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Major societal and environmental challenges involve complex systems that have diverse multi-scale interacting processes. Consider, for example, how droughts and water reserves affect crop production and how agriculture and industrial needs affect water quality and availability. Preventive measures, such as delaying planting dates and adopting new agricultural practices in response to changing weather patterns, can reduce the damage caused by natural processes. Understanding how these natural and human processes affect one another allows forecasting the effects of undesirable situations and study interventions to take preventive measures. For many of these processes, there are expert models that incorporate state-of-the-art theories and knowledge to quantify a system's response to a diversity of conditions. A major challenge for efficient modeling is the diversity of modeling approaches across disciplines and the wide variety of data sources available only in formats that require complex conversions. Using expert models for particular problems requires integration of models with third-party data as well as integration of models across disciplines. Modelers face significant heterogeneity that requires resolving semantic, spatiotemporal, and execution mismatches, which are largely done by hand today and may take more than 2 years of effort. We are developing a modeling framework that uses artificial intelligence (AI) techniques to reduce modeling effort while ensuring utility for decision making. Our work to date makes several innovative contributions: (1) an intelligent user interface that guides analysts to frame their modeling problem and assists them by suggesting relevant choices and automating steps along the way; (2) semantic metadata for models, including their modeling variables and constraints, that ensures model relevance and proper use for a given decision-making problem; and (3) semantic representations of datasets in terms of modeling variables that enable automated data selection and data transformations. This framework is implemented in the MINT (Model INTegration) framework, and currently includes data and models to analyze the interactions between natural and human systems involving climate, water availability, agricultural production, and markets. Our work to date demonstrates the utility of AI techniques to accelerate modeling to support decision-making and uncovers several challenging directions for future work.

Large-scale sky surveys have played a transformative role in our understanding of astrophysical transients, only made possible by increasingly powerful machine learning-based filtering to accurately sift through the vast quantities of incoming data generated. In this paper, we present a new real-bogus classifier based on a Bayesian convolutional neural network that provides nuanced, uncertainty-aware classification of transient candidates in difference imaging, and demonstrate its application to the datastream from the GOTO wide-field optical survey. Not only are candidates assigned a well-calibrated probability of being real, but also an associated confidence that can be used to prioritise human vetting efforts and inform future model optimisation via active learning. To fully realise the potential of this architecture, we present a fully-automated training set generation method which requires no human labelling, incorporating a novel data-driven augmentation method to significantly improve the recovery of faint and nuclear transient sources. We achieve competitive classification accuracy (FPR and FNR both below 1%) compared against classifiers trained with fully human-labelled datasets, whilst being significantly quicker and less labour-intensive to build. This data-driven approach is uniquely scalable to the upcoming challenges and data needs of next-generation transient surveys. We make our data generation and model training codes available to the community., 17 pages, 12 figures, resubmitted to MNRAS following reviewer comments

Hydrologic modeling is an essential strategy for understanding and predicting natural flows, particularly where observations are lacking in either space or time or where complex terrain leads to a disconnect in the characteristic time and space scales of overland and groundwater flow. However, significant difficulties remain for the development of efficient and extensible modeling systems that operate robustly across complex regions. This paper introduces the Simulator for Hydrologic Unstructured Domains (SHUD), an integrated, multiprocess, multiscale, flexible-time-step model, in which hydrologic processes are fully coupled using the finite volume method. SHUD integrates overland flow, snow accumulation/melt, evapotranspiration, subsurface flow, groundwater flow, and river routing, thus allowing physical processes in general watersheds to be realistically captured. SHUD incorporates one-dimensional unsaturated flow, two-dimensional groundwater flow, and a fully connected river channel network with hillslopes supporting overland flow and baseflow. The paper introduces the design of SHUD, from the conceptual and mathematical description of hydrologic processes in a watershed to the model's computational structures. To demonstrate and validate the model performance, we employ three hydrologic experiments: the V-catchment experiment, Vauclin's experiment, and a model study of the Cache Creek Watershed in northern California. Ongoing applications of the SHUD model include hydrologic analyses of hillslope to regional scales (1 m2 to 106 km2), water resource and stormwater management, and interdisciplinary research for questions in limnology, agriculture, geochemistry, geomorphology, water quality, ecology, climate and land-use change. The strength of SHUD is its flexibility as a scientific and resource evaluation tool where modeling and simulation are required.

