Impacts of Using State‐of‐the‐Art Multivariate Bias Correction Methods on Hydrological Modeling Over North America

Guo, Qinglian; Chen, Jie; Zhang, X. C.; Xu, Chong‐Yu; Chen, Hua

doi:10.1029/2019wr026659

Cited by 39 publications

(21 citation statements)

References 52 publications

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“…Since GCM outputs are usually too biased to input to hydrological models for hydrological simulation at the catchment scale, a bias correction method was employed to correct GCM simulated precipitation and temperature bias using GPCC and CPC as the reference datasets, respectively. After interpolating the gridded GCM simulations to 0.25° × 0.25° grids using the inverse distance weighting method, a multivariate bias correction method called two‐stage quantile mapping (TSQM) was applied (Chen et al., 2021; Guo et al., 2019, 2020). The TSQM method corrects the distribution of single variables such as precipitation, maximum and minimum temperatures (i.e., Pre, Tmax, and Tmin), and inter‐variable correlations.…”

Section: Methodsmentioning

confidence: 99%

Impacts of Global Climate Warming on Meteorological and Hydrological Droughts and Their Propagations

Troin

Shi

et al. 2022

Earth's Future

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Meteorological to hydrological drought propagation has been widely studied to reflect the relationship between these drought categories and better understand drought mechanisms. However, global warming may alter the drought propagation features, which are not fully understood. This study aims to investigate changes in meteorological and hydrological drought conditions, especially their propagation features in 1.5–3.0°C warmer climates for 8,655 watersheds globally. First, the three‐month scale standardized precipitation index and the standardized runoff index are calculated based on the precipitation simulated by the 15 global climate models and the runoff simulated by the four hydrological models, respectively. Drought events are then identified using the run theory, followed by the calculation of drought propagation features (i.e., pooling, lag, and lengthening) for matched meteorological and hydrological droughts. As a result, both meteorological and hydrological drought conditions (i.e., duration and severity) would relieve in warmer climates due to increased precipitation for regions excluding Western North America, South America, the Mediterranean, Southern Africa, East Asia, and Australia. However, the drought conditions would be more severe during drought propagation from meteorological to hydrological droughts over most regions. During drought propagation, the worsening drought conditions over half of the regions would be more serious first and then relieved with the rising temperature. These results indicate that efforts to slow down global warming can suppress the deterioration of drought conditions in the propagation.

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Section: Methodsmentioning

confidence: 99%

Impacts of Global Climate Warming on Meteorological and Hydrological Droughts and Their Propagations

Troin

Shi

et al. 2022

Earth's Future

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“…This step is applied to all climate variables. By matching the cumulative distribution functions (CDFs) of GCM outputs with those of observations, QM has been widely used to correct the bias between GCM outputs and observations in the field of climate change [36,38]. For bias correction in the future period, DQM removes the trend of simulations firstly and then conducts quantile mapping, eventually restoring the trend.…”

Section: Datamentioning

confidence: 99%

“…However, there is a gap between the required inputs and the data provided by CMIP6 and LUH2, which have systematic bias and coarse spatial resolution. To bridge this gap, statistical downscaling methods can effectively correct systematic bias and downscale the spatial resolution and are widely used for future climate downscaling [36][37][38]. The CA-Markov model is a popular and robust land use simulation model and can potentially be adopted for spatial downscaling of land use maps [1,[39][40][41][42].…”

Section: Introductionmentioning

confidence: 99%

Future Changes in High and Low Flows under the Impacts of Climate and Land Use Changes in the Jiulong River Basin of Southeast China

