Articles | Volume 26, issue 13
https://doi.org/10.5194/hess-26-3589-2022
© Author(s) 2022. 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-26-3589-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Jul 2022
Research article |
| 13 Jul 2022
| 13 Jul 2022
Projecting end-of-century climate extremes and their impacts on the hydrology of a representative California watershed
Fadji Z. Maina
CORRESPONDING AUTHOR
Energy Geosciences Division, Lawrence Berkeley National Laboratory,
1 Cyclotron Road, M.S. 74R-316C, Berkeley, CA 94704, USA
now at: NASA Goddard Space Flight Center, Hydrological Sciences
Laboratory, Greenbelt, MD, USA
Alan Rhoades
Climate and Ecosystem Sciences Division, Lawrence Berkeley National
Laboratory, 1 Cyclotron Road, M.S. 74R-316C, Berkeley, CA 94704, USA
Erica R. Siirila-Woodburn
Energy Geosciences Division, Lawrence Berkeley National Laboratory,
1 Cyclotron Road, M.S. 74R-316C, Berkeley, CA 94704, USA
Peter-James Dennedy-Frank
Energy Geosciences Division, Lawrence Berkeley National Laboratory,
1 Cyclotron Road, M.S. 74R-316C, Berkeley, CA 94704, USA
Related authors
Fadji Z. Maina, Haruko M. Wainwright, Peter James Dennedy-Frank, and Erica R. Siirila-Woodburn
Hydrol. Earth Syst. Sci., 26, 3805–3823, https://doi.org/10.5194/hess-26-3805-2022, https://doi.org/10.5194/hess-26-3805-2022, 2022
Short summary
Short summary
We propose a hillslope clustering approach based on the seasonal changes in groundwater levels and test its performance by comparing it to several common clustering approaches (aridity index, topographic wetness index, elevation, land cover, and machine-learning clustering). The proposed approach is robust as it reasonably categorizes hillslopes with similar elevation, land cover, hydroclimate, land surface processes, and subsurface hydrodynamics, hence a similar hydrologic function.
Fadji Z. Maina, Erica R. Siirila-Woodburn, and Pouya Vahmani
Hydrol. Earth Syst. Sci., 24, 3451–3474, https://doi.org/10.5194/hess-24-3451-2020, https://doi.org/10.5194/hess-24-3451-2020, 2020
Short summary
Short summary
Projecting the changes in water resources under a no-analog future climate requires integrated hydrologic models. However, these models are plagued by several sources of uncertainty. A hydrologic model was forced with various resolutions of meteorological forcing (0.5 to 40.5 km) to assess its sensitivity to these inputs. We show that most hydrologic variables reveal biases that are seasonally and spatially dependent, which can have serious implications for calibration and water management.
Colin M. Zarzycki, Benjamin D. Ascher, Alan M. Rhoades, and Rachel R. McCrary
EGUsphere, https://doi.org/10.5194/egusphere-2023-3094, https://doi.org/10.5194/egusphere-2023-3094, 2024
Short summary
Short summary
We developed an automated workflow to detect rain-on-snow events, which cause flooding in the northeastern U.S., in climate data. Analyzing the Susquehanna River Basin, this technique identified known events affecting river flow. Comparing four gridded datasets revealed variations in event frequency and severity, driven by different snowmelt and runoff estimates. This highlights the need for accurate climate data in flood management and risk prediction for these compound extremes.
Qi Tang, Jean-Christophe Golaz, Luke P. Van Roekel, Mark A. Taylor, Wuyin Lin, Benjamin R. Hillman, Paul A. Ullrich, Andrew M. Bradley, Oksana Guba, Jonathan D. Wolfe, Tian Zhou, Kai Zhang, Xue Zheng, Yunyan Zhang, Meng Zhang, Mingxuan Wu, Hailong Wang, Cheng Tao, Balwinder Singh, Alan M. Rhoades, Yi Qin, Hong-Yi Li, Yan Feng, Yuying Zhang, Chengzhu Zhang, Charles S. Zender, Shaocheng Xie, Erika L. Roesler, Andrew F. Roberts, Azamat Mametjanov, Mathew E. Maltrud, Noel D. Keen, Robert L. Jacob, Christiane Jablonowski, Owen K. Hughes, Ryan M. Forsyth, Alan V. Di Vittorio, Peter M. Caldwell, Gautam Bisht, Renata B. McCoy, L. Ruby Leung, and David C. Bader
Geosci. Model Dev., 16, 3953–3995, https://doi.org/10.5194/gmd-16-3953-2023, https://doi.org/10.5194/gmd-16-3953-2023, 2023
Short summary
Short summary
High-resolution simulations are superior to low-resolution ones in capturing regional climate changes and climate extremes. However, uniformly reducing the grid size of a global Earth system model is too computationally expensive. We provide an overview of the fully coupled regionally refined model (RRM) of E3SMv2 and document a first-of-its-kind set of climate production simulations using RRM at an economic cost. The key to this success is our innovative hybrid time step method.
Zexuan Xu, Erica R. Siirila-Woodburn, Alan M. Rhoades, and Daniel Feldman
Hydrol. Earth Syst. Sci., 27, 1771–1789, https://doi.org/10.5194/hess-27-1771-2023, https://doi.org/10.5194/hess-27-1771-2023, 2023
Short summary
Short summary
The goal of this study is to understand the uncertainties of different modeling configurations for simulating hydroclimate responses in the mountainous watershed. We run a group of climate models with various configurations and evaluate them against various reference datasets. This paper integrates a climate model and a hydrology model to have a full understanding of the atmospheric-through-bedrock hydrological processes.
Fadji Z. Maina, Haruko M. Wainwright, Peter James Dennedy-Frank, and Erica R. Siirila-Woodburn
Hydrol. Earth Syst. Sci., 26, 3805–3823, https://doi.org/10.5194/hess-26-3805-2022, https://doi.org/10.5194/hess-26-3805-2022, 2022
Short summary
Short summary
We propose a hillslope clustering approach based on the seasonal changes in groundwater levels and test its performance by comparing it to several common clustering approaches (aridity index, topographic wetness index, elevation, land cover, and machine-learning clustering). The proposed approach is robust as it reasonably categorizes hillslopes with similar elevation, land cover, hydroclimate, land surface processes, and subsurface hydrodynamics, hence a similar hydrologic function.
Benjamin J. Hatchett, Alan M. Rhoades, and Daniel J. McEvoy
Nat. Hazards Earth Syst. Sci., 22, 869–890, https://doi.org/10.5194/nhess-22-869-2022, https://doi.org/10.5194/nhess-22-869-2022, 2022
Short summary
Short summary
Snow droughts, or below-average snowpack, can result from either dry conditions and/or rainfall instead of snowfall. Monitoring snow drought through time and across space is important to evaluate when snow drought onset occurred, its duration, spatial extent, and severity as well as what conditions created it or led to its termination. We present visualization techniques, including a web-based snow-drought-tracking tool, to evaluate snow droughts and assess their impacts in the western US.
