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Qiaohong Sun, Chiyuan Miao, and Qingyun Duan

Abstract

This study focuses on changing trends in the spatial variance and annual distribution of precipitation across mainland China during the period 1957–2014. The influence on precipitation of temperature, the East Asian summer monsoon (EASM), and related atmospheric circulation variables are examined to explore the underlying mechanisms driving the changes in precipitation. Statistically significant downward trends in the number of wet days were observed in humid regions. Large parts of southeastern China featured high temporal inequality of rainfall over the course of a year, with extreme precipitation events contributing a relatively large percentage of the total annual precipitation. Arid regions generally showed statistically significant upward trends in the number of wet days and in the fraction of extreme precipitation but a decrease in the temporal inequality. These spatial heterogeneities indicate that extreme precipitation became more widespread across mainland China. Temperature dominated the long-term changes in precipitation indices over large regions of mainland China, except in the Jianghuai region, where the weakening EASM induced greater precipitation and a more uneven annual distribution of precipitation. The effects of temperature on precipitation were region dependent and varied with precipitation intensity. This contributed to the overall decrease in the spatial variance of extreme precipitation and the increase in the temporal inequality of precipitation over eastern China. However, the EASM was more important for the interannual variability of precipitation indices over the west of northwestern China, the Yanghuai region, and some grids in southern China. The EASM exerted a zonal influence on precipitation variability through the modulation of water vapor patterns, wind fields, and convection activities.

Open access
Wentao Li, Quan J. Wang, and Qingyun Duan

Abstract

Statistical postprocessing methods can be used to correct bias and dispersion error in raw ensemble forecasts from numerical weather prediction models. Existing postprocessing models generally perform well when they are assessed on all events, but their performance for extreme events still needs to be investigated. Commonly used joint probability postprocessing models are based on the correlation between forecasts and observations. Because the correlation may be lower for extreme events as a result of larger forecast uncertainty, the dependence between forecasts and observations can be asymmetric with respect to the magnitude of the precipitation. However, the constant correlation coefficient in the traditional joint probability model lacks the flexibility to model asymmetric dependence. In this study, we formulated a new postprocessing model with a decreasing correlation coefficient to characterize asymmetric dependence. We carried out experiments using Global Ensemble Forecast System reforecasts for daily precipitation in the Huai River basin in China. The results show that, although it performs well in terms of continuous ranked probability score or reliability for all events, the traditional joint probability model suffers from overestimation for extreme events defined by the largest 2.5% or 5% of raw forecasts. On the contrary, the proposed variable-correlation model is able to alleviate the overestimation and achieves better reliability for extreme events than the traditional model. The proposed variable-correlation model can be seen as a flexible extension of the traditional joint probability model to improve the performance for extreme events.

Free access
Wentao Li, Qingyun Duan, and Quan J. Wang

Abstract

Statistical postprocessing models can be used to correct bias and dispersion errors in raw precipitation forecasts from numerical weather prediction models. In this study, we conducted experiments to investigate four factors that influence the performance of regression-based postprocessing models with normalization transformations for short-term precipitation forecasts. The factors are 1) normalization transformations, 2) incorporation of ensemble spread as a predictor in the model, 3) objective function for parameter inference, and 4) two postprocessing schemes, including distributional regression and joint probability models. The experiments on the first three factors are based on variants of a censored regression model with conditional heteroscedasticity (CRCH). For the fourth factor, we compared CRCH as an example of the distributional regression with a joint probability model. The results show that the CRCH with normal quantile transformation (NQT) or power transformation performs better than the CRCH with log–sinh transformation for most of the subbasins in Huai River basin with a subhumid climate. The incorporation of ensemble spread as a predictor in CRCH models can improve forecast skill in our research region at short lead times. The influence of different objective functions (minimum continuous ranked probability score or maximum likelihood) on postprocessed results is limited to a few relatively dry subbasins in the research region. Both the distributional regression and the joint probability models have their advantages, and they are both able to achieve reliable and skillful forecasts.

