Articles | Volume 24, issue 1
https://doi.org/10.5194/hess-24-1-2020
© Author(s) 2020. 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-24-1-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Jan 2020
Research article |
| 03 Jan 2020
| 03 Jan 2020
Significant spatial patterns from the GCM seasonal forecasts of global precipitation
Tongtiegang Zhao
CORRESPONDING AUTHOR
Center of Water Resources and Environment, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), School of Civil Engineering, Sun Yat-Sen University, Guangzhou, 510275, China
Wei Zhang
IIHR-Hydroscience & Engineering, University of Iowa, Iowa City,
52242, USA
Yongyong Zhang
Key Laboratory of Water Cycle and Related Land Surface Processes,
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
Zhiyong Liu
Center of Water Resources and Environment, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), School of Civil Engineering, Sun Yat-Sen University, Guangzhou, 510275, China
Xiaohong Chen
CORRESPONDING AUTHOR
Center of Water Resources and Environment, Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), School of Civil Engineering, Sun Yat-Sen University, Guangzhou, 510275, China
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Reliable prediction of floods depends on the quality of the input data such as precipitation. However, estimation of precipitation from the local measurements is known to be difficult, especially for extremes. Regionalization improves the estimates by increasing the quantity of data available for estimation. Here, we compare three regionalization methods based on their robustness and reliability. We apply the comparison to a dense network of daily stations within and outside Switzerland.
András Bárdossy, Jochen Seidel, and Abbas El Hachem
Hydrol. Earth Syst. Sci., 25, 583–601, https://doi.org/10.5194/hess-25-583-2021, https://doi.org/10.5194/hess-25-583-2021, 2021
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In this study, the applicability of data from private weather stations (PWS) for precipitation interpolation was investigated. Due to unknown errors and biases in these observations, a two-step filter was developed that uses indicator correlations and event-based spatial precipitation patterns. The procedure was tested and cross validated for the state of Baden-Württemberg (Germany). The biggest improvement is achieved for the shortest time aggregations.
Sigrid J. Bakke, Monica Ionita, and Lena M. Tallaksen
Hydrol. Earth Syst. Sci., 24, 5621–5653, https://doi.org/10.5194/hess-24-5621-2020, https://doi.org/10.5194/hess-24-5621-2020, 2020
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This study provides an in-depth analysis of the 2018 northern European drought. Large parts of the region experienced 60-year record-breaking temperatures, linked to high-pressure systems and warm surrounding seas. Meteorological drought developed from May and, depending on local conditions, led to extreme low flows and groundwater drought in the following months. The 2018 event was unique in that it affected most of Fennoscandia as compared to previous droughts.
Bo Dan, Xiaogu Zheng, Guocan Wu, and Tao Li
Hydrol. Earth Syst. Sci., 24, 5187–5201, https://doi.org/10.5194/hess-24-5187-2020, https://doi.org/10.5194/hess-24-5187-2020, 2020
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Data assimilation is a procedure to generate an optimal combination of the state variable in geoscience, based on the model outputs and observations. The ensemble Kalman filter (EnKF) scheme is a widely used assimilation method in soil moisture estimation. This study proposed several modifications of EnKF for improving this assimilation. The study shows that the quality of the assimilation result is improved, while the degree of water budget imbalance is reduced.
Eric Pohl, Christophe Grenier, Mathieu Vrac, and Masa Kageyama
Hydrol. Earth Syst. Sci., 24, 2817–2839, https://doi.org/10.5194/hess-24-2817-2020, https://doi.org/10.5194/hess-24-2817-2020, 2020
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Existing approaches to quantify the emergence of climate change require several user choices that make these approaches less objective. We present an approach that uses a minimum number of choices and showcase its application in the extremely sensitive, permafrost-dominated region of eastern Siberia. Designed as a Python toolbox, it allows for incorporating climate model, reanalysis, and in situ data to make use of numerous existing data sources and reduce uncertainties in obtained estimates.
Eva Mekis, Ronald E. Stewart, Julie M. Theriault, Bohdan Kochtubajda, Barrie R. Bonsal, and Zhuo Liu
Hydrol. Earth Syst. Sci., 24, 1741–1761, https://doi.org/10.5194/hess-24-1741-2020, https://doi.org/10.5194/hess-24-1741-2020, 2020
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This article provides a Canada-wide analysis of near-0°C temperature conditions (±2°C) using hourly surface temperature and precipitation type observations from 92 locations for the 1981–2011 period. Higher annual occurrences were found in Atlantic Canada, although high values also occur in other regions. Trends of most indicators show little or no change despite a systematic warming over Canada. A higher than expected tendency for near-0°C conditions was also found at some stations.
