Estimation Of Actual Evapotranspiration Research Articles

Discussion of “Evaluation of the Vapor Pressure Models in the Estimation of Actual Vapor Pressure and Evapotranspiration” by Hitesh Upreti and C. S. P. Ojha

Discussion of “Evaluation of the Vapor Pressure Models in the Estimation of Actual Vapor Pressure and Evapotranspiration” by Hitesh Upreti and C. S. P. Ojha

Estimation of Actual Evapotranspiration in Fragmented Mediterranean Areas by the Spatio-Temporal Fusion of NDVI Data

Actual evapotranspiration $(\text{ET}_{{\rm{A}}})$ is a fundamental component of the land water cycle that can be predicted by the combination of meteorological data and remotely sensed normalized difference vegetation index (NDVI) observations. The proficient application of this approach to the retrospective study of fragmented areas, however, depends on the preliminary use of spatio-temporal fusion (STF) methods capable of integrating different satellite datasets. One of these methods is the Spatial Enhancer of Vegetation Index image Series (SEVIS), which has been recently developed to improve the annual NDVI datasets based on one or a few high spatial resolution images. This STF method is currently applied to moderate resolution imaging spectroradiometer (MODIS) and TM/ETM+/OLI imagery taken over three fragmented areas in Tuscany (Central Italy), representative of different Mediterranean ecosystems, i.e., an urban grassland, a tomato field, and an olive grove. The performance of SEVIS is evaluated by comparing the $\text{ET}_{{\rm{A}}}$ estimates obtained from the original (MODIS) and synthetic (MODIS plus TM/ETM+/OLI) NDVI datasets to ground $\text{ET}_{{\rm{A}}}$ observations. The experimental results indicate that the original MODIS NDVI data cannot properly characterize the seasonal vegetation evolutions of the three study sites, which negatively affects the performance of $\text{ET}_{{\rm{A}}}$ simulation. In contrast, such evolutions are reasonably reproduced by the synthetic NDVI datasets, which improves the accuracy of the $\text{ET}_{{\rm{A}}}$ estimates both in terms of correlation and errors. The improvements are particularly evident during the summer dry period when the MODIS images are incapable of characterizing the actual vegetation response to water stress.

To Assess the Impact of Urbanization-Associated Land Use Changes on Actual Evapotranspiration and Water Balance in the Kelani River Basin, Sri Lanka

Actual Evapotranspiration (AET) is a major component of the river basin hydrological cycle over land surface and energy balances. More than 60% of input water on land is returned to the atmosphere through evapotranspiration and it greatly influences the water availability on the land surface. Estimation of AET is an essential part in various fields. Thus, this study mainly aims at assessing the impact of urbanization-associated land use changes on actual evapotranspiration and water balance in the Kelani River Basin, Sri Lanka. Thronthwaite equation and land use conditions are mainly considered in this study to estimate AET. Average monthly temperature data has been obtained from NASA MOD1C3 and data has been validated using observed temperature data processed by the Meteorological Department in Sri Lanka. Potential Evapotranspiration (PET) was calculated using monthly average temperature and fractional vegetation cover was calculated using Landsat images (TM, ETM and OLI) to identify the land use and land cover changes from year 2000 to 2018. Both potential evapotranspiration and fractional vegetation cover are used to estimate AET. TRRM data was used to get Digital Elevation Model (DEM) and Landsat images were used to calculate Normalized Differential Vegetation Index (NDVI), Normalized Differential Building Index (NDBI). All the analysis used in this study have been carried out using raster calculator, zonal statistics and pivot tools in ArcGIS 10.1 software. The study revealed that land use and land cover is a major fact to determine AET. Upper catchment of Kelani River has obtained high AET values due to the vegetation cover and the elevation. Lower part of the catchment is associated with the low values due to build - up areas. AET has decreased by 2018 and urbanization is the main reason for it. Rainy seasons reduce the AET since high humidity and reducing AET can be highlighted when considering the temporal changes of AET in Kelani river basin. Evapotranspiration as a major component of water cycle should be considered because it can be a significant fact to reduce precipitation.

