Soil moisture sensor network design for hydrological applications

Abstract. Soil moisture plays an important role in the partitioning of rainfall into evapotranspiration, infiltration, and runoff, hence a vital state variable in hydrological modelling. However, due to the heterogeneity of soil moisture in space, most existing in situ observation networks rarely provide sufficient coverage to capture the catchment-scale soil moisture variations. Clearly, there is a need to develop a systematic approach for soil moisture network design, so that with the minimal number of sensors the catchment spatial soil moisture information could be captured accurately. In this study, a simple and low-data requirement method is proposed. It is based on principal component analysis (PCA) for the investigation of the network redundancy degree and K-means cluster analysis (CA) and a selection of statistical criteria for the determination of the optimal sensor number and placements. Furthermore, the long-term (10-year) 5 km surface soil moisture datasets estimated through the advanced Weather Research and Forecasting (WRF) model are used as the network design inputs. In the case of the Emilia-Romagna catchment, the results show the proposed network is very efficient in estimating the catchment-scale surface soil moisture (i.e. with NSE and r at 0.995 and 0.999, respectively, for the areal mean estimation; and 0.973 and 0.990, respectively, for the areal standard deviation estimation). To retain 90 % variance, a total of 50 sensors in a 22 124 km2 catchment is needed, and in comparison with the original number of WRF grids (828 grids), the designed network requires significantly fewer sensors. However, refinements and investigations are needed to further improve the design scheme, which are also discussed in the paper.

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Soil Moisture Sensor Network Design for Hydrological Applications

10.5194/hess-2020-24 ◽

2020 ◽

Author(s):

Lu Zhuo ◽

Qiang Dai ◽

Binru Zhao ◽

Dawei Han

Keyword(s):

Soil Moisture ◽

Network Design ◽

Wrf Model ◽

Principal Component ◽

Optimal Number ◽

In Situ Observation ◽

Catchment Scale ◽

Moisture Sensor ◽

Soil Moisture Sensors ◽

Vital State

Abstract. Soil moisture plays an important role in the partitioning of rainfall into evapotranspiration, infiltration and runoff, hence a vital state variable in the hydrological modelling. However, due to the heterogeneity of soil moisture in space most existing in-situ observation networks rarely provide sufficient coverage to capture the catchment-scale soil moisture variations. Clearly, there is a need to develop a systematic approach for soil moisture network design, so that with the minimal number of sensors the catchment spatial soil moisture information could be captured accurately. In this study, a simple and low-data requirement method is proposed. It is based on the Principal Component Analysis (PCA) and Elbow curve for the determination of the optimal number of soil moisture sensors; and K-means Cluster Analysis (CA) and a selection of statistical criteria for the identification of the sensor placements. Furthermore, the long-term (10-year) soil moisture datasets estimated through the advanced Weather Research and Forecasting (WRF) model are used as the network design inputs. In the case of the Emilia Romagna catchment, the results show the proposed network is very efficient in estimating the catchment-scale soil moisture (i.e., with NSE and r at 0.995 and 0.999, respectively for the areal mean estimation; and 0.973 and 0.990, respectively for the areal standard deviation estimation). To retain 90 % variance, a total of 50 sensors in a 22,124 km2 catchment is needed, which in comparison with the original number of WRF grids (828 grids), the designed network requires significantly fewer sensors. However, refinements and investigations are needed to further improve the design scheme which are also discussed in the paper.

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Soil Moisture Network Design using Advanced Numerical Weather Prediction modelling and Data Mining technology for Hydrological applications

10.5194/egusphere-egu2020-7756 ◽

2020 ◽

Author(s):

Lu Zhuo ◽

Qiang Dai ◽

Dawei Han

Keyword(s):

