F. van Oorschot
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Vegetation plays a crucial role in regulating the water cycle through transpiration, which is the water flux from the subsurface to the atmosphere via roots. The amount and timing of transpiration is controlled by the interplay of seasonal energy and water supply. The latter strongly depends on the size of the root zone storage capacity (Sr), which represents the maximum accessible volume of water that vegetation can use for transpiration. Sr is primarily influenced by hydroclimatic conditions, as vegetation optimizes its root system in such a way that it guarantees water uptake and overcomes dry periods. Sr estimates are commonly derived from root zone water deficits that result from the phase shift between the seasonal signals of root zone water inflow (i.e., precipitation) and outflow (i.e., evaporation). In irrigated croplands, irrigation water serves as an additional input into the root zone. However, this aspect has been ignored in many studies, and the extent to which irrigation influences Sr estimates has never been comprehensively quantified. In this study, our objective is to quantify the influence of irrigation on Sr and identify the regional differences therein. To this end, we integrated two irrigation methods, based on the respective irrigation water use and irrigated area fractions, into the Sr estimation. We evaluated the effects compared with Sr estimates that do not consider irrigation for a sample of 4856 catchments globally with varying degrees of irrigation activity. Our results show that Sr consistently decreased when considering irrigation, with a larger effect in catchments with a larger irrigated area. For catchments with an irrigated area fraction exceeding 10 %, the median decrease in Sr was 19 and 23 mm for the two methods, corresponding to decreases of 12 % and 15 %, respectively. Sr decreased the most for catchments in tropical climates. However, the relative decrease was the largest in catchments in temperate climates. Our results demonstrate, for the first time, that irrigation has a considerable influence on Sr estimates over irrigated croplands. This effect is as strong as the effects of snowmelt that have previously been documented in catchments that have a considerable amount of precipitation falling as snow.
...
Vegetation plays a crucial role in regulating the water cycle through transpiration, which is the water flux from the subsurface to the atmosphere via roots. The amount and timing of transpiration is controlled by the interplay of seasonal energy and water supply. The latter strongly depends on the size of the root zone storage capacity (Sr), which represents the maximum accessible volume of water that vegetation can use for transpiration. Sr is primarily influenced by hydroclimatic conditions, as vegetation optimizes its root system in such a way that it guarantees water uptake and overcomes dry periods. Sr estimates are commonly derived from root zone water deficits that result from the phase shift between the seasonal signals of root zone water inflow (i.e., precipitation) and outflow (i.e., evaporation). In irrigated croplands, irrigation water serves as an additional input into the root zone. However, this aspect has been ignored in many studies, and the extent to which irrigation influences Sr estimates has never been comprehensively quantified. In this study, our objective is to quantify the influence of irrigation on Sr and identify the regional differences therein. To this end, we integrated two irrigation methods, based on the respective irrigation water use and irrigated area fractions, into the Sr estimation. We evaluated the effects compared with Sr estimates that do not consider irrigation for a sample of 4856 catchments globally with varying degrees of irrigation activity. Our results show that Sr consistently decreased when considering irrigation, with a larger effect in catchments with a larger irrigated area. For catchments with an irrigated area fraction exceeding 10 %, the median decrease in Sr was 19 and 23 mm for the two methods, corresponding to decreases of 12 % and 15 %, respectively. Sr decreased the most for catchments in tropical climates. However, the relative decrease was the largest in catchments in temperate climates. Our results demonstrate, for the first time, that irrigation has a considerable influence on Sr estimates over irrigated croplands. This effect is as strong as the effects of snowmelt that have previously been documented in catchments that have a considerable amount of precipitation falling as snow.
