scholarly journals Estimating spatial mean root-zone soil moisture from point-scale observations

2006 ◽  
Vol 3 (4) ◽  
pp. 1447-1485 ◽  
Author(s):  
A. J. Teuling ◽  
R. Uijlenhoet ◽  
F. Hupet ◽  
E. E. van Loon ◽  
P. A. Troch
Keyword(s):  
Soil Moisture ◽  
Root Zone ◽  
Spatial Scales ◽  
Space Time ◽  
Time Variability ◽  
Point Scale ◽  
Area Index ◽  
Time Dynamics ◽  
Landscape Characteristics ◽  
Moisture Field

Abstract. Root zone soil moisture is a key variable in many land surface hydrology models. Often, however, there is a mismatch in the spatial scales at which models simulate soil moisture and at which soil moisture is observed. The limited spatial support of observations combined with the large spatial variability of the soil moisture field complicates model validation. The increased availability of detailed datasets on space-time variability of root-zone soil moisture allows for a posteriori analysis of the uncertainties in the relation between point-scale observations and the spatial mean. In this paper we analyze three comprehensive datasets from three different regions. We identify different strategies to select observation sites, and we present methods for quantifying the uncertainty that is associated with each strategy. In general there is a large correspondence between the different datasets with respect to the relative uncertainties for the different strategies. For all datasets, the uncertainty can be strongly reduced if some information is available that relates soil moisture at that site to the spatial mean. However this works best if the space-time dynamics of the soil moisture field are known. Selection of the site closest to the spatial mean on a single random date only leads to minor reduction of the uncertainty with respect to the spatial mean over seasonal timescales. Since soil moisture variability is the result of a complex interaction between soil, vegetation, and landscape characteristics, the soil moisture field will be correlated with some of these characteristics. Using available information, we show that the correlation with leaf area index or a wetness coefficient alone is insufficient to predict if a site is representative for the spatial mean soil moisture.

scholarly journals Estimating spatial mean root-zone soil moisture from point-scale observations

2006 ◽  
Vol 10 (5) ◽  
pp. 755-767 ◽  
Author(s):  
A. J. Teuling ◽  
R. Uijlenhoet ◽  
F. Hupet ◽  
E. E. van Loon ◽  
P. A. Troch
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Root Zone ◽  
Spatial Scales ◽  
Space Time ◽  
Point Scale ◽  
Area Index ◽  
Landscape Characteristics ◽  
Moisture Field

Abstract. Root zone soil moisture is a key variable in many land surface hydrology models. Often, however, there is a mismatch in the spatial scales at which models simulate soil moisture and at which soil moisture is observed. This complicates model validation. The increased availability of detailed datasets on space-time variability of root-zone soil moisture allows for a posteriori analysis of the uncertainties in the relation between point-scale observations and the spatial mean. In this paper we analyze three comprehensive datasets from three different regions. We identify different strategies to select observation sites. For instance, sites can be located randomly or according to the rank stability concept. For each strategy, we present methods to quantify the uncertainty that is associated with this strategy. In general there is a large correspondence between the different datasets with respect to the relative uncertainties for the different strategies. For all datasets, the uncertainty can be strongly reduced if some information is available that relates soil moisture content at that site to the spatial mean. However this works best if the space-time dynamics of the soil moisture field are known. Selection of the site closest to the spatial mean on a single random date only leads to minor reduction of the uncertainty with respect to the spatial mean over seasonal timescales. Since soil moisture variability is the result of a complex interaction between soil, vegetation, and landscape characteristics, the soil moisture field will be correlated with some of these characteristics. Using available information, we show that the correlation with leaf area index or a wetness coefficient alone is insufficient to predict if a site is representative for the spatial mean soil moisture content.

scholarly journals Non-stationarity of electrical resistivity and soil moisture relationship in heterogeneous soil system: a case study

2015 ◽  
Vol 2 (2) ◽  
pp. 955-994
Author(s):  
D. Michot ◽  
Z. Thomas ◽  
I. Adam
Keyword(s):  
Electrical Resistivity ◽  
Soil Moisture ◽  
Root Zone ◽  
Spatial Scales ◽  
Geophysical Methods ◽  
Matric Potential ◽  
Soil System ◽  
Retention Curve ◽  
Heterogeneous Soil ◽  
Change In Mean

