Challenges in modeling and predicting floods and droughts: A review

Wiley Interdisciplinary Reviews Water, Год журнала: 2021, Номер 8(3)

Опубликована: Март 11, 2021

Abstract Predictions of floods, droughts, and fast drought‐flood transitions are required at different time scales to develop management strategies targeted minimizing negative societal economic impacts. Forecasts daily seasonal scale vital for early warning, estimation event frequency hydraulic design, long‐term projections developing adaptation future conditions. All three types predictions—forecasts, estimates, projections—typically treat droughts floods independently, even though both extremes can be studied using related approaches have similar challenges. In this review, we (a) identify challenges common drought flood prediction their joint assessment (b) discuss tractable tackle these We group into four interrelated categories: data, process understanding, modeling prediction, human–water interactions. Data‐related include data availability definition. Process‐related the multivariate spatial characteristics extremes, non‐stationarities, changes in extremes. Modeling arise analysis, stochastic, hydrological, earth system, modeling. Challenges with respect interactions lie establishing links impacts, representing interactions, science communication. potential ways tackling including exploiting new sources, studying a framework, influences compounding drivers, continuous stochastic models or non‐stationary models, obtaining stakeholder feedback. Tackling one several will improve predictions help minimize impacts extreme events. This article is categorized under: Science Water >

Язык: Английский

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Increased Flood Exposure Due to Climate Change and Population Growth in the United States

Earth s Future, Год журнала: 2020, Номер 8(11)

Опубликована: Окт. 30, 2020

Abstract Precipitation extremes are increasing globally due to anthropogenic climate change. However, there remains uncertainty regarding impacts upon flood occurrence and subsequent population exposure. Here, we quantify changes in exposure hazard across the contiguous United States. We combine simulations from a model large ensemble high‐resolution hydrodynamic model—allowing us directly assess wide range of extreme precipitation magnitudes accumulation timescales. report mean increase 100‐year event ~20% (magnitude) >200% (frequency) high warming scenario, yielding ~30–127% further find nonlinear for most intense events—suggesting accelerating societal historically rare or unprecedented events 21st century.

Язык: Английский

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Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management

Hydrology and earth system sciences, Год журнала: 2021, Номер 25(7), С. 3897 - 3935

Опубликована: Июль 7, 2021

Abstract. Hydroclimatic extremes such as intense rainfall, floods, droughts, heatwaves, and wind or storms have devastating effects each year. One of the key challenges for society is understanding how these are evolving likely to unfold beyond their historical distributions under influence multiple drivers changes in climate, land cover, other human factors. Methods analysing hydroclimatic advanced considerably recent decades. Here we provide a review drivers, metrics, methods detection, attribution, management, projection nonstationary extremes. We discuss issues uncertainty associated with approaches (e.g. arising from insufficient record length, spurious nonstationarities, incomplete representation sources modelling frameworks), examine empirical simulation-based frameworks analysis extremes, identify gaps future research.

Язык: Английский

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Uncovering Flooding Mechanisms Across the Contiguous United States Through Interpretive Deep Learning on Representative Catchments

Water Resources Research, Год журнала: 2021, Номер 58(1)

Опубликована: Дек. 27, 2021

Abstract Long short‐term memory (LSTM) networks represent one of the most prevalent deep learning (DL) architectures in current hydrological modeling, but they remain black boxes from which process understanding can hardly be obtained. This study aims to demonstrate potential interpretive DL gaining scientific insights using flood prediction across contiguous United States (CONUS) as a case study. Two interpretation methods were adopted decipher machine‐captured patterns and inner workings LSTM networks. The by expected gradients method revealed three distinct input‐output relationships learned LSTM‐based runoff models 160 individual catchments. These correspond flood‐inducing mechanisms—snowmelt, recent rainfall, historical rainfall—that account for 10.1%, 60.9%, 29.0% 20,908 flow peaks identified data set, respectively. Single flooding mechanisms dominate 70.7% investigated catchments (11.9% snowmelt‐dominated, 34.4% rainfall‐dominated, 24.4% rainfall‐dominated mechanisms), remaining 29.3% have mixed mechanisms. spatial variability dominant reflects catchments' geographic climatic conditions. Moreover, additive decomposition unveils how network behaves differently retaining discarding information when emulating different types floods. Information inputs within previous time steps partially stored predict snowmelt‐induced rainfall‐induced floods, while only is retained. Overall, this provides new perspective processes extremes demonstrates prospect artificial intelligence‐assisted discovery future.

