts under the influence of heat waves are generally shorter in duration but faster in occurrence. The findings of this study have important implications for flash drought assessment, monitoring, and mitigation under the impact of heat waves.

Incorporating Plant Access to Groundwater in Existing Global, Satellite‐Based Evaporation Estimates

AbstractGroundwater is an important water source for evaporation, especially during dry conditions. Despite this recognition, plant access to groundwater is often neglected in global evaporation models. This study proposes a new, conceptual approach to incorporate plant access to groundwater in existing global evaporation models, and analyses the groundwater contribution to evaporation globally. To this end, the Global Land Evaporation Amsterdam Model (GLEAM) is used. The new GLEAM‐Hydro model relies on the linear reservoir assumption for modeling groundwater flow, and introduces a transpiration partitioning approach to estimate groundwater contributions. Model estimates are validated globally against field observations of evaporation, soil moisture, discharge and groundwater level for the time period 2015–2021, and compared to a regional groundwater model. Representing groundwater access influences evaporation in 22% of the continental surface. Globally averaged, evaporation increases by 2.5 mm year−1 (0.5% of terrestrial evaporation), but locally, evaporation can increase up to 245.2 mm year−1 (149.7%). The groundwater contribution to transpiration is highest for tall vegetation under dry conditions due to more frequent groundwater access. The temporal dynamics of the simulated evaporation improve across 75% of the stations where groundwater is a relevant water source. The skill of the model for variables such as soil moisture and runoff remains similar to GLEAM v3. The proposed approach enables a more realistic process representation of evaporation under water‐limited conditions in satellite‐data driven models such as GLEAM, and sets the ground to assimilate satellite gravimetry data in the future.

Estimating daily potential evapotranspiration using GNSS-based precipitable water vapor

Potential evapotranspiration (PET) is a crucial component of the hydrological cycle and energy balance. Although the Penman-Monteith (PM) model is the most widely used method to estimate daily PET, it requires temperature, relative humidity, solar radiation, and wind speed. In Thailand, the number of potential weather stations to provide the required data is limited, which resulted in the absence of some input variables in many locations. The objective of this study is to develop the revised potential evapotranspiration (RPET) model to estimate daily PET using Global Navigation Satellite System-derived Precipitable Water Vapor (GNSS-PWV) and temperature data. The multiple linear regression analysis was used to develop and validate the RPET model. The performance of the RPET model along with the Global Land Evaporation Amsterdam Model (GLEAM v3.2 b) and the European Centre for Medium-Range Weather Forecasts Reanalysis-5 (ERA5-Land) products was investigated using the PM model. The results revealed that the RPET model showed a strong correlation with the PM model (r = 0.85, RMSE = 0.97 mm day−1, RSR = 0.53, NSE = 0.72) under limited meteorological inputs. The RPET model performance was superior when compared to GLEAM and ERA5-Land (r = 0.80, RMSE = 1.06 mm day−1). Therefore, the proposed model is greatly suitable for daily PET estimation with only required GNSS-PWV and temperature data, and this can be implemented for drought assessment and water resources management.

Contribution of climate change and vegetation restoration to interannual variability of evapotranspiration in the agro-pastoral ecotone in northern China

Evapotranspiration (ET) is a vital variable in the water and energy cycles and significantly influenced by changing environmental conditions. However, in the data-lacking agro-pastoral ecotone in northern China (APENC) that has undergone warming and wetting climate trend and implemented a series of ecological restoration projects (ERP), the effects of climate change and vegetation greening on the magnitude, drivers, and mechanism of ET variations are not fully understood yet. In this study, we evaluated nine commonly used ET products to determine the most applicable one in the APENC based on the in-situ observations and water balance method. Subsequently, the ET variation was examined and the contribution of each factor to the ET change from 1982 to 2017 was quantified. Results show that the Global Land Evaporation Amsterdam Model (GLEAM) performed best and meets the accuracy requirements for research in the APENC. Based on the GLEAM data, our analysis reveals a notable increase in ET at a rate of 1.11 mm/y (p < 0.05). Regions with a significant increase in ET are mainly concentrated in the middle and southwest parts of the APENC, while a decreasing trend in ET is observed in the northeast region. Contributory analysis showed that precipitation (PRE) and leaf area index (LAI) were the dominant factors that controlled the ET variation, whereas air temperature indirectly affected ET variation by promoting LAI growth during 1982–2017. PRE dominated the variation of ET before the implementation of ERP (1982–1999), while the positive contribution of LAI exceeded PRE since 2000 to become the dominant factor. Our findings bridge the gap in ET research in the study region and provide important information for water resource management and ecological rehabilitation.

