Patterns Of Evapotranspiration Research Articles

Remote-Sensed Determination of Spatiotemporal Properties of Drought and Assessment of Influencing Factors in Ordos, China

Ordos drought impacts are complex; the Geodetector model is able to explore the interaction between impact factors. Based on the drought severity index (DSI), this study explored the spatio-temporal dynamics and changing trends of drought, and analyzed the driving factors of DSI spatial differentiation by using the Geodetector model. The results show that: the evapotranspiration (ET) and normalized difference vegetation index (NDVI) in Ordos showed a significant increasing trend (p < 0.05). The increasing rates were ET (4.291 mm yr−1) and NDVI (0.004 yr−1). In addition, the interannual variation of the DSI also showed a significant increase, with a trend change rate of 0.089. The spatial pattern of ET and the NDVI was low in the southwest and high in the northeast, and the spatial pattern of potential evapotranspiration (PET) was high in the southwest and low in the northeast, while the distribution of the DSI was dry in the west and wet in the east. The spatial differentiation of the DSI was mainly affected by five factors: air temperature, precipitation, land use type, soil type, and the digital elevation model (DEM), with q exceeding 0.15, which were the main driving factors of drought in the Loess Plateau. Under the interaction of multiple factors, the four combinations of temperature and the DEM, precipitation and the DEM, sunshine duration and the DEM, and relative humidity and the DEM jointly drive drought, in which precipitation (0.156) ∩ DEM (0.248) has the strongest influence on drought occurrence, and q reaches 0.389. This study directly informs specific drought management strategies or ecological conservation efforts in the region.

Groundwater table prediction and seasonal variation influenced by short rotation willow plantation on marginal riparian lands of the Prairie potholes in Canada

Shallow groundwater consumption via phreatophytic transpiration and resulting vegetation-linked groundwater table (GWT) fluctuation is a typical soil hydrological process in wetland riparian areas. However, upland and riparian land use alterations may further influence the shallow GWT fluctuation, temporally and spatially. In this multi-year field study, we investigated whether introducing short rotation willow (SRW) positively or negatively affects the shallow GWT, soil water availability, and soil health on marginal riparian lands of the Prairie Pothole Region (PPR). We compared the impact of SRW on these parameters to two common land uses: annual crop (AC) and pasture (PA). Depth to GWT was monitored via data loggers from 28 wells in two semi-arid PPR sites. The GWT depth varied by land use practices only in site B (p < 0.001; PA > SRW = AC) but not significantly in site A (p = 0.325), and the patterns were inconsistent between sites. In GWT depth prediction, the performance of Artificial Neural Network (ANN) was better than Autoregressive Integrated Moving Average (ARIMA) models but was inconsistent alike with field observations. The GWT depth responded to seasonal precipitation and potential evapotranspiration (ET) patterns. The monthly GWT fluctuations peaked between June and August due to increased precipitation, while they were lower during May and September with reduced precipitation; however, these variations were not significant (p > 0.05). Higher precipitation and lower potential ET throughout the wet year (i.e., in 2014) significantly (p < 0.05) raised GWT (i.e., decreased depth to GWT) under all land uses, and vice versa. Our study indicated that planting SRW in marginal riparian land of the PPR would not negatively impact shallow GWT or soil water availability. Moreover, the SRW plantation could also help manage soil salinity without severely depleting the soil's nutrient pools or diminishing soil quality and health indicator parameters measured during the first rotation.

Water budget-based evapotranspiration product captures natural and human-caused variability

Abstract Evapotranspiration (ET) is one of the most important yet highly uncertain components of the water cycle. Available modelled ET products do not necessarily agree with each other at various spatiotemporal scales, either due to limitations on input data and/or due to model assumptions and simplifications. Therefore, using water budget equation to estimate ET has gained attention. However, a large number of water-budget combinations with large uncertainties are available that increases ambiguity in choosing the best ET estimate. Here, the Kalman filter is employed for ingesting 96 water-budget based ET estimates and produce a global ET product with uncertainty &lt; 2 mm, and it captures the general spatiotemporal pattern of ET and the inter-annual variability over all continents. Since the water budget includes storage change due to human interventions, our ET estimates are superior over regions with strong irrigation signal, such as the Ganges basin. We verify our claim by using a modified variable infiltration capacity model that simulates irrigation activities as well. Our ET estimates have a global mean positive trend of 0.18 ± 0.02 mm/year with larger regional variations that are discussed.