Jianjun Zhang, Guangyao Gao, Bojie Fu, Cong Wang, Hoshin V. Gupta, Xiaoping Zhang, and Rui Li
Hydrol. Earth Syst. Sci., 24, 809–826, https://doi.org/10.5194/hess-24-809-2020, https://doi.org/10.5194/hess-24-809-2020, 2020
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We proposed an approach that integrates universal multifractals and a segmentation algorithm to precisely identify extreme precipitation (EP) and assess spatiotemporal EP variation over the Loess Plateau, using daily data. Our results explain how EP contributes to the widely distributed severe natural hazards. These findings are of great significance for ecological management in the Loess Plateau. Our approach is also helpful for spatiotemporal EP assessment at the regional scale.
Guoxiao Wei, Xiaoying Zhang, Ming Ye, Ning Yue, and Fei Kan
Hydrol. Earth Syst. Sci., 23, 2877–2895, https://doi.org/10.5194/hess-23-2877-2019, https://doi.org/10.5194/hess-23-2877-2019, 2019
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Accurately evaluating evapotranspiration (ET) is a critical challenge in improving hydrological process modeling. Here we evaluated four ET models (PM, SW, PT–FC, and AA) under the Bayesian framework. Our results reveal that the SW model has the best performance. This is in part because the SW model captures the main physical mechanism in ET; the other part is that the key parameters, such as the extinction factor, could be well constrained with observation data.
Chongli Di, Tiejun Wang, Xiaohua Yang, and Siliang Li
Hydrol. Earth Syst. Sci., 22, 5069–5079, https://doi.org/10.5194/hess-22-5069-2018, https://doi.org/10.5194/hess-22-5069-2018, 2018
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The original Grassberger–Procaccia algorithm for complex analysis was modified by incorporating the normal-based K-means clustering technique and the RANSAC algorithm. The calculation accuracy of the proposed method was shown to outperform traditional algorithms. The proposed algorithm was used to diagnose climate system complexity in the Hai He basin. The spatial patterns of the complexity of precipitation and air temperature reflected the influence of the dominant climate system.
César Cisneros Vaca, Christiaan van der Tol, and Chandra Prasad Ghimire
Hydrol. Earth Syst. Sci., 22, 3701–3719, https://doi.org/10.5194/hess-22-3701-2018, https://doi.org/10.5194/hess-22-3701-2018, 2018
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The influence of long-term changes in canopy structure on rainfall interception loss was studied in a 55-year old forest. Interception loss was similar at the same site (38 %), when the forest was 29 years old. In the past, the forest was denser and had a higher storage capacity, but the evaporation rates were lower. We emphasize the importance of quantifying downward sensible heat flux and heat release from canopy biomass in tall forest in order to improve the quantification of evaporation.
Sojung Park, Seon Ki Park, Jeung Whan Lee, and Yunho Park
Hydrol. Earth Syst. Sci., 22, 3435–3452, https://doi.org/10.5194/hess-22-3435-2018, https://doi.org/10.5194/hess-22-3435-2018, 2018
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Understanding the precipitation characteristics is essential to design an optimal observation network. We studied the spatial and temporal characteristics of summertime precipitation systems in Korea via geostatistical analyses on the ground-based precipitation and satellite water vapor data. We found that, under a strict standard, an observation network with higher resolution is required in local areas with frequent heavy rainfalls, depending on directional features of precipitation systems.
Wenbin Liu, Fubao Sun, Yanzhong Li, Guoqing Zhang, Yan-Fang Sang, Wee Ho Lim, Jiahong Liu, Hong Wang, and Peng Bai
Hydrol. Earth Syst. Sci., 22, 351–371, https://doi.org/10.5194/hess-22-351-2018, https://doi.org/10.5194/hess-22-351-2018, 2018
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The dynamics of basin-scale water budgets over the Tibetan Plateau (TP) are not well understood nowadays due to the lack of hydro-climatic observations. In this study, we investigate seasonal cycles and trends of water budget components (e.g. precipitation P, evapotranspiration ET and runoff Q) in 18 TP river basins during the period 1982–2011 through the use of multi-source datasets (e.g. in situ observations, satellite retrievals, reanalysis outputs and land surface model simulations).
Harsh Beria, Trushnamayee Nanda, Deepak Singh Bisht, and Chandranath Chatterjee
Hydrol. Earth Syst. Sci., 21, 6117–6134, https://doi.org/10.5194/hess-21-6117-2017, https://doi.org/10.5194/hess-21-6117-2017, 2017
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High-quality satellite precipitation forcings have provided a viable alternative to hydrologic modeling in data-scarce regions. Ageing TRMM sensors have recently been upgraded to GPM, promising enhanced spatio-temporal resolutions. Statistical and hydrologic evaluation of GPM measurements across 86 Indian river basins revealed improved low rainfall estimates with reduced effects of climatology and topography.
