Articles | Volume 24, issue 2
https://doi.org/10.5194/hess-24-809-2020
© 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-24-809-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
21 Feb 2020
Research article |
| 21 Feb 2020
| 21 Feb 2020
A universal multifractal approach to assessment of spatiotemporal extreme precipitation over the Loess Plateau of China
Jianjun Zhang
School of Land Science and Technology, China University of
Geosciences, Beijing 100083, China
State Key Laboratory of Urban and Regional Ecology, Research Center
for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Chinese Academy of
Sciences and Ministry of Water Resources, Yang ling, Shaanxi 712100, China
Key Laboratory of Land Consolidation and Rehabilitation, Ministry of Natural Resources, Beijing 100035, China
Guangyao Gao
State Key Laboratory of Urban and Regional Ecology, Research Center
for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
Bojie Fu
State Key Laboratory of Urban and Regional Ecology, Research Center
for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
Cong Wang
State Key Laboratory of Urban and Regional Ecology, Research Center
for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
Hoshin V. Gupta
CORRESPONDING AUTHOR
Department of Hydrology and Atmospheric Sciences, The University of
Arizona, Tucson, AZ 85721, USA
Xiaoping Zhang
State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Chinese Academy of
Sciences and Ministry of Water Resources, Yang ling, Shaanxi 712100, China
Rui Li
State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Chinese Academy of
Sciences and Ministry of Water Resources, Yang ling, Shaanxi 712100, China
Related authors
No articles found.
Mohammad Farmani, Hoshin V. Gupta, Ali Behrangi, Muhammad Jawad, Sadaf Moghisi, and Guo-Yue Niu
EGUsphere, https://doi.org/10.48550/arXiv.2603.25093, https://doi.org/10.48550/arXiv.2603.25093, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
Machine learning can predict streamflow accurately but often lacks physical interpretability. We develop a mass-conserving hybrid AI model that progressively adds hydrological processes, including soil storage, infiltration, ponding, and deep drainage. Tests across 15 U.S. basins show that added process structure can improve streamflow prediction, with drainage benefiting arid and snow-dominated basins but reducing skill in humid regions.
Yanzhang Huang, Guangyao Gao, Lishan Ran, Yue Wang, and Mingguo Zheng
Hydrol. Earth Syst. Sci., 30, 5473–5490, https://doi.org/10.5194/hess-30-5473-2026, https://doi.org/10.5194/hess-30-5473-2026, 2026
Short summary
Short summary
This study developed an integrative model combining sediment trapping of check dam networks with the Revised Universal Soil Loss Equation, index of connectivity, and sediment delivery ratio to reconstruct grid-scale sediment yield across the middle Yellow River Basin (1970–2020). The proposed model achieved about 20 % increase of simulation accuracy compared to ignoring check dam trapping. The sediment reduction contribution by check dams was quantified and controlling factors were detected.
Mohammad A. Farmani, Andrew Bennet, Sadaf Moghisi, Hoshin Gupta, Muhammad Jawad, Ali Behrangi, Ahmad A. Tavakoly, and Guo-Yue Niu
EGUsphere, https://doi.org/10.5194/egusphere-2026-4157, https://doi.org/10.5194/egusphere-2026-4157, 2026
Short summary
Short summary
This study presents RiverGraphNet, an artificial intelligence framework for predicting how runoff moves through connected river networks. Applied to Arizona’s Salt–Verde watershed, the method improved daily streamflow prediction relative to a standard physics-based routing model, especially for event timing and flow distributions. The results show that combining river-network structure with artificial intelligence can support better flood, reservoir, and water-resource prediction.
Benedikt Heudorfer, Hoshin Gupta, Alexander Dolich, and Ralf Loritz
EGUsphere, https://doi.org/10.5194/egusphere-2026-1965, https://doi.org/10.5194/egusphere-2026-1965, 2026
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
For most rivers, water level is not measured, making flood prediction difficult. But it's still possible with certain models. We want to improve these models and test if better models or better data help when predicting floods with these models in the United States. Results show that better data (measured by satellites) improves predictions more than better model designs. Actually, simple models often worked best. And we show that we need better measurement of landscape information.
