Nitrate subsurface transport and losses in response to its initial distributions in sloped soils: An experimental and modelling study

Transport and losses of nitrate from sloped soils are closely linked to nitrogen fertilizer management. Previous studies have always focused on different types of fertilizer applications and rarely analysed various initial nitrate distributions as a result of nitrogen fertilizer applications. Under certain conditions, both subsurface lateral saturated flow and vertical leaching dominate nitrate losses. Soil tank experiments and HYDRUS-2D modelling were used to better understand the subsurface nitrate transport and losses through lateral saturated flow and vertical leaching under various initial nitrate distributions. Low (L: 180 mg L−1), normal (N: 350 mg L−1), and high (H: 500 mg L−1) nitrate concentrations were used in five different distributions (NNNN, NLLN, LHHL, LNLN, and HNHN) along the slope of the tank. The first two treatments (NNNN and NLLN) were analysed both experimentally and numerically. Experiments were conducted under 12 rainfall events at intervals of 3 days. The HYDRUS-2D model was calibrated and validated against the experimental data and demonstrated good model performance. The other three treatments (LHHL, LNLN, and HNHN) were investigated using the calibrated model. Nitrate concentrations in purple sloped soils declined exponentially with time under intermittent rainfalls, predominantly in the upper soil layers. Non-uniform initial nitrate distributions contributed to larger differences between four locations along the slope in deeper soil layers. The non-uniform nitrate distribution either enhanced or reduced decreases in nitrate concentrations in areas with higher or lower initial nitrate concentrations, respectively. Higher nitrate concentrations at the slope foot and along the slope were reduced mainly by lateral flow and vertical leaching, respectively. Increasing nitrogen application rates increased subsurface nitrate losses. Mean subsurface lateral nitrate fluxes were twice as large as mean vertical leaching nitrate fluxes. However, due to longer leaching durations, total nitrate losses due to vertical leaching were comparable with those due to lateral flow, which indicated comparable environmental risks to surface waters and groundwater

Do we know the actual magnetopause position for typical solar wind conditions?

We compare predicted magnetopause positions at the subsolar point and four reference points in the terminator plane obtained from several empirical and numerical MHD models. Empirical models using various sets of magnetopause crossings and making different assumptions about the magnetopause shape predict significantly different magnetopause positions (with a scatter >1 RE) even at the subsolar point. Axisymmetric magnetopause models cannot reproduce the cusp indentations or the changes related to the dipole tilt effect, and most of them predict the magnetopause closer to the Earth than nonaxisymmetric models for typical solar wind conditions and zero tilt angle. Predictions of two global nonaxisymmetric models do not match each other, and the models need additional verification. MHD models often predict the magnetopause closer to the Earth than the nonaxisymmetric empirical models, but the predictions of MHD simulations may need corrections for the ring current effect and decreases of the solar wind pressure that occur in the foreshock. Comparing MHD models in which the ring current magnetic field is taken into account with the empirical Lin et al. model, we find that the differences in the reference point positions predicted by these models are relatively small for Bz=0. Therefore, we assume that these predictions indicate the actual magnetopause position, but future investigations are still needed.Key PointsEmpirical models predict significantly different magnetopause positions even at the subsolar pointAxisymmetric empirical models predict the magnetopause closer to the Earth than nonaxisymmetric empirical models for zero tilt angleResults of MHD models with the ring current magnetic field lie close to results of the nonaxisymmetric Lin et al. modelPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134087/1/jgra52758_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134087/2/jgra52758.pd

