A methodological framework to assess PMP and PMF in snow-dominated watersheds under changing climate conditions – A case study of three watersheds in Québec (Canada)

Rouhani, Hassan; Leconte, Robert

doi:10.1016/j.jhydrol.2018.04.047

Cited by 17 publications

(7 citation statements)

References 54 publications

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“…Another limitation of the study is that the presented dynamical features of PMP are simulated only under the presentday climate forcing based on a historical climate reanalysis dataset; hence, the experiments do not consider the responses of these features to future changes in climate forcing. A possible global increase in statistically estimated PMP due to the general moistening of the atmosphere under global warming has been reported by climate projection studies (Kunkel et al, 2013;Rousseau et al, 2014;Rouhani and Leconte, 2018), while the projected changes in the physically estimated PMP still remains unknown. One of the solutions to this issue is to use the PMP estimation method of this study to physically estimate the PMP of a targeted region driven by the LBCs from global climate simulations for both the historical climate and future climate under scenarios of greenhouse gas emissions so that the changes in the physically estimated PMP under the possible future changes in climate forcing can be quantified.…”

Section: Summary and Discussionmentioning

confidence: 99%

“…The research found an increase (by 0.5-6%) of PMP in the Manic-5 watershed of Canada by the end of the 21st century relative to the end of the 20th century under a greenhouse gas emission scenario. Rousseau et al (2014) and Rouhani and Leconte (2018) used similar methods and found that PMP is projected to increase significantly by up to 11% across eastern Canada under climate change. However, there is still a lack of investigation of the physical mechanism behind the potential occurrence of PMP in reality.…”

Section: Introductionmentioning

confidence: 99%

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Dynamics of Probable Maximum Precipitation Within Coastal Urban Areas in a Convection-Permitting Regional Climate Model

Liang

Yong

2022

Front. Mar. Sci.

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The west coast of Canada is strongly affected by the extreme precipitation events triggered by frequent atmospheric river (AR) activities over the eastern North Pacific. Across the region, assessing the probable maximum precipitation (PMP), can provide valuable information for resilience building of the coastal communities that are vulnerable to hydrological risks. In this study, a 3-km convection-permitting regional climate model is used to physically estimate the PMP in Vancouver. This technique maximizes the effect of AR-related water vapor transport by spatially adjusting the lateral boundary conditions (LBCs) of the model simulations for the selected AR-related extreme precipitation events. The PMP in Vancouver is identified among the simulations driven by the spatially adjusted LBCs that are corresponding with the AR-induced “worst-case scenario,” i.e., landfalling ARs hit Vancouver with optimal landfalling location and transport direction. Results suggest that the PMP in Vancouver, in terms of the maxima of the regionally averaged 72-h total precipitation for the historical extreme precipitation events, is up to 790 mm, which is 130% greater than the historical peak precipitation for the period 1980∼2017. On average, all the PMP simulations shows an overall increase by 81% in precipitation by relative to historical simulations. In addition, the PMP simulations suggested an overall decrease in snowfall by 12% due to the warmer near-surface air temperature; however, a pronounced increase in freezing rain is seen. The precipitation increase for the estimated PMP relative to the historical extreme precipitation is closely associated with the increased atmospheric moisture transport and the changes in the atmospheric dynamic factors when the AR effects are maximized. These include the enhanced low-tropospheric ascent and moisture transport convergence, which can induce stronger depletion of atmospheric moistures as indicated by the increased precipitation efficiency.

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Section: Summary and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamics of Probable Maximum Precipitation Within Coastal Urban Areas in a Convection-Permitting Regional Climate Model

Liang

Yong

2022

Front. Mar. Sci.

