Articles | Volume 26, issue 16
https://doi.org/10.5194/hess-26-4233-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-4233-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Aug 2022
Research article |
| 17 Aug 2022
| 17 Aug 2022
Quantifying overlapping and differing information of global precipitation for GCM forecasts and El Niño–Southern Oscillation
Tongtiegang Zhao
CORRESPONDING AUTHOR
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) and Center of Water Resources and Environment, School of Civil Engineering, Sun Yat-Sen University, Guangzhou, China
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) and Center of Water Resources and Environment, School of Civil Engineering, Sun Yat-Sen University, Guangzhou, China
Yu Tian
Department of Water Resources, Institute of Water Resources and Hydropower Research of China, Beijing, China
Denghua Yan
CORRESPONDING AUTHOR
Department of Water Resources, Institute of Water Resources and Hydropower Research of China, Beijing, China
Weixin Xu
School of Atmospheric Sciences, Sun Yat-Sen University, Zhuhai, China
Huayang Cai
School of Marine Engineering and Technology, Sun Yat-Sen University, Zhuhai, China
Jiabiao Wang
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) and Center of Water Resources and Environment, School of Civil Engineering, Sun Yat-Sen University, Guangzhou, China
Xiaohong Chen
CORRESPONDING AUTHOR
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) and Center of Water Resources and Environment, School of Civil Engineering, Sun Yat-Sen University, Guangzhou, China
Related authors
Tongtiegang Zhao, Qiang Li, Tongbi Tu, and Xiaohong Chen
Geosci. Model Dev., 18, 5781–5799, https://doi.org/10.5194/gmd-18-5781-2025, https://doi.org/10.5194/gmd-18-5781-2025, 2025
Short summary
Short summary
The recent WeatherBench 2 provides a versatile framework for the verification of deterministic and ensemble forecasts. In this paper, we present an explicit extension to binary forecasts of hydroclimatic extremes. Seventeen verification metrics for binary forecasts are employed, and scorecards are generated to showcase the predictive performance. The extension facilitates more comprehensive comparisons of hydroclimatic forecasts and provides useful information for forecast applications.
Tongtiegang Zhao, Zecong Chen, Yongyong Zhang, Bingyao Zhang, and Yu Li
Hydrol. Earth Syst. Sci., 29, 2429–2443, https://doi.org/10.5194/hess-29-2429-2025, https://doi.org/10.5194/hess-29-2429-2025, 2025
Short summary
Short summary
The classic logistic function characterizes the stationary relationship between drought loss and intensity. This paper accounts for time in the magnitude, shape and location parameters of the logistic function and derives nonstationary intensity loss functions. A case study is designed to test the functions for drought-affected populations by province in mainland China from 2006 to 2023. Overall, the nonstationary intensity loss functions are shown to be a useful tool for drought management.
Tongtiegang Zhao, Zexin Chen, Yu Tian, Bingyao Zhang, Yu Li, and Xiaohong Chen
Hydrol. Earth Syst. Sci., 28, 3597–3611, https://doi.org/10.5194/hess-28-3597-2024, https://doi.org/10.5194/hess-28-3597-2024, 2024
Short summary
Short summary
The local performance plays a critical part in practical applications of global streamflow reanalysis. This paper develops a decomposition approach to evaluating streamflow analysis at different timescales. The reanalysis is observed to be more effective in characterizing seasonal, annual and multi-annual features than daily, weekly and monthly features. Also, the local performance is shown to be primarily influenced by precipitation seasonality, longitude, mean precipitation and mean slope.
Qiang Li and Tongtiegang Zhao
EGUsphere, https://doi.org/10.5194/egusphere-2024-1449, https://doi.org/10.5194/egusphere-2024-1449, 2024
Preprint withdrawn
Short summary
Short summary
This paper focuses on the effect of the water balance constraint on the robustness of the long short-term memory (LSTM) network in learning rainfall-runoff relationships. Through large-sample tests, it is found that incorporating this constraint into the LSTM improves the robustness, while the improvement tends to decrease as the amount of training data increases. The results point to the compensation effects between training data and process knowledge on the LSTM’s performance.
Qiang Li and Tongtiegang Zhao
EGUsphere, https://doi.org/10.5194/egusphere-2023-2841, https://doi.org/10.5194/egusphere-2023-2841, 2024
Preprint archived
Short summary
Short summary
The lack of physical mechanism is a critical issue for the use of popular deep learning models. This paper presents an in-depth investigation of the fundamental mass balance constraint for deep learning-based rainfall-runoff prediction. The robustness against data sparsity, random parameters initialization and contrasting climate conditions are detailed. The results highlight that the water balance constraint evidently improves the robustness in particular when there is limited training data.
