Articles | Volume 14, issue 11
https://doi.org/10.5194/gmd-14-6893-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-14-6893-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
DRYP 1.0: a parsimonious hydrological model of DRYland Partitioning of the water balance
E. Andrés Quichimbo
CORRESPONDING AUTHOR
School of Earth and Environmental Sciences, Cardiff University,
Cardiff, CF10 3AT, UK
Michael Bliss Singer
School of Earth and Environmental Sciences, Cardiff University,
Cardiff, CF10 3AT, UK
Water Research Institute, Cardiff University, Cardiff, CF10 3AX, UK
Earth Research Institute, University of California Santa Barbara,
Santa Barbara, California, USA
Katerina Michaelides
School of Geographical Sciences, University of Bristol, Bristol, BS8 1SS, UK
Earth Research Institute, University of California Santa Barbara,
Santa Barbara, California, USA
Cabot Institute for the Environment, University of Bristol, Bristol, BS8 1QU, UK
Daniel E. J. Hobley
School of Earth and Environmental Sciences, Cardiff University,
Cardiff, CF10 3AT, UK
ADAS RSK Ltd, Bristol, BS3 4EB, UK
Rafael Rosolem
Cabot Institute for the Environment, University of Bristol, Bristol, BS8 1QU, UK
Faculty of Engineering, University of Bristol, Clifton, BS8 1TR, UK
Mark O. Cuthbert
School of Earth and Environmental Sciences, Cardiff University,
Cardiff, CF10 3AT, UK
Water Research Institute, Cardiff University, Cardiff, CF10 3AX, UK
School of Civil and Environmental Engineering, The University of New
South Wales, Sydney, New South Wales, Australia
Related authors
Dagmawi Teklu Asfaw, Michael Bliss Singer, Rafael Rosolem, David MacLeod, Mark Cuthbert, Edisson Quichimbo Miguitama, Manuel F. Rios Gaona, and Katerina Michaelides
Geosci. Model Dev., 16, 557–571, https://doi.org/10.5194/gmd-16-557-2023, https://doi.org/10.5194/gmd-16-557-2023, 2023
Short summary
Short summary
stoPET is a new stochastic potential evapotranspiration (PET) generator for the globe at hourly resolution. Many stochastic weather generators are used to generate stochastic rainfall time series; however, no such model exists for stochastically generating plausible PET time series. As such, stoPET represents a significant methodological advance. stoPET generate many realizations of PET to conduct climate studies related to the water balance, agriculture, water resources, and ecology.
George Blake, Katerina Michaelides, Elizabeth Kendon, Mark Cuthbert, and Michael Singer
EGUsphere, https://doi.org/10.5194/egusphere-2025-1154, https://doi.org/10.5194/egusphere-2025-1154, 2025
Short summary
Short summary
Dryland rainfall mainly falls during localised storms, with the intensity of these storms key in controlling how water moves through the landscape, but most climate models cannot represent these storms accurately. We find that if you use a model that can represent these storms to understand water resources, you end up with higher soil moisture for plants and groundwater for humans. Any studies of future water resources that don’t use high-resolution models could produce misleading projections.
Jamie Robert Cameron Brown, Ross Woods, Humberto Ribeiro da Rocha, Debora Regina Roberti, and Rafael Rosolem
EGUsphere, https://doi.org/10.5194/egusphere-2025-883, https://doi.org/10.5194/egusphere-2025-883, 2025
Short summary
Short summary
In recent years, global and regional weather datasets have emerged, but validation with real-world data is crucial, especially in diverse regions like Brazil. This study compares seven key weather variables from five datasets with measurements from 11 sites across Brazil’s main biomes. Results show varying performance across variables and timescales, with one reanalysis product outperforming others overall. Findings suggest it may be a strong choice for multi-variable studies in Brazil.
Eshrat Fatima, Rohini Kumar, Sabine Attinger, Maren Kaluza, Oldrich Rakovec, Corinna Rebmann, Rafael Rosolem, Sascha E. Oswald, Luis Samaniego, Steffen Zacharias, and Martin Schrön
Hydrol. Earth Syst. Sci., 28, 5419–5441, https://doi.org/10.5194/hess-28-5419-2024, https://doi.org/10.5194/hess-28-5419-2024, 2024
Short summary
Short summary
This study establishes a framework to incorporate cosmic-ray neutron measurements into the mesoscale Hydrological Model (mHM). We evaluate different approaches to estimate neutron counts within the mHM using the Desilets equation, with uniformly and non-uniformly weighted average soil moisture, and the physically based code COSMIC. The data improved not only soil moisture simulations but also the parameterisation of evapotranspiration in the model.
Saskia Salwey, Gemma Coxon, Francesca Pianosi, Rosanna Lane, Chris Hutton, Michael Bliss Singer, Hilary McMillan, and Jim Freer
Hydrol. Earth Syst. Sci., 28, 4203–4218, https://doi.org/10.5194/hess-28-4203-2024, https://doi.org/10.5194/hess-28-4203-2024, 2024
Short summary
Short summary
Reservoirs are essential for water resource management and can significantly impact downstream flow. However, representing reservoirs in hydrological models can be challenging, particularly across large scales. We design a new and simple method for simulating river flow downstream of water supply reservoirs using only open-access data. We demonstrate the approach in 264 reservoir catchments across Great Britain, where we can significantly improve the simulation of reservoir-impacted flow.
Manuel F. Rios Gaona, Katerina Michaelides, and Michael Bliss Singer
Geosci. Model Dev., 17, 5387–5412, https://doi.org/10.5194/gmd-17-5387-2024, https://doi.org/10.5194/gmd-17-5387-2024, 2024
Short summary
Short summary
STORM v.2 (short for STOchastic Rainfall Model version 2.0) is an open-source and user-friendly modelling framework for simulating rainfall fields over a basin. It also allows simulating the impact of plausible climate change either on the total seasonal rainfall or the storm’s maximum intensity.
Yanchen Zheng, Gemma Coxon, Ross Woods, Daniel Power, Miguel Angel Rico-Ramirez, David McJannet, Rafael Rosolem, Jianzhu Li, and Ping Feng
Hydrol. Earth Syst. Sci., 28, 1999–2022, https://doi.org/10.5194/hess-28-1999-2024, https://doi.org/10.5194/hess-28-1999-2024, 2024
Short summary
Short summary
Reanalysis soil moisture products are a vital basis for hydrological and environmental research. Previous product evaluation is limited by the scale difference (point and grid scale). This paper adopts cosmic ray neutron sensor observations, a novel technique that provides root-zone soil moisture at field scale. In this paper, global harmonized CRNS observations were used to assess products. ERA5-Land, SMAPL4, CFSv2, CRA40 and GLEAM show better performance than MERRA2, GLDAS-Noah and JRA55.
