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Global evaporation monitoring from Earth observation thermal infrared satellite missions is historically challenged due to the unavailability of any direct measurements of aerodynamic temperature. State-of-the-art one-source evaporation models use remotely sensed radiometric surface temperature as a substitute for the aerodynamic temperature and apply empirical corrections to accommodate for their inequality. This introduces substantial uncertainty in operational drought mapping over complex landscapes. By employing a non-parametric model, we show that evaporation can be directly retrieved from thermal satellite data without the need of any empirical correction. Independent evaluation of evaporation in a broad spectrum of biome and aridity yielded statistically significant results when compared with eddy covariance observations. While our simplified model provides a new perspective to advance spatio-temporal evaporation mapping from any thermal remote sensing mission, the direct retrieval of aerodynamic temperature also generates the highly required insight on the critical role of biophysical interactions in global evaporation research.

Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under short-term, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric approaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor combinations. The majority of reviewed studies compared stress proxies calculated from single-source sensor domains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analys

Remote detection and monitoring of the vegetation responses to stress became relevant for sustainable agriculture. Ongoing developments in optical remote sensing technologies have provided tools to increase our understanding of stress-related physiological processes. Therefore, this study aimed to provide an overview of the main spectral technologies and retrieval approaches for detecting crop stress in agriculture. Firstly, we present integrated views on: i) biotic and abiotic stress factors, the phases of stress, and respective plant responses, and ii) the affected traits, appropriate spectral domains and corresponding methods for measuring traits remotely. Secondly, representative results of a systematic literature analysis are highlighted, identifying the current status and possible future trends in stress detection and monitoring. Distinct plant responses occurring under short-term, medium-term or severe chronic stress exposure can be captured with remote sensing due to specific light interaction processes, such as absorption and scattering manifested in the reflected radiance, i.e. visible (VIS), near infrared (NIR), shortwave infrared, and emitted radiance, i.e. solar-induced fluorescence and thermal infrared (TIR). From the analysis of 96 research papers, the following trends can be observed: increasing usage of satellite and unmanned aerial vehicle data in parallel with a shift in methods from simpler parametric approaches towards more advanced physically-based and hybrid models. Most study designs were largely driven by sensor availability and practical economic reasons, leading to the common usage of VIS-NIR-TIR sensor combinations. The majority of reviewed studies compared stress proxies calculated from single-source sensor domains rather than using data in a synergistic way. We identified new ways forward as guidance for improved synergistic usage of spectral domains for stress detection: (1) combined acquisition of data from multiple sensors for analys

A detailed representation of plant hydraulic traits and stomatal closure in land surface models (LSMs) is a prerequisite for improved predictions of ecosystem drought response. This work presents the integration of a macroscopic root water uptake (RWU) model based on the hydraulic architecture approach in the LSM of the Terrestrial Systems Modeling Platform. The novel RWU approach is based on three parameters derived from first principles that describe the root system equivalent conductance, the compensatory RWU conductance, and the leaf water potential at stomatal closure, which defines the water stress condition for the plants. The developed RWU model intrinsically accounts for changes in the root density as well as for the simulation of the hydraulic lift process. The standard and the new RWU approach are compared by performing point-scale simulations for cropland over a sheltered minirhizotron facility in Selhausen, Germany, and validated against transpiration fluxes estimated from sap flow and soil water content measurements at different depths. Numerical sensitivity experiments are carried out using different soil textures and root distributions in order to evaluate the interplay between soil hydrodynamics and plant characteristics, and the impact of assuming time-constant plant physiological properties. Results show a good agreement between simulated and observed transpiration fluxes for both RWU models, with a more distinct response under water stress conditions and with uncertainty in the soil parameterization prevailing to the differences due to changes in the model formulation. The hydraulic RWU model exhibits also a lower sensitivity to the root distributions when simulating the onset of the water stress period. Finally, an analysis of variability across the soil and root scenarios indicates that differences in soil water content are mainly influenced by the root distribution, while the transpiration flux in both RWU models is additionally determined by

