Stochastic modeling of daily precipitation in China

Liao, Yaoming; Qiang, Zhang; Chen, Deliang

doi:10.1007/bf02837485

Cited by 38 publications

(14 citation statements)

References 14 publications

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“…The supply of water used included rainfall, groundwater, and surface water. We generated daily rainfall data in two steps following Yaoming et al (2004). In the first step, a first‐order Markov chain simulated discrete rainfall events (wet or dry days); while in the second step we used a two parameter gamma distribution to simulate the rainfall amounts on wet days.…”

Section: Methodsmentioning

confidence: 99%

Agent‐based modeling for ex ante assessment of tree crop innovations: litchis in northern Thailand

Schreinemachers

Potchanasin

Berger

et al. 2010

Agricultural Economics

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This study uses an agent-based model for ex ante assessment of agricultural innovations. The model builds on whole farm mathematical programming (MP) and extends the methodology with a spatial representation of the system, the heterogeneity of farm households and landscapes, and the interaction between farm households. We apply the model in a northern Thai watershed to study the potential of four innovations to increase the profitability of litchi orchards. Cost-benefit analysis shows that each innovation would increase the profitability of litchi growing; however, the results of the agent-based model show that at current price levels these innovations alone would not be enough to stem the decline in the area under litchis. The model was validated and the sensitivity of the results tested for variations in the irrigated water supply and liquidity. We report on how farmers responded to these results and discuss the implications for other areas in northern Thailand.JEL classifications: C61, O13, O33, Q12, Q16

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Section: Methodsmentioning

confidence: 99%

Agent‐based modeling for ex ante assessment of tree crop innovations: litchis in northern Thailand

Schreinemachers

Potchanasin

Berger

et al. 2010

Agricultural Economics

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“…Therefore, it is rather a rainfall generator that is developed and presented. Specifically, the NCC/GU-WG (Liao et al 2004) was applied in this study to each of the 220 sites in Sweden shown in Fig. 1.…”

Section: Stochastic Rainfall Generationmentioning

confidence: 99%

Projecting future local precipitation and its extremes for sweden

Chen

Achberger

et al. 2015

Geografiska Annaler: Series A, Physical Geography

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ABSTRACT. A procedure to obtain future local precipitation characteristics focused on extreme conditions has been developed based on a weather generator. The method involves six major steps: (1) the weather generator was calibrated using observed daily precipitation at 220 Swedish stations during 1961-2004; (2) present and future daily precipitation characteristics for the Swedish stations from two global climate models, namely ECHAM5 and HadCM3, were used to calculate weather generator parameters for the present and future climates at global climate model spatial scales; (3) the ratio of the weather generator parameters for the present climate simulated by the global climate models to those calculated for each station falling into the global climate model grid box were computed for all the stations; (4) these ratios were also assumed to be valid in the future climate, that way the future parameters for each station for the global climate model projected future climate could be calculated; (5) using the estimated future parameters of the weather generator, the future daily precipitation at each station could be simulated by the weather generator; (6) the simulated daily precipitation was used to compute eight indices describing mean and extreme precipitation climates. The future mean and extreme precipitation characteristics at the stations under the Second Report on Emission Scenarios A2 scenario were obtained and presented. An overall increasing trend for frequency and intensity of the indices are identified for the majority of the stations studied. The developed downscaling methodology is relatively simple but useful in deriving local precipitation changes, including changes in the precipitation extremes.

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“…Chu (2009) has compared three downscaling methods (weather generator, SDSM and REGRES) for precipitation in the Haihe River Basin and found that weather generator is the optimal method to simulate the days and amount of precipitation,especially the occurrence rate of precipitation. So in this study, the weather generator BCC/RCG-WG (Liao et al, 2004) developed by the Beijing Climate Center of CMA and Regional Climate Group at the University of Gothenburg is used to downscale the monthly precipitation at each station to daily precipitation. Tests show the weather generator BCC/RCG-WG has good effects to simulate daily precipitation, the simulations are close to the statistics of observations and the correlation between simulations and observations are very good (Liao et al, 2004(Liao et al, , 2009).…”

Section: Data Sources and Data Processingmentioning

confidence: 99%

“…So in this study, the weather generator BCC/RCG-WG (Liao et al, 2004) developed by the Beijing Climate Center of CMA and Regional Climate Group at the University of Gothenburg is used to downscale the monthly precipitation at each station to daily precipitation. Tests show the weather generator BCC/RCG-WG has good effects to simulate daily precipitation, the simulations are close to the statistics of observations and the correlation between simulations and observations are very good (Liao et al, 2004(Liao et al, , 2009). The future emission scenarios selected is the scenarios in IPCC AR4, which are A2 (high emission), A1B (medium emission) and B1 (low emission) (IPCC, 2007).…”

Section: Data Sources and Data Processingmentioning

confidence: 99%

Variations and statistical probability characteristic analysis of extreme precipitation events under climate change in Haihe River Basin, China

Xia

Zeng

et al. 2012

Hydrol. Process.

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The magnitude and frequency of regional extreme precipitation events may have variability under climate change. This study investigates the time–space variability and statistical probability characteristics of extreme precipitation under climate change in the Haihe River Basin. Hydrological alteration diagnosis methods are implemented to detect the occurrence time, style and degree of alteration such as trend and jump in the extreme precipitation series, and stationarity and serial independence are tested prior to frequency analysis. Then, the historical extreme precipitation frequency and spatio‐temporal variations analyses are conducted via generalized extreme value and generalized Pareto distributions. Furthermore, the occurrence frequency of extreme precipitation events in future is analysed on the basis of the Fourth Assessment Report of the Intergovermental Panel on Climate Change multi‐mode climate models under different greenhouse gases emission scenarios (SRES‐A2, A1B and B1). Results indicate that (1) in the past, alteration of extreme precipitation mainly occurred in the area north of 38°N. Decreasing trends of extreme precipitation are detected at most stations, whereas jump alteration is not obvious at most stations. (2) Spatial variation of estimated extreme precipitation under different return periods shows similarity. Bounded by the Taihang Mountain–Yan Mountain, extreme rainfall in the Haihe River Basin gradually reduces from the southeast to the northwest, which is consistent with the geographical features of the Haihe River Basin. (3) In the future, extreme precipitation with return period 5–20 years accounts for a significant portion of the total occurrence times. The frequency of extreme precipitation events has an increase trend under A1B and A2 scenarios. The total occurrence times of extreme precipitation under A1B senario are not more than that under B1 senario until the 2030s. Copyright © 2012 John Wiley & Sons, Ltd.

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Stochastic modeling of daily precipitation in China

Cited by 38 publications

References 14 publications

Agent‐based modeling for ex ante assessment of tree crop innovations: litchis in northern Thailand

Agent‐based modeling for ex ante assessment of tree crop innovations: litchis in northern Thailand

Projecting future local precipitation and its extremes for sweden

Variations and statistical probability characteristic analysis of extreme precipitation events under climate change in Haihe River Basin, China

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