A globally applicable framework for compound flood hazard modeling

Natural hazards and earth system sciences, Journal Year: 2023, Volume and Issue: 23(2), P. 823 - 846

Published: Feb. 27, 2023

Abstract. Coastal river deltas are susceptible to flooding from pluvial, fluvial, and coastal flood drivers. Compound floods, which result the co-occurrence of two or more these drivers, typically exacerbate impacts compared floods a single driver. While several global models have been developed, do not account for compound flooding. Local-scale provide state-of-the-art analyses but hard scale other regions as based on local datasets. Hence, there is need globally applicable hazard modeling. We develop, validate, apply framework modeling that accounts interactions between all It consists high-resolution 2D hydrodynamic Super-Fast INundation CoastS (SFINCS) model, automatically set up datasets coupled with routing model surge tide model. To test framework, we simulate historical events, Tropical Cyclone Idai Eloise in Sofala province Mozambique, compare simulated extents satellite-derived multiple days both events. Compared CaMa-Flood generally performs better terms critical success index (−0.01–0.09) hit rate (0.11–0.22) worse false-alarm ratio (0.04–0.14). Furthermore, depth maps realistic due floodplain connectivity comprehensive picture direct pluvial simulated. Using new determine dominant drivers transition zones These vary significantly events because differences magnitude time lag argue wide range plausible should be investigated obtain robust understanding interactions, important understand adaptation, preparedness, response. As setup coupling automated, reproducible, applicable, presented promising step forward towards large-scale

Language: Английский

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Large scale hydrologic and tracer aided modelling: A review

Journal of Hydrology, Journal Year: 2023, Volume and Issue: 618, P. 129177 - 129177

Published: Jan. 28, 2023

Language: Английский

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HydroMT: Automated and reproducible model building and analysis

The Journal of Open Source Software, Journal Year: 2023, Volume and Issue: 8(83), P. 4897 - 4897

Published: March 7, 2023

Many impact assessment studies rely on hydrological and hydrodynamic (hydro) models.These models typically require a large set of parameters derived from different datasets hence manual setup can be time consuming hard to reproduce.HydroMT (Hydro Model Tools) is an open-source Python package that aims make the process building model instances analyzing results automated reproducible.The provides common interface data instances, workflows transform into based (hydrological) GIS statistical methods, various methods analyze results.The user describe full instance including its forcing in single configuration file sequence workflows, making reproducible, fast, modular.The has been designed with iterative, data-centered modeling mind.First-order schematizations built for any location world by leveraging open global datasets.These later improved updating input detailed local datasets.This iterative enables quickly get initial result then informed decisions about most relevant improvements and/or required collection kick-start discussions stakeholders.Furthermore, parameter maps or easily modified sensitivity analysis calibration support these robust practices.HydroMT successfully being used several software through plugin infrastructure allow specific functionality, such as readers writers formats, extended new software.Currently supported include distributed rainfall-runoff Wflow, flood SFINCS, water quality D-Water Quality D-Emissions Delft-FIAT.

Language: Английский

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A systematic review of the hydrogeomorphological impacts of large dams in Africa

Earth-Science Reviews, Journal Year: 2025, Volume and Issue: unknown, P. 105048 - 105048

Published: Jan. 1, 2025

Language: Английский

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A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting

Frontiers in Water, Journal Year: 2022, Volume and Issue: 4

Published: Oct. 5, 2022

Probabilistic forecasting is receiving growing attention nowadays in a variety of applied fields, including hydrology. Several machine learning concepts and methods are notably relevant towards addressing the major challenges formalizing optimizing probabilistic implementations, as well equally important challenge identifying most useful ones among these implementations. Nonetheless, practically-oriented reviews focusing on such methods, how can be effectively exploited above-outlined essential endeavour, currently missing from hydrological literature. This absence holds despite pronounced intensification research efforts for benefitting this same It also substantial progress that has recently emerged, especially field post-processing, which traditionally provides hydrologists with Herein, we aim to fill specific gap. In our review, emphasize key ideas information lead effective popularizations, an emphasis support successful future implementations further scientific developments. forward-looking direction, identify open questions propose explored future.

