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

Floods are one of the most frequent and disruptive natural hazards that affect man. Annually, significant flood damage is documented worldwide. Flood mapping is a common preimpact flood hazard mitigation measure, for which advanced methods and tools (such as flood inundation models) are used to estimate potential flood extent maps that are used in spatial planning. However, these tools are affected, largely to an unknown degree, by both epistemic and aleatory uncertainty. Over the past few years, advances in uncertainty analysis with respect to flood inundation modeling show that it is appropriate to adopt Probabilistic Flood Maps (PFM) to account for uncertainty. However, the following question arises; how can probabilistic flood hazard information be incorporated into spatial planning? Thus, a consistent framework to incorporate PFMs into the decision-making is required. In this paper, a novel methodology based on Decision-Making under Uncertainty theories, in particular Value of Information (VOI) is proposed. Specifically, the methodology entails the use of a PFM to generate a VOI map, which highlights floodplain locations where additional information is valuable with respect to available floodplain management actions and their potential consequences. The methodology is illustrated with a simplified example and also applied to a real case study in the South of France, where a VOI map is analyzed on the basis of historical land use change decisions over a period of 26 years. Results show that uncertain flood hazard information encapsulated in PFMs can aid decision-making in floodplain planning.

This paper presents an analysis of uncertainty in hydraulic modelling of floods, focusing on the inaccuracy caused by inflow errors and parameter uncertainty. In particular, the study develops a method to propagate the uncertainty induced by, firstly, application of a stage-discharge rating curve and, secondly, parameterisation of a onedimensional hydraulic model by way of the power function and the conditioning of Manning's roughness coefficients. The proposed methodology was applied to a 98 km reach of the River Po, Italy. Model performance was evaluated using two independent sets of observed water levels in the river reach within a generalised likelihood uncertainty estimation framework. The inflow uncertainty was found to have a greater contribution to the overall uncertainty of the 1D model than the roughness parameters. Independent parameter conditioning and validation, as well as the uncertainty analysis, showed satisfactory model performance. When conditioned on one flood event, the model adequately simulated flood levels and high water marks for another (independent) event, as the observations were within 90% confidence interval of the simulation ensemble.

This study assessed the utility of EUDEM, a recently released digital elevation model, to support flood inundation modelling. To this end, a comparison with other topographic data sources was performed (i.e. LIDAR, light detection and ranging; SRTM, Shuttle Radar Topographic Mission) on a 98-km reach of the River Po, between Cremona and Borgoforte (Italy). This comparison was implemented using different model structures while explicitly accounting for uncertainty in model parameters and upstream boundary conditions. This approach facilitated a comprehensive assessment of the uncertainty associated with hydraulic modelling of floods. For this test site, our results showed that the flood inundation models built on coarse resolutions data (EUDEM and SRTM) and simple one-dimensional model structure performed well during model evaluation.

Water, energy and food (WEF) security are key indicators of sustainable development. Realization of sustainable development goals (SDGs) by countries is achieved through a water-energy-food-ecosystem nexus framework. Climate change is a threat to food, energy and water security in the Horn of Africa. The main aim of this review is to assess the status and prospects of WEF nexus as it relates to SDGs in the horn of Africa.  The countries considered were Ethiopia, Eritrea, Somalia and Djibouti. The review indicated that the four countries have a challenge in achieving SDGs 2, 6 and 7.  Djibouti had the highest (50.9) WEF index in the region followed by Ethiopia and Somalia at 47.5 and 36.8, respectively while Eritrea had the lowest WEF index of 35.8. The energy sub-index was the best performer in the region with an average index of 56 while water and food sub-indices were the worst at 36. Political instability, insecurity, inadequate infrastructure, weak institutional and legal framework are some of the challenges facing WEF and sustainable development in the region. Climate change adaptation measures should be incorporated into the water, energy, food and ecosystem (WEFE) nexus using an integrated approach. Modelling WEFE requires integration of models and should also focus on interactions among the sub-systems.

