Articles | Volume 15, issue 10
https://doi.org/10.5194/essd-15-4463-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-4463-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A global streamflow indices time series dataset for large-sample hydrological analyses on streamflow regime (until 2022)
Xinyu Chen
School of Environmental Science and Engineering, Southern
University of Science and Technology, Shenzhen, 518055, China
School of Environmental Science and Engineering, Southern
University of Science and Technology, Shenzhen, 518055, China
Yuning Luo
State Key Laboratory of Hydrology-Water Resources and Hydraulic
Engineering, and College of Hydrology and Water Resources, Hohai University,
Nanjing, 210098, China
Junguo Liu
School of Environmental Science and Engineering, Southern
University of Science and Technology, Shenzhen, 518055, China
Henan Provincial Key Lab of Hydrosphere and Watershed Water
Security, North China University of Water Resources and Electric Power,
Zhengzhou, 450046, China
Related authors
No articles found.
Hao Huang, Junguo Liu, Aifang Chen, Melissa Ruiz-Vásquez, and René Orth
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-376, https://doi.org/10.5194/essd-2025-376, 2025
Preprint under review for ESSD
Short summary
Short summary
Hydrological research benefits from a growing number and diversity of datasets. However, the consistency across the increasing suite of datasets is unclear, limiting the comparability of findings derived from different datasets and variables. We find overall low consistency of numerous state-of-the-art precipitation, evapotranspiration, runoff, and soil moisture datasets in terms of the water balance. Meanwhile, the water balance consistency varies across space, sources, variables, and time.
Hannes Müller Schmied, Simon Newland Gosling, Marlo Garnsworthy, Laura Müller, Camelia-Eliza Telteu, Atiq Kainan Ahmed, Lauren Seaby Andersen, Julien Boulange, Peter Burek, Jinfeng Chang, He Chen, Lukas Gudmundsson, Manolis Grillakis, Luca Guillaumot, Naota Hanasaki, Aristeidis Koutroulis, Rohini Kumar, Guoyong Leng, Junguo Liu, Xingcai Liu, Inga Menke, Vimal Mishra, Yadu Pokhrel, Oldrich Rakovec, Luis Samaniego, Yusuke Satoh, Harsh Lovekumar Shah, Mikhail Smilovic, Tobias Stacke, Edwin Sutanudjaja, Wim Thiery, Athanasios Tsilimigkras, Yoshihide Wada, Niko Wanders, and Tokuta Yokohata
Geosci. Model Dev., 18, 2409–2425, https://doi.org/10.5194/gmd-18-2409-2025, https://doi.org/10.5194/gmd-18-2409-2025, 2025
Short summary
Short summary
Global water models contribute to the evaluation of important natural and societal issues but are – as all models – simplified representation of reality. So, there are many ways to calculate the water fluxes and storages. This paper presents a visualization of 16 global water models using a standardized visualization and the pathway towards this common understanding. Next to academic education purposes, we envisage that these diagrams will help researchers, model developers, and data users.
Monica Coppo Frias, Suxia Liu, Xingguo Mo, Karina Nielsen, Heidi Ranndal, Liguang Jiang, Jun Ma, and Peter Bauer-Gottwein
Hydrol. Earth Syst. Sci., 27, 1011–1032, https://doi.org/10.5194/hess-27-1011-2023, https://doi.org/10.5194/hess-27-1011-2023, 2023
Short summary
Short summary
This paper uses remote sensing data from ICESat-2 to calibrate a 1D hydraulic model. With the model, we can make estimations of discharge and water surface elevation, which are important indicators in flooding risk assessment. ICESat-2 data give an added value, thanks to the 0.7 m resolution, which allows the measurement of narrow river streams. In addition, ICESat-2 provides measurements on the river dry portion geometry that can be included in the model.
Youjiang Shen, Dedi Liu, Liguang Jiang, Karina Nielsen, Jiabo Yin, Jun Liu, and Peter Bauer-Gottwein
Earth Syst. Sci. Data, 14, 5671–5694, https://doi.org/10.5194/essd-14-5671-2022, https://doi.org/10.5194/essd-14-5671-2022, 2022
Short summary
Short summary
A data gap of 338 Chinese reservoirs with their surface water area (SWA), water surface elevation (WSE), and reservoir water storage change (RWSC) during 2010–2021. Validation against the in situ observations of 93 reservoirs indicates the relatively high accuracy and reliability of the datasets. The unique and novel remotely sensed dataset would benefit studies involving many aspects (e.g., hydrological models, water resources related studies, and more).