Shan Sang, Yan Li, Shuang Zong, Lu Yu, Shuai Wang, Yanxu Liu, Xutong Wu, Shuang Song, Wenwu Zhao, Xuhui Wang, and Bojie Fu
Geosci. Model Dev., 19, 2039–2058, https://doi.org/10.5194/gmd-19-2039-2026, https://doi.org/10.5194/gmd-19-2039-2026, 2026
Short summary
Short summary
Regional coupled human–natural systems models are essential for regional sustainability. We developed a new model, CHANS-SD-YRB, using System Dynamics for the Yellow River Basin in China, which faces severe human-water conflicts. The model links 10 components, including Population, Economy, Energy, Food, Water, Sediment, Land, Carbon, and Climate to simulate basin's key human-natural interactions. The model is applicable for sustainable development through scenario analyses and predictions.
Ashish Manoj J, Ralf Loritz, Hoshin Gupta, and Erwin Zehe
Hydrol. Earth Syst. Sci., 29, 6115–6135, https://doi.org/10.5194/hess-29-6115-2025, https://doi.org/10.5194/hess-29-6115-2025, 2025
Short summary
Short summary
Traditional hydrological models typically operate in a forward mode, simulating streamflow and other catchment fluxes based on precipitation input. In this study, we explored the possibility of reversing this process, inferring precipitation from streamflow data, to improve flood event modelling. We then used the generated precipitation series to run hydrological models, resulting in more accurate estimates of streamflow and soil moisture.
Yichu Huang, Xiaoming Feng, Chaowei Zhou, and Bojie Fu
EGUsphere, https://doi.org/10.5194/egusphere-2024-3393, https://doi.org/10.5194/egusphere-2024-3393, 2024
Preprint archived
Short summary
Short summary
This study uses an integrated water-energy-land optimization model to explore sustainable water use pathways in the Yellow River Basin. We find water conflicts between energy and irrigation water use, and quantify the mitigation and spillover effects of water transfer. We also highlight the critical role of energy production, implying that the energy sector transformation is key to the water system of the Yellow River Basin.
Nannan An, Nan Lu, Weiliang Chen, Yongzhe Chen, Hao Shi, Fuzhong Wu, and Bojie Fu
Earth Syst. Sci. Data, 16, 1771–1810, https://doi.org/10.5194/essd-16-1771-2024, https://doi.org/10.5194/essd-16-1771-2024, 2024
Short summary
Short summary
This study generated a spatially continuous plant functional trait dataset (~1 km) in China in combination with field observations, environmental variables and vegetation indices using machine learning methods. Results showed that wood density, leaf P concentration and specific leaf area showed good accuracy with an average R2 of higher than 0.45. This dataset could provide data support for development of Earth system models to predict vegetation distribution and ecosystem functions.
Luis Andres De la Fuente, Mohammad Reza Ehsani, Hoshin Vijai Gupta, and Laura Elizabeth Condon
Hydrol. Earth Syst. Sci., 28, 945–971, https://doi.org/10.5194/hess-28-945-2024, https://doi.org/10.5194/hess-28-945-2024, 2024
Short summary
Short summary
Long short-term memory (LSTM) is a widely used machine-learning model in hydrology, but it is difficult to extract knowledge from it. We propose HydroLSTM, which represents processes like a hydrological reservoir. Models based on HydroLSTM perform similarly to LSTM while requiring fewer cell states. The learned parameters are informative about the dominant hydrology of a catchment. Our results show how parsimony and hydrological knowledge extraction can be achieved by using the new structure.
Luis Andres De la Fuente, Mohammad Reza Ehsani, Hoshin Vijai Gupta, and Laura E. Condon
EGUsphere, https://doi.org/10.5194/egusphere-2023-666, https://doi.org/10.5194/egusphere-2023-666, 2023
Preprint archived
Short summary
Short summary
Long Short-Term Memory (LSTM) is a widely-used machine learning (ML) model in hydrology. However, it is difficult to extract knowledge from it. We propose HydroLSTM which represents processes analogous to a hydrological reservoir. Models using HydroLSTM perform similarly to LSTM but require fewer cell states. The learned parameters are informative about the dominant hydroclimatic characteristics of a catchment. Our results demonstrate how hydrological knowledge is encoded in the new structure.