The patterns of potential evapotranspiration and seasonal aridity under the change in climate in the upper Blue Nile basin, Ethiopia

Evapotranspiration is one of the determinant components of the hydrological process, highly influenced by climate change due to the increase in atmospheric temperature at global and regional scales. This study was designed to evaluate the extent to which climate change affects the Potential Evapotranspiration (PET) and the consequent Aridity Index (AI) in the high-emission scenario of Representative Concentration Pathways (RCPs) in the Gilgel Abay, Ribb, Gumara, and Megech watersheds using six Global Climate Models in the 2011–2040, 2041–2070, and 2071–2100 relative to the 1971–2000 (baseline period). The average PET is simulated using the Soil Water Assessment Tool (SWAT) model. Penman-Monteith and Hargreaves methods were used in the computation of PET using the water balance technique, and the Hargreaves method was found more efficient in calibration and validation processes. The Aridity Index (AI) of watersheds is calculated using the ratio of precipitation and potential evapotranspiration. The study revealed that the change in annual average PET is showing an increasing pattern in the three time periods, and the highest rate of changes in Megech, Gilgel Abay, Ribb, and Gumara, watersheds are 16.66%, 15.53%, 14.68%, and 13.46%, respectively in the 2071–2100 time period. Seasonally, the highest rate of change in PET is 20.37% (September), 19.29% (April), 17.46% (March), and 17.02% (March) in the Megech, Gilgel Abay, Ribb, and Gumara, respectively. Similarly, the seasonal highest change in Aridity Index (AI) is also likely to be observed in the 2071–2100 in which in the dry season, it accounts –0.303 (March), –0.299 (March), –0.285 (April), and –0.276 (April) in the Ribb, Gumara, Gilgel Abay, and Megech, respectively, whereas in the rainy season, the change is 0.263, 0.258, 0.238, and 0.211 in the Gilgel Abay, Gumara, Ribb, and Megech, respectively. In general, due to the rising atmospheric temperature, the amount of moisture during dry seasons in the headwater catchments of the upper Blue Nile basin is expected to deplete in the 21st century. Therefore, it is highly recommended to use different climate change adaptation mechanisms including adopting suitable physical and biological water conservation techniques to enhance the amount of water stored in the subsurface and joining the groundwater during the rainy season.

The energy-limited water loss of an alpine shrubland on the northeastern Qinghai-Tibetan Plateau, China

Study regionAlpine shrubland on the northeastern Qinghai-Tibetan Plateau. Study focusWater provision ability is a pivotal ecological service of high-altitude alpine regions and is controlled by precipitation, evapotranspiration (ET), and soil water storage whereas the underlying ecohydrological processes remain highly unquantified. Here, we investigated continuous 19-year flux measurements to quantify the temporal patterns of ET and water budget (precipitation minus ET, P−ET), as well as 0-20 cm soil water storage change (ΔSWS). New hydrological insights for the regionAt a monthly scale, ET peaked in July (96.7 ± 26.4 mm, Mean ± S.D.) and averaged 41.7 ± 31.9 mm, whose variations were determined by the slope of the saturation vapor pressure curve at air temperature, air and soil temperatures, regardless of vegetation growth stage. P−ET averaged 18.3 ± 26.3 mm in August and September while stayed deficit during the other months. The variations in P−ET were controlled by precipitation in the May-October growing season whereas by ET in the non-growing season from November to April. ΔSWS peaked in May (28.8 ± 11.2 mm) and September (3.0 ± 2.7 mm) and almost accumulated to zero over the whole season. At annual scales, none of ET, P−ET, and ΔSWS changed significantly. ET averaged 512.2 ± 68.4 mm and exceeded precipitation (459.1 ± 58.4 mm), likely due to the lateral flow supply of uphill locations. The variations in ET were regulated directly by bulk canopy resistance and indirectly by net radiation. P−ET averaged −53.2 ± 95.4 mm and demonstrated a clear water deficit (−51.6 ± 21.0 mm) during the non-growing season. The variations of P−ET were driven jointly by precipitation and ET, with opposite but equivalent effects. The dominance of thermal conditions and energy availability on ET variability manifested an energy-limited feature of water loss in the alpine shrubland. The temporal patterns in P−ET elucidated that the alpine shrubland plays the water retention rather than water provision function through transforming variable precipitation input into stable ET loss.

Tree species explain only half of explained spatial variability in plant water sensitivity.

