Publications
computational hydrology | model analysis
data dissemination | data management
Publications
Yu, Q., Tolson, B. A., Shen, H., Han, M., Mai, J., and Lin, J. (2023):
Enhancing LSTM-based
streamflow prediction with a spatially
distributed approach
Hydrol. Earth Syst. Sci. Discuss. [preprint].
Under discussion since Nov 3, 2023.
Sabzipour, B., R. Arsenault, M. Troin, J.-L. Martel, F. Brissette, F. Brunet, J. Mai (2023):
Comparing a long
short-term memory (LSTM) neural network
with a physically-based hydrological model
for streamflow forecasting over a Canadian
catchment
Journal of Hydrology, 627(A),
130380. Accepted Sep 17, 2023.
Refsgaard, J. C., J. Mai, M. Hrachowitz, S. K. Jain, S. Stisen. (2023):
Towards more credible models in catchment hydrology to enhance hydrological process understanding: Preface
Hydrological Processes, 37(9), e14995. Accepted Sep 13, 2023.
Arsenault, R., D. Huard,
J.-L. Martel, M. Troin, J. Mai,
F. Brissette, C. Jauvin, L. Vu,
J. R. Craig, T. J. Smith, T. Logan,
B. A. Tolson, M. Han, F. Gravel,
S. Langlois (2023):
The PAVICS-Hydro
platform: A virtual laboratory for
hydroclimatic modelling and forecasting
over North America
Environmental Modelling & Software, 168, 105808. Accepted Aug 21, 2023.
full text (url) | website and Jupyter notebooks | RavenPy codes (Zenodo)
Maier, H. R., F. Zheng,
H. Gupta, J. Chen, J. Mai, D. Savic,
R. Loritz, W. Wu, D. Guo, A. Bennett,
A. Jakeman, S. Razavi J. Zhao (2023):
On how data are
partitioned in model development and
evaluation: confronting the elephant in
the room to enhance model generalization
Environmental Modelling & Software, 167, 105779. Accepted Jul 28, 2023.
Larabi, S., Mai, J., Schnorbus, M., Tolson, B., and Zwiers, F. (2023):
Towards reducing the high
cost of parameter sensitivity analysis in
hydrologic modeling: a regional parameter
sensitivity analysis approach
Hydrol. Earth Syst. Sci., 27, 3241–3263. Accepted Jul 25, 2023.
Ahmed, M. I., T. Stadnyk,
A. Pietroniro, H. Awoye, A. Bajracharya,
J. Mai, B. A. Tolson, H. Shen,
J. R. Craig, M. Gervais, K. Sagan,
S. Wruth, K. Koenig, R. Lilhare,
S. J. Déry, S. Pokorny, H. Venema,
A. Muhammad, M. Taheri (2023):
Learning from
hydrological models’ challenges: A case
study from the Nelson basin model
intercomparison project
Journal of Hydrology, 623, 12982059. Accepted Jun 11, 2023.
Gauch, M., F. Kratzert, O. Gilon, H. Gupta,
J. Mai, G. Nearing , B. Tolson, S. Hochreiter, D.
Klotz (2023):
In Defense of Metrics: Metrics Sufficiently Encode Typical Human
Preferences Regarding Hydrological Model Performance
Water Resources Research, 59(6), e2022WR033918. Accepted May 25, 2023.
Han, M., H. Shen, B.A. Tolson, J.R. Craig, J. Mai, S.G.M. Lin, N.B. Basu, F.S. Awol (2023):
BasinMaker 3.0: A GIS toolbox for distributed watershed delineation of complex lake-river routing networks
Environmental Modelling & Software, 164, 105688.
Accepted Mar 23, 2023.
Chlumsky, R., J. Mai,
J. R. Craig, and B. A. Tolson (2023):
Advancement of a blended hydrologic model for robust model performance
Hydrol. Earth Syst. Sci. Discuss. [preprint].
Under discussion since Mar 23, 2023.
