A multi-sourced assessment of the spatio-temporal dynamic of soil saturation in the MARINE flash flood model

Abstract. The MARINE hydrological model is a distributed model dedicated to flash flood simulation. Recent developments of the MARINE model are exploited in this work: on the one hand, formerly relying on water height, transfers of water through the subsurface now take place in a homogeneous soil column based on the volumetric soil water content (SSF model). On the other hand, the soil column is divided into two layers, which represent respectively the upper soil layer and the deep weathered rocks (SSF-DWF model). The aim of the present work is to assess the performances of these new representations for the simulation of soil saturation during flash flood events. An exploration of the various products available in the literature for soil moisture estimation is performed. The performances of the models are estimated with respect to several soil moisture products, either at the local scale or spatially extended: i) The gridded soil moisture product provided by the operational modeling chain SAFRAN-ISBA-MODCOU; ii) The gridded soil moisture product provided by the LDAS-Monde assimilation chain, based on the ISBA-a-gs land surface model and assimilating satellite derived data; iii) the upper soil moisture hourly measurements taken from the SMOSMANIA observation network; iv) The Soil Water Index provided by the Copernicus Global Land Service (CGLS), derived from Sentinel1/C-band SAR and ASCAT satellite data. The case study is performed over two French Mediterranean catchments impacted by flash flood events over the 2017–2019 period. The local comparison of the MARINE outputs with the SMOSMANIA measurements, as well as the comparison at the basin scale of the MARINE outputs with the gridded LDAS-Monde and CGLS data lead to the same conclusions: both the dynamics and the amplitudes of the soil moisture simulated with the SSF and SSF-DWF models are better correlated with both the SMOSMANIA measurements and the LDAS-Monde data than the outputs of the base model. The opportunity of improving the two-layers model calibration is then discussed. In conclusion, the developments presented for the representation of subsurface flow in the MARINE model enhance the soil moisture simulation during flash floods, with respect to both gridded data and local soil moisture measurements.

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A multi-sourced assessment of the spatiotemporal dynamics of soil moisture in the MARINE flash flood model

Hydrology and Earth System Sciences ◽

10.5194/hess-25-1425-2021 ◽

2021 ◽

Vol 25 (3) ◽

pp. 1425-1446

Author(s):

Judith Eeckman ◽

Hélène Roux ◽

Audrey Douinot ◽

Bertrand Bonan ◽

Clément Albergel

Keyword(s):

Soil Moisture ◽

Soil Water ◽

Flash Flood ◽

Soil Column ◽

Surface Model ◽

Flood Events ◽

Saturation Degree ◽

Local Soil ◽

Basin Scale ◽

Soil Saturation

Abstract. The MARINE (Model of Anticipation of Runoff and INundations for Extreme events) hydrological model is a distributed model dedicated to flash flood simulation. Recent developments of the MARINE model are explored in this work. On one hand, transfers of water through the subsurface, formerly relying on water height, now take place in a homogeneous soil column based on the soil saturation degree (SSF model). On the other hand, the soil column is divided into two layers, which represent, respectively, the upper soil layer and the deep weathered rocks (SSF–DWF model). The aim of the present work is to assess the accuracy of these new representations for the simulation of soil moisture during flash flood events. An exploration of the various products available in the literature for soil moisture estimation is performed. The efficiency of the models for soil saturation degree simulation is estimated with respect to several products either at the local scale or spatially distributed: (i) the gridded soil moisture product provided by the operational modeling chain SAFRAN-ISBA-MODCOU; (ii) the gridded soil moisture product provided by the LDAS-Monde assimilation chain, which is based on the ISBA-A-gs land surface model and assimilating satellite derived data; (iii) the upper soil water content hourly measurements taken from the SMOSMANIA observation network; and (iv) the Soil Water Index provided by the Copernicus Global Land Service (CGLS), which is derived from Sentinel-1 C-SAR and ASCAT satellite data. The case study is performed over two French Mediterranean catchments impacted by flash flood events over the 2017–2019 period. The local comparison of the MARINE outputs with the SMOSMANIA measurements, as well as the comparison at the basin scale of the MARINE outputs with the gridded LDAS-Monde and CGLS data, lead to the following conclusion: both the dynamics and the amplitudes of the soil saturation degree simulated with the SSF and SSF–DWF models are better correlated with both the SMOSMANIA measurements and the LDAS-Monde data than the outputs of the base model. Finally, the soil saturation degree simulated by the two-layers model for the deep layer is compared to the soil saturation degree provided by the LDAS-Monde product at corresponding depths. In conclusion, the developments presented for the representation of subsurface flow in the MARINE model enhance the soil saturation degree simulation during flash floods with respect to both gridded data and local soil moisture measurements.