Evaluation of Remote-Sensing based Estimates of Actual Evapotranspiration over (Diverse Shape and Sized) Palmiet Wetlands

Accurately quantifying actual evapotranspiration (ETa) over wetlands is important for the improved management of these ecosystems, since 65% of them are threatened by clearing or drainage in South Africa. This study evaluated a range of available estimates of ETa over six palmiet wetlands, which are key ecological structures in terms of water regulation and sediment trapping. The research compared three remote sensing based products (a local product, FruitLook, and two global data products, MOD16 ET and EEFlux) across different rainfall years (2008 to 2019). Their outputs were validated, where possible, with limited ground-based scintillometer data on the Krom palmiet wetland, which indicated that MOD16 and EEFlux were most representative of ground-based measurements. We also compared the small pixel size EEFlux data over three wetlands with ETa over increasing buffers of land cover (100, 500, 1000 m) in order to validate the perception of these wetlands being high water users. While larger wetlands had slightly higher evaporative demands than adjacent areas, ETa over a small wetland was similar to neighboring land cover. The results indicate that palmiet wetland ETa is highly variable and dependent on external factors such as climate, wetland size and seasonality.

Joint Estimation of Gross Recharge, Groundwater Usage, and Hydraulic Properties within HydroSight.

Groundwater management decisions are often founded upon estimates of aquifer hydraulic properties, recharge and the rate of groundwater usage. Too often hydraulic properties are unavailable, recharge estimates are very uncertain, and usage is unmetered or infrequently metered over only recent years or estimated using numerical groundwater models decoupled from the drivers of drawdown. This paper extends the HydroSight groundwater time-series package ( http://peterson-tim-j.github.io/HydroSight/) to allow the joint estimation of gross recharge, transmissivity, storativity, and daily usage at multiple production bores. A genetic evolutionary scheme was extended from estimating time-series model parameters to also estimating time series of usage that honor metered volumes at each production bore and produces (1) the best fit with the observed hydrograph and (2) plausible estimates of actual evapotranspiration and hence recharge. The reliability of the approach was rigorously tested. Repeated calibration of models for four bores produced estimates of transmissivity, storativity, and mean recharge that varied by a factor of 0.22-0.32, 0.13-0.2, and 0.03-0.48, respectively, when recharge boundary effects were low and the error in monthly, quarterly, and biannual metered usage was generally <10%. Application to the 30 observation bores within the Warrion groundwater management area (Australia), produced a coefficient of efficiency of ≥0.80 at 22 bores and ≥0.90 at 12 bores. The aquifer transmissivity and storativity were reasonably estimated, and were consistent with independent estimates, while mean gross recharge may be slightly overestimated. Overall, the approach allows greater insights from the available data and provides opportunity for the exploration of usage and climatic scenarios.

Combining generalized complementary relationship models with the Bayesian Model Averaging method to estimate actual evapotranspiration over China

Evapotranspiration, commonly classified as actual evapotranspiration (AET), wet evapotranspiration (WET), and potential evapotranspiration (PET), is an important component of hydrological processes. Several models have been developed for estimating AET, but none are applicable under all conditions due to the complexity of the algorithms and limitations in parameterization. However, PET and WET can be easily calculated using several simple equations based on available climate data. In this study, a regional reanalysis dataset, the China Meteorological Assimilation Driving Dataset for the SWAT model (CMADS), was used to estimate PET and WET, and a generalized complementary relationship (GCR) was used to convert from PET and WET to AET. Then a Bayesian Model Averaging (BMA) method was applied to merge the GCR-based AETs to improve the accuracy of AET estimations in China. After examining the performance of GCR and BMA models at the flux tower sites in different climate zones, we found that other PET models were better than the PM model for predicting AET using GCR. In addition, the BMA estimations were closer to flux tower observations than were those of the GCR-based AET in each climate zone. The average RMSE of BMA-merged AET was reduced to 0.66 mm/day, compared to 1.82 mm/day in the original GCR model. Moreover, the performance of AET estimated by CMADS and other AET products, such as the Global Land Data Assimilation System and the Moderate Resolution Imaging Spectroradiometer Global Evapotranspiration Project was examined at the flux tower sites. The results showed that the CMADS AET product performed better than other AET products in each climate zone with better statistical values. In general, the results showed that the GCR estimates are promising when combined with BMA for future studies to characterize global AET.

