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Modell., 205, 475, 10.1016\u002Fj.ecolmodel.2007.03.014",{"doi":757},"10.1016\u002Fj.ecolmodel.2007.03.014",{"id":759,"createTime":760,"updateTime":761,"relativeEntities":762,"slug":763,"properties":764,"entityType":109,"verifyStatus":110,"verifyTime":760,"verifyNote":111,"languages":777,"translateLanguages":24,"viewCount":25,"primaryUrl":778,"fullTextUrl":24,"authors":779,"publicationType":175,"publisherRelationship":837,"citationCount":25,"citationInfo":880,"publishDate":883,"publishYear":881,"citationAnalyzeStatus":229,"lastCitationAnalyze":884,"indexDatabases":885,"openAccess":24,"references":886,"isForceReanalyzing":364},"3a465708-cb30-476c-8045-438d01d683c3","2024-09-02T14:58:15.402+00:00","2026-07-30T21:50:00.321+00:00",[],"Early-breakthrough-of-colloids-and-bacteriophage-MS2-in-a-water-saturated-sand-column",{"openalex":765,"mag":767,"abstract":769,"title":771,"gsPaper":773,"doi":775},{"VOID":766},"W2134570311",{"VOID":768},"2134570311",{"EN":770},"\u003Cjats:p>We conducted column‐scale experiments to observe the effect of transport velocity and colloid size on early breakthrough of free moving colloids, to relate previous observations at the pore scale to a larger scale. The colloids used in these experiments were bacteriophage MS2 (0.025 μm), and 0.05‐ and 3‐μm spherical polystyrene beads, and were compared with a conservative nonsorbing tracer (KCl). The results show that early breakthrough of colloids increases with colloid size and water velocity, compared with the tracer. These results are in line with our previous observations at the pore scale that indicated that larger colloids are restricted by the size exclusion effect from sampling all paths, and therefore they tend to disperse less and move in the faster streamlines, if they are not filtered out. The measured macroscopic dispersion coefficient decreases with colloid size due to the preferential flow paths, as observed at the pore scale. Dispersivity, typically considered only a property of the medium, is in this case also a function of colloid size, in particular at low Peclet numbers due to the size exclusion effect. Other parameters for colloid transport, such as collector efficiency and colloid filtration rates, were also estimated from the experimental breakthrough curve using a numerical fitting routine. In general, we found that the estimated filtration parameters follow the clean bed filtration model, although with a lower filtration efficiency overall.\u003C\u002Fjats:p>",{"EN":772},"Early breakthrough of colloids and bacteriophage MS2 in a water‐saturated sand column",{"VOID":774},"[\"13043051242491572933\"]",{"VOID":776},"10.1029\u002F2003wr002676",[113],"https:\u002F\u002Fagupubs.onlinelibrary.wiley.com\u002Fdoi\u002F10.1029\u002F2003WR002676",[780,801,818],{"id":781,"sortIndex":25,"researcher":24,"roles":782,"affiliations":783,"properties":792},"8d9d4774-fe87-4c2c-86c2-fa6dd8fd8e28",[],[784],{"id":785,"sortIndex":25,"affiliation":786,"properties":24},"d74d5fe9-80b4-47f6-8e7a-125a759b53f0",{"id":785,"createTime":24,"updateTime":24,"relativeEntities":787,"slug":24,"properties":788,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":791,"statistic":24},[],{"title":789},{"EN":790},"Bren School of Environmental Science and Management University of California Santa Barbara California USA",[],{"orcid":793,"title":795,"gsAuthor":797,"openalex":799},{"VOID":794},"https:\u002F\u002Forcid.org\u002F0000-0002-7638-662X",{"EN":796},"Arturo A. 