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An assessment of soil moisture in the MARINE flash flood model using in situ measurements, reanalysis and satellite derived estimates

10.5194/egusphere-egu2020-1611 ◽

2020 ◽

Author(s):

Judith Eeckman ◽

Hélène Roux ◽

Bertrand Bonan ◽

Clément Albergel ◽

Audrey Douniot

Keyword(s):

Soil Moisture ◽

Water Content ◽

Soil Water ◽

Soil Water Content ◽

Model Calibration ◽

Flash Flood ◽

Soil Column ◽

Subsurface Flow ◽

Soil Layer ◽

Observation Network

The representation of soil moisture is a key factor for the simulation of flash flood in the Mediterranean region. The MARINE hydrological model is a distributed model dedicaded to flash flood simulation. Recent developments of the MARINE model lead to an improvement of the subsurface flow representation : on the one hand, the transfers through the subsurface take place in a homogeneous soil column based on the volumic soil water content instead of the water height. On the other hand, the soil column is divided into two layers, which represent respectively the upper soil layer and the deep weathered rocks. The aim of this work is to assess the performances of these new representations of the subsurface flow with respect to the soil saturation dynamics during flash flood events. The performances of the model are estimated with respect to three soil moisture products: i) the gridded soil moisture product provided by the LDAS-Monde assimilation chain. LDAS-Monde is based on the ISBA-a-gs land surface model and integrates high resolution spatial remote sensing data from the Copernicus Global Land Service for vegetation through data assimilation; ii) the upper soil moisture measurements taken from the SMOSMANIA observation network&#160;; iii) The satellite derived surface soil moisture data from Sentinel1. The case study is led over two french mediterranean catchments impacted by flash flood events over the 2017-2019 period and where one SMOSMANIA station is available. Additionnal tests for the initialisation of MARINE water content for the two soil layers are assessed. Results show first that the dynamic of the soil moisture both provided by LDAS-Monde and simulated for the upper soil layer in MARINE are locally consistent with the SMOSMANIA observations. Secondly, the use of soil water content instead of water height to describe lateral flows in MARINE is cleary more relevant with respect to both LDAS-Monde simulations and SMOSMANIA stations. The dynamic of the deep layer moisture content also appears to be consistent with the LDAS-Monde product for deeper layers. However, the bias on these values strongly rely on the calibration of the new two-layers model. The opportunity of improving the two-layers model calibration is then discussed. Finally, the impact of the soil water content initialisation is shown to be significant mainly during the flood rising, and also to be dependent on the model calibration. In conclusion, the new developments presented for the representation of subsurface flow in the MARINE model appear to enhance the soil moisture simulation during flash floods, with respect to both the LDAS-Monde product and the SMOSMANIA observation network.

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Root Response to Soil Water Status via Interaction of Crop Genotype and Environment

Agronomy ◽

10.3390/agronomy11040708 ◽

2021 ◽

Vol 11 (4) ◽

pp. 708

Author(s):

Phanthasin Khanthavong ◽

Shin Yabuta ◽

Hidetoshi Asai ◽

Md. Amzad Hossain ◽

Isao Akagi ◽

...

Keyword(s):

Soil Moisture ◽

Soil Water ◽

Root Distribution ◽

Water Status ◽

Soil Layer ◽

Shoot Biomass ◽

Soil Conditions ◽

Crop Adaptation ◽

Soil Moisture Status ◽

Root Distributions

Flooding and drought are major causes of reductions in crop productivity. Root distribution indicates crop adaptation to water stress. Therefore, we aimed to identify crop roots response based on root distribution under various soil conditions. The root distribution of four crops—maize, millet, sorghum, and rice—was evaluated under continuous soil waterlogging (CSW), moderate soil moisture (MSM), and gradual soil drying (GSD) conditions. Roots extended largely to the shallow soil layer in CSW and grew longer to the deeper soil layer in GSD in maize and sorghum. GSD tended to promote the root and shoot biomass across soil moisture status regardless of the crop species. The change of specific root density in rice and millet was small compared with maize and sorghum between different soil moisture statuses. Crop response in shoot and root biomass to various soil moisture status was highest in maize and lowest in rice among the tested crops as per the regression coefficient. Thus, we describe different root distributions associated with crop plasticity, which signify root spread changes, depending on soil water conditions in different crop genotypes as well as root distributions that vary depending on crop adaptation from anaerobic to aerobic conditions.