Carboniferous plant physiology breaks the mold

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155970/1/nph16460-sup-0001-SupInfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155970/2/nph16460_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155970/3/nph16460.pd

Stochastic upscaling of hydrodynamic dispersion and retardation factor in a physically and chemically heterogeneous tropical soil

[EN] Stochastic upscaling of flow and reactive solute transport in a tropical soil is performed using real data collected in the laboratory. Upscaling of hydraulic conductivity, longitudinal hydrodynamic dispersion, and retardation factor were done using three different approaches of varying complexity. How uncertainty propagates after upscaling was also studied. The results show that upscaling must be taken into account if a good reproduction of the flow and transport behavior of a given soil is to be attained when modeled at larger than laboratory scales. The results also show that arrival time uncertainty was well reproduced after solute transport upscaling. This work represents a first demonstration of flow and reactive transport upscaling in a soil based on laboratory data. It also shows how simple upscaling methods can be incorporated into daily modeling practice using commercial flow and transport codes.The authors thank the financial support by the Brazilian National Council for Scientific and Technological Development (CNPq) (Project 401441/2014-8). The doctoral fellowship award to the first author by the Coordination of Improvement of Higher Level Personnel (CAPES) is acknowledged. The first author also thanks the international mobility grant awarded by CNPq, through the Sciences Without Borders program (Grant Number: 200597/2015-9). The international mobility grant awarded by Santander Mobility in cooperation with the University of Sao Paulo is also acknowledged. DHI-WASI is gratefully thanked for providing a FEFLOW license.Almeida De-Godoy, V.; Zuquette, L.; Gómez-Hernández, JJ. (2019). Stochastic upscaling of hydrodynamic dispersion and retardation factor in a physically and chemically heterogeneous tropical soil. Stochastic Environmental Research and Risk Assessment. 33(1):201-216. https://doi.org/10.1007/s00477-018-1624-zS201216331Ahuja LR, Naney JW, Green RE, Nielsen DR (1984) Macroporosity to characterize spatial variability of hydraulic conductivity and effects of land management. Soil Sci Soc Am J 48:699. https://doi.org/10.2136/sssaj1984.03615995004800040001xBellin A, Lawrence AE, Rubin Y (2004) Models of sub-grid variability in numerical simulations of solute transport in heterogeneous porous formations: three-dimensional flow and effect of pore-scale dispersion. Stoch Environ Res Risk Assess 18:31–38. https://doi.org/10.1007/s00477-003-0164-2Brent RP (1973) Algorithms for minimization without derivatives. Prentice Hall, Englewood CliffsBrusseau ML (1998) Non-ideal transport of reactive solutes in heterogeneous porous media: 3. model testing and data analysis using calibration versus prediction. J Hydrol 209:147–165. https://doi.org/10.1016/S0022-1694(98)00121-8Brusseau ML, Srivastava R (1999) Nonideal transport of reactive solutes in heterogeneous porous media: 4. Analysis of the cape cod natural-gradient field experiment. Water Resour Res 35:1113–1125. https://doi.org/10.1029/1998WR900019Brutsaert W (1967) Some methods of calculating unsaturated permeability. Trans ASAE 10:400–404Cadini F, De Sanctis J, Bertoli I, Zio E (2013) Upscaling of a