Evaluation of modeled actual evapotranspiration estimates from a land surface, empirical and satellite-based models using in situ observations from a South African semi-arid savanna ecosystem

Evapotranspiration (ET) plays a crucial role in the land-atmosphere interaction and climate variability, especially in arid and semi-arid areas. Accurate estimates of ET are important in hydrological and climate modeling. This study evaluates eight ET data products from different models used for ET estimation. The data products are classified into three main categories depending on the type of modeling approaches: namely process-based land surface model, empirical models, and satellite data derived estimates. The different model estimates are evaluated against in situ measurements from the Skukuza flux tower which is situated in a semi-arid savanna in South Africa. The correlation score and cantered root mean square error computed on monthly ET averages indicate that the satellite-derived model and land surface model estimates are closer to the observed ET signal for the Skukuza site, both in-phase and magnitude. The empirical models' outputs tend to reflect a relatively pronounced departure from observations in magnitude. The normalised mean bias computed for different seasons reveals that the estimates from all modeling approaches are close to the observed signal during the transition period (March–May) to the austral summer. In general, all models overestimate ET during summer and underestimate it in winter. A qualitative analysis of the year-to-year variation for different seasons reveals that all model estimates are qualitatively consistent with the observed seasonal pattern of the signal. Satellite and process-based land surface models (LSMs) also show a response to extremes events such as drought years. The study identifies satellite-derived model outputs as a candidate for understanding spatio-temporal variability of ET across different landscapes within the study region, and process-based models to potentially be used for climate change impact studies on ET.

Integration of soil moisture as an auxiliary parameter for the anchor pixel selection process in SEBAL using Landsat 8 and Sentinel - 1A images

ABSTRACTThe sensible heat flux component calculation in the Surface Energy Balance Algorithm for Land (SEBAL) completely relies upon the anchor pixel locations. The fragmented agriculture regions with diverse crops make the selection process more challenging with less probability to correlate with ground realities. The conventional methods for anchor pixel selection have limitations in capturing the soil moisture variations beneath the canopy as they utilize only the thermal remote sensing images and ancillary datasets like land cover and crop type maps. The current research attempts to reduce the existing gap between the ground realities and simulated results by adding soil moisture as a supplementary parameter. The prospects of semi-empirical Water Cloud Model (WCM) to estimate the soil moisture content was tested in a fragmented agricultural region for different time periods. The concurrent satellite data obtained from Sentinel - 1A and Landsat 8 were utilized to supply the inputs for inversion modelling based on the Levenberg-Marquardt approach. The current research introduced ‘virtual Normalized Difference Vegetation Index’ concept to refine the WCM and yielded reliable soil moisture output for supplementing the anchor pixel selection process. The robustness of the approach was justified by considering the available energy at anchor pixel locations. The research outcomes revealed that the anchor pixel selection with and without soil moisture criterion has a significant impact on actual evapotranspiration estimation. The research also explores the scope of the synergetic use of optical and Synthetic Aperture Radar (SAR) inputs in SEBAL.

Biophysical Parameters and Actual Evapotranspiration of Bean Culture by Means of Remote Sensing

Agricultural irrigation is involved in an important chain that involves all sectors of the economy, either directly, by increasing food production, or indirectly, by withdrawing large amounts of fresh water. The relevance of this theme forces the search for alternatives to make water use as rational as possible. Evapotranspiration estimation methods based in remote sensing, such as the SAFER (Simple Algorithm for Evapotranspiration Retrieving) model, become extremely relevant in these scenarios, since it is possible to estimate this parameter in large scales. Therefore, the aim of this research was to apply the SAFER model in the estimation of bean crop actual evapotranspiration using Landsat-8 satellite image data. One of the parameters used as input in the SAFER model is the NDVI (Normalized Difference Vegetation Index), which presented a coefficient of determination (r&amp;sup2;) equal to 0.80 when compared to the crop coefficient. The actual evapotranspiration (ETa) estimated by the SAFER model were compared to the FAO 56 model estimates for later correlation between the models. This information is expected to assist the producer in a better management of water resources used in irrigation. The correlation between the two models presented a relevant coefficient of determination (r2 = 0.73), representing the potential of the SAFER model in relation to the FAO model 56.