Zongjia Zhang, Jun Liang, Yujue Zhou, Zhejun Huang, Jie Jiang, Junguo Liu, and Lili Yang
Nat. Hazards Earth Syst. Sci., 22, 4139–4165, https://doi.org/10.5194/nhess-22-4139-2022, https://doi.org/10.5194/nhess-22-4139-2022, 2022
Short summary
Short summary
An innovative multi-strategy-mode waterlogging-prediction framework for predicting waterlogging depth is proposed in the paper. The framework selects eight regression algorithms for comparison and tests the prediction accuracy and robustness of the model under different prediction strategies. Ultimately, the accuracy of predicting water depth after 30 min can exceed 86.1 %. This can aid decision-making in terms of issuing early warning information and determining emergency responses in advance.
Hanqin Tian, Zihao Bian, Hao Shi, Xiaoyu Qin, Naiqing Pan, Chaoqun Lu, Shufen Pan, Francesco N. Tubiello, Jinfeng Chang, Giulia Conchedda, Junguo Liu, Nathaniel Mueller, Kazuya Nishina, Rongting Xu, Jia Yang, Liangzhi You, and Bowen Zhang
Earth Syst. Sci. Data, 14, 4551–4568, https://doi.org/10.5194/essd-14-4551-2022, https://doi.org/10.5194/essd-14-4551-2022, 2022
Short summary
Short summary
Nitrogen is one of the critical nutrients for growth. Evaluating the change in nitrogen inputs due to human activity is necessary for nutrient management and pollution control. In this study, we generated a historical dataset of nitrogen input to land at the global scale. This dataset consists of nitrogen fertilizer, manure, and atmospheric deposition inputs to cropland, pasture, and rangeland at high resolution from 1860 to 2019.
Liguang Jiang, Silja Westphal Christensen, and Peter Bauer-Gottwein
Hydrol. Earth Syst. Sci., 25, 6359–6379, https://doi.org/10.5194/hess-25-6359-2021, https://doi.org/10.5194/hess-25-6359-2021, 2021
Short summary
Short summary
River roughness and geometry are essential to hydraulic river models. However, measurements of these quantities are not available in most rivers globally. Nevertheless, simultaneous calibration of channel geometric parameters and roughness is difficult as they compensate for each other. This study introduces an alternative approach of parameterization and calibration that reduces parameter correlations by combining cross-section geometry and roughness into a conveyance parameter.
Camelia-Eliza Telteu, Hannes Müller Schmied, Wim Thiery, Guoyong Leng, Peter Burek, Xingcai Liu, Julien Eric Stanislas Boulange, Lauren Seaby Andersen, Manolis Grillakis, Simon Newland Gosling, Yusuke Satoh, Oldrich Rakovec, Tobias Stacke, Jinfeng Chang, Niko Wanders, Harsh Lovekumar Shah, Tim Trautmann, Ganquan Mao, Naota Hanasaki, Aristeidis Koutroulis, Yadu Pokhrel, Luis Samaniego, Yoshihide Wada, Vimal Mishra, Junguo Liu, Petra Döll, Fang Zhao, Anne Gädeke, Sam S. Rabin, and Florian Herz
Geosci. Model Dev., 14, 3843–3878, https://doi.org/10.5194/gmd-14-3843-2021, https://doi.org/10.5194/gmd-14-3843-2021, 2021
Short summary
Short summary
We analyse water storage compartments, water flows, and human water use sectors included in 16 global water models that provide simulations for the Inter-Sectoral Impact Model Intercomparison Project phase 2b. We develop a standard writing style for the model equations. We conclude that even though hydrologic processes are often based on similar equations, in the end these equations have been adjusted, or the models have used different values for specific parameters or specific variables.
