Articles | Volume 26, issue 3
https://doi.org/10.5194/hess-26-589-2022
© Author(s) 2022. This work is distributed under
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the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-26-589-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Feb 2022
Research article |
| 07 Feb 2022
| 07 Feb 2022
Drivers of drought-induced shifts in the water balance through a Budyko approach
Blue Forest Conservation, Sacramento, CA, USA
Department of Civil and Environmental Engineering, University of California, Berkeley, CA 94720, USA
Francesco Avanzi
CIMA Research Foundation, via Armando Magliotto 2, 17100, Savona, Italy
Steven D. Glaser
Department of Civil and Environmental Engineering, University of California, Berkeley, CA 94720, USA
Roger C. Bales
Department of Civil and Environmental Engineering, University of California, Berkeley, CA 94720, USA
Sierra Nevada Research Institute, University of California, Merced, CA, USA
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Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-156, https://doi.org/10.5194/essd-2025-156, 2025
Revised manuscript accepted for ESSD
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Accurate rainfall data is essential, yet measuring daily precipitation worldwide is challenging. This research presents HYdroclimatic PERformance-enhanced Precipitation (HYPER-P), a dataset combining satellite, ground, and reanalysis data to estimate precipitation at a 1 km scale from 2000 to 2023. HYPER-P improves accuracy, especially in areas with few rain gauges. This dataset supports scientists and decision-makers in understanding and managing water resources more effectively.
Giulia Blandini, Francesco Avanzi, Lorenzo Campo, Simone Gabellani, Kristoffer Aalstad, Manuela Girotto, Satoru Yamaguchi, Hiroyuki Hirashima, and Luca Ferraris
EGUsphere, https://doi.org/10.5194/egusphere-2025-423, https://doi.org/10.5194/egusphere-2025-423, 2025
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Reliable SWE and snow depth estimates are key for water management in snow regions. To tackle computational challenges in data assimilation, we suggest a Long Short-Term Memory neural network for operational data assimilation in snow hydrology. Once trained, it cuts computation by 70 % versus an EnKF, with a slight RMSE increase (+6 mm SWE, +6 cm snow depth). This work advances deep learning in snow hydrology, offering an efficient, scalable, and low-cost modeling framework.
Francesca Munerol, Francesco Avanzi, Eleonora Panizza, Marco Altamura, Simone Gabellani, Lara Polo, Marina Mantini, Barbara Alessandri, and Luca Ferraris
Geosci. Commun., 7, 1–15, https://doi.org/10.5194/gc-7-1-2024, https://doi.org/10.5194/gc-7-1-2024, 2024
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To contribute to advancing education in a warming climate and prepare the next generations to play their role in future societies, we designed “Water and Us”, a three-module initiative focusing on the natural and anthropogenic water cycle, climate change, and conflicts. This study aims to introduce the initiative's educational objectives, methods, and early results.
Giulia Blandini, Francesco Avanzi, Simone Gabellani, Denise Ponziani, Hervé Stevenin, Sara Ratto, Luca Ferraris, and Alberto Viglione
The Cryosphere, 17, 5317–5333, https://doi.org/10.5194/tc-17-5317-2023, https://doi.org/10.5194/tc-17-5317-2023, 2023
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Automatic snow depth data are a valuable source of information for hydrologists, but they also tend to be noisy. To maximize the value of these measurements for real-world applications, we developed an automatic procedure to differentiate snow cover from grass or bare ground data, as well as to detect random errors. This procedure can enhance snow data quality, thus providing more reliable data for snow models.
Junyan Ding, Polly Buotte, Roger Bales, Bradley Christoffersen, Rosie A. Fisher, Michael Goulden, Ryan Knox, Lara Kueppers, Jacquelyn Shuman, Chonggang Xu, and Charles D. Koven
Biogeosciences, 20, 4491–4510, https://doi.org/10.5194/bg-20-4491-2023, https://doi.org/10.5194/bg-20-4491-2023, 2023
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We used a vegetation model to investigate how the different combinations of plant rooting depths and the sensitivity of leaves and stems to drying lead to differential responses of a pine forest to drought conditions in California, USA. We found that rooting depths are the strongest control in that ecosystem. Deep roots allow trees to fully utilize the soil water during a normal year but result in prolonged depletion of soil moisture during a severe drought and hence a high tree mortality risk.
Francesco Avanzi, Simone Gabellani, Fabio Delogu, Francesco Silvestro, Flavio Pignone, Giulia Bruno, Luca Pulvirenti, Giuseppe Squicciarino, Elisabetta Fiori, Lauro Rossi, Silvia Puca, Alexander Toniazzo, Pietro Giordano, Marco Falzacappa, Sara Ratto, Hervè Stevenin, Antonio Cardillo, Matteo Fioletti, Orietta Cazzuli, Edoardo Cremonese, Umberto Morra di Cella, and Luca Ferraris
Earth Syst. Sci. Data, 15, 639–660, https://doi.org/10.5194/essd-15-639-2023, https://doi.org/10.5194/essd-15-639-2023, 2023
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Snow cover has profound implications for worldwide water supply and security, but knowledge of its amount and distribution across the landscape is still elusive. We present IT-SNOW, a reanalysis comprising daily maps of snow amount and distribution across Italy for 11 snow seasons from September 2010 to August 2021. The reanalysis was validated using satellite images and snow measurements and will provide highly needed data to manage snow water resources in a warming climate.
