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Qingyun Duan
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, College of Hydrology and Water Resources, Hohai University, Nanjing, Jiangsu 210098, China

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Journal article
Published: 08 July 2021 in Atmospheric Research
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The Weather Research and Forecasting (WRF) model can be used to diagnose regional land-atmosphere (L-A) coupling strength in the absence of sufficient observations but subjected to uncertainties associated with model physical parameterizations. In this study, we propose a framework to quantify and reduce model physical parameterization uncertainties associated with surface fluxes and L-A coupling. An ensemble of WRF simulations with different physical schemes is used to simulate surface fluxes and land-atmosphere coupling strength over the Amazon region. The physical parameterizations investigated include cloud microphysics (MP), land surface processes (LSM), planetary boundary layer (PBL), surface layer (SL), and cumulus (CU). We perform 120 ensemble simulations using the WRF model and different combinations of six MPs, three LSMs, six PBLs and SLs and three CUs. The measurements from the GoAMAZON field campaign and satellite data are used to evaluate model performance. A Multi-way analysis of variance (ANOVA) approach is applied to quantify the relative importance of different physics processes on L-A coupling. The Tukey's test is used to sort schemes that have no significant differences into one group. The suite of physics that result in the best simulations of the corresponding variables are selected based on the Taylor skill score. Results show that the relative importance of processes and their interaction vary with the variables of interest. For example, CU was the most important process in modulating soil moisture, 2 m-humidity, latent heat, and net radiation. LSM showed dominant effects on 2 m-temperature and also has the largest impact on sensible heat and the lifting condensation level. The best physical parameterization ensembles show much narrower ranges of the variables of interest than the priori ensemble. Results of this study show the roles of different physical processes in modulating L-A interactions, quantify model uncertainties from physical processes, and provide insights for improving the model physics parameterizations.

ACS Style

Chen Wang; Yun Qian; Qingyun Duan; Maoyi Huang; Zhao Yang; Larry K. Berg; William I. Gustafson; Zhe Feng; Juxiu Liu; Jiping Quan. Quantifying physical parameterization uncertainties associated with land-atmosphere interactions in the WRF model over Amazon. Atmospheric Research 2021, 262, 105761 .

AMA Style

Chen Wang, Yun Qian, Qingyun Duan, Maoyi Huang, Zhao Yang, Larry K. Berg, William I. Gustafson, Zhe Feng, Juxiu Liu, Jiping Quan. Quantifying physical parameterization uncertainties associated with land-atmosphere interactions in the WRF model over Amazon. Atmospheric Research. 2021; 262 ():105761.

Chicago/Turabian Style

Chen Wang; Yun Qian; Qingyun Duan; Maoyi Huang; Zhao Yang; Larry K. Berg; William I. Gustafson; Zhe Feng; Juxiu Liu; Jiping Quan. 2021. "Quantifying physical parameterization uncertainties associated with land-atmosphere interactions in the WRF model over Amazon." Atmospheric Research 262, no. : 105761.

Journal article
Published: 07 June 2021 in Earth's Future
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Identifying climate change hotspot regions is critical for planning effective mitigation and adaptation activities. We use standard Euclidean distance (SED) to calculate integrated changes in precipitation and temperature means, interannual variability, and extremes between different future warming levels and a baseline period (1995–2014) using the Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model ensemble. We find consistent hotspots in the Amazon, central and western Africa, Indonesia and the Tibetan Plateau at warming levels of 1.5 °C, 2 °C and 3 °C for all scenarios explored; the Arctic, Central America and southern Africa emerge as hotspots at 4 °C warming and at the end of the 21st century under two Shared Socioeconomic Pathways scenarios, SSP3‐7.0 and SSP5‐8.5. CMIP6 models show higher SED values than CMIP5, suggesting stronger aggregated effects of climate change under the new scenarios. Hotspot time of emergence (TOE) is further investigated; TOE is defined as the year when the climate change signal first exceeds the noise of natural variability in 21st century projections. The results indicate that TOEs for warming would occur over all primary hotspots, with the earliest occurring in the Arctic and Indonesia. For precipitation, TOEs occur before 2100 in the Arctic, the Tibetan Plateau and Central America. Results using a geographical detector model show that patterns of SED are shaped by extreme hot and dry occurrences at low‐to‐medium warming, while precipitation and temperature means and extreme precipitation occurrences are the dominant influences under the high emission scenario and at high warming levels.

ACS Style

Xuewei Fan; Chiyuan Miao; Qingyun Duan; Chenwei Shen; Yi Wu. Future Climate Change Hotspots Under Different 21st Century Warming Scenarios. Earth's Future 2021, 9, 1 .

AMA Style

Xuewei Fan, Chiyuan Miao, Qingyun Duan, Chenwei Shen, Yi Wu. Future Climate Change Hotspots Under Different 21st Century Warming Scenarios. Earth's Future. 2021; 9 (6):1.

Chicago/Turabian Style

Xuewei Fan; Chiyuan Miao; Qingyun Duan; Chenwei Shen; Yi Wu. 2021. "Future Climate Change Hotspots Under Different 21st Century Warming Scenarios." Earth's Future 9, no. 6: 1.

Commentary
Published: 07 April 2021 in Water Resources Research
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This commentary explores the challenges and opportunities associated with a possible transition of Water Resources Research to a publication model where all articles are freely available upon publication (“Gold” open access). It provides a review of the status of open access publishing models, a summary of community input, and a path forward for AGU leadership. The decision to convert to open access is framed by a mix of finances and values. On the one hand, the challenge is to define who pays, and how, and what we can do to improve the affordability of publishing. On the other hand, the challenge is to increase the extent to which science is open and accessible. The next steps for the community include an incisive analysis of the financial feasibility of different cost models, and weighing the financial burden for open access against the desire to further advance open science.

