Evapotranspiration estimation considering anthropogenic heat based on remote sensing in urban area

Urbanization influences hydrologic cycle significantly on local, regional even global scale. With urbanization the water resources demand for dense population sharpened, thus it is a great challenge to ensure water supply for some metropolises such as Beijing. Urban area is traditionally considered as the area with lower evapotranspiration (ET) on account of the impervious surface and the lower wind speed. For most remote sensing models, the ET, defined as latent heat in energy budget, is estimated as the difference between net radiation and sensible heat. The sensible heat is generally higher in urban area due to the high surface temperature caused by heat island, therefore the latent heat (i.e. the ET) in urban area is lower than that in other region. We estimated water consumption from 2003 to 2012 in Beijing based on water balance method and found that the annual mean ET in urban area was about 654 mm. However, using Surface Energy Balance System (SEBS) model, the annual mean ET in urban area was only 348 mm. We attributed this inconsistence to the impact of anthropogenic heat and quantified this impact on the basis of the night-light maps. Therefore, a new model SEBS-Urban, coupling SEBS model and anthropogenic heat was developed to estimate the ET in urban area. The ET in urban area of Beijing estimated by SEBS-Urban showed a good agreement with the ET from water balance method. The findings from this study highlighted that anthropogenic heat should be included in the surface energy budget for a highly urbanized area

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-

Identifying Signatures of Natural Selection in Tibetan and Andean Populations Using Dense Genome Scan Data

High-altitude hypoxia (reduced inspired oxygen tension due to decreased barometric pressure) exerts severe physiological stress on the human body. Two high-altitude regions where humans have lived for millennia are the Andean Altiplano and the Tibetan Plateau. Populations living in these regions exhibit unique circulatory, respiratory, and hematological adaptations to life at high altitude. Although these responses have been well characterized physiologically, their underlying genetic basis remains unknown. We performed a genome scan to identify genes showing evidence of adaptation to hypoxia. We looked across each chromosome to identify genomic regions with previously unknown function with respect to altitude phenotypes. In addition, groups of genes functioning in oxygen metabolism and sensing were examined to test the hypothesis that particular pathways have been involved in genetic adaptation to altitude. Applying four population genetic statistics commonly used for detecting signatures of natural selection, we identified selection-nominated candidate genes and gene regions in these two populations (Andeans and Tibetans) separately. The Tibetan and Andean patterns of genetic adaptation are largely distinct from one another, with both populations showing evidence of positive natural selection in different genes or gene regions. Interestingly, one gene previously known to be important in cellular oxygen sensing, EGLN1 (also known as PHD2), shows evidence of positive selection in both Tibetans and Andeans. However, the pattern of variation for this gene differs between the two populations. Our results indicate that several key HIF-regulatory and targeted genes are responsible for adaptation to high altitude in Andeans and Tibetans, and several different chromosomal regions are implicated in the putative response to selection. These data suggest a genetic role in high-altitude adaption and provide a basis for future genotype/phenotype association studies necessary to confirm the role of selection-nominated candidate genes and gene regions in adaptation to altitude

Conditional Ablation of Ezh2 in Murine Hearts Reveals Its Essential Roles in Endocardial Cushion Formation, Cardiomyocyte Proliferation and Survival

Ezh2 is a histone trimethyltransferase that silences genes mainly via catalyzing trimethylation of histone 3 lysine 27 (H3K27Me3). The role of Ezh2 as a regulator of gene silencing and cell proliferation in cancer development has been extensively investigated; however, its function in heart development during embryonic cardiogenesis has not been well studied. In the present study, we used a genetically modified mouse system in which Ezh2 was specifically ablated in the mouse heart. We identified a wide spectrum of cardiovascular malformations in the Ezh2 mutant mice, which collectively led to perinatal death. In the Ezh2 mutant heart, the endocardial cushions (ECs) were hypoplastic and the endothelial-to-mesenchymal transition (EMT) process was impaired. The hearts of Ezh2 mutant mice also exhibited decreased cardiomyocyte proliferation and increased apoptosis. We further identified that the Hey2 gene, which is important for cardiomyocyte proliferation and cardiac morphogenesis, is a downstream target of Ezh2. The regulation of Hey2 expression by Ezh2 may be independent of Notch signaling activity. Our work defines an indispensible role of the chromatin remodeling factor Ezh2 in normal cardiovascular development

Surface Energy Budgets of Arctic Tundra During Growing Season

This study analyzed summer observations of diurnal and seasonal surface energy budgets across several monitoring sites within the Arctic tundra underlain by permafrost. In these areas, latent and sensible heat fluxes have comparable magnitudes, and ground heat flux enters the subsurface during short summer intervals of the growing period, leading to seasonal thaw. The maximum entropy production (MEP) model was tested as an input and parameter parsimonious model of surface heat fluxes for the simulation of energy budgets of these permafrost‐underlain environments. Using net radiation, surface temperature, and a single parameter characterizing the thermal inertia of the heat exchanging surface, the MEP model estimates latent, sensible, and ground heat fluxes that agree closely with observations at five sites for which detailed flux data are available. The MEP potential evapotranspiration model reproduces estimates of the Penman‐Monteith potential evapotranspiration model that requires at least five input meteorological variables (net radiation, ground heat flux, air temperature, air humidity, and wind speed) and empirical parameters of surface resistance. The potential and challenges of MEP model application in sparsely monitored areas of the Arctic are discussed, highlighting the need for accurate measurements and constraints of ground heat flux.Plain Language SummaryGrowing season latent and sensible heat fluxes are nearly equal over the Arctic permafrost tundra regions. Persistent ground heat flux into the subsurface layer leads to seasonal thaw of the top permafrost layer. The maximum energy production model accurately estimates the latent, sensible, and ground heat flux of the surface energy budget of the Arctic permafrost regions.Key PointThe MEP model is parsimonious and well suited to modeling surface energy budget in data‐sparse permafrost environmentsPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/150560/1/jgrd55584.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/150560/2/jgrd55584_am.pd