James C. Bennett, Quan J. Wang, David E. Robertson, Andrew Schepen, Ming Li, and Kelvin Michael
Hydrol. Earth Syst. Sci., 21, 6007–6030, https://doi.org/10.5194/hess-21-6007-2017, https://doi.org/10.5194/hess-21-6007-2017, 2017
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We assess a new streamflow forecasting system in Australia. The system is designed to meet the need of water agencies for 12-month forecasts. The forecasts perform well in a wide range of rivers. Forecasts for shorter periods (up to 6 months) are generally informative. Forecasts sometimes did not perform well in a few very dry rivers. We test several techniques for improving streamflow forecasts in drylands, with mixed success.
Konrad Bogner, Katharina Liechti, and Massimiliano Zappa
Hydrol. Earth Syst. Sci., 21, 5493–5502, https://doi.org/10.5194/hess-21-5493-2017, https://doi.org/10.5194/hess-21-5493-2017, 2017
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The enhanced availability of many different weather prediction systems nowadays makes it very difficult for flood and water resource managers to choose the most reliable and accurate forecast. In order to circumvent this problem of choice, different approaches for combining this information have been applied at the Sihl River (CH) and the results have been verified. The outcome of this study highlights the importance of forecast combination in order to improve the quality of forecast systems.
Matthew B. Switanek, Peter A. Troch, Christopher L. Castro, Armin Leuprecht, Hsin-I Chang, Rajarshi Mukherjee, and Eleonora M. C. Demaria
Hydrol. Earth Syst. Sci., 21, 2649–2666, https://doi.org/10.5194/hess-21-2649-2017, https://doi.org/10.5194/hess-21-2649-2017, 2017
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The commonly used bias correction method called quantile mapping assumes a constant function of error correction values between modeled and observed distributions. Our article finds that this function cannot be assumed to be constant. We propose a new bias correction method, called scaled distribution mapping, that does not rely on this assumption. Furthermore, the proposed method more explicitly accounts for the frequency of rain days and the likelihood of individual events.
Tesfay G. Gebremicael, Yasir A. Mohamed, Pieter v. Zaag, and Eyasu Y. Hagos
Hydrol. Earth Syst. Sci., 21, 2127–2142, https://doi.org/10.5194/hess-21-2127-2017, https://doi.org/10.5194/hess-21-2127-2017, 2017
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This study was conducted to understand the spatio-temporal variations of streamflow in the Tekezē basin. Results showed rainfall over the basin did not significantly change. However, streamflow experienced high variabilities at seasonal and annual scales. Further studies are needed to verify hydrological changes by identifying the physical mechanisms behind those changes. Findings are useful as prerequisite for studying the effects of catchment management dynamics on the hydrological processes.
Louise Crochemore, Maria-Helena Ramos, Florian Pappenberger, and Charles Perrin
Hydrol. Earth Syst. Sci., 21, 1573–1591, https://doi.org/10.5194/hess-21-1573-2017, https://doi.org/10.5194/hess-21-1573-2017, 2017
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The use of general circulation model outputs for streamflow forecasting has developed in the last decade. In parallel, traditional streamflow forecasting is commonly based on historical data. This study investigates the impact of conditioning historical data based on circulation model precipitation forecasts on seasonal streamflow forecast quality. Results highlighted a trade-off between the sharpness and reliability of forecasts.
Louise Crochemore, Maria-Helena Ramos, and Florian Pappenberger
Hydrol. Earth Syst. Sci., 20, 3601–3618, https://doi.org/10.5194/hess-20-3601-2016, https://doi.org/10.5194/hess-20-3601-2016, 2016
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This study investigates the way bias correcting precipitation forecasts can improve the skill of streamflow forecasts at extended lead times. Eight variants of bias correction approaches based on the linear scaling and the distribution mapping methods are applied to the precipitation forecasts prior to generating the streamflow forecasts. One of the main results of the study is that distribution mapping of daily values is successful in improving forecast reliability.
P. Froidevaux, J. Schwanbeck, R. Weingartner, C. Chevalier, and O. Martius
Hydrol. Earth Syst. Sci., 19, 3903–3924, https://doi.org/10.5194/hess-19-3903-2015, https://doi.org/10.5194/hess-19-3903-2015, 2015
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We investigate precipitation characteristics prior to 4000 annual floods in Switzerland since 1961. The floods were preceded by heavy precipitation, but in most catchments extreme precipitation occurred only during the last 3 days prior to the flood events. Precipitation sums for earlier time periods (like e.g. 4-14 days prior to floods) were mostly average and do not correlate with the return period of the floods.
