The challenge of unprecedented floods and droughts in risk management

Risk management has reduced vulnerability to floods and droughts globally1,2, yet their impacts are still increasing3. An improved understanding of the causes of changing impacts is therefore needed, but has been hampered by a lack of empirical data4,5. On the basis of a global dataset of 45 pairs of events that occurred within the same area, we show that risk management generally reduces the impacts of floods and droughts but faces difficulties in reducing the impacts of unprecedented events of a magnitude not previously experienced. If the second event was much more hazardous than the first, its impact was almost always higher. This is because management was not designed to deal with such extreme events: for example, they exceeded the design levels of levees and reservoirs. In two success stories, the impact of the second, more hazardous, event was lower, as a result of improved risk management governance and high investment in integrated management. The observed difficulty of managing unprecedented events is alarming, given that more extreme hydrological events are projected owing to climate change3

Panta Rhei benchmark dataset: socio-hydrological data of paired events of floods and droughts

As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management and climate adaptation. However, there is currently a lack of comprehensive, empirical data about the processes, interactions, and feedbacks in complex human–water systems leading to flood and drought impacts. Here we present a benchmark dataset containing socio-hydrological data of paired events, i.e. two floods or two droughts that occurred in the same area. The 45 paired events occurred in 42 different study areas and cover a wide range of socio-economic and hydro-climatic conditions. The dataset is unique in covering both floods and droughts, in the number of cases assessed and in the quantity of socio-hydrological data. The benchmark dataset comprises (1) detailed review-style reports about the events and key processes between the two events of a pair; (2) the key data table containing variables that assess the indicators which characterize management shortcomings, hazard, exposure, vulnerability, and impacts of all events; and (3) a table of the indicators of change that indicate the differences between the first and second event of a pair. The advantages of the dataset are that it enables comparative analyses across all the paired events based on the indicators of change and allows for detailed context- and location-specific assessments based on the extensive data and reports of the individual study areas. The dataset can be used by the scientific community for exploratory data analyses, e.g. focused on causal links between risk management; changes in hazard, exposure and vulnerability; and flood or drought impacts. The data can also be used for the development, calibration, and validation of sociohydrological models. The dataset is available to the public through the GFZ Data Services (Kreibich et al., 2023, https://doi.org/10.5880/GFZ.4.4.2023.001)

Panta Rhei benchmark dataset: socio-hydrological data of paired events of floods and droughts

As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management and climate adaptation. However, there is currently a lack of comprehensive, empirical data about the processes, interactions and feedbacks in complex human-water systems leading to flood and drought impacts. Here we present a benchmark dataset containing socio-hydrological data of paired events, i.e., two floods or two droughts that occurred in the same area. The 45 paired events occurred in 42 different study areas and cover a wide range of socio-economic and hydro-climatic conditions. The dataset is unique in covering both floods and droughts, in the number of cases assessed, and in the quantity of socio-hydrological data. The benchmark dataset comprises: 1) detailed review style reports about the events and key processes between the two events of a pair; 2) the key data table containing variables that assess the indicators which characterise management shortcomings, hazard, exposure, vulnerability and impacts of all events; 3) a table of the indicators-of-change that indicate the differences between the first and second event of a pair. The advantages of the dataset are that it enables comparative analyses across all the paired events based on the indicators-of-change and allows for detailed context- and location-specific assessments based on the extensive data and reports of the individual study areas. The dataset can be used by the scientific community for exploratory data analyses e.g. focused on causal links between risk management, changes in hazard, exposure and vulnerability and flood or drought impacts. The data can also be used for the development, calibration and validation of socio-hydrological models. The dataset is available to the public through the GFZ Data Services (Kreibich et al. 2023, link for review: https://dataservices.gfz-potsdam.de/panmetaworks/review/923c14519deb04f83815ce108b48dd2581d57b90ce069bec9c948361028b8c85/).</p

The challenge of unprecedented floods and droughts in risk management

Risk management has reduced vulnerability to floods and droughts globally1,2, yet their impacts are still increasing3. An improved understanding of the causes of changing impacts is therefore needed, but has been hampered by a lack of empirical data4,5. On the basis of a global dataset of 45 pairs of events that occurred within the same area, we show that risk management generally reduces the impacts of floods and droughts but faces difficulties in reducing the impacts of unprecedented events of a magnitude not previously experienced. If the second event was much more hazardous than the first, its impact was almost always higher. This is because management was not designed to deal with such extreme events: for example, they exceeded the design levels of levees and reservoirs. In two success stories, the impact of the second, more hazardous, event was lower, as a result of improved risk management governance and high investment in integrated management. The observed difficulty of managing unprecedented events is alarming, given that more extreme hydrological events are projected owing to climate change3

Improving operational flood ensemble prediction by the assimilation of satellite soil moisture: comparison between lumped and semi-distributed schemes

Abstract. Assimilation of remotely sensed soil moisture data (SM-DA) to correct soil water stores of rainfall-runoff models has shown skill in improving streamflow prediction. In the case of large and sparsely monitored catchments, SM-DA is a particularly attractive tool. Within this context, we assimilate satellite soil moisture (SM) retrievals from the Advanced Microwave Scanning Radiometer (AMSR-E), the Advanced Scatterometer (ASCAT) and the Soil Moisture and Ocean Salinity (SMOS) instrument, using an Ensemble Kalman filter to improve operational flood prediction within a large (&gt; 40 000 km2) semi-arid catchment in Australia. We assess the importance of accounting for channel routing and the spatial distribution of forcing data by applying SM-DA to a lumped and a semi-distributed scheme of the probability distributed model (PDM). Our scheme also accounts for model error representation by explicitly correcting bias in soil moisture and streamflow in the ensemble generation process, and for seasonal biases and errors in the satellite data. Before assimilation, the semi-distributed model provided a more accurate streamflow prediction (Nash–Sutcliffe efficiency, NSE = 0.77) than the lumped model (NSE = 0.67) at the catchment outlet. However, this did not ensure good performance at the "ungauged" inner catchments (two of them with NSE below 0.3). After SM-DA, the streamflow ensemble prediction at the outlet was improved in both the lumped and the semi-distributed schemes: the root mean square error of the ensemble was reduced by 22 and 24%, respectively; the false alarm ratio was reduced by 9% in both cases; the peak volume error was reduced by 58 and 1%, respectively; the ensemble skill was improved (evidenced by 12 and 13% reductions in the continuous ranked probability scores, respectively); and the ensemble reliability was increased in both cases (expressed by flatter rank histograms). SM-DA did not improve NSE. Our findings imply that even when rainfall is the main driver of flooding in semi-arid catchments, adequately processed satellite SM can be used to reduce errors in the model soil moisture, which in turn provides better streamflow ensemble prediction. We demonstrate that SM-DA efficacy is enhanced when the spatial distribution in forcing data and routing processes are accounted for. At ungauged locations, SM-DA is effective at improving some characteristics of the streamflow ensemble prediction; however, the updated prediction is still poor since SM-DA does not address the systematic errors found in the model prior to assimilation