Yang

Zhao

et al. 2022

Atmosphere

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Climate change and human activities have profoundly affected the world with extreme precipitation, heat waves, water scarcity, frequent floods and intense droughts. It is acknowledged that climate change will persist and perhaps intensify in the future, and it is thus meaningful to explore the quantitative impacts of these changes on hydrological regimes. The Jiulong River basin serves as an important watershed on the southeast coast of China. However, future hydrological changes under the combined impacts of climate change and land use change have been barely investigated. In this study, the climate outputs from five general circulation models (GCMs) under the Coupled Model Intercomparison Project Phase 6 (CMIP6) were corrected and spatially downscaled by a statistical downscaling method combining quantile mapping and machine learning. The future high-resolution land use maps were projected by the CA–Markov model with land use changes from the Land-Use Harmonization 2 (LUH2) as constraints. The future dynamic vegetation process was projected by the Biome-GBC model, and then, the future hydrological process under four representative concentration pathways and shared socioeconomic pathways (RCP–SSP) combined scenarios was simulated by a distributed hydrological model. Based on the copula method, the flood frequency and corresponding return periods were derived. The results demonstrated that future precipitation and air temperature would continue to rise, and future land use changes would have different developing pathways determined by the designs in various SSP–RCPs. Under the combined impacts of climate and land use change, the total available water resources will increase due to increasing precipitation, and the high flow and low flow will both increase at three stations under the four SSP–RCPs. The annual 1-day maximum discharge is projected to increase by 67–133% in the last decade of the 21st century, and the annual 7-day minimum discharge is projected to increase by 19–39%. The flood frequency analysis showed that the Jiulong River basin would face more frequent floods in the future. By the end of the 21st century, the station-average frequency of a historical 100-year flood will increase by 122% under the most optimistic scenario (SSP126) and increase by 213% under the scenario of greatest regional rivalry (SSP370). We demonstrated that climate change would be the major cause for the increase in future high flows and that land use change would dominate future changes in low flows. Finally, we recommend integrated and sustainable water management systems to tackle future challenges in this coastal basin.

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“…In other words, a quantile mapping‐based method is first used to correct the marginal distributions of precipitation and temperature, and then a distribution‐free shuffling algorithm is applied to introduce the observed correlations. The performance of this method has been evaluated and compared to other methods over the entire global land surface for climate corrections (Guo et al., 2019) and over 2,840 watersheds in Canada and the United States for hydrological simulations (Guo et al., 2020). The results showed that TSQM performed either better or comparably to other existing methods.…”

Section: Introductionmentioning

confidence: 99%

Climate Change Impact Studies: Should We Bias Correct Climate Model Outputs or Post‐Process Impact Model Outputs?

Chen

Arsenault

Brissette

et al. 2021

Water Resources Research

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Global climate models (GCMs) are major tools that provide climate projections for future climate change impact studies. However, the resolution of climate model outputs is usually too coarse to accurately resolve physical and dynamical processes of the climate system. In addition, climate model outputs are biased, sometimes significantly at the regional scale, due to limited knowledge of physical processes in the real climate system and imperfect implementation of this limited knowledge (Wilby et al., 1998;Zhuan et al., 2019). Thus, GCM simulations are rarely directly used as inputs to impact models for local and watershed-scale impact studies (Maraun et al., 2010;Wilby et al., 1998;Xu, 1999). Downscaling and/or bias correction methods are usually used to bridge the gap between climate model simulations and the data requirements of impact models Schmidli et al., 2006;Wilby et al., 2002;Zhang, 2005a). In particular, in recent years, bias correction methods have been used more frequently than other methods, such as the perfect prognosis (Wilby et al., 2002) and stochastic weather generator (Zhang, 2005a) methods, thanks to their easy application and relative good performance. Bias correction methods have become a standard approach when applying climate model outputs for climate change impact studies at the local and watershed scales.All bias correction methods are developed based on the assumption that the climate model simulation biases are constant over time. In other words, these methods assume that the magnitude of bias is the same for climate model simulations both for historical and future periods. The bias correction method first estimates the biases of climate model simulations over a historical reference period through a comparison against observations, and then the same biases are removed for the future period for climate change impact studies. Although some studies (e.g.,

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Impacts of Using State‐of‐the‐Art Multivariate Bias Correction Methods on Hydrological Modeling Over North America

Cited by 39 publications

References 52 publications

Impacts of Global Climate Warming on Meteorological and Hydrological Droughts and Their Propagations

Impacts of Global Climate Warming on Meteorological and Hydrological Droughts and Their Propagations

Future Changes in High and Low Flows under the Impacts of Climate and Land Use Changes in the Jiulong River Basin of Southeast China

Climate Change Impact Studies: Should We Bias Correct Climate Model Outputs or Post‐Process Impact Model Outputs?

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