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Probabilistic inference of ecohydrological parameters using observations from point to satellite scales

Hydrology and Earth System Sciences ◽

10.5194/hess-22-3229-2018 ◽

2018 ◽

Vol 22 (6) ◽

pp. 3229-3243 ◽

Cited By ~ 3

Author(s):

Maoya Bassiouni ◽

Chad W. Higgins ◽

Christopher J. Still ◽

Stephen P. Good

Keyword(s):

Soil Moisture ◽

Water Balance ◽

Soil Water ◽

Dry Season ◽

Soil Water Balance ◽

Rainfall Pattern ◽

Site Specific ◽

Soil Moisture Dynamics ◽

Soil Saturation ◽

Moisture Dynamics

Abstract. Vegetation controls on soil moisture dynamics are challenging to measure and translate into scale- and site-specific ecohydrological parameters for simple soil water balance models. We hypothesize that empirical probability density functions (pdfs) of relative soil moisture or soil saturation encode sufficient information to determine these ecohydrological parameters. Further, these parameters can be estimated through inverse modeling of the analytical equation for soil saturation pdfs, derived from the commonly used stochastic soil water balance framework. We developed a generalizable Bayesian inference framework to estimate ecohydrological parameters consistent with empirical soil saturation pdfs derived from observations at point, footprint, and satellite scales. We applied the inference method to four sites with different land cover and climate assuming (i) an annual rainfall pattern and (ii) a wet season rainfall pattern with a dry season of negligible rainfall. The Nash–Sutcliffe efficiencies of the analytical model's fit to soil observations ranged from 0.89 to 0.99. The coefficient of variation of posterior parameter distributions ranged from < 1 to 15 %. The parameter identifiability was not significantly improved in the more complex seasonal model; however, small differences in parameter values indicate that the annual model may have absorbed dry season dynamics. Parameter estimates were most constrained for scales and locations at which soil water dynamics are more sensitive to the fitted ecohydrological parameters of interest. In these cases, model inversion converged more slowly but ultimately provided better goodness of fit and lower uncertainty. Results were robust using as few as 100 daily observations randomly sampled from the full records, demonstrating the advantage of analyzing soil saturation pdfs instead of time series to estimate ecohydrological parameters from sparse records. Our work combines modeling and empirical approaches in ecohydrology and provides a simple framework to obtain scale- and site-specific analytical descriptions of soil moisture dynamics consistent with soil moisture observations.

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Soil Water Characteristics of Gleysols in the Bamenda (Cameroon) Wetlands and Implications for Agricultural Management Strategies

Applied and Environmental Soil Science ◽

10.1155/2021/6643208 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Alice Mufur Magha ◽

Primus Azinwi Tamfuh ◽

Lionelle Estelle Mamdem ◽

Marie Christy Shey Yefon ◽

Bertrand Kenzong ◽

...

Keyword(s):

Soil Moisture ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Soil Water ◽

Total Variance ◽

Organic Carbon Content ◽

Available Water ◽

High Soil ◽

Local Soil ◽

Very High

Water budgeting in agriculture requires local soil moisture information as crops depend mainly on moisture available at root level. The present paper aims to evaluate the soil moisture characteristics of Gleysols in the Bamenda (Cameroon) wetlands and to evaluate the link between soil moisture content and selected soil characteristics affecting crop production. The work was conducted in the field and laboratory, and data were analyzed by simple descriptive statistics. The main results showed that the soils had a silty clayey to clayey texture, high bulk density, high soil organic carbon content, and high soil organic carbon stocks. The big difference between moisture contents at wilting point and at field capacity testified to very high plant-available water content. Also, the soils displayed very high contents of readily available water and water storage contents. The soil moisture characteristics give sigmoid curves and enabled noting that the Gleysols attain their full water saturation at a range of 57.68 to 91.70% of dry soil. Clay and SOC contents show a significant positive correlation with most of the soil moisture characteristics, indicating that these soil properties are important for soil water retention. Particle density, coarse fragments, and sand contents correlated negatively with the soil moisture characteristics, suggesting that they decrease soil water-holding capacity. The principal component analysis (PCA) enabled reducing 17 variables described to only three principal components (PCs) explaining 73.73% of the total variance; the first PC alone expressed 45.12% of the total variance, associating clay, SOC, and six soil moisture characteristics, thus portraying a deep correlation between these eight variables. Construction of contoured ditches, deep tillage, and raised ridges management techniques during the rainy season while channeling water from nearby water bodies into the farmland, opportunity cropping, and usage of water cans and other irrigation strategies are used during the dry season to combat water constraints.

