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F. van Oorschot

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3 records found

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 - F. van Oorschot, F. van Oorschot, R.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.@en

Climate-controlled root zone parameters show potential to improve water flux simulations by land surface models

Journal article - F. van Oorschot, F. van Oorschot, F. van Oorschot, R.J. van der Ent, M. Hrachowitz, Andrea Alessandri, 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.@en

Influence of irrigation on root zone storage capacity estimation

Journal article - F. van Oorschot, R.J. van der Ent, Andrea Alessandri, M. Hrachowitz

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.@en

Contributed

1 records found

The performance of Earth System Models in simulating droughts

Master thesis - P.P. den Blaauwen, F. van Oorschot, R.J. van der Ent, R.W. Hut

This research evaluated the performance of Land Surface Models (LSMs) in simulating droughts, examining Land-Hist offline simulations from the Land, Surface, Snow and Soil Moisture Intercomparison Project (LS3MIP). It is well known that LSMs possess uncertainties and biases due to oversimplifications or the absence of certain physical processes (e.g., groundwater interactions and lateral connectivity). Therefore, the objective of this research was to identify the strengths and weaknesses of various LSMs and how this relates to the performances in simulating soil moisture droughts. To address this objective, eight LSMs were evaluated: CESM2, CMCC-ESM2, E3SM-1-1, EC-Earth3-Veg, HadGEM3-GC31-LL, IPSL-CM6A-LR, MIROC6, and UKESM1-0-LL. Two reference evaporation data sets (DOLCE V3 and an ensemble of FLUXCOM-RS, BESS and PML) and a reference soil moisture data set (SoMo.ml) were utilized for the evaluation. After a global analysis on the LSM evaporation characteristics, six climate diverse study areas were selected for further investigation. A long-term analysis was performed by examining the water balance and implementing the LSMs into the Budyko Framework. Subsequently, soil moisture deficits were calculated for the driest periods in time, and the resulting accumulated deficits were compared with the reference evaporation data. The timing and progression of the deficits were evaluated utilizing the reference soil moisture data. Finally, the sensitivity of the model was evaluated by examining the response of evaporation anomalies to precipitation anomalies and comparing this with the reference evaporation data. The results showed that there was a large spread in output and performance among the LSMs across all parts of the evaluation. The greatest contrasts among the LSMs were found in the dry to wet transition zones within the tropics. In this latitudinal range, the worst performing LSMs overestimated the accumulation of soil moisture deficits and the severity of droughts, while the opposite was found for the extratropical regions. Additionally, the models showed, in general, to be overly sensitive to precipitation anomalies. When ranking the implemented model bases in the LSMs based on their performance during droughts, the findings showed that the Community Land Model (implemented in CMCC-ESM2, E3SM-1-1 and CESM2) was predominantly the best performing, followed by ORCHIDEE (IPSL-CM6A-LR) and HTESSEL (EC-Earth3-Veg). MATSIRO (MIROC6) and JULES (HadGEM3-GC31-LL and UKESM1-0-LL) were the least performing model bases. From a hydrological perspective, the findings of this research could be linked to some known limitations of LSMs. Oversimplified soil and vegetation dynamics could contribute to the LSMs being overly sensitive to precipitation anomalies while the contrasts between the tropical and extratropical regions could be attributed to the representation of the soil moisture-evaporation coupling, which plays a greater role in the tropical study areas. Ultimately, this research could contribute to LS3MIP and the Land Surface Modeling community, as the results highlight the strengths and weaknesses of the LSMs in simulating soil moisture droughts. From there, this research could contribute to improving LSMs, understanding drought mechanisms, and addressing climate change impacts, especially in drought-prone regions.