Nonparametric temporal downscaling with event-based population generating algorithm for RCM daily precipitation to hourly: Model development and performance evaluation

Lee, Taesam; Park, Taewoong

doi:10.1016/j.jhydrol.2017.01.049

Cited by 15 publications

(16 citation statements)

References 32 publications

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“…In the last decades, millions of people have been affected by hydrological extremes. The risk of these hazards will increase in the future as a result of climate change and as population and infrastructure continue to increase and occupy areas exposed to higher risks [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis and Stochastic Modelling of Hydrological Extremes

Tabari

2019

Water

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Analysis of hydrological extremes is challenging due to their rarity and small sample size and the interconnections between different types of extremes and gets further complicated by an untrustworthy representation of meso-scale processes involved in extreme events by coarse spatial and temporal scale models as well as biased or missing observations due to technical difficulties during extreme conditions. The special issue “Statistical Analysis and Stochastic Modelling of Hydrological Extremes”—motivated by the need to apply and develop innovative stochastic and statistical approaches to analyze hydrological extremes under current and future climate conditions —encompass 13 research papers. Case studies presented in the papers exploit a wide range of innovative techniques for hydrological extremes analyses. The papers focus on six topics: Historical changes in hydrological extremes, projected changes in hydrological extremes, downscaling of hydrological extremes, early warning and forecasting systems for drought and flood, interconnections of hydrological extremes and applicability of satellite data for hydrological studies. This Editorial provides an overview of the covered topics and reviews the case studies relevant for each topic.

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Section: Introductionmentioning

confidence: 99%

Statistical Analysis and Stochastic Modelling of Hydrological Extremes

Tabari

2019

Water

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“…To overcome the problem of intermittency and zero values, precipitation is simulated separately from other variables. The main method for reproducing intermittency has been the multiplication of precipitation occurrence and an amount as Z = X • Y , where X is the occurrence (binary as either 0 or 1) and Y is the amount (Jeong et al, 2013;Lee and Park, 2017;Todorovic and Woolhiser, 1975). The spatial and temporal dependence in the occurrence and amount of precipitation introduce further complexity in multisite simulation.…”

Section: Introductionmentioning

confidence: 99%

“…Metaheuristic techniques, such as GA, have been popularly employed in a number of hydrometeorological applications (Chau, 2017;Fotovatikhah et al, 2018;Taormina et al, 2015;Wang et al, 2013). Although a number of variants of KNNR-GA have been applied Lee and Park, 2017), none of them can simulate multisite occurrence of precipitation whose characteristics are binary and temporally and spatially related.…”

Section: Introductionmentioning

confidence: 99%

Discrete <i>k</i>-nearest neighbor resampling for simulating multisite precipitation occurrence and model adaption to climate change

Lee

Singh

2019

Geosci. Model Dev.

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Abstract. Stochastic weather simulation models are commonly employed in water resources management, agricultural applications, forest management, transportation management, and recreational activities. Stochastic simulation of multisite precipitation occurrence is a challenge because of its intermittent characteristics as well as spatial and temporal cross-correlation. This study proposes a novel simulation method for multisite precipitation occurrence employing a nonparametric technique, the discrete version of the k-nearest neighbor resampling (KNNR), and couples it with a genetic algorithm (GA). Its modification for the study of climatic change adaptation is also tested. The datasets simulated from both the discrete KNNR (DKNNR) model and an existing traditional model were evaluated using a number of statistics, such as occurrence and transition probabilities, as well as temporal and spatial cross-correlations. Results showed that the proposed DKNNR model with GA-simulated multisite precipitation occurrence preserved the lagged cross-correlation between sites, while the existing conventional model was not able to reproduce lagged cross-correlation between stations, so long stochastic simulation was required. Also, the GA mixing process provided a number of new patterns that were different from observations, which was not feasible with the sole DKNNR model. When climate change was considered, the model performed satisfactorily, but further improvement is required to more accurately simulate specific variations of the occurrence probability.

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“…Furthermore, this linkage so far has been commonly established at the daily timestep since current climate models have some major limitations in their detailed physical modeling ability and their limited computational capability. Consequently, it requires an improved (temporal) rainfall modeling approach to describe the linkages between the ER processes over a wide range of time scales (e.g., from one day to several minutes) [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…However, very few studies are dealing with the temporal downscaling problem of GCM or RCM outputs. More specifically, some procedures have been proposed to derive the key statistics of the sub-daily ER series from those of the daily series [9,11]. Among these methods, the statistical models based on the scale-invariance (or scaling) concept has recently increasingly become an essential tool for modeling ER processes over a wide range of temporal scales [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Scale-Invariance Generalized Logistic (GLO) Model for Estimating Extreme Design Rainfalls in the Context of Climate Change

Nguyen¹

EPiC Series in Engineering

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Statistical models based on the scale-invariance (or scaling) concept has increasingly become an essential tool for modeling extreme rainfall processes over a wide range of time scales. In particular, in the context of climate change these scaling models can be used to describe the linkages between the distributions of sub-daily extreme rainfalls (ERs) and the distribution of daily ERs that is commonly provided by global or regional climate simulations. Furthermore, the Generalized Logistic distribution (GLO) has been recommended in UK for modeling of extreme hydrologic variables. Therefore, the main objective of the present study is to propose a scaling GLO model for modeling ER processes over different time scales. The feasibility and accuracy of this model were assessed using ER data from a network of 21 raingages located in Ontario, Canada. Results of this assessment based on different statistical criteria have indicated the comparable performance of the proposed scaling GLO model as compared to other popular models in practice. Furthermore, an illustrative application of the proposed model for evaluating the climate change impacts on the ERs in Ontario using the available NASA downscaled regional climate simulations has demonstrated the accuracy and robustness of the GLO model.

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Nonparametric temporal downscaling with event-based population generating algorithm for RCM daily precipitation to hourly: Model development and performance evaluation

Cited by 15 publications

References 32 publications

Statistical Analysis and Stochastic Modelling of Hydrological Extremes

Statistical Analysis and Stochastic Modelling of Hydrological Extremes

Discrete <i>k</i>-nearest neighbor resampling for simulating multisite precipitation occurrence and model adaption to climate change

Scale-Invariance Generalized Logistic (GLO) Model for Estimating Extreme Design Rainfalls in the Context of Climate Change

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Nonparametric temporal downscaling with event-based population generating algorithm for RCM daily precipitation to hourly: Model development and performance evaluation

Cited by 15 publications

References 32 publications

Statistical Analysis and Stochastic Modelling of Hydrological Extremes

Statistical Analysis and Stochastic Modelling of Hydrological Extremes

Discrete &lt;i&gt;k&lt;/i&gt;-nearest neighbor resampling for simulating multisite precipitation occurrence and model adaption to climate change

Scale-Invariance Generalized Logistic (GLO) Model for Estimating Extreme Design Rainfalls in the Context of Climate Change

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Discrete <i>k</i>-nearest neighbor resampling for simulating multisite precipitation occurrence and model adaption to climate change