Challenges and Technical Advances in Flood Early Warning Systems (FEWSs)

Flood early warning systems (FEWSs)—one of the most common flood-impact mitigation measures—are currently in operation globally. The UN Office for Disaster Risk Reduction (UNDRR) strongly advocates for an increase in their availability to reach the targets of the Sendai Framework for Disaster Risk Reduction and Sustainable Development Goals (SDGs). Comprehensive FEWS consists of four components, which includes (1) risk knowledge, (2) monitoring and forecasting, (3) warning, dissemination, and communication, and (4) response capabilities. Operational FEWSs have varying levels of complexity, depending on available data, adopted technology, and know-how. There are apparent differences in sophistication between FEWSs in developed countries that have the financial capabilities, technological infrastructure, and human resources and developing countries where FEWSs tend to be less advanced. Fortunately, recent advances in remote sensing, artificial intelligence (AI), information technologies, and social media are leading to significant changes in the mechanisms of FEWSs and provide the opportunity for all FEWSs to gain additional capability. These technologies are an opportunity for developing countries to overcome the technical limitations that FEWSs have faced so far. This chapter aims to discuss the challenges in FEWSs in brief and exposes technological advances and their benefits in flood forecasting and disaster mitigation

Improving the Accuracy of Hydrodynamic Simulations in Data Scarce Environments Using Bayesian Model Averaging: A Case Study of the Inner Niger Delta, Mali, West Africa

In this paper, the study area was the Inner Niger Delta (IND) in Mali, West Africa. The IND is threatened by climate change, increasing irrigation, and dam operations. 2D hydrodynamic modelling was used to simulate water levels, discharge, and inundation extent in the IND. Three different digital elevation models (DEM) (SRTM, MERIT, and a DEM derived from satellite images were used as a source of elevation data. Six different models were created, with different sources of elevation data and different downstream boundary conditions. Given that the performance of the models varies according to the location in the IND, the variable under consideration and the performance criteria, Bayesian Model Averaging (BMA) was used to assess the relative performance of each of the six models. The BMA weights, along with deterministic performance measures, such as the Nash Sutcliffe coefficient (NS) and the Pearson’s correlation coefficient (r), provide quantitative evidence as to which model is the best when simulating a particular hydraulic variable at a particular location. After the models were combined with BMA, both discharge and water levels could be simulated with reasonable precision (NS > 0.8). The results of this work can contribute to the more efficient management of water resources in the IND

Intercomparison of regional-scale hydrological models and climate change impacts projected for 12 large river basins worldwide—a synthesis

An intercomparison of climate change impacts projected by nine regional-scale hydrological models for 12 large river basins on all continents was performed, and sources of uncertainty were quantified in the framework of the ISIMIP project. The models ECOMAG, HBV, HYMOD, HYPE, mHM, SWAT, SWIM, VIC and WaterGAP3 were applied in the following basins: Rhine and Tagus in Europe, Niger and Blue Nile in Africa, Ganges, Lena, Upper Yellow and Upper Yangtze in Asia, Upper Mississippi, MacKenzie and Upper Amazon in America, and Darling in Australia. The model calibration and validation was done using WATCH climate data for the period 1971–2000. The results, evaluated with 14 criteria, are mostly satisfactory, except for the low flow. Climate change impacts were analyzed using projections from five global climate models under four representative concentration pathways. Trends in the period 2070–2099 in relation to the reference period 1975–2004 were evaluated for three variables: the long-term mean annual flow and high and low flow percentiles Q10 and Q90, as well as for flows in three months high- and low-flow periods denoted as HF and LF. For three river basins: the Lena, MacKenzie and Tagus strong trends in all five variables were found (except for Q10 in the MacKenzie); trends with moderate certainty for three to five variables were confirmed for the Rhine, Ganges and Upper Mississippi; and increases in HF and LF were found for the Upper Amazon, Upper Yangtze and Upper Yellow. The analysis of projected streamflow seasonality demonstrated increasing streamflow volumes during the high-flow period in four basins influenced by monsoonal precipitation (Ganges, Upper Amazon, Upper Yangtze and Upper Yellow), an amplification of the snowmelt flood peaks in the Lena and MacKenzie, and a substantial decrease of discharge in the Tagus (all months). The overall average fractions of uncertainty for the annual mean flow projections in the multi-model ensemble applied for all basins were 57% for GCMs, 27% for RCPs, and 16% for hydrological models.Intercomparison of regional-scale hydrological models and climate change impacts projected for 12 large river basins worldwide—a synthesispublishedVersio

Challenges and ways forward for sustainable weather and climate services in Africa

