Application of the Priestley–Taylor Approach in a Two-Source Surface Energy Balance Model

The Priestley–Taylor (PT) approximation for computing evapotranspiration was initially developed for conditions of a horizontally uniform saturated surface sufficiently extended to obviate any significant advection of energy. Nevertheless, the PT approach has been effectively implemented within the framework of a thermal-based two-source model (TSM) of the surface energy balance, yielding reasonable latent heat flux estimates over a range in vegetative cover and climate conditions. In the TSM, however, the PT approach is applied only to the canopy component of the latent heat flux, which may behave more conservatively than the bulk (soil + canopy) system. The objective of this research is to investigate the response of the canopy and bulk PT parameters to varying leaf area index (LAI) and vapor pressure deficit (VPD) in both natural and agricultural vegetated systems, to better understand the utility and limitations of this approximation within the context of the TSM. Micrometeorological flux measurements collected at multiple sites under a wide range of atmospheric conditions were used to implement an optimization scheme, assessing the value of the PT parameter for best performance of the TSM. Overall, the findings suggest that within the context of the TSM, the optimal canopy PT coefficient for agricultural crops appears to have a fairly conservative value of ~1.2 except when under very high vapor pressure deficit (VPD) conditions, when its value increases. For natural vegetation (primarily grasslands), the optimal canopy PT coefficient assumed lower values on average (~0.9) and dropped even further at high values of VPD. This analysis provides some insight as to why the PT approach, initially developed for regional estimates of potential evapotranspiration, can be used successfully in the TSM scheme to yield reliable heat flux estimates over a variety of land cover types

Current frameworks for reference ET and crop coefficient calculation

Estimation of evapotranspiration is under continual development and evolution, with significant developments and standardizations made during the past three decades for both reference ET (ETref) and for crop coefficients (Kc). These standardizations provide consistency and reproducibility in estimating ETref and a consistent basis for determining and expressing Kc curves, especially at the local scale. The application of the dual Kc procedure is growing, and has strong potential for improving accuracy of ET estimates as compared to the single Kc approach. This article describes current structures for estimating crop coefficients including the standardized FAO-56 dual Kc approach, with example applications. Emphasis is placed on estimation of parameters and special cases to be considered. Newer bases for establishing Kcb curves include thermal units and vegetation indices

ET mapping for agricultural water management: present status and challenges

Evapotranspiration (ET) is an essential component of the water balance. Remote sensing based agrometeorological models are presently most suited for estimating crop water use at both field and regional scales. Numerous ET algorithms have been developed to make use of remote sensing data acquired by sensors on airborne and satellite platforms. In this paper, a literature review was done to evaluate numerous commonly used remote sensing based algorithms for their ability to estimate regional ET accurately. The reported estimation accuracy varied from 67 to 97% for daily ET and above 94% for seasonal ET indicating that they have the potential to estimate regional ET accurately. However, there are opportunities to further improving these models for accurately estimating all energy balance components. The spatial and temporal remote sensing data from the existing set of earth observing satellite platforms are not sufficient enough to be used in the estimation of spatially distributed ET for on-farm irrigation management purposes, especially at a field scale level (~10 to 200 ha). This will be constrained further if the thermal sensors on future Landsat satellites are abandoned. However, research opportunities exist to improve the spatial and temporal resolution of ET by developing algorithms to increase the spatial resolution of reflectance and surface temperature data derived from Landsat/ ASTER/MODIS images using same/other-sensor high resolution multi-spectral images

Uso de sensores remotos en el seguimiento de la vegetación de dehesa y su influencia en el balance hidrológico a escala de cuenca