Haruko M. Wainwright, Sebastian Uhlemann, Maya Franklin, Nicola Falco, Nicholas J. Bouskill, Michelle E. Newcomer, Baptiste Dafflon, Erica R. Siirila-Woodburn, Burke J. Minsley, Kenneth H. Williams, and Susan S. Hubbard
Hydrol. Earth Syst. Sci., 26, 429–444, https://doi.org/10.5194/hess-26-429-2022, https://doi.org/10.5194/hess-26-429-2022, 2022
Short summary
Short summary
This paper has developed a tractable approach for characterizing watershed heterogeneity and its relationship with key functions such as ecosystem sensitivity to droughts and nitrogen export. We have applied clustering methods to classify hillslopes into
watershed zonesthat have distinct distributions of bedrock-to-canopy properties as well as key functions. This is a powerful approach for guiding watershed experiments and sampling as well as informing hydrological and biogeochemical models.
Travis A. O'Brien, Mark D. Risser, Burlen Loring, Abdelrahman A. Elbashandy, Harinarayan Krishnan, Jeffrey Johnson, Christina M. Patricola, John P. O'Brien, Ankur Mahesh, Prabhat, Sarahí Arriaga Ramirez, Alan M. Rhoades, Alexander Charn, Héctor Inda Díaz, and William D. Collins
Geosci. Model Dev., 13, 6131–6148, https://doi.org/10.5194/gmd-13-6131-2020, https://doi.org/10.5194/gmd-13-6131-2020, 2020
Short summary
Short summary
Researchers utilize various algorithms to identify extreme weather features in climate data, and we seek to answer this question: given a
plausibleweather event detector, how does uncertainty in the detector impact scientific results? We generate a suite of statistical models that emulate expert identification of weather features. We find that the connection between El Niño and atmospheric rivers – a specific extreme weather type – depends systematically on the design of the detector.
Fadji Z. Maina, Erica R. Siirila-Woodburn, and Pouya Vahmani
Hydrol. Earth Syst. Sci., 24, 3451–3474, https://doi.org/10.5194/hess-24-3451-2020, https://doi.org/10.5194/hess-24-3451-2020, 2020
Short summary
Short summary
Projecting the changes in water resources under a no-analog future climate requires integrated hydrologic models. However, these models are plagued by several sources of uncertainty. A hydrologic model was forced with various resolutions of meteorological forcing (0.5 to 40.5 km) to assess its sensitivity to these inputs. We show that most hydrologic variables reveal biases that are seasonally and spatially dependent, which can have serious implications for calibration and water management.
Leonardus van Kampenhout, Alan M. Rhoades, Adam R. Herrington, Colin M. Zarzycki, Jan T. M. Lenaerts, William J. Sacks, and Michiel R. van den Broeke
The Cryosphere, 13, 1547–1564, https://doi.org/10.5194/tc-13-1547-2019, https://doi.org/10.5194/tc-13-1547-2019, 2019
Short summary
Short summary
A new tool is evaluated in which the climate and surface mass balance (SMB) of the Greenland ice sheet are resolved at 55 and 28 km resolution, while the rest of the globe is modelled at ~110 km. The local refinement of resolution leads to improved accumulation (SMB > 0) compared to observations; however ablation (SMB < 0) is deteriorated in some regions. This is attributed to changes in cloud cover and a reduced effectiveness of a model-specific vertical downscaling technique.
Related subject area
Subject: Global hydrology | Techniques and Approaches: Mathematical applications
Integrating process-related information into an artificial neural network for root-zone soil moisture prediction
Coherence of global hydroclimate classification systems
Design flood estimation for global river networks based on machine learning models
Attributing correlation skill of dynamical GCM precipitation forecasts to statistical ENSO teleconnection using a set-theory-based approach
The spatial extent of hydrological and landscape changes across the mountains and prairies of Canada in the Mackenzie and Nelson River basins based on data from a warm-season time window
Averaging over spatiotemporal heterogeneity substantially biases evapotranspiration rates in a mechanistic large-scale land evaporation model
Rainfall Estimates on a Gridded Network (REGEN) – a global land-based gridded dataset of daily precipitation from 1950 to 2016
A framework for deriving drought indicators from the Gravity Recovery and Climate Experiment (GRACE)
Hydrological effects of climate variability and vegetation dynamics on annual fluvial water balance in global large river basins
Spatial patterns and characteristics of flood seasonality in Europe
Derived Optimal Linear Combination Evapotranspiration (DOLCE): a global gridded synthesis ET estimate
Effects of different reference periods on drought index (SPEI) estimations from 1901 to 2014
The transformed-stationary approach: a generic and simplified methodology for non-stationary extreme value analysis
Global trends in extreme precipitation: climate models versus observations
A global water cycle reanalysis (2003–2012) merging satellite gravimetry and altimetry observations with a hydrological multi-model ensemble
A generic method for hydrological drought identification across different climate regions
Simplifying a hydrological ensemble prediction system with a backward greedy selection of members – Part 1: Optimization criteria
Simplifying a hydrological ensemble prediction system with a backward greedy selection of members – Part 2: Generalization in time and space
Roiya Souissi, Mehrez Zribi, Chiara Corbari, Marco Mancini, Sekhar Muddu, Sat Kumar Tomer, Deepti B. Upadhyaya, and Ahmad Al Bitar
Hydrol. Earth Syst. Sci., 26, 3263–3297, https://doi.org/10.5194/hess-26-3263-2022, https://doi.org/10.5194/hess-26-3263-2022, 2022
Short summary
Short summary
In this study, we investigate the combination of surface soil moisture information with process-related features, namely, evaporation efficiency, soil water index and normalized difference vegetation index, using artificial neural networks to predict root-zone soil moisture. The joint use of process-related features yielded more accurate predictions in the case of arid and semiarid conditions. However, they have no to little added value in temperate to tropical conditions.
Kathryn L. McCurley Pisarello and James W. Jawitz
Hydrol. Earth Syst. Sci., 25, 6173–6183, https://doi.org/10.5194/hess-25-6173-2021, https://doi.org/10.5194/hess-25-6173-2021, 2021
Short summary
Short summary
Climate classification systems divide the Earth into zones of similar climates. We compared the within-zone hydroclimate similarity and zone shape complexity of a suite of climate classification systems, including new ones formed in this study. The most frequently used system had high similarity but high complexity. We propose the Water-Energy Clustering framework, which also had high similarity but lower complexity. This new system is therefore proposed for future hydroclimate assessments.