Solomon H. Gebrechorkos, Jian Peng, Ellen Dyer, Diego G. Miralles, Sergio M. Vicente-Serrano, Chris Funk, Hylke E. Beck, Dagmawi T. Asfaw, Michael B. Singer, and Simon J. Dadson
Earth Syst. Sci. Data, 15, 5449–5466, https://doi.org/10.5194/essd-15-5449-2023, https://doi.org/10.5194/essd-15-5449-2023, 2023
Short summary
Short summary
Drought is undeniably one of the most intricate and significant natural hazards with far-reaching consequences for the environment, economy, water resources, agriculture, and societies across the globe. In response to this challenge, we have devised high-resolution drought indices. These indices serve as invaluable indicators for assessing shifts in drought patterns and their associated impacts on a global, regional, and local level facilitating the development of tailored adaptation strategies.
Dagmawi Teklu Asfaw, Michael Bliss Singer, Rafael Rosolem, David MacLeod, Mark Cuthbert, Edisson Quichimbo Miguitama, Manuel F. Rios Gaona, and Katerina Michaelides
Geosci. Model Dev., 16, 557–571, https://doi.org/10.5194/gmd-16-557-2023, https://doi.org/10.5194/gmd-16-557-2023, 2023
Short summary
Short summary
stoPET is a new stochastic potential evapotranspiration (PET) generator for the globe at hourly resolution. Many stochastic weather generators are used to generate stochastic rainfall time series; however, no such model exists for stochastically generating plausible PET time series. As such, stoPET represents a significant methodological advance. stoPET generate many realizations of PET to conduct climate studies related to the water balance, agriculture, water resources, and ecology.
Shiuan-An Chen, Katerina Michaelides, David A. Richards, and Michael Bliss Singer
Earth Surf. Dynam., 10, 1055–1078, https://doi.org/10.5194/esurf-10-1055-2022, https://doi.org/10.5194/esurf-10-1055-2022, 2022
Short summary
Short summary
Drainage basin erosion rates influence landscape evolution through controlling land surface lowering and sediment flux, but gaps remain in understanding their large-scale patterns and drivers between timescales. We analysed global erosion rates and show that long-term erosion rates are controlled by rainfall, former glacial processes, and basin landform, whilst human activities enhance short-term erosion rates. The results highlight the complex interplay of controls on land surface processes.
William Rust, John P. Bloomfield, Mark Cuthbert, Ron Corstanje, and Ian Holman
Hydrol. Earth Syst. Sci., 26, 2449–2467, https://doi.org/10.5194/hess-26-2449-2022, https://doi.org/10.5194/hess-26-2449-2022, 2022
Short summary
Short summary
We highlight the importance of the North Atlantic Oscillation in controlling droughts in the UK. Specifically, multi-year cycles in the NAO are shown to influence the frequency of droughts and this influence changes considerably over time. We show that the influence of these varying controls is similar to the projected effects of climate change on water resources. We also show that these time-varying behaviours have important implications for water resource forecasts used for drought planning.
Heye Reemt Bogena, Martin Schrön, Jannis Jakobi, Patrizia Ney, Steffen Zacharias, Mie Andreasen, Roland Baatz, David Boorman, Mustafa Berk Duygu, Miguel Angel Eguibar-Galán, Benjamin Fersch, Till Franke, Josie Geris, María González Sanchis, Yann Kerr, Tobias Korf, Zalalem Mengistu, Arnaud Mialon, Paolo Nasta, Jerzy Nitychoruk, Vassilios Pisinaras, Daniel Rasche, Rafael Rosolem, Hami Said, Paul Schattan, Marek Zreda, Stefan Achleitner, Eduardo Albentosa-Hernández, Zuhal Akyürek, Theresa Blume, Antonio del Campo, Davide Canone, Katya Dimitrova-Petrova, John G. Evans, Stefano Ferraris, Félix Frances, Davide Gisolo, Andreas Güntner, Frank Herrmann, Joost Iwema, Karsten H. Jensen, Harald Kunstmann, Antonio Lidón, Majken Caroline Looms, Sascha Oswald, Andreas Panagopoulos, Amol Patil, Daniel Power, Corinna Rebmann, Nunzio Romano, Lena Scheiffele, Sonia Seneviratne, Georg Weltin, and Harry Vereecken
Earth Syst. Sci. Data, 14, 1125–1151, https://doi.org/10.5194/essd-14-1125-2022, https://doi.org/10.5194/essd-14-1125-2022, 2022
Short summary
Short summary
Monitoring of increasingly frequent droughts is a prerequisite for climate adaptation strategies. This data paper presents long-term soil moisture measurements recorded by 66 cosmic-ray neutron sensors (CRNS) operated by 24 institutions and distributed across major climate zones in Europe. Data processing followed harmonized protocols and state-of-the-art methods to generate consistent and comparable soil moisture products and to facilitate continental-scale analysis of hydrological extremes.
Shaini Naha, Miguel Angel Rico-Ramirez, and Rafael Rosolem
Hydrol. Earth Syst. Sci., 25, 6339–6357, https://doi.org/10.5194/hess-25-6339-2021, https://doi.org/10.5194/hess-25-6339-2021, 2021
Short summary
Short summary
Rapid growth in population in developing countries leads to an increase in food demand, and as a consequence, percentages of land are being converted to cropland which alters river flow processes. This study describes how the hydrology of a flood-prone river basin in India would respond to the current and future changes in land cover. Our findings indicate that the recurrent flood events occurring in the basin might be influenced by these changes in land cover at the catchment scale.
Tom Gleeson, Thorsten Wagener, Petra Döll, Samuel C. Zipper, Charles West, Yoshihide Wada, Richard Taylor, Bridget Scanlon, Rafael Rosolem, Shams Rahman, Nurudeen Oshinlaja, Reed Maxwell, Min-Hui Lo, Hyungjun Kim, Mary Hill, Andreas Hartmann, Graham Fogg, James S. Famiglietti, Agnès Ducharne, Inge de Graaf, Mark Cuthbert, Laura Condon, Etienne Bresciani, and Marc F. P. Bierkens
Geosci. Model Dev., 14, 7545–7571, https://doi.org/10.5194/gmd-14-7545-2021, https://doi.org/10.5194/gmd-14-7545-2021, 2021
Short summary
Short summary
Groundwater is increasingly being included in large-scale (continental to global) land surface and hydrologic simulations. However, it is challenging to evaluate these simulations because groundwater is
hiddenunderground and thus hard to measure. We suggest using multiple complementary strategies to assess the performance of a model (
model evaluation).