Journal article
(2024)
-
Fransje van Oorschot, Markus Hrachowitz, Tom Viering, Andrea Alessandri, Ruud J van der Ent
Vegetation roots play an essential role in regulating the hydrological cycle by removing water from the subsurface and releasing it to the atmosphere. However, the present understanding of the drivers of ecosystem-scale root development and their spatial variability globally is limited. This study investigates the varying roles of climate, landscape, and vegetation on the magnitude of root zone storage capacity (Sr) worldwide, which is defined as the maximum volume of subsurface moisture accessible to vegetation roots. To this aim, we quantified Sr and evaluated 21 possible climate, landscape, and vegetation controls for 3612 river catchments worldwide using a random forest machine learning model. Our findings reveal climate as primary, but spatially varying, driver of ecosystem scale Sr with landscape and vegetation characteristics playing a minor role. More specifically, we found the mean inter-storm duration as most dominant control of Sr globally, followed by mean temperature, mean precipitation, and mean topographic slope. While the inter-storm duration, temperature, and slope exhibit a consistent relation with Sr globally, the relation between precipitation and Sr varies spatially. Based on this spatial variability, we classified two different regimes: precipitation driven and energy limited. The precipitation-driven regime exhibits a positive relation between precipitation and Sr for precipitation of up to 3 mm d−1, above which the relation flattens and eventually becomes negative. The energy-limited regime exhibits a strictly negative relation between precipitation and Sr. Using the random forest model based on these three dominant climate variables and the landscape variable slope, we generated a global gridded dataset of Sr, which closely resembles other global datasets of root characteristics. This suggests that our parsimonious approach based on four globally available variables to estimate Sr on a global scale has the potential to be readily and easily integrated into the parameterization of Sr in global hydrological and land surface models. This may enhance the accuracy of global predictions of land–atmosphere exchange fluxes and hydrological extremes by providing a robust representation of both spatial and temporal variability in vegetation root characteristics.
...
Vegetation roots play an essential role in regulating the hydrological cycle by removing water from the subsurface and releasing it to the atmosphere. However, the present understanding of the drivers of ecosystem-scale root development and their spatial variability globally is limited. This study investigates the varying roles of climate, landscape, and vegetation on the magnitude of root zone storage capacity (Sr) worldwide, which is defined as the maximum volume of subsurface moisture accessible to vegetation roots. To this aim, we quantified Sr and evaluated 21 possible climate, landscape, and vegetation controls for 3612 river catchments worldwide using a random forest machine learning model. Our findings reveal climate as primary, but spatially varying, driver of ecosystem scale Sr with landscape and vegetation characteristics playing a minor role. More specifically, we found the mean inter-storm duration as most dominant control of Sr globally, followed by mean temperature, mean precipitation, and mean topographic slope. While the inter-storm duration, temperature, and slope exhibit a consistent relation with Sr globally, the relation between precipitation and Sr varies spatially. Based on this spatial variability, we classified two different regimes: precipitation driven and energy limited. The precipitation-driven regime exhibits a positive relation between precipitation and Sr for precipitation of up to 3 mm d−1, above which the relation flattens and eventually becomes negative. The energy-limited regime exhibits a strictly negative relation between precipitation and Sr. Using the random forest model based on these three dominant climate variables and the landscape variable slope, we generated a global gridded dataset of Sr, which closely resembles other global datasets of root characteristics. This suggests that our parsimonious approach based on four globally available variables to estimate Sr on a global scale has the potential to be readily and easily integrated into the parameterization of Sr in global hydrological and land surface models. This may enhance the accuracy of global predictions of land–atmosphere exchange fluxes and hydrological extremes by providing a robust representation of both spatial and temporal variability in vegetation root characteristics.
To the root of vegetation-water interactions
Improving spatiotemporal variations in global models
Vegetation strongly influences evaporation from land by transporting water from the subsurface to the atmosphere through root water uptake. The amount and timing of this water flux depends on the aboveground (e.g., the amount of leaves) and belowground (e.g., the root extent) characteristics of the vegetation. Although these vegetation characteristics vary strongly both in space and time, there is lack of adequate representation of this vegetation variability in large scale hydrological and land surface models. This causes deficiencies in representing the associated variability in modeled water and energy states and fluxes, which introduces uncertainties in future predictions of the hydrological cycle, including hydrological extremes such as droughts and floods. To address this issue, this research aims to develop more realistic model representations of spatial and temporal vegetation variability, and explore their potential for improving modeled water fluxes in large scale hydrological and land surface models.