Gang Zhao, Paul Bates, Jeffrey Neal, and Bo Pang
Hydrol. Earth Syst. Sci., 25, 5981–5999, https://doi.org/10.5194/hess-25-5981-2021, https://doi.org/10.5194/hess-25-5981-2021, 2021
Short summary
Short summary
Design flood estimation is a fundamental task in hydrology. We propose a machine- learning-based approach to estimate design floods anywhere on the global river network. This approach shows considerable improvement over the index-flood-based method, and the average bias in estimation is less than 18 % for 10-, 20-, 50- and 100-year design floods. This approach is a valid method to estimate design floods globally, improving our prediction of flood hazard, especially in ungauged areas.
Tongtiegang Zhao, Haoling Chen, Quanxi Shao, Tongbi Tu, Yu Tian, and Xiaohong Chen
Hydrol. Earth Syst. Sci., 25, 5717–5732, https://doi.org/10.5194/hess-25-5717-2021, https://doi.org/10.5194/hess-25-5717-2021, 2021
Short summary
Short summary
This paper develops a novel approach to attributing correlation skill of dynamical GCM forecasts to statistical El Niño–Southern Oscillation (ENSO) teleconnection using the coefficient of determination. Three cases of attribution are effectively facilitated, which are significantly positive anomaly correlation attributable to positive ENSO teleconnection, attributable to negative ENSO teleconnection and not attributable to ENSO teleconnection.
Paul H. Whitfield, Philip D. A. Kraaijenbrink, Kevin R. Shook, and John W. Pomeroy
Hydrol. Earth Syst. Sci., 25, 2513–2541, https://doi.org/10.5194/hess-25-2513-2021, https://doi.org/10.5194/hess-25-2513-2021, 2021
Short summary
Short summary
Using only warm season streamflow records, regime and change classifications were produced for ~ 400 watersheds in the Nelson and Mackenzie River basins, and trends in water storage and vegetation were detected from satellite imagery. Three areas show consistent changes: north of 60° (increased streamflow and basin greenness), in the western Boreal Plains (decreased streamflow and basin greenness), and across the Prairies (three different patterns of increased streamflow and basin wetness).
Elham Rouholahnejad Freund, Massimiliano Zappa, and James W. Kirchner
Hydrol. Earth Syst. Sci., 24, 5015–5025, https://doi.org/10.5194/hess-24-5015-2020, https://doi.org/10.5194/hess-24-5015-2020, 2020
Short summary
Short summary
Evapotranspiration (ET) is the largest flux from the land to the atmosphere and thus contributes to Earth's energy and water balance. Due to its impact on atmospheric dynamics, ET is a key driver of droughts and heatwaves. In this paper, we demonstrate how averaging over land surface heterogeneity contributes to substantial overestimates of ET fluxes. We also demonstrate how one can correct for the effects of small-scale heterogeneity without explicitly representing it in land surface models.
Steefan Contractor, Markus G. Donat, Lisa V. Alexander, Markus Ziese, Anja Meyer-Christoffer, Udo Schneider, Elke Rustemeier, Andreas Becker, Imke Durre, and Russell S. Vose
Hydrol. Earth Syst. Sci., 24, 919–943, https://doi.org/10.5194/hess-24-919-2020, https://doi.org/10.5194/hess-24-919-2020, 2020
Short summary
Short summary
This paper provides the documentation of the REGEN dataset, a global land-based daily observational precipitation dataset from 1950 to 2016 at a gridded resolution of 1° × 1°. REGEN is currently the longest-running global dataset of daily precipitation and is expected to facilitate studies looking at changes and variability in several aspects of daily precipitation distributions, extremes and measures of hydrological intensity.
Helena Gerdener, Olga Engels, and Jürgen Kusche
Hydrol. Earth Syst. Sci., 24, 227–248, https://doi.org/10.5194/hess-24-227-2020, https://doi.org/10.5194/hess-24-227-2020, 2020
Short summary
Short summary
GRACE-derived drought indicators enable us to detect hydrological droughts based on changes observed in all storages. By performing synthetic experiments, we find that droughts identified by existing and modified indicators are biased by trends and GRACE-based spatial noise. A modified version of the Zhao et al. (2017) indicator is found to be particularly robust against spatial noise and is therefore applied to real GRACE data over South Africa.
Jianyu Liu, Qiang Zhang, Vijay P. Singh, Changqing Song, Yongqiang Zhang, Peng Sun, and Xihui Gu
Hydrol. Earth Syst. Sci., 22, 4047–4060, https://doi.org/10.5194/hess-22-4047-2018, https://doi.org/10.5194/hess-22-4047-2018, 2018
Short summary
Short summary
Considering effective precipitation (Pe), the Budyko framework was extended to the annual water balance analysis. To reflect the mismatch between water supply (precipitation, P) and energy (potential evapotranspiration,
E0), a climate seasonality and asynchrony index (SAI) were proposed in terms of both phase and amplitude mismatch between P and E0.
Julia Hall and Günter Blöschl
Hydrol. Earth Syst. Sci., 22, 3883–3901, https://doi.org/10.5194/hess-22-3883-2018, https://doi.org/10.5194/hess-22-3883-2018, 2018
Sanaa Hobeichi, Gab Abramowitz, Jason Evans, and Anna Ukkola
Hydrol. Earth Syst. Sci., 22, 1317–1336, https://doi.org/10.5194/hess-22-1317-2018, https://doi.org/10.5194/hess-22-1317-2018, 2018
Short summary
Short summary
We present a new global ET dataset and associated uncertainty with monthly temporal resolution for 2000–2009 and 0.5 grid cell size. Six existing gridded ET products are combined using a weighting approach trained by observational datasets from 159 FLUXNET sites. We confirm that point-based estimates of flux towers provide information at the grid scale of these products. We also show that the weighted product performs better than 10 different existing global ET datasets in a range of metrics.
Myoung-Jin Um, Yeonjoo Kim, Daeryong Park, and Jeongbin Kim
Hydrol. Earth Syst. Sci., 21, 4989–5007, https://doi.org/10.5194/hess-21-4989-2017, https://doi.org/10.5194/hess-21-4989-2017, 2017
Short summary
Short summary
This study aims to understand how different reference periods (i.e., calibration periods) of climate data for estimating the drought index influence regional drought assessments. Specifically, we investigate the influence of different reference periods on historical drought characteristics such as trends, frequency, intensity and spatial extents using the Standard Precipitation Evapotranspiration Index (SPEI) estimated from the two widely used global datasets.