Mai, J. (2023):
Ten strategies towards
successful calibration of environmental
models
Journal of Hydrology, 620(A), 129414.
Accepted Mar 15, 2023.
Mei, Y., J. Mai, H. X. Do,
A. Gronewold, H. Reeves,
S. Eberts, R. Niswonger, R. S. Regan, and
R. J. Hunt (2023):
Can hydrological models
benefit from using global soil moisture,
evapotranspiration, and runoff products as
calibration targets?
Water Resources Research, 59(2), e2022WR032064.
Accepted Jan 26, 2023.
Arsenault, R., Martel, J.-L., Brunet, F., Brissette, F., and Mai, J. (2023):
Continuous streamflow prediction in ungauged basins: Long
Short-Term Memory Neural Networks clearly outperform hydrological
models
Hydrol. Earth Syst. Sci., 27, 139–157. Accepted
Jan 9, 2023.
Mai, J., Shen, H., Tolson, B. A., Gaborit, É., Arsenault, R.,
Craig, J. R., Fortin, V., Fry, L. M., Gauch, M., Klotz, D.,
Kratzert, F., O'Brien, N., Princz, D. G., Rasiya Koya, S., Roy,
T., Seglenieks, F., Shrestha, N. K., Temgoua, A. G. T., Vionnet,
V., and Waddell, J. W. (2022):
The Great Lakes Runoff Intercomparison
Project Phase 4: The Great Lakes (GRIP-GL)
Hydrol. Earth Syst. Sci., 26, 3537–3572. Highlight paper. Accepted Jun 10, 2022.
Viswanathan, M., T. K. D. Weber, S. Gayler, J. Mai, and T. Streck
(2022):
A Bayesian sequential updating approach to predict phenology of
silage maize
Biogeosciences, 19, 2187–2209. Accepted Mar 8, 2022.
Shen, H., Tolson, B. A., and Mai, J. (2022):
Time to Update the Split-Sample Approach in Hydrological Model Calibration.
Water Resources Research, 58(3), e2021WR031523. Accepted Feb 13, 2022.
Mai, J., Craig, J. R., Tolson, B. A., and R. Arsenault (2022):
The sensitivity of simulated streamflow to individual hydrologic processes across North America.
Nature Communications, 13, 455. Accepted Jan 3,
2022.
Mai, J., Craig, J. R., and Tolson, B. A. (2022):
The Pie sharing problem: Unbiased sampling of N+1 summative weights.
Environmental Modelling and Software, 148, 105282. Accepted Dec 7,
2021.
Persaud, B.D., K.A. Dukacz, G. C. Saha, A. Peterson,
L. Moradi, S. O’Hearn, E. Clary, J. Mai, M. Steeleworthy,
J.J. Venkiteswaran, H. Kheyrollah Pour, B.B. Wolfe, S.K. Carey,
J.W. Pomeroy, C.M. DeBeer, J.M. Waddington, P. Van Cappellen,
J. Lin (2021):
Ten Best Practices to Strengthen Stewardship and Sharing of
Water Science Data in Canada
Hydrological Processes, 35(11), e14385. Accepted Sep 14,
2021.
Gasset, N., Fortin, V., Dimitrijevic, M., Carrera, M., Bilodeau, B.,
Muncaster, R., Gaborit, É., Roy, G., Pentcheva, N., Bulat, M.,
Wang, X., Pavlovic, R., Lespinas, F., and Khedhaouiria, D., and
Mai, J. (2021):
A 10 km North American Precipitation and Land
Surface Reanalysis Based on the GEM Atmospheric Model
Hydrol. Earth Syst. Sci., 25, 4917-4945, 2021. Accepted Aug 6, 2021.
Loiselle, G., J.-L. Martel, A. Poulin, S. Lachance-Cloutier,
R. Turcotte, J. Fournier, J. Mai, R. Arsenault (2021):
A semi-empirical wind set-up forecasting model for Lake Champlain
Hydrological Processes, 35(6), e14240. Accepted May 10,
2021.