Modeling Actual Evapotranspiration using the Advection Aridity Model

Evapotranspiration is a major process in the hydrological cycle. Between 40 to 60% of precipitation is returned back to the atmosphere in the form of evapotranspiration. Therefore, estimation of Actual Evapotranspiration (AET) is important in studies related to hydrology, agriculture and climate. However, due to the fact that calculation of AET using conventional methods require data pertaining to climate, vegetation/crop and soil moisture levels, estimation of spatial AET becomes a very tedious task. The Complementary Relationship Areal Evapotranspiration (CRAE) hypothesis has been proposed as a simpler alternative to estimate AET from large spatial domains using only routinely observed meteorological data thereby circumventing the need for data on soil-vegetation. The objective of the present work was to apply the CRAE hypothesis in Karnataka State, India and evaluate the accuracy of estimates using MODIS satellite-derived AET estimates. The Advection Aridity (AA) model which is based on the CRAE hypothesis was chosen to estimate AET using historical climate records of the Afzalpur and Hungund stations. The AA model uses the Penman equation to estimate the Potential ET and the Priestley-Taylor equation to estimate Wet Environment ET which are needed to estimate AET. Accuracy of AET values obtained from the AA model were validated using the MODIS MOD16A2 evapotranspiration product, which provides an 8-day composite AET estimate at 500 m resolution. AET estimated by the AA model compared favorably with MOD16A2 with the average Coefficient of determination (R2)for the two stations being 0.8095. While the results of this study have shown that the AA model provides reasonably accurate AET estimates with minimum data, further studies need to be carried out to improve the accuracy through local calibration of parameters.

SENSITIVITY OF ACTUAL EVAPOTRANSPIRATION ESTIMATION USING THE SEBS MODEL TO VARIATION OF INPUT PARAMETERS (LST, DSSF, AERODYNAMICS PARAMETERS, LAI, FVC)

Abstract. Actual Evapotranspiration (AET) is a key component of the water and energy balance and hydrological regime of catchments. A land surface energy balance system model (SEBS) was used to estimate the AET of the 160100-km² Medjerda river basin in Northern Tunisia. This model uses satellite data in combination with meteorological data. In this study, we investigated the sensitivity of the AET model output to five major input variables: the 30-minute Downward Surface Shortwave solar radiation fluxes (DSSF), and Land Surface Temperatures (LST), the roughness height for momentum transfer z0m, and the influence of the spatial resolution of satellite-based Leaf Area Index (LAI) and fraction of Vegetation Cover (FVC) estimates. The DSSF product was validated using a comparison to solar radiation estimates by the Angstrom formula based on in-situ station data. Gaps in the 15-min satellite-based land surface temperature time series were filled using a sinusoidal model on pixels containing meteorological stations. One-half to two standard deviations of the errors of the regression curves were applied to analyse the sensitivity of the SEBS output. Two methods to estimate the near surface aerodynamic parameter z0m were applied and compared. Maps of LAI and FVC derived from two sensors alternatively applied as an input to the SEBS model. A sensitivity analysis, performed in the first decade of May 2010, showed that SEBS model parameterization is quite sensitive in the forestland cover type. The difference can be up to 0.3 mm day−1. For agricultural land areas, representing an important percentage of the Medjerda basin, AET estimations based on the SEBS model proved to be used to satisfy the actual evapotranspiration estimates.

