An alternate and robust approach to calibration for the estimation of land surface model parameters based on remotely sensed observations

Abstract

[1] Atmospheric models include land surface parameterizations of heat and moisture fluxes. Most parameterizations derive from early models of layered soil and vegetation canopy that account for liquid, vapor and heat diffusion, radiation processes, and surface turbulence. The number of parameters in these models ranges roughly from 10 to 50. Some parameters are known to vary over a small range and/or not significantly impact predictions. Others are highly influential (e.g., Leaf Area Index), but are currently well estimated from satellite data. Many parameters, however, are highly influential, vary over a large dynamic range, cannot be estimated from satellite data, and cannot be readily upscaled from in-situ measurements. For such parameters (e.g., maximum stomatal conductance and soil hydraulic conductivity), values are assigned based on look-up tables sorted by land cover and soil texture. Here we present a method for estimating these parameters by minimizing a measure of nonstationarity of model-predicted moisture state variable tendencies. This method has advantages over calibration: 1) it does not require flux data (e.g., evapotranspiration); and 2) the tendency terms are evaluated at the model grid and thus yield parameters that are effective for that scale. The method is demonstrated with the Noah Land Surface Model, using remotely-sensed soil moisture, at a site in California. Preliminary results indicate that the method is robust and performs better than both: 1) calibration to soil moisture observations, which can lead to large, compensating errors in drainage and evaporation; and 2) minimizing the sum of squares of innovations of soil moisture updates. Citation: Salvucci, G. D., and D. Entekhabi (2011), An alternate and robust approach to calibration for the estimation of land surface model parameters based on remotely sensed observations, Geophys. Res. Lett., 38, L16404,

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.…”

An updated treatment of soil texture and associated hydraulic properties in a global land modeling system

“…The PTF approach is used in the present paper. It is important, however, to acknowledge the existence of other approaches to estimating SHPs, including those utilizing remotely sensed soil moisture retrievals [Santanello et al, 2007;Pauwels et al, 2009;Salvucci and Entekhabi, 2011;Harrison et al, 2012]. This type of approach (not discussed further here) comes with its own set of issues; the retrievals, for example, inherently include some assumption about soil texture, which may be inconsistent with the texture assumed in the land surface model, and the optimized SHP values and the feasibility of a global calibration effort will depend on the choice of the calibration algorithm and on the coverage and quality of the remote sensing data.…”

Climatology and Forest Phenology during 1984-2013 around Western Lake Superior, USA.

“…Modeling outcomes may then deviate significantly from reality in areas where the land cover type does not change but its energetic and hydrological function is different from that bulk, categorical representation [Law et al, 2002]. Remote sensing-based phenology can be incorporated directly into the calculations of the LSMs that are coupled with regional weather and climate modeling systems [Sellers et al, 1996;Bonan et al, 2002;Sterling and Ducharne, 2008] and provide verisimilitude to the modeled vegetation biophysical traits including seasonal maxima of albedo, leaf area index (LAI), and stomatal conductance [Dorman and Sellers, 1989;Salvucci and Entekhabi, 2011].…”