Abstract. Shaanxi Province, located in northwest China and spanning multiple hydroclimatic and geological zones, has many areas largely suffering from rainfall-induced landslide and debris flow. The objectives of this study are to reveal the spatiotemporal characteristics of the two hazards and identify their major controlling factors in this region based on a region-wide, comprehensive ground-survey-based hazard inventory dataset from 2009 to 2012. We investigated the spatiotemporal characteristics of the two hazards and quantified the relationships between the occurrence rates of the two hazards and their influencing factors, including antecedent rainfall amount, rainfall duration, rainfall intensity, terrain slope, land cover type and soil type. The results show that landslide has a higher occurrence rate and more extensive distribution than debris flow in this region, while the two hazards are both concentrated in the south with ample rainfall and steep terrains. Both of the hazards show clear seasonalities: July–September for landslide and July for debris flow. Rainfall characteristics (amount, duration and intensity) and slope are the dominant factors controlling slope stability across this region. Debris flow is more sensitive to these rainfall metrics on the high-value ranges than landslide in this region. Land cover is another influencing factor but soil type does not appear to impose consistent impacts on the occurrence of the two hazards. This study not only provides important inventory data for studying the landslide and debris flow hazards but also adds valuable information for modeling and predicting the two hazards to enhance resilience to these hazards in this region.

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Abstract. The incorporation of numerical weather predictions (NWP) into a flood forecasting system can increase forecast lead times from a few hours to a few days. A single NWP forecast from a single forecast centre, however, is insufficient as it involves considerable non-predictable uncertainties and lead to a high number of false alarms. The availability of global ensemble numerical weather prediction systems through the THORPEX Interactive Grand Global Ensemble' (TIGGE) offers a new opportunity for flood forecast. The Grid-Xinanjiang distributed hydrological model, which is based on the Xinanjiang model theory and the topographical information of each grid cell extracted from the Digital Elevation Model (DEM), is coupled with ensemble weather predictions based on the TIGGE database (CMC, CMA, ECWMF, UKMO, NCEP) for flood forecast. This paper presents a case study using the coupled flood forecasting model on the Xixian catchment (a drainage area of 8826 km 2 ) located in Henan province, China. A probabilistic discharge is provided as the end product of flood forecast. Results show that the association of the Grid-Xinanjiang model and the TIGGE database gives a promising tool for an early warning of flood events several days ahead.