Change of winter wheat planting area and its impacts on groundwater depletion in the North China Plain

The North China Plain is one of the most water-stressed areas in China. Irrigation of winter wheat mainly utilizes groundwater resources, which has resulted in severe environmental problems. Accurate estimation of crop water consumption and net irrigation water consumption is crucial to guarantee the management of agricultural water resources. An actual crop evapotranspiration (ET) estimation model was proposed, by combining FAO Penman-Monteith method with remote sensing data. The planting area of winter wheat has a significant impact on water consumption; therefore, the planting area was also retrieved. The estimated ET showed good agreement with field-observed ET at four stations. The average relative bias and root mean square error (RMSE) for ET estimation were −2.2% and 25.5 mm, respectively. The results showed the planting area and water consumption of winter wheat had a decreasing trend in the Northern Hebei Plain (N-HBP) and Southern Hebei Plain (S-HBP). Moreover, in these two regions, there was a significant negative correlation between accumulated net irrigation water consumption and groundwater table. The total net irrigation water consumption in the N-HBP and S-HBP accounted for 12.9×109 m3 and 31.9×109 m3 during 2001–2016, respectively. Before and after 2001, the decline rate of groundwater table had a decreasing trend, as did the planting area of winter wheat in the N-HBP and S-HBP. The decrease of winter wheat planting area alleviated the decline of groundwater table in these two regions while the total net irrigation water consumption was both up to 28.5×109 m3 during 2001–2016 in the Northwestern Shandong Plain (NW-SDP) and Northern Henan Plain (N-HNP). In these two regions, there was no significant correlation between accumulated net irrigation water consumption and groundwater table. The Yellow River was able to supply irrigation and the groundwater table had no significant declining trend.

Comparing Remotely-Sensed Surface Energy Balance Evapotranspiration Estimates in Heterogeneous and Data-Limited Regions: A Case Study of Tanzania’s Kilombero Valley

Evapotranspiration (ET) plays a crucial role in integrated water resources planning, development and management, especially in tropical and arid regions. Determining ET is not straightforward due to the heterogeneity and complexity found in real-world hydrological basins. This situation is often compounded in regions with limited hydro-meteorological data that are facing rapid development of irrigated agriculture. Remote sensing (RS) techniques have proven useful in this regard. In this study, we compared the daily actual ET estimates derived from 3 remotely-sensed surface energy balance (SEB) models, namely, the Surface Energy Balance Algorithm for Land (SEBAL) model, the Operational Simplified Surface Energy Balance (SSEBop) model, and the Simplified Surface Balance Index (S-SEBI) model. These products were generated using the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery for a total of 44 satellite overpasses in 2005, 2010, and 2015 in the heterogeneous, highly-utilized, rapidly-developing and data-limited Kilombero Valley (KV) river basin in Tanzania, eastern Africa. Our results revealed that the SEBAL model had a relatively high ET compared to other models and the SSEBop model had relatively low ET compared to the other models. In addition, we found that the S-SEBI model had a statistically similar ET as the ensemble mean of all models. Further comparison of SEB models’ ET estimates across different land cover classes and different spatial scales revealed that almost all models’ ET estimates were statistically comparable (based on the Wilcoxon’s test and the Levene’s test at a 95% confidence level), which implies fidelity between and reliability of the ET estimates. Moreover, all SEB models managed to capture the two spatially-distinct ET regimes in KV: the stable/permanent ET regime on the mountainous parts of the KV and the seasonally varied ET over the floodplain which contains a Ramsar site (Kilombero Valley Floodplain). Our results have the potential to be used in hydrological modelling to explore and develop integrated water resources management in the valley. We believe that our approach can be applied elsewhere in the world especially where observed meteorological variables are limited.

Evapotranspiration Estimation Using Surface Energy Balance System Model: A Case Study in the Nagqu River Basin

Calculation of actual evapotranspiration (AET) is of vital importance for the study of climate change, ecosystem carbon cycling, flooding, drought, and agricultural water demand. It is one of the more important components in the hydrological cycle and surface energy balance (SEB). How to accurately estimate AET especially for the Tibetan Plateau (TP) with complex terrain remains a challenge for the scientific community. Using multi-sensor remote sensing data, meteorological forcing data, and field observations, AET was derived for the Nagqu river basin of the Northern Tibetan Plateau from a surface energy balance system (SEBS) model. As inputs for SEBS, improved algorithms and datasets for land surface albedo and a cloud-free normalized difference vegetation index (NDVI) were also constructed. The model-estimated AET were compared with results by using the combinatory method (CM). The validation indicated that the model estimates of AET agreed well with the correlation coefficient, the root mean square error, and the mean percentage error of 0.972, 0.052 mm/h, and −10.4%, respectively. The comparison between SEBS estimation and CM results also proved the feasibility of parameterization schemes for land surface parameters and AET.

