A semiparametric multivariate and multisite weather generator

Apipattanavis, Somkiat; Podestá, Guillermo; Rajagopalan, Balaji; Katz, Richard W.

doi:10.1029/2006wr005714

Cited by 106 publications

(107 citation statements)

References 52 publications

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“…The goal is to capture the basic statistical features of daily weather variables, especially temporal dependence for individual variables and contemporaneous dependence between variables. Generally based either on parametric models (Richardson 1981) or on resampling (Rajagopalan & Lall 1999), or on some combination of these 2 methods (Apipattanavis et al 2007), complications include the need to incorporate annual cycles and the desire to condition the model on large-scale atmospheric or oceanic circulation patterns such as the El Niño-Southern Oscillation (ENSO) phenomenon (Podestá et al 1999).…”

Section: Introductionmentioning

confidence: 99%

Generalized linear modeling approach to stochastic weather generators

2007

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Section: Introductionmentioning

confidence: 99%

Generalized linear modeling approach to stochastic weather generators

2007

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“…These weather generators are developed based on parametric and nonparametric methods, but the parametric method is the most commonly used one. Apipattanavis et al (2007) also presented a semi-parametric multivariate and multisite weather generator that generates precipitation states with a 3-state, first-order Markov chain and multivariate variables with a k-nearest neighbor bootstrap re sampler.There are 2 main approaches for parametric adjustments of WGs (Wilks 2010). The first involves a dayby-day change to the WG parameters based on daily variations in atmospheric circulation (Wilby et al 2002b).…”

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confidence: 99%

Downscaling of weather generator parameters to quantify hydrological impacts of climate change

Chen¹,

Brissette²,

Leconte³

2012

Clim. Res.

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A major obstacle in quantifying the hydrological impacts of climate change is the mismatch between the coarse resolution of climate model outputs (general circulation models and regional climate models) and the fine resolution requirements of hydrological models. This research presents a statistical downscaling approach combining the attributes of both the stochastic weather generator (WG) and the change factor (CF) method to overcome this problem. It is further compared against the commonly used CF method in terms of quantifying the hydrological impacts of climate change over the next century for a Canadian watershed (Quebec Province). Both downscaling methods suggested increases in winter (November−April) discharge and decreases in summer (June−October), especially for those downscaled by the WG-based method. The WG-based method predicted higher peak discharges than the CF method. The 2 downscaling methods suggested significantly different increases in annual and seasonal discharges, particularly for low flows. Hydrology results show that precipitation and temperature variability play a very important role in the runoff generating process, and that neglecting to address this variability can lead to biased results. In particular, the WG-based method has a significant advantage in simulating low flows because it takes into account the change of precipitation occurrence. The results also outline the uncertainty linked to the choice of a downscaling method. KEY WORDS: Statistical downscaling · Stochastic weather generator · Change factor method · Hydrology · Climate change Resale or republication not permitted without written consent of the publisherClim Res 51: [185][186][187][188][189][190][191][192][193][194][195][196][197][198][199][200] 2012 too many small events resulting in an overestimation of the wet day frequency, as well as biases resulting in an under/overestimation of the mean annual precipitation (Rivington et al. 2008). Dynamical and statistical downscaling methods have been developed to resolve this issue. Dynamical downscaling was developed based on dynamic formulations using the initial and time-dependent lateral boundary conditions of GCMs to achieve a higher spatial resolution by nesting regional climate models (RCM) (Caya & Laprise 1999). The spatial resolution of RCMs are much higher than that of GCMs. However, the resolution remains too coarse for small and medium-sized watersheds as well as for site-specific impact studies. Moreover, RCM data is only available for limited regions, due to the large computational cost of running the models (Solman & Nunez 1999). To overcome this problem, statistical downscaling methods have been developed. These involve linking the states of some variables representing a large scale (GCM or RCM grid scale, predictors) to the states of some variables representing a much smaller scale (catchment or site scale, predictands). A range of statistical downscaling methods have been developed and can be classified into 3 categories: transfer function (Wi...

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“…The precipitation generator is implemented following the algorithm developed by Apipattanavis et al [30] and further developed by Basinger et al [3]. Their model uses historical data which is used to generate the scenario rain events.…”

Section: Precipitation Generatormentioning

confidence: 99%

Development and Simulation of Decentralised Water and Energy Supply Concepts – Case Study of Rainwater Harvesting at the Angkor Centre for Conservation of Biodiversity in Cambodia

Czarny¹,

Präbst²,

Spinnler³

et al. 2017

J. sustain. dev. energy water environ. syst.

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Besides a sufficient energy supply, concepts for accommodations require an intelligent water management. Using the example of quarters that do not have water and energy access, a dynamic simulation model is presented in which a rainwater harvesting concept is implemented and simulated over one year using MATLAB-Simulink. The aim is to minimize respectively suspend the use of fossil energy sources and to guarantee the provision of decentralized clean drinking water. Since traditional water bodies, e.g. groundwater, are increasingly polluted and depleted, utilisation of alternative sources is prudent. Especially in rural areas, where access to drinking water is scarce, rainwater is suitable for providing potable water. Besides its beneficial chemical water properties, it is easily accessed in a decentralized manner, which makes it a preferred choice in areas with sufficient precipitation. However, access to rainwater is limited by its occurrence and contamination, calling for proper storage, utilisation, and treatment strategies. For this purpose, a rainwater harvesting system, including different water and energy management systems, was modelled and implemented using the site of the Angkor Centre for Conservation of Biodiversity in Cambodia as an example. For the simulation, a precipitation generator was implemented using real historical rain event data. An appropriate rainwater treatment process was chosen, consisting of a microfiltration and a subsequent ultrafiltration unit removing bacteriological loads entirely. Both were modelled and implemented dynamically. Using the site of the Angkor Centre of Conservation of Biodiversity, a complete rainwater harvesting plant was implemented including harvest, storage, and utilization of rainwater. Further, a renewable energy * Corresponding author Journal of Sustainable Development of Energy, Water and Environment SystemsYear 2017 Volume 5, Issue 4, pp 626-644 627 management strategy is developed, using photovoltaic modules and batteries. It was shown that the cumulative runoff meets the water demand of the Angkor Centre for Conservation of Biodiversity and that the energy demand of the rainwater system as well as the site can be met by the installed photovoltaics on the existing roof area.

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A semiparametric multivariate and multisite weather generator

Cited by 106 publications

References 52 publications

Generalized linear modeling approach to stochastic weather generators

Generalized linear modeling approach to stochastic weather generators

Downscaling of weather generator parameters to quantify hydrological impacts of climate change

Development and Simulation of Decentralised Water and Energy Supply Concepts – Case Study of Rainwater Harvesting at the Angkor Centre for Conservation of Biodiversity in Cambodia

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