The typical detection rate of $\sim1$ gamma-ray burst (GRB) per day by the \emph{Fermi} Gamma-ray Burst Monitor (GBM) provides a valuable opportunity to further our understanding of GRB physics. However, the large uncertainty of the \emph{Fermi} localization typically prevents rapid identification of multi-wavelength counterparts. We report the follow-up of 93 \emph{Fermi} GRBs with the Gravitational-wave Optical Transient Observer (GOTO) prototype on La Palma. We selected 53 events (based on favourable observing conditions) for detailed analysis, and to demonstrate our strategy of searching for optical counterparts. We apply a filtering process consisting of both automated and manual steps to 60\,085 candidates initially, rejecting all but 29, arising from 15 events. With $\approx3$ GRB afterglows expected to be detectable with GOTO from our sample, most of the candidates are unlikely to be related to the GRBs. Since we did not have multiple observations for those candidates, we cannot confidently confirm the association between the transients and the GRBs. Our results show that GOTO can effectively search for GRB optical counterparts thanks to its large field of view of $\approx40$ square degrees and its depth of $\approx20$ mag. We also detail several methods to improve our overall performance for future follow-up programs of \emph{Fermi} GRBs.

The advent of wide-field sky surveys has led to the growth of transient and variable source discoveries. The data deluge produced by these surveys has necessitated the use of machine learning (ML) and deep learning (DL) algorithms to sift through the vast incoming data stream. A problem that arises in real-world applications of learning algorithms for classification is imbalanced data, where a class of objects within the data is underrepresented, leading to a bias for over-represented classes in the ML and DL classifiers. We present a recurrent neural network (RNN) classifier that takes in photometric time-series data and additional contextual information (such as distance to nearby galaxies and on-sky position) to produce real-time classification of objects observed by the Gravitational-wave Optical Transient Observer (GOTO), and use an algorithm-level approach for handling imbalance with a focal loss function. The classifier is able to achieve an Area Under the Curve (AUC) score of 0.972 when using all available photometric observations to classify variable stars, supernovae, and active galactic nuclei. The RNN architecture allows us to classify incomplete light curves, and measure how performance improves as more observations are included. We also investigate the role that contextual information plays in producing reliable object classification., Comment: 16 pages, 12 figures, to be published in Monthly Notices of the Royal Astronomical Society

The world's longest record of river water quality (River Thames—130 years) provides a unique opportunity to understand fluvial dissolved organic carbon (DOC) concentrations dynamics. Understanding riverine DOC variability through long-term studies is crucial to capture patterns and drivers influencing sources of DOC at scales relevant for decision making. The Thames basin (United Kingdom) has undergone massive land-use change, as well as increased urbanisation and population during the period considered. We aimed to investigate the drivers of intra-annual to interannual DOC variability, assess the variability due to natural and anthropogenic factors, and understand the causes for the increased DOC variability over the period. Two approaches were used to achieve these aims. The first method was singular spectrum analysis, which was used to reconstruct the major oscillatory modes of DOC, hydroclimatic variables, and atmospheric circulation patterns and to visualise the interaction between these variables. The second approach used was generalised additive modelling, which was used to investigate other non-natural drivers of DOC variability. Our study shows that DOC variability increased by 80% over the data period, with the greatest increase occurring from the beginning of World War II onwards. The primary driver of the increase in DOC variability was the increase in the average value of fluvial DOC over the period of record, which was itself linked to the increase in basin population and diffuse DOC sources to the river due to land-use and land-management changes. Seasonal DOC variability was linked to streamflow and temperature. Our study allows to identify drivers of fluvial intra-annual and interannual DOC variability and therefore empowers actions to reduce high DOC concentrations.

