Unraveling Long-Term Flood Risk Dynamics Across the Murray-Darling Basin Using a Large-Scale Hydraulic Model and Satellite Data

River floods are one of the most devastating extreme hydrological events, with oftentimes remarkably negative effects for human society and the environment. Economic losses and social consequences, in terms of affected people and human fatalities, are increasing worldwide due to climate change and urbanization processes. Long-term dynamics of flood risk are intimately driven by the temporal evolution of hazard, exposure and vulnerability. Although needed for effective flood risk management, a comprehensive long-term analysis of all these components is not straightforward, mostly due to a lack of hydrological data, exposure information, and large computational resources required for 2-D flood model simulations at adequately high resolution over large spatial scales. This study tries to overcome these limitations and attempts to investigate the dynamics of different flood risk components in the Murray-Darling basin (MDB, Australia) in the period 1973–2014. To this aim, the LISFLOOD-FP model, i.e., a large-scale 2-D hydrodynamic model, and satellite-derived built-up data are employed. Results show that the maximum extension of flooded areas decreases in time, without revealing any significant geographical transfer of inundated areas across the study period. Despite this, a remarkable increment of built-up areas characterizes MDB, with larger annual increments across not-flooded locations compared to flooded areas. When combining flood hazard and exposure, we find that the overall extension of areas exposed to high flood risk more than doubled within the study period, thus highlighting the need for improving flood risk awareness and flood mitigation strategies in the near future.

Urban Flood Risk Assessment: Integration of Decision Making and Machine Learning

Urban flood risk mapping is an important tool for the mitigation of flooding in view of human activities and climate change. Many developing countries, however, lack sufficiently detailed data to produce reliable risk maps with existing methods. Thus, improved methods are needed that can improve urban flood risk management in regions with scarce hydrological data. Given this, we estimated the flood risk map for Rasht City (Iran), applying a composition of decision-making and machine learning methods. Flood hazard maps were produced applying six state-of-the-art machine learning algorithms such as: classification and regression trees (CART), random forest (RF), boosted regression trees (BRT), multivariate adaptive regression splines (MARS), multivariate discriminant analysis (MDA), and support vector machine (SVM). Flood conditioning parameters applied in modeling were elevation, slope angle, aspect, rainfall, distance to river (DTR), distance to streets (DTS), soil hydrological group (SHG), curve number (CN), distance to urban drainage (DTUD), urban drainage density (UDD), and land use. In total, 93 flood location points were collected from the regional water company of Gilan province combined with field surveys. We used the Analytic Hierarchy Process (AHP) decision-making tool for creating an urban flood vulnerability map, which is according to population density (PD), dwelling quality (DQ), household income (HI), distance to cultural heritage (DTCH), distance to medical centers and hospitals (DTMCH), and land use. Then, the urban flood risk map was derived according to flood vulnerability and flood hazard maps. Evaluation of models was performed using receiver-operator characteristic curve (ROC), accuracy, probability of detection (POD), false alarm ratio (FAR), and precision. The results indicated that the CART model is most accurate model (AUC = 0.947, accuracy = 0.892, POD = 0.867, FAR = 0.071, and precision = 0.929). The results also demonstrated that DTR, UDD, and DTUD played important roles in flood hazard modeling; whereas, the population density was the most significant parameter in vulnerability mapping. These findings indicated that machine learning methods can improve urban flood risk management significantly in regions with limited hydrological data.

Free Global DEMs and Flood Modelling—A Comparison Analysis for the January 2015 Flooding Event in Mocuba City (Mozambique)

Flood hazard and risk analysis in developing countries is a difficult task due to the absence or scarce availability of flow data and digital elevation models (DEMs) with the necessary quality. Up to eight DEMs (ALOS Palsar, Aster GDEM, Bare Earth DEM, SRTM DEM, Merit DEM, TanDEM-X DEM, NASA DEM, and Copernicus DEM) of different data acquisition, spatial resolution, and data processing were used to reconstruct the January 2015 flood event. The systematic flow rate record from the Mocuba city gauge station as well as international aid organisms and field data were used to define both the return period peak flows in years for different flood frequencies (Tyear) and the January 2015 flooding event peak flow. Both visual and statistical analysis of flow depth values at control point locations give us a measure of the different hydraulic modelling performance. The results related to the Copernicus DEM, both in visual and statistical approach, show a clear improvement over the results of the other free global DEMs. Under the assumption that Copernicus DEM provides the best results, a flood hazard analysis was carried out, its results being in agreement with previous data of the effects of the January 2015 flooding event in the Mocuba District. All these results highlight the step forward that Copernicus DEM represents for flood hazard analysis in developing countries, along with the use of so-called “citizen science” in the form of flooding evidence field data acquisition.

Comprehensive Assessment of Flood Hazard, Vulnerability, and Flood Risk at the Household Level in a Municipality Area: A Case Study of Nan Province, Thailand

Estimating flood hazard, vulnerability, and flood risk at the household level in the past did not fully consider all relevant parameters. The main objective of this study is to improve this drawback by developing a new comprehensive and systematic methodology considering all relevant parameters and their weighting factors. This new methodology is applied to a case study of flood inundation in a municipal area of Nan City in the Upper Nan River Basin in Thailand. Field and questionnaire surveys were carried out to collect pertinent data for input into the new methodology for estimating flood hazard, vulnerability, and risk. Designed floods for various return periods were predicted using flood simulation models for assessing flood risk. The flood risk maps constructed for the return periods of 10–500 years show a substantial increase in flood risk with the return periods. The results are consistent with past flood damages, which were significant near and along the riverbanks where ground elevation is low, population density is high, and the number of household properties are high. In conclusion, this new comprehensive methodology yielded realistic results and can be used further to assess the effectiveness of various proposed flood mitigation measures.

Flood hazard mapping and analysis under climate change using hydro-dynamic model and RCPs emission scenario in Woybo River catchment of Ethiopia

Purpose The purpose of this paper is to prepare flood hazard map and show the extent of flood hazard under climate change scenarios in Woybo River catchment. The hydraulic model, Hydrologic Engineering Center - River Analysis System (HEC-RAS) was used to simulate the floods under future climate scenarios. The impact of climate changes on severity of flooding was evaluated for the mid-term (2041–2070) and long-term (2071–2100) with relative to a baseline period (1971–2000). Design/methodology/approach Future climate scenarios were constructed from the bias corrected outputs of five regional climate models and the inflow hydrographs for 10, 25, 50 and 100 years design floods were derived from the flow which generated from HEC-hydrological modeling system; that was an input for the HEC-RAS model to generate the flood hazard maps in the catchment. Findings The results of this research show that 25.68% of the study area can be classified as very high hazard class while 28.56% of the area is under high hazard. It was also found that 20.20% is under moderate hazard and about 25.56% is under low hazard class in future under high emission scenario. The projected area to be flooded in far future relative to the baseline period is 66.3 ha of land which accounts for 62.82% from the total area. This study suggested that agricultural/crop land located at the right side of the Woybo River near the flood plain would be affected more with the 25, 50 and 100 years design floods. Originality/value Multiple climate models were assessed properly and the ensemble mean was used to prepare flood hazard map using HEC-RAS modeling.