Hydrological monitoring and seasonal forecasting: Progress and perspectives

Journal of Geographical Sciences

Volumn 26, Issue 7, 2016, Pages 904-920

  Tang, Qiuhong ^a   Zhang, Xuejun ^a,e   Duan, Qingyun ^b   Huang, Shifeng ^c   Yuan, Xing ^d   Cui, Huijuan ^a   Li, Zhe ^a   Liu, Xingcai ^a  

^a INSTITUTE OF GEOGRAPHIC SCIENCES AND NATURAL RESOURCES RESEARCH   (China)

^b BEIJING NORMAL UNIVERSITY   (China)

^c CHINA INSTITUTE OF WATER RESOURCES AND HYDROPOWER RESEARCH   (China)

^d INSTITUTE OF ATMOSPHERIC PHYSICS   (China)

^e UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

Author keywords

climate model; hydrological forecasting; hydrological monitoring; modeling; remote sensing; uncertainty

Indexed keywords

EID: 84977138913     PISSN: 1009637X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11442-016-1306-z     Document Type: Article

References (137)

1. Ajami N K, Duan Q, Sorooshian S, 2007. An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction. Water Resources Research, 43: W01403.
2. Andreadis K M, Clark E A, Wood A W et al., 2005. Twentieth-century drought in the conterminous United States. Journal of Hydrometeorology, 6(6): 985–1001.
3. Andreadis K M, Lettenmaier D P, 2006. Assimilating remotely sensed snow observations into a macroscale hydrology model. Advances in Water Resources, 29: 872–886.
4. Bao H, Zhao L, 2012. Flood forecast of Huaihe River based on TIGGE Ensemble Prediction. Journal of Hydraulic Engineering, 43(2): 216–224.
5. Barnston A G, Tippett M K, L’Heureux M L et al., 2012. Skill of real-time seasonal ENSO model predictions during 2002-11: Is our capability increasing? Bulletin of the American Meteorological Society, 93: 631–651.
6. Bastola S, Misra V, Li H, 2013. Seasonal hydrological forecasts for watersheds over the southeastern United States for the boreal summer and fall seasons. Earth Interactions, 17: 1–22.
7. Below R, Grover-Kopec E, Dilley M, 2007. Documenting drought-related disasters: A global reassessment. The Journal of Environment & Development, 16: 328–344.
8. Beven K J, Binley A M, 1992. The future of distributed models: model calibration and uncertainty prediction. Hydrological Processes, 6: 279–298.
9. Bierkens M, van Beek L, 2009. Seasonal predictability of European discharge: NAO and hydrological response time. Journal of Hydrometeorology, 10: 953–968.
10. Clark M P, Rupp D E, Woods R A et al., 2008. Hydrological data assimilation with the ensemble Kalman filter: Use of streamflow observations to update states in a distributed hydrological model. Advances in Water Resources, 31: 1309–1324.
11. Day G N, 1985. Extended streamflow forecasting using NWSRFS. Journal of Water Resources Planning and Management (ASCE), 111: 157–170.
12. De Lannoy G, Reichle R H, Arsenault K R et al., 2012. Multiscale assimilation of Advanced Microwave Scanning Radiometer-EOS snow water equivalent and Moderate Resolution Imaging Spectroradiometer snow cover fraction observations in northern Colorado. Water Resources Research, 48: W01522.
13. De Lannoy G, Reichle R H, Houser P R et al., 2010. Satellite-scale snow water equivalent assimilation into a high-resolution land surface model. Journal of Hydrometeorology, 11: 352–369.
14. De Sales F, Xue Y, 2013. Dynamic downscaling of CFS winter seasonal simulations with the UCLAETA regional climate model over the United States. Climate Dynamics, 41: 255–275.
15. Demargne J, 2014. The science of NOAA’s operational hydrologic ensemble forecast service. Bulletin of the American Meteorological Society, 95: 79–98.
16. Di C L, Yang X H, Wang X C, 2014. A four-stage hybrid model for hydrological time series forecasting. PLoS One, 9(8): e104663.
