scholarly journals Inferring Soil Moisture Memory from Streamflow Observations Using a Simple Water Balance Model

2013 ◽  
Vol 14 (6) ◽  
pp. 1773-1790 ◽  
Author(s):  
Rene Orth ◽  
Randal D. Koster ◽  
Sonia I. Seneviratne
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Weather Forecasting ◽  
Water Balance Model ◽  
Balance Model ◽  
Catchment Scale ◽  
Spatially Distributed ◽  
Optimized Model ◽  
Streamflow Data

Abstract Soil moisture is known for its integrative behavior and resulting memory characteristics. Soil moisture anomalies can persist for weeks or even months into the future, making initial soil moisture a potentially important contributor to skill in weather forecasting. A major difficulty when investigating soil moisture and its memory using observations is the sparse availability of long-term measurements and their limited spatial representativeness. In contrast, there is an abundance of long-term streamflow measurements for catchments of various sizes across the world. The authors investigate in this study whether such streamflow measurements can be used to infer and characterize soil moisture memory in respective catchments. Their approach uses a simple water balance model in which evapotranspiration and runoff ratios are expressed as simple functions of soil moisture; optimized functions for the model are determined using streamflow observations, and the optimized model in turn provides information on soil moisture memory on the catchment scale. The validity of the approach is demonstrated with data from three heavily monitored catchments. The approach is then applied to streamflow data in several small catchments across Switzerland to obtain a spatially distributed description of soil moisture memory and to show how memory varies, for example, with altitude and topography.

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

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

<p>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.</p>

scholarly journals Land Surface Controls on Hydroclimatic Means and Variability

2012 ◽  
Vol 13 (5) ◽  
pp. 1604-1620 ◽  
Author(s):  
Randal D. Koster ◽  
Sarith P. P. Mahanama
Keyword(s):  
Soil Moisture ◽  
Simple Model ◽  
Water Balance ◽  
Land Surface ◽  
Water Balance Model ◽  
Balance Model ◽  
Surface Moisture ◽  
Surface Models ◽  
Moisture Fluxes ◽  
Further Development

Abstract Hydroclimatic means and variability are determined in large part by the control of soil moisture on surface moisture fluxes, particularly evapotranspiration and runoff. This control is examined here using a simple water balance model and multidecadal observations covering the conterminous United States. Under the assumption that the relevant soil moisture–evapotranspiration and soil moisture–runoff relationships are, to first order, universal, the simple model illustrates the degree to which they interact to determine spatial distributions of hydroclimatic means and variability. In the process, the simple model provides estimates for the underlying relationships that operate in nature. The hydroclimatic sensitivities established with the simple water balance model can be used to evaluate more complex land surface models and to guide their further development, as demonstrated herein with an example.

scholarly journals A daily water balance model for representing streamflow generation process following land use change

2005 ◽  
Vol 2 (3) ◽  
pp. 821-861
Author(s):  
M. A. Bari ◽  
K. R. J. Smettem
Keyword(s):  
Land Use ◽  
Soil Moisture ◽  
Land Use Change ◽  
Water Balance ◽  
Water Balance Model ◽  
Groundwater Depth ◽  
Balance Model ◽  
Streamflow Generation ◽  
Moisture Balance ◽  
Soil Moisture Balance

Abstract. A simple conceptual water balance model representing the streamflow generation processes on a daily time step following land use change is presented. The model consists of five stores: (i) Dry, Wet and Subsurface Stores for vertical and lateral water flow, (ii) a transient Stream zone Store (iii) a saturated Goundwater Store. The soil moisture balance in the top soil Dry and Wet Stores are the most important component of the model and characterize the dynamically varying saturated areas responsible for surface runoff, interflow and deep percolation. The Subsurface Store describes the unsaturated soil moisture balance, extraction of percolated water by vegetation and groundwater recharge. The Groundwater Store controls the baseflow to stream (if any) and the groundwater contribution to the stream zone saturated areas. The daily model was developed following a "downward approach" from an earlier monthly model and performed very well in simulating daily flow generation processes observed at Ernies (control) and Lemon (53% cleared) catchments in Western Australia. Most of the model parameters were incorporated a priori from catchment attributes such as surface slope, soil depth, porosity, stream length and initial groundwater depth, and some were calibrated by matching the observed and predicted hydrographs. The predicted groundwater depth, and streamflow volumes across all time steps from daily to monthly to annual were in close agreement with observations for both catchments.

scholarly journals Benchmarking a Soil Moisture Data Assimilation System for Agricultural Drought Monitoring

2014 ◽  
Vol 15 (3) ◽  
pp. 1117-1134 ◽  
Author(s):  
Eunjin Han ◽  
Wade T. Crow ◽  
Thomas Holmes ◽  
John Bolten
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Water Balance ◽  
Soil Water ◽  
Water Balance Model ◽  
Agricultural Drought ◽  
Soil Water Balance ◽  
Drought Monitoring ◽  
Balance Model ◽  
System Component

Abstract Despite considerable interest in the application of land surface data assimilation systems (LDASs) for agricultural drought applications, relatively little is known about the large-scale performance of such systems and, thus, the optimal methodological approach for implementing them. To address this need, this paper evaluates an LDAS for agricultural drought monitoring by benchmarking individual components of the system (i.e., a satellite soil moisture retrieval algorithm, a soil water balance model, and a sequential data assimilation filter) against a series of linear models that perform the same function (i.e., have the same basic input/output structure) as the full system component. Benchmarking is based on the calculation of the lagged rank cross correlation between the normalized difference vegetation index (NDVI) and soil moisture estimates acquired for various components of the system. Lagged soil moisture/NDVI correlations obtained using individual LDAS components versus their linear analogs reveal the degree to which nonlinearities and/or complexities contained within each component actually contribute to the performance of the LDAS system as a whole. Here, a particular system based on surface soil moisture retrievals from the Land Parameter Retrieval Model (LPRM), a two-layer Palmer soil water balance model, and an ensemble Kalman filter (EnKF) is benchmarked. Results suggest significant room for improvement in each component of the system.

scholarly journals Evapotranspiration Estimates at High Spatial and Temporal Resolutions from an Energy–Water Balance Model and Satellite Data in the Capitanata Irrigation Consortium

2020 ◽  
Vol 12 (24) ◽  
pp. 4083
Author(s):  
Chiara Corbari ◽  
Drazen Skokovic Jovanovic ◽  
Luigi Nardella ◽  
Josè Sobrino ◽  
Marco Mancini
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Flash Flood ◽  
Calibration Procedure ◽  
Water Balance Model ◽  
Balance Model

The feasibility of combining remotely sensed land surface temperature data (LST) and an energy–water balance model for improving evapotranspiration estimates over time distributed in space in the Capitanata irrigation consortium is analysed. The energy–water balance FEST-EWB model (flash flood event-based spatially distributed rainfall–runoff transformation—energy–water balance model) computes continuously in time and is distributed in space soil moisture (SM) and evapotranspiration (ET) fluxes solving for a land surface temperature that closes the energy–water balance equations. The comparison between modelled and observed LST was used to calibrate the model soil parametres with a newly developed pixel to pixel calibration procedure. The effects of the calibration procedure were analysed against ground measures of soil moisture and evapotranspiration. The FEST-EWB model was run at 30 m of spatial resolution for the period between 2013 and 2018. Absolute errors of 2.5 °C were obtained for LST estimates against satellite data; while RMSE around 0.06 and 40 Wm−2 are found for ground measured soil moisture and latent heat flux, respectively.

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