The Mediterranean region is characterized by hot summers with long dry periods, a situation that may be exacerbated by the progressive global warming. In these water-limited environments where productivity of the ecosystems depends mainly on water availability, the reduction of freshwater resources can have severe consequences. An increase in aridity may lead to low productivity, land degradation and unwanted changes in land use. To reduce the vulnerability of Mediterranean landscapes it is important to improve our knowledge of the hydrological processes conditioning the water exchanges, with evapotranspiration (ET) being a key indicator of the state of ecosystems and playing a crucial role in the basin's water and energy balances. The goal of this dissertation is to improve our understanding of the evapotranspiration dynamics over Mediterranean heterogeneous and complex vegetation covers, with a focus on the dehesa ecosystem. The final aim is to contribute to the conservation of the water resources in these regions in the medium to long term, supporting the decision-making processes with quantitative, distributed, and high-quality information. To reach this goal, in this research the evaluation of remote sensing-based soil water balance (SWB) and surface energy balance (SEB) models was proposed to monitor the water consumption and water stress of typical Mediterranean vegetation at different spatial and temporal scales. In particular, the VI-ETo methodology (SWB) and the ALEXI/DisALEXI approach (SEB) have been adapted and applied. ET modeling using the VI-ETo scheme has been improved through the assessment of the vegetation layers' effective parameters. A data fusion algorithm was applied to the ET maps produced by the SEB model over the dehesa ecosystem, and we analyzed the opportunities that this high-resolution ET product in time and space can provide for water and vegetation resource management. The results have demonstrated the feasibility of both approaches (SWB and SEB models) to accurately monitor ET dynamics over the dehesa landscape, adequately reproducing the annual bimodal behavior and the response of the vegetation in periods of water deficit. The error obtained using the SWB approach (the VI-ETo method) was RMSE = 0.47 mm day-1 over the whole dehesa system (grass + trees) and over an open grassland. The monitoring of water stress for both systems with different canopy structure, using as a proxy the ET/ETo ratio, and the stress coefficient (Ks), was successful. Improvements on the specific spectral properties of oak trees and layer-specific parameters were included into the modeling. We also analyzed the influence of the spectral properties of oak trees and another typical Mediterranean tree canopy, the olive orchard, in the VI-ETo model. We found that the use of appropriate values of the parameter SAVImax (0.51 for oak trees and 0.57 for olive trees) had notable implications in the computation of ET and water stress, in contrast to using a generic value for Mediterranean crops (SAVImax= 0.75). The accuracy of this water balance-based approach was also evaluated over two heterogeneous Mediterranean basins, with a mosaic of holm oaks and grasslands, shrubs, coniferous plantations, and irrigated horticultural crops. The annual discharge flows of both watersheds, which were determined from the modeled ET data and using a simple surface water balance, were very similar to those obtained with the HBV hydrological model, and to the values measured at the outlet of one of the basins, corroborating the usefulness of the VI-ETo methodology on these vegetation types. On the other hand, the resulting ET series (30 m, daily) derived with the SEB approach (ALEXI/DisALEXI method) and the STARFM fusion algorithm provided an RMSE value of 0.67 mm day-1, which was considered an acceptable error for management purposes. This error was slightly lower compared to using simpler interpolation methods, probably due to the high temporal frequency and better spatial representation of the flux tower footprint of the fused time series. The analysis of ET patterns over small heterogeneous vegetated patches that form the dehesa structure revealed the importance of having fine resolution information at field scale to distinguish the water consumed by the different vegetation components, which influences the provision of many ecosystem services. For example, it was key for identifying phenology dates of grasslands, or understanding the hydrological functioning of riverside dense evergreen vegetation with high ET rates during the whole year, in contrast with the herbaceous areas. Accurately modeling these different behaviors of dehesa microclimates is useful to support farmers‘ management and provide recommendations tailored for each structural component and requirements.La región mediterránea se caracteriza por veranos calurosos con largos períodos sin precipitaciones, situación que puede agravarse con el progresivo calentamiento global. En estos ambientes donde la productividad de los