Timing of human-induced climate change emergence from internal climate variability for hydrological impact studies

Zhuan, Meijia; Chen, Jie; Shen, Mingxi; Xu, Chong‐Yu; Chen, Hua; Xiong, Lihua

doi:10.2166/nh.2018.059

Cited by 42 publications

(30 citation statements)

References 33 publications

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“…Climate model (GCMs [global climate models] or RCMs [regional climate models]) outputs are usually used as inputs to environmental models (e.g., hydrological models) for assessing the climate change impacts on the environment (e.g., streamflow) (Li et al ., ; Piniewski et al ., ; Wang et al ., ; Zhuan et al ., ). However, their coarse resolution and/or inherent biases often hinder the use of GCM outputs directly for impact studies, due to the inherent limitations in climate models and our limited knowledge of climate systems (Xu, ; Giorgi and Coppola, ; Chen et al ., ; Wilcke et al ., ; Rajbhandari et al ., ).…”

Section: Introductionmentioning

confidence: 97%

Bias nonstationarity of global climate model outputs: The role of internal climate variability and climate model sensitivity

Chen

et al. 2018

Intl Journal of Climatology

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Bias correction methods are developed based on the assumption that the biases of climate model outputs are stationary, that is, the characteristics of the bias are constant over time. However, recent studies have shown the biases are not always stationary. The objectives of this study are to investigate the impacts of bias nonstationarity of climate-model-simulated precipitation and temperature on future climate projections, and the roles of internal climate variability (ICV) and climate model sensitivity (CMS) in bias nonstationarity. A pseudoreality approach is used in this study, in which each of the 24 climate model simulations is alternately selected as a reference to estimate the biases (defined as pseudobias to distinguish it from actual bias estimated by observations) of 23 other simulations. The absolute ratio of the change in pseudobias between two periods to the corresponding climate change signal is calculated to assess the impacts of bias nonstationarity on future climate projections. Furthermore, the roles of ICV and CMS are investigated by comparing the changes in pseudobias between historical and future periods relative to the baseline period. The results show that biases of climate-model-simulated mean annual and seasonal temperature and precipitation vary with time. Bias nonstationarity of temperature is not significant in future temperature projections, while the bias nonstationarity of precipitation plays an important role in future precipitation projections. In addition, the contributions of ICV and CMS to bias nonstationarity are both relatively small for temperature, even though the latter contributes slightly more than the former. However, ICV makes a large contribution to the bias nonstationarity of precipitation for the historical period. In the far future period, the role of CMS is as important as ICV. These results imply that the impacts of ICV and CMS may need to be considered when developing and evaluating a bias correction method, especially for precipitation projections.

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Section: Introductionmentioning

confidence: 97%

Bias nonstationarity of global climate model outputs: The role of internal climate variability and climate model sensitivity

Chen

et al. 2018

Intl Journal of Climatology

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“…Lumped hydrological models are still widely used in flood forecasting and flood risk assessment (e.g., El Alfy, 2016;Jie et al, 2016;Refsgaard et al, 1988;Thiboult & Anctil, 2015;Huang & Hattermann, 2018;Su et al, 2018), water resource assessments (e.g., Kizza et al, 2013;Xu et al, 1996;Koivusalo et al, 2017), and impact studies of climate change on water resources (e.g., Awan et al, 2016;Chen et al, 2007Chen et al, & 2012Yan et al, 2016;Guo et al, 2018;Zhuan et al, 2018). However, the performance of lumped hydrological models is substantially influenced by model inputs (Oudin et al, 2006;Chang et al, 2017;Pechlivanidis et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

The effect of rain gauge density and distribution on runoff simulation using a lumped hydrological modelling approach