Chlumsky, R., Mai, J., Craig, J. R., and Tolson, B. A. (2021):
Simultaneous calibration of hydrologic model structure and
parameters using a blended model
Water Resources Research, 57(5), e2020WR029229. Accepted Apr 14,
2021.
Mai, J. , B. A. Tolson, H. Shen, É. Gaborit, V. Fortin, N. Gasset,
H. Awoye, T. A. Stadnyk, L. M. Fry, E. A. Bradley, F. Seglenieks,
A. G. Temgoua, D. G. Princz, S. Gharari, A. Haghnegahdar,
M. E. Elshamy, S. Razavi, M. Gauch, J. Lin, X. Ni, Y. Yuan,
M. McLeod, N. B. Basu, R. Kumar, O. Rakovec, L. Samaniego,
S. Attinger, N. K. Shrestha, P. Daggupati, T. Roy, S. Wi,
T. Hunter, J. R. Craig, and A. Pietroniro (2021):
The Great Lakes Runoff Intercomparison
Project Phase 3: Lake Erie (GRIP-E)
Journal of Hydrologic Engineering, 26(9), 05021020. Accepted Feb
16, 2021. Best Case Study award 2023 by ASCE-EWRI.
de Rooij, G. H., J. Mai, and R. Madi (2021):
Sigmoidal water retention function with improved behaviour in
dry and wet soils
Hydrol. Earth Syst. Sci., 25, 983-1007. Accepted Jan 20, 2021.
Gauch, M., Mai, J., and Lin, J. (2021):
The Proper Care and Feeding of CAMELS: How Limited Training
Data Affects Streamflow Prediction
Environmental Modelling & Software, 135, 104926. Accepted Nov 5, 2020.
Mai, J., Craig, J. R., and Tolson, B. A. (2020):
Simultaneously Determining Global Sensitivities of Model
Parameters and Model Structure
Hydrol. Earth Syst. Sci., 24, 5835–5858. Accepted Oct 27, 2020.
Mai, J., Arsenault, R., Tolson, B. A., Latraverse, M., and
Demeester, K. (2020):
Application of Parameter Screening To Derive Optimal Initial
State Adjustments for Streamflow Forecasting
Water Resources Research, 56(9), e2020WR027960.
Spieler, D., Mai, J., Craig, J. R., Tolson, B. A., and
Schütze, N. (2020):
Automatic Model Structure Identification for Conceptual
Hydrologic Models
Water Resources Research, 56(9), e2019WR027009.
Han, M., Mai, J., Tolson, B. A., Craig, J. R., Gaborit, É.,
Liu, H., and Lee, K. (2020):
Subwatershed-based lake and river routing products for
hydrologic and land surface models applied over Canada
Canadian Water Resources Journal, 0, 1-15.
Craig, J. R., Brown, G., Chlumsky, R., Jenkinson, R. W., Jost, G.,
Lee, K., Mai, J., Serrer, M., Sgro, N., Shafii, M., Snowdon,
A. P., and Tolson, B. A. (2020):
Flexible watershed simulation with the Raven hydrological
modelling framework
Environmental Modelling & Software, 129, 104728.
Gauch, M., Bai, J., Mai, J., and Lin, J. (2020):
An Open-Source Interface to the Canadian Surface Prediction
Archive.
JCDL ’20, August 1–5, 2020, Virtual Event,
China.
Mai, J., Kornelsen, K. C., Tolson, B. A., Fortin, V.,
Gasset, N., Bouhemhem, D., Schaefer, D., Leahy, M., Anctil, F., and
Coulibaly, P. (2020):
The Canadian Surface Prediction Archive (CaSPAr): A Platform to
Enhance Environmental Modeling in Canada and Globally
Bull. Amer. Meteor. Soc., 101, E341–E356.
Gauch, M., Mai, J., Gharari, S., and Lin, J. (2019):
Data-Driven vs. Physically-Based Streamflow Prediction
Models
Proceedings of the 9th International Workshop on Climate
Informatics, October 2019, Paris, France.
