Bias correction of climate model outputs influences watershed model nutrient load predictions

Miralha, Lorrayne; Muenich, Rebecca Logsdon; Scavia, Donald; Wells, Karlie A.; Steiner, Allison L.; Kalcic, Margaret; Apostel, Anna; Basile, Samantha; Kirchhoff, Christine

doi:10.1016/j.scitotenv.2020.143039

Cited by 25 publications

(15 citation statements)

References 44 publications

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“…Our results suggest that using MBCs such as 3DBC sometimes worsens the prediction results. This suggestion may be helpful for analyzing the results of Miralha et al ( 2020 ), wherein QM performed better than MBCs in the NPS model.…”

Section: Discussionmentioning

confidence: 87%

“…Similar to Miralha et al ( 2020 ), this study used both observed and climate model-simulated daily precipitation ( Pr ), maximum temperature ( T max ), and minimum temperature ( T min ) data considering their availability, robustness, and impact on NPS. The observed data were taken from the China Meteorological Science Data Sharing Service Network ( http://data.cma.cn ) and covered the period from 1981 to 2015.…”

Section: Study Area and Datamentioning

confidence: 99%

“…Even the COVID-19 pandemic, which occurred along with a sudden reduction in greenhouse gas emissions, has not seemed to change the trend of global warming (Forster et al 2020 ). As a vital component of the climate system, precipitation also changes in its intensity and frequency with rising temperatures, thus bringing challenges to watershed adaptation/elimination management (Miralha et al 2020 ; Zhang et al 2019a ). Moreover, recent attention has been given to the temporal unevenness of precipitation because this unevenness can change the potential risks, e.g., both the rainfall duration and antecedent dry period can influence the first-flush effect of nonpoint source pollution (NPS; e.g., Chen 2020 ; Feng et al 2013 ).…”

Section: Introductionmentioning

confidence: 99%

“…NPS refers to accumulated pollutants (e.g., fertilizers) in a watershed entering the downstream receiving water body in a wide, dispersed, and trace form under the flushing and transport of precipitation, thus leading to eutrophication, damaging the aquatic environment, and threatening water security (Miralha et al 2020 ; Mosley 2015 ). Precipitation plays an important role in the processing of NPS, as it is not only the key driving force of NPS (in the way of precipitation runoff and/or soil erosion), but it can also affect the physicochemical properties of soils (e.g., moisture content, pH, C/N ratio, temperature), reshaping the surface water flux, groundwater flux, and ecohydrological flux accordingly (Ouyang et al 2019 ; Shortridge 2019 ).…”

Section: Introductionmentioning

confidence: 99%

“…Precipitation plays an important role in the processing of NPS, as it is not only the key driving force of NPS (in the way of precipitation runoff and/or soil erosion), but it can also affect the physicochemical properties of soils (e.g., moisture content, pH, C/N ratio, temperature), reshaping the surface water flux, groundwater flux, and ecohydrological flux accordingly (Ouyang et al 2019 ; Shortridge 2019 ). Precipitation is also the dominant input of both physics-based and statistics-based NPS models, indicating the importance of precipitation characteristics to NPS (Miralha et al 2020 ; Varekar et al 2021 ). Although the precipitation amount is considered a preferred feature of precipitation, precipitation unevenness, which describes the extent to which falling precipitation is asymmetrical over time, can also affect NPS (Nobre et al 2020 ; Qiu et al 2018 ).…”

Section: Introductionmentioning

confidence: 99%

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Precipitation projection over Daqing River Basin (North China) considering the evolution of dependence structures

Gao

Liu

et al. 2021

Environ Sci Pollut Res

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Understanding dynamic future changes in precipitation can provide prior information for nonpoint source pollution simulations under global warming. However, the evolution of the dependence structure and the unevenness characteristics of precipitation are rarely considered. This study applied a two-stage bias correction to daily precipitation and max/min temperature data in the Daqing River Basin (DQRB) with the HadGEM3-RA climate model. Validated from 1981 to 2015, future scenarios under two emission paths covering 2031–2065 and 2066–2100 were projected to assess variations in both the amount and unevenness of precipitation. The results suggested that, overall, the two-stage bias correction could reproduce the marginal distributions of variables and the evolution process of the dependence structure. In the future, the amount of precipitation in the plains is expected to increase more than that in the mountains, while precipitation unevenness, as measured by relative entropy, shows a slight increase in the mountains and a decrease in the plains, with enhanced seasonality. Conditioned on rising temperatures, high-/low-intensity precipitation tends to intensify/weaken precipitation unevenness. Additionally, the potential application of the bias correction method used herein and the possible impacts of uneven precipitation on nonpoint source pollution are given for further analyses. This study can provide useful information for future nonpoint source pollution simulations in the DQRB. Supplementary Information The online version contains supplementary material available at 10.1007/s11356-021-16066-9.

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Section: Discussionmentioning

confidence: 87%

Section: Study Area and Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Precipitation projection over Daqing River Basin (North China) considering the evolution of dependence structures

Gao

Liu

et al. 2021

Environ Sci Pollut Res

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Environmental thresholds for phytoplankton group dynamics in the Western Lake Erie

Miralha

Errera

Hood³

2020

Preprint

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Development of an ensemble Bayesian inference‐based copula approach for bivariate risk evaluation of extreme precipitation under climate change

Sun

et al. 2022

Intl Journal of Climatology

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In this study, an ensemble Bayesian inference-based copula approach (EBICA) has been proposed to evaluate the joint risk of extreme precipitation indices under climate change. EBICA incorporates GCMs, quantile delta mappingbased bias correction method, Monte Carlo Markov Chain (MCMC)-based Bayesian inference method, and copula functions into an integration, where 17 GCMs and 3 copulas are explored EBICA can reflect the inherent uncertainties in GCM and copula selections, as well as deal with the uncertainty caused by copula parameters when modelling the dependent structures.Ensemble results of GCMs and copulas are also analysed to promote the projection accuracy of dynamic features of extreme precipitation (characterized by precipitation intensity, very heavy precipitation amount and frequency, and maximum 1 or 5-day precipitation amount). EBICA is then applied to Fujian Province to investigate climate change impacts on extreme precipitation during 2021-2099. Major findings can be summarized as (a) GCM is the primary source of uncertainties that impacts extreme precipitation projection, and the ensemble of GCMs suggests that climate change would exacerbate future precipitation intensity, amount, and frequency over Fujian Province; (b) mountainous area in Fujian is more easily encountered with very heavy precipitation amount and coastal area is more easily faced with maximum 1-day or consecutive 5-day precipitation under climate change; (c) Fujian Province would averagely encounter with shorter co-occurrence period of [SDII, R95p] and [R20mm, R95p], corresponding to higher risk level in very heavy precipitation amount and frequency, especially in the north part. Generally, these findings could help lay the foundation for forecasting and warning of extreme precipitation events and reducing associated risk losses.

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Bias correction of climate model outputs influences watershed model nutrient load predictions

Cited by 25 publications

References 44 publications

Precipitation projection over Daqing River Basin (North China) considering the evolution of dependence structures

Precipitation projection over Daqing River Basin (North China) considering the evolution of dependence structures

Environmental thresholds for phytoplankton group dynamics in the Western Lake Erie

Development of an ensemble Bayesian inference‐based copula approach for bivariate risk evaluation of extreme precipitation under climate change

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