Soil Moisture ◽

Network Design ◽

Weather Prediction ◽

Wrf Model ◽

Principal Component ◽

Optimal Number ◽

In Situ Observation ◽

Catchment Scale ◽

Soil Moisture Sensors ◽

Vital State

Soil moisture plays an important role in the partitioning of rainfall into evapotranspiration, infiltration and runoff, hence a vital state variable in the hydrological modelling. However, due to the heterogeneity of soil moisture in space most existing in-situ observation networks rarely provide sufficient coverage to capture the catchment-scale soil moisture variations. Clearly, there is a need to develop a systematic approach for soil moisture network design, so that with the minimal number of sensors the catchment spatial soil moisture information could be captured accurately. In this study, a simple and low-data requirement method is proposed. It is based on the Principal Component Analysis (PCA) and Elbow curve for the determination of the optimal number of soil moisture sensors; and K-means Cluster Analysis (CA) and a selection of statistical criteria for the identification of the sensor placements. Furthermore, the long-term (10-year) soil moisture datasets estimated through the advanced Weather Research and Forecasting (WRF) model are used as the network design inputs. In the case of the Emilia Romagna catchment, the results show the proposed network is very efficient in estimating the catchment-scale soil moisture (i.e., with NSE and r at 0.995 and 0.999, respectively for the areal mean estimation; and 0.973 and 0.990, respectively for the areal standard deviation estimation). To retain 90% variance, a total of 50 sensors in a 22,124 km2 catchment is needed, which in comparison with the original number of WRF grids (828 grids), the designed network requires significantly fewer sensors. However, refinements and investigations are needed to further improve the design scheme which are also discussed in the paper.

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Near‐surface soil moisture retrieval from ASAR Wide Swath imagery using a Principal Component Analysis

International Journal of Remote Sensing ◽

10.1080/01431160701442088 ◽

2008 ◽

Vol 29 (10) ◽

pp. 2925-2942 ◽

Cited By ~ 14

Author(s):

X. Kong ◽

S. R. Dorling

Keyword(s):

Principal Component Analysis ◽

Soil Moisture ◽

Surface Soil ◽

Principal Component ◽

Component Analysis ◽

Surface Soil Moisture ◽

Near Surface ◽

Wide Swath

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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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Satellite soil moisture data assimilation for improved operational continental water balance prediction

10.5194/hess-2020-485 ◽

2020 ◽

Author(s):

Siyuan Tian ◽

Luigi J. Renzullo ◽

Robert C. Pipunic ◽

Julien Lerat ◽

Wendy Sharples ◽

...

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Water Balance ◽

Surface Soil ◽

Root Zone ◽

In Situ Observation ◽

Surface Soil Moisture ◽

Time Step ◽

Australian Water

Abstract. A simple and effective two-step data assimilation framework was developed to improve soil moisture representation in an operational large-scale water balance model. The first step is the sequential state updating process that exploits temporal covariance statistics between modelled and satellite-derived soil moisture to produce analysed estimates. The second step is to use analysed surface moisture estimates to impart mass conservation constraints (mass redistribution) on related states and fluxes of the model in a post-analysis adjustment after the state updating at each time step. In this study, we apply the data assimilation framework to the Australian Water Resources Assessment Landscape model (AWRA-L) and evaluate its impact on the model's accuracy against in-situ observations across water balance components. We show that the correlation between simulated surface soil moisture and in-situ observation increases from 0.54 (open-loop) to 0.77 (data assimilation). Furthermore, indirect verification of root-zone soil moisture using remotely sensed vegetation time series across cropland areas results in significant improvements of 0.11 correlation units. The improvements gained from data assimilation can persist for more than one week in surface soil moisture estimates and one month in root-zone soil moisture estimates, thus demonstrating the efficacy of this data assimilation framework.

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Satellite soil moisture data assimilation for improved operational continental water balance prediction

Hydrology and Earth System Sciences ◽

10.5194/hess-25-4567-2021 ◽

2021 ◽

Vol 25 (8) ◽

pp. 4567-4584

Author(s):

Siyuan Tian ◽

Luigi J. Renzullo ◽

Robert C. Pipunic ◽

Julien Lerat ◽

Wendy Sharples ◽

...

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Water Balance ◽

Surface Soil ◽

Root Zone ◽

In Situ Observation ◽

Surface Soil Moisture ◽

Time Step ◽

Australian Water

Abstract. A simple and effective two-step data assimilation framework was developed to improve soil moisture representation in an operational large-scale water balance model. The first step is a Kalman-filter-type sequential state updating process that exploits temporal covariance statistics between modelled and satellite-derived soil moisture to produce analysed estimates. The second step is to use analysed surface moisture estimates to impart mass conservation constraints (mass redistribution) on related states and fluxes of the model using tangent linear modelling theory in a post-analysis adjustment after the state updating at each time step. In this study, we assimilate satellite soil moisture retrievals from both Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) missions simultaneously into the Australian Water Resources Assessment Landscape model (AWRA-L) using the proposed framework and evaluate its impact on the model's accuracy against in situ observations across water balance components. We show that the correlation between simulated surface soil moisture and in situ observation increases from 0.54 (open loop) to 0.77 (data assimilation). Furthermore, indirect verification of root-zone soil moisture using remotely sensed Enhanced Vegetation Index (EVI) time series across cropland areas results in significant improvements from 0.52 to 0.64 in correlation. The improvements gained from data assimilation can persist for more than 1 week in surface soil moisture estimates and 1 month in root-zone soil moisture estimates, thus demonstrating the efficacy of this data assimilation framework.

