Calibration of a large-scale hydrological model using satellite-based soil moisture and evapotranspiration products

Abstract. A considerable number of river basins around the world lack sufficient ground observations of hydro-meteorological data for effective water resources assessment and management. Several approaches can be developed to increase the quality and availability of data in these poorly gauged or ungauged river basins, and among those, the use of earth observations products has recently become promising. Earth observations of various environmental variables can be used potentially to increase the knowledge about the hydrological processes in the basin and to improve streamflow model estimates, via assimilation or calibration. The present study aims to calibrate the large-scale hydrological model PCR-GLOBWB using satellite-based products of evapotranspiration and soil moisture for the Moroccan Oum Er Rbia basin. Daily simulations at a spatial resolution of 5 arcmin × 5 arcmin are performed with varying parameters values for the 32-year period 1979–2010. Five different calibration scenarios are inter-compared: (i) reference scenario using the hydrological model with the standard parameterization, (ii) calibration using in-situ observed discharge time series, (iii) calibration using GLEAM actual evapotranspiration time series, (iv) calibration using ESA CCI surface soil moisture time series and (v) step-wise calibration using GLEAM actual evapotranspiration and ESA CCI surface soil moisture time series. The impact on discharge estimates of precipitation in comparison with model parameters calibration is investigated using three global precipitation products, including EI, WFDEI and MSWEP. Results show that GLEAM evapotranspiration and ESA CCI soil moisture may be used for model calibration resulting in reasonable discharge estimates (NSE values from 0.5 to 0.75), although better model performance is achieved when the model is calibrated with in-situ streamflow observations. Independent calibration based on only evapotranspiration or soil moisture observations improves model predictions to a lesser extent. Precipitation input affects to discharge estimates more than calibrating model parameters. The use of WFDEI precipitation leads to the lowest model performances. Apart from the in-situ discharge calibration scenario, the highest discharge improvement is obtained when EI and MSWEP precipitation products are used in combination with a step-wise calibration approach based on evapotranspiration and soil moisture observations. This study opens up the possibility to use globally available earth observations and reanalysis products of precipitation, evapotranspiration and soil moisture into large-scale hydrological models to estimate discharge at a river basin scale.

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Calibration of a large-scale hydrological model using satellite-based soil moisture and evapotranspiration products

Hydrology and Earth System Sciences ◽

10.5194/hess-21-3125-2017 ◽

2017 ◽

Vol 21 (6) ◽

pp. 3125-3144 ◽

Cited By ~ 47

Author(s):

Patricia López López ◽

Edwin H. Sutanudjaja ◽

Jaap Schellekens ◽

Geert Sterk ◽

Marc F. P. Bierkens

Keyword(s):

Time Series ◽

Soil Moisture ◽

Large Scale ◽

Hydrological Model ◽

Surface Soil ◽

Reanalysis Data ◽

Model Parameters ◽

Surface Soil Moisture ◽

Earth Observations

Abstract. A considerable number of river basins around the world lack sufficient ground observations of hydro-meteorological data for effective water resources assessment and management. Several approaches can be developed to increase the quality and availability of data in these poorly gauged or ungauged river basins; among them, the use of Earth observations products has recently become promising. Earth observations of various environmental variables can be used potentially to increase knowledge about the hydrological processes in the basin and to improve streamflow model estimates, via assimilation or calibration. The present study aims to calibrate the large-scale hydrological model PCRaster GLOBal Water Balance (PCR-GLOBWB) using satellite-based products of evapotranspiration and soil moisture for the Moroccan Oum er Rbia River basin. Daily simulations at a spatial resolution of 5  ×  5 arcmin are performed with varying parameters values for the 32-year period 1979–2010. Five different calibration scenarios are inter-compared: (i) reference scenario using the hydrological model with the standard parameterization, (ii) calibration using in situ-observed discharge time series, (iii) calibration using the Global Land Evaporation Amsterdam Model (GLEAM) actual evapotranspiration time series, (iv) calibration using ESA Climate Change Initiative (CCI) surface soil moisture time series and (v) step-wise calibration using GLEAM actual evapotranspiration and ESA CCI surface soil moisture time series. The impact on discharge estimates of precipitation in comparison with model parameters calibration is investigated using three global precipitation products, including ERA-Interim (EI), WATCH Forcing methodology applied to ERA-Interim reanalysis data (WFDEI) and Multi-Source Weighted-Ensemble Precipitation data by merging gauge, satellite and reanalysis data (MSWEP). Results show that GLEAM evapotranspiration and ESA CCI soil moisture may be used for model calibration resulting in reasonable discharge estimates (NSE values from 0.5 to 0.75), although better model performance is achieved when the model is calibrated with in situ streamflow observations. Independent calibration based on only evapotranspiration or soil moisture observations improves model predictions to a lesser extent. Precipitation input affects discharge estimates more than calibrating model parameters. The use of WFDEI precipitation leads to the lowest model performances. Apart from the in situ discharge calibration scenario, the highest discharge improvement is obtained when EI and MSWEP precipitation products are used in combination with a step-wise calibration approach based on evapotranspiration and soil moisture observations. This study opens up the possibility of using globally available Earth observations and reanalysis products of precipitation, evapotranspiration and soil moisture in large-scale hydrological models to estimate discharge at a river basin scale.