17. Döll P, Fiedler K, Zhang J, 2009. Global-scale analysis of river flow alterations due to water withdrawals and reservoirs. Hydrology and Earth System Sciences, 13: 2413–2432.
18. Duan Q, Schaake J, Andréassian V et al., 2006. Model Parameter Estimation Experiment (MOPEX): An overview of science strategy and major results from the second and third workshops. Journal of Hydrology, 320: 3–17.
19. Duan Q, Sorooshian S, Gupta V, 1992. Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resources Research, 28(4): 1015–1031.
20. Entekhabi D, Njoku E G, O’Neill P E et al., 2010. The Soil Moisture Active Passive (SMAP) Mission. Proceedings of the IEEE, 98: 704–716.
21. Evensen G, 1997. Advanced data assimilation in strongly nonlinear dynamics. Monthly Weather Review, 125: 1342–1354.
22. Ferguson I M, Maxwell R M, 2012. Human impacts on terrestrial hydrology: Climate change versus pumping and irrigation. Environmental Research Letters, 7: 044022.
23. Fujii H, Koike T, Imaoka K, 2009. Improvement of the AMSR-E algorithm for soil moisture estimation by introducing a fractional vegetation coverage dataset derived from MODIS data. Journal of the Remote Sensing Society of Japan, 29(1): 282–292.
24. Gao H, Tang Q, Ferguson C R et al., 2010. Estimating the water budget of major U.S. river basins via remote sensing. International Journal of Remote Sensing, 31: 3955–3978.
25. Garen D C, 1992. Improved techniques in regression-based streamflow volume forecasting. Journal of Water Resources Planning and Management (ASCE), 118: 654–670.
26. Gerten D, Rost S, von Bloh W et al., 2008. Causes of change in 20th century global river discharge. Geophysical Research Letters, 35: L20405.
27. Glahn H R, Lowry D A, 1972. The use of Model Output Statistics (MOS) in objective weather forecasting. Journal of Applied Meteorology and Climatology, 11(8): 1203–1211.
28. Haddeland I, Clark D B, Franssen W et al., 2011. Multimodel estimate of the global terrestrial water balance: Setup and first results. Journal of Hydrometeorology, 12: 869–884.
29. Hamlet A F, Lettenmaier D P, 1999. Columbia River streamflow forecasting based on ENSO and PDO climate signals. Journal of Water Resources Planning and Management (ASCE), 125(6): 333–341.
30. Han E, Crow W T, Holmes T et al., 2014. Benchmarking a Soil Moisture Data Assimilation System for Agricultural Drought Monitoring. Journal of Hydrometeorology, 15: 1117–1134.
31. He X, Zhao T, Yang D, 2013. Prediction of monthly inflow to the Danjiangkou reservoir by distributed hydrological model and hydro-climatic teleconnections. Journal of Hydroelectric Engineering, 32(3): 4–9.
32. Hirabayashi Y, Kanae S, Emori S et al., 2008. Global projections of changing risks of floods and droughts in a changing climate. Hydrological Sciences Journal, 53(4): 754–772.
33. Hirabayashi Y, Mahendran R, Koirala S et al., 2013. Global flood risk under climate change. Nature Climate Change, 3: 816–821.
34. Hong Y, Adler R F, Hossain F et al., 2007. A first approach to global runoff simulation using satellite rainfall estimation. Water Resources Research, 43: W08502.
35. Hou A Y, Kakar R K, Neeck S et al., 2014. The Global Precipitation Measurement Mission. Bulletin of the American Meteorological Society, 95: 701–722.
36. Houborg R, Rodell M, Li B et al., 2012. Drought indicators based on model-assimilated Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage observations. Water Resources Research, 48: W07525.
37. Hu S S, Zheng H X, Liu C M et al., 2012. Assessing the impacts of climate variability and human activities on streamflow in the water source area of Baiyangdian Lake. Acta Geographica Sinica, 67(1): 62–70. (in Chinese)