G. H. Fang, J. Yang, Y. N. Chen, and C. Zammit
Hydrol. Earth Syst. Sci., 19, 2547–2559, https://doi.org/10.5194/hess-19-2547-2015, https://doi.org/10.5194/hess-19-2547-2015, 2015
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This study compares the effects of five precipitation and three temperature correction methods on precipitation, temperature, and streamflow through loosely coupling RCM (RegCM) and a distributed hydrological model (SWAT) in terms of frequency-based indices and time-series-based indices. The methodology and results can be used for other regions and other RCM and hydrologic models, and for impact studies of climate change on water resources at a regional scale.
M. S. Siam and E. A. B. Eltahir
Hydrol. Earth Syst. Sci., 19, 1181–1192, https://doi.org/10.5194/hess-19-1181-2015, https://doi.org/10.5194/hess-19-1181-2015, 2015
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This paper explains the different natural modes of interannual variability in the flow of the Nile River and also presents a new index based on the sea surface temperature (SST) over the southern Indian Ocean to forecast the flow of the Nile River. It also presents a new hybrid forecasting algorithm that can be used to predict the Nile flow based on indices of the SST in the eastern Pacific and southern Indian oceans.
D. Halwatura, A. M. Lechner, and S. Arnold
Hydrol. Earth Syst. Sci., 19, 1069–1091, https://doi.org/10.5194/hess-19-1069-2015, https://doi.org/10.5194/hess-19-1069-2015, 2015
G. Panthou, T. Vischel, T. Lebel, G. Quantin, and G. Molinié
Hydrol. Earth Syst. Sci., 18, 5093–5107, https://doi.org/10.5194/hess-18-5093-2014, https://doi.org/10.5194/hess-18-5093-2014, 2014
D. Masson and C. Frei
Hydrol. Earth Syst. Sci., 18, 4543–4563, https://doi.org/10.5194/hess-18-4543-2014, https://doi.org/10.5194/hess-18-4543-2014, 2014
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The question of how to utilize information from the physiography/topography in the spatial interpolation of rainfall is a long-standing discussion in the literature. In this study we test ideas that go beyond the approach in popular interpolation schemes today. The key message of our study is that these ideas can at best marginally improve interpolation accuracy, even in a region where a clear benefit would intuitively be expected.
A. Casanueva, C. Rodríguez-Puebla, M. D. Frías, and N. González-Reviriego
Hydrol. Earth Syst. Sci., 18, 709–725, https://doi.org/10.5194/hess-18-709-2014, https://doi.org/10.5194/hess-18-709-2014, 2014
D. H. Yan, D. Wu, R. Huang, L. N. Wang, and G. Y. Yang
Hydrol. Earth Syst. Sci., 17, 2859–2871, https://doi.org/10.5194/hess-17-2859-2013, https://doi.org/10.5194/hess-17-2859-2013, 2013
F. Yusof, I. L. Kane, and Z. Yusop
Hydrol. Earth Syst. Sci., 17, 1311–1318, https://doi.org/10.5194/hess-17-1311-2013, https://doi.org/10.5194/hess-17-1311-2013, 2013
J. H. Lee, J. Timmermans, Z. Su, and M. Mancini
Hydrol. Earth Syst. Sci., 16, 4291–4302, https://doi.org/10.5194/hess-16-4291-2012, https://doi.org/10.5194/hess-16-4291-2012, 2012
L. Gudmundsson, J. B. Bremnes, J. E. Haugen, and T. Engen-Skaugen
Hydrol. Earth Syst. Sci., 16, 3383–3390, https://doi.org/10.5194/hess-16-3383-2012, https://doi.org/10.5194/hess-16-3383-2012, 2012
T. Bosshard, S. Kotlarski, T. Ewen, and C. Schär
Hydrol. Earth Syst. Sci., 15, 2777–2788, https://doi.org/10.5194/hess-15-2777-2011, https://doi.org/10.5194/hess-15-2777-2011, 2011
S. Nie, J. Zhu, and Y. Luo
Hydrol. Earth Syst. Sci., 15, 2437–2457, https://doi.org/10.5194/hess-15-2437-2011, https://doi.org/10.5194/hess-15-2437-2011, 2011
