scholarly journals Root-zone soil moisture estimation using data-driven methods

2014 ◽  
Vol 50 (4) ◽  
pp. 2946-2962 ◽  
Author(s):  
Kurt C. Kornelsen ◽  
Paulin Coulibaly
Keyword(s):  
Soil Moisture ◽  
Root Zone ◽  
Data Driven ◽  
Using Data ◽  
Moisture Estimation

Soil Moisture Initialization Input Scale Effect on Parameter Value Identification of a Physically Based Distributed Hydrologic Modelling

2020 ◽  
Author(s):  
Nawa Raj Pradhan ◽  
Steven Brown ◽  
Ian Floyd
Keyword(s):  
Soil Moisture ◽  
Vegetation Index ◽  
Hydrological Model ◽  
Root Zone ◽  
Soil Column ◽  
Estimation Method ◽  
Model Parameters ◽  
Analysis Model ◽  
Soil Moisture Data ◽  
Moisture Estimation

<p>Data acquisition and an efficient processing method for hydrological model initialization, such as soil moisture, and parameter value identification are critical for a physics based distributed watershed modelling of flood and flood related disasters such as sediment and debris flow. Site measurements can provide relatively accurate estimates of soil moisture, but such techniques are limited due to the need for a variety of measurement accessories, which are difficult to obtain to cover a large area sufficiently. Available satellite-based digital soil moisture data is at 9 kilometers to 50 kilometers in resolution which completely filters the soil moisture details at the hill slope scale. Moreover, available satellite-based digital soil moisture data represents only a few centimeters of the top soil column that informs nothing about the effective root-zone wetness. A recently developed soil moisture estimation method called SERVES (Soil moisture Estimation of Root zone through Vegetation index-based Evapotranspiration fraction and Soil properties) overcomes this limitation of satellite-based soil moisture data by estimating distributed root zone soil moisture at 30 meter resolution. In this study, a distributed watershed hydrological model of a sub-catchment of Reynolds Creek Experimental Watershed was developed with GSSHA (Gridded Surface Sub-surface Hydrological Analysis) Model. SERVES soil moisture estimated at 30 meter resolution was deployed in the watershed hydrological parameter value calibration and identification process. The 30 meter resolution SERVES soil moisture data was resampled to 4500 meter and 9000 meter resolutions and was separately employed in the calibrated hydrological model to determine the effect soil moisture resolution  has on the simulated outputs and the model parameters. It was found that the simulated discharge significantly decreased as the initial soil moisture resolution was coarsened. To compensate for this underestimated simulated discharge, the soil hydraulic conductivity value decreased logarithmically with respect to the decreased resolutions. This study will reduce parameter value identification uncertainty especially in flood and soil erosion modelling at multi scale watershed in a changing climate.</p>

scholarly journals Satellite Imagery-Based SERVES Soil Moisture for the Analysis of Soil Moisture Initialization Input Scale Effects on Physics-Based Distributed Watershed Hydrologic Modelling

2020 ◽  
Vol 12 (13) ◽  
pp. 2108
Author(s):  
Nawa Raj Pradhan ◽  
Ian Floyd ◽  
Stephen Brown
Keyword(s):  
Soil Moisture ◽  
Vegetation Index ◽  
Hydrological Model ◽  
Root Zone ◽  
Scale Effects ◽  
Distributed Simulation ◽  
Soil Column ◽  
Estimation Method ◽  
Soil Moisture Data ◽  
Moisture Estimation

Data acquisition and an efficient processing method for hydrological model initialization, such as soil moisture and parameter value identification are critical for a physics-based distributed watershed modelling of flood and flood related disasters such as sediment and debris flow. Site measurements can provide accurate estimates of soil moisture, but such techniques are limited due to the number of physical sensors required to cover a large area effectively. Available satellite-based digital soil moisture data ranges from 9 km to 20 km in resolution which obscures the soil moisture details of a hill slope scale. This resolution limitation of available satellite-based distributed soil moisture data has impacted critical analysis of soil moisture resolution variance on physics-based distributed simulation results. Moreover, available satellite-based digital soil moisture data represents only a few centimeters of the top soil column and that would inform little about the effective root-zone wetness. A recently developed soil moisture estimation method called SERVES (Soil moisture Estimation of Root zone through Vegetation index-based Evapotranspiration fraction and Soil properties) overcomes this limitation of satellite-based soil moisture data by estimating distributed effective root zone soil moisture at 30 m resolution. In this study, a distributed watershed hydrological model of a sub-catchment of Reynolds Creek Experimental Watershed was developed with the GSSHA (Gridded Surface Sub-surface Hydrological Analysis) Model. SERVES soil moisture estimated at 30 m resolution was deployed in the watershed hydrological parameter value calibration and identification process. The 30 m resolution SERVES soil moisture data was resampled to 4500 m and 9000 m resolutions and was separately employed in the calibrated hydrological model to determine the soil moisture resolution effect on the model simulated outputs and the model parameter values. It was found that the simulated discharge is underestimated, infiltration rate/volume is overestimated and higher soil moisture state distribution is filtered out as the initial soil moisture resolution was coarsened. To compensate for this disparity in the simulated results, the soil saturated hydraulic conductivity value decreased with respect to the decreased resolutions.