38. Huang X, Xiao Q N, Barker D M et al., 2009. Four-dimensional variational data assimilation for WRF: Formulation and preliminary results. Monthly Weather Review, 137: 299–314.
39. Huffman G J, Bolvin D T, Nelkin E J et al., 2007. The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. Journal of Hydrometeorology, 8(1): 38–55.
40. Kavetski D, Franks S W, Kuczera G, 2002. Confronting input uncertainty in environmental modeling. In: Duan Q, Gupta H, Sorooshian S et al. (eds.). Calibration of Watershed Models. Water Science and Application Series 6, American Geophysical Union, Washington D. C.: 49–68.
41. Kim S, Liu Y Y, Johnson F M et al., 2015. A global comparison of alternate AMSR2 soil moisture products: Why do they differ? Remote Sensing of Environment, 161: 43–52.
42. Koster R D, Mahanama S, Livneh B et al., 2010. Skill in streamflow forecasts derived from large-scale estimates of soil moisture and snow. Nature Geoscience, 3: 613–616.
43. Krishnamurti T N, Kishtawal C M, LaRow T E et al., 1999. Improved weather and seasonal climate forecasts from multimodel superensemble. Science, 285: 1548–1550
44. Krzysztofowicz R, Sigrest A A, 1999. Calibration of probabilistic quantitative precipitation forecasts. Weather Forecast, 14(3): 427–442.
45. Kwon H-H, Brown C, Xu K et al., 2009. Seasonal and annual maximum streamflow forecasting using climate information: Application to the Three Gorges Dam in the Yangtze River basin, China. Hydrological Sciences Journal, 54: 582–595.
46. Lan Y H, Ding Y J, Kang E et al., 2003. The relationship between ENSO cycle and high and low-flow in the upper Yellow River. Journal of Geographical Sciences, 13(1): 105–111.
47. Leng G, Huang M, Tang Q et al., 2013. Modeling the effects of irrigation on land surface fluxes and states over the conterminous United States: Sensitivity to input data and model parameters. Journal of Geophysical Research: Atmospheres, 118(17): 9789–9803.
48. Leng G, Huang M, Tang Q et al., 2014. Modeling the effects of groundwater-fed irrigation on terrestrial hydrology over the conterminous United States. Journal of Hydrometeorology, 15: 957–972.
49. Leng G Y, Tang Q H, Huang S Z et al., 2016. Assessments of joint hydrological extreme risks in a warming climate in China. International Journal of Climatology, 36: 1632–1642
50. Leng G Y, Tang Q H, Rayburg S, 2015. Climate change impacts on meteorological, agricultural and hydrological droughts in China. Global and Planetary Change, 126: 23–34.
51. Li H, Luo L, Wood E F, 2008. Seasonal hydrologic predictions of low-flow conditions over eastern USA during the 2007 drought. Atmospheric Science Letters, 9: 61–66.
52. Li H, Luo L, Wood E F, Schaake J, 2009. The role of initial conditions and forcing uncertainties in seasonal hydrologic forecasting. Journal of Geophysical Research: Atmospheres, 114: D04114.
53. Li Q Z, Yan N N, Zhang F F et al., 2010. Drought monitoring and its impacts assessment in Southwest China using remote sensing in the spring of 2010. Acta Geographica Sinica, 65(7): 771–780. (in Chinese)
54. Li X, Liu S M, Xiao Q et al., 2013. Heihe Watershed Allied Telemetry Experimental Research (HiWATER): Scientific objectives and experimental design. Bulletin of the American Meteorological Society, 94: 1145–1160
55. Li X, Toshio K, Mahadevan P, 2004. A very fast simulated re-annealing (VFSA) approach for land data assimilation. Computers & Geosciences, 30: 239–248.
56. Li Y, Hu J, Wang J et al., 2008. Application of Ensemble Streamflow Prediction (ESP) to medium- and long-term water resources prediction. Journal of China Hydrology, 28(1): 25–27.
