Multi-site, multivariate weather generator using maximum entropy bootstrap

Srivastav, Roshan; Simonović́, Slobodan P.

doi:10.1007/s00382-014-2157-x

Cited by 29 publications

(13 citation statements)

References 33 publications

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“…Srivastava and Simonovic (2014) [18] developed a non-parametric multisite, multivariate maximum entropy based weather generator (MEBWG) for generating daily precipitation and minimum and maximum temperature. The three main steps involved in MBE are: 1) orthogonal transformation of daily climate variables at multiple sites to remove spatial correlation; 2) use of maximum entropy bootstrap (MEB) to generate synthetic replicates of climate variables and 3) inverse orthogonal transformation of synthetic climate variables to re-established spatial correlation.…”

Section: Maximum Entropy Based Weather Generator (Mebwg)mentioning

confidence: 99%

“…Six Downscaling methods applied in this study are as follows: 1) bias corrected spatial disaggregation (BCSD) [11] [15], 2) bias correction constructed analogues with quantile mapping reordering (BCCAQ) [16], 3) delta change method coupled with a non-parametric K-nearest neighbor weather generator [17], 4) delta change method coupled with maximum entropy based weather generator [18], 5) non-parametric statistical downscaling model based on the kernel regression [19], and 6) beta regression based statistical downscaling model [20]. BCSD and BCAAQ were successfully applied across Canada in the past, however these methods cannot explicitly capture changes in daily extremes [16] where other four downscaling methods can capture changes in daily extremes and can produce extreme values outside of the historical boundaries [18]- [21]. The above mentioned six downscaling methods are used to quantify the amount of uncertainty arising from different types of statistical downscaling methods and compare it with other sources of uncertainties.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty in Precipitation Projection under Changing Climate Conditions: A Regional Case Study

Mandal¹,

Breach²,

Simonović́³

2016

AJCC

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This study investigates different sources of uncertainty in the assessment of the climate change impacts on total monthly precipitation in the Campbell River basin, British Columbia, Canada. Four global climate models (GCMs), three greenhouse gas emission scenarios (RCPs) and six downscaling methods (DSMs) are used in the assessment. These sources of uncertainty are analyzed separately for two future time periods (2036 to 2065 and 2066 to 2095). An uncertainty metric is calculated based on the variation in simulated precipitation due to choice of GCMs, emission scenarios and downscaling models. The results show that the selection of a downscaling method provides the largest amount of uncertainty when compared to the choice of GCM and/or emission scenario. However, the choice of GCM provides a significant amount of uncertainty if downscaling methods are not considered. This assessment work is conducted at ten different locations in the Campbell River basin.

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Section: Maximum Entropy Based Weather Generator (Mebwg)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Uncertainty in Precipitation Projection under Changing Climate Conditions: A Regional Case Study

Mandal¹,

Breach²,

Simonović́³

2016

AJCC

Self Cite

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“…As stated previously, many downscaling methods require considerable effort regarding parameter estimation and verification. Even though the application of such methods might not be limited by computing power, approaches that have a simple structure and that are easy to implement remain highly desired (Clark et al, 2004;Bárdossy and Pegram, 2012;Li, 2014;Srivastav and Simonovic, 2015;Scheuerer et al, 2017). The proposed method first generates daily series weather for the future period at single sites.…”

Section: Why Was the Proposed Gcm Downscaling Methods Used?mentioning

confidence: 99%

“…Numerous multisite downscaling methods have been developed, e.g., dynamic methods based on regional climate models (Cooley and Sain, 2010;Bárdossy and Pegram, 2012;Pegram and Bárdossy, 2013), empirical scaling methods (Allerup, 1996;Bürger and Chen, 2005), generalized linear models Yang et al, 2005;Lu and Qin, 2014;Asong et al, 2016), artificial neural networks (Harpham and Wilby, 2005;Cannon, 2008), nonhomogeneous hidden Markov models (Charles et al, 1999;Bellone et al, 2000;Fu et al, 2013), and weather generators (Wilks, 1999a;Qian et al, 2002;Mehrotra and Sharma, 2010;Khalili et al, 2013;Srivastav and Simonovic, 2015). Thus far, the application to hydrological modeling of most of these methods has been limited, except for the stochastic weather generator.…”

Section: Introductionmentioning

confidence: 99%

Evaluating climate change impacts on streamflow variability based on a multisite multivariate GCM downscaling method in the Jing River of China

Jin

2017

Hydrol. Earth Syst. Sci.

