Regional Scale Evapotranspiration Research Articles

Comparison of Remotely Sensed Evapotranspiration Models Over Two Typical Sites in an Arid Riparian Ecosystem of Northwestern China

Accurate estimates of evapotranspiration (ET) are essential for the conservation of ecosystems and sustainable management of water resources in arid and semiarid regions. Over the last two decades, several empirical remotely sensed ET models (ERSETMs) had been developed and extensively used for regional-scale ET estimation in arid and semiarid ecosystems. These ERSETMs were constructed by combining datasets from different sites and relating measured daily ET to corresponding meteorological data and vegetation indices at the site scale. Then, regional-scale ET on a pixel basis can be estimated, using the established ERSETMs. The estimation accuracy of these ERSETMs at the site scale plays a fundamental and crucial role in regional-scale ET estimation. Recent studies have revealed that ET estimates from some of these models have significant uncertainties at different spatiotemporal scales. However, little information is available on the performance of these ERSETMs at the site scale. In this study, we compared eight ERSETMs, using ET measurements from 2013 to 2018 for two typical eddy covariance sites (Tamarix site and Populus site) in an arid riparian ecosystem of Northwestern China, intending to provide a guide for the selection of these models. Results showed that the Nagler-2013 model and the Yuan-2016 model outperformed the other models. There were substantial differences in the ET estimation of the eight ERSETMs at daily, monthly, and seasonal scales. The mean ET of the growing season from 2013 to 2018 ranged from 465.93 to 519.65 mm for the Tamarix site and from 386.22 to 437.05 mm for the Populus site, respectively. The differences in model structures and characterization of both meteorological conditions and vegetation factors were the primary sources of different model performance. Our findings provide useful information for choosing models and obtaining accurate ET estimation in arid regions.

Effects of Distinguishing Vegetation Types on the Estimates of Remotely Sensed Evapotranspiration in Arid Regions

Accurate estimates of evapotranspiration (ET) in arid ecosystems are important for sustainable water resource management due to competing water demands between human and ecological environments. Several empirical remotely sensed ET models have been constructed and their potential for regional scale ET estimation in arid ecosystems has been demonstrated. Generally, these models were built using combined measured ET and corresponding remotely sensed and meteorological data from diverse sites. However, there are usually different vegetation types or mixed vegetation types in these sites, and little information is available on the estimation uncertainty of these models induced by combining different vegetation types from diverse sites. In this study, we employed the most popular one of these models and recalibrated it using datasets from two typical vegetation types (shrub Tamarix ramosissima and arbor Populus euphratica) in arid ecosystems of northwestern China. The recalibration was performed in the following two ways: using combined datasets from the two vegetation types, and using a single dataset from specific vegetation type. By comparing the performance of the two methods in ET estimation for Tamarix ramosissima and Populus euphratica, we investigated and compared the accuracy of ET estimation at the site scale and the difference in annual ET estimation at the regional scale. The results showed that the estimation accuracy of daily, monthly, and yearly ET was improved by distinguishing the vegetation types. The method based on the combined vegetation types had a great influence on the estimation accuracy of annual ET, which overestimated annual ET about 9.19% for Tamarix ramosissima and underestimated annual ET about 11.50% for Populus euphratica. Furthermore, substantial difference in annual ET estimation at regional scale was found between the two methods. The higher the vegetation coverage, the greater the difference in annual ET. Our results provide valuable information on evaluating the estimation accuracy of regional scale ET using empirical remotely sensed ET models for arid ecosystems.

Regional evapotranspiration from an image-based implementation of the Surface Temperature Initiated Closure (STIC1.2) model and its validation across an aridity gradient in the conterminous US