Daniel Power, Miguel Angel Rico-Ramirez, Sharon Desilets, Darin Desilets, and Rafael Rosolem
Geosci. Model Dev., 14, 7287–7307, https://doi.org/10.5194/gmd-14-7287-2021, https://doi.org/10.5194/gmd-14-7287-2021, 2021
Short summary
Short summary
Cosmic-ray neutron sensors estimate root-zone soil moisture at sub-kilometre scales. There are national-scale networks of these sensors across the globe; however, methods for converting neutron signals to soil moisture values are inconsistent. This paper describes our open-source Python tool that processes raw sensor data into soil moisture estimates. The aim is to allow a user to ensure they have a harmonized data set, along with informative metadata, to facilitate both research and teaching.
Maria Magdalena Warter, Michael Bliss Singer, Mark O. Cuthbert, Dar Roberts, Kelly K. Caylor, Romy Sabathier, and John Stella
Hydrol. Earth Syst. Sci., 25, 3713–3729, https://doi.org/10.5194/hess-25-3713-2021, https://doi.org/10.5194/hess-25-3713-2021, 2021
Short summary
Short summary
Intensified drying of soil and grassland vegetation is raising the impact of fire severity and extent in Southern California. While browned grassland is a common sight during the dry season, this study has shown that there is a pronounced shift in the timing of senescence, due to changing climate conditions favoring milder winter temperatures and increased precipitation variability. Vegetation may be limited in its ability to adapt to these shifts, as drought periods become more frequent.
William Rust, Mark Cuthbert, John Bloomfield, Ron Corstanje, Nicholas Howden, and Ian Holman
Hydrol. Earth Syst. Sci., 25, 2223–2237, https://doi.org/10.5194/hess-25-2223-2021, https://doi.org/10.5194/hess-25-2223-2021, 2021
Short summary
Short summary
In this paper, we find evidence for the cyclical behaviour (on a 7-year basis) in UK streamflow records that match the main cycle of the North Atlantic Oscillation. Furthermore, we find that the strength of these 7-year cycles in streamflow is dependent on proportional contributions from groundwater and the response times of the underlying groundwater systems. This may allow for improvements to water management practices through better understanding of long-term streamflow behaviour.
Isaac Kipkemoi, Katerina Michaelides, Rafael Rosolem, and Michael Bliss Singer
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-48, https://doi.org/10.5194/hess-2021-48, 2021
Manuscript not accepted for further review
Short summary
Short summary
The work is a novel investigation of the role of temporal rainfall resolution and intensity in affecting the water balance of soil in a dryland environment. This research has implications for what rainfall data are used to assess the impact of climate and climate change on the regional water balance. This information is critical for anticipating the impact of a changing climate on dryland communities globally who need it to know when to plant their seeds or where livestock pasture is available.
Gabriel C. Rau, Mark O. Cuthbert, R. Ian Acworth, and Philipp Blum
Hydrol. Earth Syst. Sci., 24, 6033–6046, https://doi.org/10.5194/hess-24-6033-2020, https://doi.org/10.5194/hess-24-6033-2020, 2020
Short summary
Short summary
This work provides an important generalisation of a previously developed method that quantifies subsurface barometric efficiency using the groundwater level response to Earth and atmospheric tides. The new approach additionally allows the quantification of hydraulic conductivity and specific storage. This enables improved and rapid assessment of subsurface processes and properties using standard pressure measurements.
Cited articles
Abbott, M. B., Bathurst, J. C., Cunge, J. A., O'Connell, P. E., and
Rasmussen, J.: An introduction to the European Hydrological System –
Systeme Hydrologique Europeen, “SHE”, 2: Structure of a physically-based,
distributed modelling system, J. Hydrol., 87, 61–77,
https://doi.org/10.1016/0022-1694(86)90115-0, 1986.
Abdulrazzak, M. J.: Losses of flood water from alluvial channels, Arid Soil
Res. Rehab., 9, 15–24, https://doi.org/10.1080/15324989509385870, 1995.
Albergel, C., Rüdiger, C., Pellarin, T., Calvet, J.-C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., Piguet, B., and Martin, E.: From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations, Hydrol. Earth Syst. Sci., 12, 1323–1337, https://doi.org/10.5194/hess-12-1323-2008, 2008.
Albergel, C., Dutra, E., Munier, S., Calvet, J.-C., Munoz-Sabater, J., de Rosnay, P., and Balsamo, G.: ERA-5 and ERA-Interim driven ISBA land surface model simulations: which one performs better?, Hydrol. Earth Syst. Sci., 22, 3515–3532, https://doi.org/10.5194/hess-22-3515-2018, 2018.
Alfieri, L., Lorini, V., Hirpa, F. A., Harrigan, S., Zsoter, E., Prudhomme,
C., and Salamon, P.: A global streamflow reanalysis for 1980–2018, J.
Hydrol. X, 6, 100049, https://doi.org/10.1016/j.hydroa.2019.100049, 2020.
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop
evapotranspiration-Guidelines for computing crop water requirements-FAO
Irrigation and drainage paper 56, FAO Rome, 300, D05109, 1998.
Anderson, M. P., Woessner, W. W., and Hunt, R. J.: Applied Groundwater
Modeling: Simulation of Flow and Advective Transport, Academic Press – Elsevier, the Netherlands, 632 pp., 2015.
Aryal, S. K., Silberstein, R. P., Fu, G., Hodgson, G., Charles, S. P., and
McFarlane, D.: Understanding spatio-temporal rainfall-runoff changes in a
semi-arid region, Hydrol. Process., 34, 2510–2530,
https://doi.org/10.1002/hyp.13744, 2020.
Assouline, S.: Infiltration into soils: Conceptual approaches and solutions,
Water Resour. Res., 49, 1755–1772, https://doi.org/10.1002/wrcr.20155,
2013.
Atmospheric and Geospace Sciences Division of the National Science Foundation: Kendall, available at: http://cosmos.hwr.arizona.edu/Probes/StationDat/010/index.php, last access: 20 June 2021.
Bakker, M., Post, V., Langevin, C. D., Hughes, J. D., White, J., Starn, J.,
and Fienen, M. N.: FloPy: Python package for creating, running, and
post-processing MODFLOW-based models, U.S. Geological Survey,
https://doi.org/10.5066/F7BK19FH, 2016a.
Bakker, M., Post, V., Langevin, C. D., Hughes, J. D., White, J. T., Starn,
J. J., and Fienen, M. N.: Scripting MODFLOW Model Development Using Python
and FloPy, Groundwater, 54, 733–739, https://doi.org/10.1111/gwat.12413,
2016b.
Barnhart, K. R., Hutton, E. W. H., Tucker, G. E., Gasparini, N. M., Istanbulluoglu, E., Hobley, D. E. J., Lyons, N. J., Mouchene, M., Nudurupati, S. S., Adams, J. M., and Bandaragoda, C.: Short communication: Landlab v2.0: a software package for Earth surface dynamics, Earth Surf. Dynam., 8, 379–397, https://doi.org/10.5194/esurf-8-379-2020, 2020.