ACS Style

Martyn P. Clark; Charles H. Luce; Amir AghaKouchak; Wouter Berghuijs; Cédric H. David; Qingyun Duan; Shemin Ge; Ilja van Meerveld; Chunmiao Zheng; Marc B. Parlange; Scott W. Tyler. Open Science: Open Data, Open Models, …and Open Publications? Water Resources Research 2021, 57, 1 .

AMA Style

Martyn P. Clark, Charles H. Luce, Amir AghaKouchak, Wouter Berghuijs, Cédric H. David, Qingyun Duan, Shemin Ge, Ilja van Meerveld, Chunmiao Zheng, Marc B. Parlange, Scott W. Tyler. Open Science: Open Data, Open Models, …and Open Publications? Water Resources Research. 2021; 57 (4):1.

Chicago/Turabian Style

Martyn P. Clark; Charles H. Luce; Amir AghaKouchak; Wouter Berghuijs; Cédric H. David; Qingyun Duan; Shemin Ge; Ilja van Meerveld; Chunmiao Zheng; Marc B. Parlange; Scott W. Tyler. 2021. "Open Science: Open Data, Open Models, …and Open Publications?" Water Resources Research 57, no. 4: 1.

Editorial
Published: 26 March 2021 in Reviews of Geophysics
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The editorial board thanks the 2020 peer reviewers

ACS Style

Fabio Florindo; Annmarie G. Carlton; Paolo D'Odorico; Qingyun Duan; Jasper S. Halekas; Gesine Mollenhauer; Eelco J. Rohling; Robert G. Bingham; Emily E. Brodsky; Michel C. Crucifix; Andrew Gettelman; Alan Robock. Thank You to Our Peer Reviewers for 2020. Reviews of Geophysics 2021, 59, 1 .

AMA Style

Fabio Florindo, Annmarie G. Carlton, Paolo D'Odorico, Qingyun Duan, Jasper S. Halekas, Gesine Mollenhauer, Eelco J. Rohling, Robert G. Bingham, Emily E. Brodsky, Michel C. Crucifix, Andrew Gettelman, Alan Robock. Thank You to Our Peer Reviewers for 2020. Reviews of Geophysics. 2021; 59 (1):1.

Chicago/Turabian Style

Fabio Florindo; Annmarie G. Carlton; Paolo D'Odorico; Qingyun Duan; Jasper S. Halekas; Gesine Mollenhauer; Eelco J. Rohling; Robert G. Bingham; Emily E. Brodsky; Michel C. Crucifix; Andrew Gettelman; Alan Robock. 2021. "Thank You to Our Peer Reviewers for 2020." Reviews of Geophysics 59, no. 1: 1.

Journal article
Published: 05 March 2021 in Journal of Hydrology
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The South-to-North water diversion Middle Route Project (MRP) is expected to alleviate the long-term groundwater storage (GWS) depletion in North China Plain (NCP) after the beginning of its operation in December 2014. This study aims to investigate the effect of MRP on GWS by comparing GWS changes before (2003–2014) and after (2015–2018) the MRP operation. The analysis was conducted by using groundwater level data from 617 wells in NCP, and then evaluated against satellite-based water storage data from Gravity Recovery and Climate Experiment (GRACE) and its Follow-On missions. On average in NCP, a decreasing trend of −19.1 ± 5.1 mm/yr was seen in GWS based on well observations during 2003–2014, but a recovery trend of +1.8 ± 0.7 mm/yr was found during 2015–2018. The GWS recovery was most prominent in subregions where groundwater over-utilization had occurred in NCP. GRACE exhibited the capacity to detect the regional GWS depletion during 2003–2014, but difficult to distinguish the sub-regional GWS recovery during 2015–2018. The potential causes for GWS recovery were found to be complicated, not only caused by the reduction of groundwater pumping as accelerated by MRP-diverted water, but also the increasing precipitation recharge of aquifers and the enhanced management of groundwater system. The findings highlight that GWS in NCP has started a gradual transition from unsustainable depletion to sub-regional recovery as benefit from the MRP water diversion.

ACS Style

Chong Zhang; Qingyun Duan; Pat J.-F. Yeh; Yun Pan; Huili Gong; Hamid Moradkhani; Wei Gong; Xiaohui Lei; Weihong Liao; Lei Xu; Zhiyong Huang; Longqun Zheng; Xueru Guo. Sub-regional groundwater storage recovery in North China Plain after the South-to-North water diversion project. Journal of Hydrology 2021, 597, 126156 .

AMA Style

Chong Zhang, Qingyun Duan, Pat J.-F. Yeh, Yun Pan, Huili Gong, Hamid Moradkhani, Wei Gong, Xiaohui Lei, Weihong Liao, Lei Xu, Zhiyong Huang, Longqun Zheng, Xueru Guo. Sub-regional groundwater storage recovery in North China Plain after the South-to-North water diversion project. Journal of Hydrology. 2021; 597 ():126156.

Chicago/Turabian Style

Chong Zhang; Qingyun Duan; Pat J.-F. Yeh; Yun Pan; Huili Gong; Hamid Moradkhani; Wei Gong; Xiaohui Lei; Weihong Liao; Lei Xu; Zhiyong Huang; Longqun Zheng; Xueru Guo. 2021. "Sub-regional groundwater storage recovery in North China Plain after the South-to-North water diversion project." Journal of Hydrology 597, no. : 126156.