Abstract. Recent studies have highlighted the need for improved characterizations of aerodynamic conductance and temperature (gA and T0) in thermal remote-sensing-based surface energy balance (SEB) models to reduce uncertainties in regional-scale evapotranspiration (ET) mapping. By integrating radiometric surface temperature (TR) into the Penman–Monteith (PM) equation and finding analytical solutions of gA and T0, this need was recently addressed by the Surface Temperature Initiated Closure (STIC) model. However, previous implementations of STIC were confined to the ecosystem-scale using flux tower observations of infrared temperature. This study demonstrates the first regional-scale implementation of the most recent version of the STIC model (STIC1.2) that integrates the Moderate Resolution Imaging Spectroradiometer (MODIS) derived TR and ancillary land surface variables in conjunction with NLDAS (North American Land Data Assimilation System) atmospheric variables into a combined structure of the PM and Shuttleworth–Wallace (SW) framework for estimating ET at 1 km × 1 km spatial resolution. Evaluation of STIC1.2 at 13 core AmeriFlux sites covering a broad spectrum of climates and biomes across an aridity gradient in the conterminous US suggests that STIC1.2 can provide spatially explicit ET maps with reliable accuracies from dry to wet extremes. When observed ET from one wet, one dry, and one normal precipitation year from all sites were combined, STIC1.2 explained 66 % of the variability in observed 8-day cumulative ET with a root mean square error (RMSE) of 7.4 mm/8-day, mean absolute error (MAE) of 5 mm/8-day, and percent bias (PBIAS) of −4 %. These error statistics showed relatively better accuracies than a widely used but previous version of the SEB-based Surface Energy Balance System (SEBS) model, which utilized a simple NDVI-based parameterization of surface roughness (zOM), and the PM-based MOD16 ET. SEBS was found to overestimate (PBIAS = 28 %) and MOD16 was found to underestimate ET (PBIAS = −26 %). The performance of STIC1.2 was better in forest and grassland ecosystems as compared to cropland (20 % underestimation) and woody savanna (40 % overestimation). Model inter-comparison suggested that ET differences between the models are robustly correlated with gA and associated roughness length estimation uncertainties which are intrinsically connected to TR uncertainties, vapor pressure deficit (DA), and vegetation cover. A consistent performance of STIC1.2 in a broad range of hydrological and biome categories, as well as the capacity to capture spatio-temporal ET signatures across an aridity gradient, points to the potential for this simplified analytical model for near-real-time ET mapping from regional to continental scales.

Evapotranspiration Estimates Derived Using Multi-Platform Remote Sensing in a Semiarid Region

Evapotranspiration (ET) is a key component of the water balance, especially in arid and semiarid regions. The current study takes advantage of spatially-distributed, near real-time information provided by satellite remote sensing to develop a regional scale ET product derived from remotely-sensed observations. ET is calculated by scaling PET estimated from Moderate Resolution Imaging Spectroradiometer (MODIS) products with downscaled soil moisture derived using the Soil Moisture Ocean Salinity (SMOS) satellite and a second order polynomial regression formula. The MODis-Soil Moisture ET (MOD-SMET) estimates are validated using four flux tower sites in southern Arizona USA, a calibrated empirical ET model, and model output from Version 2 of the North American Land Data Assimilation System (NLDAS-2). Validation against daily eddy covariance ET indicates correlations between 0.63 and 0.83 and root mean square errors (RMSE) between 40 and 96 W/m2. MOD-SMET estimates compare well to the calibrated empirical ET model, with a −0.14 difference in correlation between sites, on average. By comparison, NLDAS-2 models underestimate daily ET compared to both flux towers and MOD-SMET estimates. Our analysis shows the MOD-SMET approach to be effective for estimating ET. Because it requires limited ancillary ground-based data and no site-specific calibration, the method is applicable to regions where ground-based measurements are not available.

Analysis of the contribution of groundwater to evapotranspiration in an arid irrigation district with shallow water table

Groundwater from the shallow aquifers can supply substantial water for evapotranspiration of crops and other vegetation (the groundwater supported evapotranspiration). Regional scale evapotranspiration and the groundwater contribution to evapotranspiration were estimated by the soil water balance equation in Jiefangzha sub-district (JFZSD), a sub-district with shallow aquifers in Inner Mongolia, China. Estimates used an 8-year (2006–2013) hydrological dataset including soil moisture, the depth to water table, irrigation amounts, rainfall data, and drainage water flow. Soil moisture and the groundwater depth varied substantially with irrigation, the fluctuations in rainfall, and the stage of crop growth. The soil moisture with very shallow groundwater depth was greater than it was with groundwater at greater depth. The 8-year mean evapotranspiration was estimated to be 664mm, the mean section evapotranspiration were ranged from 538mm to 899mm during the crop growth period in JFZSD. The mean groundwater supported evapotranspiration (ETg) was estimated to be 228mm, with variation from 145mm to 412mm during the crop growth period in JFZSD. Analysis of the positive correlation between evapotranspiration and the sum of irrigation and rainfall, and the analysis of the negative correlation between ETg/ET and the sum of irrigation and rainfall, reflect the need of groundwater to meet the evapotranspiration demand. Approximately 20% to 40% of the evapotranspiration is from the shallow aquifers in JFZSD. With the policy of decreasing the amount of Yellow River water diverted to the HID in the future, the more groundwater would be used. However, the local groundwater in return depends on irrigation. Besides, the fraction of drainage to irrigation is already low, suggesting that future decreases in water deliveries will lead to soil salinization and loss of productivity in the sub district. These results could be useful for the government to understand the significant role of the groundwater and make reasonable water use policy in the semiarid agricultural regions.