57. Liu F, Chen S L, Dong P et al., 2012. Spatial and temporal variability of water discharge in the Yellow River Basin over the past 60 years. Journal of Geographical Sciences, 22(6): 1013–1033.
58. Liu J, Zhang J, 2005. Development and prospects of hydrological forecasting technique in China. Journal of China Hydrology, 25(6): 1–5. (in Chinese)
59. Liu Y, Weerts A H, Clark M et al., 2012. Advancing data assimilation in operational hydrologic forecasting: Progresses, challenges, and emerging opportunities. Hydrology and Earth System Sciences, 16: 3863–3887.
60. Lu G H, Wu Z Y, Wen L et al., 2008. Real-time flood forecast and flood alert map over the Huaihe River Basin in China using a coupled hydro-meteorological modeling system. Science in China, Series E: Technological Sciences, 51(7): 1049–1063.
61. Luo L, Wood E F, 2007. Monitoring and predicting the 2007 U.S. drought. Geophysical Research Letters, 34: L22702.
62. Luo L, Wood E F, 2008. Use of Bayesian merging techniques in a multimodel seasonal hydrologic ensemble prediction system for the eastern United States. Journal of Hydrometeorology, 9: 866–884.
63. Luo L, Wood E F, Pan M, 2007. Bayesian merging of multiple climate model forecasts for seasonal hydrological predictions. Journal of Geophysical Research: Atmospheres, 112: D10102.
64. Ma F, Yuan X, Ye A, 2015. Seasonal drought predictability and forecast skill over China. Journal of Geophysical Research, 120: 8264–8275.
65. Maurer E P, Lettenmaier D P, Mantua N J, 2004. Variability and potential sources of predictability of North American runoff. Water Resources Research, 40(9): W09306.
66. Maurer E P, Wood A W, Adam J C et al., 2002. A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States. Journal of Climate, 15: 3237–3251.
67. Milly P C D, Wetherald R T, Dunne K A et al., 2002. Increasing risk of great floods in a changing climate. Nature, 415: 514–517.
68. Mitchell K E, Lohmann D, Houser P R et al., 2004. The multi-institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system. Journal of Geophysical Research: Atmospheres, 109: D07S90.
69. Mo K C, Lettenmaier D P, 2014. Hydrologic prediction over the conterminous United States using the National Multi-Model Ensemble. Journal of Hydrometeorology, 15: 1457–1472.
70. Mo K C, Shukla S, Lettenmaier D P et al., 2012. Do Climate Forecast System (CFSv2) forecasts improve seasonal SM prediction? Geophysical Research Letters, 39: L23703.
71. Nijssen B, Shukla S, Lin C Y et al., 2014. A prototype global drought information system based on multiple land surface models. Journal of Hydrometeorology, 15: 1661–1676.
72. Pagano T C, Garen D C, Perkins T R et al., 2009. Daily updating of operational statistical seasonal water supply forecasts for the western U.S. Journal of the American Water Resources Association (JAWRA), 45: 767–778.
73. Pan M, Sahoo A K, Troy T J, 2012. Multisource estimation of long-term terrestrial water budget for major global river basins. Journal of Climate, 25: 3191–3206.
74. Pan M, Wood E F, Wojcik R et al., 2008. Estimation of regional terrestrial water cycle using multi-sensor remote sensing observations and data assimilation. Remote Sensing of Environment, 112(4): 1282–1294.
75. Pozzi W, Sheffield J, Stefanski R et al., 2013. Toward global drought early warning capability: Expanding international cooperation for the development of a framework for monitoring and forecasting. Bulletin of the American Meteorological Society, 94: 776–785.
76. Raftery A E, Gneiting T, Balabdaoui F et al., 2005. Using Bayesian model averaging to calibrate forecast ensembles. Monthly Weather Review, 133: 1155–1174.
77. Robertson D E, Wang Q J, 2012. A Bayesian approach to predictor selection for seasonal streamflow forecasting. Journal of Hydrometeorology, 13: 155–171.