Gauch, M., Mai, J., Gharari, S.,and Lin, J. (2019):
Streamflow Prediction with Limited Spatially-Distributed Input
Data
Proceedings of the NeurIPS 2019 Workshop on Tackling Climate
Change with Machine Learning, December 2019, Vancouver, BC,
Canada.
Thober, S., Cuntz, M., Kelbling, M., Kumar, R., Mai, J.,
and Samaniego L. (2019):
The multiscale Routing Model mRM v1.0: simple river routing at
resolutions from 1 to 50 km
Geosci. Model Dev., 12, 2501-2521.
Mai, J. and Tolson, B.A. (2019):
Model Variable Augmentation (MVA) for Diagnostic Assessment of
Sensitivity Analysis Results
Water Resources Research, 55(4), 2631-2651.
Liu, H., Thiboult, A., Tolson, B.A., Anctil, F., and Mai, J. (2019):
Efficient treatment of climate data uncertainty in ensemble
Kalman filter (EnKF) based on an existing historical climate
ensemble dataset
Journal of Hydrology, 568, 985-996.
Bumberger, J., Mai, J., Schmidt, F., Luenenschloss, P.,
Wagner, N., and Toepfer, H. (2018):
Spatial Retrieval of Broadband Dielectric Spectra
Sensors 2018, 18(9), 2780.
Zink, M., Samaniego, L., Kumar, R., Thober, S., Mai, J., Schäfer, D., Marx, A. (2018):
A national scale planning tool for agricultural droughts in
Germany
In: Friesen, J., Rodríguez-Sinobas, L. (eds.)
Advanced tools for integrated water resources management
Advances in Chemical Pollution, Environmental Management and
Protection 3
Elsevier, p. 147-169
Zink, M., Mai, J., Cuntz, M., and Samaniego, L. (2018):
Conditioning a Hydrologic Model using Patterns of Remotely Sensed Land Surface Temperature
Water Resources Research, 54, 2976–2998.
Jahanpour, M., Tolson, B. A., and Mai, J. (2018):
PADDS Algorithm Assessment for Biobjective Water Distribution System Benchmark Design Problems
Journal of Water Resources Planning and Management 144 (3), 1193 - 1219.
Demirel, M.C., Mai, J., González, G. M., Koch, J., Samaniego, L., Stisen, S. (2018):
Combining satellite data and appropriate objective functions
for improved spatial pattern performance of a distributed
hydrologic model
Hydrology and Earth System Sciences 22 (2), 1299-1315
Madi, R., de Rooij, G. H., Mielenz, H., and Mai, J. (2018):
Parametric soil water retention models: A critical evaluation of expressions for the full moisture range
Hydrol. Earth Syst. Sci. 22 (2), 1193 - 1219
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. (2017):
Improving Calibration and Validation of Cosmic-Ray Neutron
Sensors in the Light of Spatial Sensitivity
Hydrol. Earth Syst. Sci. 21 (10), 5009 - 5030
Zink, M., Samaniego, L., Kumar, R., Thober, S., Mai, J., Schäfer,
D., Marx, A. (2016):
The German drought monitor
Environ. Res. Lett. 11 (7), art. 074002.