Chapter 2 focuses on model representations of spatial and temporal variability of aboveground vegetation characteristics based on satellite remote sensing data. Interannual variability of land cover and leaf area index (LAI) from latest global remote sensing datasets are integrated into the land surfacemodel Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL). Furthermore, datasets of LAI and the fraction of green vegetation cover are used to develop and integrate a spatially and temporally varying model parameterization of the effective vegetation cover. The effects of these three implementations on simulated hydrology are evaluated using offline (land-only) model simulations. The results show that the enhanced variability of aboveground vegetation characteristics considerably improves the simulated variability of evaporation and near-surface soil moisture. These improvements are connected to a framework that describes how the implemented vegetation variability influences internal model interactions between vegetation, soil moisture, and evaporation.
Chapter 3 evaluates how climate-controlled root zone parameters influence water flux simulations with the land surfacemodel HTESSEL. To this aim catchment scale root zone storage capacity Sr (mm), defined as the maximum volume of subsurfacemoisture that can be accessed by the vegetation roots, is estimated using the memory method. In this method Sr is derived from soil water deficits, reflecting the ability of vegetation to adapt to the local climate conditions by sizing their roots in such a way to guarantee continuous access to water, keeping memory of past water deficit conditions. Climatecontrolled Sr is estimated with the memory method for 15 catchments in Australia to adequately represent the spatial variability of the vegetation roots. These estimates are integrated into HTESSEL, replacing the static root representation based on soil types and uniform soil depth. The results of offline model simulations show that climatecontrolled Sr representation significantly improves the timing of modeled discharge in the study regions. This suggests that a climate-controlled representation of the model Sr has potential for improving water flux simulations by land surface models in a global context.
Chapter 4 presents the influence of irrigation on the estimation of Sr with the memory method. The memory method Sr is derived from the seasonal patterns of root zone water input and output. Besides precipitation as input, irrigation supplies additional water to the root zone in irrigated agricultural fields. However, the influence of irrigation on the memory method Sr estimates has not been assessed previously. In this study two methods based on different globally available irrigation datasets are developed to account for irrigation in the memory method for estimating Sr. The Sr estimates fromthese two methods are compared to a case without considering irrigation for a large sample of catchments globally. The results show, for the first time, that irrigation considerably reduces Sr in regions with extensive irrigation, highlighting the relevance of irrigation for adequately estimating ecosystem scale Sr.
Chapter 5 investigates the influence of climate, landscape, and vegetation variables on Sr globally. So far, there is limited insight on the controls of global-scale root development and their spatial variation. A random forest model is used to predict Sr as estimated with the memory method based on 21 variables for a large sample of catchments globally. The results indicate that hydro-climatic variables are the dominant, but spatially varying, driver of ecosystemscale Sr, while landscape and vegetation play aminor role. Based on the importance of the various drivers, a reduced parsimoniousmodel using four variables is used to predict Sr on a global scale. These predictions largely resemble other global estimates of root characteristics based on more complex methods and datasets. This indicates that the here developed parsimonious model to estimate global scale Sr based on four simple globally available variables adequately represents the spatial variability of Sr globally. Together with the results from Chapter 2, it can be concluded that integration of these estimates into large scale hydrological and land surface models has potential to improve model water fluxes.
The findings of this dissertation directly contribute to the large scale hydrological and climate model communities by providing methods to adequately represent spatial and temporal vegetation variability. The results demonstrate the potential of these methods to improve modeled water fluxes by large scale hydrological and land surface models, with major implications for the accuracy of hydrological and climate predictions. This dissertation lays the foundation for future research aimed at further improving the realism of model vegetation variability. ...
Chapter 2 focuses on model representations of spatial and temporal variability of aboveground vegetation characteristics based on satellite remote sensing data. Interannual variability of land cover and leaf area index (LAI) from latest global remote sensing datasets are integrated into the land surfacemodel Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL). Furthermore, datasets of LAI and the fraction of green vegetation cover are used to develop and integrate a spatially and temporally varying model parameterization of the effective vegetation cover. The effects of these three implementations on simulated hydrology are evaluated using offline (land-only) model simulations. The results show that the enhanced variability of aboveground vegetation characteristics considerably improves the simulated variability of evaporation and near-surface soil moisture. These improvements are connected to a framework that describes how the implemented vegetation variability influences internal model interactions between vegetation, soil moisture, and evaporation.
