Suitability of the height above nearest drainage (HAND) model for flood inundation mapping in data-scarce regions: a comparative analysis with hydrodynamic models

Unprecedented floods from extreme rainfall events worldwide emphasize the need for flood inundation mapping for floodplain management and risk reduction. Access to flood inundation maps and risk evaluation tools remains challenging in most parts of the world, particularly in rural regions, leading to decreased flood resilience. The use of hydraulic and hydrodynamic models in rural areas has been hindered by excessive data and computational requirements. In this study, we mapped the flood inundation in Huron Creek watershed, Michigan, USA for an extreme rainfall event (1000-year return period) that occurred in 2018 (Father’s Day Flood) using the Height Above Nearest Drainage (HAND) model and a synthetic rating curve developed from LIDAR DEM. We compared the flood inundation extent and depth modeled by the HAND with flood inundation characteristics predicted by two hydrodynamic models, viz., HEC-RAS 2D and SMS-SRH 2D. The flood discharge of the event was simulated using the HEC-HMS hydrologic model. Results suggest that, in different channel segments, the HAND model produces different degrees of concurrence in both flood inundation extent and depth when compared to the hydrodynamic models. The differences in flood inundation characteristics produced by the HAND model are primarily due to the uncertainties associated with optimal parameter estimation of the synthetic rating curve. Analyzing the differences between the HAND and hydrodynamic models also highlights the significance of terrain characteristics in model predictions. Based on the comparable predictive capability of the HAND model to map flood inundation areas during extreme rainfall events, we demonstrate the suitability of the HAND-based approach for mitigating flood risk in data-scarce, rural regions

Use of HAND terrain descriptor for estimating flood-prone areas in river basins

O mapeamento de áreas inundáveis em uma bacia hidrográfica é fundamental para o gerenciamento do risco de inundações, estratégias mitigadoras e sistemas de previsão e alerta, entre outros benefícios. Uma abordagem para esse mapeamento é com base no descritor do terreno HAND (Height Above Nearest Drainage), derivado diretamente do Modelo Digital de Elevação (MDE), no qual cada pixel apresenta a diferença de elevação desse ponto em relação ao ponto da rede de drenagem ao qual ele se conecta. Considerando a bacia do rio Mamanguape (3.522,7 km²; Paraíba) como área de estudo, esta pesquisa adotou esse método e verificou sua aplicabilidade quanto a cinco aspectos: consideração de uma área mínima variável espacialmente para denotar o início da drenagem; impacto de considerar o MDE sem depressões; avaliação da condiçãohidrostática; efeito de incorporação de uma rede vetorial existente; análise comparativa à morfologia da bacia em termos do perfil longitudinal dos rios. Os resultados indicaram que adotar um valor uniforme de área mínima de contribuição para início da rede de drenagem é uma simplificação que deveria ser evitada, adotando-se a variação espacial de tal parâmetro, que influi no total e na distribuição espacial das áreas inundadas. Além disso, considerar o MDE sem depressões leva a maiores valores do HAND e menor área inundada (diferença variou de 3% a 99%), comparativamente ao MDE com depressões, embora apenas 3,1% dos pixels representem depressões. É recomendado considerar o MDE sem depressões, ao passo que o pré-processamento por incorporação de rede vetorial (stream burning) gera resultados incoerentes quanto à relação do HAND com o padrão morfológico representado no MDE. Concluiuse, ainda, que a estimativa de áreas inundáveis pelo HAND não garante a condição hidrostática, mas esse desacordo abrange uma região de extensão desprezível para fins práticos.The flood hazard mapping in a river basin is crucial for flooding risk management, mitigation strategies, and flood forecasting and warning systems, among other benefits. One approach for this mapping is based on the HAND (Height Above Nearest Drainage) terrain descriptor, directly derived from the Digital Elevation Model (DEM), in which each pixel represents the elevation difference of this point in relation to the river drainage network to which it is connected. Considering the Mamanguape river basin (3,522.7 km²; state of Paraíba, Brazil) as the study location, the present research applied this method and verified it as for five aspects: consideration of a spatially variable minimum drainage area for denoting the river drainage initiation; the impact of considering a depressionless DEM; evaluation of hydrostatic condition; effect of incorporating an existing river vector network; and comparative analysis of basin morphology regarding longitudinal river profiles. According to the results, adopting a uniform minimum drainage area for the river network initiation is a simplification that should be avoided, using a spatially variable approach, which influences the amount and spatial distribution of flooded areas. Additionally, considering the depressionless DEM leads to higher values of HAND and to a smaller flooded area (difference ranging between 3% and 99%), when compared with the use of DEM with depression, despite 3.1% of the pixels representing depressions. The use of the depressionless DEM is recommended, whereas the DEM pre-processing by incorporating a vector network (stream burning) generates dubious results regarding the relation between HAND and the morphological pattern presented in the DEM. Moreover, the estimation of flooded areas based on HAND does not guarantee the hydrostatic condition, but this disagreement comprises a negligible area for practical purposes