Spatiotemporal patterns of plant water uptake, loss, and storage exert a first-order control on photosynthesis and evapotranspiration. Many studies of plant responses to water stress have focused on differences between species because of their different stomatal closure, xylem conductance, and root traits. However, several other ecohydrological factors are also relevant, including soil hydraulics, topographically driven redistribution of water, plant adaptation to local climatic variations, and changes in vegetation density. Here, we seek to understand the relative importance of the dominant species for regional-scale variations in woody plant responses to water stress. We map plant water sensitivity (PWS) based on the response of remotely sensed live fuel moisture content to variations in hydrometeorology using an auto-regressive model. Live fuel moisture content dynamics are informative of PWS because they directly reflect vegetation water content and therefore patterns of plant water uptake and evapotranspiration. The PWS is studied using 21,455 wooded locations containing U.S. Forest Service Forest Inventory and Analysis plots across the western United States, where species cover is known and where a single species is locally dominant. Using a species-specific mean PWS value explains 23% of observed PWS variability. By contrast, a random forest driven by mean vegetation density, mean climate, soil properties, and topographic descriptors explains 43% of observed PWS variability. Thus, the dominant species explains only 53% (23% compared to 43%) of explainable variations in PWS. Mean climate and mean NDVI also exert significant influence on PWS. Our results suggest that studies of differences between species should explicitly consider the environments (climate, soil, topography) in which observations for each species are made, and whether those environments are representative of the entire species range.

The impact of extreme precipitation on water use efficiency along vertical vegetation belts in Hengduan Mountain during 2001 and 2020

In the context of climate change, extreme precipitation events are continuously increasing and impact the water‑carbon coupling of ecosystems. The vertical vegetation zonation, as a characteristic of mountain ecosystems, reflects the differences in vegetation response to climate change at different elevations. In this study, we used the water use efficiency (WUE) as an indicator to evaluate the water‑carbon relationship. By using MODIS data, we analyzed the spatiotemporal patterns of gross primary productivity (GPP), evapotranspiration (ET), and WUE from 2001 to 2020, as well as the responses of WUE to extreme wetness factor Number of precipitation days (R0.1), extreme dryness factor Consecutive dry days (CDD), and meteorological factors under the vertical vegetation zonation. Our results showed that annual GPP and ET displayed a significant increasing trend between 2001 and 2020, whereas WUE showed a weak decreasing trend. Spatially, GPP and WUE decreased with increasing elevation. Analyzing the WUE of mountainous ecosystems as a unified whole may not precisely capture the reactions of vegetation to severe rainfall occurrences. In fact, across different vegetation belts in mountainous areas, there exists a negative correlation between WUE and R0.1, and a positive correlation with CDD. In terms of meteorological factors, the temporal variation of GPP was primarily associated with vapor pressure deficit (VPD) and temperature (Ta), while those of ET was mainly related to soil water content (SWC). WUE was affected by a combination of meteorological factors and had a certain degree of variation between different altitude intervals. These findings contribute to a better understanding and prediction of the relationship between extreme rainfall climate and water‑carbon coupling in mountainous areas.

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.

Inclusion of bedrock vadose zone in dynamic global vegetation models is key for simulating vegetation structure and function

Abstract. Across many upland environments, soils are thin and plant roots extend into fractured and weathered bedrock where moisture and nutrients can be obtained. Root water extraction from unsaturated weathered bedrock is widespread and, in many environments, can explain gradients in vegetation community composition, transpiration, and plant sensitivity to climate. Despite increasing recognition of its importance, the “rock moisture” reservoir is rarely incorporated into vegetation and Earth system models. Here, we address this weakness in a widely used dynamic global vegetation model (DGVM; LPJ-GUESS). First, we use a water flux-tracking deficit approach to more accurately parameterize plant-accessible water storage capacity across the contiguous United States, which critically includes the water in bedrock below depths typically prescribed by soil databases. Secondly, we exploit field-based knowledge of contrasting plant-available water storage capacity in weathered bedrock across two bedrock types in the Northern California Coast Ranges as a detailed case study. For the case study in Northern California, climate and soil water storage capacity are similar at the two study areas, but the site with thick weathered bedrock and ample rock moisture supports a temperate mixed broadleaf–needleleaf evergreen forest, whereas the site with thin weathered bedrock and limited rock moisture supports an oak savanna. The distinct biomes, seasonality and magnitude of transpiration and primary productivity, and baseflow magnitudes only emerge from the DGVM when a new and simple subsurface storage structure and hydrology scheme is parameterized with storage capacities extending beyond the soil into the bedrock. Across the contiguous United States, the updated hydrology and subsurface storage improve annual evapotranspiration estimates as compared to satellite-derived products, particularly in seasonally dry regions. Specifically, the updated hydrology and subsurface storage allow for enhanced evapotranspiration through the dry season that better matches actual evapotranspiration patterns. While we made changes to both the subsurface water storage capacity and the hydrology, the most important impacts on model performance derive from changes to the subsurface water storage capacity. Our findings highlight the importance of rock moisture in explaining and predicting vegetation structure and function, particularly in seasonally dry climates. These findings motivate efforts to better incorporate the rock moisture reservoir into vegetation, climate, and landscape evolution models.

