Described is the development of a regional forecasting system for landslide hazard threat level, suitable for use operationally by forecasting and disaster management agencies. The system utilizes spatially distributed operational hydrologic models to estimate depth-integrated soil moisture on basin scales of order 160 km 2 , with forcing of remotely sensed and on-site precipitation data. The depth-integrated soil moisture data and the precipitation forcing are used together with regional databases of landslide occurrence to develop threshold curves in the precipitation/soil moisture space that allow the prediction of landslide hazard threat level on satellite-derived rainfall pixel scales. Predetermined susceptibility maps may then be used together with the real-time prediction of hazard threat level for a particular rainfall pixel to determine the slopes within the pixel that are more likely to fail in real time and to characterize a given pixel as susceptible or nonsusceptible to landsliding for real-time prediction. The operational system development requires global satellite precipitation estimates with short latency, real-time precipitation data from sparse rain gauges in the region, and a regional database of historical landslide events with location and timing information. Parametric databases that support the operational hydrologic model consist of soil texture by depth and land-use/land-cover information. The case study presented is for the country of El Salvador. The study shows the feasibility of the regional system development and the validation of the assumed existence of a threshold curve in twodimensional space consisting of the depth-integrated soil moisture and of the forcing precipitation. The resulting threshold curve, when examined with data from the period 2006-2011 in El Salvador, resulted in warnings of landslide occurrence with frequency that spanned the range between 1 and 5 % of the days for the basins identified to be susceptible to landsliding.

No abstract

No abstract

Flash Flood Guidance consists of indices that estimate the amount of rain of a certain duration that is needed over a given small basin in order to cause minor flooding. Backwater catchment inundation from swollen rivers or regional groundwater inputs are not significant over the spatial and temporal scales for the majority of upland flash flood prone basins, as such, these effects are not considered. However, some lowland areas and flat terrain near large rivers experience standing water long after local precipitation has ceased. NASA is producing an experimental product from the MODIS that detects standing water. These observations were assimilated into the hydrologic model in order to more accurately represent soil moisture conditions within basins, from sources of water from outside of the basin. Based on the upper soil water content, relations are used to derive an error estimate for the modeled soil saturation fraction; whereby, the soil saturation fraction model state can be updated given the availability of satellite observed inundation. Model error estimates were used in a Monte Carlo ensemble forecast of soil water and flash flood potential. Numerical experiments with six months of data -December 2011 showed that MODIS inundation data, when assimilated to correct soil moisture estimates, increased the likelihood that bankfull flow would occur, over non-assimilated modeling, at catchment outlets for approximately 44% of basin-days during the study time period. While this is a much more realistic representation of conditions, no actual events occurred allowing for validation during the time period.