Articles | Volume 26, issue 5
https://doi.org/10.5194/hess-26-1319-2022
© 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-26-1319-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Coupled effects of observation and parameter uncertainty on urban groundwater infrastructure decisions
Marina R. L. Mautner
CORRESPONDING AUTHOR
Department of Land Air and Water Resources, University of California Davis, Davis, CA, USA
Department of Civil and Environmental Engineering, University of California Davis, Davis, CA, USA
Laura Foglia
Department of Land Air and Water Resources, University of California Davis, Davis, CA, USA
Jonathan D. Herman
Department of Civil and Environmental Engineering, University of California Davis, Davis, CA, USA
Related authors
No articles found.
Nusrat Molla, John DeIonno, Thilo Gross, and Jonathan Herman
Earth Syst. Dynam., 13, 1677–1688, https://doi.org/10.5194/esd-13-1677-2022, https://doi.org/10.5194/esd-13-1677-2022, 2022
Short summary
Short summary
How the structure of resource governance systems affects how they respond to change is not yet well understood. We model the stability of thousands of different governance systems, revealing that greater diversity and interdependence among actors are destabilizing, while venue shopping and advocacy organizations are stabilizing. This study suggests that complexity in governance corresponds to responsiveness to change, while providing insight into managing them to balance adaptivity and stability
Stephen R. Maples, Laura Foglia, Graham E. Fogg, and Reed M. Maxwell
Hydrol. Earth Syst. Sci., 24, 2437–2456, https://doi.org/10.5194/hess-24-2437-2020, https://doi.org/10.5194/hess-24-2437-2020, 2020
Short summary
Short summary
In this study, we use a combination of local- and global-sensitivity analyses to evaluate the relative importance of (1) the configuration of subsurface alluvial geology and (2) the hydraulic properties of geologic facies on recharge processes. Results show that there is a large variation of recharge rates possible in a typical alluvial aquifer system and that the configuration proportion of sand and gravel deposits in the subsurface have a large impact on recharge rates.
Robert Reinecke, Laura Foglia, Steffen Mehl, Jonathan D. Herman, Alexander Wachholz, Tim Trautmann, and Petra Döll
Hydrol. Earth Syst. Sci., 23, 4561–4582, https://doi.org/10.5194/hess-23-4561-2019, https://doi.org/10.5194/hess-23-4561-2019, 2019
Short summary
Short summary
Recently, the first global groundwater models were developed to better understand surface-water–groundwater interactions and human water use impacts. However, the reliability of model outputs is limited by a lack of data as well as model assumptions required due to the necessarily coarse spatial resolution. In this study we present the first global maps of model sensitivity according to their parameterization and build a foundation to improve datasets, model design, and model understanding.
Robert Reinecke, Laura Foglia, Steffen Mehl, Tim Trautmann, Denise Cáceres, and Petra Döll
Geosci. Model Dev., 12, 2401–2418, https://doi.org/10.5194/gmd-12-2401-2019, https://doi.org/10.5194/gmd-12-2401-2019, 2019
Short summary
Short summary
G³M is a new global groundwater model (http://globalgroundwatermodel.org) that simulates lateral and vertical flows as well as exchanges with surface water bodies like rivers, lakes, and wetlands for the whole globe except Antarctica and Greenland. The newly developed model framework enables an efficient integration into established global hydrological models. This paper presents the G³M concept and specific model design decisions together with first results under a naturalized equilibrium.
N. W. Chaney, J. D. Herman, P. M. Reed, and E. F. Wood
Hydrol. Earth Syst. Sci., 19, 3239–3251, https://doi.org/10.5194/hess-19-3239-2015, https://doi.org/10.5194/hess-19-3239-2015, 2015
Short summary
Short summary
Land surface modeling is playing an increasing role in global monitoring and prediction of extreme hydrologic events. However, uncertainties in parameter identifiability limit the reliability of model predictions. This study makes use of petascale computing to perform a comprehensive evaluation of land surface modeling for global flood and drought monitoring and suggests paths forward to overcome the challenges posed by parameter uncertainty.