Huayang Cai, Bo Li, Junhao Gu, Tongtiegang Zhao, and Erwan Garel
Ocean Sci., 19, 603–614, https://doi.org/10.5194/os-19-603-2023, https://doi.org/10.5194/os-19-603-2023, 2023
Short summary
Short summary
For many problems concerning water resource utilization in estuaries, it is essential to be able to express observed salinity distributions based on simple theoretical models. In this study, we propose an analytical salt intrusion model inspired from a theory for predictions of flood hydrographs in watersheds. The newly developed model can be well calibrated using a minimum of three salinity measurements along the estuary and has been successfully applied in 21 estuaries worldwide.
Huayang Cai, Hao Yang, Pascal Matte, Haidong Pan, Zhan Hu, Tongtiegang Zhao, and Guangliang Liu
Ocean Sci., 18, 1691–1702, https://doi.org/10.5194/os-18-1691-2022, https://doi.org/10.5194/os-18-1691-2022, 2022
Short summary
Short summary
Quantifying spatial–temporal water level dynamics is essential for water resources management in estuaries. In this study, we propose a simple yet powerful regression model to examine the influence of the world’s largest dam, the Three Gorges Dam (TGD), on the spatial–temporal water level dynamics within the Yangtze River estuary. The presented method is particularly useful for determining scientific strategies for sustainable water resources management in dam-controlled estuaries worldwide.
Tongtiegang Zhao, Haoling Chen, Quanxi Shao, Tongbi Tu, Yu Tian, and Xiaohong Chen
Hydrol. Earth Syst. Sci., 25, 5717–5732, https://doi.org/10.5194/hess-25-5717-2021, https://doi.org/10.5194/hess-25-5717-2021, 2021
Short summary
Short summary
This paper develops a novel approach to attributing correlation skill of dynamical GCM forecasts to statistical El Niño–Southern Oscillation (ENSO) teleconnection using the coefficient of determination. Three cases of attribution are effectively facilitated, which are significantly positive anomaly correlation attributable to positive ENSO teleconnection, attributable to negative ENSO teleconnection and not attributable to ENSO teleconnection.
Tongtiegang Zhao, Qiang Li, Tongbi Tu, and Xiaohong Chen
Geosci. Model Dev., 18, 5781–5799, https://doi.org/10.5194/gmd-18-5781-2025, https://doi.org/10.5194/gmd-18-5781-2025, 2025
Short summary
Short summary
The recent WeatherBench 2 provides a versatile framework for the verification of deterministic and ensemble forecasts. In this paper, we present an explicit extension to binary forecasts of hydroclimatic extremes. Seventeen verification metrics for binary forecasts are employed, and scorecards are generated to showcase the predictive performance. The extension facilitates more comprehensive comparisons of hydroclimatic forecasts and provides useful information for forecast applications.
Keke Zhao, Denghua Yan, Tianling Qin, Chenhao Li, Dingzhi Peng, and Yifan Song
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-291, https://doi.org/10.5194/essd-2025-291, 2025
Preprint under review for ESSD
Short summary
Short summary
This study presents a high-quality daily weather dataset for all of China from 1961 to 2021, including air temperature, atmospheric pressure, relative humidity, and sunshine duration. It was produced using a reconstruction framework that combines thousands of ground observations with landform and elevation data. The dataset provides consistent weather information even in mountainous regions and supports studies on land surface and water processes, climate change, and environmental impacts.
Tongtiegang Zhao, Zecong Chen, Yongyong Zhang, Bingyao Zhang, and Yu Li
Hydrol. Earth Syst. Sci., 29, 2429–2443, https://doi.org/10.5194/hess-29-2429-2025, https://doi.org/10.5194/hess-29-2429-2025, 2025
Short summary
Short summary
The classic logistic function characterizes the stationary relationship between drought loss and intensity. This paper accounts for time in the magnitude, shape and location parameters of the logistic function and derives nonstationary intensity loss functions. A case study is designed to test the functions for drought-affected populations by province in mainland China from 2006 to 2023. Overall, the nonstationary intensity loss functions are shown to be a useful tool for drought management.
Tongtiegang Zhao, Zexin Chen, Yu Tian, Bingyao Zhang, Yu Li, and Xiaohong Chen
Hydrol. Earth Syst. Sci., 28, 3597–3611, https://doi.org/10.5194/hess-28-3597-2024, https://doi.org/10.5194/hess-28-3597-2024, 2024
Short summary
Short summary
The local performance plays a critical part in practical applications of global streamflow reanalysis. This paper develops a decomposition approach to evaluating streamflow analysis at different timescales. The reanalysis is observed to be more effective in characterizing seasonal, annual and multi-annual features than daily, weekly and monthly features. Also, the local performance is shown to be primarily influenced by precipitation seasonality, longitude, mean precipitation and mean slope.