Simulation and evaluation of actual evapotranspiration based on inverse hydrological modeling at a basin scale

Reliable estimation of actual evapotranspiration (AET) is essential for hydrological, ecological, and land-surface models. The water balance method is a useful tool for estimating AET. However, it usually neglects soil moisture (SM), which is a dominant factor in AET simulation. Based on field observations, an inverse hydrological modeling method was proposed for regional AET simulation. The method can be applied to estimate AET in other basins, where observations of SM are available. A nonlinear relationship between AET and potential evapotranspiration, a soil water constraint factor, was developed and calibrated for the studied basin. Results of the AET evaluation indicated this relationship was able to estimate AET accurately for both monthly and two-week series, as the Nash and Sutcliffe efficiency coefficient (NSE) was 0.903 and 0.613, respectively. The meteorologically based complementary relationship (CR) method was also able to estimate monthly AET accurately, in particular the revised CR method. But it showed poor performance when estimating AET at a shorter timescale. The performance of the revised CR method with a calibrated parameter was better than that of the initial CR method, as the NSE improved from 0.52 to 0.70. Overall, the performance of the nonlinear relationship with calibrated parameters was better than that of the CR method. In conclusion, monthly AET can be accurately estimated using the CR method based only on meteorological data.

Estimating evapotranspiration of pistachio crop based on SEBAL algorithm using Landsat 8 satellite imagery

Stationary actual evapotranspiration (ET) estimation methods deal with some issues such as maintenance problems, the point data recording for a particular location, the complex hydrological process calculation, and requiring the expert workers. To solve this, ET estimation methods like Surface Energy Balance Algorithm for Land (SEBAL) method can be used based on the satellite images. However, the efficiency of the SEBAL model varies in ET estimation for different products and environmental conditions. On the other hand, pistachio is an important crop of Iran which little investigation were accomplished to estimate its ET using the presented models. Therefore, the purpose of this study is to investigate the efficiency of SEBAL algorithm in estimating ET of pistachio crop. In this regard, actual ET of a 100 hectares pistachio farm, located in Safaeieh region in the Semnan province, Iran, was selected as the study area. Implementations were performed using 29 Landsat 8 Operational Land Imager (OLI) images during the 2013–2017 period. The results were verified and compared with the results of evapotranspiration in standard condition estimated by the Intelligent Meteorological instrument (iMetos-Pessl) as the ground truth. The results of this study indicate the determination coefficient and Root Mean Square Error (RMSE) of the estimated actual ET for pistachio plant as 0.8 and 2.5 mm, respectively. Moreover, the results indicate the high spatial variability of ET in the pistachio growth period and the need to use a satellite data analysis method, like the SEBAL method, to prepare the ET map. Overall, the results show the good efficiency of the SEBAL model for estimating the actual ET of the pistachio product.

Evapotranspiration evaluation models based on machine learning algorithms—A comparative study

The constant need to increase agricultural production, together with the more and more frequent drought events in many areas of the world, requires a more careful assessment of irrigation needs and, therefore, a more accurate estimation of actual evapotranspiration. In recent years, several water management issues have been addressed by means of models derived from Artificial Intelligence research. When using machine learning based models, the main challenging aspects are represented by the choice of the best possible algorithm, the choice of adequately representative variables and the availability of appropriate data sets.Machine learning algorithms may be a powerful tool for the prediction of actual evapotranspiration, when a time series of few years is available. Starting from the measurements of a sufficient number of climatic parameters it is possible to obtain forecasting models characterized by very high accuracy and precision. Three different evapotranspiration models have been compared in this study. The models differ in the input variables. Four variants of each model were applied, varying the machine learning algorithm: M5P Regression Tree, Bagging, Random Forest and Support Vector Regression. The data refers to an experimental site in Central Florida, characterized by humid subtropical climate.The best outcomes have been provided by Model 1, whose input variables were net solar radiation, sensible-heat flux, moisture content of the soil, wind speed, mean relative humidity and mean temperature. Model 3, built from data only of mean temperature, mean relative humidity and net solar radiation, has provided still satisfactory results. Model 2, which adds the wind speed to the input variables of Model 3, has provided results that are absolutely comparable to those of Model 3 itself.