Journal article
Published: 29 January 2021 in Journal of Hydrology
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Land surface models are important tools to represent and predict the spatiotemporal variability of evapotranspiration, which is a key variable in terrestrial water, energy and carbon cycles. However, evapotranspiration estimates from land surface models may suffer from various uncertainties in land surface modeling. Therefore, assessing the performance of evapotranspiration simulation plays a vital role in understanding the deficiencies of land surface modeling. Most of the evaluation studies of modeled evapotranspiration relied on comparisons with flux site observations (point scale) and water budget-derived evapotranspiration (basin scale), which have certain drawbacks and limitations. Thus, the evaluation results may be misleading for understanding the performance of land surface models on representing the spatial variability of evapotranspiration. In this study, a thorough spatial evaluation of the new and reprocessed Global Land Data Assimilation System evapotranspiration products is performed across China based on three bias-insensitive spatial evaluation methods, including the empirical orthogonal function analysis, the connectivity analysis and the fractions skill score. These evapotranspiration products were estimated from three land surface models, namely Noah, VIC and CLSM. The conventional evapotranspiration evaluation against eddy covariance measurements is also performed. The results show that all three products have consistent trends with the observed evapotranspiration series at both daily and monthly time scales. Noah and VIC have comparable performances in terms of different statistic metrics and outperform CLSM at both time scales. The spatial evaluation methods can provide additional valuable information to diagnose the model errors. VIC has the worst spatial performance during the warm months. Despite its inferior performance in late winter and early spring, Noah, overall, has the best spatial performance among the three. The gained insights of this study can help to improve the spatial performance of these models and further promote the system development.

ACS Style

Ruochen Sun; Qingyun Duan; Jiahu Wang. Understanding the spatial patterns of evapotranspiration estimates from land surface models over China. Journal of Hydrology 2021, 595, 126021 .

AMA Style

Ruochen Sun, Qingyun Duan, Jiahu Wang. Understanding the spatial patterns of evapotranspiration estimates from land surface models over China. Journal of Hydrology. 2021; 595 ():126021.

Chicago/Turabian Style

Ruochen Sun; Qingyun Duan; Jiahu Wang. 2021. "Understanding the spatial patterns of evapotranspiration estimates from land surface models over China." Journal of Hydrology 595, no. : 126021.

Journal article
Published: 26 October 2020 in Water Resources Research
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The South‐to‐North Water Diversion Middle Route Project (MRP), which started its operation in December 2014, was designed to transfer water from Danjiangkou Reservoir (DR) in Hanjiang River Basin to North China Plain (NCP) to alleviate water shortage and long‐term groundwater depletion in the water‐receiving region. This study investigates the effectiveness of actual MRP operation during 2015‐2018 using the observed water budget data collected from DR and the groundwater level data from 559 monitoring wells. Assuming that MRP was in operation during 2005‐2014, ensemble water diversion simulations were performed to study the sensitivity of MRP effectiveness to two important factors: the downstream water demand of DR (Dwd) and the ratio (Ir) of water diversion volume (Qd) replacing groundwater pumping in NCP. Even though the observed and simulated mean annual Qd during 2015‐2018 (i.e., 4.3 km3/yr and 7.0 km3/yr, respectively) failed to meet the original water delivery target of 9.5 km3/yr due to its short operation and the coincidence with a dry cycle, MRP is effective in groundwater recovery as an increasing trend (+0.3 km3/yr) in groundwater storage (GWS) was observed in NCP during 2015‐2018. MRP’s effectiveness is sensitive to Dwd and Ir. Dwd should not exceed 23.0 km3/yr to guarantee Qd reaching the original target, and Ir should not be less than 33% to guarantee GWS recovery. Those findings suggest that a reasonable decrease of Dwd and an increase of Ir are the recommended pathway to ensure the effectiveness of MRP in meeting both water delivery and groundwater recovery targets.

ACS Style

Chong Zhang; Qingyun Duan; Pat J.‐F. Yeh; Yun Pan; Huili Gong; Wei Gong; Zhenhua Di; Xiaohui Lei; Weihong Liao; Zhiyong Huang; Longqun Zheng; Xueru Guo. The Effectiveness of the South‐to‐North Water Diversion Middle Route Project on Water Delivery and Groundwater Recovery in North China Plain. Water Resources Research 2020, 56, 1 .

AMA Style

Chong Zhang, Qingyun Duan, Pat J.‐F. Yeh, Yun Pan, Huili Gong, Wei Gong, Zhenhua Di, Xiaohui Lei, Weihong Liao, Zhiyong Huang, Longqun Zheng, Xueru Guo. The Effectiveness of the South‐to‐North Water Diversion Middle Route Project on Water Delivery and Groundwater Recovery in North China Plain. Water Resources Research. 2020; 56 (10):1.

Chicago/Turabian Style

Chong Zhang; Qingyun Duan; Pat J.‐F. Yeh; Yun Pan; Huili Gong; Wei Gong; Zhenhua Di; Xiaohui Lei; Weihong Liao; Zhiyong Huang; Longqun Zheng; Xueru Guo. 2020. "The Effectiveness of the South‐to‐North Water Diversion Middle Route Project on Water Delivery and Groundwater Recovery in North China Plain." Water Resources Research 56, no. 10: 1.