J. D. Herman, J. B. Kollat, P. M. Reed, and T. Wagener
Hydrol. Earth Syst. Sci., 17, 5109–5125, https://doi.org/10.5194/hess-17-5109-2013, https://doi.org/10.5194/hess-17-5109-2013, 2013
J. D. Herman, J. B. Kollat, P. M. Reed, and T. Wagener
Hydrol. Earth Syst. Sci., 17, 2893–2903, https://doi.org/10.5194/hess-17-2893-2013, https://doi.org/10.5194/hess-17-2893-2013, 2013
Related subject area
Subject: Water Resources Management | Techniques and Approaches: Uncertainty analysis
Robust multi-objective optimization under multiple uncertainties using the CM-ROPAR approach: case study of water resources allocation in the Huaihe River basin
Actionable human-water systems modeling under uncertainty
Evaluating the impact of post-processing medium-range ensemble streamflow forecasts from the European Flood Awareness System
Disentangling sources of future uncertainties for water management in sub-Saharan river basins
Possibilistic response surfaces: incorporating fuzzy thresholds into bottom-up flood vulnerability analysis
Future hot-spots for hydro-hazards in Great Britain: a probabilistic assessment
Evaluation of impacts of future climate change and water use scenarios on regional hydrology
Planning for climate change impacts on hydropower in the Far North
Describing the interannual variability of precipitation with the derived distribution approach: effects of record length and resolution
Dissolved oxygen prediction using a possibility theory based fuzzy neural network
Projected changes in US rainfall erosivity
Approximating uncertainty of annual runoff and reservoir yield using stochastic replicates of global climate model data
Assessment of precipitation and temperature data from CMIP3 global climate models for hydrologic simulation
Robust global sensitivity analysis of a river management model to assess nonlinear and interaction effects
Sensitivity and uncertainty in crop water footprint accounting: a case study for the Yellow River basin
Irrigation efficiency and water-policy implications for river basin resilience
On an improved sub-regional water resources management representation for integration into earth system models
Statistical analysis of error propagation from radar rainfall to hydrological models
The implications of climate change scenario selection for future streamflow projection in the Upper Colorado River Basin
Prioritization of water management under climate change and urbanization using multi-criteria decision making methods
Crop yields response to water pressures in the Ebro basin in Spain: risk and water policy implications
Jitao Zhang, Dimitri Solomatine, and Zengchuan Dong
Hydrol. Earth Syst. Sci., 28, 3739–3753, https://doi.org/10.5194/hess-28-3739-2024, https://doi.org/10.5194/hess-28-3739-2024, 2024
Short summary
Short summary
Faced with the problem of uncertainty in the field of water resources management, this paper proposes the Copula Multi-objective Robust Optimization and Probabilistic Analysis of Robustness (CM-ROPAR) approach to obtain robust water allocation schemes based on the uncertainty of drought and wet encounters and the uncertainty of inflow. We believe that this research article not only highlights the significance of the CM-ROPAR approach but also provides a new concept for uncertainty analysis.
Laura Gil-García, Nazaret M. Montilla-López, Carlos Gutiérrez-Martín, Ángel Sánchez-Daniel, Pablo Saiz-Santiago, Josué M. Polanco-Martínez, Julio Pindado, and C. Dionisio Pérez-Blanco
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-61, https://doi.org/10.5194/hess-2024-61, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
This paper presents an interdisciplinary model for quantifying uncertainties in water allocation under climate change. It combines climate, hydrological, and microeconomic experiments with a decision support system. Multi-model analyses reveal potential futures for water management policies, emphasizing nonlinear climate responses. As illustrated in the Douro River Basin, minor water allocation changes have significant economic impacts, stresssing the need for adaptation strategies.
Gwyneth Matthews, Christopher Barnard, Hannah Cloke, Sarah L. Dance, Toni Jurlina, Cinzia Mazzetti, and Christel Prudhomme
Hydrol. Earth Syst. Sci., 26, 2939–2968, https://doi.org/10.5194/hess-26-2939-2022, https://doi.org/10.5194/hess-26-2939-2022, 2022
Short summary
Short summary
The European Flood Awareness System creates flood forecasts for up to 15 d in the future for the whole of Europe which are made available to local authorities. These forecasts can be erroneous because the weather forecasts include errors or because the hydrological model used does not represent the flow in the rivers correctly. We found that, by using recent observations and a model trained with past observations and forecasts, the real-time forecast can be corrected, thus becoming more useful.
Alessandro Amaranto, Dinis Juizo, and Andrea Castelletti
Hydrol. Earth Syst. Sci., 26, 245–263, https://doi.org/10.5194/hess-26-245-2022, https://doi.org/10.5194/hess-26-245-2022, 2022
Short summary
Short summary
This study aims at designing water supply strategies that are robust against climate, social, and land use changes in a sub-Saharan river basin. We found that robustness analysis supports the discovery of policies enhancing the resilience of water resources systems, benefiting the agricultural, energy, and urban sectors. We show how energy sustainability is affected by water availability, while urban and irrigation resilience also depends on infrastructural interventions and land use changes.
Thibaut Lachaut and Amaury Tilmant
Hydrol. Earth Syst. Sci., 25, 6421–6435, https://doi.org/10.5194/hess-25-6421-2021, https://doi.org/10.5194/hess-25-6421-2021, 2021
Short summary
Short summary
Response surfaces are increasingly used to identify the hydroclimatic conditions leading to a water resources system's failure. Partitioning the surface usually requires performance thresholds that are not necessarily crisp. We propose a methodology that combines the inherent uncertainty of response surfaces with the ambiguity of performance thresholds. The proposed methodology is illustrated with a multireservoir system in Canada for which some performance thresholds are imprecise.