dual-permeability Monte Carlo simulation model for contaminant transport in fractured networks by genetic algorithm parameter identification. Stoch Environ Res Risk Assess 27:505–516. https://doi.org/10.1007/s00477-012-0595-8Cambardella CA, Moorman TB, Parkin TB, Karlen DL, Novak JM, Turco RF, Konopka AE (1994) Field-scale variability of soil properties in central iowa soils. Soil Sci Soc Am J 58:1501. https://doi.org/10.2136/sssaj1994.03615995005800050033xCapilla JE, Rodrigo J, Gómez-Hernández JJ (1999) Simulation of non-Gaussian transmissivity fields honoring piezometric data and integrating soft and secondary information. Math Geol 31:907–927. https://doi.org/10.1023/A:1007580902175Cassiraga EF, Fernàndez-Garcia D, Gómez-Hernández JJ (2005) Performance assessment of solute transport upscaling methods in the context of nuclear waste disposal. Int J Rock Mech Min Sci 42:756–764. https://doi.org/10.1016/j.ijrmms.2005.03.013Corey AT (1977) Mechanics of heterogeneous fluids in porous media. Water Resources Publications, Fort Collins, CO, p 259Dagan G (1989) Flow and transport in porous formations. Springer, Berlin. https://doi.org/10.1007/978-3-642-75015-1Dagan G (2004) On application of stochastic modeling of groundwater flow and transport. Stoch Environ Res Risk Assess. https://doi.org/10.1007/s00477-004-0191-7de Azevedo AAB, Pressinotti MMN, Massoli M (1981) Sedimentological studies of the Botucatu and Pirambóia formations in the region of Santa Rita do Passa Quatro (In portuguese). Rev do Inst Geológico 2:31–38. https://doi.org/10.5935/0100-929X.19810003Deng H, Dai Z, Wolfsberg AV, Ye M, Stauffer PH, Lu Z, Kwicklis E (2013) Upscaling retardation factor in hierarchical porous media with multimodal reactive mineral facies. Chemosphere 91:248–257. https://doi.org/10.1016/j.chemosphere.2012.10.105Diersch H-JG (2014) Finite element modeling of flow, mass and heat transport in porous and fractured media. Springer, Berlin. https://doi.org/10.1007/978-3-642-38739-5Dippenaar MA (2014) Porosity reviewed: quantitative multi-disciplinary understanding, recent advances and applications in vadose zone hydrology. Geotech Geol Eng 32:1–19. https://doi.org/10.1007/s10706-013-9704-9Fagundes JRT, Zuquette LV (2011) Sorption behavior of the sandy residual unconsolidated materials from the sandstones of the Botucatu Formation, the main aquifer of Brazil. Environ Earth Sci 62:831–845. https://doi.org/10.1007/s12665-010-0570-yFenton GA, Griffiths DV (2008) Risk assessment in geotechnical engineering. Wiley, p 463Fernàndez-Garcia D, Gómez-Hernández JJ (2007) Impact of upscaling on solute transport: Traveltimes, scale dependence of dispersivity, and propagation of uncertainty. Water Resour Res. https://doi.org/10.1029/2005WR004727Fernàndez-Garcia D, Llerar-Meza G, Gómez-Hernández JJ (2009) Upscaling transport with mass transfer models: mean behavior and propagation of uncertainty. Water Resour Res. https://doi.org/10.1029/2009WR007764Feyen L, Gómez-Hernández JJ, Ribeiro PJ, Beven KJ, De Smedt F (2003a) A Bayesian approach to stochastic capture zone delineation incorporating tracer arrival times, conductivity measurements, and hydraulic head observations. Water Resour Res. https://doi.org/10.1029/2002WR001544Feyen L, Ribeiro PJ, Gómez-Hernández JJ, Beven KJ, De Smedt F (2003b) Bayesian methodology for stochastic capture zone delineation incorporating transmissivity measurements and hydraulic head observations. J Hydrol 271:156–170. https://doi.org/10.1016/S0022-1694(02)00314-1Forsythe GE, Malcolm MA, Moler CB (1976) Computer methods for mathematical computations. Prentice-Hall, Englewood Cliffs, p 259Freeze R, Cherry J (1979) Groundwater. PrenticeHall Inc, Englewood cliffs, p 604Frippiat CC, Holeyman AE (2008) A comparative review of upscaling methods for solute transport in heterogeneous porous media. J Hydrol 362:150–176. https://doi.org/10.1016/j.jhydrol.2008.08.015Fu J, Gómez-Hernández JJ (2009) Uncertainty assessment and data worth in groundwater flow and mass transport modeling using a blocking Markov chain Monte Carlo method. J Hydrol 364:328–341. https://doi.org/10.1016/j.jhydrol.2008.11.014Gelhar LW, Axness CL (1983) Three-dimensional stochastic analysis of macrodispersion in aquifers. Water Resour Res 19:161–180. https://doi.org/10.1029/WR019i001p00161Gelhar LW, Welty C, Rehfeldt KR (1992) A critical review of data on field-scale dispersion in aquifers. Water Resour Res 28:1955–1974. https://doi.org/10.1029/92WR00607Giacheti HL, Rohm SA, Nogueira JB, Cintra JCA (1993) Geotechnical properties of the Cenozoic sediment (in protuguese). In: Albiero JH, Cintra JCA (eds) Soil from the interior of São Paulo. ABMS, Sao Paulo, pp 143–175Gómez-Hernandez JJ (1990) A stochastic approach to the simulation of block conductivity fields conditional upon data measured at a smaller scale. Stanford University, StanfordGómez-Hernández JJ, Gorelick SM (1989) Effective groundwater model parameter values: influence of spatial variabiity of hydraulic conductivity, leackance, and recharge. Water Resour Res 25:405–419Gómez-Hernández JJ, Journel A (1993) Joint sequential simulation of multigaussian fields. In: Geostatistics Tróia’92. pp 85–94. https://doi.org/10.1007/978-94-011-1739-5_8Gómez-Hernández JJ, Wen X-H (1994) Probabilistic assessment of travel times in groundwater modeling. Stoch Hydrol Hydraul 8:19–55. https://doi.org/10.1007/BF01581389Gómez-Hernández JJ, Fu J, Fernandez-Garcia D (2006) Upscaling retardation factors in 2-D porous media. In: Bierkens MFP, Gehrels JC, Kovar K (eds) Calibration and reliability in groundwater modelling: from uncertainty to decision making: proceedings of the ModelCARE 2005 conference held in The Hague, The Netherlands, 6–9 June, 2005. IAHS Publication, pp 130–136Goovaerts P (1999) Geostatistics in soil science: state-of-the-art and perspectives. Geoderma 89:1–45. https://doi.org/10.1016/S0016-7061(98)00078-0Jarvis NJ (2007) A review of non-equilibrium water fl ow and solute transport in soil macropores: principles, controlling factors and consequences for water quality. Eur J Soil Sci 58:523–546. https://doi.org/10.4141/cjss2011-050Jellali S, Diamantopoulos E, Kallali H, Bennaceur S, Anane M, Jedidi N (2010) Dynamic sorption of ammonium by sandy soil in fixed bed columns: evaluation of equilibrium and non-equilibrium transport processes. J Environ Manag 91:897–905. https://doi.org/10.1016/j.jenvman.2009.11.006Journel AG, Gomez-Hernandez JJ (1993) Stochastic imaging of the wilmington clastic sequence. SPE Form Eval 8:33–40. https://doi.org/10.2118/19857-PAJournel A, Deutsch C, Desbarats A (1986) Power averaging for block effective permeability. Proc SPE Calif Reg Meet. https://doi.org/10.2118/15128-MSKronberg BI, Fyfe WS, Leonardos OH, Santos AM (1979) The chemistry of some Brazilian soils: element mobility during intense weathering. Chem Geol 24:211–229. https://doi.org/10.1016/0009-2541(79)90124-4Lake LW (1988) The origins of anisotropy (includes associated papers 18394 and 18458). J Pet Technol 40:395–396. https://doi.org/10.2118/17652-PALawrence AE, Rubin Y (2007) Block-effective macrodispersion for numerical simulations of sorbing solute transport in heterogeneous porous formations. Adv Water Resour 30:1272–1285. https://doi.org/10.1016/j.advwatres.2006.11.005Lemke LD, Barrack WA II, Abriola LM, Goovaerts P (2004) Matching solute breakthrough with deterministic and stochastic aquifer models. Groundwater 42:920–934Li L, Zhou H, Gómez-Hernández JJ (2011a) A comparative study of three-dimensional hydraulic conductivity upscaling at the macro-dispersion experiment (MADE) site, Columbus Air Force Base, Mississippi (USA). J Hydrol 404:278–293. https://doi.org/10.1016/j.jhydrol.2011.05.001Li L, Zhou H, Gómez-Hernández JJ (2011b) Transport upscaling using multi-rate mass transfer in three-dimensional highly heterogeneous porous media. Adv Water