Chapter 3 evaluates how climate-controlled root zone parameters influence water flux simulations with the land surfacemodel HTESSEL. To this aim catchment scale root zone storage capacity Sr (mm), defined as the maximum volume of subsurfacemoisture that can be accessed by the vegetation roots, is estimated using the memory method. In this method Sr is derived from soil water deficits, reflecting the ability of vegetation to adapt to the local climate conditions by sizing their roots in such a way to guarantee continuous access to water, keeping memory of past water deficit conditions. Climatecontrolled Sr is estimated with the memory method for 15 catchments in Australia to adequately represent the spatial variability of the vegetation roots. These estimates are integrated into HTESSEL, replacing the static root representation based on soil types and uniform soil depth. The results of offline model simulations show that climatecontrolled Sr representation significantly improves the timing of modeled discharge in the study regions. This suggests that a climate-controlled representation of the model Sr has potential for improving water flux simulations by land surface models in a global context.
Chapter 4 presents the influence of irrigation on the estimation of Sr with the memory method. The memory method Sr is derived from the seasonal patterns of root zone water input and output. Besides precipitation as input, irrigation supplies additional water to the root zone in irrigated agricultural fields. However, the influence of irrigation on the memory method Sr estimates has not been assessed previously. In this study two methods based on different globally available irrigation datasets are developed to account for irrigation in the memory method for estimating Sr. The Sr estimates fromthese two methods are compared to a case without considering irrigation for a large sample of catchments globally. The results show, for the first time, that irrigation considerably reduces Sr in regions with extensive irrigation, highlighting the relevance of irrigation for adequately estimating ecosystem scale Sr.
Chapter 5 investigates the influence of climate, landscape, and vegetation variables on Sr globally. So far, there is limited insight on the controls of global-scale root development and their spatial variation. A random forest model is used to predict Sr as estimated with the memory method based on 21 variables for a large sample of catchments globally. The results indicate that hydro-climatic variables are the dominant, but spatially varying, driver of ecosystemscale Sr, while landscape and vegetation play aminor role. Based on the importance of the various drivers, a reduced parsimoniousmodel using four variables is used to predict Sr on a global scale. These predictions largely resemble other global estimates of root characteristics based on more complex methods and datasets. This indicates that the here developed parsimonious model to estimate global scale Sr based on four simple globally available variables adequately represents the spatial variability of Sr globally. Together with the results from Chapter 2, it can be concluded that integration of these estimates into large scale hydrological and land surface models has potential to improve model water fluxes.
The findings of this dissertation directly contribute to the large scale hydrological and climate model communities by providing methods to adequately represent spatial and temporal vegetation variability. The results demonstrate the potential of these methods to improve modeled water fluxes by large scale hydrological and land surface models, with major implications for the accuracy of hydrological and climate predictions. This dissertation lays the foundation for future research aimed at further improving the realism of model vegetation variability. ...
Vegetation strongly influences evaporation from land by transporting water from the subsurface to the atmosphere through root water uptake. The amount and timing of this water flux depends on the aboveground (e.g., the amount of leaves) and belowground (e.g., the root extent) characteristics of the vegetation. Although these vegetation characteristics vary strongly both in space and time, there is lack of adequate representation of this vegetation variability in large scale hydrological and land surface models. This causes deficiencies in representing the associated variability in modeled water and energy states and fluxes, which introduces uncertainties in future predictions of the hydrological cycle, including hydrological extremes such as droughts and floods. To address this issue, this research aims to develop more realistic model representations of spatial and temporal vegetation variability, and explore their potential for improving modeled water fluxes in large scale hydrological and land surface models.