Predictive modelling benchmark of nitrate Vulnerable Zones at a regional scale based on Machine learning and remote sensing

Nitrate leaching losses from arable lands into groundwater were a main driver in designating Nitrate Vulnerable Zones (NVZs) according to the Nitrates Directive, with a view to enhancing their water quality. Despite this, developing common strategies for effective water quality control in these areas remains a challenge in the European Union. This paper evaluates the performance of the Random Forest (RF) machine learning algorithm combined with Feature Selection (FS) techniques in predicting nitrate pollution in NVZs groundwater bodies in different periods and using updated environmental features in Andalusia, Spain. A set of forty-four features extrinsic to groundwater bodies were used as environmental predictors, with an aim to make this methodology exportable to other regions. Phenological features obtained through remote-sensing techniques were included to measure the dynamics of agricultural activity. In addition, other dynamic features derived from weather and livestock effluents were included to analyse seasonal and interannual changes in nitrate pollution. Three feature stacks and two nitrate databases were used in the predictive modelling: Period 1 (2009), with 321 nitrate samples for training; Period 2 (2010), with 282 nitrate samples for validation and initial spatial prediction; and Period 3 (2017), to assess the changes in the probability of groundwater nitrate content exceeding 50 mg/L. Random Forest as a wrapper with four sequential search methods was considered: sequential backward selection (SBS), sequential forward selection (SFS), sequential forward floating selection (SFFS) and sequential backward floating selection (SBFS). From among all the Feature Selection methods applied, Random Forest with SFS had the best performance (overall accuracy = 0.891 and six predictor features) and linked the highest probability of nitrate pollution with three dynamic features: the Normalized Difference Vegetation Index (NDVI) base level, NDVI value for the end of the growing season and accumulated manure production of livestock farms; and three static features: slope, sediment depositional areas and valley depth

Lake Huron Shoreline Analysis

Lake Huron is a popular tourist destination and is home to several businesses and residents. Since the shoreline is dynamic and is subject to change over the years due to several factors such as a change in water level, soil type, human encroachment, etc., these locations tend to encounter floods due to increased water levels and wind speed. This causes erosion and loss to the properties along the shoreline. This study is based on two areas of interest named Pinery Provincial Park and Sauble Beach which are located on the shoreline of Lake Huron where Pinery Provincial Park is a naturally maintained shoreline and Sauble Beach is altered by humans to make it a tourism-oriented beach. The project investigates and compares the changes in shorelines between both locations to study the effects of two different shoreline maintenance practices. The change is then further studied by adding a dimension of water levels from 1970 to 2021 and future level changes. A software application named Digital Shoreline Analysis System (DSAS) version 5.0 was used within ESRI’s ArcMap 10.8 to perform shoreline change analysis. DSAS produces results in the form of the following statistics: shoreline change envelope (SCE), net shoreline movement (NSM), endpoint rate (EPR), linear regression rate (LRR), and weighted linear regression (WLR). EPR was used to analyze shoreline change rate and NSM was used to map flooding and erosion hazards. This project also examines the areas which may flood in the future due to climate change and unprecedented water level rise by using the following two approaches: a) Hypothetical situation was considered in which there would be ±2m water level change on top of forecasted water levels by US Army Corps of Engineers and Fisheries and Oceans Canada and b) Built-in Kalman Filter Model in DSAS was used to predict the shoreline for next 10 and 20 years. Based on these approaches, flood and erosion hazard maps were created considering variables such as water level, slope, elevation, and bathymetry. After analysis, a 3D model was created to showcase the areas which could be impacted based on the first approach in future flooding scenarios. The analysis is accompanied by the study of shoreline management strategies commonly used in Canada and based on the results of the analysis, recommendations for future management strategies will be made to minimize the impact of the flood. Lastly, the overall results of Sauble Beach and Pinery Provincial Park are compared and discussed in section 6. The results indicate that the Pinery Provincial Park shoreline has a stable shoreline compared to Sauble Beach. Pinery Provincial Park is having about 50% fewer erosion rates and negative shoreline movement than Sauble Beach. It cannot be neglected that both study areas are facing increased erosion and decreased accretion over the years but the human interference and the Sauble Beach municipality’s neglecting towards sustainable tourism practices has resulted in losing its beach at a higher rate. This project suggests adapting green shoreline management techniques to both the study area. These results may be useful for the authorities, local government agencies, and NGOs that are tasked with developing and implementing shoreline management plans

Improving Flood Inundation and Streamflow Forecasts in Snowmelt Dominated Regions