Surface Resistance Controls Differences in Evapotranspiration Between Croplands and Prairies in U.S. Corn Belt Sites

AbstractWater returned to the atmosphere as evapotranspiration (ET) is approximately 1.6x global river discharge and has wide‐reaching impacts on groundwater and streamflow. In the U.S. Midwest, widespread land conversion from prairie to pasture to cropland has altered spatiotemporal patterns of ET, yet there is not consensus on the direction of change or the mechanisms controlling changes. We measured ET at three locations within the Long‐Term Agroecosystem Research network along a latitudinal gradient with paired rainfed cropland and prairie sites at each location. At the northern locations, the Upper Mississippi River Basin (UMRB) and Kellogg Biological Station (KBS), the cropland has annual ET that is 84 and 29 mm/year (22% and 5%) higher, respectively, caused primarily by higher ET during springtime when fields are fallow. At the southern location, the Central Mississippi River Basin (CMRB), the prairie has 69 mm/year (11%) higher ET, primarily due to a longer growing season. Differences in climate and that the CMRB prairie is remnant native prairie, while the UMRB and KBS prairies are restored, make it challenging to attribute differences to specific mechanisms. To accomplish this, we examine the energy balance using the Two‐Resistance Method (TRM). Results from the TRM demonstrate that higher surface conductance in croplands is the primary factor leading to higher springtime ET from croplands, relative to prairies. Results from this study provide insight into impacts of warm season grasses on the hydrology of the U.S. Corn Belt by providing a mechanistic understanding of how land use change affects the water budget.

Variation and Transformation of Evapotranspiration at Different Scales in a Desert Steppe

Water resources are severely scarce in desert steppes, and precipitation rarely collects in rivers or is transformed into groundwater. Evapotranspiration (ET) is the primary “export” of precipitation conversion and is the main mechanism for water vapor exchange between the underlying surface and atmosphere. ET changes have certain scale effects. This study focused on the natural grasslands in the Xilamuren Desert Steppe and analyzed and estimated the ET patterns at different scales, including micro-, point-, and surface scales, using observational data from instruments such as a photosynthetic meter, Eddy-covariance system (EC), and large-aperture scintillometer (LAS) from the Ecological Hydrology National Field Science Observation Station in the northern foothills of the Yin Mountains, Inner Mongolia. The spatial scale was extended based on this analysis. The results showed that at the microscale, the diurnal variation in the photosynthetic and transpiration rates of Leymus chinensis followed a bimodal curve. In July and August (high-temperature months), photosynthetic and transpiration rates were almost synchronous. In May and October, when the temperature was moderate, the transpiration rate was delayed compared to the photosynthetic rate at the first peak, and the second peak was significantly smaller than the first peak. At the point-scale, the daily average ET during the growing season was 1.37 mm·d−1 and the total cumulative ET was 251 mm. Transpiration levels exhibited significant seasonal variation in the following order: July &gt; August &gt; June &gt; September &gt; May &gt; October. At the surface-scale, the daily average ET during the growing season was 1.60 mm·d−1 and the total cumulative ET was 294 mm, which was 17% higher than that of the point-scale. The surface-scale ET was estimated using the observed values of the EC and the scale relationship formula and was optimized using different spatial scales of crop coefficients. This well reflected the ET patterns at the surface-scale. Therefore, this study proposes a spatial scale expansion method for a homogeneous underlying surface, verifies its value, and provides methodological support for estimating ET in cases of data scarcity.