Cecile M. M. Kittel, Liguang Jiang, Christian Tøttrup, and Peter Bauer-Gottwein
Hydrol. Earth Syst. Sci., 25, 333–357, https://doi.org/10.5194/hess-25-333-2021, https://doi.org/10.5194/hess-25-333-2021, 2021
Short summary
Short summary
In poorly instrumented catchments, satellite altimetry offers a unique possibility to obtain water level observations. Improvements in instrument design have increased the capabilities of altimeters to observe inland water bodies, including rivers. In this study, we demonstrate how a dense Sentinel-3 water surface elevation monitoring network can be established at catchment scale using publicly accessible processing platforms. The network can serve as a useful supplement to ground observations.
Cited articles
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017.
Addor, N., Nearing, G., Prieto, C., Newman, A., Le Vine, N., and Clark, M.
P.: A ranking of hydrological signatures based on their predictability in
space, Water Resour. Res., 54, 8792–8812, 2018.
Addor, N., Do, H. X., Alvarez-Garreton, C., Coxon, G., Fowler, K., and
Mendoza, P. A.: Large-sample hydrology: recent progress, guidelines for new
datasets and grand challenges, Hydrolog. Sci. J., 65, 712–725,
2020.
Alvarez-Garreton, C., Mendoza, P. A., Boisier, J. P., Addor, N., Galleguillos, M., Zambrano-Bigiarini, M., Lara, A., Puelma, C., Cortes, G., Garreaud, R., McPhee, J., and Ayala, A.: The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset, Hydrol. Earth Syst. Sci., 22, 5817–5846, https://doi.org/10.5194/hess-22-5817-2018, 2018.
Baker, D. B., Richards, R. P., Loftus, T. T., and Kramer, J. W.: A new
flashiness index: Characteristics and applications to midwestern rivers and
streams, J. Am. Water Resour. As., 40,
503–522, 2004.
Barichivich, J., Gloor, E., Peylin, P., Brienen, R. J. W., Schöngart,
J., Espinoza, J. C., and Pattnayak, K. C.: Recent intensification of Amazon
flooding extremes driven by strengthened Walker circulation, Sci.
Adv., 4, eaat8785, 10.1126/sciadv.aat8785, 2018.
Botter, G., Basso, S., Rodriguez-Iturbe, I., and Rinaldo, A.: Resilience of
river flow regimes, P. Natl. Acad. Scie. USA, 110,
12925–12930, https://doi.org/10.1073/pnas.1311920110, 2013.
Brouziyne, Y., De Girolamo, A. M., Aboubdillah, A., Benaabidate, L.,
Bouchaou, L., and Chehbouni, A.: Modeling alterations in flow regimes under
changing climate in a Mediterranean watershed: An analysis of
ecologically-relevant hydrological indicators, Ecol. Inform., 61,
101219, https://doi.org/10.1016/j.ecoinf.2021.101219, 2021.
Chagas, V. B. P., Chaffe, P. L. B., Addor, N., Fan, F. M., Fleischmann, A. S., Paiva, R. C. D., and Siqueira, V. A.: CAMELS-BR: hydrometeorological time series and landscape attributes for 897 catchments in Brazil, Earth Syst. Sci. Data, 12, 2075–2096, https://doi.org/10.5194/essd-12-2075-2020, 2020.
Chen, X., Quan, Q., Zhang, K., and Wei, J.: Spatiotemporal characteristics
and attribution of dry/wet conditions in the Weihe River Basin within a
typical monsoon transition zone of East Asia over the recent 547 years,
Environ. Model. Softw., 143, 105116,
https://doi.org/10.1016/j.envsoft.2021.105116, 2021.
Chen, X., Jiang, L., Luo, Y., and Liu, J.: A global streamflow indices time
series dataset, Science Data Bank [dataset],
https://doi.org/10.57760/sciencedb.07227, 2023a.
Chen, X., Zhang, K., Luo, Y., Zhang, Q., Zhou, J., Fan, Y., Huang, P., Yao,
C., Chao, L., and Bao, H.: A distributed hydrological model for semi-humid
watersheds with a thick unsaturated zone under strong anthropogenic impacts:
a case study in Haihe River Basin, J. Hydrol., 623, 129765,
https://doi.org/10.1016/j.jhydrol.2023.129765, 2023b.