An intrinsic gamma-ray burst afterglow Long gamma-ray bursts (GRBs) are emitted by relativistic jets generated during the collapse of a massive star in a distant galaxy. The GRB itself lasts only a few seconds but is followed by an afterglow that can persist for hours or days. The H.E.S.S. Collaboration observed the afterglow of GRB 190829A, a nearby long GRB. The proximity of this burst allowed it to be detected at tera–electron volt energies that would otherwise be absorbed in the intergalactic medium. By analyzing the spectrum and light curve at x-ray and gamma-ray wavelengths, the authors show that the afterglow cannot be explained by standard models. Science , abe8560, this issue p. 1081

Full list of authors: EHT MWL Science Working Group; Algaba, J. C.; Anczarski, J.; Asada, K.; Baloković, M.; Chandra, S.; Cui, Y. -Z.; Falcone, A. D.; Giroletti, M.; Goddi, C.; Hada, K.; Haggard, D.; Jorstad, S.; Kaur, A.; Kawashima, T.; Keating, G.; Kim, J. -Y.; Kino, M.; Komossa, S.; Kravchenko, E. V.; Krichbaum, T. P.; Lee, S. -S.; Lu, R. -S.; Lucchini, M.; Markoff, S.; Neilsen, J.; Nowak, M. A.; Park, J.; Principe, G.; Ramakrishnan, V.; Reynolds, M. T.; Sasada, M.; Savchenko, S. S.; Williamson, K. E.; Event Horizon Telescope Collaboration; Akiyama, Kazunori; Alberdi, Antxon; Alef, Walter; Anantua, Richard; Azulay, Rebecca; Baczko, Anne-Kathrin; Ball, David; Barrett, John; Bintley, Dan; Benson, Bradford A.; Blackburn, Lindy; Blundell, Raymond; Boland, Wilfred; Bouman, Katherine L.; Bower, Geoffrey C.; Boyce, Hope; Bremer, Michael; Brinkerink, Christiaan D.; Brissenden, Roger; Britzen, Silke; Broderick, Avery E.; Broguiere, Dominique; Bronzwaer, Thomas; Byun, Do-Young; Carlstrom, John E.; Chael, Andrew; Chan, Chi-Kwan; Chatterjee, Shami; Chatterjee, Koushik; Chen, Ming-Tang; Chen, Yongjun; Chesler, Paul M.; Cho, Ilje; Christian, Pierre; Conway, John E.; Cordes, James M.; Crawford, Thomas M.; Crew, Geoffrey B.; Cruz-Osorio, Alejandro; Davelaar, Jordy; de Laurentis, Mariafelicia; Deane, Roger; Dempsey, Jessica; Desvignes, Gregory; Dexter, Jason; Doeleman, Sheperd S.; Eatough, Ralph P.; Falcke, Heino; Farah, Joseph; Fish, Vincent L.; Fomalont, Ed; Ford, H. Alyson; Fraga-Encinas, Raquel; Friberg, Per; Fromm, Christian M.; Fuentes, Antonio; Galison, Peter; Gammie, Charles F.; García, Roberto; Gentaz, Olivier; Georgiev, Boris; Gold, Roman; Gómez, José L.; Gómez-Ruiz, Arturo I.; Gu, Minfeng; Gurwell, Mark; Hecht, Michael H.; Hesper, Ronald; Ho, Luis C.; Ho, Paul; Honma, Mareki; Huang, Chih-Wei L.; Huang, Lei; Hughes, David H.; Ikeda, Shiro; Inoue, Makoto; Issaoun, Sara; James, David J.; Jannuzi, Buell T.; Janssen, Michael; Jeter, Britton; Jiang, Wu; Jiménez-Rosales, Alejandra; Johnson, Michael