Yongzhe Chen, Xiaoming Feng, Bojie Fu, Haozhi Ma, Constantin M. Zohner, Thomas W. Crowther, Yuanyuan Huang, Xutong Wu, and Fangli Wei
Earth Syst. Sci. Data, 15, 897–910, https://doi.org/10.5194/essd-15-897-2023, https://doi.org/10.5194/essd-15-897-2023, 2023
Short summary
Short summary
This study presented a long-term (2002–2021) above- and belowground biomass dataset for woody vegetation in China at 1 km resolution. It was produced by combining various types of remote sensing observations with adequate plot measurements. Over 2002–2021, China’s woody biomass increased at a high rate, especially in the central and southern parts. This dataset can be applied to evaluate forest carbon sinks across China and the efficiency of ecological restoration programs in China.
Jinxia An, Guangyao Gao, Chuan Yuan, Juan Pinos, and Bojie Fu
Hydrol. Earth Syst. Sci., 26, 3885–3900, https://doi.org/10.5194/hess-26-3885-2022, https://doi.org/10.5194/hess-26-3885-2022, 2022
Short summary
Short summary
An in-depth investigation was conducted of all rainfall-partitioning components at inter- and intra-event scales for two xerophytic shrubs. Inter-event rainfall partitioning amount and percentage depended more on rainfall amount, and rainfall intensity and duration controlled intra-event rainfall-partitioning variables. One shrub has larger branch angle, small branch and smaller canopy area to produce stemflow more efficiently, and the other has larger biomass to intercept more rainfall.
Jonathan M. Frame, Frederik Kratzert, Daniel Klotz, Martin Gauch, Guy Shalev, Oren Gilon, Logan M. Qualls, Hoshin V. Gupta, and Grey S. Nearing
Hydrol. Earth Syst. Sci., 26, 3377–3392, https://doi.org/10.5194/hess-26-3377-2022, https://doi.org/10.5194/hess-26-3377-2022, 2022
Short summary
Short summary
The most accurate rainfall–runoff predictions are currently based on deep learning. There is a concern among hydrologists that deep learning models may not be reliable in extrapolation or for predicting extreme events. This study tests that hypothesis. The deep learning models remained relatively accurate in predicting extreme events compared with traditional models, even when extreme events were not included in the training set.
Shuang Song, Shuai Wang, Xutong Wu, Yongyuan Huang, and Bojie Fu
Hydrol. Earth Syst. Sci., 26, 2035–2044, https://doi.org/10.5194/hess-26-2035-2022, https://doi.org/10.5194/hess-26-2035-2022, 2022
Short summary
Short summary
A reasonable assessment of the contribution of the water resources in a river basin to domestic crops supplies will be the first step in balancing the water–food nexus. Our results showed that although the Yellow River basin had reduced its virtual water outflow, its importance to crop production in China had been increasing when water footprint networks were considered. Our complexity-based approach provides a new perspective for understanding changes in a basin with a severe water shortage.
Bojie Fu, Xutong Wu, Zhuangzhuang Wang, Xilin Wu, and Shuai Wang
Earth Syst. Dynam., 13, 795–808, https://doi.org/10.5194/esd-13-795-2022, https://doi.org/10.5194/esd-13-795-2022, 2022
Short summary
Short summary
To understand the dynamics of a coupled human and natural system (CHANS) and promote its sustainability, we propose a conceptual
pattern–process–service–sustainabilitycascade framework. The use of this framework is systematically illustrated by a review of CHANS research experience in China's Loess Plateau in terms of coupling landscape patterns and ecological processes, linking ecological processes to ecosystem services, and promoting social–ecological sustainability.