Tradeoffs Between Temporal and Spatial Pattern Calibration and Their Impacts on Robustness and Transferability of Hydrologic Model Parameters to Ungauged Basins

AbstractOptimization of spatially consistent parameter fields is believed to increase the robustness of parameter estimation and its transferability to ungauged basins. The current paper extends previous multi‐objective and transferability studies by exploring the value of both multi‐basin and spatial pattern calibration of distributed hydrologic models as compared to single‐basin and single‐objective model calibrations, with respect to tradeoffs, performance and transferability. The mesoscale Hydrological Model (mHM) is used across six large central European basins. Model simulations are evaluated against streamflow observations at the basin outlets and remotely sensed evapotranspiration patterns. Several model validation experiments are performed through combinations of single‐ (temporal evaluation through discharge) and multi‐objective (temporal and spatial evaluation through discharge and spatial evapotranspiration patterns) calibrations with holdout experiments saving alternating basins for model evaluation. The study shows that there are very minimal tradeoffs between spatial and temporal performance objectives and that a joint calibration of multiple basins using multiple objective functions provides the most robust estimations of parameter fields that perform better when transferred to ungauged basins. The study indicates that particularly the multi‐basin calibration approach is key for robust parametrizations, and that the addition of an objective function tailored for matching spatial patterns of ET fields alters the spatial parameter fields while significantly improving the spatial pattern performance without any tradeoffs with discharge performance. In light of model equifinality, the minimal tradeoff between spatial and temporal performance shows that adding spatial pattern evaluation to the traditional temporal evaluation of hydrological models can assist in identifying optimal parameter sets.

Effect of climate change on reference evapotranspiration at the subnational scale: case study of Egypt

PurposeThis study’s fundamental objective is to assess climate change impact on reference evapotranspiration (ETo) patterns in Egypt under the latest shared socioeconomic pathways (SSPs) of climate change scenarios. Additionally, the study considered the change in the future solar radiation and actual vapor pressure and predicted them from historical data, as these factors significantly impact changes in the ETo.Design/methodology/approachThe study utilizes data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) models to analyze reference ETo. Six models are used, and an ArcGIS tool is created to calculate the monthly average ETo for historical and future periods. The tool considers changes in actual vapor pressure and solar radiation, which are the primary factors influencing ETo.FindingsThe research reveals that monthly reference ETo in Egypt follows a distinct pattern, with the highest values concentrated in the southern region during summer and the lowest values in the northern part during winter. This disparity is primarily driven by mean air temperature, which is significantly higher in the southern areas. Looking ahead to the near future (2020–2040), the data shows that Aswan, in the south, continues to have the highest annual ETo, while Kafr ash Shaykh, in the north, maintains the lowest. This pattern remains consistent in the subsequent period (2040–2060). Additionally, the study identifies variations in ETo , with the most significant variability occurring in Shamal Sina under the SSP585 scenario and the least variability in Aswan under the SSP370 scenario for the 2020–2040 time frame.Originality/valueThis study’s originality lies in its focused analysis of climate change effects on ETo, incorporating crucial factors like actual vapor pressure and solar radiation. Its significance becomes evident as it projects ETo patterns into the near and distant future, providing indispensable insights for long-term planning and tailored adaptation strategies. As a result, this research serves as a valuable resource for policymakers and researchers in need of in-depth, region-specific climate change impact assessments.

Surface flux equilibrium estimates of evaporative fraction and evapotranspiration at global scale: Accuracy evaluation and performance comparison