Original paper
Published: 09 October 2020 in Advances in Atmospheric Sciences
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Regional climate models (RCMs) participating in the Coordinated Regional Downscaling Experiment (CORDEX) have been widely used for providing detailed climate change information for specific regions under different emissions scenarios. This study assesses the effects of three common bias correction methods and two multi-model averaging methods in calibrating historical (1980–2005) temperature simulations over East Asia. Future (2006–49) temperature trends under the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios are projected based on the optimal bias correction and ensemble averaging method. Results show the following: (1) The driving global climate model and RCMs can capture the spatial pattern of annual average temperature but with cold biases over most regions, especially in the Tibetan Plateau region. (2) All bias correction methods can significantly reduce the simulation biases. The quantile mapping method outperforms other bias correction methods in all RCMs, with a maximum relative decrease in root-mean-square error for five RCMs reaching 59.8% (HadGEM3-RA), 63.2% (MM5), 51.3% (RegCM), 80.7% (YSU-RCM) and 62.0% (WRF). (3) The Bayesian model averaging (BMA) method outperforms the simple multi-model averaging (SMA) method in narrowing the uncertainty of bias-corrected results. For the spatial correlation coefficient, the improvement rate of the BMA method ranges from 2% to 31% over the 10 subregions, when compared with individual RCMs. (4) For temperature projections, the warming is significant, ranging from 1.2°C to 3.5°C across the whole domain under the RCP8.5 scenario. (5) The quantile mapping method reduces the uncertainty over all subregions by between 66% and 94%.

ACS Style

Chenwei Shen; Qingyun Duan; Chiyuan Miao; Chang Xing; Xuewei Fan; Yi Wu; Jingya Han. Bias Correction and Ensemble Projections of Temperature Changes over Ten Subregions in CORDEX East Asia. Advances in Atmospheric Sciences 2020, 37, 1191 -1210.

AMA Style

Chenwei Shen, Qingyun Duan, Chiyuan Miao, Chang Xing, Xuewei Fan, Yi Wu, Jingya Han. Bias Correction and Ensemble Projections of Temperature Changes over Ten Subregions in CORDEX East Asia. Advances in Atmospheric Sciences. 2020; 37 (11):1191-1210.

Chicago/Turabian Style

Chenwei Shen; Qingyun Duan; Chiyuan Miao; Chang Xing; Xuewei Fan; Yi Wu; Jingya Han. 2020. "Bias Correction and Ensemble Projections of Temperature Changes over Ten Subregions in CORDEX East Asia." Advances in Atmospheric Sciences 37, no. 11: 1191-1210.

Journal article
Published: 18 September 2020 in Journal of Geophysical Research: Atmospheres
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Simulations from the models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6), which represent the most recent generation of climate models, are now available. Understanding the performance of these models in simulating historical climate extremes can provide a basis for producing reliable future climate projections. Here, we assess the simulation of 16 indices of temperature extremes defined by the Expert Team on Climate Change Detection and Indices using results from 24 CMIP6 models as compared with results from CMIP5. Comparisons with observations and reanalyses indicate that the CMIP6 models could capture the spatial patterns and temporal variations of the observed temperature extremes well for some indices, although less well for others. Based on spatial and temporal skill scores, CMIP6 ensemble means were more skillful in simulating absolute and threshold indices of extreme temperature than CMIP5 ensemble means were, but the performances of both the CMIP5 and CMIP6 ensemble means in simulating the spatial patterns for duration and percentile indices were relatively unsatisfactory (spatial skill scores S < 0.3). Furthermore, our results suggest that there have been improvements in spatial pattern skill scores in some individual CMIP6 models relative to CMIP5 model scores for summer days, tropical nights, cold spell duration, and diurnal temperature range.

ACS Style

Xuewei Fan; Chiyuan Miao; Qingyun Duan; Chenwei Shen; Yi Wu. The Performance of CMIP6 Versus CMIP5 in Simulating Temperature Extremes Over the Global Land Surface. Journal of Geophysical Research: Atmospheres 2020, 125, 1 .

AMA Style

Xuewei Fan, Chiyuan Miao, Qingyun Duan, Chenwei Shen, Yi Wu. The Performance of CMIP6 Versus CMIP5 in Simulating Temperature Extremes Over the Global Land Surface. Journal of Geophysical Research: Atmospheres. 2020; 125 (18):1.

Chicago/Turabian Style

Xuewei Fan; Chiyuan Miao; Qingyun Duan; Chenwei Shen; Yi Wu. 2020. "The Performance of CMIP6 Versus CMIP5 in Simulating Temperature Extremes Over the Global Land Surface." Journal of Geophysical Research: Atmospheres 125, no. 18: 1.

Journal article
Published: 10 August 2020 in Earth's Future
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With the increasing pressure from population growth and economic development, northern China (NC) faces a grand challenge of water scarcity, which can be further exacerbated by climatic and societal changes. The South‐to‐North Water Diversion (SNWD) project is designed to mitigate the water scarcity in NC. However, few studies have quantified the impact of the SNWD on water scarcity within the context of climatic and societal changes and its potential effects on economic and agricultural food in the region. We used water supply stress index (WaSSI) to quantify water scarcity within the context of environmental change in NC, and developed a method to estimate the economic and agricultural impacts of the SNWD. Focuses were put on alleviating the water supply shortage and economic and agricultural benefits for the water‐receiving NC. We find that societal changes, especially economic growth, are the major contributors to water scarcity in NC during 2009–2099. To completely mitigate the water scarcity of NC, at least an additional water supply of 13 billion m3/year (comparable to the annual diversion water by SNWD Central Route) will be necessary. Although SNWD alone cannot provide the full solution to northern China's water shortage in next few decades, it can significantly alleviate the water supply stress in NC (particularly Beijing), considerably increasing the agricultural production (more than 115 Teracalories/year) and bringing economic benefits (more than 51 billion RMB/year) through supplying industrial and domestic water use. Additionally, the transfer project could have impacts on the ecological environment in the exporting regions.