Giulia Bruno, Doris Duethmann, Francesco Avanzi, Lorenzo Alfieri, Andrea Libertino, and Simone Gabellani
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-416, https://doi.org/10.5194/hess-2022-416, 2022
Manuscript not accepted for further review
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Hydrological models often have issues during droughts. We used the distributed Continuum model over the Po river basin and independent datasets of streamflow (Q), evapotranspiration (ET), and storage. Continuum simulated Q well during wet years and moderate droughts. Performances declined for a severe drought and we explained this drop with an increased uncertainty in ET anomalies in human-affected croplands. These findings provide guidelines for assessments of model robustness during droughts.
Lorenzo Alfieri, Francesco Avanzi, Fabio Delogu, Simone Gabellani, Giulia Bruno, Lorenzo Campo, Andrea Libertino, Christian Massari, Angelica Tarpanelli, Dominik Rains, Diego G. Miralles, Raphael Quast, Mariette Vreugdenhil, Huan Wu, and Luca Brocca
Hydrol. Earth Syst. Sci., 26, 3921–3939, https://doi.org/10.5194/hess-26-3921-2022, https://doi.org/10.5194/hess-26-3921-2022, 2022
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This work shows advances in high-resolution satellite data for hydrology. We performed hydrological simulations for the Po River basin using various satellite products, including precipitation, evaporation, soil moisture, and snow depth. Evaporation and snow depth improved a simulation based on high-quality ground observations. Interestingly, a model calibration relying on satellite data skillfully reproduces observed discharges, paving the way to satellite-driven hydrological applications.
Francesco Avanzi, Simone Gabellani, Fabio Delogu, Francesco Silvestro, Edoardo Cremonese, Umberto Morra di Cella, Sara Ratto, and Hervé Stevenin
Geosci. Model Dev., 15, 4853–4879, https://doi.org/10.5194/gmd-15-4853-2022, https://doi.org/10.5194/gmd-15-4853-2022, 2022
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Knowing in real time how much snow and glacier ice has accumulated across the landscape has significant implications for water-resource management and flood control. This paper presents a computer model – S3M – allowing scientists and decision makers to predict snow and ice accumulation during winter and the subsequent melt during spring and summer. S3M has been employed for real-world flood forecasting since the early 2000s but is here being made open source for the first time.
Christian Massari, Francesco Avanzi, Giulia Bruno, Simone Gabellani, Daniele Penna, and Stefania Camici
Hydrol. Earth Syst. Sci., 26, 1527–1543, https://doi.org/10.5194/hess-26-1527-2022, https://doi.org/10.5194/hess-26-1527-2022, 2022
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Droughts are a creeping disaster, meaning that their onset, duration and recovery are challenging to monitor and forecast. Here, we provide further evidence of an additional challenge of droughts, i.e. the fact that the deficit in water supply during droughts is generally much more than expected based on the observed decline in precipitation. At a European scale we explain this with enhanced evapotranspiration, sustained by higher atmospheric demand for moisture during such dry periods.
Francesco Avanzi, Giulia Ercolani, Simone Gabellani, Edoardo Cremonese, Paolo Pogliotti, Gianluca Filippa, Umberto Morra di Cella, Sara Ratto, Hervè Stevenin, Marco Cauduro, and Stefano Juglair
Hydrol. Earth Syst. Sci., 25, 2109–2131, https://doi.org/10.5194/hess-25-2109-2021, https://doi.org/10.5194/hess-25-2109-2021, 2021
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Precipitation tends to increase with elevation, but the magnitude and distribution of this enhancement remain poorly understood. By leveraging over 11 000 spatially distributed, manual measurements of snow depth (snow courses) upstream of two reservoirs in the western European Alps, we show that these courses bear a characteristic signature of orographic precipitation. This opens a window of opportunity for improved modeling accuracy and, ultimately, our understanding of the water budget.
