Design of coastal defense structures like seawalls and breakwaters can no longer be based on stationarity assumption. In many parts of the world, an anticipated sea‐level rise (SLR) due to climate change will constitute present‐day extreme sea levels inappropriate for future coastal flood risk assessments since it will significantly increase their probability of occurrence. Here, we first show that global annual maxima sea levels (AMSLs) have been increasing in magnitude over the last decades, primarily due to a positive shift in mean sea level (MSL). Then, we apply non‐stationary extreme value theory to model the extremal behavior of sea levels with MSL as a covariate and quantify the evolution of AMSLs in the following decades using revised probabilistic sea‐level rise projections. Our analysis reveals that non‐stationary distributions exhibit distinct differences compared to simply considering stationary conditions with a change in location parameter equal to the amount of MSL rise. With the use of non‐stationary distributions, we show that by the year 2050 many locations will experience their present‐day 100‐yr return level as an event with return period less than 15 and 9 years under the moderate (RCP4.5) and high (RCP8.5) representative concentration pathways. Also, we find that by the end of this century almost all locations examined will encounter their current 100‐yr return level on an annual basis, even if CO2 concentration is kept at moderate levels (RCP4.5). Our assessment accounts for large uncertainty by incorporating ambiguities in both SLR projections and non‐stationary extreme value distribution parameters via a Monte Carlo simulation.

No abstract

Groundwater is a vital resource for both natural ecosystems and humans (Wada et al., 2014). Over 2 billion people rely on groundwater as their main freshwater resource (Famiglietti, 2014). Groundwater is also extensively used in irrigated agriculture (Siebert et al., 2010). The increased water extraction to satisfy human needs has led to groundwater depletion in many parts of the world (Aeschbach-Hertig & Gleeson, 2012;Rodell et al., 2009;Scanlon et al., 2007). Given these evolving challenges, which are expected to get worse with increasing population, assessment of groundwater recharge, that is, the amount of water that replenishes aquifers after escaping the vadose zone , is crucial for sustainable management and development of groundwater resources (Moon et al., 2004). Groundwater recharge is, however, an inherently complex process controlled by multiple factors including climate, geomorphology, vegetation characteristics and antecedent soil moisture conditions, among others (De Vries & Simmers, 2002). Several methods to obtain estimates of groundwater recharge exist, including those based on direct measurements (Flint et al., 2002) and indirect methods that often use empirical models (Reitz & Sanford, 2020), physically based land surface models (

<p>Verification provides the answer to the question "How good is my forecast?". Knowing the quality of a forecasting system provides a necessary baseline for improvement of that quality. It also contributes to forecast informed decision making, as verification provides a baseline estimate of residual uncertainties. </p><p>To monitor the quality of the forecasts produced by Deltares Global Flow Forecasting system, a prototype of an operational forecast verification system was developed. The verification system comprises various components including the Ensemble Verification System (EVS), the Deltares OpenArchive and the Delft-FEWS forecast production system. Relevant verification metrics are computed by the EVS, which are subsequently stored and displayed in the forecasting system. This will allow for robust, automated forecast verification, and the usage of this information during the real-time forecasting process.  </p><p>Future work on the system will include a post-processing routine which will cast the verification information in a format suitable for publication to both existing and prospective GLOFFIS clients. Over the years, the system's outcomes are expected to provide a significant contribution to the quality of the GLOFFIS forecasts. </p><p>In parallel, the system is being applied to various other operational hydrological forecasting systems around the globe.</p>

<p>Operational near real-time flood forecasting relies heavily on adequate spatial interpolation of precipitation forcing which bears a huge impact on the accuracy of hydrologic forecasts. In this study, the generalized REGNIE (genRE) interpolation technique is examined. The genRE approach was shown to enhance the traditional Inverse Distance Weighting (IDW) method with information from existing observed climatological precipitation data sets (Van Osnabrugge, 2017). The successful application of the genRE method with a re-analysis precipitation data set, expands the applicability of the method as detailed re-analysis data sets become more prevalent while high density observation networks remain scarce.</p><p>Here, the approach is extended to use climatological precipitation data from the Met Éireann’s Re-Analysis (MÉRA). Investigations are carried out using hourly precipitation accumulations for two major flood events induced by Atlantic storms in the Suir River Basin, Ireland. Alongside genRE, the following techniques are comparatively explored: Inverse Distance Weighting (IDW), Ordinary Kriging (OK) and Regression Kriging (RK). Cross-validation is applied in order to compare the different interpolation methods, while spatial maps and correlation coefficients are utilized for assessing the skill of the interpolators to emulate the climatology of MÉRA. In the process, a preliminary intercomparison between the observed precipitation and MÉRA precipitation for the two events is also made.</p><p>In a statistical sense, cross-validation results verify that genRE performs slightly better than all three interpolation techniques for both events studied. Overall, OK is found to be the most inadequate approach, specifically in terms of preserving the original variance in observed precipitation. MÉRA manages to reproduce the temporal variations of observations in a good manner for both events, whereas it displays less skill when considering spatial variations especially where topography has a major influence. Finally, genRE outperforms all other interpolators in mimicking the climatological conditions of MÉRA for both events.</p><p> </p><p>Van Osnabrugge, B., Weerts, A.H. and Uijlenhoet, R., 2017. genRE: A method to extend gridded precipitation climatology data sets in near real-time for hydrological forecasting purposes. Water Resources Research, 53(11), pp.9284-9303.</p>