Batelaan, O. and Smedt, F. D.: SEEPAGE, a New MODFLOW DRAIN Package,
Groundwater, 42, 576–588,
https://doi.org/10.1111/j.1745-6584.2004.tb02626.x, 2004.
Batelis, S.-C., Rahman, M., Kollet, S., Woods, R., and Rosolem, R.: Towards
the representation of groundwater in the Joint UK Land Environment
Simulator, Hydrol. Process., 34, 2843–2863,
https://doi.org/10.1002/hyp.13767, 2020.
Becker, R., Gebremichael, M., and Märker, M.: Impact of soil surface and
subsurface properties on soil saturated hydraulic conductivity in the
semi-arid Walnut Gulch Experimental Watershed, Arizona, USA, Geoderma, 322,
112–120, https://doi.org/10.1016/j.geoderma.2018.02.023, 2018.
Beven, K. and Binley, A.: The future of distributed models: model
calibration and uncertainty prediction, Hydrol. Process., 6, 279–298, 1992.
Braun, J. and Willett, S. D.: A very efficient O(n), implicit and parallel
method to solve the stream power equation governing fluvial incision and
landscape evolution, Geomorphology, 180–181, 170–179,
https://doi.org/10.1016/j.geomorph.2012.10.008, 2013.
Brooks, R. H. and Corey, A. T.: Hydraulic Properties of Porous Media,
Hydrology Paper No. 3, Fort Collins,
Colorado State University, 40 pp., 1964.
Chen, F. and Dudhia, J.: Coupling an Advanced Land Surface–Hydrology Model
with the Penn State–NCAR MM5 Modeling System. Part I: Model Implementation
and Sensitivity, Mon. Weather Rev., 129, 569–585,
https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2, 2001.
Cherlet, M., Hutchinson, C., Reynolds, J., Hill, J., Sommer, S., and von
Maltitz, G.: World atlas of desertification, Publication Office
of the European Union, Luxembourg, 3rd ed., 295 pp., 2018.
Chow, V. T., Maidment, D. R., and Mays, L. W.: Applied Hydrology, McGraw-Hill Companies, Singapore, 588 pp., 1988.
Clapp, R. B. and Hornberger, G. M.: Empirical equations for some soil
hydraulic properties, Water Resour. Res., 14, 601–604,
https://doi.org/10.1029/WR014i004p00601, 1978.
Clark, M. P., Fan, Y., Lawrence, D. M., Adam, J. C., Bolster, D., Gochis, D.
J., Hooper, R. P., Kumar, M., Leung, L. R., Mackay, D. S., Maxwell, R. M.,
Shen, C., Swenson, S. C., and Zeng, X.: Improving the representation of
hydrologic processes in Earth System Models, Water Resour. Res., 51,
5929–5956, https://doi.org/10.1002/2015WR017096, 2015.
Coes, A. L. and Pool, D. R.: Ephemeral-stream channel and basin-floor
infiltration and recharge in the Sierra Vista subwatershed of the Upper San
Pedro Basin, Southeastern Arizona: Chapter J in Ground-water recharge in the
arid and semiarid southwestern United States, Professional Paper 1703, U.S.
Geological Survey, 2007.
Craig, J. R., Liu, G., and Soulis, E. D.: Runoff–infiltration partitioning
using an upscaled Green–Ampt solution, Hydrol. Process., 24, 2328–2334,
https://doi.org/10.1002/hyp.7601, 2010.
Cuthbert, M. O., Acworth, R. I., Andersen, M. S., Larsen, J. R., McCallum,
A. M., Rau, G. C., and Tellam, J. H.: Understanding and quantifying focused,
indirect groundwater recharge from ephemeral streams using water table
fluctuations, Water Resour. Res., 52, 827–840,
https://doi.org/10.1002/2015WR017503, 2016.
Cuthbert, M. O., Gleeson, T., Moosdorf, N., Befus, K. M., Schneider, A.,
Hartmann, J., and Lehner, B.: Global patterns and dynamics of
climate–groundwater interactions, Nat. Clim. Change, 9, 137–141,
https://doi.org/10.1038/s41558-018-0386-4, 2019a.
Cuthbert, M. O., Taylor, R. G., Favreau, G., Todd, M. C., Shamsudduha, M.,
Villholth, K. G., MacDonald, A. M., Scanlon, B. R., Kotchoni, D. O. V.,
Vouillamoz, J.-M., Lawson, F. M. A., Adjomayi, P. A., Kashaigili, J.,
Seddon, D., Sorensen, J. P. R., Ebrahim, G. Y., Owor, M., Nyenje, P. M.,
Nazoumou, Y., Goni, I., Ousmane, B. I., Sibanda, T., Ascott, M. J.,
Macdonald, D. M. J., Agyekum, W., Koussoubé, Y., Wanke, H., Kim, H.,
Wada, Y., Lo, M.-H., Oki, T., and Kukuric, N.: Observed controls on
resilience of groundwater to climate variability in sub-Saharan Africa,
Nature, 572, 230–234, https://doi.org/10.1038/s41586-019-1441-7, 2019b.
Dai, Y., Xin, Q., Wei, N., Zhang, Y., Shangguan, W., Yuan, H., Zhang, S.,
Liu, S., and Lu, X.: A Global High-Resolution Data Set of Soil Hydraulic and
Thermal Properties for Land Surface Modeling, J. Adv. Model. Earth Sy.,
11, 2996–3023, https://doi.org/10.1029/2019MS001784, 2019.
Datry, T., Bonada, N., and Boulton, A. (Eds.): Intermittent Rivers and
Ephemeral Streams, Ecology and Management, Academic Press – Elsevier, the Netherlands,
https://doi.org/10.1016/B978-0-12-803835-2.09997-6, 2017.
de Graaf, I. E. M., Sutanudjaja, E. H., van Beek, L. P. H., and Bierkens, M. F. P.: A high-resolution global-scale groundwater model, Hydrol. Earth Syst. Sci., 19, 823–837, https://doi.org/10.5194/hess-19-823-2015, 2015.
Desilets, D. and Zreda, M.: Footprint diameter for a cosmic-ray soil
moisture probe: Theory and Monte Carlo simulations, Water Resour. Res., 49,
3566–3575, https://doi.org/10.1002/wrcr.20187, 2013.
Desilets, D., Zreda, M., and Ferré, T. P. A.: Nature’s neutron probe: Land surface hydrology at an elusive scale with cosmic rays, Water Resour. Res., 46, W11505, https://doi.org/10.1029/2009WR008726, 2010.
Emmerich, W. E. and Verdugo, C. L.: Long-term carbon dioxide and water flux
database, Walnut Gulch Experimental Watershed, Arizona, United States, Water
Resour. Res., 44, W05S09, https://doi.org/10.1029/2006WR005693, 2008.