Qiang Li and Tongtiegang Zhao
EGUsphere, https://doi.org/10.5194/egusphere-2024-1449, https://doi.org/10.5194/egusphere-2024-1449, 2024
Preprint withdrawn
Short summary
Short summary
This paper focuses on the effect of the water balance constraint on the robustness of the long short-term memory (LSTM) network in learning rainfall-runoff relationships. Through large-sample tests, it is found that incorporating this constraint into the LSTM improves the robustness, while the improvement tends to decrease as the amount of training data increases. The results point to the compensation effects between training data and process knowledge on the LSTM’s performance.
Qiang Li and Tongtiegang Zhao
EGUsphere, https://doi.org/10.5194/egusphere-2023-2841, https://doi.org/10.5194/egusphere-2023-2841, 2024
Preprint archived
Short summary
Short summary
The lack of physical mechanism is a critical issue for the use of popular deep learning models. This paper presents an in-depth investigation of the fundamental mass balance constraint for deep learning-based rainfall-runoff prediction. The robustness against data sparsity, random parameters initialization and contrasting climate conditions are detailed. The results highlight that the water balance constraint evidently improves the robustness in particular when there is limited training data.
Huayang Cai, Bo Li, Junhao Gu, Tongtiegang Zhao, and Erwan Garel
Ocean Sci., 19, 603–614, https://doi.org/10.5194/os-19-603-2023, https://doi.org/10.5194/os-19-603-2023, 2023
Short summary
Short summary
For many problems concerning water resource utilization in estuaries, it is essential to be able to express observed salinity distributions based on simple theoretical models. In this study, we propose an analytical salt intrusion model inspired from a theory for predictions of flood hydrographs in watersheds. The newly developed model can be well calibrated using a minimum of three salinity measurements along the estuary and has been successfully applied in 21 estuaries worldwide.
Ying Li, Chenghao Wang, Ru Huang, Denghua Yan, Hui Peng, and Shangbin Xiao
Hydrol. Earth Syst. Sci., 26, 6413–6426, https://doi.org/10.5194/hess-26-6413-2022, https://doi.org/10.5194/hess-26-6413-2022, 2022
Short summary
Short summary
Spatial quantification of oceanic moisture contribution to the precipitation over the Tibetan Plateau (TP) contributes to the reliable assessments of regional water resources and the interpretation of paleo archives in the region. Based on atmospheric reanalysis datasets and numerical moisture tracking, this work reveals the previously underestimated oceanic moisture contributions brought by the westerlies in winter and the overestimated moisture contributions from the Indian Ocean in summer.
Baisha Weng, Zhaoyu Dong, Yuheng Yang, Denghua Yan, Mengyu Li, and Yuhang Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2022-1290, https://doi.org/10.5194/egusphere-2022-1290, 2022
Preprint archived
Short summary
Short summary
The study selected a structural equation model to construct the turnover rate of amino sugars with soil physicochemical properties and extracellular enzymes under the warming and increased precipitation scenarios. The results of this study answer the mechanism of action of warming and precipitation on the effect of soil amino sugars which will play an important scientific and technical support role in the development of plateau agriculture and carbon and nitrogen cycles.
Huayang Cai, Hao Yang, Pascal Matte, Haidong Pan, Zhan Hu, Tongtiegang Zhao, and Guangliang Liu
Ocean Sci., 18, 1691–1702, https://doi.org/10.5194/os-18-1691-2022, https://doi.org/10.5194/os-18-1691-2022, 2022
Short summary
Short summary
Quantifying spatial–temporal water level dynamics is essential for water resources management in estuaries. In this study, we propose a simple yet powerful regression model to examine the influence of the world’s largest dam, the Three Gorges Dam (TGD), on the spatial–temporal water level dynamics within the Yangtze River estuary. The presented method is particularly useful for determining scientific strategies for sustainable water resources management in dam-controlled estuaries worldwide.
Erwan Garel, Ping Zhang, and Huayang Cai
Ocean Sci., 17, 1605–1621, https://doi.org/10.5194/os-17-1605-2021, https://doi.org/10.5194/os-17-1605-2021, 2021
Short summary
Short summary
Understanding tidal hydrodynamics is essential for water resources management in estuarine environments. In this study, we propose an analytical model to examine the fortnightly water level variations due to tidal motions alone in tide-dominated estuaries. Details of the analytical model show that changes in the mean depth or length of semi-arid estuaries affect the fortnightly tide amplitude, which has significant potential impacts on the estuarine ecosystem management.