Abstract. Root uptake is the most decisive key in water transfer involving soil and vegetation. It depends on water availability which can be evaluated by punctual measurements. Additionally, surface geophysical methods such as Electrical Resistivity Tomography (ERT) provide larger spatial scales. This paper focuses on investigating temporal and spatial soil moisture changes, along a toposequence crossed by a hedgerow, using ERT and punctual measurements. 10 ERT were performed over the studied period for a 28 m long transect and compared to matric potential and groundwater level measurements. Soil Volumetric Water Content (VWC) was predicted using two methods (i) from ER using Waxman and Smits model (ii) and from matric potential using experimental retention curve fitted by Van Genuchten model. Probability Density Functions (Pdfs) of our set of data show that the largest change, in mean values of ER as well as matric potential, was observed in the topsoil layer. We then analyzed the consistency between ER and punctual measurements in this layer by extracting the arrays in the junction between ER grids and punctual measurements. Pdfs of ER maps at each monitoring time (from T01 to T10) were also calculated to select the more contrasted distributions corresponding to the wettest (T06) and driest states (T10). Results of ER were consistent with matric potential measurements with two different behaviors for locations inside and outside the root zone. A strong correlation (r = 0.9) between VWC values from Waxman and Smits model and those obtained from retention curve was observed outside the root zone. The heterogeneous soil system inside the root zone shows a different pattern in this relationship. The shift in the relationship between ER and soil moisture for the locations outside and inside the root zone highlights the non-stationarity in heterogeneous soil system. Such systems were actually related to the high hedgerow root density and also to a particular topographical context (ditch and bank) which is encountered in Brittany and over north-west of Europe.

Changing sensitivity of global vegetation productivity to hydro-climate drivers

2021 ◽  
Author(s):  
Wantong Li ◽  
Matthias Forkel ◽  
Mirco Migliavacca ◽  
Markus Reichstein ◽  
Sophia Walther ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Root Zone ◽  
Growing Season ◽  
Climatic Conditions ◽  
Terrestrial Vegetation ◽  
Spatial And Temporal Patterns ◽  
Vegetation Productivity ◽  
Area Index ◽  
Global Vegetation

<p>Terrestrial vegetation couples the global water, energy and carbon exchange between the atmosphere and the land surface. Thereby, vegetation productivity is determined by a multitude of energy- and water-related variables. While the emergent sensitivity of productivity to these variables has been inferred from Earth observations, its temporal evolution during the last decades is unclear, as well as potential changes in response to trends in hydro-climatic conditions. In this study, we analyze the changing sensitivity of global vegetation productivity to hydro-climate conditions by using satellite-observed vegetation indices (i.e. NDVI) at the monthly timescale from 1982–2015. Further, we repeat the analysis with simulated leaf area index and gross primary productivity from the TRENDY vegetation models, and contrast the findings with the observation-based results. We train a random forest model to predict anomalies of productivity from a comprehensive set of hydro-meteorological variables (temperature, solar radiation, vapor pressure deficit, surface and root-zone soil moisture and precipitation), and to infer the sensitivity to each of these variables. By training models from temporal independent subsets of the data we detect the evolution of sensitivity across time. Results based on observations show that vegetation sensitivity to energy- and water-related variables has significantly changed in many regions across the globe. In particular we find decreased (increased) sensitivity to temperature in very warm (cold) regions. Thereby, the magnitude of the sensitivity tends to differ between the early and late growing seasons. Likewise, we find changing sensitivity to root-zone soil moisture with increases predominantly in the early growing season and decreases in the late growing season. For better understanding the mechanisms behind the sensitivity changes, we analyse land-cover changes, hydro-climatic trends, and abrupt disturbances (e.g. drought, heatwave events or fires could result in breaking points of sensitivity evolution in the local interpretation). In summary, this study sheds light on how and where vegetation productivity changes its response to the drivers under climate change, which can help to understand possibly resulting changes in spatial and temporal patterns of land carbon uptake.</p>

Area-representative validation of remotely sensed high resolution soil moisture using a cosmic-ray neutron sensor

2020 ◽  
Author(s):  
Dragana Panic ◽  
Isabella Pfeil ◽  
Andreas Salentinig ◽  
Mariette Vreugdenhil ◽  
Wolfgang Wagner ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Root Zone ◽  
Spatial Scales ◽  
Temporal Frequency ◽  
Cosmic Ray ◽  
Irrigation Scheduling ◽  
Water Index ◽  
The Impact