Maierdang Keyimu, Zongshan Li, Bojie Fu, Guohua Liu, Fanjiang Zeng, Weiliang Chen, Zexin Fan, Keyan Fang, Xiuchen Wu, and Xiaochun Wang
Clim. Past, 17, 2381–2392, https://doi.org/10.5194/cp-17-2381-2021, https://doi.org/10.5194/cp-17-2381-2021, 2021
Short summary
Short summary
We created a residual tree-ring width chronology and reconstructed non-growth-season precipitation (NGSP) over the period spanning 1600–2005 in the southeastern Tibetan Plateau (SETP), China. Reconstruction model verification as well as similar variations of NGSP reconstruction and Palmer Drought Severity Index reconstructions from the surrounding region indicate the reliability of the present reconstruction. Our reconstruction is representative of NGSP variability of a large region in the SETP.
Cited articles
Anagnostopoulou, C. and Tolika, K.: Extreme precipitation in Europe:
statistical threshold selection based on climatological criteria, Theor.
Appl. Climatol., 107, 479-489, 2012.
Bao, J., Sherwood, S. C., Alexander, L. V., and Evans, J. P.: Future increases in extreme precipitation exceed observed scaling rates, Nat. Clim. Change, 7, 128–132, 2017.
Beguería, S., Vicente-Serrano, S. M., López-Moreno, J. I., and
García-Ruiz, J. M.: Annual and seasonal mapping of peak intensity, magnitude and duration of extreme precipitation events across a climatic
gradient, northeast Spain, Int. J. Climatol., 29, 1759–1779, 2009.
Bernaola Galván, P., Ivanov, P. C., Amaral, L. A. N., and Stanley, H. E.: Scale invariance in the nonstationarity of human heart rate, Phys. Rev. Lett., 87, 168–105, 2001.
Cai, Q.: Soil erosion and management on the Loess Plateau, J. Geogr. Sci.,
11, 53–70, 2001.
Deidda, R. and Puliga, M.: Sensitivity of goodness-of-fit statistics to
rainfall data rounding off, Phys. Chem. Earth, 31, 1240–1251, 2006.
Donat, M. G., Lowry, A. L., Alexander, L. V., O'Gorman, P. A., and Maher, N.: More extreme precipitation in the world's dry and wet regions, Nat. Clim. Change, 6, 508–513, https://doi.org/10.1038/NCLIMATE2941, 2016.
Dong, Q., Chen, X., and Chen, T.: Characteristics and Changes of Extreme
Precipitation in the Yellow–Huaihe and Yangtze–Huaihe Rivers Basins, China, J. Climate, 24, 3781–3795, 2011.
Douglas, E. M. and Barros, A. P.: Probable maximum precipitation estimation
using multifractals: application in the Eastern United States, J. Hydrometeorol., 4, 1012–1024, 2003.
Du, H., Wu, Z., Zong, S., Meng, X., and Wang, L.: Assessing the characteristics of extreme precipitation over Northeast China using the
multifractal detrended fluctuation analysis, J. Geophys. Res.-Atmos., 118,
52013, https://doi.org/10.1002/jgrd.50487, 2013.
Dulière, V., Zhang, Y., and Salathé Jr, E. P.: Extreme precipitation
and temperature over the US Pacific Northwest: A comparison between observations, reanalysis data, and regional models, J. Climate, 24,
1950–1964, 2011.
Eekhout, J. P. C., Hunink, J. E., Terink, W., and de Vente, J.: Why increased extreme precipitation under climate change negatively affects water security, Hydrol. Earth Syst. Sci., 22, 5935–5946, https://doi.org/10.5194/hess-22-5935-2018, 2018.
Feng, X. M., Sun, G., Fu, B. J., Su, C. H., Liu, Y., and Lamparski, H.: Regional effects of vegetation restoration on water yield across the Loess Plateau, China, Hydrol. Earth Syst. Sci., 16, 2617–2628, https://doi.org/10.5194/hess-16-2617-2012, 2012.
Feng, X., Fu, B., Piao, S., Wang, S., Ciais, P., Zeng, Z., Lü, Y., Zeng,
Y., Li, Y., and Jiang, X.: Revegetation in China's Loess Plateau is approaching sustainable water resource limits, Nat. Clim. Change, 6, 1019–1022, 2016.