Cited articles
Abatzoglou, J. T., Redmond, K. T., and Edwards, L. M.: Classification of
regional climate variability in the state of California, J. Appl. Meteorol. Clim., 48, 1527–1541, https://doi.org/10.1175/2009JAMC2062.1, 2009. a
Ackerly, D. D., Loarie, S. R., Cornwell, W. K., Weiss, S. B., Hamilton, H.,
Branciforte, R., and Kraft, N. J.: The geography of climate change:
Implications for conservation biogeography, Div. Distrib., 16, 476–487, https://doi.org/10.1111/j.1472-4642.2010.00654.x, 2010. a
Allerup, P., Madsen, H., and Vejen, F.: Correction of precipitation based on
off-site weather information, Atmos. Res., 53, 231–250,
https://doi.org/10.1016/S0169-8095(99)00051-4, 2000. a
Alvarez-Garreton, C., Pablo Boisier, J., Garreaud, R., Seibert, J., and Vis,
M.: Progressive water deficits during multiyear droughts in basins with long
hydrological memory in Chile, Hydrol. Earth Syst. Sci., 25, 429–446, https://doi.org/10.5194/hess-25-429-2021, 2021. a
Avanzi, F., Rungee, J., Maurer, T., Bales, R., Ma, Q., Glaser, S., and Conklin, M.: Climate elasticity of evapotranspiration shifts the water balance of Mediterranean climates during multi-year droughts, Hydrol. Earth Syst. Sci., 24, 4317–4337, https://doi.org/10.5194/hess-24-4317-2020, 2020. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t
Avanzi, F., Ercolani, G., Gabellani, S., Cremonese, E., Pogliotti, P., Filippa, G., Morra Di Cella, U., Ratto, S., Stevenin, H., Cauduro, M., and Juglair, S.: Learning about precipitation lapse rates from snow course data improves water balance modeling, Hydrol. Earth Syst. Sci., 25, 2109–2131,
https://doi.org/10.5194/hess-25-2109-2021, 2021. a, b
Bales, R. C., Guo, Q., Shen, D., McConnell, J. R., Du, G., Burkhart, J. F.,
Spikes, V. B., Hanna, E., and Cappelen, J.: Annual accumulation for
Greenland updated using ice core data developed during 2000–2006 and analysis of daily coastal meteorological data, J. Geophys. Res.-Atmos., 114, D06116, https://doi.org/10.1029/2008JD011208, 2009. a
Bales, R. C., Goulden, M. L., Hunsaker, C. T., Conklin, M. H., Hartsough, P. C., O'Geen, A. T., Hopmans, J. W., and Safeeq, M.: Mechanisms controlling
the impact of multi-year drought on mountain hydrology, Scient. Rep., 8, 1–8, https://doi.org/10.1038/s41598-017-19007-0, 2018. a, b, c, d, e, f, g, h
Berghuijs, W. R., Woods, R. A., and Hrachowitz, M.: A precipitation shift from snow towards rain leads to a decrease in streamflow, Nat. Clim. Change,
4, 583–586, https://doi.org/10.1038/nclimate2246, 2014. a, b, c
Bolger, B. L., Park, Y. J., Unger, A. J., and Sudicky, E. A.: Simulating the
pre-development hydrologic conditions in the San Joaquin Valley, California,
J. Hydrol., 411, 322–330, https://doi.org/10.1016/j.jhydrol.2011.10.013, 2011. a
Brown, L. R. and Bauer, M. L.: Effects of hydrologic infrastructure on flow
regimes of California's Central Valley rivers: Implications for fish populations, River Res. Appl., 26, 751–765, https://doi.org/10.1002/rra.1293, 2010. a
Chen, X., Alimohammadi, N., and Wang, D.: Modeling interannual variability of
seasonal evaporation and storage change based on the extended Budyko framework, Water Resour. Res., 49, 6067–6078, https://doi.org/10.1002/wrcr.20493, 2013. a
Coron, L., Andréassian, V., Perrin, C., Lerat, J., Vaze, J., Bourqui, M.,
and Hendrickx, F.: Crash testing hydrological models in contrasted climate
conditions: An experiment on 216 Australian catchments, Water Resour. Res., 48, 1–17, https://doi.org/10.1029/2011WR011721, 2012. a
Dai, A.: Increasing drought under global warming in observations and models, Nat. Clim. Change, 3, 52–58, https://doi.org/10.1038/nclimate1633, 2013. a
Daly, C., Halbleib, M., Smith, J. I., Gibson, W. P., Doggett, M. K., Taylor,
G. H., Curtis, J., and Pasteris, P. P.: Physiographically sensitive mapping
of climatological temperature and precipitation across the conterminous
United States, Int. J. Climatol., 28, 2031–2064, https://doi.org/10.1002/joc.1688, 2008. a, b
Dettinger, M. D. and Cayan, D. R.: Interseasonal covariability of Sierra Nevada streamflow and San Francisco Bay salinity, J. Hydrol., 277, 164–181, https://doi.org/10.1016/S0022-1694(03)00078-7, 2003. a
Ejeta, M. Z.: Validation of predicted meteorological drought in California