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A performance assessment method for SAR satellite-derived surface soil moisture data using a soil-water balance model, meteorological observations, and soil pedotransfer functions.

10.5194/egusphere-egu2020-3387 ◽

2020 ◽

Author(s):

John Beale ◽

Toby Waine ◽

Ronald Corstanje ◽

Jonathan Evans

Keyword(s):

Regression Analysis ◽

Soil Moisture ◽

Water Balance ◽

Surface Soil ◽

Cosmic Ray ◽

Water Balance Model ◽

Balance Model ◽

Model Parameters ◽

Surface Soil Moisture

The validation of surface soil moisture products derived from SAR satellites data is challenged by the difficulty of reliably measuring in-situ soil moisture at shallow soil depths of a few centimetres, consistent with the penetration depth of the microwave beam. Our analysis shows that the apparent accuracy of the remote sensing products is underestimated by comparison with inconsistent probe data or measurements at greater soil depths. Our alternative approach uses in-situ meteorological measurements to determine rainfall and potential evapotranspiration, to be used with soil hydrological properties as inputs to a water balance model to estimate surface soil moisture independently of the satellite data. In-situ soil moisture measurements are used to validate and refine the model parameters. The choice of appropriate soil hydrological parameters with which to convert remotely sensed surface soil moisture indices to volumetric moisture content is shown to have a significant impact on the bias and offset in the regression analysis. To illustrate this, Copernicus SSM data is analysed by this method for a number of COSMOS-UK soil moisture monitoring sites, showing a significant improvement in the coefficient of determination, bias and offset over regression analysis using in-situ measurements from soil moisture probes or the cosmic ray soil moisture sensor itself. This will benefit users of such products in agriculture, for example, in determining actual soil moisture deficit.

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Combining satellite radar altimetry, SAR surface soil moisture and GRACE total storage changes for hydrological model calibration in a large poorly gauged catchment

Hydrology and Earth System Sciences ◽

10.5194/hess-15-1729-2011 ◽

2011 ◽

Vol 15 (6) ◽

pp. 1729-1743 ◽

Cited By ~ 65

Author(s):

C. Milzow ◽

P. E. Krogh ◽

P. Bauer-Gottwein

Keyword(s):

Soil Moisture ◽

Model Calibration ◽

Hydrological Model ◽

Surface Soil ◽

Remote Sensing Data ◽

Model Parameters ◽

Altimetry Data ◽

Surface Soil Moisture ◽

Radar Altimetry ◽

Condition Model

Abstract. The availability of data is a major challenge for hydrological modelling in large parts of the world. Remote sensing data can be exploited to improve models of ungauged or poorly gauged catchments. In this study we combine three datasets for calibration of a rainfall-runoff model of the poorly gauged Okavango catchment in Southern Africa: (i) surface soil moisture (SSM) estimates derived from radar measurements onboard the Envisat satellite; (ii) radar altimetry measurements by Envisat providing river stages in the tributaries of the Okavango catchment, down to a minimum river width of about one hundred meters; and (iii) temporal changes of the Earth's gravity field recorded by the Gravity Recovery and Climate Experiment (GRACE) caused by total water storage changes in the catchment. The SSM data are shown to be helpful in identifying periods with over-respectively underestimation of the precipitation input. The accuracy of the radar altimetry data is validated on gauged subbasins of the catchment and altimetry data of an ungauged subbasin is used for model calibration. The radar altimetry data are important to condition model parameters related to channel morphology such as Manning's roughness. GRACE data are used to validate the model and to condition model parameters related to various storage compartments in the hydrological model (e.g. soil, groundwater, bank storage etc.). As precipitation input the FEWS-Net RFE, TRMM 3B42 and ECMWF ERA-Interim datasets are considered and compared.