European Centre for Medium-Range Weather Forecasts: ERA5 hourly data, ECMWF [data set], available at:
https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form (last access: 20 June 2021), 2018.
Ewen, J., Parkin, G., and O'Connell, P. E.: SHETRAN: Distributed River Basin
Flow and Transport Modeling System, J. Hydrol. Eng., 5, 250–258,
https://doi.org/10.1061/(ASCE)1084-0699(2000)5:3(250), 2000.
Fan, Y., Li, H., and Miguez-Macho, G.: Global Patterns of Groundwater Table
Depth, Science, 339, 940–943, https://doi.org/10.1126/science.1229881,
2013.
Franz, T. E., Zreda, M., Ferre, T. P. A., Rosolem, R., Zweck, C., Stillman,
S., Zeng, X., and Shuttleworth, W. J.: Measurement depth of the cosmic ray
soil moisture probe affected by hydrogen from various sources, Water Resour.
Res., 48, W08515, https://doi.org/10.1029/2012WR011871, 2012.
Franz, T. E., Zreda, M., Rosolem, R., and Ferre, T. P. A.: A universal calibration function for determination of soil moisture with cosmic-ray neutrons, Hydrol. Earth Syst. Sci., 17, 453–460, https://doi.org/10.5194/hess-17-453-2013, 2013.
Giordano, M.: Global Groundwater? Issues and Solutions, Annu. Rev. Env.
Resour., 34, 153–178,
https://doi.org/10.1146/annurev.environ.030308.100251, 2009.
Goodrich, D. C., Lane, L. J., Shillito, R. M., Miller, S. N., Syed, K. H.,
and Woolhiser, D. A.: Linearity of basin response as a function of scale in
a semiarid watershed, Water Resour. Res., 33, 2951–2965,
https://doi.org/10.1029/97WR01422, 1997.
Goodrich, D. C., Keefer, T. O., Unkrich, C. L., Nichols, M. H., Osborn, H.
B., Stone, J. J., and Smith, J. R.: Long-term precipitation database, Walnut
Gulch Experimental Watershed, Arizona, United States, Water Resour. Res.,
44, W08515, https://doi.org/10.1029/2006WR005782, 2008.
Goodrich, D. C., Williams, D. G., Unkrich, C. L., Hogan, J. F., Scott, R.
L., Hultine, K. R., Pool, D., Goes, A. L., and Miller, S.: Comparison of
Methods to Estimate Ephemeral Channel Recharge, Walnut Gulch, San Pedro
River Basin, Arizona, in: Groundwater Recharge in a Desert Environment: The
Southwestern United States, American Geophysical Union (AGU), 77–99,
2013.
Goodrich, D. C., Kepner, W. G., Levick, L. R., and Wigington, P. J.:
Southwestern Intermittent and Ephemeral Stream Connectivity, JAWRA J. Am.
Water Resour. Assoc., 54, 400–422, https://doi.org/10.1111/1752-1688.12636,
2018.
Harbaugh, A. W.: MODFLOW-2005, The U.S. Geological Survey Modular
Ground-Water Model – the Ground-Water Flow Process, U.S. Geological Survey
Techniques and Methods 6-A16, 2005.
Harbaugh, A. W., Banta, E. R., Hill, M. C., and McDonald, M. G.: MODFLOW-2000, The U.S. Geological Survey Modular Ground-Water Model – User Guide to Modularization Concepts and the Ground-Water Flow Process, Geological Survey (U.S.), Denver, CO, https://doi.org/10.3133/ofr200092, 2000-92, 121, 2000.
Heilman, P., Nichols, M. H., Goodrich, D. C., Miller, S. N., and Guertin, D.
P.: Geographic information systems database, Walnut Gulch Experimental
Watershed, Arizona, United States, Water Resour. Res., 44, W05S11,
https://doi.org/10.1029/2006WR005777, 2008.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., Chiara, G. D., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková,
M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P.
de, Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5
global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049,
https://doi.org/10.1002/qj.3803, 2020.
Hobley, D. E. J., Adams, J. M., Nudurupati, S. S., Hutton, E. W. H., Gasparini, N. M., Istanbulluoglu, E., and Tucker, G. E.: Creative computing with Landlab: an open-source toolkit for building, coupling, and exploring two-dimensional numerical models of Earth-surface dynamics, Earth Surf. Dynam., 5, 21–46, https://doi.org/10.5194/esurf-5-21-2017, 2017.
Holtan, H. N.: Time-condensation in hydrograph-analysis, EOS T. Am.
Geophys. Union, 26, 407–413, https://doi.org/10.1029/TR026i003p00407, 1945.
Huang, J., Yu, H., Guan, X., Wang, G., and Guo, R.: Accelerated dryland
expansion under climate change, Nat. Clim. Change, 6, nclimate2837,
https://doi.org/10.1038/nclimate2837, 2015.
Huang, J., Li, Y., Fu, C., Chen, F., Fu, Q., Dai, A., Shinoda, M., Ma, Z.,
Guo, W., Li, Z., Zhang, L., Liu, Y., Yu, H., He, Y., Xie, Y., Guan, X., Ji,
M., Lin, L., Wang, S., Yan, H., and Wang, G.: Dryland climate change: Recent
progress and challenges, Rev. Geophys., 55, 719–778,
https://doi.org/10.1002/2016RG000550, 2017.
Hughes, A., Mansour, M., Robins, N., and Peach, D.: Numerical Modelling of
Run-off Recharge in a Catchment in the West Bank, MODFLOW More 2006 Manag.
Ground-Water Syst. Conf. Proc., 1, 385–389, 2006.
Hughes, D. A.: A simple approach to estimating channel transmission losses
in large South African river basins, J. Hydrol., 25, 100619,
https://doi.org/10.1016/j.ejrh.2019.100619, 2019.
Ivanov, V. Y., Vivoni, E. R., Bras, R. L., and Entekhabi, D.: Catchment
hydrologic response with a fully distributed triangulated irregular network
model, Water Resour. Res., 40, W11102, https://doi.org/10.1029/2004WR003218, 2004.
Kampf, S. K. and Burges, S. J.: A framework for classifying and comparing
distributed hillslope and catchment hydrologic models, Water Resour. Res.,
43, W05423, https://doi.org/10.1029/2006WR005370, 2007.
Keefer, T. O., Moran, M. S., and Paige, G. B.: Long-term meteorological and
soil hydrology database, Walnut Gulch, W05S07, https://doi.org/10.1029/2006WR005702,
2008.
Kipkemoi, I., Michaelides, K., Rosolem, R., and Singer, M. B.: Climatic expression of rainfall on soil moisture dynamics in drylands, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2021-48, 2021.
Lahmers, T. M., Gupta, H., Castro, C. L., Gochis, D. J., Yates, D., Dugger,
A., Goodrich, D., and Hazenberg, P.: Enhancing the Structure of the
WRF-Hydro Hydrologic Model for Semiarid Environments, J. Hydrometeorol., 20,
691–714, https://doi.org/10.1175/JHM-D-18-0064.1, 2019.
Leenaars, J. G. B., Claessens, L., Heuvelink, G. B. M., Hengl, T., Ruiperez
González, M., van Bussel, L. G. J., Guilpart, N., Yang, H., and Cassman,
K. G.: Mapping rootable depth and root zone plant-available water holding
capacity of the soil of sub-Saharan Africa, Geoderma, 324, 18–36,
https://doi.org/10.1016/j.geoderma.2018.02.046, 2018.
Marçais, J., de Dreuzy, J.-R., and Erhel, J.: Dynamic coupling of
subsurface and seepage flows solved within a regularized partition
formulation, Adv. Water Resour., 109, 94–105,
https://doi.org/10.1016/j.advwatres.2017.09.008, 2017.
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.
Mayes, M., Caylor, K. K., Singer, M. B., Stella, J. C., Roberts, D., and
Nagler, P.: Climate sensitivity of water use by riparian woodlands at
landscape scales, Hydrol. Process., 34, 4884–4903,
https://doi.org/10.1002/hyp.13942, 2020.
Mein, R. G. and Larson, C. L.: Modeling infiltration during a steady rain,
Water Resour. Res., 9, 384–394, https://doi.org/10.1029/WR009i002p00384,
1973.
Messager, M. L., Lehner, B., Cockburn, C., Lamouroux, N., Pella, H.,
Snelder, T., Tockner, K., Trautmann, T., Watt, C., and Datry, T.: Global
prevalence of non-perennial rivers and streams, Nature, 594, 391–397,
https://doi.org/10.1038/s41586-021-03565-5, 2021.
Michaelides, K. and Wainwright, J.: Modelling the effects of
hillslope–channel coupling on catchment hydrological response, Earth Surf.
Proc. Land., 27, 1441–1457, https://doi.org/10.1002/esp.440, 2002.
Michaelides, K. and Wilson, M. D.: Uncertainty in predicted runoff due to
patterns of spatially variable infiltration, Water Resour. Res., 43, W02415,
https://doi.org/10.1029/2006WR005039, 2007.
Michaelides, K., Hollings, R., Singer, M. B., Nichols, M. H., and Nearing,
M. A.: Spatial and temporal analysis of hillslope–channel coupling and
implications for the longitudinal profile in a dryland basin, Earth Surf.
Proc. Land., 43, 1608–1621, https://doi.org/10.1002/esp.4340, 2018.
Miller, S. N., Youberg, A., Guertin, D. P., and Goodrich, D. C.: Channel morphology investigations using Geographic Information Systems and field research, in: Land Stewardship in the 21st Century: The Contributions of Watershed Management, Tucson, Arizona, 13–16 March 2000, 415–419, 2000.
Mudd, S. M.: Investigation of the hydrodynamics of flash floods in ephemeral
channels: Scaling analysis and simulation using a shock-capturing flow model
incorporating the effects of transmission losses, J. Hydrol., 324, 65–79,
https://doi.org/10.1016/j.jhydrol.2005.09.012, 2006.
Nash, I. E. and Sutcliffe, I. V.: River flow forecasting through conceptual
models, J. Hydrol., 10, 282–290, 1970.
Noorduijn, S. L., Shanafield, M., Trigg, M. A., Harrington, G. A., Cook, P.
G., and Peeters, L.: Estimating seepage flux from ephemeral stream channels
using surface water and groundwater level data, Water Resour. Res., 50,
1474–1489, https://doi.org/10.1002/2012WR013424, 2014.
Pelletier, J. D., Broxton, P. D., Hazenberg, P., Zeng, X., Troch, P. A.,
Niu, G.-Y., Williams, Z., Brunke, M. A., and Gochis, D.: A gridded global
data set of soil, intact regolith, and sedimentary deposit thicknesses for
regional and global land surface modeling, J. Adv. Model. Earth Sy., 8,
41–65, https://doi.org/10.1002/2015MS000526, 2016.
Philip, J. R.: Theory of Infiltration: The infiltration equation and its solutions, Soil Sci., 171, S34–S46, 1957.
Pilgrim, D. H., Chapman, T. G., and Doran, D. G.: Problems of
rainfall-runoff modelling in arid and semiarid regions, Hydrolog. Sci. J., 33,
379–400, https://doi.org/10.1080/02626668809491261, 1988.
Power, D., Rico-Ramirez, M. A., Desilets, S., Desilets, D., and Rosolem, R.: Cosmic-Ray neutron Sensor PYthon tool (crspy): An open-source tool for the processing of cosmic-ray neutron and soil moisture data, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2021-77, in review, 2021.
Quichimbo, E. A., Singer, M. B., and Cuthbert, M. O.: Characterising
groundwater–surface water interactions in idealised ephemeral stream
systems, Hydrol. Process., 34, 3792–3806,
https://doi.org/10.1002/hyp.13847, 2020.
Quichimbo, E. A., Cuthbert, M. O., Singer, M. B., Michaelides, K., Rosolem, R., and Hobley, D. E. J.: DRYP 1.0: A parsimonious hydrological model of DRYland Partitioning of the water balance, In DRYP 1.0: A parsimonious hydrological model of DRYland Partitioning of the water balance (1.0), Zenodo, https://doi.org/10.5281/zenodo.5061988, 2021.
Rahman, M., Rosolem, R., Kollet, S. J., and Wagener, T.: Towards a
computationally efficient free-surface groundwater flow boundary condition
for large-scale hydrological modelling, Adv. Water Resour., 123, 225–233,
https://doi.org/10.1016/j.advwatres.2018.11.015, 2019.
Rawls, W. J., Brakensiek, D. L., and Saxtonn, K. E.: Estimation of Soil
Water Properties, T. ASAE, 25, 1316–1320,
https://doi.org/10.13031/2013.33720, 1982.
Reinecke, R., Foglia, L., Mehl, S., Trautmann, T., Cáceres, D., and Döll, P.: Challenges in developing a global gradient-based groundwater model (G3M v1.0) for the integration into a global hydrological model, Geosci. Model Dev., 12, 2401–2418, https://doi.org/10.5194/gmd-12-2401-2019, 2019.
Renard, K. G.: The hydrology of semiarid rangeland watersheds, Rep. ARS-41-162, Agricultural Research Service, United States Department of Agriculture, Washington, D.C., 28 pp., 1970.
Renard, K. G., Nichols, M. H., Woolhiser, D. A., and Osborn, H. B.: A brief
background on the U.S. Department of Agriculture Agricultural Research
Service Walnut Gulch Experimental Watershed, Water Resour. Res., 44, W05S02,
https://doi.org/10.1029/2006WR005691, 2008.