Cuntz, M., Mai, J., Samaniego, L., Clark, M., Wulfmeyer, V., Branch, O., Attinger, S., Thober, S. (2016):
The impact of standard and hard-coded parameters on the hydrologic fluxes in the Noah-MP land surface model
J. Geophys. Res. 121 (18), 10676 - 10700
Nijzink, R.C., Samaniego, L., Mai, J., Kumar, R., Thober, S., Zink, M., Schäfer, D., Savenije, H.H.G., Hrachowitz, M. (2016):
The importance of topography-controlled sub-grid process heterogeneity and semi-quantitative prior constraints in distributed hydrological models
Hydrol. Earth Syst. Sci. 20 (3), 1151 - 1176
Rakovec, O., Kumar, R., Mai, J., Cuntz, M., Thober, S., Zink, M., Attinger, S., Schäfer, D., Schrön, M., Samaniego, L. (2016):
Multiscale and multivariate evaluation of water fluxes and states over European river basins
J. Hydrometeorol. 17 (1), 287 - 307
Zech, A., Müller, S., Mai, J., Heße, F., Attinger, S. (2016):
Extending Theis' solution: Using transient pumping tests to estimate parameters of aquifer heterogeneity
Water Resour. Res. 52 (8), 6156 - 6170
Marx, A., Samaniego, L., Kumar, R., Thober, S., Mai, J., Zink, M. (2016):
Der Dürremonitor: aktuelle Information zur Bodenfeuchte in Deutschland
In: Wernecke, G., Ebner von Eschenbach, A.-D., Strunck, Y., Kirschbauer, L., Müller, A. (Hrsg.)
Wasserressourcen: Wissen im Flussgebieten vernetzen. Beiträge zum Tag der Hydrologie am 17./18. März 2016 in Koblenz, ausgerichtet von der Hochschule Koblenz und der Bundesanstalt für Gewässerkunde
Forum für Hydrologie und Wasserbewirtschaftung 37
Deutsche Vereinigung für Wasserwirtschaft, Abwasser und Abfall (DWA), Hennef, S. 131 - 142
Kumar, R., Musuuza, J.L., Van Loon, A.F., Teuling, A.J., Barthel, R., Ten Broek, J., Mai, J., Samaniego, L., Attinger, S. (2016):
Multiscale evaluation of the Standardized Precipitation Index as a groundwater drought indicator
Hydrol. Earth Syst. Sci. 20 (3), 1117 - 1131
Thober, S., Kumar, R., Sheffield, J., Mai, J., Schäfer, D., Samaniego, L. (2015):
Seasonal soil moisture drought prediction over Europe using the North American Multi-Model Ensemble (NMME)
J. Hydrometeorol. 16 (6), 2329 - 2344
Cuntz, M., Mai, J., Zink, M.,
Thober, S., Kumar, R., Schäfer, D.,
Schrön, M., Craven, J.R., Rakovec, O.,
Spieler, D., Prykhodko, V., Dalmasso, G.,
Musuuza, J., Langenberg, B., Attinger, S.,
Samaniego, L. (2015):
Computationally inexpensive identification of noninformative model parameters by sequential screening
Water Resour. Res. 51 (8), 6417 - 6441
Thober, S., Mai, J., Zink, M., Samaniego, L. (2014):
Stochastic temporal disaggregation of monthly precipitation for regional gridded data sets
Water Resour. Res. 50 (11), 8714 - 8735
Wehrer, M., Mai, J., Attinger, S., Totsche, K.U. (2013):
Kinetic control of contaminant release from NAPLs – Information potential of concentration time profiles
Environ. Pollut. 179 , 301 - 314
Mai, J., Trump, S., Lehmann, I., Attinger, S. (2013):
Parameter importance in FRAP acquisition and analysis: a simulation approach
Biophys. J. 104 (9), 2089 - 2097
Göhler, M., Mai, J., Cuntz, M. (2013):
Use of eigendecomposition in a parameter sensitivity analysis of the Community Land Model
J. Geophys. Res. 118 (2), 904 - 921
Michaelson, J., Trump, S., Rudzok, S., Gräbsch, C., Madureira, D.J., Dautel, F., Mai, J., Attinger, S., Schirmer, K., von Bergen, M., Lehmann, I., Beyer, A. (2011):
Transcriptional signatures of regulatory and toxic responses to benzo-[a]-pyrene exposure
BMC Genomics 12 , art. 502
Mai, J., Trump, S., Ali, R., Schiltz, R.L., Hager, G., Hanke, T., Lehmann, I., Attinger, S. (2011):
Are assumptions about the model type necessary in reaction-diffusion modeling? A FRAP application
Biophys. J. 100 (5), 1178 - 1188.