D.; Jung, Taehyun; Karami, Mansour; Karuppusamy, Ramesh; Kettenis, Mark; Kim, Dong-Jin; Kim, Jongsoo; Kim, Junhan; Koay, Jun Yi; Kofuji, Yutaro; Koch, Patrick M.; Koyama, Shoko; Kramer, Michael; Kramer, Carsten; Kuo, Cheng-Yu; Lauer, Tod R.; Levis, Aviad; Li, Yan-Rong; Li, Zhiyuan; Lindqvist, Michael; Lico, Rocco; Lindahl, Greg; Liu, Jun; Liu, Kuo; Liuzzo, Elisabetta; Lo, Wen-Ping; Lobanov, Andrei P.; Loinard, Laurent; Lonsdale, Colin; MacDonald, Nicholas R.; Mao, Jirong; Marchili, Nicola; Marrone, Daniel P.; Marscher, Alan P.; Martí-Vidal, Iván; Matsushita, Satoki; Matthews, Lynn D.; Medeiros, Lia; Menten, Karl M.; Mizuno, Izumi; Mizuno, Yosuke; Moran, James M.; Moriyama, Kotaro; Moscibrodzka, Monika; Müller, Cornelia; Musoke, Gibwa; Mejías, Alejandro Mus; Nagai, Hiroshi; Nagar, Neil M.; Nakamura, Masanori; Narayan, Ramesh; Narayanan, Gopal; Natarajan, Iniyan; Nathanail, Antonios; Neri, Roberto; Ni, Chunchong; Noutsos, Aristeidis; Okino, Hiroki; Olivares, Héctor; Ortiz-León, Gisela N.; Oyama, Tomoaki; Özel, Feryal; Palumbo, Daniel C. M.; Patel, Nimesh; Pen, Ue-Li; Pesce, Dominic W.; Piétu, Vincent; Plambeck, Richard; Popstefanija, Aleksandar; Porth, Oliver; Pötzl, Felix M.; Prather, Ben; Preciado-López, Jorge A.; Psaltis, Dimitrios; Pu, Hung-Yi; Rao, Ramprasad; Rawlings, Mark G.; Raymond, Alexander W.; Rezzolla, Luciano; Ricarte, Angelo; Ripperda, Bart; Roelofs, Freek; Rogers, Alan; Ros, Eduardo; Rose, Mel; Roshanineshat, Arash; Rottmann, Helge; Roy, Alan L.; Ruszczyk, Chet; Rygl, Kazi L. J.; Sánchez, Salvador; Sánchez-Arguelles, David; Savolainen, Tuomas; Schloerb, F. Peter; Schuster, Karl-Friedrich; Shao, Lijing; Shen, Zhiqiang; Small, Des; Sohn, Bong Won; Soohoo, Jason; Sun, He; Tazaki, Fumie; Tetarenko, Alexandra J.; Tiede, Paul; Tilanus, Remo P. J.; Titus, Michael; Toma, Kenji; Torne, Pablo; Trent, Tyler; Traianou, Efthalia; Trippe, Sascha; van Bemmel, Ilse; van Langevelde, Huib Jan; van Rossum, Daniel R.; Wagner, Jan; Ward-Thompson, Derek; Wardle, John; Weintroub, Jonathan; Wex, Norbert; Wharton, Robert; Wielgus, Maciek; Wong, George N.; Wu, Qingwen; Yoon, Doosoo; Young, André; Young, Ken; Younsi, Ziri; Yuan, Feng; Yuan, Ye-Fei; Zensus, J. Anton; Zhao, Guang-Yao; Zhao, Shan-Shan; Fermi Large Area Telescope Collaboration; Principe, G.; Giroletti, M.; D'Ammando, F.; Orienti, M.; H. E. S. S. Collaboration; Abdalla, H.; Adam, R.; Aharonian, F.; Benkhali, F. Ait; Angüner, E. O.; Arcaro, C.; Armand, C.; Armstrong, T.; Ashkar, H.; Backes, M.; Baghmanyan, V.; Barbosa Martins, V.; Barnacka, A.; Barnard, M.; Becherini, Y.; Berge, D.; Bernlöhr, K.; Bi, B.; Böttcher, M.; Boisson, C.; Bolmont, J.; de Lavergne, M. De Bony; Breuhaus, M.; Brun, F.; Brun, P.; Bryan, M.; Büchele, M.; Bulik, T.; Bylund, T.; Caroff, S.; Carosi, A.; Casanova, S.; Chand, T.; Chen, A.; Cotter, G.; Curyło, M.; Damascene