Bayesian change-point analysis in hydrometeorological time series. Part 1. The normal model revisited

Perreault, Luc; Bernier, Jacques; Bobée, Bernard; Parent, Éric

doi:10.1016/s0022-1694(00)00270-5

Cited by 151 publications

(111 citation statements)

References 31 publications

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“…Easterling and Peterson, 1995) or Bayesian approaches (e.g. Perreault et al, 2000a, b) have been used to extract the change point locations and magnitudes of the inhomogeneities. There now exist a number of different modern multiple-breakpoint detection packages which can be used to homogenise data sets, e.g.…”

Section: Introductionmentioning

confidence: 99%

Pairwise homogeneity assessment of HadISD

et al. 2014

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Abstract. We report on preliminary steps in the homogenisation of HadISD, a sub-daily, station-based data set covering 1973-2013. Using temperature, dew point temperature, sealevel pressure and wind speeds, change points are detected using the Pairwise Homogenisation Algorithm from Menne and Williams Jr (2009). Monthly-mean values and monthlymean diurnal ranges (temperature and dew point temperature) or monthly-maximum values (wind speeds) are processed using the full network of 6103 stations in HadISD. Where multiple change points are detected within 1 year, they are combined and the average date is used. Under the assumption that the underlying true population of inhomogeneity magnitudes is Gaussian, inhomogeneity magnitudes as small as around 0.5 • C, 0.5 hPa or 0.5 m s −1 have been successfully detected. The change point dates and inhomogeneity magnitudes for each of the calculation methods will be provided alongside the data set to allow users to select stations which have different levels of homogeneity. We give an example application of this change point information in calculating global temperature values from HadISD and comparing these to CRUTEM4. Removing the most inhomogeneous stations results in a better match between HadISD and CRUTEM4 when matched to the same coverage. However, further removals of stations with smaller and fewer inhomogeneities worsen the match.

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Section: Introductionmentioning

confidence: 99%

Pairwise homogeneity assessment of HadISD

et al. 2014

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“…Alternatively, when the goal is a post de facto evaluation of an LID intervention, time series analysis can be conducted on rainfall and hydrograph data, provided quality data are available both before and after the intervention. A variety of time series tools are available including graphical methods, 137 153 Interpretation of time series data can be complicated by climate variability over the time of observation. 140,154−157 The gold standard for assessing the hydrologic impact of landuse change is paired (or triplicated) catchment studies, in which the catchment of interest is paired with a control catchment (and a reference catchment, in the case of a triplicate design) of similar climate and physiography.…”

Section: Optimizing Lid Selection At the Catchment Scalementioning

confidence: 99%

From Rain Tanks to Catchments: Use of Low-Impact Development To Address Hydrologic Symptoms of the Urban Stream Syndrome

Askarizadeh

Rippy

Fletcher

et al. 2015

Environ. Sci. Technol.

138

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Catchment urbanization perturbs the water and sediment budgets of streams, degrades stream health and function, and causes a constellation of flow, water quality, and ecological symptoms collectively known as the urban stream syndrome. Low-impact development (LID) technologies address the hydrologic symptoms of the urban stream syndrome by mimicking natural flow paths and restoring a natural water balance. Over annual time scales, the volumes of stormwater that should be infiltrated and harvested can be estimated from a catchment-scale water-balance given local climate conditions and preurban land cover. For all but the wettest regions of the world, a much larger volume of stormwater runoff should be harvested than infiltrated to maintain stream hydrology in a preurban state. Efforts to prevent or reverse hydrologic symptoms associated with the urban stream syndrome will therefore require: (1) selecting the right mix of LID technologies that provide regionally tailored ratios of stormwater harvesting and infiltration; (2) integrating these LID technologies into next-generation drainage systems; (3) maximizing potential cobenefits including water supply augmentation, flood protection, improved water quality, and urban amenities; and (4) long-term hydrologic monitoring to evaluate the efficacy of LID interventions.

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“…Taking inspiration from other research fields such as hydrology (Rao, 1996;Perreault et al, 2000aPerreault et al, , 2000b and avalanche science (Eckert et al, 2007(Eckert et al, , 2008 in which dealing with stochasticity is more common, this paper aims at defining a bouncing model explicitly distinguishing these different sources of variability.…”

Section: F Bourrier Et Al: Stochastic Bouncing Modelmentioning

confidence: 99%

Bayesian stochastic modeling of a spherical rock bouncing on a coarse soil

Bourrier¹,

Eckert²,

Nicot³

et al. 2009

Nat. Hazards Earth Syst. Sci.

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Abstract. Trajectory analysis models are increasingly used for rockfall hazard mapping. However, classical approaches only partially account for the variability of the trajectories. In this paper, a general formulation using a Taylor series expansion is proposed for the quantification of the relative importance of the different processes that explain the variability of the reflected velocity vector after bouncing. A stochastic bouncing model is obtained using a statistical analysis of a large numerical data set. Estimation is performed using hierarchical Bayesian modeling schemes. The model introduces information on the coupling of the reflected and incident velocity vectors, which satisfactorily expresses the mechanisms associated with boulder bouncing.The approach proposed is detailed in the case of the impact of a spherical boulder on a coarse soil, with special focus on the influence of soil particles' geometrical configuration near the impact point and kinematic parameters of the rock before bouncing. The results show that a first-order expansion is sufficient for the case studied and emphasize the predominant role of the local soil properties on the reflected velocity vector's variability. The proposed model is compared with classical approaches and the interest for rockfall hazard assessment of reliable stochastic bouncing models in trajectory simulations is illustrated with a simple case study.

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Bayesian change-point analysis in hydrometeorological time series. Part 1. The normal model revisited

Cited by 151 publications

References 31 publications

Pairwise homogeneity assessment of HadISD

Pairwise homogeneity assessment of HadISD

From Rain Tanks to Catchments: Use of Low-Impact Development To Address Hydrologic Symptoms of the Urban Stream Syndrome

Bayesian stochastic modeling of a spherical rock bouncing on a coarse soil

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