Accurate estimation of terrestrial evapotranspiration (ET) is crucial to understand the water cycle and partitioning of turbulent energy fluxes at the land surface. However, large-scale ET estimation is always difficult to achieve due to the complexity of controlling factors and the heterogeneity of landscapes. A recent model, referred to as the surface flux equilibrium (SFE) model, proposes that evaporative fraction (EF, the ratio of ET to available energy at the surface) can be estimated accurately from near-surface specific humidity and air temperature, which makes it applicable to common weather stations and climate reanalysis datasets. However, previous validation only focused on the evaluation of this model at site and basin scale for ET estimation, making the real performance of the SFE model for EF estimation heretofore unknown. There also exists a gap in the comparison between the SFE model and other existing products in the representation of global ET patterns and variation trend. Against this background, a comprehensive validation was performed in this study to evaluate the accuracy of the SFE model for both EF and ET estimation. The validation against 136 flux towers worldwide shows that the ground-based SFE model achieved EF estimation with a root-mean-square-error (RMSE) of 0.22 and 0.16 at daily and monthly scale, respectively. The corresponding RMSE of ET estimation was 0.94 mm/day and 16.37 mm/month. The application of the SFE model on a global scale was achieved by using two reanalysis products (ERA5-Land and GLDAS-CLSM) as inputs respectively. The results indicate that the SFE model did hold potential to reduce the RMSE of these two products for ET estimation, but the cost was the decrease in correlation coefficient (r). The comparison with five existing global ET products (BESS, GLEAM, AVHRR, MOD16, and SSEBop) shows that the SFE model performed on the basis of ERA5-Land product has achieved moderate accuracy among these products. Judged from r, mean absolute error, RMSE, and bias, its accuracy was ranked as fourth, fourth, second, and second, respectively. However, a further evaluation over different land cover types suggests that the ERA-based SFE model has significant superiority over cropland, relative superiority over forest and savannas, and significant inferiority over shrubland and grassland. This analysis is the first global validation of the SFE model for EF estimation as well as its comparison with existing global ET products. The validation analysis proves that this simple model has generally achieved comparable accuracy to existing complex models in the representation of global ET patterns and variation trend.

Calibration of Land-Use-Dependent Evaporation Parameters in Distributed Hydrological Models Using MODIS Evaporation Time Series Data

The land-use-specific calibration of evapotranspiration parameters in hydrologic modeling is challenging due to the lack of appropriate reference data. We present a MODIS-based calibration approach of vegetation-related evaporation parameters for two mesoscale catchments in western Germany with the physically based distributed hydrological model WaSiM-ETH. Time series of land-use-specific actual evapotranspiration (ETa) patterns were generated from MOD16A2 evapotranspiration and CORINE land-cover data from homogeneous image pixels for the major land-cover types in the region. Manual calibration was then carried out for 1D single-cell models, each representing a specific land-use type based on aggregated 11-year mean ETa values using SKout and PBIAS as objective functions (SKout &gt; 0.8, |PBIAS| &lt; 5%). The spatio-temporal evaluation on the catchment scale was conducted by comparing the simulated ETa pattern to six daily ETa grids derived from LANDSAT data. The results show a clear overall improvement in the SPAEF (spatial efficiency metric) for most land-use types, with some deficiencies for two scenes in spring and late summer due to phenological variation and a particularly dry hydrological system state, respectively. The presented method demonstrates a significant improvement in the simulation of ETa regarding both time and spatial scale.

Combining UAV thermography, point cloud analysis and machine learning for assessing small‐scale evapotranspiration patterns in a tropical rainforest

AbstractMicroclimate and vegetation structure control evapotranspiration (ET) from land surfaces at stand and landscape scales. Tropical rainforests are among the most diverse and complex terrestrial ecosystems, harbouring vast plant and animal species throughout their dense multistory canopy. They contribute substantially to global precipitation through their high ET. However, there is little information about ET influences at very small spatial scales under given climatic conditions. In a tropical rainforest on Sumatra, we studied the relationship between pixel‐level ET as derived from high‐resolution (~10 cm), near‐surface thermography from an unmanned aerial vehicle (UAV) and canopy structure as derived from red–green–blue (RGB) image and three‐dimensional (3D) point cloud analyses. The 16 derived potential predictors encompassed vegetation height, height variability, vegetation density and reflectance variables. Using regression models, several of the studied variables had a significant linear relationship with ET, but the explained variance was only marginal. However, applying a random forest algorithm including forward feature selection and target oriented cross validation explained substantial parts of the pixel‐level variance in ET (R2 = 0.56–0.65), thus indicating multiple non‐linear relationships with interactions among predictor variables. Therein, green leaf index, leaf area density and vegetation height were often the most important variables for the prediction outcome, but their sequence varied among the four study plots. Overall, combining canopy structure variables derived from RGB photogrammetry explained relatively large parts of spatial ET variations. Our study thus indicates the large potential of combining UAV‐based thermography and photogrammetry techniques with machine learning approaches to better understand ET but also suggests that more work remains to be done in explaining ET patterns at very small spatial scales.

