Using remotely-sensed estimates of soil moisture to infer soil texture and hydraulic properties across a semi-arid watershed

Abstract

Near-surface soil moisture is a critical component of land surface energy and water balance studies encompassing a wide range of disciplines. However, the processes of infiltration, runoff, and evapotranspiration in the vadose zone of the soil are not easy to quantify or predict because of the difficulty in accurately representing soil texture and hydraulic properties in land surface models. This study approaches the problem of parameterizing soils from a unique perspective based on components originally developed for operational estimation of soil moisture for mobility assessments. Estimates of near-surface soil moisture derived fiom passive (L-band) microwave remote sensing were acquired on six dates during the Monsoon '90 experiment in southeastern Arizona, and used to calibrate hydraulic properties in an offline land surface model and infer information on the soil conditions of the region. Specifically, a robust parameter estimation tool (PEST) was used to calibrate the Noah land surface model and run at very high spatial resolution across the Walnut Gulch Experimental Watershed. Errors in simulated versus observed soil moisture were minimized by adjusting the soil texture, which in turn controls the hydraulic properties through the use of pedotransfer functions. By estimating a continuous range of widely applicable soil properties such as sand, silt, and clay percentages rather than applying rigid soil texture classes, lookup tables, or large parameter sets as in previous studies, the physical accuracy and consistency of the resulting soils could then be assessed.In addition, the sensitivity of this calibration method to the number and timing of microwave retrievals is determined in relation to the temporal patterns in precipitation and soil drying. The resultant soil properties were applied to an extended time period demonstrating the improvement in simulated soil moisture over that using default or county-level soil parameters. The methodology is also applied to an independent case at Walnut Gulch using a new soil moisture product fiom active (C-band) radar imagery with much lower spatial and temporal resolution. Overall, results demonstrate the potential to gain physically meaningful soils information using simple parameter estimation with few but appropriately timed remote sensing retrievals. This study exarnines the ability of microwave remote sensing estimates of soil moisture to be used to calibrate a land surface model and, in the process, infer soil textural and hydraulic properties across spatially heterogeneous landscapes. Results also demonstrate the limitations and potential improvements in simulating soil moisture evolution using a combination of remote sensing, modeling, and parameter estimation techniques. Watershed. E r r m in simulated versus observed soil moistwe were m&mized by adjustbg the soil texae, which k ttt.m eontrols the hytlraa1-iit:propefiies though k use of pedob-msfer h e k s . By estimating a continms range of widely applicable soil prope~es swh sts sad, silt, and cl...

Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review

“…It has been demonstrated that batch calibration using soil moisture can result in a better match between modelled and observed soil moisture [139]. However, these studies focused on improved surface or root-zone soil moisture simulation through either conceptual models or physically-based models [138,[140][141][142][143]. From the flood forecasting perspective, a more important question is how the batch calibration using soil moisture affects the streamflow simulation/forecasting.…”

Impact of Land Model Calibration on Coupled Land–Atmosphere Prediction

“…In fact, Santanello et al (2007) 795 modified the FXEXP parameter in their study to be 1.0, due to the semi-arid region and inability coverage (e.g. Mitchell et al 2004, LeMone et al 2010, Trier et al 2011.…”

“…for mixed crops or soil 191 types). This can be a problem, particularly for soils where larger differences in soil parameters 192 have been observed within a soil type than between types (Feddes et al 1993;Soet and Stricker 193 2003; Gutmann and Small 2005;Santanello et al 2007). …”

“…In order to combat these limitations, numerous attempts have been made to calibrate (or 195 'optimize') LSM parameters using observations of state variables such as soil moisture and 196 surface temperature as constraints (Gupta et al 1999;Hess 2001;Hogue et al 2005;Liu et al 197 2003Liu et al 197 , 2004Liu et al 197 , 2005Santanello et al 2007;Peters-Lidard et al 2008;Harrison et al 2012 calibration, and highlight the computational difficulty in calibrating large parameter sets in 220 coupled models (which has precluded the calibration of a full 3-D mesoscale model to date). As 221 a result, they suggest a stepwise procedure of offline before coupled calibration as an alternative.…”

“…where EF is the evaporative fraction, defined as feedback of the atmosphere (through the PBL) on surface fluxes (EF atm ) (Santanello et al 2007;249 van Heerwaarden et al 2009 of LoCo research and the related diagnostic framework can be found in Santanello et al (2009Santanello et al ( , 263 2011aSantanello et al ( , 2011bSantanello et al ( , 2012; hereafter referred to as S09, S11a, S11b, S12 OPT/UE) algorithms to be applied to the LSMs to exploit further the information content from 296 observations. The algorithms (e.g.…”