Sea level rise (SLR), a well-documented and urgent aspect of anthropogenic global warming, threatens population and assets located in low-lying coastal regions all around the world. Common flood hazard assessment practices typically account for one driver at a time (e.g., either fluvial flooding only or ocean flooding only), whereas coastal cities vulnerable to SLR are at risk for flooding from multiple drivers (e.g., extreme coastal high tide, storm surge, and river flow). Here, we propose a bivariate flood hazard assessment approach that accounts for compound flooding from river flow and coastal water level, and we show that a univariate approach may not appropriately characterize the flood hazard if there are compounding effects. Using copulas and bivariate dependence analysis, we also quantify the increases in failure probabilities for 2030 and 2050 caused by SLR under representative concentration pathways 4.5 and 8.5. Additionally, the increase in failure probability is shown to be strongly affected by compounding effects. The proposed failure probability method offers an innovative tool for assessing compounding flood hazards in a warming climate.sea level rise | coastal flooding | compound extremes | copula | failure probability F looding hazard, characterized by the intensity/frequency of flood events (1), is an important consideration in local level planning and adaptation (2). Coastal cities are especially demanding sites for flood hazard assessment because of exposure to multiple flood drivers such as coastal water level (WL), river discharge, and precipitation (3, 4). Furthermore, dependence among the flood drivers [e.g., coastal surge/tide, sea level rise (SLR), and river flow] can lead to compound events (5) in which the simultaneous or sequential occurrence of extreme or nonextreme events may lead to an extreme event or impact (6). For example, in estuarine systems, the interplay between coastal WL and freshwater inflow determines the surface WL (and hence the flood probability) at subtidal (7) and tidal (8-11) frequencies.In the United States, flood hazard assessment practices are typically based on univariate methods. For example, procedures for rivers often treat oceanic contributions (e.g., tides and storm surges) using static base flood levels (e.g., ref. 12), and do not consider the dynamic effects of coastal WL (e.g., ref. 13). Similarly, flood hazard procedures for coastal WLs (e.g., ref. 14) do not account for terrestrial factors such as river discharge or direct precipitation into urban areas. Previous studies indicate that univariate extreme event analysis may not correctly estimate the probability of a given hydrologic event (15,16). This points to the potential importance of multivariate analysis of extreme events in coastal/estuarine systems and consideration of compounding effects between flood drivers (6). Bivariate extreme event analysis has been explored in a coastal context with different variables and in different areas (5, 17-33) (see SI Appendix, Table S2 for more details). B...

Humans create vast quantities of wastewater through inefficiencies and poor management of water systems. The wasting of water poses sustainability challenges, depletes energy reserves, and undermines human water security and ecosystem health. Here we review emerging approaches for reusing wastewater and minimizing its generation. These complementary options make the most of scarce freshwater resources, serve the varying water needs of both developed and developing countries, and confer a variety of environmental benefits. Their widespread adoption will require changing how freshwater is sourced, used, managed, and priced.

International audienceThe Surface Water and Ocean Topography (SWOT) satellite mission planned for launch in 2020 will map river elevations and inundated area globally for rivers >100 m wide. In advance of this launch, we here evaluated the possibility of estimating discharge in ungauged rivers using synthetic, daily ‘‘remote sensing’’ measurements derived from hydraulic models corrupted with minimal observational errors. Five discharge algorithms were evaluated, as well as the median of the five, for 19 rivers spanning a range of hydraulic and geomorphic conditions. Reliance upon a priori information, and thus applicability to truly ungauged reaches, varied among algorithms: one algorithm employed only global limits on velocity and depth, while the other algorithms relied on globally available prior estimates of discharge. We found at least one algorithm able to estimate instantaneous discharge to within 35% relative root-mean-squared error (RRMSE) on 14/16 nonbraided rivers despite out-of-bank flows, multichannel planforms, and backwater effects. Moreover, we found RRMSE was often dominated by bias; the median standard deviation of relativeresiduals across the 16 nonbraided rivers was only 12.5%. SWOT discharge algorithm progress is therefore encouraging, yet future efforts should consider incorporating ancillary data or multialgorithm synergy to improve results