<p>Reliable measurements of soil moisture (SM) are required for many applications worldwide, e.g., for flood and drought forecasting, and for improving the agricultural water use efficiency (e.g., irrigation scheduling). For the retrieval of large-scale SM datasets with a high temporal frequency, remote sensing methods have proven to be a valuable data source. (Sub-)daily SM is derived, for example, from observations of the Advanced Scatterometer (ASCAT) since 2007. These measurements are available on spatial scales of several square kilometers and are in particular useful for applications that do not require fine spatial resolutions but long and continuous time series. Since the launch of the first Sentinel-1 satellite in 2015, the derivation of SM at a spatial scale of 1 km has become possible for every 1.5-4 days over Europe (SSM1km) [1]. Recently, efforts have been made to combine ASCAT and Sentinel-1 to a Soil Water Index (SWI) product, in order to obtain a SM dataset with daily 1 km resolution (SWI1km) [2]. Both datasets are available over Europe from the Copernicus Global Land Service (CGLS, https://land.copernicus.eu/global/). As the quality of such a dataset is typically best over grassland and agricultural areas, and degrades with increasing vegetation density, validation is of high importance for the further development of the dataset and for its subsequent use by stakeholders.</p><p>Traditionally, validation studies have been carried out using in situ SM sensors from ground networks. Those are however often not representative of the area-wide satellite footprints. In this context, cosmic-ray neutron sensors (CRNS) have been found to be valuable, as they provide integrated SM estimates over a much larger area (about 20 hectares), which comes close to the spatial support area of the satellite SM product. In a previous study, we used CRNS measurements to validate ASCAT and S1 SM over an agricultural catchment, the Hydrological Open Air Laboratory (HOAL), in Petzenkirchen, Austria. The datasets were found to agree, but uncertainties regarding the impact of vegetation were identified.</p><p>In this study, we validated the SSM1km, SWI1km and a new S1-ASCAT SM product, which is currently developed at TU Wien, using CRNS. The new S1-ASCAT-combined dataset includes an improved vegetation parameterization, trend correction and snow masking. The validation has been carried out in the HOAL and on a second site in Marchfeld, Austria’s main crop producing area. As microwaves only penetrate the upper few centimeters of the soil, we applied the soil water index concept [3] to obtain soil moisture estimates of the root zone (approximately 0-40 cm) and thus roughly corresponding to the depth of the CRNS measurements. In the HOAL, we also incorporated in-situ SM from a network of point-scale time-domain-transmissivity sensors distributed within the CRNS footprint. The datasets were compared to each other by calculating correlation metrics. Furthermore, we investigated the effect of vegetation on both the satellite and the CRNS data by analyzing detailed information on crop type distribution and crop water content.</p><p>[1] Bauer-Marschallinger et al., 2018a: https://doi.org/10.1109/TGRS.2018.2858004<br>[2] Bauer-Marschallinger et al., 2018b: https://doi.org/10.3390/rs10071030<br>[3] Wagner et al., 1999: https://doi.org/10.1016/S0034-4257(99)00036-X</p>

scholarly journals Observed and modelled ecosystem respiration and gross primary production of a grassland in southwestern France

2010 ◽  
Vol 7 (1) ◽  
pp. 429-462 ◽  
Author(s):  
C. Albergel ◽  
J.-C. Calvet ◽  
A.-L. Gibelin ◽  
S. Lafont ◽  
J.-L. Roujean ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Primary Production ◽  
Root Zone ◽  
Ecosystem Respiration ◽  
Gross Primary Production ◽  
Net Ecosystem Exchange ◽  
Co2 Flux ◽  
Single Equation ◽  
Complex Model ◽  
Area Index

Abstract. In this work, a simple representation of the soil moisture effect on the ecosystem respiration is implemented into the A-gs version of the Interactions between Soil, Biosphere, and Atmosphere (ISBA) model. It results in an improvement of the modelled CO2 flux over a grassland, in southwestern France. The former temperature-only dependent respiration formulation used in ISBA-A-gs is not able to model the limitation of the respiration under dry conditions. In addition to soil moisture and soil temperature, the only parameter required in this formulation is the ecosystem respiration parameter Re25. It can be estimated by the mean of eddy covariance measurements of turbulent nighttime CO2 flux (i.e. ecosystem respiration). The resulting correlation between observed and modelled net ecosystem exchange is r2=0.63 with a bias of −2.18 μmol m−2 s−1. It is shown that when CO2 observations are not available, it is possible to use a more complex model, able to represent the heterotrophic respiration and all the components of the autotrophic respiration, to estimate Re25 with similar results. The modelled ecosystem respiration estimates are provided by the Carbon Cycle (CC) version of ISBA (ISBA-CC). ISBA-CC is a version of ISBA able to simulate all the respiration components whereas ISBA-A-gs uses a single equation for ecosystem respiration. ISBA-A-gs is easier to handle and more convenient than ISBA-CC for practical use in atmospheric or hydrological models. Surface water and energy flux observations as well as gross primary production (GPP) estimates are compared with model outputs. The dependence of GPP to air temperature is investigated. The observed GPP is less sensitive to temperature than the modelled GPP. Finally, the simulations of the ISBA-A-gs model are analysed over a seven year period (2001–2007). Modelled soil moisture and leaf area index (LAI) are confronted with the observed root-zone soil moisture content (m3 m−3), and with LAI estimates derived from surface reflectance measurements.