Using Neural Networks to Estimate Site-Specific Crop Evapotranspiration with Low-Cost Sensors

Irrigation efficiency is facilitated by matching irrigation rates to crop water demand based on estimates of actual evapotranspiration (ET). In production settings, monitoring of water demand is typically accomplished by measuring reference ET rather than actual ET, which is then adjusted approximately using simplified crop coefficients based on calendars of crop maturation. Methods to determine actual ET are usually limited to use in research experiments for reasons of cost, labor and requisite user skill. To pair monitoring and research methods, we co-located eddy covariance sensors with on-farm weather stations over two different irrigated crops (vegetable beans and hazelnuts). Neural networks were used to train a neural network and utilize on-farm weather sensors to report actual ET as measured by the eddy covariance method. This approach was able to robustly estimate ET from as few as four sensor parameters (temperature, solar radiation, humidity and wind speed) with training time as brief as one week. An important limitation found with this machine learning method is that the trained network is only valid under environmental and crop conditions similar to the training period. The small number of required sensors and short training times demonstrate that this approach can estimate site-specific and crop specific ET. With additional field validation, this approach may offer a new method to monitor actual crop water demand for irrigation management.

Remote sensing of water use and water stress in the African savanna ecosystem at local scale – Development and validation of a monitoring tool

Savannas are among the most productive biomes of Africa, where they comprise half of its surface. They support wildlife, livestock, rangelands, crops, and livelihoods, playing an important socioeconomic role in rural areas. These water-limited ecosystems with seasonal water availability are highly sensitive to changes in both climate conditions, and in land-use/management practices. Although monitoring programs for African savanna water use have been established in certain areas, most of them are largely restricted to point based measurements or coarse scales, and are not fully capable to provide distributed timely information for planning purposes. In this study we develop a mechanism for monitoring the water used by African savanna from fine scale (meters) to watershed scale, integrating the effects of the water stress. Our hypothesis is that the Ecosystem Stress Index (ESI) is a valuable tool to downscale estimates of actual evapotranspiration at coarse scale, to high resolutions. To monitor savanna water fluxes in a semi-continuous way this study integrates two different ET-estimation approaches: KC-FAO56 model, integrating reflectance-based “crop” coefficients (SPOT 4 & 5 satellites), is used to derive unstressed savanna evapotranspiration (with high spatial resolution), and the two-source surface energy balance model -TSEB, integrating radiometric surface temperature (AATSR satellites) allows the determination of water stress across savannas (ESI, with low spatial resolution). The difference between estimated and observed surface fluxes derived from TSEB (RMSDLE = 53 Wm-2, RMSDH = 50 Wm-2, RMSDRn = 60 Wm-2, RMSDG = 21 Wm-2) were of the same magnitude as the uncertainties derived from the flux measurement system, being sufficiently accurate to be employed in a distributed way and on a more regular basis. The approach of ESI to downscale ET proved to be useful, and errors between estimated and observed daily ET (RMSD 0.6 mmday−1) were consistent with the results of other studies in savanna ecosystems. The modelling framework proposed provided an accurate representation of the natural landscape heterogeneity and local conditions, with the potential of providing information suitable from local to broader scales.