Partitioning blue and green water sources of evapotranspiration using the water evaluation and planning (WEAP) model

Abstract Understanding agricultural water use by sources such as rainwater (green water) or irrigation water (blue water) is vital to manage scarce freshwater resources. Tracing sources of evapotranspiration increases water management efficiency. This study proposes a two-bucket soil moisture water balance approach to trace sources of evapotranspiration over irrigated landscapes in the Awash Basin, Ethiopia, which is modeled in the water evaluation and planning model. Algorithms are devised to separate actual evapotranspiration into blue and green sources. The results showed annual basin renewable water of 15.78 BCM in the form of streamflow (4.65 BCM), stored water (3.54 BCM), and groundwater percolation (7.59 BCM). Annual actual evapotranspiration over irrigated landscapes was 805 MCM, which was separated as blue evapotranspiration (88.4%) and green evapotranspiration (11.6%). The findings also demonstrate that blue evapotranspiration predominantly occurs during dry months, indicating heavy reliance on stored water for irrigated landscapes. Subbasin-level analyses showed varying blue/green evapotranspiration patterns based on precipitation and irrigated agriculture. Irrigation accounted for less than 3% of the streamflow in upstream basins, while midstream and downstream basins utilized up to 30 and 70%, respectively. The complementary use of rainfall and irrigation in most parts of the basin is considered to be of interest to water managers.

Integrating Tracers and Soft Data Into Multi‐Criteria Calibration: Implications From Distributed Modeling in a Riparian Wetland

AbstractCalibrating distributed hydrological models often leads to equifinality due to complex model structures, which can be further exacerbated in wetlands due to spatio‐temporal heterogeneity in ecohydrological processes. Here, step‐wise calibrations of the physically‐based distributed model EcH2O‐iso was conducted in a data‐rich wetland by minimizing a weighted average of the errors on discharge, stream isotopes, groundwater (GW) isotopes, and soil moisture. Results showed multi‐criteria calibration outperformed single‐criterion calibration as it strongly increased the overall performance, yet only marginally degraded performance of each calibration target. Isotopes were highlighted as appropriate auxiliary data as they effectively constrained the model with relatively small weights (0.1). However, those parameter sets that minimize the errors could still lead to physically implausible simulations of uncalibrated internal states or fluxes. This was further demonstrated by an approach developed to check internal fluxes based on soft data (transpiration and lateral flow), suggesting 54% of optimized models gave “right answers for the wrong reasons.” By excluding those models against soft data, such an approach further constrained equifinality, and unraveled potential inconsistencies between observations and calibration. Modeling represented the wetland as a slow‐draining system mainly fed by GW, but also influenced by near‐surface flow during winter or summer convectional events. Further, heterogeneity in hydrological functioning was partly attributed to distinct evapotranspiration patterns between contrasting vegetation communities. Therefore, this study not only provided insights into wetland functioning, but also revealed potential equifinality even with abundant data for calibration, and potential solutions based on the integration of isotopes and soft data.

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 &lt; 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.

A framework for estimating actual evapotranspiration through spatial heterogeneity-based machine learning approaches

Actual evapotranspiration (ET) is a key variable controlling the exchange of energy and water in terrestrial ecosystems. The spatial heterogeneity of ET patterns poses a great challenge for regional ET estimation over heterogeneous landscapes. In this study, we proposed a framework for estimating ET through interactive detector for spatial associations (IDSA)-based machine learning (ML) approaches by combining data from remote sensing and four flux towers distributed in the Hai River Basin. The IDSA model was applied to explore individual and interactive determinants of ET over the Hai River Basin. In addition, the geographical regions of ET were determined according to the spatial heterogeneity. Then we simulated ET in each geographical region separately using three ML models, including random forest, gradient boosting decision tree, and Cubist. General ML models without considering spatial heterogeneity were used for comparison, and evaluations were conducted with the eddy covariance flux tower observations at the site scale, also with water balance ET at the basin scale. The results demonstrated that the spatial patterns of ET were difficult to be explained by individual environmental variables (64.9 %). The maximum interpretability was improved by about 11.2 % through the interaction of air temperature (T) and normalized different vegetation index (NDVI). The framework developed in this study had excellent performance with coefficient of determination (R2) ranging from 0.807 to 0.811, root mean square error (RMSE) ranging from 0.654 mm/day to 0.661 mm/day, and mean absolute error (MAE) ranging from 0.470 mm/day to 0.485 mm/day. It was superior to general ML models at the site and basin scales. Additionally, the ET simulated by IDSA-based ML models were in good agreement with the reference product, further illustrating its reliability. The proposed framework provides a theoretical basis for in-depth understanding of spatial heterogeneity of ET and a new perspective for ET prediction over large scales.