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Thermal Inertia Approach Using a Heat Budget Model to Estimate the Spatial Distribution of Surface Soil Moisture over a Semiarid Grassland in Central Mongolia

Journal of Hydrometeorology ◽

10.1175/jhm-d-17-0040.1 ◽

2018 ◽

Vol 19 (1) ◽

pp. 245-265 ◽

Cited By ~ 7

Author(s):

Dai Matsushima ◽

Jun Asanuma ◽

Ichirow Kaihotsu

Keyword(s):

Time Series ◽

Soil Moisture ◽

Surface Soil ◽

Heat Budget ◽

Atmospheric Stability ◽

Thermal Inertia ◽

In Situ Observation ◽

Surface Soil Moisture ◽

Budget Model

Abstract Thermal inertia is a physical parameter that evaluates soil thermal properties with an emphasis on the stability of the temperature when the soil is affected by heating/cooling. Thermal inertia can be retrieved from a heat budget formulation as a parameter when the time series of Earth surface temperature and forcing variables, such as insolation and air temperature, are given. In this study, a two-layer, linearized heat budget model was employed for the retrieval of thermal inertia over a grassland in a semiarid region. Application of different formulations to the aerodynamic conductance with respect to atmospheric stability significantly improved the accuracy of the thermal inertia retrieval. The retrieved values of thermal inertia were well correlated with in situ surface soil moisture at multiple ground stations. The daily time series of thermal inertia–derived soil moisture qualitatively agreed well with in situ soil moisture after antecedent rainfalls, which was found after fitting the time series to an exponentially decaying function. On the contrary, AMSR2 soil moisture mostly did not agree with in situ soil moisture. The results of the estimation showed high accuracy: the root-mean-square error was 0.038 m3 m−3 compared to the in situ data and was applied to an area of 2° × 2° in which the in situ observation locations were included. The spatiotemporal distribution of surface soil moisture was mapped at a 0.03° × 0.03° spatial resolution in the study area as 10- or 11-day averages over a vegetation growth period of 2012.

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Combining time series of Sentinel-1 and -2 with in-situ data for estimating soil moisture at crop field scale

10.5194/egusphere-egu2020-13647 ◽

2020 ◽

Author(s):

Marc Padilla ◽

Ana Pérez ◽

Mirta Pinilla

Keyword(s):

Time Series ◽

Soil Moisture ◽

Surface Soil ◽

Correlation Coefficients ◽

Field Size ◽

Model Parameters ◽

Field Scale ◽

In Situ Data ◽

Crop Field

Soil moisture is one of the key variables for crop modeling and scheduling farm operations. Current available soil moisture products are generated at global or regional scales and its spatial resolution (~1km2) is usually too coarse for common small farms. Within the framework of the EU Horizon2020 funded TWIGA project, we intended to provide improved soil moisture estimates at crop field scale. The advantage of focusing at the scale of a single crop is that the algorithm selection can be based more on the retrieval accuracy rather than on the computing performance.Time series of Sentinel-1 SAR backscatter (at VH and VV polarizations) and Sentinel-2 NDVI observations, on each crop field, were assimilated with a semiempirical polarimetric backscattering model for bare soil surfaces (Oh) coupled with a Water Cloud model (WCM). Some of the model parameters are the actual variables of interest to be estimated, in our case the daily surface soil moistures. They were estimated by a Bayesian inversion approach. The key advantage of using WCM, is that the effects of vegetation on backscatter can be taken into account, and therefore soil moisture estimates are available even when vegetation is present. The empirical model parameters (surface roughness, and A and B parameters of WCM) were calibrated with in-situ data from four stations in Ghana, with observations every 30 minutes from May to October 2019 at 10 cm depth. The calibration was based on a hierarchical Bayesian regression, to take into account that model parameter distributions might vary across land cover types and across in-situ stations themselves. The validation was based on the comparison between the soil moisture observations of one in-situ station and estimates from the model couple calibrated with the data from the other three in-situ stations. That procedure was repeated for each station. Correlation coefficients were above 0.64 and root mean square error bellow 0.065 m3/m3 in two out of the four stations. Accuracy tended to be dependent on field size, due to the well known SAR speckle noise. The station with the lowest accuracy was locate on a 30x30m2 field. Accuracy was additionally affected by likely sudden changes on the surface soil or vegetation during the analysis time windows. Correlation coefficients were higher (~0.85) on the time periods without such sudden changes.Given the results of the current study, we would recommend that the location of eventual future in-situ stations should be preferably placed on larger fields, larger than 30x30m2. Further research would be needed to improve the model and understand better its limitations for an eventual operational implementation.&#160;

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Estimation of surface soil moisture in alpine areas based on medium spatial resolution SAR time-series and upscaled in-situ measurements

10.1117/12.2071252 ◽

2014 ◽

Cited By ~ 1

Author(s):

F. Greifeneder ◽

C. Notarnicola ◽

G. Cuozzo ◽

G. Bertoldi ◽

S. Della Chiesa ◽

...

