Towards multi-objective optimization of large-scale fluvial landscaping measures

Straatsma, Menno; Fliervoet, Jan M.; Kabout, Johan A. H.; Baart, Fedor; Kleinhans, Maarten G.

doi:10.5194/nhess-19-1167-2019

Cited by 13 publications

(17 citation statements)

References 65 publications

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“…Because of the large number of vegetation types (ecotopes) relative to the number of observations, we classified each ecotope as either a suitable or an unsuitable corncrake habitat, based on information on habitat requirements provided by Schäffer2004 and Koffijberg (2004). For elevation, we used a 25 m resolution elevation map (Schellekens et al 2014, Straatsma et al 2019). For each record (presence or pseudo‐absence), we then calculated the proportion of suitable habitat area (%) and the mean elevation (m a.s.l.)…”

Section: Methodsmentioning

confidence: 99%

Disentangling drivers of spatial autocorrelation in species distribution models

et al. 2020

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Species distribution models (SDMs) are frequently used to understand the influence of site properties on species occurrence. For robust model inference, SDMs need to account for the spatial autocorrelation of virtually all species occurrence data. Current methods do not routinely distinguish between extrinsic and intrinsic drivers of spatial autocorrelation, although these may have different implications for conservation. Here, we present and test a method that disentangles extrinsic and intrinsic drivers of spatial autocorrelation using repeated observations of a species. We focus on unknown habitat characteristics and conspecific interactions as extrinsic and intrinsic drivers, respectively. We model the former with spatially correlated random effects and the latter with an autocovariate, such that the spatially correlated random effects are constant across the repeated observations whereas the autocovariate may change. We tested the performance of our model on virtual species data and applied it to observations of the corncrake Crex crex in the Netherlands. Applying our model to virtual species data revealed that it was well able to distinguish between the two different drivers of spatial autocorrelation, outperforming models with no or a single component for spatial autocorrelation. This finding was independent of the direction of the conspecific interactions (i.e. conspecific attraction versus competitive exclusion). The simulations confirmed that the ability of our model to disentangle both drivers of autocorrelation depends on repeated observations. In the case study, we discovered that the corncrake has a stronger response to habitat characteristics compared to a model that did not include spatially correlated random effects, whereas conspecific interactions appeared to be less important. This implies that future conservation efforts should primarily focus on maximizing habitat availability. Our study shows how to systematically disentangle extrinsic and intrinsic drivers of spatial autocorrelation. The method we propose can help to correctly identify the main drivers of species distributions.

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Section: Methodsmentioning

confidence: 99%

Disentangling drivers of spatial autocorrelation in species distribution models

et al. 2020

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“…With an open-source code, accessible inputs, and robust representation of the probability of flood inundation for floods with a large range of frequencies, states, municipalities, or other stakeholders could execute the model. Additionally, floodplain maps may be amended relatively easily to shifts in inputs from either an improved understanding of process, uncertainty, or current or future changes, although updates to the model code may enhance the functionality of such evaluations (e.g., [ 52 ]).…”

Section: Discussionmentioning

confidence: 99%

Improving flood hazard datasets using a low-complexity, probabilistic floodplain mapping approach

et al. 2021

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As runoff patterns shift with a changing climate, it is critical to effectively communicate current and future flood risks, yet existing flood hazard maps are insufficient. Modifying, extending, or updating flood inundation extents is difficult, especially over large scales, because traditional floodplain mapping approaches are data and resource intensive. Low-complexity floodplain mapping techniques are promising alternatives, but their simplistic representation of process falls short of capturing inundation patterns in all situations or settings. To address these needs and deficiencies, we formalize and extend the functionality of the Height Above Nearest Drainage (i.e., HAND) floodplain mapping approach into the probHAND model by incorporating an uncertainty analysis. With publicly available datasets, the probHAND model can produce probabilistic floodplain maps for large areas relatively rapidly. We describe the modeling approach and then provide an example application in the Lake Champlain Basin, Vermont, USA. Uncertainties translate to on-the-ground changes to inundated areas, or floodplain widths, in the study area by an average of 40%. We found that the spatial extent of probable inundation captured the distribution of observed and modeled flood extents well, suggesting that low-complexity models may be sufficient for representing inundation extents in support of flood risk and conservation mapping applications, especially when uncertainties in parameter inputs and process simplifications are accounted for. To improve the accuracy of flood hazard datasets, we recommend investing limited resources in accurate topographic datasets and improved flood frequency analyses. Such investments will have the greatest impact on decreasing model output variability, therefore increasing the certainty of flood inundation extents.

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“…2 Flood damages 2.1 Towards risk-based flood management in highly productive paddy rice cultivation -concept development and application to the Mekong Delta (Triet et al, 2018) Flood risk assessment involves proper estimation of hazard and consequent damage. Compared with a large number of hazard studies, research on flood damage is rather limited, particularly in agricultural lands.…”

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confidence: 99%

“…Compared with a large number of hazard studies, research on flood damage is rather limited, particularly in agricultural lands. Triet et al (2018) present a large-scale flood risk assessment for the agricultural sector in the Vietnamese Mekong Delta. The authors focus on rice paddies, which are the predominant crop type in the delta.…”

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confidence: 99%

Preface: Advances in flood risk assessment and management

Prieto

Patel

Han

2020

Nat. Hazards Earth Syst. Sci.

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Abstract. Floods are among Earth's most common and most destructive natural hazards, affecting human lives and properties directly and indirectly around the world. The frequency and magnitude of extreme flooding have been increasing in many parts of the world in recent decades (see, e.g. Berghuijs et al., 2017; Blöschl et al., 2019a; Marijnissen et al., 2019), hampering human well-being and economic growth in both developed and developing countries. Flood risk management carries out the flood risk assessment and uses appropriate resources (human, finance, science and technology, and nature) to control the flood risk (Han, 2011), which is an urgent challenge for the scientific and engineering communities to address. In a similar way to “Twenty-three unsolved problems in hydrology” (Blöschl et al., 2019b), despite decades of research in this field, there are still many unsolved problems in floods as well. This special issue “Flood Risk Assessment and Management” is an outcome of the session “Flood Risk Assessment and Management” in the Naturals Hazards Division at the European Geosciences Union (EGU) General Assembly held in Vienna, Austria. The session series has been organized annually at EGU since 2018. This special issue presents a wide range of in-depth research studies based on flood modelling (including hydrological modelling and hydrodynamic modelling), hazard mapping, flood damage and risk assessment as well as studies that focus on flood relief prioritization, mitigation strategies and flood policies. Extraordinary floods and debris flows are also included due to dam and dike breaks and extreme storms over gullies in mountain areas. The nine articles in this special issue are broadly introduced in the following three categories.

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Towards multi-objective optimization of large-scale fluvial landscaping measures

Cited by 13 publications

References 65 publications

Disentangling drivers of spatial autocorrelation in species distribution models

Disentangling drivers of spatial autocorrelation in species distribution models

Improving flood hazard datasets using a low-complexity, probabilistic floodplain mapping approach

Preface: Advances in flood risk assessment and management

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