A system of metrics for the assessment and improvement of aquatic ecosystem models

In this paper, we introduce the CSPS framework for the hierarchical assessment of aquatic ecosystem models built on a range of metrics and characteristic signatures relevant to aquatic ecosystem condition. The framework is comprised of four levels: 0) conceptual validation; 1) comparison of simulated state variables with observations (‘state validation’); 2) comparison of fluxes with measured process rates (‘process validation’); and 3) assessment of system-level emergent properties, patterns and relationships (‘system validation’). Of these, only levels 0 and 1 are routinely undertaken at present. To highlight a diverse range of contexts relevant to the aquatic ecosystem modelling community, we present several case studies of improved validation approaches using the level 0–3 assessment hierarchy. We envision that the community–driven adoption of these metrics will lead to more rigorously assessed models, ultimately accelerating advances in model structure and function, and improved confidence in model predictions

Decision making under uncertainty in environmental projects using mathematical simulation modeling

The final publication is available at Springer via http://dx.doi.org/10.1007/s12665-016-6135-yIn decision-making processes, reliability and risk aversion play a decisive role. The aim of this study is to perform an uncertainty assessment of the effects of future scenarios of sustainable groundwater pumping strategies on the quantitative and chemical status of an aquifer. The good status of the aquifer is defined according to the terms established by the EU Water Framework Directive (WFD). A decision support systems (DSS) is presented, which makes use of a stochastic inverse model (GC method) and geostatistical approaches to calibrate equally likely realizations of hydraulic conductivity (K) fields for a particular case study. These K fields are conditional to available field data, including hard and soft information. Then, different future scenarios of groundwater pumping strategies are generated, based on historical information and WFD standards, and simulated for each one of the equally likely K fields. The future scenarios lead to different environmental impacts and levels of socioeconomic development of the region and, hence, to a different degree of acceptance among stakeholders. We have identified the different stakeholders implied in the decision-making process, the objectives pursued and the alternative actions that should be considered by stakeholders in a public participation project (PPP). The MonteCarlo simulation provides a highly effective way for uncertainty assessment and allows presenting the results in a simple and understandable way even for non-experts stakeholders. The methodology has been successfully applied to a real case study and lays the foundations to performa PPP and stakeholders' involvement in a decisionmaking process as required by the WFD. The results of the methodology can help the decision-making process to come up with the best policies and regulations for a groundwater system under uncertainty in groundwater parameters and management strategies and involving stakeholders with conflicting interests.Llopis Albert, C.; Palacios Marqués, D.; Merigó -Lindahl, JM. (2016). Decision making under uncertainty in environmental projects using mathematical simulation modeling. Environmental Earth Sciences. 75(19):1-11. doi:10.1007/s12665-016-6135-yS1117519Arhonditsis GB, Perhar G, Zhang W, Massos E, Shi M, Das A (2008) Addressing equifinality and uncertainty in eutrophication models. Water Resour Res 44:W01420. doi: 10.1029/2007WR005862Capilla JE, Llopis-Albert C (2009) Gradual conditioning of non-gaussian transmissivity fields to flow and mass transport data. 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Comput Geosci 16(4):395–440Harbaugh AW, Banta ER, Hill MC and McDonald MG (2000) MODFLOW- 2000, The US geological survey modular groundwater model-user guide to modularization concepts and the groundwater flow process. US Geol. Surv. Open-File Rep 00–92, 12Hu LY (2000) Gradual deformation and iterative calibration of Gaussian related stochastic models. Math Geol 32(1):87–108Jagelke J, Barthel R (2005) Conceptualization and implementation of a regional groundwater model for the Neckar catchment in the framework of an integrated regional model. Adv Geosci 5:105–111Llopis-Albert C (2008) Stochastic inverse modeling conditional to flow, mass transport and secondary information. Universitat Politècnica de València, València. ISBN 978-84-691-9796-7Llopis-Albert C, Capilla JE (2009a) Gradual conditioning of non-gaussian transmissivity fields to flow and mass transport data. Demonstration on a synthetic aquifer. J Hydrol 371:53–55. doi: 10.1016/j.jhydrol.2009.03.014Llopis-Albert C, Capilla JE (2009b) Gradual conditioning of non-gaussian transmissivity fields to flow and mass transport data. Application to the macrodispersion experiment (MADE-2) site, on Columbus air force base in Mississippi (USA). J Hydrol 371:75–84. doi: 10.1016/j.jhydrol.2009.03.016Llopis-Albert C, Capilla JE (2010a) Stochastic simulation of non-gaussian 3D conductivity fields in a fractured medium with multiple statistical populations: a case study. J Hydrol Eng 15(7):554–566. doi: 10.1061/(ASCE)HE.1943-5584.0000214Llopis-Albert C, Capilla JE (2010b) Stochastic inverse modeling of hydraulic conductivity fields taking into account independent stochastic structures: a 3D case study. J Hydrol 391:277–288. doi: 10.1016/j.jhydrol.2010.07.028Llopis-Albert C, Pulido-Velazquez D (2014) Discussion about the validity of sharp-interface models to deal with seawater intrusion in coastal aquifers. Hydrol Process 28(10):3642–3654Llopis-Albert C, Pulido-Velazquez D (2015) Using MODFLOW code to approach transient hydraulic head with a sharp-interface solution. Hydrol Process 29(8):2052–2064. doi: 10.1002/hyp.10354Llopis-Albert C, Palacios-Marqués D, Merigó JM (2014) A coupled stochastic inverse-management framework for dealing with nonpoint agriculture pollution under groundwater parameter uncertainty. J Hydrol 511:10–16. doi: 10.1016/j.jhydrol.2014.01.021Llopis-Albert C, Merigó JM, Palacios-Marqués D (2015) Structure adaptation in stochastic inverse methods for integrating information. Water Resour Manage 29(1):95–107. doi: 10.1007/s11269-014-0829-2Llopis-Albert C, Merigó JM, Xu Y (2016) A coupled stochastic inverse/sharp interface seawater intrusion approach for coastal aquifers under groundwater parameter uncertainty. J Hydrol 540:774–783. doi: 10.1016/j.jhydrol.2016.06.065McDonald MG and Harbaugh AW (1988) A modular three-dimensional finite-difference groundwater flow model. US geological survey technical manual of water resources investigation, Book 6, US geological survey, Reston, Virginia, 586Molina JL, Pulido-Velazquez M, Llopis-Albert C, Peña-Haro S (2013) Stochastic hydro-economic model for groundwater quality management using Bayesian networks. Water Sci Technol 67(3):579–586. doi: 10.2166/wst.2012.598Peña-Haro S, Llopis-Albert C, Pulido-Velazquez M (2010) Fertilizer standards for controlling groundwater nitrate pollution from agriculture: El Salobral-Los Llanos case study, Spain. J Hydrol 392:174–187. doi: 10.1016/j.jhydrol.2010.08.006Peña-Haro S, Pulido-Velazquez M, Llopis-Albert C (2011) Stochastic hydro-economic modeling for optimal management of agricultural groundwater nitrate pollution under hydraulic conductivity uncertainty. 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Planktonic events may cause polymictic-dimictic regime shifts in temperate lakes