Gagnon, J.-S., Lovejoy, S., and Schertzer, D.: Multifractal earth topography, Nonlin. Processes Geophys., 13, 541–570, https://doi.org/10.5194/npg-13-541-2006, 2006.
Haddad, K., Rahman, A., and Green, J.: Design rainfall estimation in Australia: a case study using L moments and generalized least squares
regression, Stoch. Environ. Res. Risk A., 25, 815–825, 2011.
Herold, N., Behrangi, A., and Alexander, L. V.: Large uncertainties in
observed daily precipitation extremes over land, J. Geophys. Res.-Atmos.,
122, 668–681, 2017.
Huang, J., Yu, H., Guan, X., Wang, G., and Guo, R.: Accelerated dryland
expansion under climate change, Nat. Clim. Change, 6, 166–171, 2016.
Hubert, P., Tessier, Y., Lovejoy, S., Schertzer, D., Schmitt, F., Ladoy, P.,
Carbonnel, J., Violette, S., and Desurosne, I.: Multifractals and extreme
rainfall events, Geophys. Res. Lett., 20, 931–934, 1993.
IPCC – Intergovernmental Panel on Climate Change: Climate Change 2007:
Synthesis Report. Contribution of Working Groups I, II and III to the Fourth
Assessment Report of the Intergovernmental Panel on Climate Change, edited
by: Core Writing Team, Pachauri, R. K. and Reisinger, A., Geneva, Switzerland, 104 pp., 2007.
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L.,
Iredell, M., Saha, S., White, G., and Woollen, J.: The NCEP/NCAR 40-year
reanalysis project, B. Am. Meteorol. Soc., 77, 437–471, 1996.
Lavallée, D., Lovejoy, S., Schertzer, D., and Ladoy, P.: Nonlinear
variability and landscape topography: analysis and simulation, in: Fractals
in geography, edited by: Lam, N. S.-N. and Cola, L. D., PTR Prentice-Hall,
London, 1993.
Li, Y., Cai, W., and Campbell, E.: Statistical modeling of extreme rainfall
in southwest Western Australia, J. Climate, 18, 852–863, 2005.
Li, Z., Zheng, F.-L., Liu, W.-Z., and Flanagan, D. C.: Spatial distribution
and temporal trends of extreme temperature and precipitation events on the
Loess Plateau of China during 1961–2007, Quatern. Int., 226, 92–100, 2010a.
Li, Z., Zheng, F., and Liu, W.: Analyzing the spatial temporal changes of
extreme precipitation events in the loess plateau from 1961 to 2007, J. Nat.
Res., 25, 291–299, 2010b.
Li, Z., Xu, X., Xu, C., Liu, M., Wang, K., and Yu, B.: Annual Runoff is Highly Linked to Precipitation Extremes in Karst Catchments of Southwest China, J. Hydrometeorol., 18, 2745–2759, 2017.
Liu, B., Chen, J., Chen, X., Lian, Y., and Wu, L.: Uncertainty in determining extreme precipitation thresholds, J. Hydrol., 503, 233–245, 2013.
Lovejoy, S. and Schertzer, D.: Scaling and multifractal fields in the solid earth and topography, Nonlin. Processes Geophys., 14, 465–502, https://doi.org/10.5194/npg-14-465-2007, 2007.
Lovejoy, S. and Schertzer, D.: The weather and Climate: emergent laws and
multifractal cascades, Cambridge University Press, Cambridge, 2013.
Ludwig, W. and Probst, J. L.: River sediment discharge to the oceans;
present-day controls and global budgets, Am. J. Sci., 298, 265-295, 1998.
Manola, I., van den Hurk, B., De Moel, H., and Aerts, J. C. J. H.: Future extreme precipitation intensities based on a historic event, Hydrol. Earth Syst. Sci., 22, 3777–3788, https://doi.org/10.5194/hess-22-3777-2018, 2018.
Miao, C., Sun, Q., Duan, Q., and Wang, Y.: Joint analysis of changes in
temperature and precipitation on the Loess Plateau during the period 1961–2011, Clim. Dynam., 47, 3221–3234, 2016.