78. Rodell M, Houser P R, Jambor U et al., 2004. The Global Land Data Assimilation System. Bulletin of the American Meteorological Society, 85: 381–394.
79. Saha S, Moorthi S, Wu X R et al., 2014. The NCEP climate forecast system version 2. Journal of Climate, 27: 2185–2208.
80. Schaake J, Demargne J, Hartman R et al., 2007. Precipitation and temperature ensemble forecasts from single- value forecasts. Hydrology and Earth System Sciences, 4: 655–717.
81. Sheffield J, Ferguson C R, Troy T J et al., 2009. Closing the terrestrial water budget from satellite remote sensing. Geophysical Research Letters, 36: L07403.
82. Sheffield J, Wood E F, 2007. Characteristics of global and regional drought, 1950–2000: Analysis of soil moisture data from off-line simulation of the terrestrial hydrologic cycle. Journal of Geophysical Research: Atmospheres, 112: D17115.
83. Sheffield J, Wood E F, 2008. Global trends and variability in soil moisture and drought characteristics, 1950–2000, from observation-driven simulations of the terrestrial hydrologic cycle. Journal of Climate, 21: 432–458.
84. Sheffield J, Wood E F, Chaney N et al., 2014. A drought monitoring and forecasting system for sub-Sahara African water resources and food security. Bulletin of the American Meteorological Society, 95: 861–882.
85. Shukla S, Lettenmaier D P, 2011a. Seasonal hydrologic prediction in the United States: Understanding the role of initial hydrologic conditions and seasonal climate forecast skill. Hydrology and Earth System Sciences, 15: 3529–3538.
86. Shukla S, Lettenmaier D P, 2013. Multi-RCM ensemble downscaling of NCEP CFS winter season forecasts: Implications for seasonal hydrologic forecast skill. Journal of Geophysical Research: Atmospheres, 118: 10770–10790.
87. Shukla S, Steinemann A C, Lettenmaier D P, 2011b. Drought monitoring for Washington State: Indicators and applications. Journal of Hydrometeorology, 12: 66–83.
88. Shukla S, Sheffield J, Wood E F et al., 2013. On the sources of global land surface hydrologic predictability. Hydrology and Earth System Sciences, 17: 2781–2796.
89. Singh V P, Cui H, 2015. Entropy theory for streamflow forecasting. Environmental Processes, 2: 449–460.
90. Sinha T, Sankarasubramanian A, 2013. Role of climate forecasts and initial conditions in developing streamflow and soil moisture forecasts in a rainfall-runoff regime. Hydrology and Earth System Sciences, 17: 721–733.
91. Smith D M, Scaife A A, Kirtman B P, 2012. What is the current state of scientific knowledge with regard to seasonal to decadal forecasting. Environmental Research Letters, 7: 015602.
92. Staudinger M, Seibert J, 2014. Predictability of low flow: An assessment with simulation experiments. Journal of Hydrology, 519: 1383–1393.
93. Tang Q, Oki T, Kanae S et al., 2007. The influence of precipitation variability and partial irrigation within grid cells on a hydrological simulation. Journal of Hydrometeorology, 8: 499–512.
94. Tang Q, Oki T, Kanae S et al., 2008. Hydrological cycles change in the Yellow River basin during the last half of the 20th century. Journal of Climate, 21: 1790–1806.
95. Tang Q, Wood A W, Lettenmaier D P, 2009a. Real-time precipitation estimation based on index station percentiles. Journal of Hydrometeorology, 10: 266–277.
96. Tang Q, Gao H, Lu H et al., 2009b. Remote sensing: Hydrology. Progress in Physical Geography, 33: 490–509.
97. Tang Q, Peterson S, Cuenca R H et al., 2009c. Satellite-based near-real-time estimation of irrigated crop water consumption. Journal of Geophysical Research: Atmospheres, 114: D05114.
98. Tapley B D, Bettadpur S, Ries J C et al., 2004. GRACE measurements of mass variability in the Earth system. Science, 305(5683): 503–505.