Statistical blending of global-gridded climatological products: an approach to inverse hydrological model

Abstract The growing use of global-scale environmental products in hydro-climatic modeling has increased the variety of their applications and the complications of their uncertainties and evaluations. Researchers have recently turned to statistical blending of these products to achieve optimal modeling. The proposed statistical blending in this study includes five large-scale and satellite precipitation (CHIRPS, ERA5-Land of ECMWF, GPM (IMERG), TRMM, and Terra) and evapotranspiration (GLEAM, SSEBop, MODIS, Terra, and ERA) products committed in three modeling scenarios. The blending procedures are organized using a conceptual water balance model to achieve the best precipitation and evapotranspiration results for the conceptual production of streamflow using hydrological inverse modeling. Based on the results, the proposed blending procedures of precipitation and evapotranspiration improved the performance of the model using different statistical metrics. In addition, the results show the conformity of the pattern and behavior of the blended precipitation calculated using the moving least square method in the study area. This happened by changing the estimation based on in situ values, particularly in cold months considering the orographic/snow effects. The combining method provides a good fusion procedure to improve the realistic estimation of precipitation and evapotranspiration in ungagged watersheds as well.

Reconstruction of a spatially seamless, daily SMAP (SSD_SMAP) surface soil moisture dataset from 2015 to 2021

Surface soil moisture (SSM) is a vital component in terrestrial hydrological processes. As a type of important microwave remote sensing-based SSM dataset, the Soil Moisture Active Passive (SMAP) SSM data were available since 2015 and have been applied to many studies. However, the spatial gaps in the SMAP SSM data affect greatly its applicability. To address this issue, this study developed a method to fill the spatial gaps in the SMAP SSM data, generating a spatially seamless, daily SMAP (SSD_SMAP) SSM dataset from 2015 to 2021. The method makes full use of the long SMAP SSM time-series data with inherent spatial gaps. To deal with the continuously changed spatial gaps in the daily SMAP data, we used a highly comparative time-series analysis (HCTSA) to dig the temporal profiles of the SMAP SSM time-series, producing 17 seamless HCTSA-based time-series characteristics (HTCs). Both spatially seamless topography characteristics (TCs) and location characteristics (LCs) were also considered. Moreover, a random forest (RF) model was trained to construct the relation between the valid daily SMAP data at the prediction time and the seamless auxiliary data (i.e., 17 HTCs coupled with TCs and LCs). Subsequently, the constructed RF model was migrated to fill the spatial gaps of the SMAP data at the corresponding time. The proposed method was validated in both simulated gaps (real data as reference) and real gaps (in-situ SSM data as reference). The produced SSD_SMAP dataset was also compared with the model-based Global Land Evaporation Amsterdam Model (GLEAM) dataset. The results indicated that the SSD_SMAP data are closer to the in-situ data, with an unbiased root mean square error (ubRMSE) of ∼ 0.040 m3/m3. The SSD_SMAP dataset can provide data support for global and regional research in related fields.

Phenophase-based comparison of field observations to satellite-based actual evaporation estimates of a natural woodland: miombo woodland, southern Africa

Abstract. The trend and magnitude of actual evaporation across the phenophases of miombo woodlands are unknown. This is because estimating evaporation in African woodland ecosystems continues to be a challenge, as flux observation towers are scant if not completely lacking in most ecosystems. Furthermore, significant phenophase-based discrepancies in both trend and magnitude exist among the satellite-based evaporation estimates (i.e. Global Land Evaporation Amsterdam Model (GLEAM), moderate resolution imaging spectroradiometer (MODIS), operational simplified surface energy balance (SSEBop), and water productivity through open-access remotely sensed derived data (WaPOR)), making it difficult to ascertain which of the estimates are close to field conditions. Despite the many limitations with estimation of evaporation in woodlands, the development and application of the distributed temperature system (DTS) is providing deepened insights and improved accuracy in woodland energy partitioning for evaporation assessment. In this study, the Bowen ratio distributed temperature sensing (BR-DTS) approach is used to partition available energy and estimate actual evaporation across three canopy phenophases of the miombo woodland, covering the entire 2021 dry season (May–October) and early rain season (November–December) at a representative site in Mpika in Zambia, southern Africa. To complement the field experiment, four satellite-based evaporation estimates are compared to the field observations. Our results show that actual evaporation of the miombo woodland appears to follow the trend of the net radiation, with the lowest values observed during the phenophase with the lowest net radiation in the cool dry season and the highest values during the phenophase with peak net radiation in the early rainy season. It appears the continued transpiration during the driest period in the dormant phenophase (with lowest canopy cover and photosynthetic activities) may be influenced by the species-dependent adapted physiological attributes such as access to moisture in deep soils (i.e. due to deep rooting), plant water storage, and the simultaneous leaf fall and leaf flush among miombo plants. Of the four satellite-based evaporation estimates, only the WaPOR has a similar trend to the field observations across the three phenophases. However, all four satellite-based estimates underestimate the actual evaporation during the dormant and green-up phenophases. Large coefficients of variation in actual evaporation estimates among the satellite-based estimates exist in the dormant and green-up phenophases and are indicative of the difficulty in estimating actual evaporation in these phenophases. The differences between field observations and satellite-based evaporation estimates can be attributed to the model structure, processes, and inputs.