ecosistemas depende principalmente de la disponibilidad de agua, la reducción de los recursos hídricos puede tener graves consecuencias. Un aumento de la aridez puede conducir a una baja productividad, degradación de la tierra y cambios no deseados en el uso del suelo. Para reducir la vulnerabilidad de las zonas mediterráneas es importante profundizar en el estudio de los procesos hidrológicos que condicionan los intercambios de agua, siendo la evapotranspiración (ET) un indicador clave del estado de los ecosistemas y jugando un papel crucial en los balances hídricos y energéticos de la cuenca. El objetivo de esta tesis es mejorar nuestro conocimiento sobre la dinámica de la evapotranspiración en cubiertas mediterráneas heterogéneas y complejas, con el foco en el ecosistema de dehesa. El objetivo final es contribuir a la conservación de los recursos hídricos de estas regiones en el medio-largo plazo, apoyando en los procesos de toma de decisiones con información cuantitativa, distribuida y de calidad. Para alcanzar este objetivo, en esta investigación se propuso evaluar modelos de balance de agua en el suelo (SWB) y balance de energía en superficie (SEB) basados en el uso de sensores remotos, para el seguimiento del consumo de agua y el estrés hídrico de la vegetación mediterránea a diferentes escalas espaciales y temporales. En particular, se ha adaptado y aplicado la metodología VI-ETo (SWB) y el enfoque ALEXI/DisALEXI (SEB). Se ha mejorado el modelado de ET utilizando el esquema VI-ETo mediante la evaluación de los parámetros efectivos de las capas de vegetación. Se aplicó un algoritmo de fusión de datos remotos a los mapas de ET generados por el modelo SEB sobre el ecosistema de dehesa, y estudiamos las oportunidades que este producto de ET con alta resolución espacial y temporal puede aportar en la gestión de los recursos hídricos y de los ecosistemas. Los resultados han demostrado la viabilidad de ambos enfoques (modelos SWB y SEB) para monitorear con precisión la dinámica de la ET sobre el ecosistema de dehesa, reproduciendo adecuadamente el comportamiento bimodal anual y la respuesta de la vegetación en períodos de déficit hídrico. El error obtenido usando el enfoque SWB (el método VI-ETo) fue RMSE = 0.47 mm día-1, tanto para el sistema dehesa (pasto + árboles) como para una zona de pastizal. El seguimiento del estrés hídrico para ambos sistemas con diferente estructura de vegetación, utilizando la relación ET/ETo y el coeficiente de estrés (Ks), fue satisfactorio. Se incluyeron en el modelado mejoras sobre las propiedades espectrales específicas de las encinas y los parámetros específicos de los diferentes estratos de vegetación. También analizamos la influencia de las propiedades espectrales de las encinas y otra cubierta mediterránea, el olivar, en el modelo VI-ETo. Encontramos que el uso de valores apropiados del parámetro SAVImax (0,51 para robles y 0,57 para olivos) tuvo un efecto significativo en la determinación del consumo de agua y estrés hídrico, en comparación con usar un valor genérico para cultivos mediterráneos (SAVImax = 0,75). La precisión de este enfoque basado en el balance hídrico también se evaluó en dos cuencas mediterráneas heterogéneas, con un mosaico de encinas y pastizales, arbustos, plantaciones de coníferas y cultivos hortícolas de regadío. Los caudales de descarga anual de ambas cuencas, determinados a partir de los datos de ET modelados y utilizando un balance hídrico superficial muy simple, fueron muy similares a los obtenidos con el modelo hidrológico HBV, y a los valores medidos en la salida de una de las cuencas, corroborando la utilidad de la metodología VI-ETo sobre estas formaciones vegetales. Por otra parte, la serie final de ET (30 m, diaria) derivada del enfoque SEB (método ALEXI/DisALEXI) y del algoritmo de fusión STARFM proporcionó un valor de RMSE de 0,67 mm día-1, considerado un error aceptable para fines de manejo. Este error fue ligeramente inferior a los obtenidos usando métodos de interpolación más simples, debido probablemente a la alta frecuencia temporal y una mejor representación espacial del footprint de la torre de medida de flujos en la serie temporal fusionada. El análisis de los patrones de la ET sobre pequeñas manchas de vegetación heterogéneas, que forman la estructura de la dehesa, reveló la importancia de tener información con alta resolución a escala de campo para distinguir el agua consumida por los diferentes componentes de la vegetación, que tienen influencia en el aprovisionamiento de muchos servicios ecosistémicos. Por ejemplo, fue clave para identificar ciertas fechas fenológicas de los pastizales, o entender el funcionamiento hidrológico de la vegetación densa de hoja perenne en zonas de ribera con altas tasas de ET durante todo el año, en comparación con zonas de especies herbáceas. Modelar con precisión estos comportamientos diferentes de los microclimas de la dehesa es útil para apoyar la gestión de los agricultores y ofrecer recomendaciones adaptadas a cada componente y necesidades estructurales

