Attributing runoff changes to climate variability and human activities: uncertainty analysis using four monthly water balance models

Hydrological simulations to delineate the impacts of climate variability and human activities are subjected to uncertainties related to both parameter and structure of the hydrological models. To analyze the impact of these uncertainties on the model performance and to yield more reliable simulation results, a global calibration and multimodel combination method that integrates the Shuffled Complex Evolution Metropolis (SCEM) and Bayesian Model Averaging of four monthly water balance models was proposed. The method was applied to the Weihe River Basin, the largest tributary of the Yellow River, to determine the contribution of climate variability and human activities to runoff changes. The change point, which was used to determine the baseline period (1956–1990) and human-impacted period (1991–2009), was derived using both cumulative curve and Pettitt’s test. Results show that the combination method from SCEM provides more skillful deterministic predictions than the best calibrated individual model, resulting in the smallest uncertainty interval of runoff changes attributed to climate variability and human activities. This combination methodology provides a practical and flexible tool for attribution of runoff changes to climate variability and human activities by hydrological models.     

Probabilistic GIS-based method for delineation of urban flooding risk hotspots

Identifying urban flooding risk hotspots is one of the first steps in an integrated methodology for urban flood risk assessment and mitigation. This work employs three GIS-based frameworks for identifying urban flooding risk hotspots for residential buildings and urban corridors. This is done by overlaying a map of potentially flood-prone areas [estimated through the topographic wetness index (TWI)], a map of residential areas and urban corridors [extracted from a city-wide assessment of urban morphology types (UMT)], and a geo-spatial census dataset. A maximum likelihood method (MLE) is employed for estimating the threshold used for identifying the flood-prone areas (the TWI threshold) based on the inundation profiles calculated for various return periods within a given spatial window. Furthermore, Bayesian parameter estimation is employed in order to estimate the TWI threshold based on inundation profiles calculated for more than one spatial window. For different statistics of the TWI threshold (e.g. MLE estimate, 16th percentile, 50th percentile), the map of the potentially flood-prone areas is overlaid with the map of urban morphology units, identified as residential and urban corridors, in order to delineate the urban hotspots for both UMT. Moreover, information related to population density is integrated by overlaying geo-spatial census datasets in order to estimate the number of people affected by flooding. Differences in exposure characteristics have been assessed for a range of different residential types. As a demonstration, urban flooding risk hotspots are delineated for different percentiles of the TWI value for the city of Addis Ababa, Ethiopia.