scholarly journals Modeling the monthly mean soil-water balance with a statistical-dynamical ecohydrology model as coupled to a two-component canopy model

2010 ◽  
Vol 14 (10) ◽  
pp. 2099-2120 ◽  
Author(s):  
J. P. Kochendorfer ◽  
J. A. Ramírez
Keyword(s):  
Soil Moisture ◽  
Water Stress ◽  
Water Balance ◽  
Soil Water ◽  
Soil Texture ◽  
Root Zone ◽  
Soil Water Balance ◽  
Area Index ◽  
Two Component ◽  
Canopy Model

Abstract. The statistical-dynamical annual water balance model of Eagleson (1978) is a pioneering work in the analysis of climate, soil and vegetation interactions. This paper describes several enhancements and modifications to the model that improve its physical realism at the expense of its mathematical elegance and analytical tractability. In particular, the analytical solutions for the root zone fluxes are re-derived using separate potential rates of transpiration and bare-soil evaporation. Those potential rates, along with the rate of evaporation from canopy interception, are calculated using the two-component Shuttleworth-Wallace (1985) canopy model. In addition, the soil column is divided into two layers, with the upper layer representing the dynamic root zone. The resulting ability to account for changes in root-zone water storage allows for implementation at the monthly timescale. This new version of the Eagleson model is coined the Statistical-Dynamical Ecohydrology Model (SDEM). The ability of the SDEM to capture the seasonal dynamics of the local-scale soil-water balance is demonstrated for two grassland sites in the US Great Plains. Sensitivity of the results to variations in peak green leaf area index (LAI) suggests that the mean peak green LAI is determined by some minimum in root zone soil moisture during the growing season. That minimum appears to be close to the soil matric potential at which the dominant grass species begins to experience water stress and well above the wilting point, thereby suggesting an ecological optimality hypothesis in which the need to avoid water-stress-induced leaf abscission is balanced by the maximization of carbon assimilation (and associated transpiration). Finally, analysis of the sensitivity of model-determined peak green LAI to soil texture shows that the coupled model is able to reproduce the so-called "inverse texture effect", which consists of the observation that natural vegetation in dry climates tends to be most productive in sandier soils despite their lower water holding capacity. Although the determination of LAI based on complete or near-complete utilization of soil moisture is not a new approach in ecohydrology, this paper demonstrates its use for the first time with a new monthly statistical-dynamical model of the water balance. Accordingly, the SDEM provides a new framework for studying the controls of soil texture and climate on vegetation density and evapotranspiration.

scholarly journals Suitability of modelled and remotely sensed essential climate variables for monitoring Euro-Mediterranean droughts

2014 ◽  
Vol 7 (3) ◽  
pp. 931-946 ◽  
Author(s):  
C. Szczypta ◽  
J.-C. Calvet ◽  
F. Maignan ◽  
W. Dorigo ◽  
F. Baret ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Interannual Variability ◽  
Land Surface ◽  
Root Zone ◽  
Mediterranean Area ◽  
Remotely Sensed ◽  
Climatic Conditions ◽  
Leaf Life Span ◽  
Moisture Profile ◽  
Area Index

Abstract. Two new remotely sensed leaf area index (LAI) and surface soil moisture (SSM) satellite-derived products are compared with two sets of simulations of the ORganizing Carbon and Hydrology In Dynamic EcosystEms (ORCHIDEE) and Interactions between Soil, Biosphere and Atmosphere, CO2-reactive (ISBA-A-gs) land surface models. We analyse the interannual variability over the period 1991–2008. The leaf onset and the length of the vegetation growing period (LGP) are derived from both the satellite-derived LAI and modelled LAI. The LGP values produced by the photosynthesis-driven phenology model of ISBA-A-gs are closer to the satellite-derived LAI and LGP than those produced by ORCHIDEE. In the latter, the phenology is based on a growing degree day model for leaf onset, and on both climatic conditions and leaf life span for senescence. Further, the interannual variability of LAI is better captured by ISBA-A-gs than by ORCHIDEE. In order to investigate how recent droughts affected vegetation over the Euro-Mediterranean area, a case study addressing the summer 2003 drought is presented. It shows a relatively good agreement of the modelled LAI anomalies with the observations, but the two models underestimate plant regrowth in the autumn. A better representation of the root-zone soil moisture profile could improve the simulations of both models. The satellite-derived SSM is compared with SSM simulations of ISBA-A-gs only, as ORCHIDEE has no explicit representation of SSM. Overall, the ISBA-A-gs simulations of SSM agree well with the satellite-derived SSM and are used to detect regions where the satellite-derived product could be improved. Finally, a correspondence is found between the interannual variability of detrended SSM and LAI. The predictability of LAI is less pronounced using remote sensing observations than using simulated variables. However, consistent results are found in July for the croplands of the Ukraine and southern Russia.