Cheng, Y., Sang, Y., Wang, Z., Guo, Y., and Tang, Y.: Effects of Rainfall
and Underlying Surface on Flood Recession–The Upper Huaihe River Basin
Case, Int. J. Disast. Risk Sc., 12, 111–120,
10.1007/s13753-020-00310-w, 2021.
Clark, M. P., Rupp, D. E., Woods, R. A., Tromp-van Meerveld, H., Peters,
N., and Freer, J.: Consistency between hydrological models and field
observations: linking processes at the hillslope scale to hydrological
responses at the watershed scale, Hydrol. Process., 23, 311–319, 2009.
Clausen, B. and Biggs, B.: Flow variables for ecological studies in
temperate streams: groupings based on covariance, J. Hydrol., 237,
184–197, 2000.
Colls, M., Timoner, X., Font, C., Sabater, S., and Acuña, V.: Effects of
Duration, Frequency, and Severity of the Non-flow Period on Stream Biofilm
Metabolism, Ecosystems, 22, 1393–1405, https://doi.org/10.1007/s10021-019-00345-1, 2019.
Court, A.: Measures of streamflow timing, J. Geophys. Res.,
67, 4335–4339, 1962.
Coxon, G., Addor, N., Bloomfield, J. P., Freer, J., Fry, M., Hannaford, J., Howden, N. J. K., Lane, R., Lewis, M., Robinson, E. L., Wagener, T., and Woods, R.: CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain, Earth Syst. Sci. Data, 12, 2459–2483, https://doi.org/10.5194/essd-12-2459-2020, 2020.
Crochemore, L., Isberg, K., Pimentel, R., Pineda, L., Hasan, A., and
Arheimer, B.: Lessons learnt from checking the quality of openly accessible
river flow data worldwide, Hydrolog. Sci. J., 65, 699–711,
https://doi.org/10.1080/02626667.2019.1659509, 2020.
Cushman, R. M.: Review of Ecological Effects of Rapidly Varying Flows
Downstream from Hydroelectric Facilities, North Am. J.
Fish. Manage., 5, 330–339, https://doi.org/10.1577/1548-8659(1985)5<330:ROEEOR>2.0.CO;2, 1985.
Delaigue, O., Brigode, P., Andréassian, V., Perrin, C., Etchevers, P.,
Soubeyroux, J.-M., Janet, B., and Nans, A.: CAMELS-FR: A large sample
hydroclimatic dataset for France to explore hydrological diversity and
support model benchmarking, IAHS-2022 Scientific Assembly, Montpellier, France, 29 May–3 June 2022, IAHS2022-521, https://doi.org/10.5194/iahs2022-521, 2022.
Do, H. X., Westra, S., and Leonard, M.: A global-scale investigation of
trends in annual maximum streamflow, J. Hydrol., 552, 28–43,
https://doi.org/10.1016/j.jhydrol.2017.06.015, 2017.
Do, H. X., Gudmundsson, L., Leonard, M., and Westra, S.: The Global Streamflow Indices and Metadata Archive (GSIM) – Part 1: The production of a daily streamflow archive and metadata, Earth Syst. Sci. Data, 10, 765–785, https://doi.org/10.5194/essd-10-765-2018, 2018.
Ebeling, P., Kumar, R., Lutz, S. R., Nguyen, T., Sarrazin, F., Weber, M., Büttner, O., Attinger, S., and Musolff, A.: QUADICA: water QUAlity, DIscharge and Catchment Attributes for large-sample studies in Germany, Earth Syst. Sci. Data, 14, 3715–3741, https://doi.org/10.5194/essd-14-3715-2022, 2022.
Estrany, J., Garcia, C., and Batalla, R. J.: Hydrological response of a
small mediterranean agricultural catchment, J. Hydrol., 380,
180–190, 2010.
Euser, T., Winsemius, H. C., Hrachowitz, M., Fenicia, F., Uhlenbrook, S., and Savenije, H. H. G.: A framework to assess the realism of model structures using hydrological signatures, Hydrol. Earth Syst. Sci., 17, 1893–1912, https://doi.org/10.5194/hess-17-1893-2013, 2013.