using analogous orbital geometries, Hydrol. Process., 28, 3703–3713, 2013. a
Ejeta, M. Z., Arora, S. K., Kadir, T., and Yin, H.: California Central Valley
Unimpaired Flow Data, Tech. rep., California Department of Water Resources,
Sacramento, CA, available at:
https://www.waterboards.ca.gov/waterrights/water_issues/programs/bay_delta/bay_delta_plan/water_quality_control_planning/docs/sjrf_spprtinfo/dwr_2007a.pdf
(last access: 16 July 2021), 2007. a, b
EROS Data Center: National Elevatoin Dataset, available at:
https://www.usgs.gov/core-science-systems/national-geospatial-program/national-map (last access: 17 July 2021), 1999. a
Fu, B.: On the Calculation of the Evaporation from Land Surface, Chinese J. Atmos. Sci., 5, 23–31, 1981. a
Gnann, S. J., Woods, R. A., and Howden, N. J.: Is There a Baseflow Budyko
Curve?, Water Resour. Res., 55, 2838–2855, https://doi.org/10.1029/2018WR024464, 2019. a
Goulden, M. L. and Bales, R. C.: Mountain runoff vulnerability to increased
evapotranspiration with vegetation expansion, P. Natl. Acad. Sci. USA, 111, 14071–14075, https://doi.org/10.1073/pnas.1319316111, 2014. a
Goulden, M. L. and Bales, R. C.: California forest die-off linked to multi-year deep soil drying in 2012–2015 drought, Nat. Geosci., 12, 632–637, https://doi.org/10.1038/s41561-019-0388-5, 2019. a, b, c, d
Goulden, M. L., Anderson, R. G., Bales, R. C., Kelly, A. E., Meadows, M., and
Winston, G. C.: Evapotranspiration along an elevation gradient in California's Sierra Nevada, J. Geophys. Res.-Biogeo., 117, 1–13, https://doi.org/10.1029/2012JG002027, 2012. a, b
Graf, A., Klosterhalfen, A., Arriga, N., Bernhofer, C., Bogena, H., Bornet, F., Brüggemann, N., Brümmer, C., Buchmann, N., Chi, J., Chipeaux, C.,
Cremonese, E., Cuntz, M., Dušek, J., El-Madany, T. S., Fares, S., Fischer, M., Foltýnová, L., Gharun, M., Ghiasi, S., Gielen, B.,
Gottschalk, P., Grünwald, T., Heinemann, G., Heinesch, B., Heliasz, M.,
Holst, J., Hörtnagl, L., Ibrom, A., Ingwersen, J., Jurasinski, G., Klatt, J., Knohl, A., Koebsch, F., Konopka, J., Korkiakoski, M., Kowalska, N., Kremer, P., Kruijt, B., Lafont, S., Léonard, J., De Ligne, A., Longdoz, B., Loustau, D., Magliulo, V., Mammarella, I., Manca, G., Mauder, M., Migliavacca, M., Mölder, M., Neirynck, J., Ney, P., Nilsson, M., Paul-Limoges, E., Peichl, M., Pitacco, A., Poyda, A., Rebmann, C., Roland,
M., Sachs, T., Schmidt, M., Schrader, F., Siebicke, L., Šigut, L., Tuittila, E. S., Varlagin, A., Vendrame, N., Vincke, C., Völksch, I., Weber, S., Wille, C., Wizemann, H. D., Zeeman, M., and Vereecken, H.: Altered energy partitioning across terrestrial ecosystems in the European drought year 2018: Energy partitioning in the drought 2018, Philos. T. Roy. Soc. B, 375, 1810, https://doi.org/10.1098/rstb.2019.0524, 2020. a
Greve, P., Gudmundsson, L., Orlowsky, B., and Seneviratne, S. I.: A
two-parameter Budyko function to represent conditions under which
evapotranspiration exceeds precipitation, Hydrol. Earth Syst. Sci., 20, 2195–2205, https://doi.org/10.5194/hess-20-2195-2016, 2016. a
Groeneveld, D. P., Baugh, W. M., Sanderson, J. S., and Cooper, D. J.: Annual
groundwater evapotranspiration mapped from single satellite scenes, J. Hydrol., 344, 146–156, https://doi.org/10.1016/j.jhydrol.2007.07.002, 2007. a
Guan, B., Waliser, D. E., Ralph, F. M., Fetzer, E. J., and Neiman, P. J.:
Hydrometeorological characteristics of rain-on-snow events associated with
atmospheric rivers, Geophys. Res. Lett., 43, 2964–2973, https://doi.org/10.1002/2016GL067978, 2016. a
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of
the mean squared error and NSE performance criteria: Implications for improving hydrological modelling, J. Hydrol., 377, 80–91,
https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009. a
Hahm, W. J., Dralle, D. N., Rempe, D. M., Bryk, A. B., Thompson, S. E., Dawson, T. E., and Dietrich, W. E.: Low Subsurface Water Storage Capacity Relative to Annual Rainfall Decouples Mediterranean Plant Productivity and Water Use From Rainfall Variability, Geophys. Res. Lett., 46, 6544–6553, https://doi.org/10.1029/2019GL083294, 2019a. a
Hahm, W. J., Rempe, D. M., Dralle, D. N., Dawson, T. E., Lovill, S. M., Bryk,
A. B., Bish, D. L., Schieber, J., and Dietrich, W. E.: Lithologically