Язык: Английский

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Reservoir regulation affects droughts and floods at local and regional scales

Environmental Research Letters, Год журнала: 2021, Номер 16(12), С. 124016 - 124016

Опубликована: Ноя. 5, 2021

Abstract Hydrological extremes can be particularly impactful in catchments with high human presence where they are modulated by intervention such as reservoir regulation. Still, we know little about how operation affects droughts and floods, at a regional scale. Here, I present large data set of natural regulated catchment pairs the United States assess regulation local drought flood characteristics. My results show that (1) hazard scale reducing severity (i.e. intensity/magnitude deficit/volume) but increasing duration; (2) spatial connectedness number co-experiences events with) winter summer; (3) alleviation effect is only weakly affected purpose for both floods. conclude characteristics substantially regulation, an aspect should neither neglected nor climate impact assessments.

Язык: Английский

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Extreme floods in Europe: going beyond observations using reforecast ensemble pooling

Hydrology and earth system sciences, Год журнала: 2022, Номер 26(2), С. 469 - 482

Опубликована: Янв. 31, 2022

Abstract. Assessing the rarity and magnitude of very extreme flood events occurring less than twice a century is challenging due to lack observations such rare events. Here we develop new approach, pooling reforecast ensemble members from European Flood Awareness System (EFAS), increase sample size available estimate frequency local regional We assess added value pooling, determine where in Central Europe one might expect most events, evaluate how event severity related physiographic meteorological catchment characteristics. work with set 234 catchments Global Runoff Data Centre matched EFAS for which performance simulated floods good when compared observed streamflow. pool EFAS-simulated 10 perturbed lead times ranging 22 46 d, are only weakly dependent (<0.25 average correlation across times). The resulting large (130 time series instead 1) enables analyses occur century. demonstrate that produces more robust estimates considerably reduced uncertainty bounds (by ∼80 % on average) observation-based but may equally introduce biases arising meteorology hydrological model. Our results show that, given return period, specific highest steep, cold, wet regions comparably low strong flow regulation through dams. Furthermore, our pooled indicate probability flooding higher Great Britain Scandinavia. conclude an efficient approach derive model performance.

Язык: Английский

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Drought Spatial Extent and Dependence Increase During Drought Propagation From the Atmosphere to the Hydrosphere

Geophysical Research Letters, Год журнала: 2024, Номер 51(6)

Опубликована: Март 22, 2024

Abstract As droughts propagate both in time and space, their impacts increase because of changes drought properties. Because temporal spatial propagation are mostly studied separately, it is yet unknown how extent connectedness change as though the hydrological cycle from precipitation to streamflow groundwater. Here, we use a large‐sample dataset 70 catchments Central Europe study local characteristics. We show that leads longer, later, fewer with larger extents. 75% P‐ET, among these 20% further 10% Of droughts, 40% Drought dependence during along pathway thanks synchronizing effects land‐surface but decreases again for groundwater sub‐surface heterogeneity.