Chapter 2 focuses on model representations of spatial and temporal variability of aboveground vegetation characteristics based on satellite remote sensing data. Interannual variability of land cover and leaf area index (LAI) from latest global remote sensing datasets are integrated into the land surfacemodel Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL). Furthermore, datasets of LAI and the fraction of green vegetation cover are used to develop and integrate a spatially and temporally varying model parameterization of the effective vegetation cover. The effects of these three implementations on simulated hydrology are evaluated using offline (land-only) model simulations. The results show that the enhanced variability of aboveground vegetation characteristics considerably improves the simulated variability of evaporation and near-surface soil moisture. These improvements are connected to a framework that describes how the implemented vegetation variability influences internal model interactions between vegetation, soil moisture, and evaporation.
Chapter 3 evaluates how climate-controlled root zone parameters influence water flux simulations with the land surfacemodel HTESSEL. To this aim catchment scale root zone storage capacity Sr (mm), defined as the maximum volume of subsurfacemoisture that can be accessed by the vegetation roots, is estimated using the memory method. In this method Sr is derived from soil water deficits, reflecting the ability of vegetation to adapt to the local climate conditions by sizing their roots in such a way to guarantee continuous access to water, keeping memory of past water deficit conditions. Climatecontrolled Sr is estimated with the memory method for 15 catchments in Australia to adequately represent the spatial variability of the vegetation roots. These estimates are integrated into HTESSEL, replacing the static root representation based on soil types and uniform soil depth. The results of offline model simulations show that climatecontrolled Sr representation significantly improves the timing of modeled discharge in the study regions. This suggests that a climate-controlled representation of the model Sr has potential for improving water flux simulations by land surface models in a global context.
Chapter 4 presents the influence of irrigation on the estimation of Sr with the memory method. The memory method Sr is derived from the seasonal patterns of root zone water input and output. Besides precipitation as input, irrigation supplies additional water to the root zone in irrigated agricultural fields. However, the influence of irrigation on the memory method Sr estimates has not been assessed previously. In this study two methods based on different globally available irrigation datasets are developed to account for irrigation in the memory method for estimating Sr. The Sr estimates fromthese two methods are compared to a case without considering irrigation for a large sample of catchments globally. The results show, for the first time, that irrigation considerably reduces Sr in regions with extensive irrigation, highlighting the relevance of irrigation for adequately estimating ecosystem scale Sr.
Chapter 5 investigates the influence of climate, landscape, and vegetation variables on Sr globally. So far, there is limited insight on the controls of global-scale root development and their spatial variation. A random forest model is used to predict Sr as estimated with the memory method based on 21 variables for a large sample of catchments globally. The results indicate that hydro-climatic variables are the dominant, but spatially varying, driver of ecosystemscale Sr, while landscape and vegetation play aminor role. Based on the importance of the various drivers, a reduced parsimoniousmodel using four variables is used to predict Sr on a global scale. These predictions largely resemble other global estimates of root characteristics based on more complex methods and datasets. This indicates that the here developed parsimonious model to estimate global scale Sr based on four simple globally available variables adequately represents the spatial variability of Sr globally. Together with the results from Chapter 2, it can be concluded that integration of these estimates into large scale hydrological and land surface models has potential to improve model water fluxes.
The findings of this dissertation directly contribute to the large scale hydrological and climate model communities by providing methods to adequately represent spatial and temporal vegetation variability. The results demonstrate the potential of these methods to improve modeled water fluxes by large scale hydrological and land surface models, with major implications for the accuracy of hydrological and climate predictions. This dissertation lays the foundation for future research aimed at further improving the realism of model vegetation variability.
Chapter 2 focuses on model representations of spatial and temporal variability of aboveground vegetation characteristics based on satellite remote sensing data. Interannual variability of land cover and leaf area index (LAI) from latest global remote sensing datasets are integrated into the land surfacemodel Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL). Furthermore, datasets of LAI and the fraction of green vegetation cover are used to develop and integrate a spatially and temporally varying model parameterization of the effective vegetation cover. The effects of these three implementations on simulated hydrology are evaluated using offline (land-only) model simulations. The results show that the enhanced variability of aboveground vegetation characteristics considerably improves the simulated variability of evaporation and near-surface soil moisture. These improvements are connected to a framework that describes how the implemented vegetation variability influences internal model interactions between vegetation, soil moisture, and evaporation.