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P., 1995, Fluorescently labeled virus probes show that natural virus populations can control structure of marine microbial communities, Appl. Environ. Microbiol., 61, 3623, 10.1128\u002Faem.61.10.3623-3627.1995",{"doi":941},"10.1128\u002Faem.61.10.3623-3627.1995",{"id":24,"text":943,"url":24,"identifiers":944},"IMSL 1991 Visual Numerics Houston Tex.",{},{"id":24,"text":946,"url":24,"identifiers":947},"10.1016\u002FS0021-9797(03)00254-6",{"doi":946},{"id":24,"text":949,"url":24,"identifiers":950},"10.1021\u002Fes9604323",{"doi":949},{"id":24,"text":952,"url":24,"identifiers":953},"10.1006\u002Fjcis.2000.7097",{"doi":952},{"id":24,"text":955,"url":24,"identifiers":956},"10.2134\u002Fjeq1982.00472425001100030004x",{"doi":955},{"id":24,"text":958,"url":24,"identifiers":959},"10.1061\u002F(ASCE)0733-9372(1995)121:12(869)",{"doi":958},{"id":24,"text":961,"url":24,"identifiers":962},"10.1002\u002Fj.1551-8833.1996.tb06518.x",{"doi":961},{"id":24,"text":964,"url":24,"identifiers":965},"10.1016\u002FS0169-7722(01)00216-9",{"doi":964},{"id":24,"text":967,"url":24,"identifiers":968},"10.1021\u002Fes00063a602",{"doi":967},{"id":24,"text":970,"url":24,"identifiers":971},"10.1111\u002Fj.1745-6584.2000.tb00211.x",{"doi":970},{"id":24,"text":973,"url":24,"identifiers":974},"Ogata A., 1970, Theory of dispersion in a granular medium: A review of the theoretical aspects of dispersion of fluid flowing through a porous material, U. S. Geol. Surv. Prof. Pap., 411‐I",{},{"id":24,"text":976,"url":24,"identifiers":977},"10.1029\u002F1999WR900171",{"doi":976},{"id":24,"text":979,"url":24,"identifiers":980},"10.1016\u002F0043-1354(94)90029-9",{"doi":979},{"id":24,"text":982,"url":24,"identifiers":983},"W. H. Press S. A. Teukolsky W. T. Vetterling B. P. Flannery 1992 Cambridge Univ. Press New York",{},{"id":24,"text":985,"url":24,"identifiers":986},"10.1002\u002Faic.690220316",{"doi":985},{"id":24,"text":988,"url":24,"identifiers":989},"10.1002\u002Faic.690280544",{"doi":988},{"id":24,"text":991,"url":24,"identifiers":992},"10.2134\u002Fjeq1981.00472425001000030001x",{"doi":991},{"id":24,"text":994,"url":24,"identifiers":995},"10.1029\u002F1999WR900059",{"doi":994},{"id":24,"text":997,"url":24,"identifiers":998},"W. B. Russel D. A. Saville W. R. Schowalter 1989 Cambridge Univ. Press New York",{},{"id":24,"text":1000,"url":24,"identifiers":1001},"10.1016\u002F0927-7757(95)03384-X",{"doi":1000},{"id":24,"text":1003,"url":24,"identifiers":1004},"10.1029\u002F2001WR001223",{"doi":1003},{"id":24,"text":1006,"url":24,"identifiers":1007},"10.1080\u002F10643380091184174",{"doi":1006},{"id":24,"text":1009,"url":24,"identifiers":1010},"10.1029\u002F1998WR900108",{"doi":1009},{"id":24,"text":1012,"url":24,"identifiers":1013},"10.1029\u002F95WR00199",{"doi":1012},{"id":24,"text":1015,"url":24,"identifiers":1016},"10.1023\u002FA:1006596412177",{"doi":1015},{"id":24,"text":1018,"url":24,"identifiers":1019},"10.1080\u002F00288330.2000.9516924",{"doi":1018},{"id":24,"text":1021,"url":24,"identifiers":1022},"10.1029\u002F2002WR001583",{"doi":1021},{"id":24,"text":1024,"url":24,"identifiers":1025},"10.1016\u002F0021-9797(74)90337-3",{"doi":1024},{"id":24,"text":1027,"url":24,"identifiers":1028},"10.1006\u002Fjcis.1998.5541",{"doi":1027},{"id":24,"text":1030,"url":24,"identifiers":1031},"Yates M. V., 1992, VIRTUS, a model of virus transport in unsaturated soils, Appl. Environ. Microbiol., 58, 1609, 10.1128\u002Faem.58.5.1609-1616.1992",{"doi":1032},"10.1128\u002Faem.58.5.1609-1616.1992",{"id":24,"text":1034,"url":24,"identifiers":1035},"10.1021\u002Fes60058a005",{"doi":1034},{"id":24,"text":1037,"url":24,"identifiers":1038},"10.1111\u002Fj.1745-6584.2001.tb02471.x",{"doi":1037},{"id":1040,"createTime":1041,"updateTime":1042,"relativeEntities":1043,"slug":1044,"properties":1045,"entityType":109,"verifyStatus":110,"verifyTime":1062,"verifyNote":111,"languages":1063,"translateLanguages":1064,"viewCount":25,"primaryUrl":1066,"fullTextUrl":24,"authors":1067,"publicationType":175,"publisherRelationship":1160,"citationCount":25,"citationInfo":1204,"publishDate":1207,"publishYear":1205,"citationAnalyzeStatus":1208,"lastCitationAnalyze":1209,"indexDatabases":1210,"openAccess":24,"references":1211,"isForceReanalyzing":364},"14c612f1-44cc-4b84-8078-526004ccef02","2025-02-04T09:08:56.021+00:00","2026-07-29T00:00:52.088+00:00",[],"Macroroughness-and-variations-in-reach-averaged-flow-resistance-in-steep-mountain-streams",{"mag":1046,"gsPaper":1048,"keywords":1050,"openalex":1052,"abstract":1054,"title":1057,"doi":1060},{"VOID":1047},"1545011082",{"VOID":1049},"[\"1907089879426102404\"]",{"VI":1051},"",{"VOID":1053},"W1545011082",{"EN":1055,"VI":1056},"\u003Cjats:p>Steep