G. Ibarra-Berastegi, J. Saénz, A. Ezcurra, A. Elías, J. Diaz Argandoña, and I. Errasti
Hydrol. Earth Syst. Sci., 15, 1895–1907, https://doi.org/10.5194/hess-15-1895-2011, https://doi.org/10.5194/hess-15-1895-2011, 2011
R. Schiemann, R. Erdin, M. Willi, C. Frei, M. Berenguer, and D. Sempere-Torres
Hydrol. Earth Syst. Sci., 15, 1515–1536, https://doi.org/10.5194/hess-15-1515-2011, https://doi.org/10.5194/hess-15-1515-2011, 2011
A. Lü, S. Jia, W. Zhu, H. Yan, S. Duan, and Z. Yao
Hydrol. Earth Syst. Sci., 15, 1273–1281, https://doi.org/10.5194/hess-15-1273-2011, https://doi.org/10.5194/hess-15-1273-2011, 2011
G. Wang, A. J. Dolman, and A. Alessandri
Hydrol. Earth Syst. Sci., 15, 57–64, https://doi.org/10.5194/hess-15-57-2011, https://doi.org/10.5194/hess-15-57-2011, 2011
F. Garavaglia, J. Gailhard, E. Paquet, M. Lang, R. Garçon, and P. Bernardara
Hydrol. Earth Syst. Sci., 14, 951–964, https://doi.org/10.5194/hess-14-951-2010, https://doi.org/10.5194/hess-14-951-2010, 2010
Cited articles
Alfieri, L., Burek, P., Dutra, E., Krzeminski, B., Muraro, D., Thielen, J.,
and Pappenberger, F.: GloFAS – global ensemble streamflow forecasting and
flood early warning, Hydrol. Earth Syst. Sci., 17, 1161–1175,
https://doi.org/10.5194/hess-17-1161-2013, 2013.
Ambaum, M. H. P., Hoskins, B. J., and Stephenson, D. B.: Arctic oscillation
or North Atlantic oscillation? (vol 14, pg 3495, 2001), J. Climate, 15, 3495–3507, https://doi.org/10.1175/1520-0442(2002)015<0553:c>2.0.co;2, 2002.
Anselin, L.: Local Indicators Of Spatial Association – LISA, Geogr. Anal., 27, 93–115, https://doi.org/10.1111/j.1538-4632.1995.tb00338.x, 1995.
Anselin, L., Syabri, I., and Kho, Y.: GeoDa: An introduction to spatial data
analysis, Geogr. Anal., 38, 5-22, https://doi.org/10.1111/j.0016-7363.2005.00671.x, 2006.
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, A., Finger, P., Meyer-Christoffer, A., Rudolf, B., and Ziese, M.:
GPCC full data reanalysis version 6.0 at 1.0: monthly land-surface
precipitation from rain-gauges built on GTS-based and historic data, Global
Precipitation Climatology Centre (GPCC), Berlin, Germany, https://doi.org/10.5065/D6000072, 2011.
Becker, E., van den Dool, H., and Zhang, Q.: Predictability and Forecast Skill in NMME, J. Climate, 27, 5891–5906, https://doi.org/10.1175/jcli-d-13-00597.1, 2014.
Bellprat, O., Guemas, V., Doblas-Reyes, F., and Donat, M. G.: Towards reliable extreme weather and climate event attribution, Nat. Commun., 10, 1732, https://doi.org/10.1038/s41467-019-09729-2, 2019.
Chen, L.-C., Van den Dool, H., Becker, E., and Zhang, Q.: ENSO Precipitation
and Temperature Forecasts in the North American Multimodel Ensemble: Composite Analysis and Validation, J. Climate, 30, 1103–1125,
https://doi.org/10.1175/jcli-d-15-0903.1, 2017.
Cliff, A. D., and Ord, J. K.: Spatial processes: models & applications,
Pion Ltd, https://doi.org/10.1086/412797, 1981.
Crochemore, L., Ramos, M. H., and Pappenberger, F.: Bias correcting precipitation forecasts to improve the skill of seasonal streamflow forecasts, Hydrol. Earth Syst. Sci., 20, 3601–3618, https://doi.org/10.5194/hess-20-3601-2016, 2016.
Di Luzio, M., Johnson, G. L., Daly, C., Eischeid, J. K., and Arnold, J. G.:
Constructing retrospective gridded daily precipitation and temperature datasets for the conterminous United States, J. Appl. Meteorol. Clim., 47, 475–497, https://doi.org/10.1175/2007jamc1356.1, 2008.
Doblas-Reyes, F. J., Garcia-Serrano, J., Lienert, F., Biescas, A. P., and
Rodrigues, L. R. L.: Seasonal climate predictability and forecasting: status
and prospects, Wiley Interdisciplin. Rev.-Clim. Change, 4, 245–268,
https://doi.org/10.1002/wcc.217, 2013.