Reynolds, J. F., Smith, D. M. S., Lambin, E. F., Turner, B. L., Mortimore,
M., Batterbury, S. P. J., Downing, T. E., Dowlatabadi, H., Fernández, R.
J., Herrick, J. E., Huber-Sannwald, E., Jiang, H., Leemans, R., Lynam, T.,
Maestre, F. T., Ayarza, M., and Walker, B.: Global Desertification: Building
a Science for Dryland Development, Science, 316, 847–851,
https://doi.org/10.1126/science.1131634, 2007.
Richardson, A. D., Hollinger, D. Y., Burba, G. G., Davis, K. J., Flanagan,
L. B., Katul, G. G., William Munger, J., Ricciuto, D. M., Stoy, P. C.,
Suyker, A. E., Verma, S. B., and Wofsy, S. C.: A multi-site analysis of
random error in tower-based measurements of carbon and energy fluxes, Agr.
Forest Meteorol., 136, 1–18, https://doi.org/10.1016/j.agrformet.2006.01.007,
2006.
Rosolem, R., Shuttleworth, W. J., Zreda, M., Franz, T. E., Zeng, X., and
Kurc, S. A.: The Effect of Atmospheric Water Vapor on Neutron Count in the
Cosmic-Ray Soil Moisture Observing System, J. Hydrometeorol., 14,
1659–1671, https://doi.org/10.1175/JHM-D-12-0120.1, 2013.
Schaake, J. C., Koren, V. I., Duan, Q.-Y., Mitchell, K., and Chen, F.:
Simple water balance model for estimating runoff at different spatial and
temporal scales, J. Geophys. Res.-Atmos., 101, 7461–7475,
https://doi.org/10.1029/95JD02892, 1996.
Schmidt, A., Hanson, C., Chan, W. S., and Law, B. E.: Empirical assessment
of uncertainties of meteorological parameters and turbulent fluxes in the
AmeriFlux network, J. Geophys. Res.-Biogeo., 117, G04014,
https://doi.org/10.1029/2012JG002100, 2012.
Schreiner-McGraw, A., Ajami, H., and Vivoni, E. R.: Extreme weather events
and transmission losses in arid streams, Environ. Res. Lett., 14, 084002,
https://doi.org/10.1088/1748-9326/ab2949, 2019.
Schrön, M., Köhli, M., Scheiffele, L., Iwema, J., Bogena, H. R., Lv, L., Martini, E., Baroni, G., Rosolem, R., Weimar, J., Mai, J., Cuntz, M., Rebmann, C., Oswald, S. E., Dietrich, P., Schmidt, U., and Zacharias, S.: Improving calibration and validation of cosmic-ray neutron sensors in the light of spatial sensitivity, Hydrol. Earth Syst. Sci., 21, 5009–5030, https://doi.org/10.5194/hess-21-5009-2017, 2017.
Scoging, H. M. and Thornes, J. B.: Infiltration characteristics in a semiarid environment, in: The Hydrology of areas of low precipitation, Canberra Symposium, Paris, 1979, International Association of Hydrological Sciences, 128, 159–168, 1979.
Scott, R. L.: Using watershed water balance to evaluate the accuracy of eddy
covariance evaporation measurements for three semiarid ecosystems, Agr.
Forest Meteorol., 150, 219–225,
https://doi.org/10.1016/j.agrformet.2009.11.002, 2010.
Scott, R.: US-Wkg: Walnut Gulch Kendall Grasslands, (2021), AmeriFlux BASE US-Wkg Walnut Gulch Kendall Grasslands, Ver. 17-5, AmeriFlux AMP [data set], https://doi.org/10.17190/AMF/1246112, 2021.
Scott, R. L., Biederman, J. A., Hamerlynck, E. P., and Barron-Gafford, G.
A.: The carbon balance pivot point of southwestern U.S. semiarid ecosystems:
Insights from the 21st century drought, J. Geophys. Res.-Biogeo.,
120, 2612–2624, https://doi.org/10.1002/2015JG003181, 2015.
Shanafield, M. and Cook, P. G.: Transmission losses, infiltration and
groundwater recharge through ephemeral and intermittent streambeds: A review
of applied methods, J. Hydrol., 511, 518–529,
https://doi.org/10.1016/j.jhydrol.2014.01.068, 2014.
Sherman, L. K.: Comparison f-curves derived by the methods of sharp and
Holtan and of Sherman and Mayer, EOS T. Am. Geophys. Union, 24,
465–467, https://doi.org/10.1029/TR024i002p00465, 1943.
Siebert, S., Burke, J., Faures, J. M., Frenken, K., Hoogeveen, J., Döll, P., and Portmann, F. T.: Groundwater use for irrigation – a global inventory, Hydrol. Earth Syst. Sci., 14, 1863–1880, https://doi.org/10.5194/hess-14-1863-2010, 2010.
Šimunek, J., Van Genuchten, M. T., and Šejna, M.: The HYDRUS
software package for simulating two-and three-dimensional movement of water,
heat, and multiple solutes in variably-saturated media, Tech. Man. Version,
1, 241, 2006.
Singer, M. B. and Michaelides, K.: How is topographic simplicity maintained
in ephemeral dryland channels?, Geology, 42, 1091–1094,
https://doi.org/10.1130/G36267.1, 2014.
Singer, M. B. and Michaelides, K.: Deciphering the expression of climate
change within the Lower Colorado River basin by stochastic simulation of
convective rainfall, Environ. Res. Lett., 12, 104011,
https://doi.org/10.1088/1748-9326/aa8e50, 2017.
Singer, M. B., Michaelides, K., and Hobley, D. E. J.: STORM 1.0: a simple, flexible, and parsimonious stochastic rainfall generator for simulating climate and climate change, Geosci. Model Dev., 11, 3713–3726, https://doi.org/10.5194/gmd-11-3713-2018, 2018.
Singer, M. B., Asfaw, D. T., Rosolem, R., Cuthbert, M. O., Miralles, D. G.,
MacLeod, D., Quichimbo, E. A., and Michaelides, K.: Hourly potential
evapotranspiration at 0.1∘ resolution for the global land surface
from 1981-present, Sci. Data, 8, 224,
https://doi.org/10.1038/s41597-021-01003-9, 2021.
Sivapalan, M. and Milly, P. C. D.: On the relationship between the time
condensation approximation and the flux concentration relation, J. Hydrol.,
105, 357–367, https://doi.org/10.1016/0022-1694(89)90113-3, 1989.
Sourthwest Watershed Research Center: Online Data access, available at: https://www.tucson.ars.ag.gov/dap/runoff_aggregate.asp, last access: 20 June 2021.
Stone, J. J., Nichols, M. H., Goodrich, D. C., and Buono, J.: Long-term
runoff database, Walnut Gulch Experimental Watershed, Arizona, United
States, Water Resour. Res., 44, W05S05, https://doi.org/10.1029/2006WR005733, 2008.