Sustainability of African weather and climate information can only be ensured by investing in improved scientific understanding, observational data, and model capability. These requirements must be underpinned by capacity development, knowledge management and partnerships of co-production, communication and coordination

Intégration du risque dans la gestion des systèmes hydriques

Analyse et gestion du risque -- La définition du risque -- L'analyse du risque -- La gestion du risque -- Problèmes divers liés au risque -- Risque et incertitude -- Le rsque dans les systèmes hydriques -- Fondements théoriques -- Sites d'application -- Modélisation de l'incertitude sur les séquences futures de débits en rivière -- Génération d'ydrogrammes sous fourme d'arbres -- Indicateurs de risque, évaluation de la gestion et aide à la décision

Assessing the performance of daily to subdaily temporal disaggregation methods for the IDF curve generation under climate change

Given the short concentration time in urban watersheds, the design of municipal water infrastructures often requires knowledge of sub-daily precipitation intensity. Sub-daily time series can be directly used in a rainfall–runoff model or to derive intensity–duration–frequency (IDF) curves and calculate the design precipitation. Given that precipitation projections are typically at a daily time scale, temporal disaggregation using techniques of variable complexity is often needed to evaluate the risk/performance of urban infrastructure in the future. This paper proposes a simple steady-state stochastic disaggregation model that generates wet/dry day occurrence using a binomial distribution and precipitation intensity using an exponential distribution. Daily precipitation data from four regional climate models (RCMs) forced with the high-emission scenario representative concentration pathway (RCP 8.5) were downscaled using the quantile mapping (QM) method. The performance of the developed method is compared to widely used temporal disaggregation methods, namely, the multiplicative random cascade model (MRC), the Hurst–Kolmogorov process (HKP), and three versions of the K-nearest neighbour (KNN) model, using the Kolmogorov–Smirnov (KS) test. The six disaggregation techniques were assessed at four stations in the South Nation River Watershed in Eastern Ontario, Canada. Results indicate that, despite its simplicity, the proposed method performed well compared to other temporal disaggregation methods when resampling the observed extreme precipitation. HIGHLIGHTS Climate change impacts on short-duration precipitation extreme events are investigated using different temporal disaggregation methods.; A simple steady-state stochastic disaggregation model is introduced to generate future sub-daily precipitation intensities.; The developed disaggregation method adequately resamples the observed short-duration extreme precipitation for application in municipal water infrastructures.

A Bayesian normal homogeneity test for the detection of artificial discontinuities in climatic series

A Bayesian Normal Homogeneity Test (BNHT) for the detection of artificial discontinuities in climatic series is presented. The test is simple to use and allows the integration of prior knowledge on the date of change from various sources of information (e.g. metadata or expert belief) in the analysis. The performance of the new test was evaluated on synthetic series with similar statistical properties as observed total annual precipitation in the southern and central parts of the province of Quebec, Canada. Different priors were used to investigate the sensitivity of the test to the choice of priors. It was found that (1) high-prior probability of no change yields low false detection rates on the homogeneous series; (2) the test has a very high power of detection on series with a single shift (the best power of detection if compared with previous methods applied to the same synthetic series); (3) shifts having a small magnitude are detectable with a low prior probability of no change and (4) when applied to series with multiple shifts with a segmentation procedure and a high probability of no change, the test proved to be performing well in detecting multiple shifts (as performing as the best techniques previously applied to the same synthetic series). An example of application to total annual precipitation in Quebec City, Canada is also presented to illustrate (1) a case for which the results are not affected by the choice of the prior parameters and (2) a case for which information about potential changes found in the metadata was integrated in the analysis and allowed the detection of a change that would not have been detected with a non-informative prior

Synthèse des techniques d'homogénéisation des séries climatiques et analyse d'applicabilité aux séries de précipitations

RésuméAu cours des dernières décennies, plusieurs méthodes d'homogénéisation ont été développées pour corriger les ruptures artificielles dans les séries climatiques. Elles ont été développées dans divers pays, pour différentes situations et avec des domaines d'application différents. Cet article présente une revue exhaustive des méthodes d'homogénéisation des séries climatiques publiées dans la littérature. Une analyse critique de ces méthodes ainsi qu'une réflexion sur leur applicabilité aux séries de précipitations totales annuelles sont également présentées.AbstractDuring the last decades, a considerable effort was spent on the development of homogenization techniques that can identify and correct anthropogenic bias in climatic series. These methods were developed in various countries, for different contexts and for different domains of applicability. The present paper is an exhaustive review of published homogenization techniques for climatic series. A critical analysis of the described methods and a discussion on their applicability to total annual precipitation are also presented