ACS Style

Yuanyuan Yin; Lei Wang; Zhongjing Wang; Qiuhong Tang; Shilong Piao; Deliang Chen; Jun Xia; Tobias Conradt; Junguo Liu; Yoshihide Wada; Ximing Cai; Zhenghui Xie; Qingyun Duan; Xiuping Li; Jing Zhou; Jianyun Zhang. Quantifying Water Scarcity in Northern China Within the Context of Climatic and Societal Changes and South‐to‐North Water Diversion. Earth's Future 2020, 8, 1 .

AMA Style

Yuanyuan Yin, Lei Wang, Zhongjing Wang, Qiuhong Tang, Shilong Piao, Deliang Chen, Jun Xia, Tobias Conradt, Junguo Liu, Yoshihide Wada, Ximing Cai, Zhenghui Xie, Qingyun Duan, Xiuping Li, Jing Zhou, Jianyun Zhang. Quantifying Water Scarcity in Northern China Within the Context of Climatic and Societal Changes and South‐to‐North Water Diversion. Earth's Future. 2020; 8 (8):1.

Chicago/Turabian Style

Yuanyuan Yin; Lei Wang; Zhongjing Wang; Qiuhong Tang; Shilong Piao; Deliang Chen; Jun Xia; Tobias Conradt; Junguo Liu; Yoshihide Wada; Ximing Cai; Zhenghui Xie; Qingyun Duan; Xiuping Li; Jing Zhou; Jianyun Zhang. 2020. "Quantifying Water Scarcity in Northern China Within the Context of Climatic and Societal Changes and South‐to‐North Water Diversion." Earth's Future 8, no. 8: 1.

Journal article
Published: 15 July 2020 in Water
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Seasonal forecasts from dynamical models are expected to be useful for drought predictions in many regions. This study investigated the usefulness of the Climate Forecast System version 2 (CFSv2) in improving meteorological drought prediction in China based on its 25-year reforecast. The six-month standard precipitation index (SPI6) was used as the drought indicator, and its persistence forecast served as the benchmark against which CFSv2 forecasts were evaluated. The analysis found that the SPI6 persistence forecast shows good skills in all regions at short lead times, and CFSv2 forecast can further improve those skills in most regions. The improvement is particularly pronounced at longer lead times and over the humid regions in the southeast. This study also examined the seasonality and regionality of persistence forecast skills and CFSv2 contributions, and reveals regions where CFSv2 forecast shows no or sometimes even negative contributions.

ACS Style

Yang Lang; Lifeng Luo; Aizhong Ye; Qingyun Duan. Do CFSv2 Seasonal Forecasts Help Improve the Forecast of Meteorological Drought over Mainland China? Water 2020, 12, 2010 .

AMA Style

Yang Lang, Lifeng Luo, Aizhong Ye, Qingyun Duan. Do CFSv2 Seasonal Forecasts Help Improve the Forecast of Meteorological Drought over Mainland China? Water. 2020; 12 (7):2010.

Chicago/Turabian Style

Yang Lang; Lifeng Luo; Aizhong Ye; Qingyun Duan. 2020. "Do CFSv2 Seasonal Forecasts Help Improve the Forecast of Meteorological Drought over Mainland China?" Water 12, no. 7: 2010.

Regular article
Published: 01 June 2020 in Journal of Meteorological Research
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Selecting proper parameterization scheme combinations for a particular application is of great interest to the Weather Research and Forecasting (WRF) model users. This study aims to develop an objective method for identifying a set of scheme combinations to form a multi-physics ensemble suitable for short-range precipitation forecasting in the Greater Beijing area. The ensemble is created by using statistical techniques and some heuristics. An initial sample of 90 scheme combinations was first generated by using Latin hypercube sampling (LHS). Then, after several rounds of screening, a final ensemble of 40 combinations were chosen. The ensemble forecasts generated for both the training and verification cases using these combinations were evaluated based on several verification metrics, including threat score (TS), Brier score (BS), relative operating characteristics (ROC), and ranked probability score (RPS). The results show that TS of the final ensemble improved by 9%-33% over that of the initial ensemble. The reliability was improved for rain ≤ 10 mm day-1, but decreased slightly for rain > 10 mm day-1 due to insufficient samples. The resolution remained about the same. The final ensemble forecasts were better than that generated from randomly sampled scheme combinations. These results suggest that the proposed approach is an effective way to select a multi-physics ensemble for generating accurate and reliable forecasts.

ACS Style

Chenwei Shen; Qingyun Duan; Wei Gong; Yanjun Gan; Zhenhua Di; Chen Wang; Shiguang Miao. An Objective Approach to Generating Multi-Physics Ensemble Precipitation Forecasts Based on the WRF Model. Journal of Meteorological Research 2020, 34, 601 -620.

AMA Style

Chenwei Shen, Qingyun Duan, Wei Gong, Yanjun Gan, Zhenhua Di, Chen Wang, Shiguang Miao. An Objective Approach to Generating Multi-Physics Ensemble Precipitation Forecasts Based on the WRF Model. Journal of Meteorological Research. 2020; 34 (3):601-620.

Chicago/Turabian Style

Chenwei Shen; Qingyun Duan; Wei Gong; Yanjun Gan; Zhenhua Di; Chen Wang; Shiguang Miao. 2020. "An Objective Approach to Generating Multi-Physics Ensemble Precipitation Forecasts Based on the WRF Model." Journal of Meteorological Research 34, no. 3: 601-620.

