ORCID Profile
0000-0002-3172-7339
Current Organisation
Wageningen University
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Publisher: Informa UK Limited
Date: 02-07-2019
Publisher: Copernicus GmbH
Date: 22-09-2023
Publisher: International Union of Geological Sciences
Date: 12-2016
Publisher: Wiley
Date: 11-04-2019
DOI: 10.1111/GCB.14626
Abstract: Although only representing 0.05% of global freshwater, or 0.001% of all global water, soil water supports all terrestrial biological life. Soil moisture behaviour in most models is constrained by hydraulic parameters that do not change. Here we argue that biological feedbacks from plants, macro-fauna and the microbiome influence soil structure, and thus the soil hydraulic parameters and the soil water content signals we observe. Incorporating biological feedbacks into soil hydrological models is therefore important for understanding environmental change and its impacts on ecosystems. We anticipate that environmental change will accelerate and modify soil hydraulic function. Increasingly, we understand the vital role that soil moisture exerts on the carbon cycle and other environmental threats such as heatwaves, droughts and floods, wildfires, regional precipitation patterns, disease regulation and infrastructure stability, in addition to agricultural production. Biological feedbacks may result in changes to soil hydraulic function that could be irreversible, resulting in alternative stable states (ASS) of soil moisture. To explore this, we need models that consider all the major feedbacks between soil properties and soil-plant-faunal-microbial-atmospheric processes, which is something we currently do not have. Therefore, a new direction is required to incorporate a dynamic description of soil structure and hydraulic property evolution into soil-plant-atmosphere, or land surface, models that consider feedbacks from land use and climate drivers of change, so as to better model ecosystem dynamics.
Publisher: Elsevier BV
Date: 09-2020
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-2649
Abstract: A solid understanding of the global water cycle and how land surface processes respond to both changes in climate and pressure due to water use is essential for society. Although Land Surface Models (LSM) and Global Hydrological Models (GHM) are able to simulate the spatiotemporal variability of the water balance relatively reliably, intercomparison studies have indicated considerable differences between the models. Each LSM and GHM present a unique set of equations, parameters and configurations that contribute to the spread of simulated hydrological responses to meteorological forcings. In order to improve our understanding of modeling uncertainties, we propose a variable importance assessment for 5 LSM/GHM (JULES, HTESSEL, PCR-GLOBWB, SURFEX and ORCHIDEE) from the EartH2Observe (E2O) project. The output of the models and the meteorological forcings were collected from the Water Resources Reanalysis Tier 2 of the E2O project, which consists of a global dataset with spatial resolution of 0.25ox0.25o. We used soil texture and land cover datasets that most resemble the inputs used by each LSM/GHM during the E2O project. The models& #8217 outputs were used to estimate 6 hydrological indices for every land cell: Evaporation-Precipitation ratio Runoff-Precipitation ratio Surface Runoff-Total Runoff ratio median Soil Moisture variation caused by a Rainfall event median Surface Runoff caused by a Rainfall event and Soil Moisture temporal autocorrelation. Then, we evaluate the input features (meteorological, land cover, and soil texture) importance to the hydrological indices of each model using machine learning. With the analysis we aim to & examine a) How much the models differ and why? b) To what extent are the output differences related to the input features or/and to the models formulation? and c) How significant is each feature to the respective hydrological index?
Publisher: American Geophysical Union (AGU)
Date: 12-2017
DOI: 10.1002/2017RG000581
No related grants have been discovered for Martine van der Ploeg.