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Chiyuan Miao, Qiaohong Sun, Dongxian Kong, and Qingyun Duan
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Newsha K. Ajami, Qingyun Duan, Xiaogang Gao, and Soroosh Sorooshian

Abstract

This paper examines several multimodel combination techniques that are used for streamflow forecasting: the simple model average (SMA), the multimodel superensemble (MMSE), modified multimodel superensemble (M3SE), and the weighted average method (WAM). These model combination techniques were evaluated using the results from the Distributed Model Intercomparison Project (DMIP), an international project sponsored by the National Weather Service (NWS) Office of Hydrologic Development (OHD). All of the multimodel combination results were obtained using uncalibrated DMIP model simulations and were compared against the best-uncalibrated as well as the best-calibrated individual model results. The purpose of this study is to understand how different combination techniques affect the accuracy levels of the multimodel simulations. This study revealed that the multimodel simulations obtained from uncalibrated single-model simulations are generally better than any single-member model simulations, even the best-calibrated single-model simulations. Furthermore, more sophisticated multimodel combination techniques that incorporated bias correction step work better than simple multimodel average simulations or multimodel simulations without bias correction.

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Feng Ma, Lifeng Luo, Aizhong Ye, and Qingyun Duan

Abstract

Meteorological and hydrological droughts can bring different socioeconomic impacts. In this study, we investigated meteorological and hydrological drought characteristics and propagation using the standardized precipitation index (SPI) and standardized streamflow index (SSI), over the upstream and midstream of the Heihe River basin (UHRB and MHRB, respectively). The correlation analysis and cross-wavelet transform were adopted to explore the relationship between meteorological and hydrological droughts in the basin. Three modeling experiments were performed to quantitatively understand how climate change and human activities influence hydrological drought and propagation. Results showed that meteorological drought characteristics presented little difference between UHRB and MHRB, while hydrological drought events are more frequent in the MHRB. In the UHRB, there were positive relationships between meteorological and hydrological droughts, whereas drought events became less frequent but longer when meteorological drought propagated into hydrological drought. Human activities have obviously changed the positive correlation to negative in the MHRB, especially during warm and irrigation seasons. The propagation time varied with seasonal climate characteristics and human activities, showing shorter values due to higher evapotranspiration, reservoir filling, and irrigation. Quantitative evaluation showed that climate change was inclined to increase streamflow and propagation time, contributing from −57% to 63%. However, more hydrological droughts and shorter propagation time were detected in the MHRB because human activities play a dominant role in water consumption with contribution rate greater than (−)89%. This study provides a basis for understanding the mechanism of hydrological drought and for the development of improved hydrological drought warning and forecasting system in the HRB.

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NOAA'S ADVANCED HYDROLOGIC PREDICTION SERVICE

Building Pathways for Better Science in Water Forecasting

John McEnery, John Ingram, Qingyun Duan, Thomas Adams, and Lee Anderson

The National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS) Advanced Hydrologic Prediction Service (AHPS) program was established to meet our nation's need for more precise flash-flood forecast information. AHPS uses NOAA investments in remote sensing, precipitation forecasts, climate predictions, data automation, hydrologic science, and operational forecast system technologies. AHPS establishes a pathway for the infusion of new verified science and technology, and expands the use of NWS climate, weather, and water analyses and information products. State-of-the-art science is used for improved operational forecasting of floods, and drought conditions. The objective is to deliver more precise forecast information over greater temporal scales (hours, days, and months) and to depict the magnitude and certainty of occurrence for events ranging from droughts to floods. The AHPS program improves flash-flood forecasts, and provides ensemble streamflow forecasting and flood-forecast maps. AHPS information is accessible to customers by the internet with texts and graphics. This paper describes AHPS forecasting services and their implementation status.

