Comment on hess-2020-669

An efficient calibration with remotely sensed (RS) data is important for accurate predictions at ungauged catchments. This study investigates the advantages of streamflow-sensitive regionalization on calibration with RS evapotranspiration (ET). Regionalization experiments are performed at 28 catchments in Australia. The catchments are classified into three groups based on annual rainfall and runoff coefficients. Streamflow, RS ET, and a multi-objective RS ET-streamflow calibration are performed using the DiffeRential Evolution Adaptive Metropolis algorithm in each catchment. Simplified Australian Water Resource Assessment-Landscape model is calibrated for a selection of five parameters. Posterior probability distributions of parameters from three calibrations performed at donor catchments in each group are inspected to find the parameter for regionalization in the individual group. In group 1 of wetter catchments, regionalization of parameter FsoilEmax (soil evaporation scaling factor) helps to simplify the calibration without any deterioration in ET, soil moisture (SM) and streamflow predictions. Regionalization of parameter Beta (coefficient describing rate of hydraulic conductivity increase with water content) in group 2 assists to improve the streamflow predictions with no decrement in ET and SM predictions. However, regionalization is not able to provide satisfactory results in group 3. Group 3 includes low-yielding catchments, with average annual rainfall below 1000 mm/year and runoff coefficient less than 0.1, where traditional streamflow calibration also fails to produce accurate results. This study concludes that streamflow-sensitive regionalization is effective for improving the efficacy of RS ET calibration in wetter catchments.

Information about the hydrological behaviour of a river basin prior to setting up, calibrating and validating a distributed hydrological model requires extensive datasets that are hardly available for many parts of the world due to insufficient monitoring networks. In this study, the focus was on prevailing spatio-temporal patterns of remotely sensed evapotranspiration (ET) that enabled conclusions to be drawn about the hydrological behaviour and spatial peculiarities of a river basin at rather high spatial resolution. The prevailing spatio-temporal patterns of ET were identified using a principal component analysis of a time series of 644 images of MODIS ET covering the Wami River basin (Tanzania) between the years 2000 and 2013. The time series of the loadings on the principal components were analysed for seasonality and significant long-term trends. The spatial patterns of principal component scores were tested for significant correlation with elevations and slopes, and for differences between different soil texture and land use classes. The results inferred that the temporal and spatial patterns of ET were related to those of preceding rainfalls. At the end of the dry season, high ET was maintained only in areas of shallow groundwater and in cloud forest nature reserves. A region of clear reduction of ET in the long-term was related to massive land use change. The results also confirmed that most soil texture and land use classes differed significantly. Moreover, ET was exceptionally high in natural forests and loam soil, and very low in bushland and sandy-loam soil. Clearly, this approach has shown great potential of publicly available remote sensing data in providing a sound basis for water resources management as well as for distributed hydrological models in data-scarce river basins at lower latitudes.

Using the three-layer variable infiltration capacity (VIC-3L) hydrological model and the successive interpolation approach (SIA) of climate factors, the authors studied the effect of different land cover types on the surface hydrological cycle. Daily climate data from 1992 to 2001 and remotely-sensed leaf area index (LAI) are used in the model. The model is applied to the Baohe River basin, a subbasin of the Yangtze River basin, China, with an area of 2500 km2. The vegetation cover types in the Baohe River basin consist mostly of the mixed forest type (∼85%). Comparison of the modeled results with the observed discharge data suggests that: (1) Daily discharges over the period of 1992–2001 simulated with inputs of remotely-sensed land cover data and LAI data can generally produce observed discharge variations, and the modeled annual total discharge agrees with observations with a mean difference of 1.4%. The use of remote sensing images also makes the modeled spatial distributions of evapotranspiration physically meaningful. (2) The relative computing error (RCE) of the annual average discharge is −24.8% when the homogeneous broadleaf deciduous forestry cover is assumed for the watershed. The error is 21.8% when a homogeneous cropland cover is assumed and −14.32% when an REDC (Resource and Environment Database of China) land cover map is used. The error is reduced to 1.4% when a remotely-sensed land cover at 1000-m resolution is used.