Lorenzo Mentaschi, Michalis Vousdoukas, Evangelos Voukouvalas, Ludovica Sartini, Luc Feyen, Giovanni Besio, and Lorenzo Alfieri
Hydrol. Earth Syst. Sci., 20, 3527–3547, https://doi.org/10.5194/hess-20-3527-2016, https://doi.org/10.5194/hess-20-3527-2016, 2016
Short summary
Short summary
The climate is subject to variations which must be considered
studying the intensity and frequency of extreme events.
We introduce in this paper a new methodology
for the study of variable extremes, which consists in detecting
the pattern of variability of a time series, and applying these patterns
to the analysis of the extreme events.
This technique comes with advantages with respect to the previous ones
in terms of accuracy, simplicity, and robustness.
B. Asadieh and N. Y. Krakauer
Hydrol. Earth Syst. Sci., 19, 877–891, https://doi.org/10.5194/hess-19-877-2015, https://doi.org/10.5194/hess-19-877-2015, 2015
Short summary
Short summary
We present a systematic comparison of changes in historical extreme precipitation in station observations (HadEX2) and 15 climate models from the CMIP5 (as the largest and most recent sets of available observational and modeled data sets), on global and continental scales for 1901-2010, using both parametric (linear regression) and non-parametric (the Mann-Kendall as well as Sen’s slope estimator) methods, taking care to sample observations and models spatially and temporally in comparable ways.
A. I. J. M. van Dijk, L. J. Renzullo, Y. Wada, and P. Tregoning
Hydrol. Earth Syst. Sci., 18, 2955–2973, https://doi.org/10.5194/hess-18-2955-2014, https://doi.org/10.5194/hess-18-2955-2014, 2014
M. H. J. van Huijgevoort, P. Hazenberg, H. A. J. van Lanen, and R. Uijlenhoet
Hydrol. Earth Syst. Sci., 16, 2437–2451, https://doi.org/10.5194/hess-16-2437-2012, https://doi.org/10.5194/hess-16-2437-2012, 2012
D. Brochero, F. Anctil, and C. Gagné
Hydrol. Earth Syst. Sci., 15, 3307–3325, https://doi.org/10.5194/hess-15-3307-2011, https://doi.org/10.5194/hess-15-3307-2011, 2011
D. Brochero, F. Anctil, and C. Gagné
Hydrol. Earth Syst. Sci., 15, 3327–3341, https://doi.org/10.5194/hess-15-3327-2011, https://doi.org/10.5194/hess-15-3327-2011, 2011
Cited articles
Abbott, M. B., Bathurst, J. C., Cunge, J. A., Oconnell, P. E., and Rasmussen, J.: An introduction to the european hydrological system: Systeme hydrologique Europeen, She. 2. Structure of a physically-based, distributed modeling system, J. Hydrol., 87, 61–77, 1986.
Allan, R. P., Barlow, M., Byrne, M. P., Cherchi, A., Douville, H., Fowler,
H. J., Gan, T. Y., Pendergrass, A. G., Rosenfeld, D., Swann, A. L. S., Wilcox, L. J., and Zolina, O.: Advances in understanding large-scale responses of the water cycle to climate change, Ann. N.Y. Acad. Sci., 1472, 49–75, https://doi.org/10.1111/nyas.14337, 2020.
Alo, C. A. and Wang, G.: Hydrological impact of the potential future vegetation response to climate changes projected by 8 GCMs, J. Geophys. Res.-Biogeo., 113, G03011, https://doi.org/10.1029/2007JG000598, 2008.
Bales, R. C., Molotch, N. P., Painter, T. H., Dettinger, M. D., Rice, R.,
and Dozier, J.: Mountain hydrology of the western United States, Water
Resour. Res., 42, 690, https://doi.org/10.1029/2005WR004387, 2006.
Barnett, T. P., Adam, J. C., and Lettenmaier, D. P.: Potential impacts of a
warming climate on water availability in snow-dominated regions, Nature, 438, 303–309, https://doi.org/10.1038/nature04141, 2005.
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.
Bixio, A. C., Gambolati, G., Paniconi, C., Putti, M., Shestopalov, V. M.,
Bublias, V. N., Bohuslavsky, A. S., Kasteltseva, N. B., and Rudenko, Y. F.: Modeling groundwater-surface water interactions including effects of morphogenetic depressions in the Chernobyl exclusion zone, Environ. Geol., 42, 162–177, 2002.
Boryan, C., Yang, Z., Mueller, R., and Craig, M.: Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program, Geocarto Int., 26, 341–358, https://doi.org/10.1080/10106049.2011.562309, 2011.
Cayan, D. R., Maurer, E. P., Dettinger, M. D., Tyree, M., and Hayhoe, K.:
Climate change scenarios for the California region. Climatic Change, 87, 21–42, https://doi.org/10.1007/s10584-007-9377-6, 2008.
Celia, M. A., Bouloutas, E. T., and Zarba, R. L.: A general mass-conservative numerical solution for the unsaturated flow equation, Water Resour. Res., 26, 1483–1496, https://doi.org/10.1029/WR026i007p01483, 1990.
Christensen, L., Tague, C. L., and Baron, J. S.: Spatial patterns of simulated transpiration response to climate variability in a snow dominated mountain ecosystem, Hydrol. Process., 22, 3576–3588, https://doi.org/10.1002/hyp.6961, 2008.
Collins, W. D., Bitz, C. M., Blackmon, M. L., Bonan, G. B., Bretherton, C. S., Carton, J. A., Chang, P., Doney, S. C., Hack, J. J., Henderson, T. B., Kiehl, J. T., Large, W. G., McKenna, D. S., Santer, B. D., and Smith, R. D.: The Community Climate System Model Version 3 (CCSM3), J. Climate, 19, 2122–2143, https://doi.org/10.1175/JCLI3761.1, 2006.
Condon, L. E., Maxwell, R. M., and Gangopadhyay, S.: The impact of subsurface conceptualization on land energy fluxes, Adv. Water Resour., 60, 188–203, https://doi.org/10.1016/j.advwatres.2013.08.001, 2013.
Condon, L. E., Atchley, A. L., and Maxwell, R. M.: Evapotranspiration depletes groundwater under warming over the contiguous United States, Nat.
Commun., 11, 873, https://doi.org/10.1038/s41467-020-14688-0, 2020.
Cook, E. R., Woodhouse, C. A., Eakin, C. M., Meko, D. M., and Stahle, D. W.: Long-Term Aridity Changes in the Western United States, Science, 306, 1015–1018, https://doi.org/10.1126/science.1102586, 2004.
Cosgrove, B. A., Lohmann, D., Mitchell, K. E., Houser, P. R., Wood, E. F., Schaake, J. C., Robock, A., Marshall, C., Sheffield, J., Duan, Q., Luo, L., Higgins, R. W., Pinker, R. T., Tarpley, J. D., and Meng, J.:: Real-time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project, J. Geophys. Res.-Atmos., 108, 8842, https://doi.org/10.1029/2002JD003118, 2003.