Language: Английский

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The critical need to foster computational reproducibility

Environmental Research Letters, Journal Year: 2022, Volume and Issue: 17(4), P. 041005 - 041005

Published: March 11, 2022

Abstract The climate crisis illustrates the critical need for earth and environmental models to assess Earth’s past future by translating emissions into signals subsequent impacts regarding floods, droughts, or heatwaves, as well resource availability. While computational grow in relevance guiding policies public discourse, our trust these is put test. A recent study estimates that 93% of hydrology water resources published studies cannot be reproduced. In this perspective, we question whether are amid a reproducibility sciences peek behind curtain everyday research. Software development has become an integral part research most areas, including sciences, where data processing algorithms increasingly sophisticated solve challenges time. Paradoxically, poses threat scientific progress: Reproducibility, essential pillar science, difficult reach even This trend particularly worrisome results have potentially controversial implications stakeholders policymakers may influence opinion decisions long years, progress towards Open Science led more publishers demanding access source code alongside peer-reviewed manuscripts; but still find less reproducible cited frequently. We argue insufficiently understand how science community currently attempts reproduce what they face effort. To do scientists attribute lack possible solutions? perspective survey on think necessary paint picture fosters thus trust.

Language: Английский

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Where should hydrology go? An early-career perspective on the next IAHS Scientific Decade: 2023–2032

Hydrological Sciences Journal, Journal Year: 2023, Volume and Issue: 68(4), P. 529 - 541

Published: Feb. 9, 2023

This paper shares an early-career perspective on potential themes for the upcoming International Association of Hydrological Sciences (IAHS) Scientific Decade (SD). opinion synthesizes six discussion sessions in western Europe identifying three that all offer a different hydrological threats world faces and could serve to direct broader community: “Tipping points thresholds hydrology,” “Intensification water cycle,” “Water services under pressure.” Additionally, four trends were distinguished concerning way which research is conducted: big data, bridging science practice, open science, inter- multidisciplinarity. These will provide valuable input future discussions theme next IAHS SD. We encourage other scientists voice their by organizing own commenting this make initiative grow from regional global movement.

Language: Английский

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Assessing Hydrology, Biogeochemistry, and Organic Micropollutants in an Urban Stream‐Aquifer System: An Interdisciplinary Data Set

Journal of Geophysical Research Biogeosciences, Journal Year: 2024, Volume and Issue: 129(1)

Published: Jan. 1, 2024

Abstract Urban expansion leads to increasing water pollution, impacting both human health and ecosystems. This decline in quality often stems from insufficient wastewater treatment, along with runoff urban agricultural areas. Water degradation challenges our efforts for sustainable management hinders progress toward the UN's Sustainable Development Goals (SDGs), particularly SDG6. Within aquatic environments, hyporheic zone—the subsurface area where surface groundwater mix—plays a crucial role facilitating pollutant turnover overall health. Mixing between generates diverse microhabitats streambed varying levels of oxygen, temperature, chemical composition, which turn allows microbial communities strive. The complexity exchange flows within zone associated processes pollutants nutrients can only be assessed detailed, cross‐disciplinary data sets including about hydrology, climatology, biogeochemistry, composition. However, integrated this kind are seldom available. As result, drivers behind dynamics stream‐aquifer systems still not fully understood. Addressing knowledge deficit, we present comprehensive unique set an system Switzerland spanning over 6 months. Incorporating hydrometric, tracer, nutrient, organic micropollutant data, help shed light on intricate mechanisms governing flows, as well nutrient cycling environments.

Language: Английский

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Protocol for extracting flow hydrograph shape metrics for use in time-series flood hydrology analysis

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(1), P. e0315796 - e0315796

Published: Jan. 10, 2025

The shape characteristics of flow hydrographs hold essential information for understanding, monitoring and assessing changes in flood hydrology at reach catchment scales. However, the analysis individual is time consuming, making hundreds or thousands them unachievable. A method protocol needed to ensure that datasets being generated, metrics produced, have been consistently derived validated. In this lab protocol, we present workflows Python extracting with any available temporal resolution from Open Access publicly gauging station records. workflow identifies morphologically-defined types (i.e. in-channel fresh, high overbank flood) uses classify hydrographs. It then calculates several at-a-station upstream-to-downstream hydrograph including kurtosis, skewness, peak stage, arrival time, rate-of-rise, peak-to-peak travel wave celerity, attenuation, attenuation index. Some require GIS-derived data, such as area channel distance between gauges. output dataset provides quantified which can be used track over characterise catchments regions. are flexible enough allow additional indicators added swapped out, use a different classification suits local conditions. could considered change detection tool identify where occurring target more sophisticated modelling exercises explain detected. We demonstrate using 117 records coastal rivers New South Wales (NSW), Australia.

Language: Английский

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Preface: Hydrological Sciences in the Anthropocene – a structured community effort

Proceedings of the International Association of Hydrological Sciences, Journal Year: 2025, Volume and Issue: 385, P. 501 - 511

Published: Feb. 12, 2025

Language: Английский

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