Zeng

Chen

Guo

et al. 2018

Journal of Hydrology

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Most lumped hydrological models use areal average precipitation data as model input. Though weather-radar-based and satellite-based precipitation estimation methods have been proposed in recent years, the rain gauge is still the most widely used precipitation-measuring tool. Optimal selection of rain gauge number and location will improve the accuracy of areal average precipitation estimations with minimum cost. In this study, the impacts of rain gauge density and distribution on lumped hydrological modelling uncertainty with different catchment sizes are analysed. To this end, the performances of a lumped hydrological model, the Xinanjiang model, in a densely gauged river basin, the Xiangjiang River basin, and its sub-basins under different gauge density and distribution are compared. First, seven levels of rain gauge density are defined. For each density level, several samples of different rain gauge distributions are randomly selected. Then, the areal average precipitation of each sample is estimated and used as input to the Xinanjiang model. Finally, the model is calibrated using the shuffled complex evolution (SCE-UA) algorithm, and model uncertainty is evaluated via the Bayesian method. The results show that 1) imperfect precipitation inputs measured by a sparse and irregular rain gauge network will lead to substantial uncertainty in model parameter estimation and flood simulation; 2) the impacts of imperfect precipitation estimates on model efficiency can be reduced to some extent through the adjustment of model parameters; 3) modelling uncertainty is reduced by increasing the rain gauge density or optimizing the rain gauge distribution pattern; and 4) the improvement in lumped model efficiency is no longer significant when the rain gauge density exceeds a certain threshold, but a further increase in rain gauge density will reduce model parameter uncertainty and the width of the runoff confidence interval.

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“…The first stage involves correcting the distribution of precipitation amounts on wet days and the wet-day frequency of the projected GCM-simulated precipitation using the QM-based method by Chen et al [5]. This bias correction method is conceptually simple and widely used [10,21,29,43,44]. The second stage involves generating the temporal sequence of precipitation occurrence using the first-order two-state Markov chain based on the first stage corrected monthly precipitation amounts.…”

Section: Methodsmentioning

confidence: 99%

A Markov Chain-Based Bias Correction Method for Simulating the Temporal Sequence of Daily Precipitation

et al. 2020

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Bias correction methods are routinely used to correct climate model outputs for hydrological and agricultural impact studies. Even though superior bias correction methods can correct the distribution of daily precipitation amounts, as well as the wet-day frequency, they usually fail to correct the temporal sequence or structure of precipitation occurrence. To solve this problem, we presented a hybrid bias correction method for simulating the temporal sequence of daily precipitation occurrence. We did this by combining a first-order two-state Markov chain with a quantile-mapping (QM) based bias correction method. Specifically, a QM-based method was used to correct the distributional attributes of daily precipitation amounts and the wet-day frequency simulated by climate models. Then, the sequence of precipitation occurrence was simulated using the first-order two-state Markov chain with its parameters adjusted based on linear relationships between QM-corrected mean monthly precipitation and the transition probabilities of precipitation occurrence. The proposed Markov chain-based bias correction (MCBC) method was compared with the QM-based method with respect to reproducing the temporal structure of precipitation occurrence over 10 meteorological stations across China. The results showed that the QM-based method was unable to correct the temporal sequence, with the cumulative frequency of wet- and dry-spell length being considerably underestimated for most stations. The MCBC method can could reproduce the temporal sequence of precipitation occurrence, with the generated cumulative frequency of wet- and dry-spell lengths fitting that of the observation well. The proposed method also performed reasonably well with respect to reproducing the mean, standard deviation, and the longest length of observed wet- and dry-spells. Overall, the MCBC method can simulate the temporal sequence of precipitation occurrence, along with correcting the distributional attributes of precipitation amounts. This method can be used with crop and hydrological models in climate change impact studies at the field and small watershed scales.

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Timing of human-induced climate change emergence from internal climate variability for hydrological impact studies

Cited by 42 publications

References 33 publications

Bias nonstationarity of global climate model outputs: The role of internal climate variability and climate model sensitivity

Bias nonstationarity of global climate model outputs: The role of internal climate variability and climate model sensitivity

The effect of rain gauge density and distribution on runoff simulation using a lumped hydrological modelling approach

A Markov Chain-Based Bias Correction Method for Simulating the Temporal Sequence of Daily Precipitation

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