scholarly journals Estimating Root Zone Moisture from Surface Soil Using Limited Data

2017 ◽  
Vol 24 (4) ◽  
pp. 501-516
Author(s):  
Wen-Zhi Zeng ◽  
Guo-Qing Lei ◽  
Hong-Ya Zhang ◽  
Ming-Hai Hong ◽  
Chi Xu ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Soil Moisture ◽  
Linear Regression ◽  
Monte Carlo Simulations ◽  
Linear Regression Model ◽  
Surface Soil ◽  
Root Zone ◽  
Surface Moisture ◽  
Moisture Estimation ◽  
Hydrus 1D

Abstract For estimation of root-zone moisture content from EO-1/Hyperion imagery, surface soil moisture was first predicted by hyperspectral reflectance data using partial least square regression (PLSR) analysis. The textures of more than 300 soil samples extracted from a 900 m × 900 m field site located within the Hetao Irrigation District in China were used to parameterize the HYDRUS-1D numerical model. The study area was spatially discretized into 18,000 compartments (30 m × 30 m × 0.02 m), and Monte Carlo simulations were applied to generate 2000 different soil-particle size distributions for each compartment. Soil hydraulic properties for each realization were determined by application of artificial neural network analysis and used to parameterize HYDRUS-1D to simulate averaged soil-moisture contents within the root zone (0-40 cm) and surface (approximately 0-4 cm). Then the link between surface moisture and root zone was established by use of linear regression analysis, resulting in R and RMSE of 0.38 and 0.03, respectively. Kriging and co-kriging with observed surface moisture, and co-kriging with surface moisture obtained from Hyperion imagery were also used to estimate root-zone moisture. Results indicated that PLSR is a powerful tool for soil moisture estimation from hyperspectral data. Furthermore, co-kriging with observed surface moisture had the highest R (0.41) and linear regression model, and HYDRUS Monte Carlo simulations had a lowest RMSE (0.03) among the four methods. In regions that have similar climatic and soil conditions to our study area, a linear regression model with HYDRUS Monte Carlo simulations is a practical method for root-zone moisture estimation before sowing and it can be easily coupled with remote sensing technology.

scholarly journals Fuzzy and process modelling of contour ridge water dynamics

2018 ◽  
Vol 378 ◽  
pp. 3-10
Author(s):  
Alexander Mhizha ◽  
John Ndiritu
Keyword(s):  
Fuzzy Logic ◽  
Soil Moisture ◽  
Rainwater Harvesting ◽  
Root Zone ◽  
Process Modelling ◽  
Water Dynamics ◽  
Field Scale ◽  
Model Soil ◽  
Distributed Process ◽  
Using Data

Abstract. Contour ridges are an in-situ rainwater harvesting technology developed initially for soil erosion control but are currently also widely promoted for rainwater harvesting. The effectiveness of contour ridges depends on geophysical, hydro-climatic and socio economic factors that are highly varied in time and space. Furthermore, field-scale data on these factors are often unavailable. This together with the complexity of hydrological processes at field scale limits the application of classical distributed process modelling to highly-instrumented experimental fields. This paper presents a framework that combines fuzzy logic and process-based approach for modelling contour ridges for rainwater harvesting where detailed field data are not available. Water balance for a representative contour-ridged field incorporating the water flow processes across the boundaries is integrated with fuzzy logic to incorporate the uncertainties in estimating runoff. The model is tested using data collected during the 2009/2010 and 2010/2011 rainfall seasons from two contour-ridged fields in Zhulube located in the semi-arid parts of Zimbabwe. The model is found to replicate soil moisture in the root zone reasonably well (NSE = 0.55 to 0.66 and PBIAS = −1.3 to 6.1 %). The results show that combining fuzzy logic and process based approaches can adequately model soil moisture in a contour ridged-field and could help to assess the water dynamics in contour ridged fields.