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Abstract. Projected hydrological variability is important for future resource and hazard management of water supplies because changes in hydrological variability can cause more disasters than changes in the mean state. However, climate change scenarios downscaled from Earth System Models (ESMs) at single sites cannot meet the requirements of distributed hydrologic models for simulating hydrological variability. This study developed multisite multivariate climate change scenarios via three steps: (i) spatial downscaling of ESMs using a transfer function method, (ii) temporal downscaling of ESMs using a single-site weather generator, and (iii) reconstruction of spatiotemporal correlations using a distribution-free shuffle procedure. Multisite precipitation and temperature change scenarios for 2011-2040 were generated from five ESMs under four representative concentration pathways to project changes in streamflow variability using the Soil and Water Assessment Tool (SWAT) for the Jing River, China. The correlation reconstruction method performed realistically for intersite and intervariable correlation reproduction and hydrological modeling. The SWAT model was found to be well calibrated with monthly streamflow with a model efficiency coefficient of 0.78. It was projected that the annual mean precipitation would not change, while the mean maximum and minimum temperatures would increase significantly by 1.6 ± 0.3 and 1.3 ± 0.2 • C; the variance ratios of 2011-2040 to 1961-2005 were 1.15 ± 0.13 for precipitation, 1.15 ± 0.14 for mean maximum temperature, and 1.04 ± 0.10 for mean minimum temperature. A warmer climate was predicted for the flood season, while the dry season was projected to become wetter and warmer; the findings indicated that the intra-annual and interannual variations in the future climate would be greater than in the current climate. The total annual streamflow was found to change insignificantly but its variance ratios of 2011-2040 to 1961-2005 increased by 1.25 ± 0.55. Streamflow variability was predicted to become greater over most months on the seasonal scale because of the increased monthly maximum streamflow and decreased monthly minimum streamflow. The increase in streamflow variability was attributed mainly to larger positive contributions from increased precipitation variances rather than negative contributions from increased mean temperatures.

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“…These transfer functions are used together with future climate model data to obtain bias-corrected future climate model data. Downscaling is performed following a change factor approach and by using two different weather generators: KNN-CAD (version 4) (King et al 2012) and Multisite multivariate weather generator model (M3EB) (Srivastav and Simonovic 2014). The former is a semi-parametric weather generator while the latter is a non-parametric type weather generator.…”

Section: Analysis Performed and Methods Usedmentioning

confidence: 99%

Towards Reducing Climate Change Impact Assessment Process Uncertainty

Gaur

Simonović́

2015

Environ. Process.

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Three different sources of process-based uncertainty in climate change impact studies have been identified. It has been demonstrated that uncertainty can arise from: a) differences in the process approach, b) differences in the methods used in each step of the process, and c) differences in the space, time and distribution scales considered for analysis. The discussion of possible reasons behind the observed differences in climate change assessment analyses follows. It has been recommended that the selection of robust methods, which not only perform well on historical timeline but also keep the climate models changes intact, is the key to reduce process-based uncertainty in climate projections. Further, it is advised that future climate projections be analysed at all space, time, distribution coordinates located within the domain of interest in order to obtain a comprehensive picture of the changes projected by the climate models.

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Multi-site, multivariate weather generator using maximum entropy bootstrap

Cited by 29 publications

References 33 publications

Uncertainty in Precipitation Projection under Changing Climate Conditions: A Regional Case Study

Uncertainty in Precipitation Projection under Changing Climate Conditions: A Regional Case Study

Evaluating climate change impacts on streamflow variability based on a multisite multivariate GCM downscaling method in the Jing River of China

Towards Reducing Climate Change Impact Assessment Process Uncertainty

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