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Xueli Huo, Zhongfang Liu, Qingyun Duan, Pengmei Hao, Yanyan Zhang, Yonghong Hao, and Hongbin Zhan

Abstract

The Niangziguan Springs (NS) discharge is used as a proxy indicator of the variability of the karst groundwater system in relation to major climate indices such as El Niño–Southern Oscillation (ENSO), Pacific decadal oscillation (PDO), Indian summer monsoon (ISM), and west North Pacific monsoon (WNPM). The relationships between spring discharge and these climate indices are determined using the multitaper method (MTM), continuous wavelet transform (CWT), and wavelet transform coherence (WTC). Significant periodic components of spring discharge in the 1-, 3.4-, and 26.8-yr periodicities are identified and reconstructed for further investigation of the correlation between spring discharge and large-scale climate patterns on these time scales. Correlation coefficients and WTC between spring discharge and the climate indices indicate that variability in spring discharge is significantly and positively correlated with monsoon indices in the 1-yr periodicity and negatively correlated with ENSO in the 3.4-yr periodicity and PDO in the 26.8-yr periodicity. This suggests that the oscillations of the spring discharge on annual, interannual, and interdecadal time scales are dominated by monsoon, ENSO, and PDO in the NS basin, respectively. Results show that monsoons modulate the spring discharge by affecting local meteorological parameters. ENSO and PDO impact the variability of the NS discharge by affecting the climate conditions in northern China.

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Zhenghui Xie, Fei Yuan, Qingyun Duan, Jing Zheng, Miaoling Liang, and Feng Chen

Abstract

This paper presents a methodology for regional parameter estimation of the three-layer Variable Infiltration Capacity (VIC-3L) land surface model with the goal of improving the streamflow simulation for river basins in China. This methodology is designed to obtain model parameter estimates from a limited number of calibrated basins and then regionalize them to uncalibrated basins based on climate characteristics and large river basin domains, and ultimately to continental China. Fourteen basins from different climatic zones and large river basins were chosen for model calibration. For each of these basins, seven runoff-related model parameters were calibrated using a systematic manual calibration approach. These calibrated parameters were then transferred within the climate and large river basin zones or climatic zones to the uncalibrated basins. To test the efficiency of the parameter regionalization method, a verification study was conducted on 19 independent river basins in China. Overall, the regionalized parameters, when evaluated against the a priori parameter estimates, were able to reduce the model bias by 0.4%–249.8% and relative root-mean-squared error by 0.2%–119.1% and increase the Nash–Sutcliffe efficiency of the streamflow simulation by 1.9%–31.7% for most of the tested basins. The transferred parameters were then used to perform a hydrological simulation over all of China so as to test the applicability of the regionalized parameters on a continental scale. The continental simulation results agree well with the observations at regional scales, indicating that the tested regionalization method is a promising scheme for parameter estimation for ungauged basins in China.

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Qiaohong Sun, Chiyuan Miao, Amir AghaKouchak, Iman Mallakpour, Duoying Ji, and Qingyun Duan

Abstract

Predicting the changes in teleconnection patterns and related hydroclimate extremes can provide vital information necessary to adapt to the effects of the El Niño–Southern Oscillation (ENSO). This study uses the outputs of global climate models to assess the changes in ENSO-related dry/wet patterns and the frequency of severe dry/wet events. The results show anomalous precipitation responding asymmetrically to La Niña and El Niño, indicating the teleconnections may not simply be strengthened. A “dry to drier, wet to wetter” annual anomalous precipitation pattern was projected during La Niña phases in some regions, with drier conditions over southern North America, southern South America, and southern central Asia, and wetter conditions in Southeast Asia and Australia. These results are robust, with agreement from the 26 models and from a subset of 8 models selected for their good performance in capturing observed patterns. However, we did not observe a similar strengthening of anomalous precipitation during future El Niño phases, for which the uncertainties in the projected influences are large. Under the RCP4.5 emissions scenario, 45 river basins under El Niño conditions and 39 river basins under La Niña conditions were predicted to experience an increase in the frequency of severe dry events; similarly, 59 river basins under El Niño conditions and 61 river basins under La Niña conditions were predicted to have an increase in the frequency of severe wet events, suggesting a likely increase in the risk of floods. Our results highlight the implications of changes in ENSO patterns for natural hazards, disaster management, and engineering infrastructure.

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