Tongtiegang Zhao, Haoling Chen, Quanxi Shao, Tongbi Tu, Yu Tian, and Xiaohong Chen
Hydrol. Earth Syst. Sci., 25, 5717–5732, https://doi.org/10.5194/hess-25-5717-2021, https://doi.org/10.5194/hess-25-5717-2021, 2021
Short summary
Short summary
This paper develops a novel approach to attributing correlation skill of dynamical GCM forecasts to statistical El Niño–Southern Oscillation (ENSO) teleconnection using the coefficient of determination. Three cases of attribution are effectively facilitated, which are significantly positive anomaly correlation attributable to positive ENSO teleconnection, attributable to negative ENSO teleconnection and not attributable to ENSO teleconnection.
Ying Li, Chenghao Wang, Hui Peng, Shangbin Xiao, and Denghua Yan
Hydrol. Earth Syst. Sci., 25, 4759–4772, https://doi.org/10.5194/hess-25-4759-2021, https://doi.org/10.5194/hess-25-4759-2021, 2021
Short summary
Short summary
Precipitation change in the Three Gorges Reservoir Region (TGRR) plays a critical role in the operation and regulation of the Three Gorges Dam and the protection of residents and properties. We investigated the long-term contribution of moisture sources to precipitation changes in this region with an atmospheric moisture tracking model. We found that southwestern source regions (especially the southeastern tip of the Tibetan Plateau) are the key regions that control TGRR precipitation changes.
Hailong Wang, Kai Duan, Bingjun Liu, and Xiaohong Chen
Hydrol. Earth Syst. Sci., 25, 4741–4758, https://doi.org/10.5194/hess-25-4741-2021, https://doi.org/10.5194/hess-25-4741-2021, 2021
Short summary
Short summary
Using remote sensing and reanalysis data, we examined the relationships between vegetation development and water resource availability in a humid subtropical basin. We found overall increases in total water storage and surface greenness and vegetation production, and the changes were particularly profound in cropland-dominated regions. Correlation analysis implies water availability leads the variations in greenness and production, and irrigation may improve production during dry periods.
Jun Li, Zhaoli Wang, Xushu Wu, Jakob Zscheischler, Shenglian Guo, and Xiaohong Chen
Hydrol. Earth Syst. Sci., 25, 1587–1601, https://doi.org/10.5194/hess-25-1587-2021, https://doi.org/10.5194/hess-25-1587-2021, 2021
Short summary
Short summary
We introduce a daily-scale index, termed the standardized compound drought and heat index (SCDHI), to measure the key features of compound dry-hot conditions. SCDHI can not only monitor the long-term compound dry-hot events, but can also capture such events at sub-monthly scale and reflect the related vegetation activity impacts. The index can provide a new tool to quantify sub-monthly characteristics of compound dry-hot events, which are vital for releasing early and timely warning.
Leicheng Guo, Chunyan Zhu, Huayang Cai, Zheng Bing Wang, Ian Townend, and Qing He
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-75, https://doi.org/10.5194/hess-2021-75, 2021
Revised manuscript not accepted
Short summary
Short summary
Overtide is a shallow water tidal component and its interaction with astronomical tides induces tidal wave deformation, which is an important process that controls sediment transport. We use a numerical tidal model to examine overtide changes in estuaries under varying river discharges and find spatially nonlinear changes and the threshold of an intermediate river that benefits maximal overtide generation. The findings inform management of sediment transport and flooding risk in estuaries.
Tian Lan, Kairong Lin, Chong-Yu Xu, Zhiyong Liu, and Huayang Cai
Hydrol. Earth Syst. Sci., 24, 5859–5874, https://doi.org/10.5194/hess-24-5859-2020, https://doi.org/10.5194/hess-24-5859-2020, 2020
Cited articles
Anghileri, D., Voisin, N., Castelletti, A., Pianosi, F., Nijssen, B., and Lettenmaier, D. P.: Value of long-term streamflow forecasts to reservoir operations for water supply in snow-dominated river catchments: Value of Long-Term Forecasts to Reservoir Operations, Water Resour. Res., 52, 4209–4225, https://doi.org/10.1002/2015WR017864, 2016.
Barnston, A. G., Tippett, M. K., L'Heureux, M. L., Li, S., and DeWitt, D. G.: Skill of Real-Time Seasonal ENSO Model Predictions during 2002–11: Is Our Capability Increasing?, B. Am. Meteorol. Soc., 93, 631–651, https://doi.org/10.1175/BAMS-D-11-00111.1, 2012.
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical weather prediction, Nature, 525, 47–55, https://doi.org/10.1038/nature14956, 2015.