Journal article
Published: 15 April 2020 in Journal of Geophysical Research: Atmospheres
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Lack of hydrological data of high spatiotemporal resolution has been a major challenge for understanding the rainfall‐runoff relationship on the Loess Plateau of China. In this study, we examined this relationship across 82 watersheds on the plateau for two time periods: P1 (1971–1987) and P2 (2008–2016). We found linear relationships between the annual rainfall series and annual surface runoff series in P1 that were stronger in the southeast and that declined toward the northwest, but this spatial pattern was slightly disrupted during P2. The mean annual runoff coefficient ranged from 0.056 to 0.126 for the 25th and 75th percentiles, respectively, during the P1 period, but the corresponding values were smaller in P2 (0.035 and 0.088, respectively). In total, the runoff coefficient decreased for 87% of the watersheds, with an average reduction of 0.033. For most watersheds of the plateau, we found a stable threshold value for the monthly runoff coefficient when monthly rainfall reached a certain value (i.e., a critical point). Between P1 and P2, the threshold value of the runoff coefficient decreased for 57 watersheds as a result of soil conservation measures. The rainfall critical point represents the minimum monthly rainfall to maintain the stable relationship between monthly infiltration and monthly rainfall in the process of runoff production. This value became greater on the middle regions of the plateau during P2 (increasing by 28% on average). Check dams, vegetation, and land use all have profoundly influenced the relationship between rainfall and surface runoff in this region.

ACS Style

Chiyuan Miao; Haiyan Zheng; Juying Jiao; XiaoMing Feng; Qingyun Duan; Ephraim Mpofu. The Changing Relationship Between Rainfall and Surface Runoff on the Loess Plateau, China. Journal of Geophysical Research: Atmospheres 2020, 125, 1 .

AMA Style

Chiyuan Miao, Haiyan Zheng, Juying Jiao, XiaoMing Feng, Qingyun Duan, Ephraim Mpofu. The Changing Relationship Between Rainfall and Surface Runoff on the Loess Plateau, China. Journal of Geophysical Research: Atmospheres. 2020; 125 (8):1.

Chicago/Turabian Style

Chiyuan Miao; Haiyan Zheng; Juying Jiao; XiaoMing Feng; Qingyun Duan; Ephraim Mpofu. 2020. "The Changing Relationship Between Rainfall and Surface Runoff on the Loess Plateau, China." Journal of Geophysical Research: Atmospheres 125, no. 8: 1.

Journal article
Published: 26 March 2020 in Water
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Land surface evapotranspiration (ET) is important in land-atmosphere interactions of water and energy cycles. However, regional ET simulation has a great uncertainty. In this study, a highly-efficient parameter optimization framework was applied to improve ET simulations of the Community Land Model version 4.0 (CLM4) in China. The CLM4 is a model at land scale, and therefore, the monthly ET observation was used to evaluate the simulation results. The optimization framework consisted of a parameter sensitivity analysis (also called parameter screening) by the multivariate adaptive regression spline (MARS) method and sensitivity parameter optimization by the adaptive surrogate modeling-based optimization (ASMO) method. The results show that seven sensitive parameters were screened from 38 adjustable parameters in CLM4 using the MARS sensitivity analysis method. Then, using only 133 model runs, the optimal values of the seven parameters were found by the ASMO method, demonstrating the high efficiency of the method. For the optimal parameters, the ET simulations of CLM4 were improved by 7.27%. The most significant improvement occurred in the Tibetan Plateau region. Additional ET simulations from the validation years were also improved by 5.34%, demonstrating the robustness of the optimal parameters. Overall, the ASMO method was found to be efficient for conducting parameter optimization for CLM4, and the optimal parameters effectively improved ET simulation of CLM4 in China.

ACS Style

Chong Zhang; Zhenhua Di; Qingyun Duan; Zhenghui Xie; Wei Gong. Improved Land Evapotranspiration Simulation of the Community Land Model Using a Surrogate-Based Automatic Parameter Optimization Method. Water 2020, 12, 943 .

AMA Style

Chong Zhang, Zhenhua Di, Qingyun Duan, Zhenghui Xie, Wei Gong. Improved Land Evapotranspiration Simulation of the Community Land Model Using a Surrogate-Based Automatic Parameter Optimization Method. Water. 2020; 12 (4):943.

Chicago/Turabian Style

Chong Zhang; Zhenhua Di; Qingyun Duan; Zhenghui Xie; Wei Gong. 2020. "Improved Land Evapotranspiration Simulation of the Community Land Model Using a Surrogate-Based Automatic Parameter Optimization Method." Water 12, no. 4: 943.

Journal article
Published: 25 February 2020 in Reviews of Geophysics
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On behalf of the authors and readers of Reviews of Geophysics (RoG), the American Geophysical Union (AGU), and the broader scientific community, the editors wish to wholeheartedly thank those who reviewed manuscripts for RoG in 2019.

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Fabio Florindo; Ann Marie Carlton; Paolo D'odorico; Qingyun Duan; Jasper S. Halekas; Gesine Mollenhauer; Eelco J. Rohling. Thank You to Our Peer Reviewers for 2019. Reviews of Geophysics 2020, 58, 1 .

AMA Style

Fabio Florindo, Ann Marie Carlton, Paolo D'odorico, Qingyun Duan, Jasper S. Halekas, Gesine Mollenhauer, Eelco J. Rohling. Thank You to Our Peer Reviewers for 2019. Reviews of Geophysics. 2020; 58 (1):1.

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Fabio Florindo; Ann Marie Carlton; Paolo D'odorico; Qingyun Duan; Jasper S. Halekas; Gesine Mollenhauer; Eelco J. Rohling. 2020. "Thank You to Our Peer Reviewers for 2019." Reviews of Geophysics 58, no. 1: 1.