Fowler, K. J. A., Acharya, S. C., Addor, N., Chou, C., and Peel, M. C.: CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia, Earth Syst. Sci. Data, 13, 3847–3867, https://doi.org/10.5194/essd-13-3847-2021, 2021.
Gehrke, P. C., Brown, P., Schiller, C. B., Moffatt, D. B., and Bruce, A. M.:
River regulation and fish communities in the Murray-Darling river system,
Australia, Regul. River., 11, 363–375,
https://doi.org/10.1002/rrr.3450110310, 1995.
Gnann, S. J., McMillan, H. K., Woods, R. A., and Howden, N. J. K.: Including
Regional Knowledge Improves Baseflow Signature Predictions in Large Sample
Hydrology, Water Resour. Res., 57, e2020WR028354,
https://doi.org/10.1029/2020WR028354, 2021a.
Gnann, S. J., Coxon, G., Woods, R. A., Howden, N. J., and McMillan, H. K.:
TOSSH: A toolbox for streamflow signatures in hydrology, Environ.
Model. Softw., 138, 104983, https://doi.org/10.1016/j.envsoft.2021.104983, 2021b.
Gocic, M. and Trajkovic, S.: Analysis of changes in meteorological variables
using Mann-Kendall and Sen's slope estimator statistical tests in Serbia,
Global Planet. Change, 100, 172–182,
https://doi.org/10.1016/j.gloplacha.2012.10.014, 2013.
Goeking, S. A. and Tarboton, D. G.: Variable Streamflow Response to Forest
Disturbance in the Western US: A Large-Sample Hydrology Approach, Water
Resour. Res., 58, e2021WR031575,
https://doi.org/10.1029/2021WR031575, 2022.
Gudmundsson, L., Do, H. X., Leonard, M., and Westra, S.: The Global Streamflow Indices and Metadata Archive (GSIM) – Part 2: Quality control, time-series indices and homogeneity assessment, Earth Syst. Sci. Data, 10, 787–804, https://doi.org/10.5194/essd-10-787-2018, 2018.
Gudmundsson, L., Leonard, M., Do, H. X., Westra, S., and Seneviratne, S. I.:
Observed trends in global indicators of mean and extreme streamflow,
Geophys. Res. Lett., 46, 756–766, 2019.
Gudmundsson, L., Boulange, J., Do, H. X., Gosling, S. N., Grillakis, M. G.,
Koutroulis, A. G., Leonard, M., Liu, J., Müller Schmied, H.,
Papadimitriou, L., Pokhrel, Y., Seneviratne, S. I., Satoh, Y., Thiery, W.,
Westra, S., Zhang, X., and Zhao, F.: Globally observed trends in mean and
extreme river flow attributed to climate change, Science, 371, 1159–1162,
https://doi.org/10.1126/science.aba3996, 2021.
Gupta, H. V., Perrin, C., Blöschl, G., Montanari, A., Kumar, R., Clark, M., and Andréassian, V.: Large-sample hydrology: a need to balance depth with breadth, Hydrol. Earth Syst. Sci., 18, 463–477, https://doi.org/10.5194/hess-18-463-2014, 2014.
Harmon, B., Logan, L., Spiese, C., and Rahrig, R.: Flow alterations in
rivers due to unconventional oil and gas development in the Ohio River
basin, Sci. Total Environ., 856, 159126,
https://doi.org/10.1016/j.scitotenv.2022.159126, 2022.
Horner, I.: Design and evaluation of hydrological signatures for the
diagnostic and improvement of a process-based distributed hydrological
model, Université Grenoble Alpes, 2020.
Jacobson, R., Bouska, K., Bulliner, E., Lindner, G., and Paukert, C.:
Geomorphic Controls on Floodplain Connectivity, Ecosystem Services, and
Sensitivity to Climate Change: An Example From the Lower Missouri River,
Water Resour. Res., 58, e2021WR031204, https://doi.org/10.1029/2021WR031204, 2022.
Klingler, C., Schulz, K., and Herrnegger, M.: LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe, Earth Syst. Sci. Data, 13, 4529–4565, https://doi.org/10.5194/essd-13-4529-2021, 2021.
Lane, E. W. and Lei, K.: Stream Flow Variability,
T. Am. Soc. Civ. Eng., 115, 1084–1098,
https://doi.org/10.1061/TACEAT.0006394, 1950.