Worldwide ecosystems primary production and survival are largely limited by soil water availability, and affected by plant traits that control water uptake, transport, and release in the atmosphere. In order to understand how these complex systems will react to future climates, and optimally adjust their management, the development of experimental and modelling approaches is crucial. In particular, integrating process-based representations plant hydraulics and stomatal closure in land surface models (LSMs) is a major challenge as the limited computational resources necessitate the use of simplified descriptions of these processes. We present methods used to identify scale consistent emergent principles governing water flow in plants. We show that simplistic mechanistic expressions arise from the cell to the root system scale, and along stems whose geometry, saturated conductivity, sensitivity to cavitation, and leaf area vary continuously with height. Our results support that cavitation is a whole-stem emergent property; in which compounding effects of gravity and vertical traits variations reported experimentally all substantially contribute to the occurrence of hydraulic failure in tall trees, and need to be accounted for in LSMs. Furthermore, a macroscopic solution of water flow in plant hydraulic architectures was implemented in the Terrestrial Systems Modeling Platform. As compared to the standard approach, this new model of water capture characteristically exhibits a different sensitivity of the onset of water stress to root distribution and soil properties.

3D models of root growth, architecture and function are evolving as they become important tools that aid the design of agricultural management schemes and the selection of beneficial root traits. However, while benchmarking is common for water and solute transport models in soil, 3D root-soil interaction models have never been systematically analysed. Several interacting processes might induce disagreement between models: root growth, sink term definitions of root water and solute uptake and representation of the rhizosphere. The extent of discrepancies is currently unknown. Thus, a framework for quantitatively comparing such models is required. We propose, in a first step, to define benchmarking scenarios that test individual components of the complex models: root architecture, water flow in soil and roots, solute transport in soil and roots. While the latter will focus mainly on comparing numerical aspects, the root architectural models have to be compared at a conceptual level as they generally differ in process representation. Therefore defining common inputs that recreate reference root systems in all models will be a key challenge. In a second step, benchmarking scenarios for the coupled root-rhizosphere-soil models can be defined. We expect that the results of step 1 will enable us to better interpret differences found in step 2. We expect that this benchmarking will result in improved models, with which we can simulate various scenarios with greater confidence, avoiding that future work is based on accidental results caused by bugs, numerical errors or conceptual misunderstandings and will set a standard for future model development.

Numerical models are modern tools for capturing the spatial and temporal variability in the land-surface hydrologic response to rainfall and for understanding the physical relations between internal watershed pro cesses and observed streamflow. This thesis presents the improvement and application of a distributed hydrologic model distinguished by its path-based description of surface flow across the drainage basin and by its coupling of the surface and subsurface components that contribute to the catchment response. As a research tool for hydrologic forecasting and experimentation, the CATchment HY drology (CATHY) model fully incorporates spatial heterogeneities in basin topography, surface descriptors, and hydrometeorological forcing to produce dynamic maps of hydrologic states and fluxes. These capabilities allow investigation of theoretical questions and practical problems in hydrologic science and water resources engineering. Three related themes are developed in this thesis. First, an assessment via a comparative analysis of two different approaches for handling surface- subsurface interactions is conducted. This intercomparison study considers the coupling between the land surface and the subsurface via an explicit exchange term resolved by continuity principles implemented in the ParFlow model (a fully-coupled approach) and via special treatment of atmospheric boundary conditions in CATHY (a sequential approach). Different test cases are used to investigate the models' sensitivity to factors such as vertical mesh resolution, time step size, and slope angle. Second, surface-subsurface interactions are evaluated in a climate change scenario extracted from projections generated by the Canadian Regional Climate Model (CRCM). We assess the sensitivity of the hydrological response (river discharge, aquifer recharge, and soil water storage ) to future climate conditions for the des Anglais catchment located in southwestern Quebec (Canada). In this context, we also c