Floods have profound impacts on populations worldwide in terms of both loss of life and property. A global inventory of floods is an important tool for quantifying the spatial and temporal distribution of floods and for evaluating global flood prediction models. Several global hazard inventories currently exist; however, their utility for spatiotemporal analysis of global floods is limited. The existing flood catalogs either fail to record the geospatial area over which the flood impacted or restrict the types of flood events included in the database according to a set of criteria, limiting the scope of the inventory. To improve upon existing databases, and make it more comprehensive, we have compiled a digitized Global Flood Inventory (GFI) for the period 1998-2008 which also geo-references each flood event by latitude and longitude. This technical report presents the methodology used to compile the GFI and preliminary findings on the spatial and temporal distributions of the flooding events that are contained in the inventory.

In this study we developed an impact factor formula (IFF) to quantitatively attribute separately the impacts of climate change and local human activities on hydrological response (i.e. run-off) in a sub-basin of Yellow River for the period 1950-2000. Using the daily climatic data, we first calibrated and verified the variable infiltration capacity (VIC) hydrological model to the baseline period 1955-1970. Then we developed the basin's natural run-off for the following three decades using the VIC model without considering local human impacts, as the VIC model is benchmarked by the 1960's hydrological regime.On the basis of observed precipitation, run-off and reconstructed natural run-off data from 1971 to 2000, we quantified their long-term trend, decadal and annual variations. Using daily climatic observations, we showed that the precipitation and run-off have decreased from the baseline decade, the 1960s, indicating a drier hydrological regime for recent decades. We further applied the IFF to quantitatively attribute separately the impacts of reduced precipitation and increased temperatures from climate change and then of local human activities on hydrological run-off response. It was found that climate change has a greater impact than human activities on the basin's run-off for the three consecutive decades. The pCC (percentage change of run-off due to climate change impact) is found to be 89% followed by 66% and 56% in 1970s, 1980s and 1990s, respectively. Over the decades, pHA (percentage change of run-off due to human activities) has continuously increased from 11% to 44%. If the trend continues, in future, the pHA is going to outweigh pCC in this basin. This study provides a quantitative assessment methodology for water resources managers to understand the changing process of the hydrological cycle and attribute its causative factors in a sub-basin of the Yellow River.

Abstract. Study of hydro-climatology at a range of temporal scales is important in understanding and ultimately mitigating the potential severe impacts of hydrological extreme events such as floods and droughts. Using daily in-situ data over the last two decades combined with the recently available multiple-years satellite remote sensing data, we analyzed and simulated, with a distributed hydrologic model, the hydro-climatology in Nzoia, one of the major contributing sub-basins of Lake Victoria in the East African highlands. The basin, with a semi arid climate, has no sustained base flow contribution to Lake Victoria. The short spell of high discharge showed that rain is the prime cause of floods in the basin. There is only a marginal increase in annual mean discharge over the last 21 years. The 2-, 5-and 10-year peak discharges, for the entire study period showed that more years since the mid 1990's have had high peak discharges despite having relatively less annual rain. The study also presents the hydrologic model calibration and validation results over the Nzoia basin. The spatiotemporal variability of the water cycle components were quantified using a hydrologic model, with in-situ and multi-satellite remote sensing datasets. The model is calibrated using daily observed discharge data for the period between 1985 and 1999, for which model performance is estimated with a Nash Sutcliffe Efficiency (NSCE) of 0.87 and 0.23% bias. The model validation showed an error metrics with NSCE of 0.65 and 1.04% bias. Moreover, the hydrologic capability of satellite precipitation (TRMM-3B42 V6) is evaluated. In terms of reconstruction of the water cycle components the spatial distribution and time series of modeling results for precipitation and runoff showed considerable agreement with the monthly Correspondence to: Y. Hong (yanghong@ou.edu) model runoff estimates and gauge observations. Runoff values responded to precipitation events that occurred across the catchment during the wet season from March to early June. The spatially distributed model inputs, states, and outputs, were found to be useful for understanding the hydrologic behavior at the catchment scale. The monthly peak runoff is observed in the months of April, May and November. The analysis revealed a linear relationship between rainfall and runoff for both wet and dry seasons. Satellite precipitation forcing data showed the potential to be used not only for the investigation of water balance but also for addressing issues pertaining to sustainability of the resources at the catchment scale.

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