Lane, R. A. and Kay, A. L.: Climate Change Impact on the Magnitude and
Timing of Hydrological Extremes Across Great Britain, Front. Water, 3, 684982,
https://doi.org/10.3389/frwa.2021.684982, 2021.
Lane, R. A., Coxon, G., Freer, J., Seibert, J., and Wagener, T.: A large-sample investigation into uncertain climate change impacts on high flows across Great Britain, Hydrol. Earth Syst. Sci., 26, 5535–5554, https://doi.org/10.5194/hess-26-5535-2022, 2022.
McMillan, H., Westerberg, I., and Branger, F.: Five guidelines for selecting
hydrological signatures, Hydrol. Process., 31, 4757–4761, 2017.
Nearing, G. S., Kratzert, F., Sampson, A. K., Pelissier, C. S., Klotz, D.,
Frame, J. M., Prieto, C., and Gupta, H. V.: What Role Does Hydrological
Science Play in the Age of Machine Learning?, Water Resour. Res., 57,
e2020WR028091, https://doi.org/10.1029/2020WR028091, 2021.
Olden, J. D. and Poff, N. L.: Redundancy and the choice of hydrologic
indices for characterizing streamflow regimes, River Res.
Appl., 19, 101–121, https://doi.org/10.1002/rra.700,
2003.
Paine, L.: River Cultures in World History–Rescuing a Neglected Resource,
Fudan Journal of the Humanities and Social Sciences, 12, 457–472,
https://doi.org/10.1007/s40647-018-0220-4, 2019.
Palmer, M. and Ruhi, A.: Linkages between flow regime, biota, and ecosystem
processes: Implications for river restoration, Science, 365, eaaw2087,
https://doi.org/10.1126/science.aaw2087, 2019.
Poff, N. L. and Ward, J. V.: Implications of Streamflow Variability and
Predictability for Lotic Community Structure: A Regional Analysis of
Streamflow Patterns, Can. J. Fish. Aquat. Sci., 46,
1805–1818, https://doi.org/10.1139/f89-228, 1989.
Poff, N. L., Allan, J. D., Bain, M. B., Karr, J. R., Prestegaard, K. L.,
Richter, B. D., Sparks, R. E., and Stromberg, J. C.: The natural flow
regime, BioScience, 47, 769–784, 1997.
Posavec, K., Bačani, A., and Nakić, Z.: A Visual Basic Spreadsheet
Macro for Recession Curve Analysis, Groundwater, 44, 764–767,
https://doi.org/10.1111/j.1745-6584.2006.00226.x, 2006.
Richter, B. D., Baumgartner, J. V., Powell, J., and Braun, D. P.: A Method
for Assessing Hydrologic Alteration within Ecosystems, Conserv. Biol.,
10, 1163–1174,
https://doi.org/10.1046/j.1523-1739.1996.10041163.x, 1996.
Rood, S., Mahoney, J., Reid, D., and Zilm, L.: Instream Flows and the
Decline of Riparian Cottonwoods Along the St. Mary River, Alberta, Can.
J. Bot., 73, 1250–1260, https://doi.org/10.1139/b95-136, 1995.
Safeeq, M., Grant, G. E., Lewis, S. L., and Tague, C. L.: Coupling snowpack
and groundwater dynamics to interpret historical streamflow trends in the
western United States, Hydrol. Process., 27, 655–668, 2013.
Sauquet, E., Shanafield, M., Hammond, J. C., Sefton, C., Leigh, C., and
Datry, T.: Classification and trends in intermittent river flow regimes in
Australia, northwestern Europe and USA: A global perspective, J.
Hydrol., 597, 126170,
https://doi.org/10.1016/j.jhydrol.2021.126170, 2021.
Sawicz, K., Wagener, T., Sivapalan, M., Troch, P. A., and Carrillo, G.: Catchment classification: empirical analysis of hydrologic similarity based on catchment function in the eastern USA, Hydrol. Earth Syst. Sci., 15, 2895–2911, https://doi.org/10.5194/hess-15-2895-2011, 2011.