Much effort has been dedicated to expanding hydrological forecasting capabilities and improving understanding of the continental-scale hydrological modeling used to predict future hydrologic conditions and quantify consequences of climate change. In 2016, the National Oceanic and Atmospheric Administration’s (NOAA) Office of Water Prediction implemented the National Water Model (NWM) to provide nationally consistent, operational hydrologic forecasting capability across the continental U.S. The primary goal of this research was to develop hydrological tools that include modeling of flood inundation mapping and snowmelt contributions to river flow in snowmelt-dominated regions across the Western U.S. This dissertation first presents terrain analysis enhancements developed to reduce the overestimation of flooded areas, observed where barriers such as roads cross rivers, from the continental-scale flood inundation mapping method that uses NWM streamflow forecasts. Then, it reports on a systematic evaluation of the NWM snow outputs against observed snow water equivalent (SWE) and snow-covered area fraction (SCAF) at point locations across the Western U.S. This evaluation identified the potential causes responsible for discrepancies in the model snow outputs and suggests opportunities for future research directed towards model improvements. Then, it presents improvements to SWE modeling by quantifying the improvements when using better model inputs and implementing humidity information in separating precipitation into rain and snow. These results inform understanding of continental-scale hydrologic processes and how they should be modeled

Surface water – groundwater interactions: A case of a shallow semi-closed lake catchment in northern Tanzania

A Dissertation Submitted in Partial Fulfilment of the Requirements for the Degree of Doctor of Philosophy in Water Resources Engineering of the Nelson Mandela African Institution of Science and TechnologyConjunctive use of surface water and groundwater is rapidly growing in many developing countries as an adaptation strategy to climate variability and change. However, the interactions between the groundwater and the surface water systems are not adequately understood, especially among the East African rift valley lakes, where data paucity has limited studies and reporting on the spatial influence of catchment heterogeneity. In its humble contribution to sustainable water development, this study aimed to present a platform for understanding the influence of climatic variation and anthropogenic activities on surface water–groundwater interactions. To be relevant locally, Lake Babati, a freshwater lake in Northern Tanzania that provides the community with fish, freshwater, and a habit for hippopotamus, was studied. The study applied hydrological simulation, grey relational analysis, and stepwise regression analysis to model the hydrological behaviour of the lake. Further, it used hydrogeochemistry and environmental isotopes to identify groundwater fluxes and draw the conceptual understanding of surface water – groundwater interaction and applied topography-based indices to spatially map groundwater potentials within the catchment. The results showed that Lake Babati level is significantly declining (p-value < 0.01) at a rate of 25 mm per annum. The lake level decline could not be explained by climatic variability since the decline occurred when both evaporation and rainfall showed no significant changes either seasonally or annually. Instead, the consistent decline of the lake level in all seasons could be due to the expansion of the spillway, which effectively lowered the lake reservoir level and increased the lake outflow in rainy seasons. The hydro-geochemistry and isotopes data showed that the lake water and groundwater interact and are in hydraulic connections. Further, using Height Above Nearest Drainage based and Topography Wetness Index based methods, the study developed two groundwater potential maps to predict groundwater spatial variability and guide groundwater prospecting efforts and subsequent development. Given that Lake Babati is in a hydraulic connection with the groundwater, its consistent decline will likely impact the groundwater system. Similarly, abstracting groundwater at unsustainable rates could lower the lake levels further. Therefore, integrated water resources management is required for sustainable water resources development and management in the catchment. Mandatory and continuous monitoring of the water resources (groundwater levels, river flows, and lake levels) is recommended to generate quality in situ data for future studies

Flood Extent and Volume Estimation using Multi-Temporal Synthetic Aperture Radar.

Ph. D. Thesis.Satellite imagery has the potential to monitor flooding across wide geographical regions. Recent launches have improved the spatial and temporal resolution of available data, with the European Space Agency (ESA) Copernicus programme providing global imagery at no end-user cost. Synthetic Aperture Radar (SAR) is of particular interest due to its ability to map flooding independent of weather conditions. Satellite-derived flood observations have real-world application in flood risk management and validation of hydrodynamic models. This thesis presents a workflow for estimating flood extent, depth and volume utilising ESA Sentinel-1 SAR imagery. Flood extents are extracted using a combination of change detection, variable histogram thresholding and object-based region growing. An innovative technique has been developed for estimating flood shoreline heights by combining the inundation extents with high-resolution terrain data. A grid-based framework is used to derive the water surface from the shoreline heights, from which water depth and volume are calculated. The methodology is applied to numerous catchments across the north of England that suffered from severe flooding throughout the winter of 2015-16. Extensive flooding has been identified throughout the study region, with peak inundation occurring on 29th December 2015. On this date, over 100 km2 of flooding is identified in the Ouse catchment, equating to a water volume of 0.18 km3. The SAR flood extents are validated against satellite optical imagery, achieving a Total Accuracy of 91% and a Critical Success Index of 77%. The derived water surfaces have an average error of 3 cm and an RMSE of 98 cm compared to river stage measurements. The methods developed are robust and globally applicable, shown with an additional study along the Mackenzie River in Australia. The presented methodology, alongside the increased temporal resolution provided by Sentinel-1, highlights the potential for accurate, reliable mapping of flood dynamics using satellite imagery.NERC, (DREAM) CD