Controlled Subsurface Critical Zone Thickness and Water Storage Capacity
Determine Regional Plant Community Composition, Water Resour. Res., 55, 3028–3055, https://doi.org/10.1029/2018WR023760, 2019b. a
Hamon, W.: Computation of direct runoff amounts from storm rainfall, Int. Assoc. Hydrolog. Sci. Publ., 63, 52–62, 1963. a
He, M., Russo, M., and Anderson, M.: Hydroclimatic characteristics of the
2012–2015 California drought from an operational perspective, Climate, 5,
1987–1992, https://doi.org/10.3390/cli5010005, 2017. a, b
Hofste, R. W., Reig, P., and Schleifer, L.: 17 Countries, Home to One-Quarter
of the World's Population, Face Extremely High Water Stress, Tech. rep.,
World Resources Institute, Washington, DC, available at:
https://www.wri.org/blog/2019/08/17-countries-home-one-quarter-world-population-face-
extremely-high-water-stress (last access: 13 October 2020), 2019. a
Hrachowitz, M. and Clark, M. P.: The complementary merits of competing modelling philosophies in hydrology, Hydrol. Earth Syst. Sci., 21, 3953–3973, https://doi.org/10.5194/hess-21-3953-2017, 2017. a
Huang, G. and Kadir, T.: Estimates of Natural and Unimpaired Flows for the
Central Valley of California: Water Years 1922–2014, Tech. rep., California
Department of Water Resources, Sacramento, CA, available at:
https://www.waterboards.ca.gov/waterrights/water_issues/programs/bay_delta/california_waterfix/exhibits/docs/petitioners_exhibit/dwr/part2_rebuttal/dwr_1384.pdf
(last access: 16 July 2021), 2016. a, b
Huang, S., Li, P., Huang, Q., Leng, G., Hou, B., and Ma, L.: The propagation
from meteorological to hydrological drought and its potential influence factors, J. Hydrol., 547, 184–195, https://doi.org/10.1016/j.jhydrol.2017.01.041, 2017. a
Ishida, K., Gorguner, M., Ercan, A., Trinh, T., and Kavvas, M. L.: Trend
analysis of watershed-scale precipitation over Northern California by means of dynamically-downscaled CMIP5 future climate projections, Sci. Total Environ., 592, 12–24, https://doi.org/10.1016/j.scitotenv.2017.03.086, 2017. a
Jaramillo, F., Cory, N., Arheimer, B., Laudon, H., Van Der Velde, Y., Hasper,
T. B., Teutschbein, C., and Uddling, J.: Dominant effect of increasing forest biomass on evapotranspiration: Interpretations of movement in Budyko space, Hydrol. Earth Syst. Sci., 22, 567–580, https://doi.org/10.5194/hess-22-567-2018, 2018. a, b, c, d
Kirchner, J. W.: Catchments as simple dynamical systems: Catchment
characterization, rainfall-runoff modeling, and doing hydrology backward,
Water Resour. Res., 45, 2429, https://doi.org/10.1029/2008WR006912, 2009. a
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube
basin under an ensemble of climate change scenarios, J. Hydrol., 424–425, 264–277, https://doi.org/10.1016/j.jhydrol.2012.01.011, 2012. a
Li, D., Pan, M., Cong, Z., Zhang, L., and Wood, E.: Vegetation control on water and energy balance within the Budyko framework, Water Resour. Res., 49, 969–976, https://doi.org/10.1002/wrcr.20107, 2013. a, b
Li, Y., Liu, C., Yu, W., Tian, D., and Bai, P.: Response of streamflow to
environmental changes: A Budyko-type analysis based on 144 river basins over
China, Sci. Total Environ., 664, 824–833, https://doi.org/10.1016/j.scitotenv.2019.02.011, 2019. a
Liu, J., Zhang, Q., Singh, V. P., and Shi, P.: Contribution of multiple
climatic variables and human activities to streamflow changes across China,
J. Hydrol., 545, 145–162, https://doi.org/10.1016/j.jhydrol.2016.12.016, 2017. a
Lundquist, J. D., Hughes, M., Henn, B., Gutmann, E. D., Livneh, B., Dozier, J., and Neiman, P.: High-elevation precipitation patterns: Using snow
measurements to assess daily gridded datasets across the Sierra Nevada, California, J. Hydrometeorol., 16, 1773–1792, https://doi.org/10.1175/JHM-D-15-0019.1, 2015. a
Ma, Y., Zhang, Y., Yang, D., and Farhan, S. B.: Precipitation bias variability versus various gauges under different climatic conditions over the Third Pole Environment (TPE) region, Int. J. Climatol., 35, 1201–1211, https://doi.org/10.1002/joc.4045, 2015. a
Masih, I., Maskey, S., Mussá, F. E., and Trambauer, P.: A review of droughts on the African continent: A geospatial and long-term perspective, Hydrol. Earth Syst. Sci., 18, 3635–3649, https://doi.org/10.5194/hess-18-3635-2014, 2014. a