Tarek, M., Brissette, F. P., and Arsenault, R.: Evaluation of the ERA5 reanalysis as a potential reference dataset for hydrological modelling over North America, Hydrol. Earth Syst. Sci., 24, 2527–2544, https://doi.org/10.5194/hess-24-2527-2020, 2020.
Taylor, R. G., Scanlon, B., Döll, P., Rodell, M., Beek, R. van, Wada,
Y., Longuevergne, L., Leblanc, M., Famiglietti, J. S., Edmunds, M., Konikow,
L., Green, T. R., Chen, J., Taniguchi, M., Bierkens, M. F. P., MacDonald,
A., Fan, Y., Maxwell, R. M., Yechieli, Y., Gurdak, J. J., Allen, D. M.,
Shamsudduha, M., Hiscock, K., Yeh, P. J.-F., Holman, I., and Treidel, H.:
Ground water and climate change, Nat. Clim. Change, 3, nclimate1744,
https://doi.org/10.1038/nclimate1744, 2012.
Taylor, R. G., Todd, M. C., Kongola, L., Maurice, L., Nahozya, E., Sanga,
H., and MacDonald, A. M.: Evidence of the dependence of groundwater
resources on extreme rainfall in East Africa, Nat. Clim. Change, 3,
374–378, https://doi.org/10.1038/nclimate1731, 2013.
Twine, T. E., Kustas, W. P., Norman, J. M., Cook, D. R., Houser, P. R.,
Meyers, T. P., Prueger, J. H., Starks, P. J., and Wesely, M. L.: Correcting
eddy-covariance flux underestimates over a grassland, Agr. Forest Meteorol.,
103, 279–300, https://doi.org/10.1016/S0168-1923(00)00123-4, 2000.
Vergnes, J.-P., Decharme, B., Alkama, R., Martin, E., Habets, F., and
Douville, H.: A Simple Groundwater Scheme for Hydrological and Climate
Applications: Description and Offline Evaluation over France, J.
Hydrometeorol., 13, 1149–1171, https://doi.org/10.1175/JHM-D-11-0149.1,
2012.
Wagner, W., Lemoine, G., and Rott, H.: A Method for Estimating Soil Moisture
from ERS Scatterometer and Soil Data, Remote Sens. Environ., 70, 191–207,
https://doi.org/10.1016/S0034-4257(99)00036-X, 1999.
Walker, W. R.: Guidelines for Designing and Evaluating Surface Irrigation Systems, FAO Irrigation and Drainage Paper No. 45, FAO, Rome, 1989.
Wang, H. F. and Anderson, M. P.: Introduction to Groundwater Modeling:
Finite Difference and Finite Element Methods, W.H.Freeman & Co Ltd, San
Francisco, 237 pp., 1982.
Wang, L., Chen, W., Huang, G., and Zeng, G.: Changes of the transitional
climate zone in East Asia: past and future, Clim. Dynam., 49, 1463–1477,
https://doi.org/10.1007/s00382-016-3400-4, 2017.
Wheater, H., Sorooshian, S., and Sharma, K. D. (Eds.): Hydrological
Modelling in Arid and Semi-Arid Areas, 1 edition, Cambridge University
Press, Cambridge, New York, 222 pp., 2007.
White, R. P. and Nackoney, J.: Drylands, People, and Ecosystem Goods and Services, World Resources Institute, Washington, D.C., 58 pp., 2003.
Wood, E. F., Lettenmaier, D. P., Liang, X., Lohmann, D., Boone, A., Chang,
S., Chen, F., Dai, Y., Dickinson, R. E., Duan, Q., Ek, M., Gusev, Y. M.,
Habets, F., Irannejad, P., Koster, R., Mitchel, K. E., Nasonova, O. N.,
Noilhan, J., Schaake, J., Schlosser, A., Shao, Y., Shmakin, A. B., Verseghy,
D., Warrach, K., Wetzel, P., Xue, Y., Yang, Z. L., and Zeng, Q. C.: The
project for intercomparison of land-surface parameterization schemes (PILPS)
phase 2(c) Red-Arkansas River basin experiment: 1. Experiment description
and summary intercomparisons, Global Planet. Change, 19, 115–135,
https://doi.org/10.1016/S0921-8181(98)00044-7, 1998.
Woodward, C. S. and Dawson, C. N.: Analysis of Expanded Mixed Finite Element
Methods for a Nonlinear Parabolic Equation Modeling Flow into Variably
Saturated Porous Media, SIAM J. Numer. Anal. Phila., 37, 701–724,
https://doi.org/10.1137/S0036142996311040, 2000.
Woolhiser, D. A., Smith, R., and Goodrich, D. C.: KINEROS: a kinematic runoff and erosion model: documentation and user manual, U.S. Department of Agriculture, Washington, D.C., 77 pp., 1990.
Zimmer, M. A., Kaiser, K. E., Blaszczak, J. R., Zipper, S. C., Hammond, J.
C., Fritz, K. M., Costigan, K. H., Hosen, J., Godsey, S. E., Allen, G. H.,
Kampf, S., Burrows, R. M., Krabbenhoft, C. A., Dodds, W., Hale, R., Olden,
J. D., Shanafield, M., DelVecchia, A. G., Ward, A. S., Mims, M. C., Datry,
T., Bogan, M. T., Boersma, K. S., Busch, M. H., Jones, C. N., Burgin, A. J.,
and Allen, D. C.: Zero or not? Causes and consequences of zero-flow stream
gage readings, WIREs Water, 7, e1436, https://doi.org/10.1002/wat2.1436,
2020.
Zoccatelli, D., Marra, F., Armon, M., Rinat, Y., Smith, J. A., and Morin, E.: Contrasting rainfall-runoff characteristics of floods in desert and Mediterranean basins, Hydrol. Earth Syst. Sci., 23, 2665–2678, https://doi.org/10.5194/hess-23-2665-2019, 2019.
Zreda, M., Shuttleworth, W. J., Zeng, X., Zweck, C., Desilets, D., Franz, T., and Rosolem, R.: COSMOS: the COsmic-ray Soil Moisture Observing System, Hydrol. Earth Syst. Sci., 16, 4079–4099, https://doi.org/10.5194/hess-16-4079-2012, 2012.
Short summary
Understanding and quantifying water partitioning in dryland regions are of key importance to anticipate the future impacts of climate change in water resources and dryland ecosystems. Here, we have developed a simple hydrological model (DRYP) that incorporates the key processes of water partitioning in drylands. DRYP is a modular, versatile, and parsimonious model that can be used to anticipate and plan for climatic and anthropogenic changes to water fluxes and storage in dryland regions.
Understanding and quantifying water partitioning in dryland regions are of key importance to...