Lila Collet, Shaun Harrigan, Christel Prudhomme, Giuseppe Formetta, and Lindsay Beevers
Hydrol. Earth Syst. Sci., 22, 5387–5401, https://doi.org/10.5194/hess-22-5387-2018, https://doi.org/10.5194/hess-22-5387-2018, 2018
Short summary
Short summary
Floods and droughts cause significant damages and pose risks to lives worldwide. In a climate change context this work identifies hotspots across Great Britain, i.e. places expected to be impacted by an increase in floods and droughts. By the 2080s the western coast of England and Wales and northeastern Scotland would experience more floods in winter and droughts in autumn, with a higher increase in drought hazard, showing a need to adapt water management policies in light of climate change.
Seungwoo Chang, Wendy Graham, Jeffrey Geurink, Nisai Wanakule, and Tirusew Asefa
Hydrol. Earth Syst. Sci., 22, 4793–4813, https://doi.org/10.5194/hess-22-4793-2018, https://doi.org/10.5194/hess-22-4793-2018, 2018
Short summary
Short summary
It is important to understand potential impacts of climate change and human water use on streamflow and groundwater levels. This study used climate models with an integrated hydrologic model to project future streamflow and groundwater level in Tampa Bay for a variety of future water use scenarios. Impacts of different climate projections on streamflow were found to be much stronger than the impacts of different human water use scenarios, but both were significant for groundwater projection.
Jessica E. Cherry, Corrie Knapp, Sarah Trainor, Andrea J. Ray, Molly Tedesche, and Susan Walker
Hydrol. Earth Syst. Sci., 21, 133–151, https://doi.org/10.5194/hess-21-133-2017, https://doi.org/10.5194/hess-21-133-2017, 2017
Short summary
Short summary
We know that climate is changing quickly in the Far North (the Arctic and sub-Arctic). Hydropower continues to grow in this region because water resources are perceived to be plentiful. However, with changes in glacier extent and permafrost, and more extreme events, will those resources prove reliable into the future? This study amasses the evidence that quantitative hydrology modeling and uncertainty assessment have matured to the point where they should be used in water resource planning.
Claudio I. Meier, Jorge Sebastián Moraga, Geri Pranzini, and Peter Molnar
Hydrol. Earth Syst. Sci., 20, 4177–4190, https://doi.org/10.5194/hess-20-4177-2016, https://doi.org/10.5194/hess-20-4177-2016, 2016
Short summary
Short summary
We show that the derived distribution approach is able to characterize the interannual variability of precipitation much better than fitting a probabilistic model to annual rainfall totals, as long as continuously gauged data are available. The method is a useful tool for describing temporal changes in the distribution of annual rainfall, as it works for records as short as 5 years, and therefore does not require any stationarity assumption over long periods.
Usman T. Khan and Caterina Valeo
Hydrol. Earth Syst. Sci., 20, 2267–2293, https://doi.org/10.5194/hess-20-2267-2016, https://doi.org/10.5194/hess-20-2267-2016, 2016
Short summary
Short summary
This paper contains a new two-step method to construct fuzzy numbers using observational data. In addition an existing fuzzy neural network is modified to account for fuzzy number inputs. This is combined with possibility-theory based intervals to train the network. Furthermore, model output and a defuzzification technique is used to estimate the risk of low Dissolved Oxygen so that water resource managers can implement strategies to prevent the occurrence of low Dissolved Oxygen.
M. Biasutti and R. Seager
Hydrol. Earth Syst. Sci., 19, 2945–2961, https://doi.org/10.5194/hess-19-2945-2015, https://doi.org/10.5194/hess-19-2945-2015, 2015
Short summary
Short summary
We estimate future changes in US erosivity from the most recent ensemble projections of daily and monthly rainfall accumulation. The expectation of overall increase in erosivity is confirmed by these calculations, but a quantitative assessment is marred by large uncertainties. Specifically, the uncertainty in the method of estimation of erosivity is more consequential than that deriving from the spread in climate simulations, and leads to changes of uncertain sign in parts of the south.