mountain streams typically feature macroroughness elements like large immobile boulders or channel‐spanning bedforms such as step‐pool sequences. The effects of macroroughness on resistance and flow velocity are not well understood and appropriate field parameters for representing macroroughness in flow velocity equations have not been identified. The prediction of flow velocity in rough and steep streams therefore remains challenging. We measured flow velocity and several macroroughness parameters, i.e., boulder concentration, boulder diameter and protrusion, and roughness of longitudinal channel profiles in six reaches of steep mountain streams with plane bed\u002Friffle, step‐pool, and cascade channel morphologies. The between‐site variations in flow resistance can be explained to a large degree by nondimensionalization of discharge and flow velocity using channel slope and a characteristic roughness length. Using any of our roughness parameters as the characteristic roughness length, this nondimensionalization leads to a similarity collapse of the entire data set. The remaining differences in flow resistance among the streams are related to dimensionless measures of macroroughness that describe the concentration of boulders or step density in a reach. Boulder concentration represents the measure best describing the data and is used in a simple regression equation for flow velocity. The predictions were better than predictions by the variable power law equation proposed by Ferguson. Although the regression might not be statistically significant, the observed trends suggest that boulder concentration partly explains the residual variance of between‐site variation of flow resistance.\u003C\u002Fjats:p>","\u003Cjats:p>Các dòng suối núi dốc thường có đặc điểm nhám vĩ mô với các yếu tố như đá tảng lớn không di chuyển hoặc các hình dạng đáy trải rộng như chuỗi bậc hồ. Các tác động của nhám vĩ mô đối với kháng lực và tốc độ dòng chảy vẫn chưa được hiểu rõ và chưa xác định được các thông số hiện trường phù hợp để đại diện cho nhám vĩ mô trong các phương trình tốc độ dòng chảy. Do đó, việc dự đoán tốc độ dòng chảy trong các dòng suối thô và dốc vẫn là một thách thức. Chúng tôi đã đo tốc độ dòng chảy và một số tham số nhám vĩ mô, cụ thể là nồng độ đá tảng, đường kính và độ nhô của đá tảng, cũng như độ nhám của các hồ sơ dòng chảy dọc tại sáu đoạn của các dòng suối núi dốc với các hình thái đáy phẳng\u002Friffle, chuỗi bậc hồ và thác nước. Các biến thể giữa các địa điểm về kháng lực dòng chảy có thể được giải thích phần lớn thông qua việc chuẩn hóa không chiều bởi lưu lượng và tốc độ dòng chảy sử dụng độ dốc của kênh và một chiều dài nhám đặc trưng. Sử dụng bất kỳ một trong các tham số nhám của chúng tôi như là chiều dài nhám đặc trưng, việc chuẩn hóa này dẫn đến sự sụp đổ về mức độ tương tự của toàn bộ bộ dữ liệu. Sự khác biệt còn lại trong kháng lực dòng chảy giữa các dòng suối liên quan đến các thước đo vô chiều của nhám vĩ mô mô tả nồng độ của đá tảng hoặc mật độ bậc trong một đoạn. Nồng độ đá tảng là thước đo tốt nhất mô tả dữ liệu và được sử dụng trong một phương trình hồi quy đơn giản cho tốc độ dòng chảy. Các dự đoán thu được tốt hơn so với các dự đoán bằng phương trình công suất biến đổi được đề xuất bởi Ferguson. 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The few attempts toward sub‐annual reconstructions have relied on statistical disaggregation, which uses none or little proxy information. Here, we develop a novel framework that optimizes proxy combinations to simultaneously produce seasonal and annual reconstructions. Importantly, the framework ensures that total seasonal flow matches annual flow closely. This mass balance criterion is necessary to avoid misguiding water management decisions, such as the allocation of water rights or dam release decisions. Using the framework, and leveraging a multi‐species network of ring width and cellulose \u003Cjats:inline-graphic xmlns:xlink=\"http:\u002F\u002Fwww.w3.org\u002F1999\u002Fxlink\" xlink:href=\"graphic\u002Fwrcr25462-math-0001.png\" xlink:title=\"urn:x-wiley:00431397:media:wrcr25462:wrcr25462-math-0001\" \u002F>O in Southeast Asia, we reconstruct seasonal and annual inflow to Thailand's largest reservoir. The reconstructions are statistically skillful. 