Remotely sensed evapotranspiration (ETRS) shows promise for enhancing hydrological models, especially in regions lacking in situ streamflow observations. However, model calibration studies showed conflicting results regarding the ability of ETRS products to improve streamflow simulation. Rather than relying on model calibration, here we produce the first randomized experiment that explores the full streamflow–ET skill distribution, and also the first probabilistic assessment of the value of different global ETRS products for streamflow simulation. Using 280,000 randomized SWAT (Soil and Water Assessment Tool) model runs across seven catchments and four ETRS products, we show that the relationship between ET and streamflow skills is complex, and simultaneous improvement in both skills is only possible in a limited range. Parameter sensitivity analysis indicates that the most sensitive parameters can have opposite contributions to ET and streamflow skills, leading to skill trade-offs. Conditional probability assessment reveals that models with good ET skills are likely to produce good streamflow skills, but not vice versa. We suggest that randomized experiments such as ours should be performed before model calibration to determine whether using ETRS is worthwhile, and to help in interpreting the calibration results.

Thailand is characterized by typical tropical monsoon climate, and is suffering serious water related problems, including seasonal drought and flooding. These issues are highly related to the hydrological processes, e.g., precipitation and evapotranspiration (ET), which are helpful to understand and cope with these problems. It is critical to study the spatiotemporal pattern of ET in Thailand to support the local water resource management. In the current study, daily ET was estimated over Thailand by ETMonitor, a process-based model, with mainly satellite earth observation datasets as input. One major advantage of the ETMonitor algorithm is that it introduces the impact of soil moisture on ET by assimilating the surface soil moisture from microwave remote sensing, and it reduces the dependence on land surface temperature, as the thermal remote sensing is highly sensitive to cloud, which limits the ability to achieve spatial and temporal continuity of daily ET. The ETMonitor algorithm was further improved in current study to take advantage of thermal remote sensing. In the improved scheme, the evaporation fraction was first obtained by land surface temperature—vegetation index triangle method, which was used to estimate ET in the clear days. The soil moisture stress index (SMSI) was defined to express the constrain of soil moisture on ET, and clear sky SMSI was retrieved according to the estimated clear sky ET. Clear sky SMSI was then interpolated to cloudy days to obtain the SMSI for all sky conditions. Finally, time-series ET at daily resolution was achieved using the interpolated spatio-temporal continuous SMSI. Good agreements were found between the estimated daily ET and flux tower observations with root mean square error ranging between 1.08 and 1.58 mm d−1, which showed better accuracy than the ET product from MODerate resolution Imaging Spectroradiometer (MODIS), especially for the forest sites. Chi and Mun river basins, located in Northeast Thailand, were selected to analyze the spatial pattern of ET. The results indicate that the ET had large fluctuation in seasonal variation, which is predominantly impacted by the monsoon climate.

Abstract. Water recycled through transpiring forests influences the spatial distribution of precipitation in the Amazon and has been shown to play a role in the initiation of the wet season. However, due to the challenges and costs associated with measuring evapotranspiration (ET) directly and high uncertainty in remote-sensing ET retrievals, the spatial and temporal patterns in Amazon ET remain poorly understood. In this study, we estimated ET over the Amazon and 10 sub-basins using a catchment-balance approach, whereby ET is calculated directly as the balance between precipitation, runoff, and change in groundwater storage. We compared our results with ET from remote-sensing datasets, reanalysis, models from Phase 5 and Phase 6 of the Coupled Model Intercomparison Projects (CMIP5 and CMIP6 respectively), and in situ flux tower measurements to provide a comprehensive overview of current understanding. Catchment-balance analysis revealed a gradient in ET from east to west/southwest across the Amazon Basin, a strong seasonal cycle in basin-mean ET primarily controlled by net incoming radiation, and no trend in ET over the past 2 decades. This approach has a degree of uncertainty, due to errors in each of the terms of the water budget; therefore, we conducted an error analysis to identify the range of likely values. Satellite datasets, reanalysis, and climate models all tended to overestimate the magnitude of ET relative to catchment-balance estimates, underestimate seasonal and interannual variability, and show conflicting positive and negative trends. Only two out of six satellite and model datasets analysed reproduced spatial and seasonal variation in Amazon ET, and captured the same controls on ET as indicated by catchment-balance analysis. CMIP5 and CMIP6 ET was inconsistent with catchment-balance estimates over all scales analysed. Overall, the discrepancies between data products and models revealed by our analysis demonstrate a need for more ground-based ET measurements in the Amazon as well as a need to substantially improve model representation of this fundamental component of the Amazon hydrological cycle.