M. C. Peel, R. Srikanthan, T. A. McMahon, and D. J. Karoly
Hydrol. Earth Syst. Sci., 19, 1615–1639, https://doi.org/10.5194/hess-19-1615-2015, https://doi.org/10.5194/hess-19-1615-2015, 2015
Short summary
Short summary
We present a proof-of-concept approximation of within-GCM uncertainty using non-stationary stochastic replicates of monthly precipitation and temperature projections and investigate the impact of within-GCM uncertainty on projected runoff and reservoir yield. Amplification of within-GCM variability from precipitation to runoff to reservoir yield suggests climate change impact assessments ignoring within-GCM uncertainty would provide water resources managers with an unjustified sense of certainty
T. A. McMahon, M. C. Peel, and D. J. Karoly
Hydrol. Earth Syst. Sci., 19, 361–377, https://doi.org/10.5194/hess-19-361-2015, https://doi.org/10.5194/hess-19-361-2015, 2015
Short summary
Short summary
Here we assess GCM performance from a hydrologic perspective. We identify five better performing CMIP3 GCMs that reproduce grid-scale climatological statistics of observed precipitation and temperature over global land regions for future hydrologic simulation. GCM performance in reproducing observed mean and standard deviation of annual precipitation, mean annual temperature and mean monthly precipitation and temperature was assessed and ranked, and five better performing GCMs were identified.
L. J. M. Peeters, G. M. Podger, T. Smith, T. Pickett, R. H. Bark, and S. M. Cuddy
Hydrol. Earth Syst. Sci., 18, 3777–3785, https://doi.org/10.5194/hess-18-3777-2014, https://doi.org/10.5194/hess-18-3777-2014, 2014
L. Zhuo, M. M. Mekonnen, and A. Y. Hoekstra
Hydrol. Earth Syst. Sci., 18, 2219–2234, https://doi.org/10.5194/hess-18-2219-2014, https://doi.org/10.5194/hess-18-2219-2014, 2014
C. A. Scott, S. Vicuña, I. Blanco-Gutiérrez, F. Meza, and C. Varela-Ortega
Hydrol. Earth Syst. Sci., 18, 1339–1348, https://doi.org/10.5194/hess-18-1339-2014, https://doi.org/10.5194/hess-18-1339-2014, 2014
N. Voisin, H. Li, D. Ward, M. Huang, M. Wigmosta, and L. R. Leung
Hydrol. Earth Syst. Sci., 17, 3605–3622, https://doi.org/10.5194/hess-17-3605-2013, https://doi.org/10.5194/hess-17-3605-2013, 2013
D. Zhu, D. Z. Peng, and I. D. Cluckie
Hydrol. Earth Syst. Sci., 17, 1445–1453, https://doi.org/10.5194/hess-17-1445-2013, https://doi.org/10.5194/hess-17-1445-2013, 2013
B. L. Harding, A. W. Wood, and J. R. Prairie
Hydrol. Earth Syst. Sci., 16, 3989–4007, https://doi.org/10.5194/hess-16-3989-2012, https://doi.org/10.5194/hess-16-3989-2012, 2012
J.-S. Yang, E.-S. Chung, S.-U. Kim, and T.-W. Kim
Hydrol. Earth Syst. Sci., 16, 801–814, https://doi.org/10.5194/hess-16-801-2012, https://doi.org/10.5194/hess-16-801-2012, 2012
S. Quiroga, Z. Fernández-Haddad, and A. Iglesias
Hydrol. Earth Syst. Sci., 15, 505–518, https://doi.org/10.5194/hess-15-505-2011, https://doi.org/10.5194/hess-15-505-2011, 2011
Cited articles
Bakker, M., Post, V., Hughes, J. D., Langevin, C. D., White, J. T., Leaf, A. T., Paulinski, S. R., Bellino, J. C., Morway, E. D., Toews, M. W., Larsen, J. D., Fienen, M. N., Starn, J. J., and Brakenhoff, D.: FloPy v3.2.12 — release candidate: U.S. Geological Survey Software Release, 31 May 2019 [code], https://doi.org/10.5066/F7BK19FH, 2019. a
Bárdossy, A.: Calibration of hydrological model parameters for ungauged catchments, Hydrol. Earth Syst. Sci., 11, 703–710, https://doi.org/10.5194/hess-11-703-2007, 2007. a
Beven, K.: Facets of uncertainty: epistemic uncertainty, non-stationarity,
likelihood, hypothesis testing, and communication,
