Stochastic Bias Correction and Uncertainty Estimation of Satellite-Retrieved Soil Moisture Products

To apply satellite-retrieved soil moisture to a short-range weather prediction, we review a stochastic approach for reducing foot print scale biases and estimating its uncertainties. First, we discuss a challenge of representativeness errors. Before describing retrieval errors in more detail, we clarify a conceptual difference between error and uncertainty in basic metrological terms of the International Organization for Standardization (ISO), and briefly summarize how current retrieval algorithms deal with a challenge of land surface heterogeneity. As compared to relative approaches such as Triple Collocation, or cumulative distribution function (CDF) matching that aim for climatology stationary errors at time-scale of years, we address a stochastic approach for reducing instantaneous retrieval errors at time-scale of several hours to days. The stochastic approach has a potential as a global scheme to resolve systematic errors introducing from instrumental measurements, geo-physical parameters, and surface heterogeneity across the globe, because it does not rely on the ground measurements or reference data to be compared with

HISTORICAL AND FORECASTED KENTUCKY SPECIFIC SLOPE STABILITY ANALYSES USING REMOTELY RETRIEVED HYDROLOGIC AND GEOMORPHOLOGIC DATA

Hazard analyses of rainfall-induced landslides have typically been observed to experience a lack of inclusion of measurements of soil moisture within a given soil layer at a site of interest. Soil moisture is a hydromechanical variable capable of both strength gains and reductions within soil systems. However, in situ monitoring of soil moisture at every site of interest is an unfeasible goal. Therefore, spatiotemporal estimates of soil moisture that are representative of in-situ conditions are required for use in subsequent landslide hazard analyses. This study brings together various techniques for the acquisition, modeling, and forecasting of spatiotemporal retrievals of soil moisture across areas of Eastern Kentucky for use in hazard analyses. These techniques include: A novel approach for determination of satellite-based soil moisture retrieval correction factors for use in acquisition of low orbit-based soil moisture retrievals in site-specific analyses, unique spatiotemporal modeling of soil moisture at various depths within the soil layer through assimilation of satellite-based and land surface modeled soil moisture estimates, and the development of a novel workflow to effectively provide 7-day forecasts of soil moisture for use in subsequent forecasting of landslide hazards. Soil moisture retrieved through the previous approaches was implemented within landslide hazard and susceptibility analyses across known rainfall-induced landslides within Eastern Kentucky. Investigated analyses were conducted through a coupling of spatial soil moisture retrievals with that of site-specific geomorphologic data. These analyses proved capable in the detection of incipient failure conditions indicative of landslide occurrence over these known investigated slides. These soil moisture-based analyses show that inclusion of soil moisture, as hydromechanical variable, yields a more capable hazard analysis approach. Additionally, these analyses serve as a means to gain a better understanding of the coupled hydro-mechanical behavior associated with the initiation of rainfall-induced landslides