According to 230 flux site observations, intra-day and diurnal ground heat flux (G) accounted for 19.2 % and 28.8 % of the corresponding net radiation, respectively. This indicates that G plays an important role in remote sensing (RS) evapotranspiration (ET) models. The accuracy of the five G simulation methods in the surface energy balance-based RS ET models was evaluated using half-hourly observations. The linear coefficient (LC) method and the methods embedded with the normalized difference vegetation index (NDVI) were able to accurately simulate a half-hourly G series at most sites. The mean and median Nash-Sutcliffe efficiency (NSE) values of all sites were generally higher than 0.50 in each half-hour period. However, the two methods embedded by fractional vegetation coverage showed poor performance in most half-hour periods, except the sunrise and sunset periods, with the mean and median NSE values of all sites below 0.20. The accuracy of each method varied significantly at different sites and at half-hour intervals. The highest accuracy was exhibited during sunrise periods (6:00–7:00), followed by sunset periods (17:00–18:00). There were 92 % (211/230) sites with an NSE of the LC method greater than 0.50 at 6:30. It showed a slightly higher accuracy during night periods than during daytime periods. The lowest accuracy was observed at noon periods (10:00–15:30). The sites with an NSE exceeding 0.50 only accounted for 51 % (118/230) and 43 % (100/230) at 10:30 and 13:30, respectively. The accuracy of Northern Hemisphere sites was generally higher than that of Southern Hemisphere sites. In general, the highest and lowest accuracies were observed at the high- and low-latitude sites, respectively. The performance of the LC method and the methods embedded with NDVI were generally satisfactory at the Eurasian and North American sites, with the NSE values of most sites exceeding 0.70. Conversely, it exhibited relatively poor performance at the African, South American, and Oceanian sites, especially the African sites. Both the temporal and spatial distributions of the accuracy of the G simulation were positively correlated with the correlation between G and the net radiation. Although the G simulation methods accurately simulated the G series at most sites and time periods, their performance was poor at some sites and time periods. The application of RS ET datasets covering these sites requires caution. The optimal parameter value for each method varied greatly at different sites. This indicates that the fixed parameter values in the G simulation methods are not appropriate. Further improvement of G simulations at these sites and time periods is recommended for the RS ET modelers.

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

Evapotranspiration (ET) is a key factor to further our understanding of climate change processes, especially on the Tibetan Plateau, which is sensitive to global change. Herein, the spatial patterns of ET are examined, and the effects of environmental factors on ET at different scales are explored from the years 2000 to 2012. The results indicated that a steady trend in ET was detected over the past decade. Meanwhile, the spatial distribution shows an increase of ET from the northwest to the southeast, and the rate of change in ET is lower in the middle part of the Tibetan Plateau. Besides, the positive effect of radiation on ET existed mainly in the southwest. Based on the environment gradient transects, the ET had positive correlations with temperature (R>0.85, p<0.0001), precipitation (R > 0.89, p < 0.0001), and NDVI (R > 0.75, p < 0.0001), but a negative correlation between ET and radiation (R = 0.76, p < 0.0001) was observed. We also found that the relationships between environmental factors and ET differed in the different grassland ecosystems, which indicated that vegetation type is one factor that can affect ET. Generally, the results indicate that ET can serve as a valuable ecological indicator.

Comparison of satellite-based evapotranspiration models over terrestrial ecosystems in China

The impacts of tillage practices, majorly conventional tillage (CT) and no-till (NT), on soil hydraulic properties have been studied in recent decades. In this paper, we incorporated an auto-calibration algorithm into the Soil and Water Assessment Tool (SWAT) model and calibrated the model at eight field sites with soil water content (SWC) observations in the Pataha Creek Watershed, WA, USA. The Green–Ampt method in SWAT was chosen to determine infiltration and surface runoff. Parameter uncertainty was quantified by “relatively optimal” parameter sets filtered by a critical objective function value. Cluster analysis was adopted to obtain equal-sized parameter sets for each site and to compare parameter sets between tillage practices. The centers of these clusters were employed as a sample of parameter values. The clustered parameter sets were then used in scenario analysis to examine the impacts of cropland tillage practices on lateral flow, runoff and evapotranspiration (ET). The model parameters (e.g., soil hydraulic properties) were significantly different between CT and NT. In particular, higher bulk density, larger available water capacity, and higher effective hydraulic conductivity were found for NT than for CT. SWCs at three depths of the NT sites were significantly higher than those of CT sites, which could be attributed to tillage practices. However, higher available water capacity at NT sites indicated that the NT soil had a higher capacity to hold water. Thus the mean net changes in SWC during a year were not significantly different between CT and NT. The statistically different model parameters neither resulted in statistical differences in annual outputs (e.g., runoff and ET) nor substantial differences in monthly outputs. Our study indicates that the tillage impacts on hydrological processes are site-specific and scale-dependent.