Shih, S.-S., Liu, C.-H., and Ning, J.-H.: In-river weir effects on the
alteration of flow regime and regarding structural stream habitat, J.
Hydrol., 615, 128670, https://doi.org/10.1016/j.jhydrol.2022.128670, 2022.
Singh, S. K., Pahlow, M., Booker, D. J., Shankar, U., and Chamorro, A.:
Towards baseflow index characterisation at national scale in New Zealand,
J. Hydrol., 568, 646–657, 2019.
Sun, A. Y., Jiang, P., Mudunuru, M. K., and Chen, X.: Explore
Spatio-Temporal Learning of Large Sample Hydrology Using Graph Neural
Networks, Water Resour. Res., 57, e2021WR030394,
https://doi.org/10.1029/2021WR030394, 2021.
The Nature Conservancy: Indicators of Hydrologic Alteration Version 7.1
User's Manual, 2009.
Tonkin, J. D., Merritt, D., Olden, J. D., Reynolds, L. V., and Lytle, D. A.:
Flow regime alteration degrades ecological networks in riparian ecosystems,
Nat. Ecol. Evol., 2, 86–93, 2018.
Torabi Haghighi, A., Yaraghi, N., Sönmez, M. E., Darabi, H., Kum, G.,
Çelebi, A., and Kløve, B.: An index-based approach for assessment of
upstream-downstream flow regime alteration, J. Hydrol., 600,
126697, https://doi.org/10.1016/j.jhydrol.2021.126697, 2021.
Tramblay, Y., Rouché, N., Paturel, J.-E., Mahé, G., Boyer, J.-F., Amoussou, E., Bodian, A., Dacosta, H., Dakhlaoui, H., Dezetter, A., Hughes, D., Hanich, L., Peugeot, C., Tshimanga, R., and Lachassagne, P.: ADHI: the African Database of Hydrometric Indices (1950–2018), Earth Syst. Sci. Data, 13, 1547–1560, https://doi.org/10.5194/essd-13-1547-2021, 2021.
Troin, M., Martel, J.-L., Arsenault, R., and Brissette, F.: Large-sample
study of uncertainty of hydrological model components over North America,
J. Hydrol., 609, 127766,
https://doi.org/10.1016/j.jhydrol.2022.127766, 2022.
UKIH: Institute of Hydrology: Low Flow Studies Report No 3, Institute of
Hydrology, Wallingford, UK, 1980.
Wasko, C., Nathan, R., and Peel, M. C.: Trends in global flood and
streamflow timing based on local water year, Water Resour. Res., 56,
e2020WR027233, https://doi.org/10.1029/2020WR027233, 2020.
Westerberg, I. K. and McMillan, H. K.: Uncertainty in hydrological signatures, Hydrol. Earth Syst. Sci., 19, 3951–3968, https://doi.org/10.5194/hess-19-3951-2015, 2015.
Worku, F. F., Werner, M., Wright, N., van der Zaag, P., and Demissie, S. S.: Flow regime change in an endorheic basin in southern Ethiopia, Hydrol. Earth Syst. Sci., 18, 3837–3853, https://doi.org/10.5194/hess-18-3837-2014, 2014.
Yang, Y., Roderick, M. L., Yang, D., Wang, Z., Ruan, F., McVicar, T. R.,
Zhang, S., and Beck, H. E.: Streamflow stationarity in a changing world,
Environ. Res. Lett., 16, 064096, https://doi.org/10.1088/1748-9326/ac08c1, 2021.
Yin, J., Gentine, P., Zhou, S., Sullivan, S. C., Wang, R., Zhang, Y., and
Guo, S.: Large increase in global storm runoff extremes driven by climate
and anthropogenic changes, Nat. Commun., 9, 4389,
10.1038/s41467-018-06765-2, 2018.
Short summary
River flow is experiencing changes under the impacts of climate change and human activities. For example, flood events are occurring more often and are more destructive in many places worldwide. To deal with such issues, hydrologists endeavor to understand the features of extreme events as well as other hydrological changes. One key approach is analyzing flow characteristics, represented by hydrological indices. Building such a comprehensive global large-sample dataset is essential.
River flow is experiencing changes under the impacts of climate change and human activities. For...
Altmetrics
Final-revised paper
Preprint