On the variability of cold region flooding

Matti, Bettina; Dahlke, H. E.; Lyon, Steve W.

doi:10.1016/j.jhydrol.2016.01.055

Cited by 32 publications

(23 citation statements)

References 49 publications

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“…The Gumbel distribution is an Extreme I type distribution. This distribution can reflect the extreme probability characteristics of some meteorological variables (Matti et al, 2016). The probability distribution function is shown in (Equation (3)).…”

Section: The Return Period Of Extreme Snowfallsupporting

confidence: 58%

“…The Gumbel distribution is an Extreme I type distribution. This distribution can reflect the extreme probability characteristics of some meteorological variables (Matti et al ., ). The probability distribution function is shown in (Equation ).The extreme snowfall calculation using the Gumbel distribution was conducted according to (Equations –),

F ((), x) = \exp ({}, - \exp ([], - α ((), x - β))),

where α > 0 is the scale parameter, β is the location parameter, and x is the snowfall.…”

Section: Methodology and Data Setsmentioning

confidence: 99%

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Change in the spatiotemporal pattern of snowfall during the cold season under climate change in a snow‐dominated region of China

Bai

Shi²,

Shi

et al. 2019

Intl Journal of Climatology

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The spatiotemporal pattern of precipitation is significantly changing with global climate change. Snowfall is a solid phase of precipitation and an important water resource. With two gridded data sets of APHRO (Asia Precipitation‐Highly‐Resolved Observational Data Integration Towards Evaluation of Water Resources) and CN05.1, this study analyses the changes in the spatiotemporal pattern of snowfall in a snow‐dominant region of China from 1961 to 2015. The results indicate the significant increasing trend of winter snowfall in horizontal and altitude dimension in snow‐dominant regions, but the winter snowing season length shortened. For the frequency of snowfall intensity level, light, and heavy snowfall and snowstorms increased, but moderate snowfall showed no change. However, the intensity of extreme snowfall in once‐in‐a‐century was decreasing in all of the snow‐dominant regions. In the altitude dimension, the increasing trend in snow‐dominant conditions was not uniform, which may be related to change in air temperature and water vapour through the vertical atmospheric levels. The upwards trend in snowfall may be caused by the increase of atmospheric water content rather than the change of snowy weather conditions. In addition, the change values of climate indices can also contribute to snowfall increasing in snow‐dominant regions.

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Section: The Return Period Of Extreme Snowfallsupporting

confidence: 58%

“…The Gumbel distribution is an Extreme I type distribution. This distribution can reflect the extreme probability characteristics of some meteorological variables (Matti et al ., ). The probability distribution function is shown in (Equation ).The extreme snowfall calculation using the Gumbel distribution was conducted according to (Equations –),

F ((), x) = \exp ({}, - \exp ([], - α ((), x - β))),

where α > 0 is the scale parameter, β is the location parameter, and x is the snowfall.…”

Section: Methodology and Data Setsmentioning

confidence: 99%

Change in the spatiotemporal pattern of snowfall during the cold season under climate change in a snow‐dominated region of China

Bai

Shi²,

Shi

et al. 2019

Intl Journal of Climatology

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“…Since the early reviews of Barnett et al (2005) and Milly et al (2005), which offered a global perspective, more recent studies have investigated the response on continental and regional scales. In mountainous regions that supply fresh water to downstream populated areas, reduced snowpack in recent decades has led to concerns about water availability for human consumption, agriculture and fisheries (Stewart, 2009;Jiménez Cisneros et al, 2014), while changes in the timing of the freshet and a widespread decrease in the snowto-rain ratio (Danco et al, 2016) have implications for flood magnitude and frequency (Matti et al, 2016). An understanding of the mechanisms behind these changes has been greatly aided by the use of process-based hydrologic models (Park and Markus, 2014;Duan et al, 2017), which permit the analysis of a host of variables that respond to historical climate forcings via physically consistent relationships.…”

Section: Study Domain and Motivationmentioning

confidence: 99%

Examining controls on peak annual streamflow and floods in the Fraser River Basin of British Columbia

Curry

Zwiers

2018

Hydrol. Earth Syst. Sci.