Resour 34:478–489. https://doi.org/10.1016/j.advwatres.2011.01.001Logsdon Keller KE, Moorman TB (2002) Measured and predicted solute leaching from multiple undisturbed soil columns. Soil Sci Soc Am J 66:686–695. https://doi.org/10.2136/sssaj2002.6860Lourens A, van Geer FC (2016) Uncertainty propagation of arbitrary probability density functions applied to upscaling of transmissivities. Stoch Environ Res Risk Assess 30:237–249. https://doi.org/10.1007/s00477-015-1075-8Mahapatra IC, Singh KN, Pillai KG, Bapat SR (1985) Rice soils and their management. Indian J Agron 30:R1–R41Morakinyo JA, Mackay R (2006) Geostatistical modelling of ground conditions to support the assessment of site contamination. Stoch Environ Res Risk Assess 20:106–118. https://doi.org/10.1007/s00477-005-0015-4Moslehi M, de Barros FPJ, Ebrahimi F, Sahimi M (2016) Upscaling of solute transport in disordered porous media by wavelet transformations. Adv Water Resour 96:180–189. https://doi.org/10.1016/j.advwatres.2016.07.013Osinubi KJ, Nwaiwu CM (2005) Hydraulic conductivity of compacted lateritic soil. J Geotech Geoenviron Eng 131:1034–1041. https://doi.org/10.1061/(ASCE)1090-0241(2005)131:8(1034)Remy N (2004) SGeMS: stanford geostatistical modeling software. Softw Man. https://doi.org/10.1007/978-1-4020-3610-1_89Renard P, de Marsily G (1997) Calculating equivalent permeability: a review. Adv Water Resour 20:253–278. https://doi.org/10.1016/S0309-1708(96)00050-4Robin MJL, Sudicky EA, Gillham RW, Kachanoski RG (1991) Spatial variability of strontium distribution coefficients and their correlation with hydraulic conductivity in the Canadian forces base borden aquifer. Water Resour Res 27:2619–2632. https://doi.org/10.1029/91WR01107Salamon P, Fernàndez-Garcia D, Gómez-Hernández JJ (2007) Modeling tracer transport at the MADE site: the importance of heterogeneity. Water Resour Res. https://doi.org/10.1029/2006WR005522Sánchez-Vila X, Carrera J, Girardi JP (1996) Scale effects in transmissivity. J Hydrol 183:1–22. https://doi.org/10.1016/S0022-1694(96)80031-XScheibe T, Yabusaki S (1998) Scaling of flow and transport behavior in heterogeneous groundwater systems. Adv Water Resour 22:223–238. https://doi.org/10.1016/S0309-1708(98)00014-1Selvadurai PA, Selvadurai APS (2014) On the effective permeability of a heterogeneous porous medium: the role of the geometric mean. Philos Mag 94:2318–2338. https://doi.org/10.1080/14786435.2014.913111Shackelford CD (1994) Critical concepts for column testing. J Geotech Eng 120:1804–1828. https://doi.org/10.1016/0148-9062(95)96996-OŠimůnek J, van Genuchten MT, Šejna M, Toride N, Leij FJ (1999) The STANMOD computer software for evaluating solute transport in porous media using analytical solutions of convection-dispersion equation. Riverside, CaliforniaTaskinen A, Sirviö H, Bruen M (2008) Modelling effects of spatial variability of saturated hydraulic conductivity on autocorrelated overland flow data: linear mixed model approach. Stoch Environ Res Risk Assess 22:67–82. https://doi.org/10.1007/s00477-006-0099-5Tuli A, Hopmans JW, Rolston DE, Moldrup P (2005) Comparison of air and water permeability between disturbed and undisturbed soils. Soil Sci Soc Am J 69:1361. https://doi.org/10.2136/sssaj2004.0332Tyukhova AR, Willmann M (2016) Conservative transport upscaling based on information of connectivity. Water Resour Res 52:6867–6880. https://doi.org/10.1002/2015WR018331van Genuchten MTh (1980) Determining transport parameters from solute displacement experiments. Research Report 118. U.S. Salinity Lab., Riverside, CAVanderborght J, Timmerman A, Feyen J (2000) Solute transport for steady-state and transient flow in soils with and without macropores. Soil Sci Soc Am J 64:1305–1317. https://doi.org/10.2136/sssaj2000.6441305xVanmarcke E (2010) Random fields: analysis and synthesis. World Scientific. MIT Press, Cambridge, MA, p 364Vishal V, Leung JY (2017) Statistical scale-up of 3D