A wide variety of processes controls the time of occurrence, duration, extent, and severity of river floods. Classifying flood events by their causative processes may assist in enhancing the accuracy of local and regional flood frequency estimates and support the detection and interpretation of any changes in flood occurrence and magnitudes. This paper provides a critical review of existing causative classifications of instrumental and preinstrumental series of flood events, discusses their validity and applications, and identifies opportunities for moving toward more comprehensive approaches. So far no unified definition of causative mechanisms of flood events exists. Existing frameworks for classification of instrumental and preinstrumental series of flood events adopt different perspectives: hydroclimatic (large‐scale circulation patterns and atmospheric state at the time of the event), hydrological (catchment scale precipitation patterns and antecedent catchment state), and hydrograph‐based (indirectly considering generating mechanisms through their effects on hydrograph characteristics). All of these approaches intend to capture the flood generating mechanisms and are useful for characterizing the flood processes at various spatial and temporal scales. However, uncertainty analyses with respect to indicators, classification methods, and data to assess the robustness of the classification are rarely performed which limits the transferability across different geographic regions. It is argued that more rigorous testing is needed. There are opportunities for extending classification methods to include indicators of space–time dynamics of rainfall, antecedent wetness, and routing effects, which will make the classification schemes even more useful for understanding and estimating floods.This article is categorized under: Science of Water > Water Extremes Science of Water > Hydrological Processes Science of Water > Methods

The response of fluvial systems to land use and climate change varies depending on catchment size. While forcing-response mechanisms of small catchments are reasonably well understood, the response of larger drainage basins is less clear. In particular, the impact of land use and climate change on the Rhine system is poorly understood because of the catchment size (185 000 km²) and the long history of human cultivation, which started approximately 7500 years ago. A sediment budget is calculated to specify the amount of alluvial sediment that was deposited during the Holocene and to estimate long-term soil erosion rates. The results suggest that 59±14 X 10⁹ t of Holocene alluvial sediment is stored in the non-alpine part of the Rhine catchment (South and Central Germany, Eastern France, The Netherlands). About 50% of Holocene alluvial sediment is deposited along the trunk valley and the delta (Upper Rhine, Lower Rhine, coastal plain), while the rest is stored along the tributary valleys. The floodplain sediment storage corresponds to a mean erosion rate of 0.55±0.16 t/ha per yr (38.5±10.7 mm/kyr) across the Rhine catchment outside the Alps. This Holocene-averaged estimate amounts for sediments that were delivered to the channel network and is at the lower limit of erosion rates from other studies of different methodology.

The number of personal weather stations (PWSs) with data available through the internet is increasing gradually in many parts of the world. The purpose of this study is to investigate the applicability of these data for the spatial interpolation of precipitation using a novel approach based on indicator correlations and rank statistics. Due to unknown errors and biases of the observations, rainfall amounts from the PWS network are not considered directly. Instead, it is assumed that the temporal order of the ranking of these data is correct. The crucial step is to find the stations which fulfil this condition. This is done in two steps – first, by selecting the locations using the time series of indicators of high precipitation amounts. Then, the remaining stations are then checked for whether they fit into the spatial pattern of the other stations. Thus, it is assumed that the quantiles of the empirical distribution functions are accurate. These quantiles are then transformed to precipitation amounts by a quantile mapping using the distribution functions which were interpolated from the information from the German National Weather Service (Deutscher Wetterdienst – DWD) data only. The suggested procedure was tested for the state of Baden-Württemberg in Germany. A detailed cross validation of the interpolation was carried out for aggregated precipitation amount of 1, 3, 6, 12 and 24 h. For each of these temporal aggregations, nearly 200 intense events were evaluated, and the improvement of the interpolation was quantified. The results show that the filtering of observations from PWSs is necessary as the interpolation error after the filtering and data transformation decreases significantly. The biggest improvement is achieved for the shortest temporal aggregations.

Study regionThe study was conducted with data from the city state of Singapore, a densely populated and highly urbanized island state close to the equator.Study focusThe study provides a basic statistical characterization of Singapore's precipitation regime. It quantifies the effects of daily and yearly cycles and the connections to the El Niño Southern Oscillation and Sea Surface Temperature on hourly rainfall amounts. A regional regression analysis was applied to detect trends in hourly and daily precipitation extremes.New hydrological insights for the region•Rainfall events in the early morning and in the afternoon exhibit different spatio-temporal characteristics.•Singapore's precipitation activity is influenced by the El Niño Southern Oscillation, but not during the North-East Monsoon season. During the La Niña phase, seasonal precipitation totals are higher and the arrival of the most intense precipitation events is shifted towards morning hours.•The rainfall regime has been changing during the investigation period from 1981 to 2010. Rainfall totals as well as hourly and daily extremes increased. The increase in daily extremes was the fastest, changing the temporal scaling of the extremes.•The predicted changes in precipitation scaling towards higher daily sums have implications for the management of stormwater run-off