Maskey, S. and Trambauer, P.: Hydrological Modeling for Drought Assessment,
Elsevier Inc., Delft, the Netherlands, https://doi.org/10.1016/B978-0-12-394846-5.00010-2, 2015. a
Mastrotheodoros, T., Pappas, C., Molnar, P., Burlando, P., Manoli, G., Parajka, J., Rigon, R., Szeles, B., Bottazzi, M., Hadjidoukas, P., and Fatichi, S.: More green and less blue water in the Alps during warmer summers, Nat. Clim. Change, 10, 155–161, https://doi.org/10.1038/s41558-019-0676-5, 2020. a, b
McVicar, T. R., Roderick, M. L., Donohue, R. J., and Van Niel, T. G.: Less
bluster ahead? ecohydrological implications of global trends of terrestrial
near-surface wind speeds, Ecohydrology, 5, 381–388, https://doi.org/10.1002/eco.1298,
2012. a
Mernild, S. H., Hanna, E., McConnell, J. R., Sigl, M., Beckerman, A. P., Yde,
J. C., Cappelen, J., Malmros, J. K., and Steffen, K.: Greenland precipitation trends in a long-term instrumental climate context (1890–2012): evaluation of coastal and ice core records, Int. J. Climatol., 35, 303–320, https://doi.org/10.1002/joc.3986, 2015. a
Moussa, R. and Lhomme, J. P.: The Budyko functions under non-steady-state
conditions, Hydrol. Earth Syst. Sci., 20, 4867–4879,
https://doi.org/10.5194/hess-20-4867-2016, 2016. a
Ning, T., Li, Z., and Liu, W.: Vegetation dynamics and climate seasonality
jointly control the interannual catchment water balance in the Loess Plateau
under the Budyko framework, Hydrol. Earth Syst. Sci., 21, 1515–1526, https://doi.org/10.5194/hess-21-1515-2017, 2017. a
Ning, T., Zhou, S., Chang, F., Shen, H., Li, Z., and Liu, W.: Interaction of
vegetation, climate and topography on evapotranspiration modelling at different time scales within the Budyko framework, Agr. Forest Meteorol., 275, 59–68, https://doi.org/10.1016/j.agrformet.2019.05.001, 2019. a, b, c, d, e
Ning, T., Li, Z., Feng, Q., Chen, W., and Li, Z.: Effects of forest cover change on catchment evapotranspiration variation in China, Hydrol. Process., 34, 2219–2228, https://doi.org/10.1002/hyp.13719, 2020. a
O'Grady, A. P., Carter, J. L., and Bruce, J.: Can we predict groundwater
discharge from terrestrial ecosystems using existing eco-hydrological concepts?, Hydrol. Earth Syst. Sci., 15, 3731–3739, https://doi.org/10.5194/hess-15-3731-2011, 2011. a
Oroza, C. A., Bales, R. C., Stacy, E. M., Zheng, Z., and Glaser, S. D.:
Long-Term Variability of Soil Moisture in the Southern Sierra: Measurement
and Prediction, Vadose Zone J., 17, 170178, https://doi.org/10.2136/vzj2017.10.0178, 2018. a, b
Oudin, L., Andréassian, V., Lerat, J., and Michel, C.: Has land cover a
significant impact on mean annual streamflow? An international assessment
using 1508 catchments, J. Hydrol., 357, 303–316, https://doi.org/10.1016/j.jhydrol.2008.05.021, 2008. a
Peterson, T. J., Saft, M., Peel, M., and John, A.: Watersheds may not recover
from drought, Science, 372, 745–749, https://doi.org/10.1126/science.abd5085, 2021. a
Petheram, C., Potter, N., Vaze, J., Chiew, F., and Zhang, L.: Towards better
understanding of changes in rainfall-runoff relationships during the recent
drought in south-eastern Australia, in: MODSIM 2011 – 19th International
Congress on Modelling and Simulation – Sustaining Our Future: Understanding
and Living with Uncertainty, December 2011, 3622–3628, available at: https://www.mssanz.org.au/modsim2011/I6/petheram.pdf (last access: 9 October 2020), 2011. a, b
Pike, J. G.: The estimation of annual run-off from meteorological data in a
tropical climate, J. Hydrol., 2, 116–123, https://doi.org/10.1016/0022-1694(64)90022-8, 1964. a, b
Potter, N. J., Petheram, C., and Zhang, L.: Sensitivity of streamflow to
rainfall and temperature in south-eastern Australia during the Millennium
drought, in: MODSIM 2011 – 19th International Congress on Modelling and
Simulation – Sustaining Our Future: Understanding and Living with Uncertainty, November 2014, 3636–3642, available at: https://www.mssanz.org.au/modsim2011/I6/potter.pdf (last access: 10 October 2020), 2011. a, b
Raleigh, M. S. and Lundquist, J. D.: Comparing and combining SWE estimates
from the SNOW-17 model using PRISM and SWE reconstruction, Water Resour. Res., 48, 1–16, https://doi.org/10.1029/2011WR010542, 2012. a