Chapter 3 evaluates how climate-controlled root zone parameters influence water flux simulations with the land surfacemodel HTESSEL. To this aim catchment scale root zone storage capacity Sr (mm), defined as the maximum volume of subsurfacemoisture that can be accessed by the vegetation roots, is estimated using the memory method. In this method Sr is derived from soil water deficits, reflecting the ability of vegetation to adapt to the local climate conditions by sizing their roots in such a way to guarantee continuous access to water, keeping memory of past water deficit conditions. Climatecontrolled Sr is estimated with the memory method for 15 catchments in Australia to adequately represent the spatial variability of the vegetation roots. These estimates are integrated into HTESSEL, replacing the static root representation based on soil types and uniform soil depth. The results of offline model simulations show that climatecontrolled Sr representation significantly improves the timing of modeled discharge in the study regions. This suggests that a climate-controlled representation of the model Sr has potential for improving water flux simulations by land surface models in a global context.
Chapter 4 presents the influence of irrigation on the estimation of Sr with the memory method. The memory method Sr is derived from the seasonal patterns of root zone water input and output. Besides precipitation as input, irrigation supplies additional water to the root zone in irrigated agricultural fields. However, the influence of irrigation on the memory method Sr estimates has not been assessed previously. In this study two methods based on different globally available irrigation datasets are developed to account for irrigation in the memory method for estimating Sr. The Sr estimates fromthese two methods are compared to a case without considering irrigation for a large sample of catchments globally. The results show, for the first time, that irrigation considerably reduces Sr in regions with extensive irrigation, highlighting the relevance of irrigation for adequately estimating ecosystem scale Sr.
Chapter 5 investigates the influence of climate, landscape, and vegetation variables on Sr globally. So far, there is limited insight on the controls of global-scale root development and their spatial variation. A random forest model is used to predict Sr as estimated with the memory method based on 21 variables for a large sample of catchments globally. The results indicate that hydro-climatic variables are the dominant, but spatially varying, driver of ecosystemscale Sr, while landscape and vegetation play aminor role. Based on the importance of the various drivers, a reduced parsimoniousmodel using four variables is used to predict Sr on a global scale. These predictions largely resemble other global estimates of root characteristics based on more complex methods and datasets. This indicates that the here developed parsimonious model to estimate global scale Sr based on four simple globally available variables adequately represents the spatial variability of Sr globally. Together with the results from Chapter 2, it can be concluded that integration of these estimates into large scale hydrological and land surface models has potential to improve model water fluxes.
The findings of this dissertation directly contribute to the large scale hydrological and climate model communities by providing methods to adequately represent spatial and temporal vegetation variability. The results demonstrate the potential of these methods to improve modeled water fluxes by large scale hydrological and land surface models, with major implications for the accuracy of hydrological and climate predictions. This dissertation lays the foundation for future research aimed at further improving the realism of model vegetation variability.