Journal article
Published: 10 January 2020 in Atmosphere
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Typhoon precipitation and intensity forecasting plays an important role in disaster prevention and mitigation in the typhoon landfall area. However, the issue of improving forecast accuracy is very challenging. In this study, the Weather Research and Forecasting (WRF) model typhoon simulations on precipitation and central 10-m maximum wind speed (10-m wind) were improved using a systematic parameter optimization framework consisting of parameter screening and adaptive surrogate modeling-based optimization (ASMO) for screening sensitive parameters. Six of the 25 adjustable parameters from seven physics components of the WRF model were screened by the Multivariate Adaptive Regression Spline (MARS) parameter sensitivity analysis tool. Then the six parameters were optimized using the ASMO method, and after 178 runs, the 6-hourly precipitation, and 10-m wind simulations were finally improved by 6.83% and 13.64% respectively. The most significant improvements usually occurred with the maximum precipitation or the highest wind speed. Additional typhoon events from other years were simulated to validate that the WRF optimal parameters were reasonable. The results demonstrated that the improvements in 6-hourly precipitation and 10-m wind were 4.78% and 8.54% respectively. Overall, the ASMO optimization method is an effective and highly efficient way to improve typhoon precipitation and intensity simulation using a numerical weather prediction model.

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Zhenhua Di; Qingyun Duan; Chenwei Shen; Zhenghui Xie. Improving WRF Typhoon Precipitation and Intensity Simulation Using a Surrogate-Based Automatic Parameter Optimization Method. Atmosphere 2020, 11, 89 .

AMA Style

Zhenhua Di, Qingyun Duan, Chenwei Shen, Zhenghui Xie. Improving WRF Typhoon Precipitation and Intensity Simulation Using a Surrogate-Based Automatic Parameter Optimization Method. Atmosphere. 2020; 11 (1):89.

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Zhenhua Di; Qingyun Duan; Chenwei Shen; Zhenghui Xie. 2020. "Improving WRF Typhoon Precipitation and Intensity Simulation Using a Surrogate-Based Automatic Parameter Optimization Method." Atmosphere 11, no. 1: 89.

Journal article
Published: 23 December 2019 in Environmental Modelling & Software
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Most of the commonly available sensitivity analysis methods cannot reliably compute the interaction effect. Even though the Sobol’ type methods that use Monte Carlo simulation can evaluate the interaction effect, the result is either inaccurate or requires an extraordinary number of model runs to obtain a reasonable estimate. In this study, we evaluate the sparse polynomial chaos (SPC) method as a reasonable way to estimate the interaction effect. This method is evaluated on two mathematical test functions (Ishigami and Sobol’ G) and two hydrologic models (HBV-SASK and SAC-SMA). Our results show the SPC method needs about a sample size of 30 to 70 times the number of dimensions of the parameter space to evaluate the interaction effects of hydrologic models. Our findings are significant for hydrologic simulation and model calibration, as we aim to improve the understanding of complex interactions among model components and to reduce model uncertainty.

ACS Style

Heng Wang; Wei Gong; Qingyun Duan; Zhenhua Di. Evaluation of parameter interaction effect of hydrological models using the sparse polynomial chaos (SPC) method. Environmental Modelling & Software 2019, 125, 104612 .

AMA Style

Heng Wang, Wei Gong, Qingyun Duan, Zhenhua Di. Evaluation of parameter interaction effect of hydrological models using the sparse polynomial chaos (SPC) method. Environmental Modelling & Software. 2019; 125 ():104612.

Chicago/Turabian Style

Heng Wang; Wei Gong; Qingyun Duan; Zhenhua Di. 2019. "Evaluation of parameter interaction effect of hydrological models using the sparse polynomial chaos (SPC) method." Environmental Modelling & Software 125, no. : 104612.

Journal article
Published: 02 November 2019 in Atmospheric Research
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Modeling tools can be used to diagnose regional land-atmosphere (L-A) coupling strength in the absence of sufficient observations, but subject to uncertainties associated with parameters in model physical parameterizations. Different sensitivity analysis (SA) approaches may lead to different conclusions about the underlying sensitivities. In this study, we quantify simulation uncertainties related to parameter perturbations, and use different approaches to conduct parameter SA on the WRF model pertaining to L-A coupling strength for simulations over the Amazon region. A total of twenty parameters from the Yonsei University (YSU) planetary boundary layer (PBL) and the revised MM5 surface layer (SL) schemes were selected in this analysis. Three different SA methods, the Morris One-at-A-Time (MOAT) method, the Multivariate Adaptive Regression Splines (MARS) method, and the Sobol’ method, were employed to analyze seven WRF-simulated variables and five L-A coupling metrics. Results show that 1) parameter perturbations cause large simulation uncertainties which are comparable to those in the observations; 2) three different SA methods give consistent L-A coupling strength outcomes; 3) six out of the twenty parameters contribute 80%–95% of the total variance in the metrics analyzed, and first-order effects dominate over interaction effects; 4) the twelve variables/metrics of interest show similar sensitivity patterns to the selected parameters, which is consistent across all the methods used. Physical mechanisms for how the sensitive parameters act in determining the L-A coupling strength and associated variables also are illustrated. Our results will help quantifying L-A coupling strength and establishing a basis for parameter calibration over the Amazon region.

ACS Style

Chen Wang; Yun Qian; Qingyun Duan; Maoyi Huang; Larry K. Berg; Hyeyum H. Shin; Zhe Feng; Ben Yang; Jiping Quan; Songyou Hong; Junhua Yan. Assessing the sensitivity of land-atmosphere coupling strength to boundary and surface layer parameters in the WRF model over Amazon. Atmospheric Research 2019, 234, 104738 .