We compared flood mapping techniques using a one‐dimensional (1D) hydraulic model HEC‐RAS and two‐dimensional (2D) LISFLOOD‐FP for a 10‐km reach of Gorgan River in Iran. Both models were run using the same hydrologic input data. The input into the models was a steady discharge of 90 cm, corresponds to a flood peak occurred on March 25, 2012. Flood maps generated using these two models were compared with an observed flood inundation map, using F‐statistic. The roughness coefficients of the models were calibrated by maximizing the value of the F‐statistic. Based on the F‐statistic, LISFLOOD‐FP gives a slightly better result (F = 0.69) than HEC‐RAS (F = 0.67). Visual comparison of the flood extents generated by the two models showed reasonably good agreement. Validation was done using a flood event occurred on May 31, 2014. The LISFLOOD‐FP model gave a better result for validation as well. The 2D model showed more consistency in comparison with the 1D model.

SUMMARYFloods are natural events that can disrupt vulnerable societies and cause significant damages. Floodplain mapping, i.e. the assessment of the areas that can potentially be flooded, can help reduce the negative impact of flood events by supporting the process of landuse planning in areas exposed to flood risk. Flood inundation modelling is one of the most common approaches to develop floodplain maps.The recent literature has shown that hydraulic modelling of floods is affected by numerous sources of uncertainty that can be reduced (but not eliminated) via calibration and validation. For instance, many studies have shown that models may fail to simulate flood events of magnitude different from that of calibration and validation events. This can be caused by the fact that river flow mechanisms are nonlinear and are characterised by thresholds that demarcate flow regimes.One of the challenges in using uncertain outcomes is that decision makers (e.g. spatial planners) often have to take decisive binary actions, for instance, either to change the landuse (e.g. urbanize) or not. From the perspective of a modeller, one can provide precise (but potentially wrong) results based on both expert knowledge and the results of calibrated-and-validated models. However, this is neither prudent nor pragmatic, given that expert knowledge is variable and unavoidably subjective. As a matter of fact, different modellers using the same input data and models often attain different results. Thus, it is more scientifically sound to provide the results of flood inundation models in probabilistic terms.The objective of this thesis is to contribute to the scientific work on assessing Probabilistic flood hazard maps are generated using a Monte Carlo approach to capture the impact of these sources of uncertainty. Lastly, a new methodology for assessing the benefits of flood hazard mitigation measures (i.e. the KULTURisk framework as a result of an EU FP7 project) was used.The utility of probabilistic model output is then assessed using two approaches: (i)Value of Information, and (ii) Prospect theory. Implementation of these two approaches is based on the premise of a welfare trajectory, whereby the value of (and vii generated from) assets and investments in the floodplain accrue over time. Thus, the occurrence of a flood event results in damages that lower the welfare trajectory.Landuse in the floodplain can be altered based on the needs of the community as well as on potential flood risk. In this case, a higher investment yields higher returns,

The Artificial Neural Network (ANN) modeling has been applied successfully in hydrology to predict future flows based on the previous rainfall-runoff values. For a long time, flooding has been experienced in the surrounding areas of the Rift Valley lakes including Lake Baringo fed by River Perkerra due to the rising water levels because of the above normal rainfall season resulting in massive socioeconomic losses. The study aims at predicting the occurrence of floods in River Perkerra using ANN model with the input data being 417 consistent pairs of daily rainfall and discharge, simulated runoff as the output. The model was trained, tested and validated producing a best fit regression with R2 of 0.951 for training, 0.938 for validation, 0.953 for testing giving an average of 0.949 indicating a close relationship between the input and output values. The overall best validation performance, RMSE was 0.9204 m3/s indicating high efficiency of the FFNN model developed to predict floods. Flows greater than 14 m3/s, Q1, were the extreme flood events closely associated with the socioeconomic losses. This prediction of Q1 value is crucial in the formulation and implementation of measures and policies by the County Government that will mitigate adverse impacts of predicted floods in the catchment.