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Evaluation of soil moisture in CMIP5 simulations over contiguous United States using in situ and satellite observations

10.5194/hess-2016-477 ◽

2016 ◽

Author(s):

Shanshui Yuan ◽

Steven M. Quiring

Keyword(s):

United States ◽

Soil Moisture ◽

Soil Column ◽

Regional Analysis ◽

Soil Layer ◽

Model Performance ◽

Coupled Model ◽

Cmip5 Models ◽

Near Surface

Abstract. This study provides a comprehensive evaluation of soil moisture simulations in the Coupled Model Intercomparison Project Phase 5 (CMIP5) extended historical experiment (2003 to 2012). Soil moisture from in situ and satellite sources are used to evaluate CMIP5 simulations in the contiguous United States (CONUS). Both near-surface (0–10 cm) and soil column (0–100 cm) simulations from more than 14 CMIP5 models are evaluated during the warm season (April–September). Multi-model ensemble means and the performance of individual models are assessed at a monthly time scale. Our results indicate that CMIP5 models can reproduce the seasonal variability in soil moisture over CONUS. However, the models tend to overestimate the magnitude of both near-surface and soil-column soil moisture in the western U.S. and underestimate it in the eastern U.S. There are large variations in model performance, especially in the near-surface. There are significant regional and inter-model variations in performance. Results of a regional analysis show that in deeper soil layer, the CMIP5 soil moisture simulations tend to be most skillful in the southern U.S. Based on both the satellite-derived and in situ soil moisture, CESM1, CCSM4 and GFDL-ESM2M perform best in the 0–10 cm soil layer and CESM1, CCSM4, GFDL-ESM2M and HadGEM2-ES perform best in the 0–100 cm soil layer.

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Towards an operationally capable satellite-based 0-100 cm soil moisture dataset from C3S

10.5194/egusphere-egu21-12268 ◽

2021 ◽

Author(s):

Adam Pasik ◽

Wolfgang Preimesberger ◽

Bernhard Bauer-Marschallinger ◽

Wouter Dorigo

Keyword(s):

Soil Moisture ◽

Soil Profile ◽

Land Surface ◽

Root Zone ◽

Soil Layer ◽

Land Surface Model ◽

In Situ Measurements ◽

Surface Model ◽

T Values

Multiple satellite-based global surface soil moisture (SSM) datasets are presently available, these however, address exclusively the top layer of the soil (0-5cm). Meanwhile, root-zone soil moisture cannot be directly quantified with remote sensing but can be estimated from SSM using a land surface model. Alternatively, soil water index (SWI; calculated from SSM as a function of time needed for infiltration) can be used as a simple approximation of root-zone conditions. SWI is a proxy for deeper layers of the soil profile which control evapotranspiration, and is hence especially important for studying hydrological processes over vegetation-covered areas and meteorological modelling.Here we introduce the advances in our work on the first operationally capable SWI-based root-zone soil moisture dataset from C3S Soil Moisture v201912 COMBINED product, spanning the period 2002-2020. The uniqueness of this dataset lies in the fact that T-values (temporal lengths ruling the infiltration) characteristic of SWI were translated into particular soil depths making it much more intuitive, user-friendly and easily applicable. Available are volumetric soil moisture values for the top 1 m of the soil profile at 10 cm intervals, where the optimal T-value (T-best) for each soil layer is selected based on a range of correlation metrics with in situ measurements from the International Soil Moisture Network (ISMN) and the relevant soil and climatic parameters. Additionally we present the results of an extensive global validation against in situ measurements (ISMN) as well as the results of investigations into the relationship between a range of soil and climate characteristics and the optimal T-values for particular soil depths.

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A regionally explicit, global SWI calibration based on ISMN observations

10.5194/egusphere-egu21-496 ◽

2021 ◽

Author(s):

Manolis G. Grillakis

Keyword(s):