99. Thober S, Kumar R, Sheffield J et al., 2015. Seasonal soil moisture drought prediction over Europe using the North American Multi-Model Ensemble (NMME). Journal of Hydrometeorology, 16: 2329–2344.
100. Tobin K J, Bennett M E, 2010. Adjusting satellite precipitation data to facilitate hydrologic modeling. Journal of Hydrometeorology, 11: 966–978.
101. van Dijk A, Peña-Arancibia J L, Wood E F et al., 2013. Global analysis of seasonal streamflow predictability using an ensemble prediction system and observations from 6192 small catchments worldwide. Water Resources Research, 49: 2729–2746.
102. Van Shaar J R, Haddeland I, Lettenmaier D P, 2002. Effects of land cover changes on the hydrologic response of interior Columbia River Basin forested catchments. Hydrological Processes, 16(13): 2499–2520.
103. Vogt J V, Barbosa P, Hofer B et al., 2011. Developing a European drought observatory for monitoring assessing and forecasting droughts across the European continent. AGU Fall Meeting Abstracts 1, NH24A–07.
104. Wang A, Lettenmaier D P, Sheffield J, 2011a. Soil moisture drought in China, 1950–2006. Journal of Climate, 24: 3257–3271.
105. Wang C, Duan Q, Gong W et al., 2014. An evaluation of adaptive surrogate modeling based optimization with two benchmark problems. Environmental Modelling & Software, 60: 167–179.
106. Wang C, Duan Q, Tong C H et al., 2016. A GUI platform for uncertainty quantification of complex dynamical models. Environmental Modelling & Software, 76: 1–12.
107. Wang E, Zhang Y, Luo J et al., 2011b. Monthly and seasonal streamflow forecasts using rainfall-runoff modeling and historical weather data. Water Resources Research, 47: W05516.
108. Wang X, Barker D M, Snyder C et al., 2008. A hybrid ETKF-3DVAR data assimilation scheme for the WRF model. Part I: Observing system simulation experiment. Monthly Weather Review, 136: 5116–5131.
109. Werner K, Brandon D, Clark M et al., 2004. Climate index weighting schemes for NWS ESP-based seasonal volume forecasts. Journal of Hydrometeorology, 5: 1076–1090.
110. Werner K, Brandon D, Clark M et al., 2005. Incorporating medium-range numerical weather model output into the ensemble streamflow prediction system of the National Weather Service. Journal of Hydrometeorology, 6: 101–114.
111. Westra S, Sharma A, Brown C et al., 2008. Multivariate streamflow forecasting using independent component analysis. Water Resources Research, 44: W02437.
112. Wilhite D A, 2000. Drought as a natural hazard: Concepts and definitions. In: Wilhite D A. Droughts: A Global Assessment. London: Routledge, 3–18.
113. Wood A W, Kumar A, Lettenmaier D P, 2005. A retrospective assessment of National Centers for Environmental Prediction climate model-based ensemble hydrologic forecasting in the western United States. Journal of Geophysical Research: Atmospheres, 110: D04105.
114. Wood A W, Lettenmaier D P, 2006. A test bed for new seasonal hydrologic forecasting approaches in the western United States. Bulletin of the American Meteorological Society, 87: 1699–1712.
115. Wood A W, Lettenmaier D P, 2008. An ensemble approach for attribution of hydrologic prediction uncertainty. Geophysical Research Letters, 35: L14401.
116. Wood A W, Maurer E P, Kumar A et al., 2002. Long-range experimental hydrologic forecasting for the eastern United States. Journal of Geophysical Research: Atmospheres, 107: 4429.
117. Wu H, Adler R F, Tian Y et al., 2014. Real-time global flood estimation using satellite-based precipitation and a coupled land surface and routing model. Water Resources Research, 50: 2693–2717.
