Defining high-flow seasons using temporal streamflow patterns from a global model

Lee, Donghoon; Ward, Philip J.; Block, Paul

doi:10.5194/hess-19-4689-2015

Cited by 22 publications

(30 citation statements)

References 44 publications

(63 reference statements)

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“…Although a high‐flow value (seasonal maximum or upper percentile streamflow) may represent a more traditional flood characterization, this value may also include variability due to local climate/hydrology may potentially be more significant than those from season‐ahead large‐scale climate drivers. For this study, we use seasonal peak‐flow, calculated as average streamflow in the peak (3 month) season according to the volume‐based threshold method and daily streamflow data, as defined in Lee et al (). Daily gridded streamflow is obtained from the PCR‐GLOBWB (PCRaster GLOBal Water Balance) model, described in the following section; peak‐flow seasons (Figure ) are validated with corresponding GRDC streamflow observations and actual flood records.…”

Section: Methodsmentioning

confidence: 99%

Attribution of Large‐Scale Climate Patterns to Seasonal Peak‐Flow and Prospects for Prediction Globally

Lee

Ward

Block

2018

Water Resources Research

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Flood‐related fatalities and impacts on society surpass those from all other natural disasters globally. While the inclusion of large‐scale climate drivers in streamflow (or high‐flow) prediction has been widely studied, an explicit link to global‐scale long‐lead prediction is lacking, which can lead to an improved understanding of potential flood propensity. Here we attribute seasonal peak‐flow to large‐scale climate patterns, including the El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), and Atlantic Multidecadal Oscillation (AMO), using streamflow station observations and simulations from PCR‐GLOBWB, a global‐scale hydrologic model. Statistically significantly correlated climate patterns and streamflow autocorrelation are subsequently applied as predictors to build a global‐scale season‐ahead prediction model, with prediction performance evaluated by the mean squared error skill score (MSESS) and the categorical Gerrity skill score (GSS). Globally, fair‐to‐good prediction skill (20% ≤ MSESS and 0.2 ≤ GSS) is evident for a number of locations (28% of stations and 29% of land area), most notably in data‐poor regions (e.g., West and Central Africa). The persistence of such relevant climate patterns can improve understanding of the propensity for floods at the seasonal scale. The prediction approach developed here lays the groundwork for further improving local‐scale seasonal peak‐flow prediction by identifying relevant global‐scale climate patterns. This is especially attractive for regions with limited observations and or little capacity to develop flood early warning systems.

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Section: Methodsmentioning

confidence: 99%

Attribution of Large‐Scale Climate Patterns to Seasonal Peak‐Flow and Prospects for Prediction Globally

Lee

Ward

Block

2018

Water Resources Research

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“…Changes in the timing of the annual flood, for example, have widespread impacts on flood-based farming systems and therefore the livelihoods for populations who adapt their floodplain management and agricultural practices to the normally experienced rise and fall of the flood wave (Paul, 1984;van Steenbergen, 1997). Accordingly, research has been carried out to characterize the distribution of hydrological regimes globally (Dettinger & Diaz, 2000;Lee et al, 2015), to explain the largest seasonality gradients (Hastenrath, 1996), and to understand how hydrological regimes might be impacted by climate change (Blöschl et al, 2017;Burn et al, 2016;Cunderlik & Ouarda, 2009). The objective of this paper is to investigate whether there are significant differences in the timing of annual floods between different modes of climate variability across Africa, where there is a strong link between ©2019.…”

Section: Introductionmentioning

confidence: 99%

Climate Variability Alters Flood Timing Across Africa

Ficchì

Stephens

2019

Geophysical Research Letters

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Modes of climate variability are known to influence rainy season onset, but there is less understanding of how they impact flood timing. We use streamflow reanalysis and gauged observation data sets to examine the influence of the Indian Ocean Dipole and El Niño–Southern Oscillation across sub‐Saharan Africa. We find significant changes in flood timing between positive and negative phases of both Indian Ocean Dipole and El Niño–Southern Oscillation; in some cases the difference in the timing of annual flood events is more than three months. Sensitivity to one or other mode of variability differs regionally. Changes in flood timing are larger than variability in rainy season onset reported in the literature, highlighting the need to understand how the hydrological system alters climate variability signals seen in rainy season onset, length, and rainfall totals. Our insights into flood timing could support communities who rely on flood‐based farming systems to adapt to climate variability.

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“…As introduced in Marx et al (2018), low flows could cause shortage of surface or subsurface water resources, which has the potential to impact hydrological drought. High flows are indicators for flooding (Lee et al, 2015), and are usually related to heavy rainfall (Groisman et al, 2001).…”

Section: The Attribution Methodsmentioning

confidence: 99%

High‐Resolution Land Surface Modeling of Hydrological Changes Over the Sanjiangyuan Region in the Eastern Tibetan Plateau: 2. Impact of Climate and Land Cover Change

Yuan

2018

J Adv Model Earth Syst

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Known as the “China Water Tower,” the Sanjiangyuan region over the eastern Tibetan Plateau experienced significant hydrological changes during the past few decades, potentially affecting the security of food, energy, and water over the downstream areas. Previous studies attributed the hydrological changes to global warming or land cover change, and obtained contrary conclusions on the dominant drivers. Here we show that natural climate change is mainly responsible for most hydrological changes (except low flows) over the Sanjiangyuan region, followed by the anthropogenic climate change, while land cover contributed the least. The newly developed Conjunctive Surface‐Subsurface Process version 2 land surface model that was evaluated comprehensively in the first part of this series was used to conduct a set of high‐resolution simulations driven by Coupled Model Intercomparison Project phase 5 detection and attribution experiment outputs with natural or anthropogenic forcings. By using an integrated hydroclimate attribution framework, anthropogenic climate change was found to cause significant ground temperature warming and soil frozen period shortening. However, significant decreasing trends in annual mean streamflow and high flows over the Yellow River headwater region and the terrestrial water storage averaged over the Sanjiangyuan region were mainly caused by natural climate change, with contribution by 57–97%. The contributions from land cover change are less than 11%. This study suggests that adaptations are more important than mitigations for the water resource management over the Sanjiangyuan and its downstream regions, because natural climate change outweighed human‐induced climate change in the headwater region.

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Defining high-flow seasons using temporal streamflow patterns from a global model

Cited by 22 publications

References 44 publications

Attribution of Large‐Scale Climate Patterns to Seasonal Peak‐Flow and Prospects for Prediction Globally

Attribution of Large‐Scale Climate Patterns to Seasonal Peak‐Flow and Prospects for Prediction Globally

Climate Variability Alters Flood Timing Across Africa

High‐Resolution Land Surface Modeling of Hydrological Changes Over the Sanjiangyuan Region in the Eastern Tibetan Plateau: 2. Impact of Climate and Land Cover Change

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