Soil Moisture ◽

Soil Water ◽

Large Scale ◽

Root Zone ◽

Soil Depth ◽

European Space Agency ◽

Soil Layer ◽

Added Value ◽

Water Index ◽

Soil Moisture Data

Remote sensing has proven to be an irreplaceable tool for monitoring soil moisture. The European Space Agency (ESA), through the Climate Change Initiative (CCI), has provided one of the most substantial contributions in the soil water monitoring, with almost 4 decades of global satellite derived and homogenized soil moisture data for the uppermost soil layer. Yet, due to the inherent limitations of many of the remote sensors, only a limited soil depth can be monitored. To enable the assessment of the deeper soil layer moisture from surface remotely sensed products, the Soil Water Index (SWI) has been established as a convolutive transformation of the surface soil moisture estimation, under the assumption of uniform hydraulic conductivity and the absence of transpiration. The SWI uses a single calibration parameter, the T-value, to modify its response over time.Here the Soil Water Index (SWI) is calibrated using ESA CCI soil moisture against in situ observations from the International Soil Moisture Network and then use Artificial Neural Networks (ANNs) to find the best physical soil, climate, and vegetation descriptors at a global scale to regionalize the calibration of the T-value. The calibration is then used to assess a root zone related soil moisture for the period 2001 &#8211; 2018.The results are compared against the European Centre for Medium-Range Weather Forecasts, ERA5 Land reanalysis soil moisture dataset, showing a good agreement, mainly over mid-latitudes. The results indicate that there is added value to the results of the machine learning calibration, comparing to the uniform T-value. This work contributes to the exploitation of ESA CCI soil moisture data, while the produced data can support large scale soil moisture related studies.

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No evidence that earthworms increase soil greenhouse gas emissions (CO2 and N2O) in the presence of plants and soil-moisture fluctuations

10.5194/egusphere-egu21-8599 ◽

2021 ◽

Author(s):

Pierre Ganault ◽

Johanne Nahmani ◽

Yvan Capowiez ◽

Isabelle Bertrand ◽

Bruno Buatois ◽

...

Keyword(s):

Soil Moisture ◽

Greenhouse Gases ◽

Greenhouse Gas Emissions ◽

Greenhouse Gas ◽

Soil Water ◽

Water Status ◽

Soil Layer ◽

Agricultural Practices ◽

Biodiversity Loss ◽

Gas Emissions

Accelerating climate change and biodiversity loss calls for agricultural practices that can sustain productivity with lower greenhouse gas emissions while maintaining biodiversity. Biodiversity-friendly agricultural practices have been shown to increase earthworm populations, but according to a recent meta-analyses, earthworms could increase soil CO2 and N2O emissions by 33 and 42%, respectively. However, to date, many studies reported idiosyncratic and inconsistent effects of earthworms on greenhouse gases, indicating that the underlying mechanisms are not fully understood. Here we report the effects of earthworms (anecic, endogeic and their combination) with or without plants on CO2 and N2O emissions in the presence of soil-moisture fluctuations from a mesocosms experiment. The experimental set-up was explicitly designed to account for the engineering effect of earthworms (i.e. burrowing) and investigate the consequences on soil macroporosity, soil water dynamic, and microbial activity. We found that plants reduced N2O emissions by 19.80% and that relative to the no earthworm control, the cumulative N2O emissions were 17.04, 34.59 and 44.81% lower in the anecic, both species and endogeic species, respectively. CO2 emissions were not significantly affected by the plants or earthworms but depended on the interaction between earthworms and soil water content, an interaction that was also observed for the N2O emissions. Soil porosity variables measured by X-ray tomography suggest that the earthworm effects on CO2 and N2O emissions were mediated by the burrowing patterns affecting the soil aeration and water status. N2O emissions decreased with the volume occupied by macropores in the deeper soil layer, whereas CO2 emissions decreased with the macropore volume in the top soil layer. This study suggests that experimental setups without plants and in containers where the earthworm soil engineering effects via burrowing and casting on soil water status are minimized may be responsible, at least in part, for the reported positive earthworm effects on greenhouse gases.

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Incorporating Hydraulic Lift into a Land Surface Model and Its Effects on Surface Soil Moisture Prediction

Journal of Hydrometeorology ◽

10.1175/jhm-385.1 ◽

2004 ◽

Vol 5 (6) ◽

pp. 1181-1191 ◽

Cited By ~ 18

Author(s):

Diandong Ren ◽

Ming Xue ◽

Ann Henderson-Sellers

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Energy Exchange ◽

Atmospheric Surface Layer ◽

Soil Layer ◽

Land Surface Model ◽

Hydraulic Lift ◽

Surface Model ◽

Daily Cycle ◽

Dry Conditions

Abstract In comparison with the Oklahoma Atmospheric Surface-layer Instrumentation System (OASIS) measurements, the Simulator for Hydrology and Energy Exchange at the Land Surface (SHEELS), a multilayer soil hydrological model, simulates a much faster drying of the superficial soil layer (5 cm) for a densely vegetated area at the OASIS site in Norman, Oklahoma, under dry conditions. Further, the measured superficial soil moisture contents also show a counterintuitive daily cycle that moistens the soil during daytime and dries the soil at night. The original SHEELS model fails to simulate this behavior. This work proposes a treatment of hydraulic lift processes associated with stressed vegetation and shows via numerical experiments that both problems reported above can be much alleviated by including the hydraulic lift effect associated with stressed vegetation.

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