Surface Energy Balance-Based Model for Estimating Evapotranspiration Taking into Account Spatial Variability in Weather

Reliable estimates of evapotranspiration (ET) from vegetation are needed for many types of water-resource investigations. How well models can estimate ET from vegetation varies, depending on the capabilities of the model as well as the nature of the targeted vegetation. Model accuracy also depends heavily on the quality and quantity of the data used. Several ET models have been developed that use an energy balance approach in which the data used by the models are derived from satellite imagery. This research introduces an enhanced surface energy balance-based model, the remote sensing of evapotranspiration or ReSET model, for estimating ET. ReSET is an ET estimation model that takes into consideration the spatial variability in weather parameters, which makes it particularly applicable for calculating regional scale ET. ReSET also has the capability of interpolating between the available weather stations in time and space. The model's accuracy at daily and seasonal time scales is evaluated in several case studies.

Estimation of Regional Evapotranspiration by Combining Aircraftand Ground-Based Measurements

Two methods are examined for combining measurements from instrumented aircraftand towers to estimate regional scale evapotranspiration. Aircraft data provided spatially averaged values of properties of the surface, the evaporative fraction and maximum stomatal conductance. These quantities are less sensitive to meteorological conditions than the turbulent fluxes of heat and water vapour themselves. The methods allowed aircraft data collected over several days to be averaged and thus to reduce the random error associated with the temporal under-sampling inherent in aircraft measurements. Evaporative fraction is estimated directly from the aircraft data, while maximum stomatal conductance is estimated by coupling the Penman–Monteith equation to a simple model relating surface conductance to the incoming shortwave radiation and specific humidity saturation deficit. The spatial averages of evaporative fraction and maximum stomatal conductance can then be used with routine tower data to estimate the regional scale evapotranspiration. Data from aircraft flights and six ground based sites during the OASIS field campaign in south–east New South Wales in 1995 have been used to check the methods. Both the evaporative fraction and the maximum stomatal conductance derived from the aircraft data give information on the spatial variability of the surface energy budget at scales from 10 to 100 km. Daily averaged latent heat fluxes estimated using these methods for the OASIS study region agree with the available observations in quasi-stationary conditions or in weakly non-stationary conditions when the data from several aircraft flights are averaged to reduce the impact of short term imbalances in the surface energy budget.

Regional scale evapotranspiration estimation using satellite derived albedo and surface temperature

In the studies reteted to surface energy balance, satellite data provides important inputs for estimating regional surface albedo and evapotranspiration. The paper describes the use of satellite data in determining the surface emissivity over heterogeneous a’reas by taking Normalized Difference Vegetation Index (NDVI) as modulating parameter at pixel resolution. The estimated emissivity values have been used to find the surface temperature at the pixel scale. Landsat-TM-visible, NIR, TIR bands data and some ground meteorological data have been used in an energy balance model for estimating surface albedo and evapotranspiration. The ET values derived from the model are in good agreement with the values obtained with. ‘CENTURY MODEL’ and ground observations over the area, suggesting the possible use of this approach fot regional scale studies on evapotranspiration.

Using spatial context in satellite data to infer regional scale evapotranspiration

An estimate of evapotranspiration is developed by relating variations of satellite-derived surface temperature to a vegetation index computed from satellite visible and near-infrared data. The method requires independent estimates of evapotranspiration for a completely vegetated area and for a nonvegetated area, although such areas need not appear in the satellite data. A regional estimate of evapotranspiration is derived despite the lack of precise estimates for individual satellite measurements. The method requires spatial variability in the satellite data: it does not apply in uniform areas. In addition, a property is identified which permits discrimination of cirrus clouds from areas of varying soil moisture. >