Variable Rate Irrigation Using a Spatial Evapotranspiration Model With Remote Sensing Imagery and Soil Water Content Measurements

Variable rate irrigation may help in intensification of agriculture by producing more yield per unit inputs. Real time spatial information about water balance components is important for designing VRI prescription maps. This work involved use of a spatial evapotranspiration model for studying spatial variability in an agricultural field at the Eastern Nebraska Research and Extension Center near Mead, Nebraska. Imagery from unmanned aerial systems and Landsat were used as input for the spatial evapotranspiration model. Other inputs into the model were soil water content measurements from neutron probes, weather data, crop data, previous irrigation prescriptions, and soil properties for the field. The work included comparison of VRI treatments with uniform irrigation and rainfed treatments in terms of yield potential and reduced water withdrawal. Uniform irrigation methods included uniform irrigation managed using soil water content measurements from neutron probe and rainfed treatment. The model was updated and improved during the study period in attempt to more accurately model water balance components and manage VRI. Mean total prescribed irrigation depth was significantly larger for VRI using Landsat than uniform treatments for soybean in 2017. It was significantly lower for VRI using Landsat than other irrigated treatments for soybean in 2018. Maize yield in 2017 was significantly greater for VRI using Landsat and uniform treatments than the rainfed treatment. No other significant yield differences were observed in 2017 and 2018. Future research may focus on inclusion of thermal infrared UAS imagery, and an advanced method soil water content measurements in the model. Advisor: Derek M. Heere

The partitioning of evapotranspiration in apple orchards from planting until full-bearing age and implications for water resources management

Philosophiae Doctor - PhDOrchard evapotranspiration (ET) is a complex flux which has been the subject of many studies. It often includes transpiration from the trees, cover crops and weeds, evaporation from the soil, mulches, and other orchard artefacts. Studies of evapotranspiration in orchards often quantify tree water use and soil evaporation, treating the water use from the understorey vegetation on the orchard floor as negligible. Therefore, there is a paucity of information; first about the water use of cover crops in general, and secondly about the contribution of cover crops to whole orchard ET. This information is important, especially in semi-arid regions like South Africa where water resources are already under great strain and the situation is predicted to worsen in future due to climate change

Evapotranspiration estimates derived using thermal-based satellite remote sensing and data fusion for irrigation management in California vineyards

Irrigation in the Central Valley of California is essential for successful wine grape production. With reductions in water availability in much of California due to drought and competing water-use interests, it is important to optimize irrigation management strategies. In the current study, we investigate the utility of satellite-derived maps of evapotranspiration (ET) and the ratio of actual-to-reference ET (fRET) based on remotely sensed land-surface temperature (LST) imagery for monitoring crop water use and stress in vineyards. The Disaggregated Atmosphere Land EXchange Inverse (ALEXI/DisALEXI) surface-energy balance model, a multi-scale ET remote-sensing framework with operational capabilities, is evaluated over two Pinot noir vineyard sites in central California that are being monitored as part of the Grape Remote-Sensing Atmospheric Profile and Evapotranspiration eXperiment (GRAPEX). A data fusion approach is employed to combine ET time-series retrievals from multiple satellite platforms to generate estimates at both the high spatial (30 m) and temporal (daily) resolution required for field-scale irrigation management. Comparisons with micrometeorological data indicate reasonable model performance, with mean absolute errors of 0.6 mm day−1 in ET at the daily time step and minimal bias. Values of fRET agree well with tower observations and reflect known irrigation. Spatiotemporal analyses illustrate the ability of ALEXI/DisALEXI/data fusion package to characterize heterogeneity in ET and fRET both within a vineyard and over the surrounding landscape. These findings will inform the development of strategies for integrating ET mapping time series into operational irrigation management framework, providing actionable information regarding vineyard water use and crop stress at the field and regional scale and at daily to multi-annual time scales.info:eu-repo/semantics/acceptedVersio