Diagnosing Product Variability in the Soil Moisture Response to Precipitation on the Tibetan Plateau

Abstract Previous studies show that some soil moisture products have a good agreement with in situ measurements on the Tibetan Plateau (TP). However, the soil moisture response to precipitation variability in different products is yet to be assessed. In this study, we focus on the soil moisture response to precipitation variability across weekly to decadal time scales in satellite observations and reanalyses. The response of soil moisture to precipitation variability differs between products, with large uncertainties observed for variations in weekly accumulated precipitation. Using June 2009 as an example, weekly mean anomalous soil moisture varies by up to 25% between products. Across decadal time scales, soil moisture trends vary spatially and across different products. In light of the soil moisture response to precipitation at different time scales, we conclude that remote sensing products developed as part of the European Space Agency’s (ESA) Water Cycle Multimission Observation Strategy and Soil Moisture Climate Change Initiative (CCI) projects are the most reliable, followed by the Global Land Evaporation Amsterdam Model (GLEAM) dataset. Even products that strongly agree with in situ observations on daily time scales, such as the Global Land Data Assimilation System (GLDAS), show inconsistent soil moisture responses to decadal precipitation trends. European Centre for Medium-Range Weather Forecasts (ECWMF) reanalysis products have a relatively poor agreement with in situ observations compared to satellite observations and land-only reanalysis datasets. Unsurprisingly, products which show a consistent soil moisture response to precipitation variability are those mostly aligned to observations or describe the physical relationship between soil moisture and precipitation well. Significance Statement We focus on soil moisture responses to precipitation across weekly to decadal time scales by using multiple satellite observations and reanalysis products. Several soil moisture products illustrate good consistency with in situ measurements in different biomes on the Tibetan Plateau, while the response to precipitation variability differs between products, with large uncertainties observed for variations in weekly accumulated precipitation. The response of soil moisture to decadal trends in boreal summer precipitation varies spatially and temporally across products. Based on the assessments of the soil moisture response to precipitation variability across different time scales, we conclude that remote sensing products developed as part of the European Space Agency’s Water Cycle Multimission Observation Strategy and Soil Moisture Climate Change Initiative (CCI) projects are the most reliable, followed by the Global Land Evaporation Amsterdam Model (GLEAM) dataset. Reanalysis products from ECWMF show inconsistent soil moisture responses to precipitation. The results highlight the importance of using multiple soil moisture products to understand the surface response to precipitation variability and to inform developments in soil moisture modeling and satellite retrievals.

Performance of machine learning algorithms for multi-step ahead prediction of reference evapotranspiration across various agro-climatic zones and cropping seasons

This study forecasts multi-step-ahead Potential Evapotranspiration (ETO) in India using globally available fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) gridded climate reanalysis products (ERA5). For this purpose, the potential of six machine learning approaches are examined across different agro climatic zones and cropping seasons. Support Vector Regression (SVR), Multivariate Adaptive Regression Splines (MARS), Random Forest (RF), Multi-Layer Perceptron (MLP), one-dimensional Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models are first evaluated at the station level for up to 28-day-ahead prediction of daily ETO using meteorological station data as well as gridded ERA5 products. Using meteorological observations, at all three stations, SVR performs the best for a prediction horizon (PH) up to 2-days whereas LSTM performs the best for PH of 7-days. Using ERA5 datasets as input, among the three stations, the best performance is observed in Nagpur, where the best models for real-time and 28-day ahead prediction are LSTM (R2 = 0.847 and MAE = 0.474 mm/day) and RF (R2 = 0.722 and MAE = 0.635 mm/day) respectively. As the PH increases from 2-day to 28-day-ahead, models using ERA5 datasets performs better than those using station observations for most of the cases. Although the prediction performance drops initially with the increase in lead time, the drop in performance between 7-day and 28-day-ahead prediction is negligible. Evaluation of gridwise ETO prediction across entire India, using Global Land Evaporation Amsterdam Model (GLEAM) dataset as a reference, indicates MLP and CNN as the top performing models. Considering the crop seasons, model performance during Rabi season (October-March) ranged from 0.103 to 0.145 mm/day (MAE) and 0.977 to 0.988 (R2), which is better than the Kharif season (June-September) where MAE ranged from 0.140 to 0.234 mm/day and R2 ranged from 0.906 to 0.962. During the Rabi season, the ETO prediction performance of the arid agro-climatic zone is found to be superior to the other three agro-climatic zones, with the highest range of R2 (0.939 to 0.955) and lowest range of MAE (0.146 to 0.182 mm/day). Even the worst prediction performance, which is observed in the Humid region during the Rabi season, is also reasonably good (R2 = 0.656 to 0.79); thereby establishing the potential of the proposed models in multi-step ahead ETO prediction across various agro-climatic zones and cropping seasons.