particle-tracking simulation for non-Fickian dispersive solute transport modeling. Environ Res Risk Assess, Stoch. https://doi.org/10.1007/s00477-017-1501-1Wen X-H, Gómez-Hernández JJ (1996) Upscaling hydraulic conductivities in heterogeneous media: an overview. J Hydrol 183:ix–xxxii. https://doi.org/10.1016/S0022-1694(96)80030-8Wen XH, Gómez-Hernández JJ (1998) Numerical modeling of macrodispersion in heterogeneous media: a comparison of multi-Gaussian and non-multi-Gaussian models. J Contam Hydrol 30:129–156. https://doi.org/10.1016/S0169-7722(97)00035-1Wen XH, Capilla JE, Deutsch CV, Gómez-Hernández JJ, Cullick AS (1999) A program to create permeability fields that honor single-phase flow rate and pressure data. Comput Geosci 25:217–230. https://doi.org/10.1016/S0098-3004(98)00126-5Wilding LP, Drees LR (1983) Spatial variability and pedology. In: Wilding LP, Smeck NE, Hall GF (eds) Pedogenesis and soil taxonomy: the soil orders. Elsevier, Amsterdam, pp 83–116Willmann M, Carrera J, Guadagnini A (2006) Block-upscaling of transport in heterogeneous aquifers. h2ogeo.upc.edu 1–7Xu Z, Meakin P (2013) Upscaling of solute transport in heterogeneous media with non-uniform flow and dispersion fields. Appl Math Model 37:8533–8542. https://doi.org/10.1016/j.apm.2013.03.070Zech A, Attinger S, Cvetkovic V, Dagan G, Dietrich P, Fiori A, Rubin Y, Teutsch G (2015) Is unique scaling of aquifer macrodispersivity supported by field data? Water Resour Res 51:7662–7679. https://doi.org/10.1002/2015WR017220Zhou H, Li L, Gómez-Hernández JJ (2010) Three-dimensional hydraulic conductivity upscaling in groundwater modeling. Comput Geosci 36:1224–1235. https://doi.org/10.1016/j.cageo.2010.03.008Zhou H, Li L, Hendricks Franssen H-J, Gómez-Hernández JJ (2012) Pattern recognition in a bimodal aquifer using the normal-score ensemble Kalman filter. Math Geosci 44:169–185. https://doi.org/10.1007/s11004-011-9372-

Iron Behaving Badly: Inappropriate Iron Chelation as a Major Contributor to the Aetiology of Vascular and Other Progressive Inflammatory and Degenerative Diseases

The production of peroxide and superoxide is an inevitable consequence of aerobic metabolism, and while these particular "reactive oxygen species" (ROSs) can exhibit a number of biological effects, they are not of themselves excessively reactive and thus they are not especially damaging at physiological concentrations. However, their reactions with poorly liganded iron species can lead to the catalytic production of the very reactive and dangerous hydroxyl radical, which is exceptionally damaging, and a major cause of chronic inflammation. We review the considerable and wide-ranging evidence for the involvement of this combination of (su)peroxide and poorly liganded iron in a large number of physiological and indeed pathological processes and inflammatory disorders, especially those involving the progressive degradation of cellular and organismal performance. These diseases share a great many similarities and thus might be considered to have a common cause (i.e. iron-catalysed free radical and especially hydroxyl radical generation). The studies reviewed include those focused on a series of cardiovascular, metabolic and neurological diseases, where iron can be found at the sites of plaques and lesions, as well as studies showing the significance of iron to aging and longevity. The effective chelation of iron by natural or synthetic ligands is thus of major physiological (and potentially therapeutic) importance. As systems properties, we need to recognise that physiological observables have multiple molecular causes, and studying them in isolation leads to inconsistent patterns of apparent causality when it is the simultaneous combination of multiple factors that is responsible. This explains, for instance, the decidedly mixed effects of antioxidants that have been observed, etc...Comment: 159 pages, including 9 Figs and 2184 reference