Miao, C., Duan, Q., Sun, Q., Lei, X., and Li, H.: Non-uniform changes in
different categories of precipitation intensity across China and the associated large-scale circulations, Environ. Res. Lett., 14, 025004, https://doi.org/10.1088/1748-9326/aaf306, 2019.
Min, S., Zhang, X., Zwiers, F. W., and Hegerl, G. C.: Human contribution to
more-intense precipitation extremes, Nature, 470, 378–381, 2011.
Ministry of Water Resources and National Bureau of Statistics: Bulletin of First National Census for Water, China Water & Power Press, Beijing, 2013.
Oliver, M. A. and Webster, R.: Kriging: a method of interpolation for
geographical information systems, Int. J. Geogr. Inf. Syst., 4, 313–332, 1990.
Pandey, G., Lovejoy, S., and Schertzer, D.: Multifractal analysis of daily
river flows including extremes for basins of five to two million square
kilometres, one day to 75 years, J. Hydrol., 208, 62–81, 1998.
Papalexiou, S. M., Koutsoyiannis, D., and Makropoulos, C.: How extreme is extreme? An assessment of daily rainfall distribution tails, Hydrol. Earth Syst. Sci., 17, 851–862, https://doi.org/10.5194/hess-17-851-2013, 2013.
Parisi, G. and Frisch, U.: A multifractal model of intermittency, in:
Turbulence and predictability in geophysical fluid dynamics and climate
dynamics, edited by: Ghil, M., Benzi, R., and Parisi, G., Elsevier North Holland, New-York, 1985.
Pecl, G. T., Araújo, M. B., Bell, J. D., Blanchard, J., Bonebrake, T. C., Chen, I.-C., Clark, T. D., Colwell, R. K., Danielsen, F., and Evengård, B.: Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being, Science, 355, eaai9214, https://doi.org/10.1126/science.aai9214, 2017.
Pfahl, S., O'Gorman, P. A., and Fischer, E. M.: Understanding the regional
pattern of projected future changes in extreme precipitation, Nat. Clim.
Change, 7, 423–427, https://doi.org/10.1038/NCLIMATE3287, 2017.
Ran, D., Liu, L., Zhao, L., Bai, Z., Liu, B., and Wang, H.: The soil
conservation practices and streamflow and sediment load changes in the
Hekou-Longmen region of middle reaches of Yellow River, Yellow River Water Conservancy Press, Zhengzhou, 2000.
Ren, M.-E.: Sediment discharge of the Yellow River, China: past, present and
future – a synthesis, Adv. Earth Sci., 21, 551–563, 2006.
Rustomji, P., Zhang, X., Hairsine, P., Zhang, L., and Zhao, J.: River sediment load and concentration responses to changes in hydrology and
catchment management in the Loess Plateau region of China, Water Resour. Res., 44, W00A04, https://doi.org/10.1029/2007WR006656, 2008.
Schertzer, D. and Lovejoy, S.: Physical modeling and analysis of rain and clouds by anisotropic scaling multiplicative processes, J. Geophys. Res.-Atmos., 92, 9693–9714, 1987.
Shi, H. and Shao, M.: Soil and water loss from the Loess Plateau in China, J. Arid Environ., 45, 9–20, 2000.
Stow, D., Daeschner, S., Hope, A., Douglas, D., Petersen, A., Myneni, R.,
Zhou, L., and Oechel, W.: Variability of the seasonally integrated normalized difference vegetation index across the north slope of Alaska in the 1990s, Int. J. Remote Sens., 24, 1111–1117, 2003.
Sunyer, M. A., Hundecha, Y., Lawrence, D., Madsen, H., Willems, P., Martinkova, M., Vormoor, K., Bürger, G., Hanel, M., Kriauǐūnienė, J., Loukas, A., Osuch, M., and Yücel, I.: Inter-comparison of statistical downscaling methods for projection of extreme precipitation in Europe, Hydrol. Earth Syst. Sci., 19, 1827–1847, https://doi.org/10.5194/hess-19-1827-2015, 2015.
Svensson, C. and Jones, D. A.: Dependence between sea surge, river flow and precipitation in south and west Britain, Hydrol. Earth Syst. Sci., 8, 973–992, https://doi.org/10.5194/hess-8-973-2004, 2004.