Hydrolog. Sci. J., 61, 1652–1665, https://doi.org/10.1080/02626667.2015.1031761, 2016. a
Bhaskar, A. S., Beesley, L., Burns, M. J., Fletcher, T. D., Hamel, P., Oldham,
C. E., and Roy, A. H.: Will it rise or will it fall? Managing the complex
effects of urbanization on base flow, Freshw. Science, 35, 293–310,
https://doi.org/10.1086/685084, 2016. a
Borgonovo, E.: A new uncertainty importance measure,
Reliab. Eng. Syst. Safe, 92, 771–784, https://doi.org/10.1016/j.ress.2006.04.015, 2007. a
Borgonovo, E. and Plischke, E.: Sensitivity analysis: A review of recent
advances, Eur. J. Oper. Res., 248, 869–887,
https://doi.org/10.1016/j.ejor.2015.06.032, 2016. a
Brunner, P., Doherty, J., and Simmons, C. T.: Uncertainty assessment and
implications for data acquisition in support of integrated hydrologic
models, Water Resour. Res., 48, 1–18, https://doi.org/10.1029/2011WR011342,
2012. a
Dams, J., Woldeamlak, S. T., and Batelaan, O.: Predicting land-use change and its impact on the groundwater system of the Kleine Nete catchment, Belgium, Hydrol. Earth Syst. Sci., 12, 1369–1385, https://doi.org/10.5194/hess-12-1369-2008, 2008. a
Dams, J., Salvadore, E., Van Daele, T., Ntegeka, V., Willems, P., and Batelaan, O.: Spatio-temporal impact of climate change on the groundwater system, Hydrol. Earth Syst. Sci., 16, 1517–1531, https://doi.org/10.5194/hess-16-1517-2012, 2012. a
Doherty, J. and Moore, C.: Decision Support Modeling: Data Assimilation,
Uncertainty Quantification, and Strategic Abstraction, Groundwater, 58,
327–337, https://doi.org/10.1111/gwat.12969, 2020. a
Doherty, J. and Simmons, C. T.: La modélisation de nappe comme support
de décision: Réflexions sur un cadre conceptuel unifié,
Hydrogeol. J., 21, 1531–1537, https://doi.org/10.1007/s10040-013-1027-7, 2013. a
Fletcher, S., Strzepek, K., Alsaati, A., and de Weck, O.: Learning and
flexibility for water supply infrastructure planning under groundwater
resource uncertainty, Environ. Res. Lett., 14, 114022,
https://doi.org/10.1088/1748-9326/ab4664, 2019. a
Foster, S. S. D., Lawrence, A., and Morris, B.: Groundwater in urban
development: assessing management needs and formulating policy strategies,
no. 390 in World Bank technical paper series, World Bank, Washington,
D.C, ISBN 978-0-8213-4072-1, 1998. a
Ganji, A., Maier, H. R., and Dandy, G. C.: A modified Sobol' sensitivity
analysis method for decision-making in environmental problems, Environ. Modell. Softw., 75, 15–27, https://doi.org/10.1016/j.envsoft.2015.10.001,
2016. a
Guillaume, J. H. A., Hunt, R. J., Comunian, A., Blakers, R. S., and Fu, B.:
Methods for Exploring Uncertainty in Groundwater Management Predictions,
in: Integrated Groundwater Management, edited by: Jakeman, A. J., Barreteau, O., Hunt, R. J., Rinaudo, J. D., and Ross, A., 711–737, Springer International
Publishing, https://doi.org/10.1007/978-3-319-23576-9_28, 2016. a
Hadka, D., Herman, J., Reed, P., and Keller, K.: An open source framework for
many-objective robust decision making, Environ. Modell. Softw.,
74, 114–129, https://doi.org/10.1016/j.envsoft.2015.07.014, 2015. a
Herman, J. D., Reed, P. M., and Wagener, T.: Time-varying sensitivity analysis
clarifies the effects of watershed model formulation on model behavior,
Water Resour. Res., 49, 1400–1414, https://doi.org/10.1002/wrcr.20124, 2013. a
Herman, J. D., Quinn, J. D., Steinschneider, S., Giuliani, M., and Fletcher,
S.: Climate Adaptation as a Control Problem: Review and Perspectives on
Dynamic Water Resources Planning Under Uncertainty, Water Resour.