Rana, G. and Katerji, N.: Measurement and estimation of actual evapotranspiration in the field under Mediterranean climate: A review, Eur. J. Agron., 13, 125–153, https://doi.org/10.1016/S1161-0301(00)00070-8, 2000. a
Rasmussen, R., Baker, B., Kochendorfer, J., Meyers, T., Landolt, S., Fischer,
A. P., Black, J., Thériault, J. M., Kucera, P., Gochis, D., Smith, C.,
Nitu, R., Hall, M., Ikeda, K., and Gutmann, E.: How well are we measuring
snow: The NOAA/FAA/NCAR winter precipitation test bed, B. Am. Meteorol. Soc., 93, 811–829, https://doi.org/10.1175/BAMS-D-11-00052.1, 2012. a, b
Roche, J. W., Goulden, M. L., and Bales, R. C.: Estimating evapotranspiration
change due to forest treatment and fire at the basin scale in the Sierra
Nevada, California, Ecohydrology, 11, e1978, https://doi.org/10.1002/eco.1978, 2018. a
Roderick, M. L. and Farquhar, G. D.: A simple framework for relating
variations in runoff to variations in climatic conditions and catchment
properties, Water Resour. Res., 47, 1–11, https://doi.org/10.1029/2010WR009826, 2011. a
Shao, Q., Traylen, A., and Zhang, L.: Nonparametric method for estimating the
effects of climatic and catchment characteristics on mean annual
evapotranspiration, Water Resour. Res., 48, 1–13, https://doi.org/10.1029/2010WR009610, 2012. a
Shen, Q., Cong, Z., and Lei, H.: Evaluating the impact of climate and
underlying surface change on runoff within the Budyko framework: A study
across 224 catchments in China, J. Hydrol., 554, 251–262,
https://doi.org/10.1016/j.jhydrol.2017.09.023, 2017. a, b
Tague, C. and Grant, G. E.: Groundwater dynamics mediate low-flow response to
global warming in snow-dominated alpine regions, Water Resour. Res., 45, 1–12, https://doi.org/10.1029/2008WR007179, 2009. a
Teuling, A. J., Van Loon, A. F., Seneviratne, S. I., Lehner, I., Aubinet, M.,
Heinesch, B., Bernhofer, C., Grünwald, T., Prasse, H., and Spank, U.:
Evapotranspiration amplifies European summer drought, Geophys. Res. Lett., 40, 2071–2075, https://doi.org/10.1002/grl.50495, 2013. a, b, c
Thomas, H. A.: Improved methods for national water assessment, water resources contract: WR15249270, Tech. rep., US Geological Survey,
https://doi.org/10.3133/70046351, 1981. a, b
Trenberth, K. E., Dai, A., van der Schrier, G., Jones, P. D., Barichivich, J., Briffa, K. R., and Sheffield, J.: Global warming and changes in drought,
Nat. Clim. Change, 4, 17–22, https://doi.org/10.1038/nclimate2067, 2014. a
Troch, P. A., Lahmers, T., Meira, A., Mukherjee, R., Pedersen, J. W., Roy, T., and Valdés-Pineda, R.: Catchment coevolution: A useful framework for
improving predictions of hydrological change?, Water Resour. Res., 51,
4903–4922, https://doi.org/10.1002/2015WR017032, 2015. a, b
Van Loon, A. F.: Hydrological drought explained, Wiley Interdisciplin. Rev.: Water, 2, 359–392, https://doi.org/10.1002/wat2.1085, 2015. a
Vaze, J., Post, D. A., Chiew, F. H. S., Perraud, J.-M., Viney, N. R., and Teng, J.: Climate non-stationarity – Validity of calibrated rainfall–runoff
models for use in climate change studies, J. Hydrol., 394, 447–457, https://doi.org/10.1016/j.jhydrol.2010.09.018, 2010. a
Wang, D.: Evaluating interannual water storage changes at watersheds in
Illinois based on long-term soil moisture and groundwater level data, Water
Resour. Res., 48, 1–12, https://doi.org/10.1029/2011WR010759, 2012. a
Wang, D. and Alimohammadi, N.: Responses of annual runoff, evaporation, and
storage change to climate variability at the watershed scale, Water Resour. Res., 48, W05546, https://doi.org/10.1029/2011WR011444, 2012. a
Wang, D. and Hejazi, M.: Quantifying the relative contribution of the climate
and direct human impacts on mean annual streamflow in the contiguous United
States, Water Resour. Res., 47, W00J12, https://doi.org/10.1029/2010WR010283, 2011. a
Wang, D. and Tang, Y.: A one-parameter Budyko model for water balance captures emergent behavior in darwinian hydrologic models, Geophys. Res. Lett., 41, 4569–4577, https://doi.org/10.1002/2014GL060509, 2014. a
Wang, S., Pan, M., Mu, Q., Shi, X., Mao, J., Brümmer, C., Jassal, R. S.,
Krishnan, P., Li, J., and Andrew Black, T.: Comparing evapotranspiration
from eddy covariance measurements, water budgets, remote sensing, and land
surface models over Canada, J. Hydrometeorol., 16, 1540–1560,
https://doi.org/10.1175/JHM-D-14-0189.1, 2015. a