Estimación espacial de la evapotranspiración mediante imágenes de satélite Landsat y el modelo SEBAL en el humedal Paraíso - Huacho

Estimación espacial de la evapotranspiración mediante imágenes de satélite Landsat y el modelo SEBAL en el humedal Paraíso - Huacho Spatial estimation of evapotranspiration by Landsat satellite images and the SEBAL model in the Paraíso wetland – Huacho Erick Garcia y Miguel Ángel Lleellish Autoridad Nacional del Agua (ANA), Calle diecisiete Nº 355, Urb. El Palomar - San Isidro Lima, Perú. Ministerio del Ambiente (MINAM), Calle Los Nogales Nº 236, San Isidro Lima, Perú. DOI: https://doi.org/10.33017/RevECIPeru2011.0026/ RESUMEN Los humedales están entre los ecosistemas más valiosos del planeta porque albergan una gran biodiversidad. Además, brindan muchas funciones relacionada con los recursos hídricos, como ser fuentes naturales de agua, reguladores del ciclo hidrológico y el clima, zonas de descarga y recarga de acuíferos, barreras naturales contra las inundaciones y la intrusión marina en los acuíferos costeros, entre otras. El conocimiento de las demandas ecológicas de agua en los humedales es de vital importancia para incluir a estos ecosistemas en una gestión integrada de los recursos hídricos. En este estudio, se presentan los resultados obtenidos en la estimación de la evapotranspiración en el humedal Paraíso de Huacho, mediante la aplicación del modelo de balance de energía superficial conocido como SEBAL a una subescena del satélite Landsat 7 ETM+ con fecha 04 de abril de 2000, la cual coincide con el inicio de la época de afloramiento en el humedal. La estimación de la evapotranspiración actual (ETa) estuvo en el rango de 0-6.3 mm/día, con los valores más bajos en las zonas desérticas y las parcelas en descanso, y los valores más altos en los espejos de agua y la vegetación del humedal. El humedal evapotranspira en altas tasas, con una ETa máxima de 4.9 mm/día y un valor medio de 4.6 mm/día para la fecha analizada. La evapotranspiración de referencia (ETo) estimada mediante el método FAO Penman-Monteith, para la fecha de paso del satélite, fue de 3.9 mm/día y con la inclusión del coeficiente del cultivo (Kc) dio valores de 4.2 y 4.3 mm/día, para la vegetación del humedal y para el cultivo de alfalfa respectivamente. Estos dos valores están cerca a la ETa de 4.6 y 3.9 mm/día estimada por SEBAL para la misma vegetación y que se encuentra en condiciones optimas de crecimiento. Los resultados obtenidos demuestran que es factible estimar la ETa mediante imágenes de satélite con un error medio relativo menor al 10%. Descriptores: teledetección, sensoramiento remoto, evapotranspiración, humedales, EBAL. ABSTRACT Wetlands are among the most valuables ecosystems in the world, because support a great biodiversity. Besides, provides many hydrological functions such as surface water sources, regulation of hydrological cycle, weather and aquifer recharge and discharge zones, they are also natural barriers to flooding and sea intrusion in coastal aquifer. The knowledge of the environmental water demands for wetlands is of vital importance to include these ecosystems in water resources management. In this study, the results obtained in the evapotranspiration estimation in the Paraíso wetland are presented. The results were obtained by the application of the surface energy balance model known as SEBAL over a sub scene of Landsat ETM+ satellite dated April 8, 2000, this coincide with the beginning of upwelling season in the wetland are presented. Actual evapotranspiration (ETa) estimates was in the range of 0-6.3 mm/day, the lowest values were in desert and fallow land areas and the highest values were in surface water bodies and wetland vegetations. The wetland evapotranspiration rate was high, with a maximum ET of 4.9 mm/day and average value of 4.6 mm/day. The reference evapotransporation (ETo) estimated in the satellite overpass date was 3.9 mm/day and adjusted with the crop coefficient (Kc) was 4.2 y 4.3 mm/day, for wetland vegetation and alfalfa crops. This values area close to the ETa of 4.6 y 3.9 mm/day estimated by SEBAL for the same vegetation in healthy conditions. The results obtained shows that are feasible to estimate the ETa by satellite images with a relative mean error lower than 10%. Keywords: remote sensing, evapotranspiration, wetlands, SEBAL.