Water transparency affects the thermal structure of lakes, and within certain lake depth ranges, it can determine whether a lake mixes regularly (polymictic regime) or stratifies continuously (dimictic regime) from spring through summer. Phytoplankton biomass can influence transparency but the effect of its seasonal pattern on stratification is unknown. Therefore we analysed long term field data from two lakes of similar depth, transparency and climate but one polymictic and one dimictic, and simulated a conceptual lake with a hydrodynamic model. Transparency in the study lakes was typically low during spring and summer blooms and high in between during the clear water phase (CWP), caused when zooplankton graze the spring bloom. The effect of variability of transparency on thermal structure was stronger at intermediate transparency and stronger during a critical window in spring when the rate of lake warming is highest. Whereas the spring bloom strengthened stratification in spring, the CWP weakened it in summer. The presence or absence of the CWP influenced stratification duration and under some conditions determined the mixing regime. Therefore seasonal plankton dynamics, including biotic interactions that suppress the CWP, can influence lake temperatures, stratification duration, and potentially also the mixing regime

Influence of ocean–atmospheric oscillations on lake ice phenology in eastern North America

Our results reveal long-term trends in ice out dates (1836–2013) for twelve lakes in Maine, New Brunswick and New Hampshire, in eastern North America. The trends are remarkably coherent between lakes (rs = 0.462–0.933, p < 0.01) and correlate closely with the March–April (MA) instrumental temperature records from the region (rs = 0.488–0.816, p < 0.01). This correlation permits use of ice out dates as a proxy to extend the shorter MA instrumental record (1876–2013). Mean ice out dates trended progressively earlier during the recovery from the Little Ice Age through to the 1940s, and gradually became later again through to the late 1970s, when ice out dates had returned to values more typical of the late nineteenth century. Post-1970’s ice out dates resumed trending toward earlier dates, with the twenty-first century being characterized by the earliest ice out dates on record. Spectral and wavelet time series analysis indicate that ice out is influenced by several teleconnections including the Quasi-biennial Oscillation, El Niño-Southern Oscillation, North Atlantic Oscillation, as well as a significant correlation between inland lake records and the Atlantic Multidecadal Oscillation. The relative influence of these teleconnections is variable with notable shifts occurring after ~1870, ~1925, and ~1980–2000. The intermittent expression of these cycles in the ice out and MA instrumental record is not only influenced by absolute changes in the intensity of the various teleconnections and other climate drivers, but through phase interference between teleconnections, which periodically damps the various signals