Cristea, N. C., Lundquist, J. D., Loheide, S. P., Lowry, C. S., and Moore, C. E.: Modelling how vegetation cover affects climate change impacts on streamflow timing and magnitude in the snowmelt-dominated upper Tuolumne
Basin, Sierra Nevada, Hydrol. Process., 28, 3896–3918, https://doi.org/10.1002/hyp.9909, 2014.
Daly, C., Halbleib, M., Smith, J. I., Gibson, W. P., Doggett, M. K., Taylor, G. H., Curtis, J., and Pasteris, P. P.: Physiographically 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.
Dettinger, M.: Climate Change, Atmospheric Rivers, and Floods in California
– A Multimodel Analysis of Storm Frequency and Magnitude Changes1, J. Am. Water Resour. Assoc., 47, 514–523, https://doi.org/10.1111/j.1752-1688.2011.00546.x, 2011.
Dettinger, M. and Anderson, M. L.: Storage in California's reservoirs and
snowpack in this time of drought, San Francisco Estuary and Watershed
Science, 13, https://doi.org/10.15447/sfews.2015v13iss2art1, 2015.
Dettinger, M., Redmond, K., and Cayan, D.: Winter Orographic Precipitation
Ratios in the Sierra Nevada – Large-Scale Atmospheric Circulations and
Hydrologic Consequences, J. Hydrometeorol., 5, 1102–1116, https://doi.org/10.1175/JHM-390.1, 2004.
Dettinger, M. D.: Atmospheric Rivers as Drought Busters on the U.S. West
Coast, J. Hydrometeorol., 14, 1721–1732, https://doi.org/10.1175/JHM-D-13-02.1, 2013.
Dierauer, J. R., Whitfield, P. H., and Allen, D. M.: Climate Controls on
Runoff and Low Flows in Mountain Catchments of Western North America, Water
Resour. Res., 54, 7495–7510, https://doi.org/10.1029/2018WR023087, 2018.
Faunt, C. C. and Geological Survey (US) (Eds.): Groundwater availability of
the Central Valley Aquifer, California, US Geological Survey professional
paper, US Geological Survey, Reston, VA, 225 pp., 2009.
Faunt, C. C., Belitz, K., and Hanson, R. T.: Development of a three-dimensional model of sedimentary texture in valley-fill deposits of Central Valley, California, USA, Hydrogeo. J., 18, 625–649, https://doi.org/10.1007/s10040-009-0539-7, 2010.
Ferguson, I. M. and Maxwell, R. M.: Role of groundwater in watershed response and land surface feedbacks under climate change, Water Resour. Res., 46, 1–15, https://doi.org/10.1029/2009WR008616, 2010.
Ficklin, D. L., Luo, Y., and Zhang, M.: Climate change sensitivity assessment of streamflow and agricultural pollutant transport in California's Central Valley using Latin hypercube sampling, Hydrol. Process., 27, 2666–2675, https://doi.org/10.1002/hyp.9386, 2013.
Gates, W. L: AMIP: the atmospheric model intercomparison project, B. Am.
Meteorol. Soc., 73, 1962–1970, https://doi.org/10.1175/1520-0477(1992)073<1962:ATAMIP>2.0.CO;2, 1992.
Gent, P. R., Danabasoglu, G., Donner, L. J., Holland, M. M., Hunke, E. C., Jayne, S. R., Lawrence, D. M., Neale, R. B., Rasch, P. J., Vertenstein, M., Worley, P. H., Yang, Z.-L., and Zhang, M.: The Community Climate System Model Version 4, J. Climate, 24, 4973–4991, https://doi.org/10.1175/2011JCLI4083.1, 2011.
Geologic Map of California: Geologic Map of California, https://maps.conservation.ca.gov/cgs/gmc/ (last access: 17 October 2018), 2015.
Gershunov, A., Shulgina, T., Clemesha, R. E. S., Guirguis, K., Pierce, D. W., Dettinger, M. D., Lavers, D. A., Cayan, D. R., Polade, S. D., Kalansky, J., and Ralph, F. M.:: Precipitation regime change in Western North America: The role of Atmospheric Rivers, Sci. Rep., 9, 9944, https://doi.org/10.1038/s41598-019-46169-w, 2019.
Gettelman, A. and Morrison, H.: Advanced Two-Moment Bulk Microphysics for
Global Models. Part I: Off-Line Tests and Comparison with Other Schemes, J. Climate, 28, 1268–1287, https://doi.org/10.1175/JCLI-D-14-00102.1, 2015.
Gilbert, J. M. and Maxwell, R. M.: Examining regional groundwater–surface water dynamics using an integrated hydrologic model of the San Joaquin River
basin, Hydrol. Earth Syst. Sci., 21, 923–947, https://doi.org/10.5194/hess-21-923-2017, 2017.
Gleick, P. H.: The development and testing of a water balance model for
climate impact assessment: Modeling the Sacramento Basin, Water Resour. Res., 23, 1049–1061, https://doi.org/10.1029/WR023i006p01049, 1987.
Griffin, D. and Anchukaitis, K. J.: How unusual is the 2012–2014 California drought?, Geophys. Res. Lett., 41, 9017–9023, https://doi.org/10.1002/2014GL062433, 2014.
Haarsma, R. J., Roberts, M. J., Vidale, P. L., Senior, C. A., Bellucci, A.,
Bao, Q., Chang, P., Corti, S., Fučkar, N. S., Guemas, V., von Hardenberg, J., Hazeleger, W., Kodama, C., Koenigk, T., Leung, L. R., Lu, J., Luo, J.-J., Mao, J., Mizielinski, M. S., Mizuta, R., Nobre, P., Satoh, M., Scoccimarro, E., Semmler, T., Small, J., and von Storch, J.-S.: High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6, Geosci. Model Dev., 9, 4185–4208, https://doi.org/10.5194/gmd-9-4185-2016, 2016.
Harbaugh, A. W., Banta, E. R., Hill, M. C., and McDonald, M. G.: MODFLOW-2000, the US Geological Survey modular ground-water model: User guide to modularization concepts and the ground-water flow process, US Geological Survey, Reston, VA, 2000.
Harpold, A. A. and Molotch, N. P.: Sensitivity of soil water availability
to changing snowmelt timing in the western U.S., Geophys. Res. Lett., 42, 8011–8020, https://doi.org/10.1002/2015GL065855, 2015.