Becker, E. J., Kirtman, B. P., L'Heureux, M., Muñoz, Á. G., and Pegion, K.: A Decade of the North American Multimodel Ensemble (NMME): Research, Application, and Future Directions, B. Am. Meteorol. Soc., 103, E973–E995, https://doi.org/10.1175/BAMS-D-20-0327.1, 2022.
Bennett, J. C., Wang, Q. J., Li, M., Robertson, D. E., and Schepen, A.: Reliable long-range ensemble streamflow forecasts: Combining calibrated climate forecasts with a conceptual runoff model and a staged error model: Long-Range Ensemble Streamflow Forecasts, Water Resour. Res., 52, 8238–8259, https://doi.org/10.1002/2016WR019193, 2016.
Cai, W., Yang, K., Wu, L., Huang, G., Santoso, A., Ng, B., Wang, G., and Yamagata, T.: Opposite response of strong and moderate positive Indian Ocean Dipole to global warming, Nat. Clim. Change, 11, 27–32, https://doi.org/10.1038/s41558-020-00943-1, 2021.
Cash, B. A., Manganello, J. V., and Kinter, J. L.: Evaluation of NMME temperature and precipitation bias and forecast skill for South Asia, Clim. Dynam., 53, 7363–7380, https://doi.org/10.1007/s00382-017-3841-4, 2019.
Chen, M., Shi, W., Xie, P., Silva, V. B. S., Kousky, V. E., Wayne Higgins, R., and Janowiak, J. E.: Assessing objective techniques for gauge-based analyses of global daily precipitation, J. Geophys. Res., 113, D04110, https://doi.org/10.1029/2007JD009132, 2008.
Corti, S., Palmer, T., Balmaseda, M., Weisheimer, A., Drijfhout, S., Dunstone, N., Hazeleger, W., Kröger, J., Pohlmann, H., Smith, D., von Storch, J.-S., and Wouters, B.: Impact of Initial Conditions versus External Forcing in Decadal Climate Predictions: A Sensitivity Experiment*, J. Climate, 28, 4454–4470, https://doi.org/10.1175/JCLI-D-14-00671.1, 2015.
Climate Prediction Center: Niño3.4
index, https://www.cpc.ncep.noaa.gov/data/indices/, last access: 6 August 2022.
Delworth, T. L., Cooke, W. F., Adcroft, A., Bushuk, M., Chen, J., Dunne, K. A., Ginoux, P., Gudgel, R., Hallberg, R. W., Harris, L., Harrison, M. J., Johnson, N., Kapnick, S. B., Lin, S., Lu, F., Malyshev, S., Milly, P. C., Murakami, H., Naik, V., Pascale, S., Paynter, D., Rosati, A., Schwarzkopf, M. D., Shevliakova, E., Underwood, S., Wittenberg, A. T., Xiang, B., Yang, X., Zeng, F., Zhang, H., Zhang, L., and Zhao, M.: SPEAR: The Next Generation GFDL Modeling System for Seasonal to Multidecadal Prediction and Projection, J. Adv. Model. Earth Sy., 12, e2019MS001895, https://doi.org/10.1029/2019MS001895, 2020.
Efron, B. and Tibshirani, R.: Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy, Stat. Sci., 1, 54–75, https://doi.org/10.1214/ss/1177013815, 1986.
Emerton, R., Cloke, H. L., Stephens, E. M., Zsoter, E., Woolnough, S. J., and Pappenberger, F.: Complex picture for likelihood of ENSO-driven flood hazard, Nat. Commun., 8, 14796, https://doi.org/10.1038/ncomms14796, 2017.
Hamlet, A. F. and Lettenmaier, D. P.: Columbia River Streamflow Forecasting Based on ENSO and PDO Climate Signals, J. Water Res. Plan. Man., 125, 333–341, https://doi.org/10.1061/(ASCE)0733-9496(1999)125:6(333), 1999.
Hamouda, M. E., Pasquero, C., and Tziperman, E.: Decoupling of the Arctic Oscillation and North Atlantic Oscillation in a warmer climate, Nat. Clim. Change, 11, 137–142, https://doi.org/10.1038/s41558-020-00966-8, 2021.
He, X., Estes, L., Konar, M., Tian, D., Anghileri, D., Baylis, K., Evans, T. P., and Sheffield, J.: Integrated approaches to understanding and reducing drought impact on food security across scales, Curr. Opin. Env. Sust., 40, 43–54, https://doi.org/10.1016/j.cosust.2019.09.006, 2019.