Uncertainties of three high-resolution actual evapotranspiration products across China: Comparisons and applications

Magnitude and variation of large-scale actual evapotranspiration (AET) are of much concern under global climate change. In this study, uncertainties in three mainstream high-resolution monthly AET products, namely Global Land Evaporation Amsterdam Model (GLEAM) V3.5a, Global Land Data Assimilation System (GLDAS) V2.0 and V2.1 Noah Land Surface Model (NOAH), and complementary-relationship-based (CR) AET, were evaluated quantitatively from the different perspectives of the grid, climate zone and land cover type, and then were used to develop an integrated monthly land surface AET dataset (AETm) based on the Bayesian Three-Cornered Hat method (BTCH). Further, spatiotemporal changes and drivers of annual AETm were analyzed. Results showed GLEAM dataset had generally the best spatiotemporal performances in China, especially in relatively warm months (e.g., from April to September), followed by CR product which mainly demonstrated its excellent performance in relatively cold months (e.g., winter). The BTCH method could substantially mitigate the uncertainties of three paternal AET products, and AETm dataset well reproduced spatiotemporal patterns of the observations over heterogeneous landscapes in China. During 1982–2017, annual AETm increased in 84.82% (52.75% with p < 0.01) of China's land surface with a countrywide rate of 1.27 ± 0.12 mm/a2 (p < 0.01). Annual AETm variations in most of the temperate continental zone, northeast mountain plateau zone, southwest subtropical monsoon zone and most of the temperate monsoon zone were mainly dominated by water availability, while those in most of the subtropical monsoon zone and southeast mountain plateau zone were primarily attributed to energy availability, indicating that climate change dominates annual AETm variations. These results would be beneficial for the selection and application of AET datasets in China, and a deeper understanding of climate change and regional response.

Bridging Scales: An Approach to Evaluate the Temporal Patterns of Global Transpiration Products Using Tree‐Scale Sap Flow Data

AbstractTranspiration is a key process driving energy, water and thus carbon dynamics. Global transpiration products are fundamental for understanding and predicting vegetation processes. However, validation of these transpiration products is limited, mainly due to lack of suitable data sets. We propose a method to use SAPFLUXNET, the first quality‐controlled global tree sap flow (SF) database, for evaluating transpiration products at global scale. Our method is based on evaluating temporal mismatches, rather than absolute values, by standardizing both transpiration and SF products. We evaluate how transpiration responses to hydro‐meteorological variation from the Global Land Evaporation Amsterdam Model (GLEAM), a widely used global transpiration product, compare to in situ responses from SAPFLUXNET field data. Our results show GLEAM and SAPFLUXNET temporal trends are in good agreement, but diverge under extreme conditions. Their temporal mismatches differ depending on the magnitude of transpiration and are not random, but linked to energy and water availability. Despite limitations, we show that the new global SAPFLUXNET data set is a valuable tool to evaluate T products and identify problematic assumptions and processes embedded in models. The approach we propose can, therefore, be the foundation for a wider use of SAPFLUXNET, a new, independent, source of information, to understand the mechanisms controlling global transpiration fluxes.

Developing a dynamic semi-distributed hydrological model considering interactions between soil moisture and evapotranspiration: application of bulk transfer method

ABSTRACT Empirically-derived relationships are mainly used to estimate evapotranspiration (ET) in water balance models. Due to a lack of effective factors and correct conceptual relationships, the resulting ET may be inaccurate. Therefore, a hydrological model was developed by combining water balance and bulk transfer theory, called semi-distributed coupled water balance and bulk transfer (SD-CWBT). According to it, a dynamic interrelationship between ET and soil moisture was correctly established to estimate ET via physical concepts. Global gridded ET retrievals, which provide the spatiotemporal distribution of ET at a global scale, including ECMWF Reanalysis v5-Land (ERA5-Land), TERRA, Global Land Evaporation Amsterdam Model (GLEAM), and Operational Simplified Surface Energy Balance (SSEBop), were employed to evaluate the model. The results indicated the SD-CWBT, with Nash–Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE), and Root Mean Squared Error (RMSE) of [0.6, 0.95], [0.68, 0.96], and [12.61, 0.99] for the streamflow and groundwater level simulation, respectively, had better performance compared to the alternatives. Furthermore, the estimated spatiotemporal distributions of ET agreed well with the climatic-vegetation conditions.