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“…Their historical data over 1,615 10-km grids across ARB are collected from a raster-gridded data set (NLWIS, 2007) (climate.weather.gc.ca). The data set is derived from, verified against and, according to (Rouhani & Leconte, 2018), well fitted with long-term in-situ observations throughout Canada. As shown in Figures 1c-1e and S14-S17 in Supporting Information S1, subarctic or boreal climates over such a large river basin in the rain shadow of the Rocky Mountains present significant tempo-spatial heterogeneities at various scales.…”

Section: Data Collection and Processingmentioning

confidence: 99%

Multifactorial Principal‐Monotonicity Inference for Macro‐Scale Distributed Hydrologic Modeling

Cheng

Huang

Dong

2022

Water Resources Research

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Macro‐scale distributed hydrologic modeling advanced hydro‐system understandings and scientized relevant human activities. However, it faces challenges of hydrometeorological heterogeneities, parametric interactions, data uncertainty, computational expensiveness, and other complexities, especially over cold regions with intense climatic changes. As an effort to address them, a multifactorial principal‐monotonicity inference (MFPMI) method is developed through integrating, extending, or improving climate classification, hydrologic modeling, and sensitivity analysis. MFPMI is applied to an undammed macro‐scale high‐latitude cold‐region watershed, Athabasca River Basin (ARB) in Canada. MFPMI mitigates the underestimation of climatic impacts on streamflows in process‐based models, hydrologic classification, large‐scale hydroclimatic data deconstruction, parametric‐interaction neglection, and climatic homogenization; its superiority is particularly evident for highly heterogeneous climates. Dominant climatic impacts on ARB streamflows of various regimes increase from tributaries to the mainstem and decrease from up‐ through down‐ to mid‐stream catchments, possibly due to the offset effects of non‐climatic factors (e.g., vegetation and soil). The impacts also decline with streamflow magnitudes, and vary with seasons, spatial scales and lead months rather than temporal resolutions. Streamflow magnitudes, catchments and metrics differ compositions of the climate conditions explaining cross‐scale uppermost discharge variations. In spite of this, streamflow increases with temperature and precipitation, and headwater climates play part of dominant roles in forcing discharges throughout ARB. Accuracy metrics differentiate parameters and accordingly structures of MFPMI models, and hydroclimatic data uncertainty is high for high flows, fine temporal scales or low climatic impacts, increasing uncertainty in hydrologic simulations and climatic‐impact estimates. This study helps advance modeling and understandings of macro‐scale cold‐region hydrology.

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“…Exploration is then made to determine if relaxing the limiting conditions of the atmospheric variables could result in a higher amount of precipitation 16 . Of the several methods to estimate PMP under historical and climate-change-altered conditions, there has recently been a proliferation of studies applying the Moisture Maximization Approach [23][24][25][26][27][28] . This approach assumes that extreme precipitation is limited by a single atmospheric variable: Precipitable Water (𝑃𝑊), or atmospheric moisture, and estimates the PMP by maximizing 𝑃𝑊 as shown in Equation (1) below:…”

Section: Introductionmentioning

confidence: 99%

An Update to Probable Maximum Precipitation Accounting for More Than Moisture Availability Alone: A Case Study of Nepal.

Wasti,

Schlef,

Kappel

et al. 2023

Preprint

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The moisture maximization approach, the most common technique for estimating Probable Maximum Precipitation (PMP), assumes that the upper limit of extreme precipitation can be estimated by maximizing atmospheric moisture availability alone. However, several atmospheric variables often play a role in the occurrence of extreme precipitation. In this paper, a generalization is proposed to incorporate other relevant atmospheric variables with a two-step multifactor maximization approach. In the first step, the dominant atmospheric variables are screened. In the second step, the maximization ratio for each variable is calculated separately and then combined as a weighted average to obtain a combined maximization ratio. When applied to a case study in Nepal, the multifactor maximization approach results in PMP estimates substantially different (up to 4x) from the conventional moisture maximization approach in regions where the PMP is particularly sensitive to factors other than atmospheric moisture availability.

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A methodological framework to assess PMP and PMF in snow-dominated watersheds under changing climate conditions – A case study of three watersheds in Québec (Canada)

Cited by 17 publications

References 54 publications

Dynamics of Probable Maximum Precipitation Within Coastal Urban Areas in a Convection-Permitting Regional Climate Model

Dynamics of Probable Maximum Precipitation Within Coastal Urban Areas in a Convection-Permitting Regional Climate Model

Multifactorial Principal‐Monotonicity Inference for Macro‐Scale Distributed Hydrologic Modeling

An Update to Probable Maximum Precipitation Accounting for More Than Moisture Availability Alone: A Case Study of Nepal.

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