He, X., Bryant, B. P., Moran, T., Mach, K. J., Wei, Z., and Freyberg, D. L.: Climate-informed hydrologic modeling and policy typology to guide managed aquifer recharge, Sci. Adv., 7, eabe6025, https://doi.org/10.1126/sciadv.abe6025, 2021.
Hidalgo, H. G. and Dracup, J. A.: ENSO and PDO Effects on Hydroclimatic Variations of the Upper Colorado River Basin, J. Hydrometeorol., 4, 5–23, https://doi.org/10.1175/1525-7541(2003)004<0005:EAPEOH>2.0.CO;2, 2003.
Huang, Z. and Zhao, T.: Predictive performance of ensemble hydroclimatic forecasts: Verification metrics, diagnostic plots and forecast attributes, WIREs Water, 9, e1580, https://doi.org/10.1002/wat2.1580, 2022.
Huang, Z., Zhao, T., Liu, Y., Zhang, Y., Jiang, T., Lin, K., and Chen, X.: Differing roles of base and fast flow in ensemble seasonal streamflow forecasting: An experimental investigation, J. Hydrol., 591, 125272, https://doi.org/10.1016/j.jhydrol.2020.125272, 2020.
IRI Data Library: forecast and observation datasets, https://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/, last access: 6 August 2022.
Johnson, S. J., Stockdale, T. N., Ferranti, L., Balmaseda, M. A., Molteni, F., Magnusson, L., Tietsche, S., Decremer, D., Weisheimer, A., Balsamo, G., Keeley, S. P. E., Mogensen, K., Zuo, H., and Monge-Sanz, B. M.: SEAS5: the new ECMWF seasonal forecast system, Geosci. Model Dev., 12, 1087–1117, https://doi.org/10.5194/gmd-12-1087-2019, 2019.
Khan, M. Z. K., Sharma, A., and Mehrotra, R.: Global seasonal precipitation forecasts using improved sea surface temperature predictions: Seasonal Precipitation Forecasts, J. Geophys. Res.-Atmos., 122, 4773–4785, https://doi.org/10.1002/2016JD025953, 2017.
Kirtman, B. P., Min, D., Infanti, J. M., Kinter, J. L., Paolino, D. A., Zhang, Q., van den Dool, H., Saha, S., Mendez, M. P., Becker, E., Peng, P., Tripp, P., Huang, J., DeWitt, D. G., Tippett, M. K., Barnston, A. G., Li, S., Rosati, A., Schubert, S. D., Rienecker, M., Suarez, M., Li, Z. E., Marshak, J., Lim, Y.-K., Tribbia, J., Pegion, K., Merryfield, W. J., Denis, B., and Wood, E. F.: The North American Multimodel Ensemble: Phase-1 Seasonal-to-Interannual Prediction; Phase-2 toward Developing Intraseasonal Prediction, B. Am. Meteorol. Soc., 95, 585–601, https://doi.org/10.1175/BAMS-D-12-00050.1, 2014.
Li, H., Luo, L., Wood, E. F., and Schaake, J.: The role of initial conditions and forcing uncertainties in seasonal hydrologic forecasting, J. Geophys. Res., 114, D04114, https://doi.org/10.1029/2008JD010969, 2009.
Li, J., Wang, Z., Wu, X., Xu, C., Guo, S., Chen, X., and Zhang, Z.: Robust Meteorological Drought Prediction Using Antecedent SST Fluctuations and Machine Learning, Water Resour. Res., 57, e2020WR029413, https://doi.org/10.1029/2020WR029413, 2021.
Li, W., Duan, Q., Miao, C., Ye, A., Gong, W., and Di, Z.: A review on statistical postprocessing methods for hydrometeorological ensemble forecasting, WIREs Water, 4, e1246, https://doi.org/10.1002/wat2.1246, 2017.
Lim, E.-P., Hudson, D., Wheeler, M. C., Marshall, A. G., King, A., Zhu, H., Hendon, H. H., de Burgh-Day, C., Trewin, B., Griffiths, M., Ramchurn, A., and Young, G.: Why Australia was not wet during spring 2020 despite La Niña, Sci. Rep.-UK, 11, 18423, https://doi.org/10.1038/s41598-021-97690-w, 2021.
Lin, H., Merryfield, W. J., Muncaster, R., Smith, G. C., Markovic, M., Dupont, F., Roy, F., Lemieux, J.-F., Dirkson, A., Kharin, V. V., Lee, W.-S., Charron, M., and Erfani, A.: The Canadian Seasonal to Interannual Prediction System Version 2 (CanSIPSv2), Weather Forecast., 35, 1317–1343, https://doi.org/10.1175/WAF-D-19-0259.1, 2020.