118. Xia Y, Mitchell K, Ek M et al., 2012. Continental-scale water and energy flux analysis and validation for the North-American Land Data Assimilation System Project Phase 2 (NLDAS-2) (Part I): Intercomparison and application of model products. Journal of Geophysical Research: Atmospheres, 117: D03109.
119. Yang D W, Li C, Ni G H et al., 2004. Application of a distributed hydrological model to Yellow River basin. Acta Geographica Sinica, 59(1): 143–154. (in Chinese)
120. Yang L, Tian F, Sun Y et al., 2014. Attribution of hydrologic forecast uncertainty within scalable forecast windows. Hydrology and Earth System Sciences, 18: 775–786.
121. Yilmaz K K, Adler R F, Tian Y et al., 2010. Evaluation of a satellite-based global flood monitoring system. International Journal of Remote Sensing, 31(14): 3763–3782.
122. Yossef N C, Winsemius H, Weerts A et al., 2013. Skill of a global seasonal streamflow forecasting system, relative roles of initial conditions and meteorological forcing. Water Resources Research, 49: 4687–4699.
123. Yuan X, Liang X Z, 2011. Improving cold season precipitation prediction by the nested CWRF-CFS system Geophysical Research Letters, 38: L02706.
124. Yuan X, Ma Z, Pan M et al., 2015a. Microwave remote sensing of short-term droughts during crop growing seasons. Geophysical Research Letters, 42: 4394–4401.
125. Yuan X, Roundy J, Wood E F et al., 2015b. Seasonal forecasting of global hydrologic extremes: System development and evaluation over GEWEX basins. Bulletin of the American Meteorological Society, 96: 1895–1912.
126. Yuan X, Wood E F, Chaney N W et al., 2013a. Probabilistic seasonal forecasting of African drought by dynamical models. Journal of Hydrometeorology, 14: 1706–1720.
127. Yuan X, Wood E F, Roundy J K et al., 2013b. CFSv2-based seasonal hydroclimatic forecasts over the conterminous United States. Journal of Climate, 26: 4828–4847.
128. Yuan X, Wood E F, Liang M, 2014. Integrating weather and climate prediction: Toward seamless hydrologic forecasting. Geophysical Research Letters, 41: 5891–5896.
129. Yuan X, Wood E F, Ma Z, 2015c. A review on climate-model-based seasonal hydrologic forecasting: Physical understanding and system development. WIREs Water, 2: 523–536.
130. Zaitchik B F, Rodell M, Reichle R H, 2008. Assimilation of GRACE terrestrial water storage data into a land surface model: Results for the Mississippi River Basin. Journal of Hydrometeorology, 9(3): 535–548.
131. Zeng H W, Li L J, Li J Y, 2012. The evaluation of TRMM Multisatellite Precipitation Analysis (TMPA) in drought monitoring in the Lancang River Basin. Journal of Geographical Sciences, 22(2): 273–282.
132. Zhai J, Su B, Krysanova V et al., 2010. Spatial variation and trends in PDSI and SPI indices and their relation to streamflow in 10 large regions of China. Journal of Climate, 23: 649–663.
133. Zhang J, Liu Z, 2006. Hydrological monitoring and flood management in China. Frontiers in Flood Research, 305: 93–101.
134. Zhang X J, Tang Q, 2015. Combining satellite precipitation and long-term ground observations for hydrological monitoring in China, Journal of Geophysical Research: Atmospheres, 120: 6426–6443.
135. Zhang X J, Tang Q H, Pan M et al., 2014. A long-term land surface hydrologic fluxes and states dataset for China. Journal of Hydrometeorology, 15: 2067–2084.
136. Zhao H G, Yang S T, Wang Z W et al., 2015. Evaluating the suitability of TRMM satellite rain-fall data for hydrological simulation using a distributed hydrological model in the Weihe River catchment in China. Journal of Geographical Sciences, 25(2): 177–195.
137. Zhou T, Nijssen B, Huffman G J et al., 2014. Evaluation of real-time satellite precipitation data for global drought monitoring. Journal of Hydrometeorology, 15: 1651–1660.