AMA Style

Chen Wang, Yun Qian, Qingyun Duan, Maoyi Huang, Larry K. Berg, Hyeyum H. Shin, Zhe Feng, Ben Yang, Jiping Quan, Songyou Hong, Junhua Yan. Assessing the sensitivity of land-atmosphere coupling strength to boundary and surface layer parameters in the WRF model over Amazon. Atmospheric Research. 2019; 234 ():104738.

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Chen Wang; Yun Qian; Qingyun Duan; Maoyi Huang; Larry K. Berg; Hyeyum H. Shin; Zhe Feng; Ben Yang; Jiping Quan; Songyou Hong; Junhua Yan. 2019. "Assessing the sensitivity of land-atmosphere coupling strength to boundary and surface layer parameters in the WRF model over Amazon." Atmospheric Research 234, no. : 104738.

Journal article
Published: 10 September 2019 in Journal of Advances in Modeling Earth Systems
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Dynamical environmental systems models are highly parameterized, having large numbers of parameters whose values are uncertain. For spatially‐distributed continental‐scale applications, such models must be run for very large numbers of grid locations. To calibrate such models, it is useful to be able to perform parameter screening, via sensitivity analysis, to identify the most important parameters. However, since this typically requires the models to be run for a large number of sampled parameter combinations, the computational burden can be huge. To make such an investigation computationally feasible, we propose a novel approach to combining spatial sampling with parameter sampling, and test it for the Noah‐MP land surface model applied across the continental USA, focusing on gross primary production (GPP) and flux of latent heat (FLH) simulations for two vegetation types. Our approach uses: a) progressive Latin hypercube sampling (PLHS) to sample at four grid levels and four parameter levels; b) a recently developed grouping‐based sensitivity analysis approach that ranks parameters by importance group rather than individually; and c) a measure of robustness to grid and parameter sampling variability. The results show that a relatively small grid sample size (i.e., 5% of the total grids) and small parameter sample size (i.e., five times the number of parameters) are sufficient to identify the most important parameters, with very high robustness to grid sampling variability and a medium level of robustness to parameter sampling variability. The results ensure a dramatic reduction in computational costs for such studies.

ACS Style

Xueli Huo; Hoshin Gupta; Guo‐Yue Niu; Wei Gong; Qingyun Duan. Parameter Sensitivity Analysis for Computationally Intensive Spatially Distributed Dynamical Environmental Systems Models. Journal of Advances in Modeling Earth Systems 2019, 11, 2896 -2909.

AMA Style

Xueli Huo, Hoshin Gupta, Guo‐Yue Niu, Wei Gong, Qingyun Duan. Parameter Sensitivity Analysis for Computationally Intensive Spatially Distributed Dynamical Environmental Systems Models. Journal of Advances in Modeling Earth Systems. 2019; 11 (9):2896-2909.

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Xueli Huo; Hoshin Gupta; Guo‐Yue Niu; Wei Gong; Qingyun Duan. 2019. "Parameter Sensitivity Analysis for Computationally Intensive Spatially Distributed Dynamical Environmental Systems Models." Journal of Advances in Modeling Earth Systems 11, no. 9: 2896-2909.

Journal article
Published: 08 July 2019 in Water Resources Research
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The community Noah land surface model with multi‐parameterization options (Noah‐MP) provides a plethora of model configurations with varying complexity for land surface modeling. The practical application of this model requires a basic understanding of the relative abilities of its various parameterization configurations in representing spatiotemporal variability and hydrologic connectivity. We designed an ensemble of 288 experiments from multi‐parameterization schemes of six physical processes to assess and reduce the structural uncertainty for land surface modeling over ten hydrologic regions in China for the period 2001–2010. The observed latent heat (LH) was well reproduced by the ensemble. Meanwhile, most experiments underestimated sensible heat (SH) throughout the year and overestimated the cold season but underestimated the warm season terrestrial water storage anomaly (TWSA). The sensitive processes and best‐performing schemes varied not only with regions but also among variables. The LH and SH were most sensitive to runoff‐groundwater (RUN), surface heat exchange coefficient (SFC), and radiation transfer (RAD). The TWSA was dominated by RUN and RAD, while largely influenced by soil moisture factor for stomatal resistance (BTR) and frozen soil permeability (INF) over some limited regions. By contrast, supercooled liquid water (FRZ) had little influence on all variables. Our optimization for individual variables produced high mean Taylor skill scores that ranged from 0.95–0.99 for LH, 0.82–0.99 for SH, and 0.63–0.95 for TWSA depending on regions. The simultaneous optimization made tradeoff among the three variables, which improved TWSA performance at the cost of reducing the skill for LH and SH over a few regions.

ACS Style

Yanjun Gan; Xin‐Zhong Liang; Qingyun Duan; Fei Chen; Jianduo Li; Yu Zhang. Assessment and Reduction of the Physical Parameterization Uncertainty for Noah‐MP Land Surface Model. Water Resources Research 2019, 55, 5518 -5538.

AMA Style

Yanjun Gan, Xin‐Zhong Liang, Qingyun Duan, Fei Chen, Jianduo Li, Yu Zhang. Assessment and Reduction of the Physical Parameterization Uncertainty for Noah‐MP Land Surface Model. Water Resources Research. 2019; 55 (7):5518-5538.

Chicago/Turabian Style

Yanjun Gan; Xin‐Zhong Liang; Qingyun Duan; Fei Chen; Jianduo Li; Yu Zhang. 2019. "Assessment and Reduction of the Physical Parameterization Uncertainty for Noah‐MP Land Surface Model." Water Resources Research 55, no. 7: 5518-5538.