Res., 56, e24389, https://doi.org/10.1029/2019WR025502, 2020. a
Herrera-Zamarrón, G., Cardona-Benavides, A., González-Hita, L.,
Gutiérrez-Ojeda, C., Hernández-Calero, R.,
Hernández-García, G., Hernández-Laloth, N.,
López-Hernández, R. I., Martínez-Morales, M., Pita de la
Paz, C., Sánchez-Díaz, L. F., Báez-Durán, J. A.,
Cruickshank-Villanueva, C., and Herrera-Revilla, I.: Estudio para obtener la
disponibilidad del acuífero de la Zona Metropolitana de la Ciudad de
México, Tech. Rep. Contract 06-CD-03-1O-0272-1-06, Secretaría
del Medio Ambiente del Gobierno del Distrito Federal, Sistema de Aguas de la
Ciudad de México (SACM), and Instituto Mexicano de Tecnología del
Agua (IMTA), Mexico City, Internal Technical Report, Contract No. 06-CD-03-1O-0272-1-06, 2005. a
Hrachowitz, M., Savenije, H., Blöschl, G., McDonnell, J., Sivapalan, M.,
Pomeroy, J., Arheimer, B., Blume, T., Clark, M., Ehret, U., Fenicia, F.,
Freer, J., Gelfan, A., Gupta, H., Hughes, D., Hut, R., Montanari, A., Pande,
S., Tetzlaff, D., Troch, P., Uhlenbrook, S., Wagener, T., Winsemius, H.,
Woods, R., Zehe, E., and Cudennec, C.: A decade of Predictions in Ungauged
Basins (PUB) – a review, Hydrolog. Sci. J., 58, 1198–1255,
https://doi.org/10.1080/02626667.2013.803183, 2013. a
Hyde, K. M. and Maier, H. R.: Distance-based and stochastic uncertainty
analysis for multi-criteria decision analysis in Excel using Visual Basic for
Applications, Environ. Modell. Softw., 21, 1695–1710,
https://doi.org/10.1016/j.envsoft.2005.08.004, 2006. a
Jing, M., Heße, F., Kumar, R., Kolditz, O., Kalbacher, T., and Attinger, S.: Influence of input and parameter uncertainty on the prediction of catchment-scale groundwater travel time distributions, Hydrol. Earth Syst. Sci., 23, 171–190, https://doi.org/10.5194/hess-23-171-2019, 2019. a
Kelleher, C., McGlynn, B., and Wagener, T.: Characterizing and reducing equifinality by constraining a distributed catchment model with regional signatures, local observations, and process understanding, Hydrol. Earth Syst. Sci., 21, 3325–3352, https://doi.org/10.5194/hess-21-3325-2017, 2017. a
Kwakkel, J. H. and Haasnoot, M.: Supporting DMDU: A Taxonomy of Approaches and
Tools, in: Decision Making under Deep Uncertainty, 355–374, Springer
International Publishing, in: Decision Making under Deep Uncertainty, edited by: Marchau, V., Walker, W., Bloemen, P., and Popper, S., https://doi.org/10.1007/978-3-030-05252-2_15, 2019. a
Lehr, C. and Lischeid, G.: Efficient screening of groundwater head monitoring data for anthropogenic effects and measurement errors, Hydrol. Earth Syst. Sci., 24, 501–513, https://doi.org/10.5194/hess-24-501-2020, 2020. a
Mai, J., Craig, J. R., and Tolson, B. A.: Simultaneously determining global sensitivities of model parameters and model structure, Hydrol. Earth Syst. Sci., 24, 5835–5858, https://doi.org/10.5194/hess-24-5835-2020, 2020. a
Maier, H., Guillaume, J., van Delden, H., Riddell, G., Haasnoot, M., and
Kwakkel, J.: An uncertain future, deep uncertainty, scenarios, robustness
and adaptation: How do they fit together?, Environ. Modell. Softw., 81, 154–164, https://doi.org/10.1016/j.envsoft.2016.03.014, 2016. a
Matott, L. S.: OSTRICH: an Optimization Software Tool, Documentation and User's Guide, Version 17.12.19, University at Buffalo Center for Computational Research, USA, 79 pp., 2017. a
Mautner, M. R. L., Foglia, L., and Herman, J. D.: mrlmautner/UrbanGW: Publication version (v2.1), Zenodo [code], https://doi.org/10.5281/zenodo.6039830, 2022. a
Mautner, M. R. L., Foglia, L., Herrera, G. S., Galán, R., and Herman, J. D.:
Urban growth and groundwater sustainability: Evaluating spatially
distributed recharge alternatives in the Mexico City Metropolitan Area,
J. Hydrol., 586, 124909, https://doi.org/10.1016/j.jhydrol.2020.124909,
2020. a, b, c, d, e, f, g
McMillan, H. K., Westerberg, I. K., and Krueger, T.: Hydrological data
uncertainty and its implications, WIREs Water, 5, 1–14,
https://doi.org/10.1002/wat2.1319, 2018. a
Megdal, S. B., Gerlak, A. K., Varady, R. G., and Huang, L. Y.: Groundwater
Governance in the United States: Common Priorities and Challenges,
Groundwater, 53, 677–684, https://doi.org/10.1111/gwat.12294, 2015. a
Mendoza, P. A., Clark, M. P., Mizukami, N., Gutmann, E. D., Arnold, J. R.,
Brekke, L. D., and Rajagopalan, B.: How do hydrologic modeling decisions
affect the portrayal of climate change impacts?, Hydrol. Process., 30,
1071–1095, https://doi.org/10.1002/hyp.10684, 2016. a
Montanari, A. and Di Baldassarre, G.: Data errors and hydrological modelling:
The role of model structure to propagate observation uncertainty, Adv.