Swendsen, R. H. and Wang, J.-S.: Nonuniversal critical dynamics in Monte Carlo simulations, Phys. Rev. Lett., 58, 86–88, 1987.
Tang, K.: The soil conservation practices and streamflow and sediment load
changes in the Hekou-Longmen region of middle reaches of Yellow River, Yellow River Water Conservancy Press, Zhengzhou, 1990.
Tang, K.: The changes of erosion, runoff and sediment in the Yellow River,
Science China Press, Beijing, 1993.
Tang, K.: Soil and water conservation in China, Science China Press, Beijing, 2004.
Tessier, Y., Lovejoy, S., and Schertzer, D.: Universal multifractals: theory
and observations for rain and clouds, J. Appl. Meteorol., 32, 223–250, 1993.
Tessier, Y., Lovejoy, S., and Schertzer, D.: Multifractal analysis and simulation of the global meteorological network, J. Appl. Meteorol., 33,
1572–1586, 1994.
Tessier, Y., Lovejoy, S., Hubert, P., Schertzer, D., and Pecknold, S.:
Multifractal analysis and modeling of rainfall and river flows and scaling,
causal transfer functions, J. Geophys. Res., 101, 26427–26440, 1996.
Walther, G.-R., Post, E., Convey, P., Menzel, A., Parmesan, C., Beebee, T. J., Fromentin, J.-M., Hoegh-Guldberg, O., and Bairlein, F.: Ecological
responses to recent climate change, Nature, 416, 389–395, 2002.
Wan, L., Zhang, X., Ma, Q., Zhang, J., Ma, T., and Sun, Y.: Spatiotemporal
characteristics of precipitation and extreme events on the Loess Plateau of
China between 1957 and 2009, Hydrol. Process., 28, 4971–4983, 2014.
Wang, G., Wang, D., Trenberth, K. E., Erfanian, A., Yu, M., Bosilovich, M.
G., and Parr, D. T.: The peak structure and future changes of the relationships between extreme precipitation and temperature, Nat. Clim. Change, 7, 268–274, 2017.
Wang, S., Fu, B., Piao, S., Lü, Y., Ciais, P., Feng, X., and Wang, Y.:
Reduced sediment transport in the Yellow River due to anthropogenic changes,
Nat. Geosci., 9, 38–41, 2015.
Wischmeier, W. H.: Use and misuse of the universal soil loss equation, J.
Soil Water Conserv., 31, 5–9, 1976.
Xin, Z., Xu, J., and Ma, Y.: Spatio-temporal Variation of Erosive Precipitation in Loess Plateau during Past 50 Years, Sci. Geogr. Sin., 29,
98–104, 2009.
Zhang, J., Zhang, T., Lei, Y., Zhang, X., and Li, R.: Streamflow regime
variations following ecological management on the Loess Plateau, China, Forests, 7, 1–18, https://doi.org/10.3390/f7010006, 2016.
Zhang, J., Zhang, X., Li, R., Chen, L., and Lin, P.: Did streamflow or suspended sediment concentration changes reduce sediment load in the middle
reaches of the Yellow River?, J. Hydrol., 546, 357–369, 2017.
Zhang, X., Walling, D. E., Quine, T. A., and Wen, A.: Use of reservoir deposits and caesium-137 measurements to investigate the erosional response of a small drainage basin in the rolling loess plateau region of China, Land
Degrad. Dev., 8, 1–16, 1997.
Zheng, F., Westra, S., and Sisson, S. A.: Quantifying the dependence between
extreme rainfall and storm surge in the coastal zone, J. Hydrol., 505, 172–187, 2013.
Zheng, F., Westra, S., and Leonard, M.: Opposing local precipitation extremes, Nat. Clim. Change, 5, 389–390, 2015.
Zhou, P. and Wang, Z.: A Study on Rainstorm Causing Soil Erosion in the Loess Plateau, J. Soil Water Conserv., 6, 1–5, 1992.
Short summary
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.
We proposed an approach that integrates universal multifractals and a segmentation algorithm to...