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There has been a growing tendency to postulate several alternative hydrologic models for a site and use model selection criteria to (1) rank these models, (2) eliminate some of them, and\u002For (3) weigh and average predictions and statistics generated by multiple models. This has led to some debate among hydrogeologists about the merits and demerits of common model selection (also known as model discrimination or information) criteria such as \u003Cjats:italic>AIC\u003C\u002Fjats:italic>, \u003Cjats:italic>AICc\u003C\u002Fjats:italic>, \u003Cjats:italic>BIC\u003C\u002Fjats:italic>, and \u003Cjats:italic>KIC\u003C\u002Fjats:italic> and some lack of clarity about the proper interpretation and mathematical representation of each criterion. We examine the model selection literature to find that (1) all published rigorous derivations of \u003Cjats:italic>AIC\u003C\u002Fjats:italic> and \u003Cjats:italic>AICc\u003C\u002Fjats:italic> require that the (true) model having generated the observational data be in the set of candidate models; (2) though \u003Cjats:italic>BIC\u003C\u002Fjats:italic> and \u003Cjats:italic>KIC\u003C\u002Fjats:italic> were originally derived by assuming that such a model is in the set, \u003Cjats:italic>BIC\u003C\u002Fjats:italic> has been rederived by Cavanaugh and Neath (1999) without the need for such an assumption; and (3) \u003Cjats:italic>KIC\u003C\u002Fjats:italic> reduces to \u003Cjats:italic>BIC\u003C\u002Fjats:italic> as the number of observations becomes large relative to the number of adjustable model parameters, implying that it likewise does not require the existence of a true model in the set of alternatives. We explain why \u003Cjats:italic>KIC\u003C\u002Fjats:italic> is the only criterion accounting validly for the likelihood of prior parameter estimates, elucidate the unique role that the Fisher information matrix plays in \u003Cjats:italic>KIC\u003C\u002Fjats:italic>, and demonstrate through an example that it imbues \u003Cjats:italic>KIC\u003C\u002Fjats:italic> with desirable model selection properties not shared by \u003Cjats:italic>AIC\u003C\u002Fjats:italic>, \u003Cjats:italic>AICc\u003C\u002Fjats:italic>, or \u003Cjats:italic>BIC\u003C\u002Fjats:italic>. Our example appears to provide the first comprehensive test of how \u003Cjats:italic>AIC\u003C\u002Fjats:italic>, \u003Cjats:italic>AICc\u003C\u002Fjats:italic>, \u003Cjats:italic>BIC\u003C\u002Fjats:italic>, and \u003Cjats:italic>KIC\u003C\u002Fjats:italic> weigh and rank alternative models in light of the models' predictive performance under cross validation with real hydrologic data.\u003C\u002Fjats:p>",{"EN":2523},"On model selection criteria in multimodel analysis",{"VOID":2525},"[\"18144707859442639241\"]",{"VOID":2527},"10.1029\u002F2008wr006803","2024-09-22T03:57:28.432+00:00",[113],"https:\u002F\u002Fagupubs.onlinelibrary.wiley.com\u002Fdoi\u002F10.1029\u002F2008WR006803",[2532,2553,2575],{"id":2533,"sortIndex":25,"researcher":24,"roles":2534,"affiliations":2535,"properties":2544},"92c22545-ea49-4197-9b3b-43fe7cf90d04",[],[2536],{"id":2537,"sortIndex":25,"affiliation":2538,"properties":24},"64d8bf5c-d537-4833-963f-82078b47a545",{"id":2537,"createTime":24,"updateTime":24,"relativeEntities":2539,"slug":24,"properties":2540,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":2543,"statistic":24},[],{"title":2541},{"EN":2542},"School of Computational Science and Department of Geological Sciences; Florida State University; Tallahassee Florida USA",[],{"orcid":2545,"title":2547,"gsAuthor":2549,"openalex":2551},{"VOID":2546},"https:\u002F\u002Forcid.org\u002F0000-0002-7080-0578",{"EN":2548},"Ming