Investigating risk, reliability and return period under the influence of large scale modes, and regional hydrological variability in hydrologic extremes

Abstract. Flood risk is projected to increase under projections of future warming climates due to an enhanced hydrological cycle. Solar geoengineering is known to reduce precipitation and slowdown the hydrological cycle, and may be therefore be expected to offset increased flood risk. We examine this hypothesis using streamflow and river discharge responses to the representative concentration pathway RCP4.5 and Geoengineering Model Intercomparison Project (GeoMIP) G4 experiments. We also calculate changes in 30, 50, 100-year flood return periods relative to the historical (1960–1999) period under the RCP4.5 and G4 scenarios. Similar spatial patterns are produced for each return period, although those under G4 are closer to historical values than under RCP4.5. Under G4 generally lower streamflows are produced on the western sides of Eurasia and North America, with higher flows on their eastern sides. In the southern hemisphere northern parts of the land masses have lower streamflow under G4, and southern parts increases relative to RCP4.5. So in general solar geoengineering does appear to reduce flood risk in most regions, but the relative effects are largely determined by this large scale geographic pattern. Both streamflow and return period show increased drying of the Amazon under both RCP4.5 and G4 scenarios, with more drying under G4.

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The role of serial European windstorm clustering for extreme seasonal losses as determined from multi-centennial simulations of high-resolution global climate model data

Natural Hazards and Earth System Science ◽

10.5194/nhess-18-2991-2018 ◽

2018 ◽

Vol 18 (11) ◽

pp. 2991-3006 ◽

Cited By ~ 4

Author(s):

Matthew D. K. Priestley ◽

Helen F. Dacre ◽

Len C. Shaffrey ◽

Kevin I. Hodges ◽

Joaquim G. Pinto

Keyword(s):

High Resolution ◽

Return Period ◽

Large Scale ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Geographic Region ◽

Near Surface ◽

Short Period

Abstract. Extratropical cyclones are the most damaging natural hazard to affect western Europe. Serial clustering occurs when many intense cyclones affect one specific geographic region in a short period of time which can potentially lead to very large seasonal losses. Previous studies have shown that intense cyclones may be more likely to cluster than less intense cyclones. We revisit this topic using a high-resolution climate model with the aim to determine how important clustering is for windstorm-related losses. The role of windstorm clustering is investigated using a quantifiable metric (storm severity index, SSI) that is based on near-surface meteorological variables (10 m wind speed) and is a good proxy for losses. The SSI is used to convert a wind footprint into losses for individual windstorms or seasons. 918 years of a present-day ensemble of coupled climate model simulations from the High-Resolution Global Environment Model (HiGEM) are compared to ERA-Interim reanalysis. HiGEM is able to successfully reproduce the wintertime North Atlantic/European circulation, and represent the large-scale circulation associated with the serial clustering of European windstorms. We use two measures to identify any changes in the contribution of clustering to the seasonal windstorm loss as a function of return period. Above a return period of 3 years, the accumulated seasonal loss from HiGEM is up to 20 % larger than the accumulated seasonal loss from a set of random resamples of the HiGEM data. Seasonal losses are increased by 10 %–20 % relative to randomized seasonal losses at a return period of 200 years. The contribution of the single largest event in a season to the accumulated seasonal loss does not change with return period, generally ranging between 25 % and 50 %. Given the realistic dynamical representation of cyclone clustering in HiGEM, and comparable statistics to ERA-Interim, we conclude that our estimation of clustering and its dependence on the return period will be useful for informing the development of risk models for European windstorms, particularly for longer return periods.

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Global streamflow and flood response to stratospheric aerosol geoengineering

Atmospheric Chemistry and Physics ◽

10.5194/acp-18-16033-2018 ◽

2018 ◽

Vol 18 (21) ◽

pp. 16033-16050 ◽

Cited By ~ 4

Author(s):

Liren Wei ◽

Duoying Ji ◽

Chiyuan Miao ◽

Helene Muri ◽

John C. Moore

Keyword(s):