Williams, C. A., Reichstein, M., Buchmann, N., Baldocchi, D., Beer, C.,
Schwalm, C., Wohlfahrt, G., Hasler, N., Bernhofer, C., Foken, T., Papale, D.,
Schymanski, S., and Schaefer, K.: Climate and vegetation controls on the
surface water balance: Synthesis of evapotranspiration measured across a
global network of flux towers, Water Resour. Res., 48, 1–13, https://doi.org/10.1029/2011WR011586, 2012. a
Wilson, K. B. and Baldocchi, D. D.: Seasonal and interannual variability of
energy fluxes over a broadleaved temperate deciduous forest in North America, Agr. Forest Meteorol., 100, 1–18, https://doi.org/10.1016/S0168-1923(99)00088-X, 2000. a
Woodhouse, C. A., Meko, D. M., MacDonald, G. M., Stahle, D. W., and Cook, E. R.: A 1,200-year perspective of 21st century drought in southwestern North America, P. Natl. Acad. Sci. USA, 107, 21283–21288, https://doi.org/10.1073/pnas.0911197107, 2010. a, b
Yang, D., Ishida, S., Goodison, B. E., and Gunther, T.: Bias correction of
daily precipitation measurements for Greenland, J. Geophys. Res., 104, 6171–6181, https://doi.org/10.1029/1998JD200110, 1999. a
Yang, D., Sun, F., Liu, Z., Cong, Z., Ni, G., and Lei, Z.: Analyzing spatial
and temporal variability of annual water-energy balance in nonhumid regions
of China using the Budyko hypothesis, Water Resour. Res., 43, 1–12,
https://doi.org/10.1029/2006WR005224, 2007. a, b, c
Yang, D., Shao, W., Yeh, P. J., Yang, H., Kanae, S., and Oki, T.: Impact of
vegetation coverage on regional water balance in the nonhumid regions of China, Water Resour. Res., 45, 1–13, https://doi.org/10.1029/2008WR006948, 2009. a, b
Zeff, H. B., Hamilton, A. L., Malek, K., Herman, J. D., Cohen, J. S.,
Medellin-Azuara, J., Reed, P. M., and Characklis, G. W.: California's
food-energy-water system: An open source simulation model of adaptive surface
and groundwater management in the Central Valley, Environ. Model. Softw., 141, 105052, https://doi.org/10.1016/j.envsoft.2021.105052, 2021.
a
Zhang, L., Dawes, W. R., and Walker, G. R.: Response of mean annual
evapotranspiration to vegetation changes at catchment scale, Water Resour. Res., 37, 701–708, https://doi.org/10.1029/2000WR900325, 2001. a, b, c
Zhang, L., Hickel, K., Dawes, W. R., Chiew, F. H., Western, A. W., and Briggs, P. R.: A rational function approach for estimating mean annual
evapotranspiration, Water Resour. Res., 40, 1–14, https://doi.org/10.1029/2003WR002710, 2004. a, b
Zhang, L., Potter, N., Hickel, K., Zhang, Y., and Shao, Q.: Water balance
modeling over variable time scales based on the Budyko framework – Model
development and testing, J. Hydrol., 360, 117–131, https://doi.org/10.1016/j.jhydrol.2008.07.021, 2008. a, b
Zhang, S., Yang, H., Yang, D., and Jayawardena, A. W.: Quantifying the effect
of vegetation change on the regional water balance within the Budyko framework, Geophys. Res. Lett., 43, 1140–1148, https://doi.org/10.1002/2015GL066952, 2016. a, b
Zhang, X., Dong, Q., Cheng, L., and Xia, J.: A Budyko-based framework for
quantifying the impacts of aridity index and other factors on annual runoff,
J. Hydrol., 579, 124224, https://doi.org/10.1016/J.JHYDROL.2019.124224, 2019. a
Zhang, Z., Glaser, S., Bales, R., Conklin, M., Rice, R., and Marks, D.: Insights into mountain precipitation and snowpack from a basin-scale
wireless-sensor network, Water Resour. Res., 53, 6626–6641,
https://doi.org/10.1002/2016WR018825, 2017. a
Zhou, J., Wang, Y., Su, B., Wang, A., Tao, H., Zhai, J., Kundzewicz, Z. W., and Jiang, T.: Choice of potential evapotranspiration formulas influences
drought assessment: A case study in China, Atmos. Res., 242, 104979, https://doi.org/10.1016/j.atmosres.2020.104979, 2020. a
Short summary
Predicting how much water will end up in rivers is more difficult during droughts because the relationship between precipitation and streamflow can change in unexpected ways. We differentiate between changes that are predictable based on the weather patterns and those harder to predict because they depend on the land and vegetation of a particular region. This work helps clarify why models are less accurate during droughts and helps predict how much water will be available for human use.
Predicting how much water will end up in rivers is more difficult during droughts because the...
