Application of remote sensed precipitation for landslide hazard assessment Dalia Kirschbaum, NASA GSFC, Code The increasing availability of remotely sensed land surface and precipitation information provides new opportunities to develop and improve upon landslide hazard assessment methods. This research considers how satellite precipitation information can be applied in a global, landslide forecasting framework. Research indicates that landslide susceptibility information must be considered at higher resolution to more effectively resolve susceptible areas. However, success in resolving when landslide activity is closely linked to appropriate characterization of the empirical rainfall intensity- duration thresholds. To determine more optimal rainfall intensity thresholds for triggering landslides over specific durations, a Genetic Algorithm Optimization tool built within the Land Information System (LIS) branch has been tested at the local scale as a possible tool for improving the characterization of rainfall triggering conditions. This work is ongoing and seeks to characterize rainfall thresholds according to geographic location and climate zone utilizing inputs on rainfall intensity, duration, and antecedent conditions. Figure 1: Distribution of rainfall-triggered landslide events obtained from online media sources and disaster databases. The database currently has over 2,800 events and 13,000 fatalities. Figure 2: Framework for the global rainfall- triggered landslide forecasting system, employing a threshold approach to highlight areas exhibiting potential landsliding conditions in near real-time. Figure 3: Example of the optimization approach for rainfall intensity-duration thresholds for a test pixel in NE India (denoted by red arrow in Fig. 1). Graph illustrates a time series of precipitation (3B42-RT) and compares the landslide events (red) to default (green) and optimized (black) algorithm results. Probability of Detection (POD): Default Run: 31% Optimized Run: 63%

Name: Dalia Kirschbaum, NASA/GSFC, Code Phone: References: Kirschbaum, D. B., Adler, R., Hong, Y., Hill, S., & Lerner-Lam, A. (2009). A global landslide catalog for hazard applications: method, results, and limitations, Natural Hazards, 52(3), Kirschbaum, D. B., Adler, R., Hong, Y., & Lerner-Lam, A. (2009). Evaluation of a preliminary satellite-based landslide hazard algorithm using global landslide inventories, Natural Hazards And Earth System Sciences, 9, Kirschbaum, D.B., Adler, R., Hong, Y., Kumar, S., Peters-Lidard, C., Lerner-Lam, A. (accepted, 2010). Advances in landslide hazard forecasting: Evaluation of a global and regional modeling approach, Environmental Earth Sciences. Kumar, Sujay V., Reichle, R.H., Harrison, K.W., Peters-Lidard, C.D., Yatheendradas, S., Santanello, J.A. (submitted, 2010). Model parameter estimation for a priori bias correction in land data assimilation: A soil moisture case study, Water Resources Research. Data Sources: TRMM Multisatellite Precipitation Analysis Real-time product (3B42-RT) Technical Description of Images: The Global landslide catalog considers all rapidly moving landslide typologies triggered by rainfall (Fig. 1). The majority of event entries are compiled from news reports, disaster databases, personal communication, and scholarly articles. The catalog is compiled on day-by-day basis and includes information on the date and time (when available) of the event, geographic and nominal location, impacts, trigger, relative classification of size and location confidence, and other details. Fig. 2 illustrates the threshold approach employed in the prototype landslide algorithm forecasting system. The algorithm elicits a forecast if rainfall exceeds a designated threshold and if the pixel considered to be highly susceptible to landslide activity. This system is operating in near real-time at the following website: Fig.3 shows initial work on employing an optimization route developed in the Land Information System (LIS) using a Genetic Algorithm methodology (Kumar et al. 2010) to find more realistic rainfall intensity and duration thresholds that may be used to improve the forecast accuracy of the global framework. The graph features a test case over a pixel (26.875N, E) in India where a number of landslides have been recorded over the monsoon seasons of 2009 and The probability of detection (POD) of the observed landslides increases by ~30% when the optimization module is used to characterize the rainfall intensity thresholds; however, the false alarm rate (FAR) also increases by 12% as a result of decreasing the rainfall threshold. This work is ongoing and shows promise in improving the characterization of rainfall thresholds, which have been shown to significantly vary by climate zone and geographic area. Scientific Significance: While landslides generally represent a localized phenomena, their impacts can be vast and have significantly impacted the lives and property of communities worldwide. In 2010 alone, over 4,800 people have been killed in rainfall-triggered landslides. This prototype landslide forecasting system, while empirically based, highlights the importance of identifying regions of potential landslide activity on a more frequent basis in order to outline existing societal hazards from landslides as well as inform local governments and aid organization of potentially hazardous regions. Remote sensing information, including both precipitation and surface products (topography – SRTM, land cover – MODIS, AVHRR, etc.), can fill large data voids in evaluating landslide hazards at a variety of spatial scales. The global landslide catalog and prototype landslide forecasting framework represents the first steps at approaching this issue from a broader perspective. We plan to employ more quantitative methods and deterministic models to better inform landslide hazard assessment and understand how data and model uncertainty impacts forecast accuracy. Relevance for future science and relationship to Decadal Survey: Future missions, such as the Global Precipitation Measurement (GPM) mission will provide more frequent and extensive estimates of precipitation at the global scale and have the potential to significantly advance landslide hazard assessment tools.