scholarly journals Modeling the monthly mean soil-water balance with a statistical-dynamical ecohydrology model as coupled to a two-component canopy model

2008 ◽  
Vol 5 (2) ◽  
pp. 579-648
Author(s):  
J. P. Kochendorfer ◽  
J. A. Ramírez
Keyword(s):  
Soil Moisture ◽  
Water Stress ◽  
Water Balance ◽  
Soil Water ◽  
Soil Texture ◽  
Root Zone ◽  
Soil Water Balance ◽  
Area Index ◽  
Two Component ◽  
Canopy Model

Abstract. The statistical-dynamical annual water balance model of Eagleson (1978) is a pioneering work in the analysis of climate, soil and vegetation interactions. This paper describes several enhancements and modifications to the model that improve its physical realism at the expense of its mathematical elegance and analytical tractability. In particular, the analytical solutions for the root zone fluxes are re-derived using separate potential rates of transpiration and bare-soil evaporation. Those potential rates, along with the rate of evaporation from canopy interception, are calculated using the two-component Shuttleworth-Wallace (1985) canopy model. In addition, the soil column is divided into two layers, with the upper layer representing the dynamic root zone. The resulting ability to account for changes in root-zone water storage allows for implementation at the monthly timescale. This new version of the Eagleson model is coined the Statistical-Dynamical Ecohydrology Model (SDEM). The ability of the SDEM to capture the seasonal dynamics of the local-scale soil-water balance is demonstrated for two grassland sites in the US Great Plains. Sensitivity of the results to variations in peak green Leaf Area Index (LAI) suggests that the mean peak green LAI is determined by some minimum in root zone soil moisture during the growing season. That minimum appears to be close to the soil matric potential at which the dominant grass species begins to experience water stress and well above the wilting point, thereby suggesting an ecological optimality hypothesis in which the need to avoid water-stress-induced leaf abscission is balanced by the maximization of carbon assimilation (and associated transpiration). Finally, analysis of the sensitivity of model-determined peak green LAI to soil texture shows that the coupled model is able to reproduce the so-called "inverse texture effect", which consists of the observation that natural vegetation in dry climates tends to be most productive in sandier soils despite their lower water holding capacity. Although the determination of LAI based on near-complete utilization of soil moisture is not a new approach in ecohydrology, this paper demonstrates its use for the first time with a new monthly statistical-dynamical model of the water balance. Accordingly, the SDEM provides a new framework for studying the controls of soil texture and climate on vegetation density and evapotranspiration.

scholarly journals Analysis of surface and root-zone soil moisture dynamics with ERS scatterometer and the hydrometeorological model SAFRAN-ISBA-MODCOU at Grand Morin watershed (France)

2008 ◽  
Vol 5 (4) ◽  
pp. 1903-1926 ◽  
Author(s):  
T. Paris Anguela ◽  
M. Zribi ◽  
S. Hasenauer ◽  
F. Habets ◽  
C. Loumagne
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Root Zone ◽  
Spatial Scales ◽  
Temporal Variations ◽  
Model Verification ◽  
Atmospheric Conditions ◽  
Soil Moisture Dynamics ◽  
Moisture Dynamics ◽  
Rms Errors

Abstract. Spatial and temporal variations of soil moisture strongly affect flooding, erosion, solute transport and vegetation productivity. Its characterization, offers an avenue to improve our understanding of complex land surface–atmosphere interactions. In this paper, soil moisture dynamics at soil surface (first centimeters) and root-zone (up to 1.5 m depth) are investigated at three spatial scales: local scale (field measurements), 8×8 km2 (hydrological model) and 25×25 km2 scale (ERS scatterometer) in a French watershed. This study points out the quality of surface and root-zone soil moisture data for SIM model and ERS scatterometer for a three year period. Surface soil moisture is highly variable because is more influenced by atmospheric conditions (rain, wind and solar radiation), and presents RMS errors up to 0.08 m3 m−3. On the other hand, root-zone moisture presents lower variability with small RMS errors (between 0.02 and 0.06 m3 m-3). These results will contribute to satellite and model verification of moisture, but also to better application of radar data for data assimilation in future.

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