Hayhoe, K., Cayan, D., Field, C. B., Frumhoff, P. C., Maurer, E. P., Miller, N. L., Moser, S. C., Schneider, S. H., Cahill, K. N., Cleland, E. E., Dale, L., Drapek, R., Hanemann, R. M., Kalkstein, L. S., Lenihan, J., Lunch, C. K., Neilson, R. P., Sheridan, S. C., and Verville, J. H.: Emissions pathways, climate change, and impacts on California, P. Natl. Acad. Sci. USA, 101, 12422–12427, https://doi.org/10.1073/pnas.0404500101, 2004.
He, M., Anderson, M., Schwarz, A., Das, T., Lynn, E., Anderson, J., Munévar, A., Vasquez, J., and Arnold, W.: Potential Changes in Runoff of California's Major Water Supply Watersheds in the 21st Century, Water, 11, 1651, https://doi.org/10.3390/w11081651, 2019.
Herrington, A. R., Lauritzen, P. H., Taylor, M. A., Goldhaber, S., Eaton, B. E., Bacmeister, J. T., Reed, K. A., and Ullrich, P. A.: Physics–Dynamics Coupling with Element-Based High-Order Galerkin Methods: Quasi-Equal-Area Physics Grid, Mon. Weather Rev., 147, 69–84, https://doi.org/10.1175/MWR-D-18-0136.1, 2019.
Homer, C., Dewitz, J., Yang, L., Jin, S., Danielson, P., Xian, G., Coulston, J., Herold, N., Wickham, J., and Megown, K.: Completion of the 2011 National Land Cover Database for the conterminous United States – representing a decade of land cover change information, Photogram. Eng. Remote Sens., 81, 345–354, 2015.
Huang, X., Rhoades, A. M., Ullrich, P. A., and Zarzycki, C. M.: An evaluation of the variable-resolution CESM for modeling California's climate, J. Adv. Model. Earth Syst., 8, 345–369, https://doi.org/10.1002/2015MS000559, 2016.
Huang, X., Stevenson, S., and Hall, A. D.: Future warming and intensification of precipitation extremes: A “double whammy” leading to increasing flood risk in California, Geophys. Res. Lett., 47, e2020GL088679, https://doi.org/10.1029/2020GL088679, 2020.
Hurrell, J. W., Holland, M. M., Gent, P. R., Ghan, S., Kay, J. E., Kushner, P. J., Lamarque, J.-F., Large, W. G., Lawrence, D., Lindsay, K., Lipscomb, W. H., Long, M. C., Mahowald, N., Marsh, D. R., Neale, R. B., Rasch, P., Vavrus, S., Vertenstein, M., Bader, D., Collins, W. D., Hack, J. J., Kiehl, J., and Marshall, S.: The Community Earth System Model: A Framework for Collaborative Research, B. Am. Meteorol. Soc., 94, 1339–1360, https://doi.org/10.1175/BAMS-D-12-00121.1, 2013.
Hurst.: Long-Term Storage Capacity of Reservoirs, Trans. Am. Soc. Civ. Eng.,
116, 770–799, 1951.
IGBP: Global plant database published – IGBP, http://www.igbp.net/news/news/news/globalplantdatabasepublished.5.1b8ae20512db692f2a6800014762.html,
last access: 17 October 2018.
Jennings, C. W., Strand, R. G., and Rogers, T. H.: Geologic Map of California, https://maps.conservation.ca.gov/cgs/gmc/ (last access: 17 October 2018), 1977.
Jones, P. W.: First- and Second-Order Conservative Remapping Schemes for
Grids in Spherical Coordinates, Mon. Weather Rev., 127, 2204–2210, https://doi.org/10.1175/1520-0493(1999)127<2204:FASOCR>2.0.CO;2, 1999.
Kollet, S. J. and Maxwell, R. M.: Integrated surface–groundwater flow
modeling: A free-surface overland flow boundary condition in a parallel
groundwater flow model, Adv. Water Resour., 29, 945–958,
https://doi.org/10.1016/j.advwatres.2005.08.006, 2006.
Koutsoyiannis, D.: Climate change, the Hurst phenomenon, and hydrological
statistics, Hydrolog.Sci. J., 48, 3–24, https://doi.org/10.1623/hysj.48.1.3.43481, 2003.
Koutsoyiannis, D.: Revisiting the global hydrological cycle: is it intensifying?, Hydrol. Earth Syst. Sci., 24, 3899–3932,
https://doi.org/10.5194/hess-24-3899-2020, 2020.
La Follette, P. T., Teuling, A. J., Addor, N., Clark, M., Jansen, K., and
Melsen, L. A.: Numerical daemons of hydrological models are summoned by
extreme precipitation, Hydrol. Earth Syst. Sci., 25, 5425–5446, https://doi.org/10.5194/hess-25-5425-2021, 2021.
Lehner, F., Deser, C., Maher, N., Marotzke, J., Fischer, E. M., Brunner, L., Knutti, R., and Hawkins, E.: Partitioning climate projection uncertainty with multiple large ensembles and CMIP5/6, Earth Syst. Dynam., 11, 491–508, https://doi.org/10.5194/esd-11-491-2020, 2020.
Lemordant, L., Gentine, P., Swann, A. S., Cook, B. I., and Scheff, J.:
Critical impact of vegetation physiology on the continental hydrologic cycle
in response to increasing CO2, P. Natl. Acad. Sci. USA, 115, 4093–4098, https://doi.org/10.1073/pnas.1720712115, 2018.
Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J.: A simple
hydrologically based model of land surface water and energy fluxes for general circulation models, J. Geophys. Res., 99, 14415–14428, https://doi.org/10.1029/94JD00483, 1994.
Maina, F. Z. and Siirila-Woodburn, E. R.: Watersheds dynamics following wildfires: Nonlinear feedbacks and implications on hydrologic responses, Hydrol. Process., 34, 33–50, https://doi.org/10.1002/hyp.13568, 2020a.
Maina, F. Z. and Siirila-Woodburn, E. R.: The Role of Subsurface Flow on
Evapotranspiration: A Global Sensitivity Analysis, Water Resour. Res., 56,
e2019WR026612, https://doi.org/10.1029/2019WR026612, 2020b.
Maina, F. Z., Siirila-Woodburn, E. R., Newcomer, M., Xu, Z., and Steefel, C.: Determining the impact of a severe dry to wet transition on watershed hydrodynamics in California, USA with an integrated hydrologic model, J. Hydrol., 580, 124358m, https://doi.org/10.1016/j.jhydrol.2019.124358, 2020a.
Maina, F. Z., Siirila-Woodburn, E. R., and Vahmani, P.: Sensitivity of
meteorological-forcing resolution on hydrologic variables, Hydrol. Earth Syst. Sci., 24, 3451–3474, https://doi.org/10.5194/hess-24-3451-2020, 2020b.
Maina, F. Z., Siirila-Woodburn, E. R., and Dennedy-Frank, P. J.: Assessing the impacts of hydrodynamic parameter uncertainties on simulated evapotranspiration in a mountainous watershed, J. Hydrol., 608, 127620, https://doi.org/10.1016/j.jhydrol.2022.127620, 2022.