Mbarubucyeye, J.; Davids, I. D.; Davies, J.; Deil, C.; Devin, J.; Dewilt, P.; Dirson, L.; Djannati-Ataï, A.; Dmytriiev, A.; Donath, A.; Doroshenko, V.; Duffy, C.; Dyks, J.; Egberts, K.; Eichhorn, F.; Einecke, S.; Emery, G.; Ernenwein, J. -P.; Feijen, K.; Fegan, S.; Fiasson, A.; de Clairfontaine, G. Fichet; Fontaine, G.; Funk, S.; Füßling, M.; Gabici, S.; Gallant, Y. A.; Giavitto, G.; Giunti, L.; Glawion, D.; Glicenstein, J. F.; Gottschall, D.; Grondin, M. -H.; Hahn, J.; Haupt, M.; Hermann, G.; Hinton, J. A.; Hofmann, W.; Hoischen, C.; Holch, T. L.; Holler, M.; Hörbe, M.; Horns, D.; Huber, D.; Jamrozy, M.; Jankowsky, D.; Jankowsky, F.; Jardin-Blicq, A.; Joshi, V.; Jung-Richardt, I.; Kasai, E.; Kastendieck, M. A.; Katarzyński, K.; Katz, U.; Khangulyan, D.; Khélifi, B.; Klepser, S.; Kluźniak, W.; Komin, Nu.; Konno, R.; Kosack, K.; Kostunin, D.; Kreter, M.; Lamanna, G.; Lemière, A.; Lemoine-Goumard, M.; Lenain, J. -P.; Levy, C.; Lohse, T.; Lypova, I.; Mackey, J.; Majumdar, J.; Malyshev, D.; Malyshev, D.; Marandon, V.; Marchegiani, P.; Marcowith, A.; Mares, A.; Martí-Devesa, G.; Marx, R.; Maurin, G.; Meintjes, P. J.; Meyer, M.; Moderski, R.; Mohamed, M.; Mohrmann, L.; Montanari, A.; Moore, C.; Morris, P.; Moulin, E.; Muller, J.; Murach, T.; Nakashima, K.; Nayerhoda, A.; de Naurois, M.; Ndiyavala, H.; Niederwanger, F.; Niemiec, J.; Oakes, L.; O'Brien, P.; Odaka, H.; Ohm, S.; Olivera-Nieto, L.; de Ona Wilhelmi, E.; Ostrowski, M.; Panter, M.; Panny, S.; Parsons, R. D.; Peron, G.; Peyaud, B.; Piel, Q.; Pita, S.; Poireau, V.; Noel, A. Priyana; Prokhorov, D. A.; Prokoph, H.; Pühlhofer, G.; Punch, M.; Quirrenbach, A.; Rauth, R.; Reichherzer, P.; Reimer, A.; Reimer, O.; Remy, Q.; Renaud, M.; Rieger, F.; Rinchiuso, L.; Romoli, C.; Rowell, G.; Rudak, B.; Ruiz-Velasco, E.; Sahakian, V.; Sailer, S.; Sanchez, D. A.; Santangelo, A.; Sasaki, M.; Scalici, M.; Schutte, H. M.; Schwanke, U.; Schwemmer, S.; Seglar-Arroyo, M.; Senniappan, M.; Seyffert, A. S.; Shafi, N.; Shiningayamwe, K.; Simoni, R.; Sinha, A.; Sol, H.; Specovius, A.; Spencer, S.; Spir-Jacob, M.; Stawarz, Ł.; Sun, L.; Steenkamp, R.; Stegmann, C.; Steinmassl, S.; Steppa, C.; Takahashi, T.; Tavernier, T.; Taylor, A. M.; Terrier, R.; Tiziani, D.; Tluczykont, M.; Tomankova, L.; Trichard, C.; Tsirou, M.; Tuffs, R.; Uchiyama, Y.; van der Walt, D. J.; van Eldik, C.; van Rensburg, C.; van Soelen, B.; Vasileiadis, G.; Veh, J.; Venter, C.; Vincent, P.; Vink, J.; Völk, H. J.; Vuillaume, T.; Wadiasingh, Z.; Wagner, S. J.; Watson, J.; Werner, F.; White, R.; Wierzcholska, A.; Wong, Yu Wun; Yusafzai, A.; Zacharias, M.; Zanin, R.; Zargaryan, D.; Zdziarski, A. A.; Zech, A.; Zhu, S. J.; Zorn, J.; Zouari, S.; Żywucka, N.; MAGIC