Return Period ◽

Flood Risk ◽

Large Scale ◽

Hydrological Cycle ◽

Stratospheric Aerosol ◽

Model Intercomparison ◽

Geographic Pattern ◽

Solar Geoengineering ◽

Intercomparison Project ◽

Future Warming

Abstract. Flood risk is projected to increase under future warming climates due to an enhanced hydrological cycle. Solar geoengineering is known to reduce precipitation and slow down the hydrological cycle and may therefore be expected to offset increased flood risk. We examine this hypothesis using streamflow and river discharge responses to Representative Concentration Pathway 4.5 (RCP4.5) and the Geoengineering Model Intercomparison Project (GeoMIP) G4 scenarios. Compared with RCP4.5, streamflow on the western sides of Eurasia and North America is increased under G4, while the eastern sides see a decrease. In the Southern Hemisphere, the northern parts of landmasses have lower streamflow under G4, and streamflow of southern parts increases relative to RCP4.5. We furthermore calculate changes in 30-, 50-, and 100-year flood return periods relative to the historical (1960–1999) period under the RCP4.5 and G4 scenarios. Similar spatial patterns are produced for each return period, although those under G4 are closer to historical values than under RCP4.5. Hence, in general, solar geoengineering does appear to reduce flood risk in most regions, but the overall effects are largely determined by this large-scale geographic pattern. Although G4 stratospheric aerosol geoengineering ameliorates the Amazon drying under RCP4.5, with a weak increase in soil moisture, the decreased runoff and streamflow leads to an increased flood return period under G4 compared with RCP4.5.

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Analysing the Return Period and Risk under the Influence of Physical Covariates on Hydrological Extreme

10.5194/egusphere-egu21-404 ◽

2021 ◽

Author(s):

Jew Das ◽

Nanduri Umamahesh ◽

Srinidhi Jha

Keyword(s):

Water Resources ◽

Return Period ◽

Large Scale ◽

Location Parameter ◽

Extreme Value ◽

Extreme Value Analysis ◽

Change Scenario ◽

Stationary Condition ◽

Value Analysis ◽

Monsoon Index

For sustainable water resources planning and management, it is necessary to redefine the concept of return period, risk, and reliability of hydrologic extreme under non-stationary condition. Thus, the present study aims to examine the return period, risk introducing physical based covariates in the location parameter of the generalised extreme value (GEV) distribution. The study is performed over the Godavari River basin, India. The expected waiting time (EWT) approach is used to make comparison of return period, risk between stationary and non-stationary approaches. From the analysis, it is found that 50% of the gauging stations are impacted by large scale modes/oscillations and regional hydrological variability, primarily by Indian Summer Monsoon Index (ISMI) and precipitation. The EWT interpretation estimates that the non-stationary return period, risk, and reliability are significantly different from stationary condition. Hence, it is concluded that return period analysis and risk assessment using non-stationary approach can be beneficial to water managers and policy makers in order to devise sustainable and resilient water resources infrastructure under climate change scenario.Keywords: Extreme value analysis; Return period; Risk; Non-stationarity; Uncertainty

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Towards global stochastic flood modelling

10.5194/egusphere-egu2020-5121 ◽

2020 ◽

Author(s):

Oliver Wing ◽

Niall Quinn ◽

Paul Bates ◽

Jeff Neal ◽

Chris Sampson ◽

...

Keyword(s):

Boundary Conditions ◽

Return Period ◽

Large Scale ◽

Hydraulic Model ◽

Statistical Dependence ◽

Hydrological Models ◽

Flood Modelling ◽

Model Based ◽

Spatial Coverage ◽

Dependence Models

Hydraulic modelling at large spatial scales is a field of enquiry approaching a state of maturity, with the flood maps produced beginning to inform wide-area planning decisions, insurance pricing and emergency response. These maps, however, are typically &#8216;static&#8217;; that is, are a spatially homogeneous representation of a given probability flood. Actual floods vary in their extremity across space: if a given location is extreme, you may expect proximal locations to be similarly extreme and distal locations to be decreasingly extreme. Methods to account for this stochastically can, broadly speaking, be split into: (i) continuous simulation via a meteorological-hydrological-hydraulic model cascade and (ii) fitting statistical dependence models to samples of river gauges, generating a synthetic event set of streamflows and simulating the hydraulics from these. The former has the benefit of total spatial coverage, but the drawbacks of high computational cost and the low skill of large-scale hydrological models in simulating absolute river discharge. The latter enables higher-fidelity hydraulics in simulating the extremes only and with more accurately defined boundary conditions, yet it is only possible to execute (ii) in gauge-rich regions &#8211; excluding most of the planet.In this work, we demonstrate that a hybrid approach of (i) & (ii) offers a promising path forward for stochastic flood modelling in gauge-poor areas. Inputting simulated streamflows from large-scale hydrological models to a conditional exceedance model which characterises the spatial dependence of discharge extremes produces a very different set of plausible flood events than when observed flows are used as boundary conditions. Yet, if the relative exceedance probability of simulated flows &#8211; internal to the hydrological model &#8211; are used in place of their absolute values (i.e. a return period instead of a value in m3s-1), the observation- and model-based dependence models produce similar events in terms of the spatial distribution of return periods. In the context of flood losses, when using Fathom-US CAT (a state-of-the-art large-scale stochastic flood loss model), the risk of an example portfolio is indistinguishable between the gauge- and model-driven framework given the uncertainty in vulnerability alone. This is providing the model-based event return period is matched up with a hydraulic model of the same return period, yet where the latter is characterised via a gauge-based approach.