Mallakpour, I., Sadegh, M., and AghaKouchak, A.: A new normal for streamflow in California in a warming climate: Wetter wet seasons and drier dry seasons,
J. Hydrol., 567, 203–211, https://doi.org/10.1016/j.jhydrol.2018.10.023, 2018.
Maurer, E. P.: Uncertainty in hydrologic impacts of climate change in the
Sierra Nevada, California, under two emissions scenarios, Climatic Change,
82, 309–325, https://doi.org/10.1007/s10584-006-9180-9, 2007.
Maurer, E. P. and Duffy, P. B.: Uncertainty in projections of streamflow
changes due to climate change in California, Geophys. Res. Lett., 32, L03704, https://doi.org/10.1029/2004GL021462, 2005.
Maxwell, R. M.: A terrain-following grid transform and preconditioner for
parallel, large-scale, integrated hydrologic modeling, Adv. Water Resour., 53, 109–117, https://doi.org/10.1016/j.advwatres.2012.10.001, 2013.
Maxwell, R. M. and Condon, L. E.: Connections between groundwater flow and
transpiration partitioning, Science, 353, 377–380, https://doi.org/10.1126/science.aaf7891, 2016.
Maxwell, R. M. and Miller, N. L.: Development of a Coupled Land Surface and Groundwater Model, J. Hydrometeorol., 6, 233–247, https://doi.org/10.1175/JHM422.1, 2005.
Mayer, T. D. and Naman, S. W.: Streamflow Response to Climate as Influenced by Geology and Elevation, J. Am. Water Resour. Assoc., 47, 724–738,
https://doi.org/10.1111/j.1752-1688.2011.00537.x, 2011.
McEvoy, D. J., Pierce, D. W., Kalansky, J. F., Cayan, D. R., and Abatzoglou, J. T.: Projected Changes in Reference Evapotranspiration in California and Nevada: Implications for Drought and Wildland Fire Danger, Earth's Future, 8,
e2020EF001736, https://doi.org/10.1029/2020EF001736, 2020.
Milly, P. C. D. and Dunne, K. A.: A Hydrologic Drying Bias in Water-Resource Impact Analyses of Anthropogenic Climate Change, J. Am. Water Resour. Assoc., 53, 822–838, https://doi.org/10.1111/1752-1688.12538, 2017.
Milly, P. C. D., Dunne, K. A., and Vecchia, A. V.: Global pattern of trends
in streamflow and water availability in a changing climate, Nature, 438, 347–350, https://doi.org/10.1038/nature04312, 2005.
Mote, P. W., Hamlet, A. F., Clark, M. P., and Lettenmaier, D. P.: Declining
mountain snowpack in western north america, B. Am. Meteorol. Soc., 86, 39–50, https://doi.org/10.1175/BAMS-86-1-39, 2005.
Musselman, K. N., Clark, M. P., Liu, C., Ikeda, K., and Rasmussen, R.: Slower snowmelt in a warmer world, Nat. Clim. Change, 7, 214–219,
https://doi.org/10.1038/nclimate3225, 2017a.
Musselman, K. N., Molotch, N. P., and Margulis, S. A.: Snowmelt response to
simulated warming across a large elevation gradient, southern Sierra Nevada,
California, The Cryosphere, 11, 2847–2866, https://doi.org/10.5194/tc-11-2847-2017, 2017b.
NCAR/UCAR: CESM Models, https://www.cesm.ucar.edu/models/, last acce4ss: 20 June 2022.
Neelin, J. D., Langenbrunner, B., Meyerson, J. E., Hall, A., and Berg, N.:
California Winter Precipitation Change under Global Warming in the Coupled
Model Intercomparison Project Phase 5 Ensemble, J. Climate, 26, 6238–6256, https://doi.org/10.1175/JCLI-D-12-00514.1, 2013.
Neitsch, S. L., Arnold, J. G., Kiniry, J. R., and Williams, J. R.: Soil and
Water Assessment tool (SWAT) user's manual version 2000, Grassland Soil and
Water Research Laboratory, Temple, TX, https://data.nal.usda.gov/dataset/swat-soil-and-water-assessment-tool (last access: 20 June 2022), 2001.
NERSC: HPSS archive: listing of /home/a/arhoades/Shared/www/Hyperion/, NERCS [data set], https://portal.nersc.gov/archive/home/a/arhoades/Shared/www/Hyperion/, last access: 20 June 2022.
Niraula, R., Meixner, T., Dominguez, F., Bhattarai, N., Rodell, M., Ajami, H., Gochis, D., and Castro, C.: How Might Recharge Change Under Projected Climate Change in the Western U.S.?, Geophys. Res. Lett., 44, 10407–10418, https://doi.org/10.1002/2017GL075421, 2017.
Niu, G.-Y., Yang, Z.-L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements, J. Geophys. Res., 116, D12109, https://doi.org/10.1029/2010JD015139, 2011.
ParFlow: ParFlow hydrologic model, https://parflow.org/#download, last accee: 20 June 2022.
Payne, A. E., Demory, M.-E., Leung, L. R., Ramos, A. M., Shields, C. A., Rutz, J. J., Siler, N., Villarini, G., Hall, A., and Ralph, F. M.: Responses and impacts of atmospheric rivers to climate change, Nat. Rev. Earth Environ., 1, 143–157, https://doi.org/10.1038/s43017-020-0030-5, 2020.
Persad, G. G., Swain, D. L., Kouba, C., and Ortiz-Partida, J. P.: Inter-model agreement on projected shifts in California hydroclimate characteristics critical to water management, Climatic Change, 162, 1493–1513, https://doi.org/10.1007/s10584-020-02882-4, 2020.
Ralph, F. M. and Dettinger, M. D.: Storms, floods, and the science of
atmospheric rivers, Eos Trans. Am. Geophys. Union, 92, 265–266, https://doi.org/10.1029/2011EO320001, 2011.
Ralph, F. Martin, Neiman, P. J., Wick, G. A., Gutman, S. I., Dettinger, M.
D., Cayan, D. R., and White, A. B.: Flooding on California's Russian River:
Role of atmospheric rivers, Geophys. Res. Lett., 33, L13801, https://doi.org/10.1029/2006GL026689, 2006.
Rhoades, A. M., Huang, X., Ullrich, P. A., and Zarzycki, C. M.: Characterizing Sierra Nevada Snowpack Using Variable-Resolution CESM, J. of Appl. Meteorol. Clim., 55, 173–196, https://doi.org/10.1175/JAMC-D-15-0156.1, 2016.