Ye",{"VOID":2550},"[\"8NhUhVMAAAAJ\"]",{"VOID":2552},"A5100709633",{"id":2554,"sortIndex":137,"researcher":24,"roles":2555,"affiliations":2556,"properties":2568},"eeac650b-7373-4efa-b5e8-ce28ea5fd5cd",[],[2557],{"id":2558,"sortIndex":25,"affiliation":2559,"properties":2565},"b176042d-de15-4969-a9c5-7024d27bbd6e",{"id":2558,"createTime":24,"updateTime":24,"relativeEntities":2560,"slug":24,"properties":2561,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":2564,"statistic":24},[],{"title":2562},{"EN":2563},"Pacific Northwest National Laboratory, Richland, Washington, UNITED STATES.",[],{"title":2566},{"VI":2567},"Pacific Northwest National Laboratory, Richland, Washington, USA",{"orcid":2569,"title":2571,"openalex":2573},{"VOID":2570},"https:\u002F\u002Forcid.org\u002F0000-0002-8714-4693",{"EN":2572},"Philip D. 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A nonlinear water temperature regression model was adapted to include discharge as a variable in addition to air temperature, and a time lag was incorporated to apply the model on a daily basis. The performance of the model was tested for a selection of study basin stations and 157 river temperature stations globally using historical series of daily river temperature, air temperature, and river discharge for the 1980–1999 period. For the study basin stations and for 87% of the global river stations, the performance of the model improved by including discharge as an input variable. Greatest improvements were found during heat wave and drought (low flow) conditions, when water temperatures are most sensitive to atmospheric influences and can reach critically high values. A sensitivity analysis showed increases in annual mean river temperatures of +1.3 °C, +2.6 °C, and +3.8 °C under air temperature increases of +2 °C, +4 °C, and +6 °C, respectively. Discharge decreases of 20% and 40% exacerbated water temperature increases by +0.3 °C and +0.8 °C on average. For several stations, maximum water temperatures on a daily basis were higher under an air temperature increase of +4 °C combined with a 40% discharge decrease compared to an air temperature increase of +6 °C (without discharge changes). Impacts of river discharge on water temperatures should therefore be incorporated to provide more accurate estimations of river temperatures during historical and future projected dry and warm periods.\u003C\u002Fjats:p>",{"EN":2823},"Global river temperatures and sensitivity to atmospheric warming and changes in river flow",{"VOID":2825},"[\"4420835570176202029\"]",{"VOID":2827},"10.1029\u002F2010wr009198",[113],"https:\u002F\u002Fagupubs.onlinelibrary.wiley.com\u002Fdoi\u002F10.1029\u002F2010WR009198",[2831,2852,2871,2888,2907],{"id":2832,"sortIndex":25,"researcher":24,"roles":2833,"affiliations":2834,"properties":2843},"e52dc2c6-c3a1-4e2b-b187-afdfd27de2d3",[],[2835],{"id":2836,"sortIndex":25,"affiliation":2837,"properties":24},"c2bd6f3d-f997-4c2e-a8d5-6679490f1279",{"id":2836,"createTime":24,"updateTime":24,"relativeEntities":2838,"slug":24,"properties":2839,"entityType":24,"verifyStatus":24,"verifyTime":24,"verifyNote":24,"languages":24,"translateLanguages":24,"viewCount":24,"url":24,"parentIds":2842,"statistic":24},[],{"title":2840},{"EN":2841},"Earth System Science and Climate Change, Wageningen University and Research Centre, Wageningen, Netherlands",[],{"orcid":2844,"title":2846,"gsAuthor":2848,"openalex":2850},{"VOID":2845},"https:\u002F\u002Forcid.org\u002F0000-0002-2597-8422",{"EN":2847},"Michelle T. 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Yet a synthesized understanding of multicentennial streamflow variability for this region is lacking. To fill this gap, we produce the first large scale streamflow reconstruction over Monsoon Asia (62 stations in 16 countries, 813 years of mean annual flow). In making this reconstruction, we develop a novel, automated, climate‐informed, and dynamic reconstruction framework that is skillful over most of the region. We show that streamflow in Monsoon Asia is spatially coherent, owing to common drivers from the Pacific, Indian, and Atlantic Oceans. We also show how these oceanic teleconnections change over space and time. 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