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Influence of multidecadal variability on high and low flows: the case of the Seine basin

Hydrology and Earth System Sciences ◽

10.5194/hess-24-1611-2020 ◽

2020 ◽

Vol 24 (4) ◽

pp. 1611-1631

Author(s):

Rémy Bonnet ◽

Julien Boé ◽

Florence Habets

Keyword(s):

Large Scale ◽

Hydrological Modeling ◽

Spring Precipitation ◽

Seine River ◽

River Flows ◽

Multidecadal Variability ◽

Low Flows ◽

The North ◽

Hydrological Variability

Abstract. The multidecadal hydroclimate variations of the Seine basin since the 1850s are investigated. Given the scarcity of long-term hydrological observations, a hydrometeorological reconstruction is developed based on hydrological modeling and a method that combines the results of a downscaled long-term atmospheric reanalysis and local observations of precipitation and temperature. This method improves previous attempts and provides a realistic representation of daily and monthly river flows. This new hydrometeorological reconstruction, available over more than 150 years while maintaining fine spatial and temporal resolutions, provides a tool to improve our understanding of the multidecadal hydrological variability in the Seine basin, as well as its influence on high and low flows. This long-term reconstruction allows analysis of the strong multidecadal variations of the Seine river flows. The main hydrological mechanisms at the origin of these variations are highlighted. Spring precipitation plays a central role by directly influencing not only the multidecadal variability in spring flows but also soil moisture and groundwater recharge, which then regulate summer river flows. These multidecadal hydroclimate variations in the Seine basin are driven by anomalies in large-scale atmospheric circulation, which themselves appear to be influenced by sea surface temperature anomalies over the North Atlantic and the North Pacific. The multidecadal hydroclimate variations also influence high and low flows over the last 150 years. The analysis of two particularly severe historical droughts, the 1921 and the 1949 events, illustrates how long-term hydroclimate variations may impact short-term drought events, particularly through groundwater–river exchanges. The multidecadal hydroclimate variations described in this study, probably of internal origin, could play an important role in the evolution of water resources in the Seine basin in the coming decades. It is therefore essential to take the associated uncertainties into account in future projections.

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Emergency Disposal Solution for Control of a Giant Landslide and Dammed Lake in Yangtze River, China

Water ◽

10.3390/w11091939 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1939 ◽

Cited By ~ 1

Author(s):

Guiya Chen ◽

Xiaofeng Zhao ◽

Yanlai Zhou ◽

Shenglian Guo ◽

Chong-Yu Xu ◽

...

Keyword(s):

Return Period ◽

Flood Risk ◽

Large Scale ◽

Water Volume ◽

Small Scale ◽

General Strategy ◽

Engineering Measures ◽

Landslide Early Warning ◽

Dammed Lake ◽

Giant Landslide

Although landslide early warning and post-assessment is of great interest for mitigating hazards, emergency disposal solutions for properly handling the landslide and dammed lake within a few hours or days to mitigate flood risk are fundamentally challenging. In this study, we report a general strategy to effectively tackle the dangerous situation created by a giant dammed lake with 770 million cubic meters of water volume and formulate an emergency disposal solution for the 25 million cubic meters of debris, composed of engineering measures of floodgate excavation and non-engineering measures of reservoirs/hydropower stations operation. Such a disposal solution can not only reduce a large-scale flood (10,000-year return period, 0.01%) into a small-scale flood (10-year return period, 10%) but minimize the flood risk as well, guaranteeing no death raised by the giant landslide.