Liu, X., Zhang, L., She, D., Chen, J., Xia, J., Chen, X., and Zhao, T.: Postprocessing of hydrometeorological ensemble forecasts based on multisource precipitation in Ganjiang River basin, China, J. Hydrol., 605, 127323, https://doi.org/10.1016/j.jhydrol.2021.127323, 2022.
Ma, F., Ye, A., Deng, X., Zhou, Z., Liu, X., Duan, Q., Xu, J., Miao, C., Di, Z., and Gong, W.: Evaluating the skill of NMME seasonal precipitation ensemble predictions for 17 hydroclimatic regions in continental China: Evaluating the Skill of NMME Seasonal Precipitation Predictions, Int. J. Climatol., 36, 132–144, https://doi.org/10.1002/joc.4333, 2016.
Madadgar, S., AghaKouchak, A., Shukla, S., Wood, A. W., Cheng, L., Hsu, K.-L., and Svoboda, M.: A hybrid statistical-dynamical framework for meteorological drought prediction: Application to the southwestern United States: A Hybrid Statistical-Dynamical Drought Prediction Framework, Water Resour. Res., 52, 5095–5110, https://doi.org/10.1002/2015WR018547, 2016.
Mason, S. J. and Goddard, L.: Probabilistic Precipitation Anomalies Associated with ENSO, B. Am. Meteorol. Soc., 82, 619–638, https://doi.org/10.1175/1520-0477(2001)082<0619:PPAAWE>2.3.CO;2, 2001.
Mei, L., Rozanov, V., Ritter, C., Heinold, B., Jiao, Z., Vountas, M., and Burrows, J. P.: Retrieval of Aerosol Optical Thickness in the Arctic Snow-Covered Regions Using Passive Remote Sensing: Impact of Aerosol Typing and Surface Reflection Model, IEEE T. Geosci. Remote, 58, 5117–5131, https://doi.org/10.1109/TGRS.2020.2972339, 2020.
Merryfield, W. J., Lee, W.-S., Boer, G. J., Kharin, V. V., Scinocca, J. F., Flato, G. M., Ajayamohan, R. S., Fyfe, J. C., Tang, Y., and Polavarapu, S.: The Canadian Seasonal to Interannual Prediction System. Part I: Models and Initialization, Mon. Weather Rev., 141, 2910–2945, https://doi.org/10.1175/MWR-D-12-00216.1, 2013.
Peel, M. C., McMahon, T. A., and Finlayson, B. L.: Continental differences in the variability of annual runoff-update and reassessment, J. Hydrol., 295, 185–197, https://doi.org/10.1016/j.jhydrol.2004.03.004, 2004.
Peng, B., Guan, K., Pan, M., and Li, Y.: Benefits of Seasonal Climate Prediction and Satellite Data for Forecasting U. S. Maize Yield, Geophys. Res. Lett., 45, 9662–9671, https://doi.org/10.1029/2018GL079291, 2018.
Pham, H. (Ed.): Springer Handbook of Engineering Statistics, Springer, London, https://doi.org/10.1007/978-1-84628-288-1, 2006.
Saha, S., Moorthi, S., Wu, X., Wang, J., Nadiga, S., Tripp, P., Behringer, D., Hou, Y.-T., Chuang, H., Iredell, M., Ek, M., Meng, J., Yang, R., Mendez, M. P., van den Dool, H., Zhang, Q., Wang, W., Chen, M., and Becker, E.: The NCEP Climate Forecast System Version 2, J. Climate, 27, 2185–2208, https://doi.org/10.1175/JCLI-D-12-00823.1, 2014.
Schepen, A., Everingham, Y., and Wang, Q. J.: On the Joint Calibration of Multivariate Seasonal Climate Forecasts from GCMs, Mon. Weather Rev., 148, 437–456, https://doi.org/10.1175/MWR-D-19-0046.1, 2020.
Sheffield, J., Wood, E. F., Chaney, N., Guan, K., Sadri, S., Yuan, X., Olang, L., Amani, A., Ali, A., Demuth, S., and Ogallo, L.: A Drought Monitoring and Forecasting System for Sub-Sahara African Water Resources and Food Security, B. Am. Meteorol. Soc., 95, 861–882, https://doi.org/10.1175/BAMS-D-12-00124.1, 2014.
Slater, L. J., Villarini, G., and Bradley, A. A.: Evaluation of the skill of North-American Multi-Model Ensemble (NMME) Global Climate Models in predicting average and extreme precipitation and temperature over the continental USA, Clim. Dynam., 53, 7381–7396, https://doi.org/10.1007/s00382-016-3286-1, 2019.