Water Resour., 51, 498–504, https://doi.org/10.1016/j.advwatres.2012.09.007,
2013. a
Moore, C. and Doherty, J.: Role of the calibration process in reducing model
predictive error, Water Resour. Res., 41, 1–14,
https://doi.org/10.1029/2004WR003501, 2005. a
Mustafa, S. M. T., Hasan, M. M., Saha, A. K., Rannu, R. P., Van Uytven, E., Willems, P., and Huysmans, M.: Multi-model approach to quantify groundwater-level prediction uncertainty using an ensemble of global climate models and multiple abstraction scenarios, Hydrol. Earth Syst. Sci., 23, 2279–2303, https://doi.org/10.5194/hess-23-2279-2019, 2019. a
Nearing, G. S., Kratzert, F., Sampson, A. K., Pelissier, C. S., Klotz, D.,
Frame, J. M., Prieto, C., and Gupta, H. V.: What Role Does Hydrological
Science Play in the Age of Machine Learning?, Water Resour. Res., 57, e2020WR028091, https://doi.org/10.1029/2020WR028091, 2021. a
OCAVM: Programa Hídrico Regional 2014–2018: Region Administrativo
Hidrológico XIII, Aguas del Valle de México, Tech. rep.,
Comisión Nacional del Agua, Tlalpan, Mexico, D.F., Programa Hídrico Regional 2014–2018 Series, 2014. a
Peters-Lidard, C. D., Clark, M., Samaniego, L., Verhoest, N. E. C., van Emmerik, T., Uijlenhoet, R., Achieng, K., Franz, T. E., and Woods, R.: Scaling, similarity, and the fourth paradigm for hydrology, Hydrol. Earth Syst. Sci., 21, 3701–3713, https://doi.org/10.5194/hess-21-3701-2017, 2017. a
Pianosi, F., Beven, K., Freer, J., Hall, J. W., Rougier, J., Stephenson, D. B.,
and Wagener, T.: Sensitivity analysis of environmental models: A systematic
review with practical workflow, Environ. Modell. Softw., 79,
214–232, https://doi.org/10.1016/j.envsoft.2016.02.008, 2016. a
Plischke, E., Borgonovo, E., and Smith, C. L.: Global sensitivity measures
from given data, Eur. J. Oper. Res., 226, 536–550,
https://doi.org/10.1016/j.ejor.2012.11.047, 2013. a
Poeter, E. P., Hill, M. C., Lu, D., Tiedeman, C., and Mehl, S. W.:
UCODE_2014, with new capabilities to define parameters unique to
predictions, calculate weights using simulated values, estimate parameters
with SVD, evaluate uncertainty with MCMC, and more, Tech. rep., Integrated
Groundwater Modeling Center (IGWMC), of the Colorado School of Mines, Report Number GWMI 2014-02, https://igwmc.mines.edu/ucode-2/ (last access: 7 March 2022), 2014. a
Qiu, J., Yang, Q., Zhang, X., Huang, M., Adam, J. C., and Malek, K.: Implications of water management representations for watershed hydrologic modeling in the Yakima River basin, Hydrol. Earth Syst. Sci., 23, 35–49, https://doi.org/10.5194/hess-23-35-2019, 2019. a
Ravalico, J. K., Maier, H. R., and Dandy, G. C.: Sensitivity analysis for
decision-making using the MORE method-A Pareto approach,
Reliab. Eng. Syst. Safe, 94, 1229–1237,
https://doi.org/10.1016/j.ress.2009.01.009, 2009. a
Ravalico, J. K., Dandy, G. C., and Maier, H. R.: Management Option Rank
Equivalence (MORE) – A new method of sensitivity analysis for
decision-making, Environ. Modell. Softw., 25, 171–181,
https://doi.org/10.1016/j.envsoft.2009.06.012, 2010. a
Razavi, S. and Gupta, H. V.: What do we mean by sensitivity analysis? the need
for comprehensive characterization of “global” sensitivity in Earth and
Environmental systems models, Water Resour. Res., 51, 3070–3092,
https://doi.org/10.1002/2014WR016527, 2015. a
Razavi, S., Jakeman, A., Saltelli, A., Prieur, C., Iooss, B., Borgonovo, E.,
Plischke, E., Lo Piano, S., Iwanaga, T., Becker, W., Tarantola, S.,
Guillaume, J. H., Jakeman, J., Gupta, H., Melillo, N., Rabitti, G.,
Chabridon, V., Duan, Q., Sun, X., Smith, S., Sheikholeslami, R., Hosseini,
N., Asadzadeh, M., Puy, A., Kucherenko, S., and Maier, H. R.: The Future of
Sensitivity Analysis: An essential discipline for systems modeling and policy
support, Environ. Modell. Softw., 137, 104954, https://doi.org/10.1016/j.envsoft.2020.104954, 2021. a, b, c
Refsgaard, J. C., van der Sluijs, J. P., Højberg, A. L., and Vanrolleghem,
P. A.: Uncertainty in the environmental modelling process – A framework and
guidance, Environ. Modell. Softw., 22, 1543–1556,
https://doi.org/10.1016/j.envsoft.2007.02.004, 2007. a
Refsgaard, J. C., Christensen, S., Sonnenborg, T. O., Seifert, D., Højberg,