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S.,
Husak, G., Rowland, J., Harrison, L., and Hoell, A.: The climate hazards
infrared precipitation with stations – a new environmental record for
monitoring extremes, Scient. Data, 2, 150066, https://doi.org/10.1038/sdata.2015.66, 2015.
Gent, P. R., Danabasoglu, G., Donner, L. J., Holland, M. M., Hunke, E. C., Jayne, S. R., Lawrence, D. M., Neale, R. B., Rasch, P. J., Vertenstein, M.,
Worley, P. H., Yang, Z. L., and Zhang, M. H.: The Community Climate System
Model Version 4, J. Climate, 24, 4973–4991, https://doi.org/10.1175/2011jcli4083.1, 2011.
Getis, A.: Reflections on spatial autocorrelation, Reg. Sci. Urban Econ., 37, 491–496, https://doi.org/10.1016/j.regsciurbeco.2007.04.005, 2007.
Gneiting, T., Balabdaoui, F., and Raftery, A. E.: Probabilistic forecasts,
calibration and sharpness, J. Roy. Stat. Soc. B, 69, 243–268,
https://doi.org/10.1111/j.1467-9868.2007.00587.x, 2007.
Hao, Y., Liu, Y. M., Weng, J. H., and Gao, Y. X.: Does the Environmental Kuznets Curve for coal consumption in China exist? New evidence from spatial
econometric analysis, Energy, 114, 1214–1223, https://doi.org/10.1016/j.energy.2016.08.075, 2016.
Hersbach, H.: Decomposition of the continuous ranked probability score for
ensemble prediction systems, Weather Forecast., 15, 559–570,
https://doi.org/10.1175/1520-0434(2000)015<0559:dotcrp>2.0.co;2, 2000.
Hudson, D., Alves, O., Hendon, H. H., Lim, E.-P., Liu, G., Luo, J.-J., MacLachlan, C., Marshall, A. G., Shi, L., Wang, G., Wedd, R., Young, G., Zhao, M., and Zhou, X.: ACCESS-S1: The new Bureau of Meteorology multi-week
to seasonal prediction system, J. South. Hemis. Earth Syst. Sci., 67, 132–159, https://doi.org/10.22499/3.6703.001, 2017.
Hurrell, J. W., Kushnir, Y., and Visbeck, M.: Climate – The North Atlantic
oscillation, Science, 291, 603–605, https://doi.org/10.1126/science.1058761, 2001.
Jia, L. W., Yang, X. S., Vecchi, G. A., Gudgel, R. G., Delworth, T. L., Rosati, A., Stern, W. F., Wittenberg, A. T., Krishnamurthy, L., Zhang, S.
Q., Msadek, R., Kapnick, S., Underwood, S., Zeng, F. R., Anderson, W. G., Balaji, V., and Dixon, K.: Improved Seasonal Prediction of Temperature and
Precipitation over Land in a High-Resolution GFDL Climate Model, J. Climate, 28, 2044–2062, https://doi.org/10.1175/jcli-d-14-00112.1, 2015.
Kapnick, S. B., Yang, X., Vecchi, G. A., Delworth, T. L., Gudgel, R., Malyshev, S., Milly, P. C. D., Shevliakova, E., Underwood, S., and Margulis, S. A.: Potential for western US seasonal snowpack prediction, P. Natl. Acad. Sci. USA, 115, 1180–1185, https://doi.org/10.1073/pnas.1716760115, 2018.
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. T., Tripp, P., Huang, J., DeWitt, D. G., Tippett, M. K., Barnston, A. G., Li, S. H., 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.
Kushnir, Y., Scaife, A. A., Arritt, R., Balsamo, G., Boer, G., Doblas-Reyes,
F., Hawkins, E., Kimoto, M., Kolli, R. K., Kumar, A., Matei, D., Matthes,
K., Muller, W. A., O'Kane, T., Perlwitz, J., Power, S., Raphael, M., Shimpo, A., Smith, D., Tuma, M., and Wu, B.: Towards operational predictions of the
near-term climate, Nat. Clim. Change, 9, 94–101, https://doi.org/10.1038/s41558-018-0359-7, 2019.
Lu, G. Y. and Wong, D. W.: An adaptive inverse-distance weighting spatial
interpolation technique, Comput. Geosci., 34, 1044–1055,
https://doi.org/10.1016/j.cageo.2007.07.010, 2008.
Luo, L., Tang, W., Lin, Z., and Wood, E. F.: Evaluation of summer temperature and precipitation predictions from NCEP CFSv2 retrospective forecast over China, Clim. Dynam., 41, 2213–2230, https://doi.org/10.1007/s00382-013-1927-1, 2013.