Thermal-Based Evaporative Stress Index for Monitoring Surface Moisture Depletion

The standard suite of indicators currently used in operational drought monitoring reflects anomalous conditions in several major components of the hydrologic budget—representing deficits in precipitation, soil moisture content, runoff, surface and groundwater storage, snowpack, and streamflow. In principle, it is useful to have a diversity of indices because drought can assume many forms (meteorological, agricultural, hydrological, and socioeconomic), over broad ranges in timescale (weeks to years), and with varied impacts of interest to different stakeholder groups. Farmers, for example, may be principally interested in soil moisture deficits, river forecasters will focus on streamflow fluctuations, and water managers will be concerned with longer-term stability in municipal water supply and reservoir levels. Only recently has actual evapotranspiration (ET) been considered as a primary indicator of drought conditions (e.g., Anderson et al., 2007b; Labedzki and Kanecka- Geszke, 2009; Li et al., 2005; Mo et al., 2010). ET is a valuable drought indicator because it reflects not only moisture availability but also the rate at which water is being consumed. Because transpiration (T) and carbon uptake by vegetation are tightly coupled through stomatal exchange, ET anomalies are indicative of vegetation health and growing conditions. In addition, the importance of so-called flash droughts is becoming increasingly evident, where hot, dry, and windy atmospheric conditions can lead to unusually rapid soil moisture depletion and, in some cases, devastating crop failure. Such events cannot be easily identified using local precipitation anomalies but should have a detectable ET signature

Machine learning models to predict daily actual evapotranspiration of citrus orchards under regulated deficit irrigation

Precise estimations of actual evapotranspiration (ETa) are essential for various environmental issues, including those related to agricultural ecosystem sustainability and water management. Indeed, the increasing demands of agricultural production, coupled with increasingly frequent drought events in many parts of the world, necessitate a more careful evaluation of crop water requirements. Artificial Intelligence-based models represent a promising alternative to the most common measurement techniques, e.g. using expensive Eddy Covariance (EC) towers. In this context, the main challenges are choosing the best possible model and selecting the most representative features. The objective of this research is to evaluate two different machine learning algorithms, namely Multi-Layer Perceptron (MLP) and Random Forest (RF), to predict daily actual evapotranspiration (ETa) in a citrus orchard typical of the Mediterranean ecosystem using different feature combinations. With many features available coming from various infield sensors, a thorough analysis was performed to measure feature importance, scatter matrix observations, and Pearson's correlation coefficient calculation, which resulted in the selection of 12 promising feature combinations. The models were calibrated under regulated deficit irrigation (RDI) conditions to estimate ETa and save irrigation water. On average up to 38.5% water savings were obtained, compared to full irrigation. Moreover, among the different input variables adopted, the soil water content (SWC) feature appears to have a prominent role in the prediction of ETa. Indeed, the presented results show that by choosing the appropriate input features, the accuracy of the proposed machine learning models remains acceptable even when the number of features is reduced to only 4. The best performance was achieved by the Random Forest method, with seven input features, obtaining a root mean square error (RMSE) and a coefficient of determination (R2) of 0.39 mm/day and 0.84, respectively. Finally, the results show that the joint use of SWC, weather and satellite data significantly improves the performance of evapotranspiration forecasts compared to models using only meteorological variables

Empirical and numerical approaches to predicting the thermal performance of green roofs

vi, 184 leaves : ill., chiefly col., maps ; 29 cm.Includes abstract and appendix.Includes bibliographical references.Green roofs are increasingly recognised as a sustainable option for reducing building energy demand. However, given the large investment required for their construction, accurate modelling methods are needed to predict their economic benefits for building owners and maximize their effectiveness. The representation of vegetation in the green roof energy balance model literature is reviewed to identify their limitations. It is concluded that an overemphasis on single source models, minimal validation periods and limited input data is likely burdening the robustness and applicability of these models. Using data collected in Calgary, Halifax and London, this study then aims to develop empirical models to offer another approach for predicting the thermal performance of green roofs. Significant multiple linear regression models highlight the importance of net radiation, air-to-surface temperature difference and humidity in predicting the substrate heat flux, with the green roof in the driest climate; Calgary, being the most effective