Rhoades, A. M., Ullrich, P. A., and Zarzycki, C. M.: Projecting 21st century snowpack trends in western USA mountains using variable-resolution CESM, Clim. Dynam., 50, 261–288, https://doi.org/10.1007/s00382-017-3606-0, 2018a.
Rhoades, A. M., Jones, A. D., and Ullrich, P. A.: The changing character of
the California Sierra Nevada as a natural reservoir, Geophys. Res. Lett., 45, 13008–13019, https://doi.org/10.1029/2018GL080308, 2018b.
Rhoades, A. M., Ullrich, P. A., Zarzycki, C. M., Johansen, H., Margulis, S. A., Morrison, H., Xu, Z., and Collins, W. D.: Sensitivity of Mountain Hydroclimate Simulations in Variable-Resolution CESM to Microphysics and Horizontal Resolution, J. Adv. Modeling Earth Syst., 10, 1357–1380, https://doi.org/10.1029/2018MS001326, 2018c.
Rhoades, A. M., Jones, A. D., O'Brien, T. A., O'Brien, J. P., Ullrich, P. A., and Zarzycki, C. M.: Influences of North Pacific Ocean domain extent on the western U.S. winter hydroclimatology in variable-resolution CESM, J. Geophys. Res.-Atmos., 125, e2019JD031977, https://doi.org/10.1029/2019JD031977, 2020a.
Rhoades, A. M., Jones, A. D., Srivastava, A., Huang, H., O'Brien, T. A., Patricola, C. M., Ullrich, P. A., Wehner, M., and Zhou, Y.: The shifting scales of western U.S. landfalling atmospheric rivers under climate change, Geophys. Res. Lett., 47, e2020GL089096, https://doi.org/10.1029/2020GL089096, 2020b.
Rhoades, A. M., Risser, M. D., Stone, D. A., Wehner, M. F., and Jones, A. D.: Implications of warming on western United States landfalling atmospheric rivers and their flood damages, Weather Clim. Extrem., 32, 100326,
https://doi.org/10.1016/j.wace.2021.100326, 2021.
Richards, L. A.: Capillary conduction of liquids through porous medium, J. Appl. Phys., 1, 318–333, https://doi.org/10.1063/1.1745010, 1931.
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, https://doi.org/10.1002/hyp.9628, 2013.
Safeeq, M., Grant, G. E., Lewis, S. L., Kramer, M. G., and Staab, B.: A hydrogeologic framework for characterizing summer streamflow sensitivity to climate warming in the Pacific Northwest, USA, Hydrol. Earth Syst. Sci., 18, 3693–3710, https://doi.org/10.5194/hess-18-3693-2014, 2014.
Safeeq, Mohammad, Grant, G. E., Lewis, S. L., and Staab, B.: Predicting
landscape sensitivity to present and future floods in the Pacific Northwest,
USA, Hydrol. Process., 29, 5337–5353, https://doi.org/10.1002/hyp.10553, 2015.
Shukla, S., Safeeq, M., AghaKouchak, A., Guan, K., and Funk, C.: Temperature impacts on the WY 2014 drought in California, Geophys. Res. Lett., 42, 4384–4393, https://doi.org/10.1002/2015GL063666, 2015.
Siirila-Woodburn, E. R., Rhoades, A. M., Hatchett, B. J., Huning, L. S.,
Szinai, J., Tague, C., Nico, P. S., Feldman, D. R., Jones, A. D., Collins, W. D., and Kaatz, L.: A low-to-no snow future and its impacts on water resources in the western United States, Nat. Rev. Earth Environ., 2, 800–819, https://doi.org/10.1038/s43017-021-00219-y, 2021.
Son, K. and Tague, C.: Hydrologic responses to climate warming for a
snow-dominated watershed and a transient snow watershed in the California
Sierra, Ecohydrology, 12, e2053, https://doi.org/10.1002/eco.2053, 2019.
Song, X., Zhang, J., Zhan, C., Xuan, Y., Ye, M., and Xu, C.: Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications, J. Hydrol., 523, 739–757, https://doi.org/10.1016/j.jhydrol.2015.02.013, 2015.
Swain, D. L., Langenbrunner, B., Neelin, J. D., and Hall, A.: Increasing
precipitation volatility in twenty-first-century California, Nat. Clim. Change, 8, 427–433, https://doi.org/10.1038/s41558-018-0140-y, 2018.
Tague, C. and Peng, H.: The sensitivity of forest water use to the timing
of precipitation and snowmelt recharge in the California Sierra: Implications for a warming climate, J. Geophys. Res.-Biogeo., 118, 875–887, https://doi.org/10.1002/jgrg.20073, 2013.
Tang, G., Li, S., Yang, M., Xu, Z., Liu, Y., and Gu, H.: Streamflow response to snow regime shift associated with climate variability in four mountain watersheds in the US Great Basin, J. Hydrol., 573, 255–266, https://doi.org/10.1016/j.jhydrol.2019.03.021, 2019.
Trefry, M. G. and Muffels, C.: FEFLOW: a finite-element ground water flow and
transport modeling tool, Ground Water, 45, 525–528, https://doi.org/10.1111/j.1745-6584.2007.00358.x, 2007.
Vicuna, S. and Dracup, J. A.: The evolution of climate change impact studies on hydrology and water resources in California, Climatic Change, 82, 327–350, https://doi.org/10.1007/s10584-006-9207-2, 2007.
Vicuna, S., Maurer, E. P., Joyce, B., Dracup, J. A., and Purkey, D.: The Sensitivity of California Water Resources to Climate Change Scenarios, J. Am. Water Resou. Assoc., 43, 482–498, https://doi.org/10.1111/j.1752-1688.2007.00038.x, 2007.
Welch, L. A. and Allen, D. M.: Hydraulic conductivity characteristics in
mountains and implications for conceptualizing bedrock groundwater flow,
Hydrogeol. J., 22, 1003–1026, https://doi.org/10.1007/s10040-014-1121-5, 2014.
Zarzycki, C. M., Levy, M. N., Jablonowski, C., Overfelt, J. R., Taylor, M.
A., and Ullrich, P. A.: Aquaplanet Experiments Using CAM's Variable-Resolution Dynamical Core, J. Climate, 27, 5481–5503,
https://doi.org/10.1175/JCLI-D-14-00004.1, 2014.
Short summary
In this work, we assess the effects of end-of-century extreme dry and wet conditions on the hydrology of California. Our results, derived from cutting-edge and high-resolution climate and hydrologic models, highlight that (1) water storage will be larger and increase earlier in the year, yet the summer streamflow will decrease as a result of high evapotranspiration rates, and that (2) groundwater and lower-order streams are very sensitive to decreases in snowmelt and higher evapotranspiration.
In this work, we assess the effects of end-of-century extreme dry and wet conditions on the...