Interannual land cover and vegetation variability based on remote sensing data in the HTESSEL land surface model
Implementation and effects on simulated water dynamics
Journal article
(2023)
-
Fransje van Oorschot, Ruud J. van der Ent, M. Hrachowitz, Emanuele Di Carlo, Franco Catalano, Souhail Boussetta, Gianpaolo Balsamo, Andrea Alessandri
Vegetation largely controls land surface–atmosphere interactions. Although vegetation is highly dynamic across spatial and temporal scales, most land surface models currently used for reanalyses and near-term climate predictions do not adequately represent these dynamics. This causes deficiencies in the variability of modeled water and energy states and fluxes from the land surface. In this study we evaluated the effects of integrating spatially and temporally varying land cover and vegetation characteristics derived from satellite observations on modeled evaporation and soil moisture in the Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL) land surface model. Specifically, we integrated interannually varying land cover from the European Space Agency Climate Change Initiative and interannually varying leaf area index (LAI) from the Copernicus Global Land Services (CGLS). Additionally, satellite data on the fraction of green vegetation cover (FCover) from CGLS were used to formulate and integrate a spatially and temporally varying effective vegetation cover parameterization. The effects of these three implementations on model evaporation fluxes and soil moisture were analyzed using historical offline (land-only) model experiments at the global scale, and model performances were quantified with global observational products of evaporation (E) and near-surface soil moisture (SMs). The interannually varying land cover consistently altered the evaporation and soil moisture in regions with major land cover changes. The interannually varying LAI considerably improved the correlation of SMs and E with respect to the reference data, with the largest improvements in semiarid regions with predominantly low vegetation during the dry season. These improvements are related to the activation of soil moisture–evaporation feedbacks during vegetation-water-stressed periods with interannually varying LAI in combination with interannually varying effective vegetation cover, defined as an exponential function of LAI. The further improved effective vegetation cover parameterization consistently reduced the errors of model effective vegetation cover, and it regionally improved SMs and E. Overall, our study demonstrated that the enhanced vegetation variability consistently improved the near-surface soil moisture and evaporation variability, but the availability of reliable global observational data remains a limitation for complete understanding of the model response. To further explain the improvements found, we developed an interpretation framework for how the model development activates feedbacks between soil moisture, vegetation, and evaporation during vegetation water stress periods.
...
Vegetation largely controls land surface–atmosphere interactions. Although vegetation is highly dynamic across spatial and temporal scales, most land surface models currently used for reanalyses and near-term climate predictions do not adequately represent these dynamics. This causes deficiencies in the variability of modeled water and energy states and fluxes from the land surface. In this study we evaluated the effects of integrating spatially and temporally varying land cover and vegetation characteristics derived from satellite observations on modeled evaporation and soil moisture in the Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL) land surface model. Specifically, we integrated interannually varying land cover from the European Space Agency Climate Change Initiative and interannually varying leaf area index (LAI) from the Copernicus Global Land Services (CGLS). Additionally, satellite data on the fraction of green vegetation cover (FCover) from CGLS were used to formulate and integrate a spatially and temporally varying effective vegetation cover parameterization. The effects of these three implementations on model evaporation fluxes and soil moisture were analyzed using historical offline (land-only) model experiments at the global scale, and model performances were quantified with global observational products of evaporation (E) and near-surface soil moisture (SMs). The interannually varying land cover consistently altered the evaporation and soil moisture in regions with major land cover changes. The interannually varying LAI considerably improved the correlation of SMs and E with respect to the reference data, with the largest improvements in semiarid regions with predominantly low vegetation during the dry season. These improvements are related to the activation of soil moisture–evaporation feedbacks during vegetation-water-stressed periods with interannually varying LAI in combination with interannually varying effective vegetation cover, defined as an exponential function of LAI. The further improved effective vegetation cover parameterization consistently reduced the errors of model effective vegetation cover, and it regionally improved SMs and E. Overall, our study demonstrated that the enhanced vegetation variability consistently improved the near-surface soil moisture and evaporation variability, but the availability of reliable global observational data remains a limitation for complete understanding of the model response. To further explain the improvements found, we developed an interpretation framework for how the model development activates feedbacks between soil moisture, vegetation, and evaporation during vegetation water stress periods.
