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.

Global climate change is anticipated to have consequences on water resources and the envi-ronment both at global and local/regional levels. Efforts towards proper management of future water resources and resolving potential water-related conflicts require the formulation of appropriate techniques to downscale the output of global climate models (GCM) to local conditions for hydrologic prediction. The paper presents an integrated framework for modeling the impact of climate change on river runoff that combines methodology for downscaling climate change scenarios for a basin scale with a hydrological model to estimate the impact of climate change on a river runoff. The modeling framework uses long-term observations of meteorological and hydrological variables together with a climate change scenario to provide a projection of future flows for the specified time horizon. The framework is based on a spatial weather generator and a distributed rainfall-runoff model. Such a configuration enables a reflection of the uncertainty of future conditions by running multiple realizations of future conditions, and also take into account the spatial variability of hydrological properties in the catchment by maintaining the physical details at a given grid size.The performance of the framework was presented for the Kaczawa basin that is one of the main left bank tributaries of the Odra River -the second biggest river in Poland. The results show simulated changes of the future river flow regime caused by climatic changes for two time horizons: 2040 and 2080.

In this study, the impacts of climate change on streamflow are investigated. The ensemble of outputs from three different Global Circulation Models models: GISS, CCCM, GFDL developed for the emission scenario A1B were analyzed to infer projected changes in climatological conditions for the region of the Upper and Middle Odra basin. Obtaining hydrological scenarios of future changes for the scale of subcatchment required simulating short-term and fine scaled weather patterns for this area. SWGEN model (Spatial Weather GENerator) was applied to downscale projected changes of climatological conditions to the ones required by hydrological model temporal and spatial resolution. Daily time series of solar radiation, temperature and precipitation were generated for the reference period 1981–2000 and for the time horizon 2030 and 2050. The generated data from SWGEN model were integrated in the hydrological model NAM to simulate streamflow under changed conditions with daily time step. The results show considerable changes in annual and seasonal runoff daily distributions for selected study catchment in the future time horizons of 2030 and 2050.

The combined analysis of precipitation and water scarcity was done with the use of the Standardized Precipitation Index (SPI) and the Standardized Runoff Index (SRI), developed as a monthly, two-variable SPI-SRI indicator to identify different classes of hydrometeorological conditions. Stochastic analysis of a long-term time series of monthly SPI-SRI indicator values was performed using a first-order Markov chain model. This provided characteristics of regional features of drought formation, evolution and persistence, as well as tools for statistical longterm drought hazard prediction. The study was carried out on two subbasins of the Odra River (Poland) of different orography and land use: the mountainous Nysa Kłodzka basin and the lowland, agricultural Prosna basin. Classification obtained with the SPI-SRI indicator was compared with the output from the NIZOWKA model that provided identification of hydrological drought events including drought duration and deficit volume. Severe and long-duration droughts corresponded to SPI-SRI Class 3 (dry meteorological and dry hydrological), while severe but short-term droughts (lasting less than 30 days) corresponded to SPI-SRI Class 4 (wet meteorological and dry hydrological). The results confirm that, in Poland, meteorologically dry conditions often shift to hydrologically dry conditions within the same month, droughts rarely last longer than 2 months and two separate drought events can be observed within the same year.

No abstract