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, Int. J. Climatol., 36, 132–144, https://doi.org/10.1002/joc.4333, 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.
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. M., 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.
Miller, H. J.: Tobler's First Law and spatial analysis, Ann. Assoc. Am. Geogr., 94, 284–289, https://doi.org/10.1111/j.1467-8306.2004.09402005.x, 2004.
Murphy, A. H.: What is a good forecast? An essay on the nature of goodness
in weather forecasting, Weather Forecast., 8, 281–293,
https://doi.org/10.1175/1520-0434(1993)008<0281:WIAGFA>2.0.CO;2, 1993.
Pappenberger, F., Cloke, H. L., and Baugh, C. A.: Cartograms for Use in Forecasting Weather-Driven Natural Hazards, Cartogr. J., 56, 134–145, https://doi.org/10.1080/00087041.2018.1534358, 2019.
Rey, S. J. and Anselin, L.: PySAL: A Python library of spatial analytical
methods, in: Handbook of applied spatial analysis, Springer, 175–193, https://doi.org/10.1007/978-3-642-03647-7_11, 2010.
Rowell, D. P.: Simulating SST Teleconnections to Africa: What is the State
of the Art?, J. Climate, 26, 5397–5418, https://doi.org/10.1175/jcli-d-12-00761.1, 2013.
Saha, S., Moorthi, S., Wu, X. R., Wang, J., Nadiga, S., Tripp, P., Behringer, D., Hou, Y. T., Chuang, H. Y., Iredell, M., Ek, M., Meng, J., Yang, R. Q., Mendez, M. P., Van Den Dool, H., Zhang, Q., Wang, W. Q., Chen, M. Y., 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.
Schamm, K., Ziese, M., Becker, A., Finger, P., Meyer-Christoffer, A., Schneider, U., Schroeder, M., and Stender, P.: Global gridded precipitation
over land: a description of the new GPCC First Guess Daily product, Earth
Syst. Sci. Data, 6, 49–60, https://doi.org/10.5194/essd-6-49-2014, 2014.
Schepen, A., Wang, Q. J., and Robertson, D.: Evidence for Using Lagged Climate Indices to Forecast Australian Seasonal Rainfall, J. Climate, 25, 1230–1246, https://doi.org/10.1175/jcli-d-11-00156.1, 2012.
Schmal, C., Myung, J., Herzel, H., and Bordyugov, G.: Moran's I quantifies
spatio-temporal pattern formation in neural imaging data, Bioinformatics, 33, 3072–3079, https://doi.org/10.1093/bioinformatics/btx351, 2017.
Sheffield, J., Wood, E. F., Chaney, N., Guan, K. Y., Sadri, S., Yuan, X., Olang, L., Abou, 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.: Weighting of NMME temperature and precipitation forecasts across Europe, J. Hydrol., 552, 646–659, https://doi.org/10.1016/j.jhydrol.2017.07.029, 2017.
Tian, D., Wood, E. F., and Yuan, X.: CFSv2-based sub-seasonal precipitation
and temperature forecast skill over the contiguous United States, Hydrol. Earth Syst. Sci., 21, 1477–1490, https://doi.org/10.5194/hess-21-1477-2017, 2017.
Tian, F. Q., Li, Y. L., Zhao, T. T. G., Hu, H. C., Pappenberger, F., Jiang, Y. Z., and Lu, H.: Evaluation of the ECMWF System 4 climate forecasts for
streamflow forecasting in the Upper Hanjiang River Basin, Hydrol. Res., 49,
1864–1879, https://doi.org/10.2166/nh.2018.176, 2018.
Tobler, W. R.: A computer movie simulating urban growth in the Detroit region, Econ. Geogr., 46, 234–240, 1970.
Vecchi, G. A., Delworth, T., Gudgel, R., Kapnick, S., Rosati, A., Wittenberg, A. T., Zeng, F., Anderson, W., Balaji, V., Dixon, K., Jia, L., Kim, H. S., Krishnamurthy, L., Msadek, R., Stern, W. F., Underwood, S. D., Villarini, G., Yang, X., and Zhang, S.: On the Seasonal Forecasting of Regional Tropical Cyclone Activity, J. Climate, 27, 7994–8016, https://doi.org/10.1175/jcli-d-14-00158.1, 2014.
Wang, Q. J., Schepen, A., and Robertson, D. E.: Merging Seasonal Rainfall
Forecasts from Multiple Statistical Models through Bayesian Model Averaging,
J. Climate, 25, 5524–5537, https://doi.org/10.1175/jcli-d-11-00386.1, 2012.
Wang, Q. J., Shao, Y., Song, Y., Schepen, A., Robertson, D. E., Ryu, D., and
Pappenberger, F.: An evaluation of ECMWF SEAS5 seasonal climate forecasts for Australia using a new forecast calibration algorithm, Environ. Model. Softw., 122, 104550, https://doi.org/10.1016/j.envsoft.2019.104550, 2019a.
