Worth of hydraulic and water chemistry observation data in terms of the reliability of surface water-groundwater exchange flux predictions under varied flow conditions

Abstract This study assesses the worth of routinely collected hydraulic data (groundwater head, stream stage and streamflow) and lesser collected water chemistry data (Radon-222, Carbon-14, electrical conductivity (EC)) in the context of making regional-scale surface water-groundwater (SW-GW) exchange flux predictions. Using integrated SW-GW flow and transport numerical models, first-order, second-moment (FOSM) analyses were employed to assess the extent of the uncertainty reduction or lack thereof in SW-GW exchange flux predictions following acquisition of hydraulic and water chemistry observation data. With a case study of the Campaspe River in the Murray-Darling Basin (Australia), we explored the apparent information content of these data during low, regular and high streamflow conditions. Also, a range of spatial and temporal prediction scales were considered: catchment-wide and reach-based spatial scales and annual and monthly temporal scales. Generally, the data worth evaluations showed significant variability across predictions that were dependent on the spatiotemporal scale of the SW-GW exchange, the magnitude and direction of the SW-GW exchange flux and the prevailing streamflow conditions. These dependencies serve to emphasise the importance of prediction specificity with respect to SW-GW exchange. Among existing data, the most worth was found in Radon-222, groundwater hydraulic head, EC, and streamflow data showing average reductions in uncertainty of 41%, 38%, 32%, and 23% respectively. Assessment of type and spatiotemporal locations of potential data showed Radon-222 to be the next most important observation type across many predictions in locations with data paucity of all data types. Hydraulic observation data types were found to inform SW-GW exchange flux best under high- and regular- streamflow conditions when the magnitude of exchange fluxes were largest, whereas the water chemistry data was of highest value for low- and regular-streamflow conditions where groundwater is discharging to the stream.

[1]  C. Kikuchi,et al.  Toward Increased Use of Data Worth Analyses in Groundwater Studies , 2017, Ground water.

[2]  Y. Rubin,et al.  A hypothesis‐driven approach to optimize field campaigns , 2012 .

[3]  Xin Wu,et al.  Systematic assessment of the uncertainty in integrated surface water‐groundwater modeling based on the probabilistic collocation method , 2014 .

[4]  Christian D Langevin,et al.  Quantifying Data Worth Toward Reducing Predictive Uncertainty , 2010, Ground water.

[5]  R. Therrien,et al.  Modeling Surface Water‐Groundwater Interaction with MODFLOW: Some Considerations , 2010, Ground water.

[6]  Christoph Tyralla,et al.  How many tracers do we need for end member mixing analysis (EMMA)? A sensitivity analysis , 2011 .

[7]  C. Lawrence,et al.  Physical hydrogeology and environmental isotopes to constrain the age, origins, and stability of a low-salinity groundwater lens formed by periodic river recharge: Murray Basin, Australia , 2010 .

[8]  L. Fifield,et al.  Cl/Br ratios and environmental isotopes as indicators of recharge variability and groundwater flow: An example from the southeast Murray Basin, Australia , 2006 .

[9]  K. Ellins,et al.  Using 222Rn to examine groundwater/surface discharge interaction in the Rio Grande de Manati, Puerto Rico , 1990 .

[10]  M Bakker,et al.  Scripting MODFLOW Model Development Using Python and FloPy , 2016, Ground water.

[11]  Catherine Moore,et al.  Role of model parameterization in risk-based decision support: An empirical exploration , 2019 .

[12]  John Doherty,et al.  Quantifying the predictive consequences of model error with linear subspace analysis , 2014 .

[13]  John Doherty,et al.  Using Prediction Uncertainty Analysis to Design Hydrologic Monitoring Networks: Example Applications from the Great Lakes Water Availability Pilot Project , 2014 .

[14]  P. Cook.  Estimating groundwater discharge to rivers from river chemistry surveys , 2013 .

[15]  C. Gerber,et al.  Advancing Physically‐Based Flow Simulations of Alluvial Systems Through Atmospheric Noble Gases and the Novel 37Ar Tracer Method , 2017 .

[16]  Henning Prommer,et al.  Using predictive uncertainty analysis to optimise tracer test design and data acquisition , 2014 .

[17]  John Doherty,et al.  Predictive uncertainty analysis of a saltwater intrusion model using null‐space Monte Carlo , 2011 .

[18]  D. Hannah,et al.  Groundwater-surface water interactions: New methods and models to improve understanding of processes and dynamics , 2010 .

[19]  T. Culver,et al.  Prediction uncertainty and data worth assessment for groundwater transport times in an agricultural catchment , 2018, Journal of Hydrology.

[20]  T. Wöhling,et al.  Quantifying River‐Groundwater Interactions of New Zealand's Gravel‐Bed Rivers: The Wairau Plain , 2018, Ground water.

[21]  J. Doherty,et al.  Uncertainty assessment and implications for data acquisition in support of integrated hydrologic models , 2012 .

[22]  Michael N. Fienen,et al.  A python framework for environmental model uncertainty analysis , 2016, Environ. Model. Softw..

[23]  Peter G. Cook,et al.  Quantifying groundwater discharge to Cockburn River, southeastern Australia, using dissolved gas tracers 222Rn and SF6 , 2006 .

[24]  F. Reinstorf,et al.  Measuring methods for groundwater – surface water interactions: a review , 2006 .

[25]  Matthew J. Knowling,et al.  On the assimilation of environmental tracer observations for model-based decision support , 2019, Hydrology and Earth System Sciences.

[26]  S. Tweed,et al.  Constraining groundwater flow, residence times, inter-aquifer mixing, and aquifer properties using environmental isotopes in the southeast Murray Basin, Australia , 2012 .

[27]  R. Hunt,et al.  Are Models Too Simple? Arguments for Increased Parameterization , 2007, Ground water.

[28]  John Doherty,et al.  Predictive error dependencies when using pilot points and singular value decomposition in groundwater model calibration , 2008 .

[29]  Albert Tarantola,et al.  Inverse problem theory - and methods for model parameter estimation , 2004 .

[30]  A. Arad,et al.  The hydrogeology, hydrochemistry and environmental isotopes of the Campaspe River aquifer system, North-Central Victoria, Australia , 1987 .

[31]  P. Cook.  Quantifying river gain and loss at regional scales , 2015 .

[32]  John Doherty,et al.  Using tree ring data as a proxy for transpiration to reduce predictive uncertainty of a model simulating groundwater-surface water-vegetation interactions , 2014 .

[33]  J. Doherty,et al.  Role of the calibration process in reducing model predictive error , 2005 .