Strazzo, S., Collins, D. C., Schepen, A., Wang, Q. J., Becker, E., and Jia, L.: Application of a Hybrid Statistical–Dynamical System to Seasonal Prediction of North American Temperature and Precipitation, Mon. Weather Rev., 147, 607–625, https://doi.org/10.1175/MWR-D-18-0156.1, 2019.
Wanders, N., Bachas, A., He, X. G., Huang, H., Koppa, A., Mekonnen, Z. T., Pagán, B. R., Peng, L. Q., Vergopolan, N., Wang, K. J., Xiao, M., Zhan, S., Lettenmaier, D. P., and Wood, E. F.: Forecasting the Hydroclimatic Signature of the 2015/16 El Niño Event on the Western United States, J. Hydrometeorol., 18, 177–186, https://doi.org/10.1175/JHM-D-16-0230.1, 2017.
Webster, P. J. and Yang, S.: Monsoon and Enso:
Selectively Interactive Systems, Q. J. Roy. Meteor. Soc., 118, 877–926, https://doi.org/10.1002/qj.49711850705, 1992.
Wood, A. W. and Lettenmaier, D. P.: A Test Bed for New Seasonal Hydrologic Forecasting Approaches in the Western United States, B. Am. Meteorol. Soc., 87, 1699–1712, https://doi.org/10.1175/BAMS-87-12-1699, 2006.
Xu, L., Chen, N., Zhang, X., and Chen, Z.: A data-driven multi-model ensemble for deterministic and probabilistic precipitation forecasting at seasonal scale, Clim. Dynam., 54, 3355–3374, https://doi.org/10.1007/s00382-020-05173-x, 2020.
Xu, W., Fletcher, T. D., Burns, M. J., and Cherqui, F.: Real Time Control of Rainwater Harvesting Systems: The Benefits of Increasing Rainfall Forecast Window, Water Resour. Res., 56, e2020WR027856, https://doi.org/10.1029/2020WR027856, 2020.
Yang, S., Li, Z., Yu, J.-Y., Hu, X., Dong, W., and He, S.: El Niño–Southern Oscillation and its impact in the changing climate, Natl. Sci. Rev., 5, 840–857, https://doi.org/10.1093/nsr/nwy046, 2018.
Yossef, N. C., Winsemius, H., Weerts, A., van Beek, R., and Bierkens, M. F. P.: Skill of a global seasonal streamflow forecasting system, relative roles of initial conditions and meteorological forcing: Skill of a Global Seasonal Streamflow Forecasting System, Water Resour. Res., 49, 4687–4699, https://doi.org/10.1002/wrcr.20350, 2013.
Yuan, X., Wood, E. F., and Liang, M.: Integrating weather and climate prediction: Toward seamless hydrologic forecasting: Seamless hydrologic forecast, Geophys. Res. Lett., 41, 5891–5896, https://doi.org/10.1002/2014GL061076, 2014.
Yuan, X., Ma, F., Wang, L., Zheng, Z., Ma, Z., Ye, A., and Peng, S.: An experimental seasonal hydrological forecasting system over the Yellow River basin – Part 1: Understanding the role of initial hydrological conditions, Hydrol. Earth Syst. Sci., 20, 2437–2451, https://doi.org/10.5194/hess-20-2437-2016, 2016.
Zhao, T., Bennett, J. C., Wang, Q. J., Schepen, A., Wood, A. W., Robertson, D. E., and Ramos, M.-H.: How Suitable is Quantile Mapping For Postprocessing GCM Precipitation Forecasts?, J. Climate, 30, 3185–3196, https://doi.org/10.1175/JCLI-D-16-0652.1, 2017.
Zhao, T., Wang, Q. J., and Schepen, A.: A Bayesian modelling approach to forecasting short-term reference crop evapotranspiration from GCM outputs, Agr. Forest Meteorol., 269–270, 88–101, https://doi.org/10.1016/j.agrformet.2019.02.003, 2019.
Zhao, T., Chen, H., Shao, Q., Tu, T., Tian, Y., and Chen, X.: Attributing correlation skill of dynamical GCM precipitation forecasts to statistical ENSO teleconnection using a set-theory-based approach, Hydrol. Earth Syst. Sci., 25, 5717–5732, https://doi.org/10.5194/hess-25-5717-2021, 2021.
Short summary
This paper develops a novel set operations of coefficients of determination (SOCD) method to explicitly quantify the overlapping and differing information for GCM forecasts and ENSO teleconnection. Specifically, the intersection operation of the coefficient of determination derives the overlapping information for GCM forecasts and the Niño3.4 index, and then the difference operation determines the differing information in GCM forecasts (Niño3.4 index) from the Niño3.4 index (GCM forecasts).
This paper develops a novel set operations of coefficients of determination (SOCD) method to...