In a case study in Tao River Basin, China, we derived a high spatial-resolution regional distribution of evapotranspiration (ET) using the single crop coefficient method and Budyko equation. We then further analyzed the spatio-temporal characteristics of this diverse eco-hydrological basin from 2001–2010. The results suggest that the single crop coefficient method based on leaf area index captures better spatial and temporal dynamics of the regional ET than did the Budyko Equation method. The rising temperature was the main reason for the increasing ET in the Tao River Basin during 2001–2010. Areas with high ET efficiency were distributed mainly in the areas where the vegetation coverage was high, and a lower runoff coefficient responded. The estimated spatial patterns of ET allowed an improved understanding of the eco-hydrological processes within the Tao River Basin and the method used might be generalized as a reference for future regional-scale eco-hydrological research.

Asymmetric environmental responses on evapotranspiration in Tibetan Plateau grassland.

Hydrological models have become an important tool for the efficient management of water resources. However, selection of appropriate models for evapotranspiration (ET) computations in river basins remains challenging to watershed managers, especially in data-scarce regions. The performance of Soil and Water Assessment Tool (SWAT)-based model for the prediction of potential and actual evapotranspiration (PET and AET) of OgunOshun river basin, Nigeria was investigated. Spatial and meteorological data was applied in setting up Mapwindow SWAT model. The three existing methods: Penman-Monteith, Priestly-Taylor (radiation-based) and Hargreaves (temperature-based), available in SWAT-were applied for the evaluation of PET and AET using soil, topographic, land-use and meteorological data as input parameters. The model results show a good correlation between the simulated and observed dataset as shown by Nasch-Sucliffe efficiency and coefficient of determination values. For the 30 year-simulation period, the predicted average PET values for PenmanMonteith, Priestley-Taylor and Hargreaves methods were 1791.516, 1684.597 and 1724.563 mm with corresponding standard deviation values of 89.322, 53.824 and 77.867 mm, respectively. The analysis indicated that Penman-Monteith and Hargreaves methods yielded almost the same results, while Priestly-Taylor method slightly differs, which establishes that it is not very suitable for arid/semi-arid regions. The study could be beneficial to watershed managers in addressing climate-related problems and for sustainable water resource management. KEYWORDS: SWAT, Potential evapotranspiration, Actual evapotranspiration, Watershed.

Accurate estimation of actual evapotranspiration (ET) is critical for understanding hydrothermal cycles and ecosystem functioning in arid regions, where water scarcity governs ecological resilience. To address persistent gaps in ET quantification, this study integrates multi-source remote sensing data, energy balance modeling, and ground-based validation that significantly enhances spatiotemporal ET accuracy in the vulnerable desert steppe ecosystems. The study utilized meteorological data from several national stations and Landsat-8 imagery to process monthly remote sensing images in 2019. The Surface Energy Balance System (SEBS) model, chosen for its ability to estimate ET over large areas, was applied to derive modeled daily ET values, which were validated by a large-weighted lysimeter. It was shown that ET varied seasonally, peaking in July at 6.40 mm/day, and reaching a minimum value in winter with 1.83 mm/day in December. ET was significantly higher in southern regions compared to central and northern areas. SEBS-derived ET showed strong agreement with lysimeter measurements, with a mean relative error of 4.30%, which also consistently outperformed MOD16A2 ET products in accuracy. This spatial heterogeneity was driven by greater vegetation coverage and enhanced precipitation in the southeast. The steppe ET showed a strong positive correlation with surface temperatures and vegetation density. Moreover, the precipitation gradients and land use were primary controllers of spatial ET patterns. The process-based SEBS frameworks demonstrate dual functionality as resource-optimized computational platforms while enabling multi-scale quantification of ET spatiotemporal heterogeneity; it was therefore a reliable tool for ecohydrological assessments in an arid steppe, providing critical insights for water resource management and drought monitoring.