A. L., and Troldborg, L.: Review of strategies for handling geological
uncertainty in groundwater flow and transport modeling, Adv. Water
Resour., 36, 36–50, https://doi.org/10.1016/j.advwatres.2011.04.006, 2012. a
Reinecke, R., Foglia, L., Mehl, S., Herman, J. D., Wachholz, A., Trautmann, T., and Döll, P.: Spatially distributed sensitivity of simulated global groundwater heads and flows to hydraulic conductivity, groundwater recharge, and surface water body parameterization, Hydrol. Earth Syst. Sci., 23, 4561–4582, https://doi.org/10.5194/hess-23-4561-2019, 2019. a
Reusser, D. E. and Zehe, E.: Inferring model structural deficits by analyzing
temporal dynamics of model performance and parameter sensitivity, Water
Resour. Res., 47, 1–15, https://doi.org/10.1029/2010WR009946, 2011. a
Rojas, R., Feyen, L., Batelaan, O., and Dassargues, A.: On the value of
conditioning data to reduce conceptual model uncertainty in groundwater
modeling, Water Resour. Res., 46, 1–20, https://doi.org/10.1029/2009WR008822,
2010. a
Şalap-Ayça, S. and Jankowski, P.: Integrating local multi-criteria
evaluation with spatially explicit uncertainty-sensitivity analysis, Spat. Cogn. Comput., 16, 106–132, https://doi.org/10.1080/13875868.2015.1137578,
2016.
a
Singh, A.: Groundwater resources management through the applications of
simulation modeling: A review, Sci. Total Environ., 499,
414–423, https://doi.org/10.1016/j.scitotenv.2014.05.048, 2014. a
Tiedeman, C. R., Ely, D. M., Hill, M. C., and O'Brien, G. M.: A method for
evaluating the importance of system state observations to model predictions,
with application to the Death Valley regional groundwater flow system, Water
Resour. Res., 40, 1–14, https://doi.org/10.1029/2004WR003313, 2004. a
Tolley, D., Foglia, L., and Harter, T.: Sensitivity Analysis and Calibration
of an Integrated Hydrologic Model in an Irrigated Agricultural Basin With a
Groundwater-Dependent Ecosystem, Water Resour. Res., 55, 7876–7901,
https://doi.org/10.1029/2018WR024209, 2019. a
Tonkin, M. J., Tiedeman, C. R., Ely, D. M., and Hill, M. C.: OPR-PPR, a
Computer Program for Assessing Data Importance to Model Predictions Using
Linear Statistics, Techniques and Methods, p. 115, Technical report, OSTI identifier: 919524, Report no. TM 6-E2, United States Geological Survey, https://doi.org/10.2172/919524, 2007. a, b
Vázquez-Suñé, E., Carrera, J., Tubau, I., Sánchez-Vila, X., and Soler, A.: An approach to identify urban groundwater recharge, Hydrol. Earth Syst. Sci., 14, 2085–2097, https://doi.org/10.5194/hess-14-2085-2010, 2010. a
Wada, Y., Bierkens, M. F. P., de Roo, A., Dirmeyer, P. A., Famiglietti, J. S., Hanasaki, N., Konar, M., Liu, J., Müller Schmied, H., Oki, T., Pokhrel, Y., Sivapalan, M., Troy, T. J., van Dijk, A. I. J. M., van Emmerik, T., Van Huijgevoort, M. H. J., Van Lanen, H. A. J., Vörösmarty, C. J., Wanders, N., and Wheater, H.: Human–water interface in hydrological modelling: current status and future directions, Hydrol. Earth Syst. Sci., 21, 4169–4193, https://doi.org/10.5194/hess-21-4169-2017, 2017. a
Wagener, T., McIntyre, N., Lees, M. J., Wheater, H. S., and Gupta, H. V.:
Towards reduced uncertainty in conceptual rainfall-runoff modelling: dynamic
identifiability analysis, Hydrol. Process., 17, 455–476,
https://doi.org/10.1002/hyp.1135, 2003. a
Zhang, X. and Liu, P.: A time-varying parameter estimation approach using split-sample calibration based on dynamic programming, Hydrol. Earth Syst. Sci., 25, 711–733, https://doi.org/10.5194/hess-25-711-2021, 2021. a
Short summary
Sensitivity analysis can be harnessed to evaluate effects of model uncertainties on planning outcomes. This study explores how observation and parameter uncertainty propagate through a hydrogeologic model to influence the ranking of decision alternatives. Using global sensitivity analysis and evaluation of aquifer management objectives, we evaluate how physical properties of the model and choice of observations for calibration can lead to variations in decision-relevant model outputs.
Sensitivity analysis can be harnessed to evaluate effects of model uncertainties on planning...