Journal article
(2021)
-
Fransje van Oorschot, Ruud J. van der Ent, Markus Hrachowitz, Andrea Alessandri
The root zone storage capacity (Sr) is the maximum volume of water in the subsurface that can potentially be accessed by vegetation for transpiration. It influences the seasonality of transpiration as well as fast and slow runoff processes. Many studies have shown that Sr is heterogeneous as controlled by local climate conditions, which affect vegetation strategies in sizing their root system able to support plant growth and to prevent water shortages. Root zone parameterization in most land surface models does not account for this climate control on root development and is based on lookup tables that prescribe the same root zone parameters worldwide for each vegetation class. These lookup tables are obtained from measurements of rooting structure that are scarce and hardly representative of the ecosystem scale. The objective of this research is to quantify and evaluate the effects of a climate-controlled representation of Sr on the water fluxes modeled by the Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL) land surface model. Climate-controlled Sr is estimated here with the "memory method"(MM) in which Sr is derived from the vegetation's memory of past root zone water storage deficits. Sr,MM is estimated for 15 river catchments over Australia across three contrasting climate regions: tropical, temperate and Mediterranean. Suitable representations of Sr,MM are implemented in an improved version of HTESSEL (Moisture Depth - MD) by accordingly modifying the soil depths to obtain a model Sr,MD that matches Sr,MM in the 15 catchments. In the control version of HTESSEL (CTR), Sr,CTR is larger than Sr,MM in 14 out of 15 catchments. Furthermore, the variability among the individual catchments of Sr,MM (117-722 mm) is considerably larger than of Sr,CTR (491-725 mm). The climate-controlled representation of Sr in the MD version results in a significant and consistent improvement of the modeled monthly seasonal climatology (1975-2010) and interannual anomalies of river discharge compared with observations. However, the effects on biases in long-term annual mean river discharge are small and mixed. The modeled monthly seasonal climatology of the catchment discharge improved in MD compared to CTR: the correlation with observations increased significantly from 0.84 to 0.90 in tropical catchments, from 0.74 to 0.86 in temperate catchments and from 0.86 to 0.96 in Mediterranean catchments. Correspondingly, the correlations of the interannual discharge anomalies improve significantly in MD from 0.74 to 0.78 in tropical catchments, from 0.80 to 0.85 in temperate catchments and from 0.71 to 0.79 in Mediterranean catchments. The results indicate that the use of climate-controlled Sr,MM can significantly improve the timing of modeled discharge and, by extension, also evaporation fluxes in land surface models. On the other hand, the method has not been shown to significantly reduce long-term climatological model biases over the catchments considered for this study.
...
The root zone storage capacity (Sr) is the maximum volume of water in the subsurface that can potentially be accessed by vegetation for transpiration. It influences the seasonality of transpiration as well as fast and slow runoff processes. Many studies have shown that Sr is heterogeneous as controlled by local climate conditions, which affect vegetation strategies in sizing their root system able to support plant growth and to prevent water shortages. Root zone parameterization in most land surface models does not account for this climate control on root development and is based on lookup tables that prescribe the same root zone parameters worldwide for each vegetation class. These lookup tables are obtained from measurements of rooting structure that are scarce and hardly representative of the ecosystem scale. The objective of this research is to quantify and evaluate the effects of a climate-controlled representation of Sr on the water fluxes modeled by the Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL) land surface model. Climate-controlled Sr is estimated here with the "memory method"(MM) in which Sr is derived from the vegetation's memory of past root zone water storage deficits. Sr,MM is estimated for 15 river catchments over Australia across three contrasting climate regions: tropical, temperate and Mediterranean. Suitable representations of Sr,MM are implemented in an improved version of HTESSEL (Moisture Depth - MD) by accordingly modifying the soil depths to obtain a model Sr,MD that matches Sr,MM in the 15 catchments. In the control version of HTESSEL (CTR), Sr,CTR is larger than Sr,MM in 14 out of 15 catchments. Furthermore, the variability among the individual catchments of Sr,MM (117-722 mm) is considerably larger than of Sr,CTR (491-725 mm). The climate-controlled representation of Sr in the MD version results in a significant and consistent improvement of the modeled monthly seasonal climatology (1975-2010) and interannual anomalies of river discharge compared with observations. However, the effects on biases in long-term annual mean river discharge are small and mixed. The modeled monthly seasonal climatology of the catchment discharge improved in MD compared to CTR: the correlation with observations increased significantly from 0.84 to 0.90 in tropical catchments, from 0.74 to 0.86 in temperate catchments and from 0.86 to 0.96 in Mediterranean catchments. Correspondingly, the correlations of the interannual discharge anomalies improve significantly in MD from 0.74 to 0.78 in tropical catchments, from 0.80 to 0.85 in temperate catchments and from 0.71 to 0.79 in Mediterranean catchments. The results indicate that the use of climate-controlled Sr,MM can significantly improve the timing of modeled discharge and, by extension, also evaporation fluxes in land surface models. On the other hand, the method has not been shown to significantly reduce long-term climatological model biases over the catchments considered for this study.