Keyword(s):

Time Series ◽

Soil Moisture ◽

Spatial Resolution ◽

Surface Soil ◽

In Situ Measurements ◽

Surface Soil Moisture

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Combining Sentinel-1 and ALOS-2 observations for soil moisture retrieval

10.5194/egusphere-egu21-8660 ◽

2021 ◽

Author(s):

Anna Balenzano ◽

Giuseppe Satalino ◽

Francesco Lovergine ◽

Davide Palmisano ◽

Francesco Mattia ◽

...

Keyword(s):

Time Series ◽

Soil Moisture ◽

Temporal Resolution ◽

Surface Soil ◽

Earth Science ◽

Observation System ◽

Final Report ◽

Surface Soil Moisture ◽

Error Characterization ◽

L Band

One of the limitations of presently available Synthetic Aperture Radar (SAR) surface soil moisture (SSM) products is their moderated temporal resolution (e.g., 3-4 days) that is non optimal for several applications, as most user requirements point to a temporal resolution of 1-2 days or less. A possible path to tackle this issue is to coordinate multi-mission SAR acquisitions with a view to the future Copernicus Sentinel-1 (C&D and Next Generation) and L-band Radar Observation System for Europe (ROSE-L).In this respect, the recent agreement between the Japanese (JAXA) and European (ESA) Space Agencies on the use of SAR Satellites in Earth Science and Applications provides a framework to develop and validate multi-frequency and multi-platform SAR SSM products. In 2019 and 2020, to support insights on the interoperability between C- and L-band SAR observations for SSM retrieval, Sentinel-1 and ALOS-2 systematic acquisitions over the TERENO (Terrestrial Environmental Observatories) Selhausen (Germany) and Apulian Tavoliere (Italy) cal/val sites were gathered. Both sites are well documented and equipped with hydrologic networks.The objective of this study is to investigate the integration of multi-frequency SAR measurements for a consistent and harmonized SSM retrieval throughout the error characterization of a combined C- and L-band SSM product. To this scope, time series of Sentinel-1 IW and ALOS-2 FBD data acquired over the two sites will be analysed. The short time change detection (STCD) algorithm, developed, implemented and recently assessed on Sentinel-1 data [e.g., Balenzano et al., 2020; Mattia et al., 2020], will be tailored to the ALOS-2 data. Then, the time series of SAR SSM maps from each SAR system will be derived separately and aggregated in an interleaved SSM product. Furthermore, it will be compared against in situ SSM data systematically acquired by the ground stations deployed at both sites. The study will assess the interleaved SSM product and evaluate the homogeneous quality of C- and L-band SAR SSM maps.&#160;&#160;ReferencesBalenzano. A., et al., &#8220;Sentinel-1 soil moisture at 1km resolution: a validation study&#8221;, submitted to Remote Sensing of Environment (2020).Mattia, F., A. Balenzano, G. Satalino, F. Lovergine, A. Loew, et al., &#8220;ESA SEOM Land project on Exploitation of Sentinel-1 for Surface Soil Moisture Retrieval at High Resolution,&#8221; final report, contract number 4000118762/16/I-NB, 2020.

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From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations

Hydrology and Earth System Sciences ◽

10.5194/hess-12-1323-2008 ◽

2008 ◽

Vol 12 (6) ◽

pp. 1323-1337 ◽

Cited By ~ 261

Author(s):

C. Albergel ◽

C. Rüdiger ◽

T. Pellarin ◽

J.-C. Calvet ◽

N. Fritz ◽

...

Keyword(s):

Soil Moisture ◽

Soil Water ◽

Surface Soil ◽

Root Zone ◽

Synthetic Data ◽

Surface Soil Moisture ◽

Data Set ◽

Water Index ◽

In Situ Observations

Abstract. A long term data acquisition effort of profile soil moisture is under way in southwestern France at 13 automated weather stations. This ground network was developed in order to validate remote sensing and model soil moisture estimates. In this paper, both those in situ observations and a synthetic data set covering continental France are used to test a simple method to retrieve root zone soil moisture from a time series of surface soil moisture information. A recursive exponential filter equation using a time constant, T, is used to compute a soil water index. The Nash and Sutcliff coefficient is used as a criterion to optimise the T parameter for each ground station and for each model pixel of the synthetic data set. In general, the soil water indices derived from the surface soil moisture observations and simulations agree well with the reference root-zone soil moisture. Overall, the results show the potential of the exponential filter equation and of its recursive formulation to derive a soil water index from surface soil moisture estimates. This paper further investigates the correlation of the time scale parameter T with soil properties and climate conditions. While no significant relationship could be determined between T and the main soil properties (clay and sand fractions, bulk density and organic matter content), the modelled spatial variability and the observed inter-annual variability of T suggest that a weak climate effect may exist.

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