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Using GRACE Satellite Gravimetry for Assessing Large-Scale Hydrologic Extremes

Remote Sensing ◽

10.3390/rs9121287 ◽

2017 ◽

Vol 9 (12) ◽

pp. 1287 ◽

Cited By ~ 18

Author(s):

Alexander Sun ◽

Bridget Scanlon ◽

Amir AghaKouchak ◽

Zizhan Zhang

Keyword(s):

Large Scale ◽

Satellite Gravimetry ◽

Grace Satellite Gravimetry ◽

Grace Satellite ◽

Hydrologic Extremes

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The role of European windstorm clustering for extreme seasonal losses as determined from a high resolution climate model

10.5194/nhess-2018-165 ◽

2018 ◽

Author(s):

Matthew D. K. Priestley ◽

Helen F. Dacre ◽

Len C. Shaffrey ◽

Kevin I. Hodges ◽

Joaquim G. Pinto

Keyword(s):

High Resolution ◽

Return Period ◽

Large Scale ◽

Western Europe ◽

Climate Model ◽

Severity Index ◽

Return Periods ◽

Near Surface ◽

Environment Model

Abstract. Extratropical cyclones are the most damaging natural hazard to affect western Europe. Serial clustering occurs when many intense cyclones affect one area in a period of time which can potentially lead to very large seasonal losses. Previous studies have shown that intense cyclones may be more likely to cluster than less intense cyclones. We revisit this topic using a high resolution climate model with the aim to determine how important clustering is for windstorm related losses. The role of windstorm clustering is investigated using a quantifiable loss-based metric (storm severity index (SSI)) based on near-surface meteorological variables (10-metre wind speed) that is used to convert a wind footprint into losses for individual windstorms or seasons. 918 years of high resolution coupled climate model data from the High-Resolution Global Environment Model (HiGEM) are compared to ERA-Interim re-analysis. HiGEM is able to successfully reproduce the wintertime North Atlantic/European circulation, and represent the large-scale circulation associated with the serial clustering of European windstorms. We use two measures to identify any changes in the contribution of clustering to the overall seasonal losses for increasing return periods. Above a return period of 3 years, the accumulated seasonal loss from HiGEM is up to 20 % larger than the accumulated seasonal loss from a set of random realisations of the HiGEM data. Seasonsal losses are increased by 10–20 % relative to randomised seasonal losses at a return period of 200 years. The contribution of the single largest event in a season to the accumulated seasonal loss does not change with return period, generally ranging between ~ 0.25–0.5. Given the realistic dynamical representation of cyclone clustering in HiGEM, and comparable statistics to ERA-Interim, we conclude that our estimation of clustering and its dependence on the return period will be useful for informing the development of risk models for European windstorms, particularly for longer return periods.

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The Kerala flood of 2018: combined impact of extreme rainfall and reservoir storage

10.5194/hess-2018-480 ◽

2018 ◽

Cited By ~ 21

Author(s):

Vimal Mishra ◽

Saran Aaadhar ◽

Harsh Shah ◽

Rahul Kumar ◽

Dushmanta Ranjan Pattanaik ◽

...

Keyword(s):

Return Period ◽

Large Scale ◽

Monsoon Season ◽

Extreme Rainfall ◽

Reservoir Storage ◽

Extreme Precipitation Events ◽

Full Capacity ◽

Significant Release ◽

Combined Impact

Abstract. Extreme precipitation events and flooding that cause losses to human lives and infrastructure have increased under the warming climate. In August 2018, the state of Kerala (India) witnessed large-scale flooding, which affected millions of people and caused 400 or more deaths. Here, we examine the return period of extreme rainfall and the potential role of reservoirs in the recent flooding in Kerala. We show that Kerala experienced 53 % above normal rainfall during the monsoon season (till August 21st) of 2018. Moreover, 1, 2, and 3-day extreme rainfall in Kerala during August 2018 had return periods of 75, 200, and 100 years. Six out of seven major reservoirs were at more than 90 % of their full capacity on August 8, 2018, before extreme rainfall in Kerala. Extreme rainfall at 1–15 days durations in August 2018 in the catchments upstream of the three major reservoirs (Idukki, Kakki, and Periyar) had the return period of more than 500 years. Extreme rainfall and almost full reservoirs resulted in a significant release of water in a short-span of time. Therefore, above normal seasonal rainfall (before August 8, 2018), high reservoir storage, and unprecedented extreme rainfall in the catchments where reservoirs are located worsened the flooding in Kerala. Reservoir operations need be improved using a skillful forecast of extreme rainfall at the longer lead time (4–7 days).

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