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Abstract. The Fraser River Basin (FRB) of British Columbia is one of the largest and most important watersheds in western North America, and home to a rich diversity of biological species and economic assets that depend implicitly upon its extensive riverine habitats. The hydrology of the FRB is dominated by snow accumulation and melt processes, leading to a prominent annual peak streamflow invariably occurring in May-July. Nevertheless, while annual peak daily streamflow (APF) during the spring freshet in the FRB is historically well correlated with basin-averaged, 1 April snow water equivalent (SWE), there are numerous occurrences of anomalously large APF in below-or near-normal SWE years, some of which have resulted in damaging floods in the region. An imperfect understanding of which other climatic factors contribute to these anomalously large APFs hinders robust projections of their magnitude and frequency.We employ the Variable Infiltration Capacity (VIC) process-based hydrological model driven by gridded observations to investigate the key controlling factors of anomalous APF events in the FRB and four of its subbasins that contribute nearly 70 % of the annual flow at Fraser-Hope. The relative influence of a set of predictors characterizing the interannual variability of rainfall, snowfall, snowpack (characterized by the annual maximum value, SWE max ), soil moisture and temperature on simulated APF at Hope (the main outlet of the FRB) and at the subbasin outlets is examined within a regression framework. The influence of large-scale climate modes of variability (the Pacific Decadal Oscillation (PDO) and the El Niño-Southern Oscillation -ENSO) on APF magnitude is also assessed, and placed in context with these more localized controls. The results indicate that next to SWE max (univariate Spearman correlation with APF ofρ = 0.64; 0.70 (observations; VIC simulation)), the snowmelt rate (ρ = 0.43 in VIC), the ENSO and PDO indices (ρ = −0.40; −0.41) and (ρ = −0.35; −0.38), respectively, and rate of warming subsequent to the date of SWE max (ρ = 0.26; 0.38), are the most influential predictors of APF magnitude in the FRB and its subbasins. The identification of these controls on annual peak flows in the region may be of use in understanding seasonal predictions or future projected streamflow changes.

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“…The ecological connectivity index(flux, F) the overall connectivity index(IFC) and possible connectivity index (PC) can reflect the connectivity of the landscape and can calculate the connectivity of each key point (or patch) in the landscape. Important value of the landscape connectivity (Matti and Dahlke and Lyon, 2016) ,is taken as the key point of landscape connectivity impact and effect analysis. The importance of the key points here refers to the importance of maintaining connectivity to the landscape.…”

Section: Analysis On the Optimum Effect Of The Key Function Of Landscmentioning

confidence: 99%

“…Traditional village landscape ecological and ecological process is the link between the links. Connectivity refers to the convenience or obstruction of landscape to ecological flow (Matti, Dahlke and Lyon, 2016;Kim, An and Kim, 2015) . The landscape of cold region with good connectivity can realize its ecological function more effectively.…”

Section: Introductionmentioning

confidence: 99%

Ecological Protection Optimization of Traditional Village Landscape in Cold Area Based on Gravity Model

2017

Revista Tecnica De La Facultad De Ingenieria Universidad Del Zu

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Ecological protection of traditional village landscape based on ecological function and process influence is one of the most important methods to improve the landscape benefit and stability. The distance between landscape and landscape influences the gravitational field of landscape protection, and it is similar to the law of universal gravitational field in logic connection. Selecting a coastal area for the study object, gravity models were used to analyze the service function of the cold area landscape ecosystem in 2014, and study the ecological protection optimization of the traditional village landscape. The results showed that the spatial heterogeneity of landscape ecological function intensity in cold region was significant, which affected the landscape ecological process and function of regional cold region, and based on the accumulated cost distance surface, ecological source and consumption path, the landscape elements such as source area, ecological corridor and ecological node are protected to optimize the traditional village landscape ecology. The results provide scientific basis for regional land use and ecological environment protection.

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On the variability of cold region flooding

Cited by 32 publications

References 49 publications

Change in the spatiotemporal pattern of snowfall during the cold season under climate change in a snow‐dominated region of China

Change in the spatiotemporal pattern of snowfall during the cold season under climate change in a snow‐dominated region of China

Examining controls on peak annual streamflow and floods in the Fraser River Basin of British Columbia

Ecological Protection Optimization of Traditional Village Landscape in Cold Area Based on Gravity Model

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