Язык: Английский

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Comparative Performance Assessment of Physical-Based and Data-Driven Machine-Learning Models for Simulating Streamflow: A Case Study in Three Catchments across the US

Journal of Hydrologic Engineering, Год журнала: 2024, Номер 29(2)

Опубликована: Фев. 8, 2024

Recent developments in computational techniques and data-driven machine-learning models (MLMs) have shown great potential capturing the rainfall-runoff relationship. However, whether MLMs outperform classical physical-based (PBMs) streamflow simulation is still controversial. In this study, we chose three representative catchments across continental United States for a comparative analysis of these two model categories, including PBMs, i.e., conceptual hydrological (EXP-HYDRO) semidistributed (SWAT), MLMs, support vector regression (SVR), backpropagation artificial neural networks (BP-ANN), deep learning model, termed long short-term memory (LSTM). Results indicate that bias SVR BP-ANN greater than PBMs under baseline input scenario, while LSTM outperforms other For delayed scenarios, perform satisfactorily. addition, show better performance high-flow regime, low-flow implying both their own merits should be jointly employed holistically to analyze streamflow. Our comparison demonstrates variable different seasonal, climatic, topographic conditions, conclude can capture relationship when coefficient variation (COV) large.

Язык: Английский

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Projected changes in Rhine River flood seasonality under global warming

Hydrology and earth system sciences, Год журнала: 2021, Номер 25(5), С. 2353 - 2371

Опубликована: Май 3, 2021

Abstract. Climatic change alters the frequency and intensity of natural hazards. In order to assess potential future changes in flood seasonality Rhine River basin, we analyse streamflow, snowmelt, precipitation evapotranspiration at 1.5, 2.0 3.0 ^∘C global warming levels. The mesoscale hydrological model (mHM) forced with an ensemble climate projection scenarios (five general circulation models under three representative concentration pathways) is used simulate present conditions both pluvial nival regimes. Our results indicate that characteristics basin are controlled by increases antecedent diminishing snowpacks. pluvial-type sub-basin Moselle River, increasing due increased encounters declining snowpacks during winter. decrease snowmelt seems counterbalance precipitation, resulting only small transient streamflow maxima. For Basin Basel, rising temperatures cause from solid liquid which enhance overall increase sums, particularly cold season. At gauge strongest maxima show up winter, when strong encounter almost unchanged snowmelt-driven runoff. analysis events for Basel suggests no point time season does a result risk flooding. Snowpacks increasingly depleted course We do not find indications merging floods warming. To refine attained results, next steps need be representation glaciers lakes set-up, coupling simulations component independent validation snow routine using satellite-based cover maps.

Язык: Английский

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Flood spatial coherence, triggers, and performance in hydrological simulations: large-sample evaluation of four streamflow-calibrated models

Hydrology and earth system sciences, Год журнала: 2021, Номер 25(1), С. 105 - 119

Опубликована: Янв. 6, 2021

Abstract. Floods cause extensive damage, especially if they affect large regions. Assessments of current, local, and regional flood hazards their future changes often involve the use hydrologic models. A reliable model ideally reproduces both local characteristics spatial aspects flooding under current climate conditions. However, uncertainties in simulated floods can be considerable yield unreliable hazard change impact assessments. This study evaluates extent to which models calibrated according standard calibration metrics such as widely used Kling–Gupta efficiency are able capture coherence triggering mechanisms. To highlight challenges related simulations, we investigate how timing, magnitude, variability represented by an ensemble hydrological when on streamflow using metric, increasingly common metric performance also flood-related studies. Specifically, compare four well-known (the Sacramento Soil Moisture Accounting model, SAC; Hydrologiska Byråns Vattenbalansavdelning HBV; variable infiltration capacity VIC; mesoscale mHM) represent (1) patterns (2) translate meteorologic variables that trigger into magnitudes. Our results show modeling challenging underestimate timing is not necessarily well captured. They further precipitation temperature always translated flow, makes assessments even more difficult for From a sample catchments with multiple models, conclude integrated alone likely have limited reliability assessments, undermining utility We underscore improved developing flood-focused, multi-objective, metrics, improving generating process representation through structure comparisons considering uncertainty input.

Язык: Английский

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