Wang, Q. J., Zhao, T., Yang, Q., and Robertson, D.: A Seasonally Coherent
Calibration (SCC) Model for Postprocessing Numerical Weather Predictions, Mon. Weather Rev., 147, 3633–3647, https://doi.org/10.1175/mwr-d-19-0108.1, 2019b.
Woldemeskel, F. M., Sivakumar, B., and Sharma, A.: Merging gauge and satellite rainfall with specification of associated uncertainty across Australia, J. Hydrol., 499, 167–176, https://doi.org/10.1016/j.jhydrol.2013.06.039, 2013.
Wu, Z., Wang, B., Li, J., and Jin, F.-F.: An empirical seasonal prediction
model of the east Asian summer monsoon using ENSO and NAO, J. Geophys. Res.-Atmos., 114, D18120, https://doi.org/10.1029/2009jd011733, 2009.
Xie, P. and Arkin, P. A.: Global Precipitation: A 17-Year Monthly Analysis
Based on Gauge Observations, Satellite Estimates, and Numerical Model Outputs, B. Am. Meteorol. Soc., 78, 2539–2558, https://doi.org/10.1175/1520-0477(1997)078<2539:gpayma>2.0.co;2, 1997.
Xie, P., Arkin, P. A., and Janowiak, J. E.: CMAP: The CPC merged analysis of
precipitation, in: Measuring precipitation from space, Springer, 319–328,
https://doi.org/10.1007/978-1-4020-5835-6, 2007.
Yuan, X., Wood, E. F., Luo, L., and Pan, M.: A first look at Climate Forecast System version 2 (CFSv2) for hydrological seasonal prediction, Geophys. Res. Lett., 38, L13402, https://doi.org/10.1029/2011GL047792, 2011.
Yuan, X., Wood, E. F., Roundy, J. K., and Pan, M.: CFSv2-based seasonal
hydroclimatic forecasts over the conterminous United States, J. Climate, 26, 4828–4847, https://doi.org/10.1175/JCLI-D-12-00683.1, 2013.
Yuan, X., Wood, E. F., and Ma, Z.: A review on climate-model-based seasonal
hydrologic forecasting: physical understanding and system development, Wiley
Interdisciplin. Rev.: Water, 2, 523–536, https://doi.org/10.1002/wat2.1088, 2015.
Yuan, Y. M., Cave, M., and Zhang, C. S.: Using Local Moran's I to identify
contamination hotspots of rare earth elements in urban soils of London, Appl. Geochem., 88, 167–178, https://doi.org/10.1016/j.apgeochem.2017.07.011, 2018.
Zhang, W., Villarini, G., Slater, L., Vecchi, G. A., and Bradley, A. A.:
Improved ENSO Forecasting Using Bayesian Updating and the North American
Multimodel Ensemble (NMME), J. Climate, 30, 9007–9025, https://doi.org/10.1175/jcli-d-17-0073.1, 2017.
Zhao, T. T. G., 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, 2017a.
Zhao, T. T. G., Liu, P., Zhang, Y. Y., and Ruan, C. Q.: Relating anomaly
correlation to lead time: Clustering analysis of CFSv2 forecasts of summer
precipitation in China, J. Geophys. Res.-Atmos., 122, 9094–9106, https://doi.org/10.1002/2017jd027018, 2017b.
Zhao, T. T. G., Chen, X. H., Liu, P., Zhang, Y. Y., Liu, B. J., and Lin, K.
R.: Relating Anomaly Correlation to Lead Time: Principal Component Analysis of NMME Forecasts of Summer Precipitation in China, J. Geophys. Res.-Atmos., 123, 6039–6052, https://doi.org/10.1029/2018jd028267, 2018.
Zhao, T. T. G., Wang, Q. J., Schepen, A., and Griffiths, M.: Ensemble
forecasting of monthly and seasonal reference crop evapotranspiration based on global climate model outputs, Agr. Forest Meteorol., 264, 114–124, https://doi.org/10.1016/j.agrformet.2018.10.001, 2019a.
Zhao, T. T. G., Zhang, Y. Y., and Chen, X. H.: Predictive performance of
NMME seasonal forecasts of global precipitation: A spatial-temporal perspective, J. Hydrol., 570, 17–25, https://doi.org/10.1016/j.jhydrol.2018.12.036, 2019b.