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Analysis of spatial variability of near-surface soil moisture to increase rainfall-runoff modelling accuracy in SW Hungary

Open Geosciences ◽

10.1515/geo-2015-0017 ◽

2015 ◽

Vol 7 (1) ◽

Cited By ~ 2

Author(s):

P. Hegedüs ◽

S. Czigány ◽

E. Pirkhoffer ◽

L. Balatonyi ◽

R. Hickey

Keyword(s):

Soil Moisture ◽

Surface Soil ◽

Watershed Scale ◽

Surface Soil Moisture ◽

Near Surface ◽

Moisture Sensor ◽

Soil Moisture Sensor ◽

Soil Moisture Data ◽

Data Points ◽

Monitoring Stations

AbstractBetween September 5, 2008 and September 5, 2009, near-surface soil moisture time series were collected in the northern part of a 1.7 km2 watershed in SWHungary at 14 monitoring locations using a portable TDR-300 soil moisture sensor. The objectives of this study are to increase the accuracy of soil moisture measurement at watershed scale, to improve flood forecasting accuracy, and to optimize soil moisture sensor density.According to our results, in 10 of 13 cases, a strong correlation exists between the measured soil moisture data of Station 5 and all other monitoring stations; Station 5 is considered representative for the entire watershed. Logically, the selection of the location of the representative measurement point(s) is essential for obtaining representative and accurate soil moisture values for the given watershed. This could be done by (i) employing monitoring stations of higher number at the exploratory phase of the monitoring, (ii) mapping soil physical properties at watershed scale, and (iii) running cross-relational statistical analyses on the obtained data.Our findings indicate that increasing the number of soil moisture data points available for interpolation increases the accuracy of watershed-scale soil moisture estimation. The data set used for interpolation (and estimation of mean antecedent soil moisture values) could be improved (thus, having a higher number of data points) by selecting points of similar properties to the measurement points from the DEM and soil databases. By using a higher number of data points for interpolation, both interpolation accuracy and spatial resolution have increased for the measured soil moisture values for the Pósa Valley.

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Catchment-scale connection between vegetation accessible storage and satellite-derived Soil Water Index

10.5194/egusphere-egu2020-6917 ◽

2020 ◽

Cited By ~ 1

Author(s):

Laurène Bouaziz ◽

Susan Steele-Dunne ◽

Jaap Schellekens ◽

Albrecht Weerts ◽

Jasper Stam ◽

...

Keyword(s):

Soil Moisture ◽

Soil Water ◽

Surface Soil ◽

Root Zone ◽

Water Storage ◽

Surface Soil Moisture ◽

Catchment Scale ◽

Near Surface ◽

Water Index ◽

Optimal Values

Estimates of water volumes stored in the root-zone of vegetation are a key element controlling the hydrological response of a catchment. Remotely-sensed soil moisture products are available globally. However, they are representative of the upper-most few centimeters of the soil. For reliable runoff predictions, we are interested in root-zone soil moisture estimates as they regulate the partitioning of precipitation to drainage and evaporation. The Soil Water Index approximates root-zone soil moisture from near-surface soil moisture and requires a single parameter representing the characteristic time length T of temporal soil moisture variability. Climate and soil properties are typically assumed to influence estimates of T, however, no clear quantitative link has yet been established and often a standard value of 20 days is assumed. In this study, we hypothesize that optimal T values are linked to the accumulated difference between precipitation (water supply) and evaporation (atmospheric water demand) during dry periods with return periods of 20 years, and, thus, to catchment-scale vegetation-accessible water storage capacities. We identify the optimal values of T that provide an adequate match between estimated SWI from several satellite-based near-surface soil moisture products (derived from AMSR2, SMAP and Sentinel-1) and modeled time series of root-zone soil moisture from a calibrated process-based model in 16 contrasting catchments of the Meuse river basin. We found that optimal values of T vary between 1 and 98 days with a median of 17 days across the studied catchments and soil moisture products. We furthermore show that T, which was previously known to increase with increasing depth of the soil layer, is positively and strongly related with catchment-scale root-zone water storage capacity, estimated based on long-term water balance data. &#160;This is useful to generate estimates of root-zone soil moisture from satellite-based surface soil moisture, as they are a key control of the response of hydrological systems.

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