Compounding effects in flood drivers challenge estimates of extreme river floods.

Estimating river flood risks under climate change is challenging, largely due to the interacting and combined influences of various flood-generating drivers. However, a more detailed quantitative analysis of such compounding effects and the implications of their interplay remains underexplored on a large scale. Here, we use explainable machine learning to disentangle compounding effects between drivers and quantify their importance for different flood magnitudes across thousands of catchments worldwide. Our findings demonstrate the ubiquity of compounding effects in many floods. Their importance often increases with flood magnitude, but the strength of this increase varies on the basis of catchment conditions. Traditional flood analysis might underestimate extreme flood hazards in catchments where the contribution of compounding effects strongly varies with flood magnitude. Overall, our study highlights the need to carefully incorporate compounding effects in flood risk assessment to improve estimates of extreme floods.

Shifts in flood generation processes exacerbate regional flood anomalies in Europe.

Anomalies in the frequency of river floods, i.e., flood-rich or -poor periods, cause biases in flood risk estimates and thus make climate adaptation measures less efficient. While observations have recently confirmed the presence of flood anomalies in Europe, their exact causes are not clear. Here we analyse streamflow and climate observations during 1960-2010 to show that shifts in flood generation processes contribute more to the occurrence of regional flood anomalies than changes in extreme rainfall. A shift from rain on dry soil to rain on wet soil events by 5% increased the frequency of flood-rich periods in the Atlantic region, and an opposite shift in the Mediterranean region increased the frequency of flood-poor periods, but will likely make singular extreme floods occur more often. Flood anomalies driven by changing flood generation processes in Europe may further intensify in a warming climate and should be considered in flood estimation and management.

Characterizing hydrograph recessions from satellite-derived soil moisture.

Hydrograph recessions, usually described through a power-law function of river flows, are among the most widely utilized hydrological signatures. Whereas theories explaining the genesis of recession exponents have been recently developed, ongoing discussions on the physical meaning and suitable proxies of recession coefficients suggest their linkage to soil moisture states of whole river basins. This work investigates the possibility to explain hydrograph recession coefficients by means of satellite-derived soil moisture products such as the Soil Water Index of Copernicus Global Land Service. Analyses of basin-averaged Soil Water Index, computed for eleven river basins from the Central and Eastern United States with varied climates and landscapes, reveal the existence of an inverse relation between mean soil moisture and its variability in time, and provide a glance into the variability of river flows. A visible signature of satellite-derived soil moisture conditions on the values of the recession coefficient as well emerges when statistics of the Soil Water Index are paired with recession properties obtained by means of hydrograph recession analysis. In particular, drier river basins characterized by larger soil moisture variability tend to exhibit larger and more variable recession coefficients. These results suggest reliability of existing conjectures about the role of soil moisture for determining recession properties, and indicate that satellite-derived products can inform understanding of the intrinsic variability of the hydrologic response in river basins.

Causative classification of river flood events.

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 ExtremesScience of Water > Hydrological ProcessesScience of Water > Methods.

Tomography of anthropogenic nitrate contribution along a mesoscale river.

Elevated nitrate concentrations are a thread for water supply and ecological integrity in surface water. Nitrate fluxes obtained by standard monitoring protocols at the catchment outlet strongly integrate spatially and temporally variable processes such as mobilization and turnover. Consequently, inference of dominant nitrate sources is often problematic and challenging in terms of effective river management and prioritization of measures. Here, we combine a spatially highly resolved assessment of nitrate concentration and fluxes along a mesoscale catchment with four years of monitoring data at two representative sites. The catchment is characterized by a strong land use gradient from pristine headwaters to lowland sub-catchments with intense agricultural land use and wastewater sources. We use nitrate concentrations in combination with hydrograph separation and isotopic fingerprinting methods to characterize and quantify nitrate source contribution. The hydrological analysis revealed a clear dominance of base flow during both campaigns. However, the absolute amounts of discharge differed considerably from one another (outlet: 1.42m3s-1 in 2014, 0.43m3s-1 in 2015). Nitrate concentrations are generally low in the pristine headwaters (<3mgL-1) and increase downstream (15 to 16mgL-1) due to the contribution of agricultural and wastewater sources. While the agricultural contribution did not vary in terms of nitrate concentration and isotopic signature between the years, the wastewater contribution strongly increased with decreasing discharge. Wastewater-borne nitrate load in the entire catchment ranged between 19% (2014) and 39% (2015). Long-term monitoring of nitrate concentration and isotopic composition in two sub-catchment exhibits a good agreement with findings from spatially monitoring. In both datasets, isotopic composition indicates that denitrification plays only a minor role. The spatially highly resolved monitoring approach helped to pinpoint hot spots of nitrate inputs into the stream while the long-term information allowed to place results into the context of intra-annual variability.