scholarly journals Root zone soil moisture estimation with Random Forest

2021 ◽  
Vol 593 ◽  
pp. 125840
Author(s):  
Coleen Carranza ◽  
Corjan Nolet ◽  
Michiel Pezij ◽  
Martine van der Ploeg
Keyword(s):  
Soil Moisture ◽  
Random Forest ◽  
Root Zone ◽  
Moisture Estimation

scholarly journals Analyzing moisture-heat coupling in a wheat-soil system using data-driven vector autoregression model

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7101 ◽  
Author(s):  
Xiaohang Feng ◽  
Xia Zhang ◽  
Zhenqi Feng ◽  
Yichang Wei
Keyword(s):  
Soil Moisture ◽  
Response Time ◽  
Soil Temperature ◽  
Vector Autoregression ◽  
Time Lag ◽  
Central China ◽  
Data Driven ◽  
Response Intensity ◽  
The Past ◽  
Using Data

Soil temperature and moisture have a close relationship, the accurate controlling of which is important for crop growth. Mechanistic models built by previous studies need exhaustive parameters and seldom consider time stochasticity and lagging effect. To circumvent these problems, this study designed a data-driven stochastic model analyzing soil moisture-heat coupling. Firstly, three vector autoregression models are built using hourly data on soil moisture and temperature at the depth of 10, 30, and 90 cm. Secondly, from impulse response functions, the time lag and intensity of two variables’ response to one unit of positive shock can be obtained, which describe the time length and strength at which temperature and moisture affect each other, indicating the degree of coupling. Thirdly, Granger causality tests unfold whether one variable’s past value helps predict the other’s future value. Analyzing data obtained from Shangqiu Experiment Station in Central China, we obtained three conclusions. Firstly, moisture’s response time lag is 25, 50, and 120 h, while temperature’s response time lag is 50, 120, and 120 h at 10, 30, and 90 cm. Secondly, temperature’s response intensity is 0.2004, 0.0163, and 0.0035 °C for 1% variation in moisture, and moisture’s response intensity is 0.0638%, 0.0163%, and 0.0050% for 1 °C variation in temperature at 10, 30, and 90 cm. Thirdly, the past value of soil moisture helps predict soil temperature at 10, 30, and 90 cm. Besides, the past value of soil temperature helps predict soil moisture at 10 and 30 cm, but not at 90 cm. We verified this model by using data from a different year and linking it to soil plant atmospheric continuum model.

scholarly journals Using data‐driven methods to explore the predictability of surface soil moisture with FLUXNET site data

2019 ◽  
Vol 33 (23) ◽  
pp. 2978-2996
Author(s):  
Jinjing Pan ◽  
Wei Shangguan ◽  
Lu Li ◽  
Hua Yuan ◽  
Shupeng Zhang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Data Driven ◽  
Surface Soil Moisture ◽  
Using Data

scholarly journals On the Efficacy of Combining Thermal and Microwave Satellite Data as Observational Constraints for Root-Zone Soil Moisture Estimation

2009 ◽  
Vol 10 (5) ◽  
pp. 1109-1127 ◽  
Author(s):  
Damian J. Barrett ◽  
Luigi J. Renzullo
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Surface Temperature ◽  
Brightness Temperature ◽  
Root Zone ◽  
Satellite Observations ◽  
Data Type ◽  
Background Error ◽  
Strong Constraint ◽  
Moisture Estimation

Abstract Data assimilation applications require the development of appropriate mathematical operators to relate model states to satellite observations. Two such “observation” operators were developed and used to examine the conditions under which satellite microwave and thermal observations provide effective constraints on estimated soil moisture. The first operator uses a two-layer surface energy balance (SEB) model to relate root-zone moisture with top-of-canopy temperature. The second couples SEB and microwave radiative transfer models to yield top-of-atmosphere brightness temperature from surface layer moisture content. Tangent linear models for these operators were developed to examine the sensitivity of modeled observations to variations in soil moisture. Assuming a standard deviation in the observed surface temperature of 0.5 K and maximal model sensitivity, the error in the analysis moisture content decreased by 11% for a background error of 0.025 m3 m−3 and by 29% for a background error of 0.05 m3 m−3. As the observation error approached 2 K, the assimilation of individual surface temperature observations provided virtually no constraint on estimates of soil moisture. Given the range of published errors on brightness temperature, microwave satellite observations were always a strong constraint on soil moisture, except under dense forest and in relatively dry soils. Under contrasting vegetation cover and soil moisture conditions, orthogonal information contained in thermal and microwave observations can be used to improve soil moisture estimation because limited constraint afforded by one data type is compensated by strong constraint from the other data type.

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