Collaboration; Acciari, V. A.; Ansoldi, S.; Antonelli, L. A.; Engels, A. Arbet; Artero, M.; Asano, K.; Baack, D.; Babić, A.; Baquero, A.; de Almeida, U. Barres; Barrio, J. A.; Becerra González, J.; Bednarek, W.; Bellizzi, L.; Bernardini, E.; Bernardos, M.; Berti, A.; Besenrieder, J.; Bhattacharyya, W.; Bigongiari, C.; Biland, A.; Blanch, O.; Bonnoli, G.; Bošnjak, Ž.; Busetto, G.; Carosi, R.; Ceribella, G.; Cerruti, M.; Chai, Y.; Chilingarian, A.; Cikota, S.; Colak, S. M.; Colombo, E.; Contreras, J. L.; Cortina, J.; Covino, S.; D'Amico, G.; D'Elia, V.; da Vela, P.; Dazzi, F.; de Angelis, A.; de Lotto, B.; Delfino, M.; Delgado, J.; Delgado Mendez, C.; Depaoli, D.; di Pierro, F.; di Venere, L.; Do Souto Espiñeira, E.; Dominis Prester, D.; Donini, A.; Dorner, D.; Doro, M.; Elsaesser, D.; Ramazani, V. Fallah; Fattorini, A.; Ferrara, G.; Fonseca, M. V.; Font, L.; Fruck, C.; Fukami, S.; García López, R. J.; Garczarczyk, M.; Gasparyan, S.; Gaug, M.; Giglietto, N.; Giordano, F.; Gliwny, P.; Godinović, N.; Green, J. G.; Green, D.; Hadasch, D.; Hahn, A.; Heckmann, L.; Herrera, J.; Hoang, J.; Hrupec, D.; Hütten, M.; Inada, T.; Inoue, S.; Ishio, K.; Iwamura, Y.; Jiménez, I.; Jormanainen, J.; Jouvin, L.; Kajiwara, Y.; Karjalainen, M.; Kerszberg, D.; Kobayashi, Y.; Kubo, H.; Kushida, J.; Lamastra, A.; Lelas, D.; Leone, F.; Lindfors, E.; Lombardi, S.; Longo, F.; López-Coto, R.; López-Moya, M.; López-Oramas, A.; Loporchio, S.; Machado de Oliveira Fraga, B.; Maggio, C.; Majumdar, P.; Makariev, M.; Mallamaci, M.; Maneva, G.; Manganaro, M.; Mannheim, K.; Maraschi, L.; Mariotti, M.; Martínez, M.; Mazin, D.; Menchiari, S.; Mender, S.; Mićanović, S.; Miceli, D.; Miener, T.; Minev, M.; Miranda, J. M.; Mirzoyan, R.; Molina, E.; Moralejo, A.; Morcuende, D.; Moreno, V.; Moretti, E.; Neustroev, V.; Nigro, C.; Nilsson, K.; Nishijima, K.; Noda, K.; Nozaki, S.; Ohtani, Y.; Oka, T.; Otero-Santos, J.; Paiano, S.; Palatiello, M.; Paneque, D.; Paoletti, R.; Paredes, J. M.; Pavletić, L.; Peñil, P.; Perennes, C.; Persic, M.; Moroni, P. G. Prada; Prandini, E.; Priyadarshi, C.; Puljak, I.; Rhode, W.; Ribó, M.; Rico, J.; Righi, C.; Rugliancich, A.; Saha, L.; Sahakyan, N.; Saito, T.; Sakurai, S.; Satalecka, K.; Saturni, F. G.; Schleicher, B.; Schmidt, K.; Schweizer, T.; Sitarek, J.; Šnidarić, I.; Sobczynska, D.; Spolon, A.; Stamerra, A.; Strom, D.; Strzys, M.; Suda, Y.; Surić, T.; Takahashi, M.; Tavecchio, F.; Temnikov, P.; Terzić, T.; Teshima, M.; Tosti, L.; Truzzi, S.; Tutone, A.; Ubach, S.; van Scherpenberg, J.; Vanzo, G.; Vazquez Acosta, M.; Ventura, S.; Verguilov, V.; Vigorito, C. F.; Vitale, V.; Vovk, I.; Will, M.; Wunderlich, C.; Zarić, D.; VERITAS Collaboration; Adams, C. B.; Benbow, W.; Brill, A.; Capasso, M.; Christiansen, J. L.; Chromey, A. J.; Daniel, M. K.; Errando, M.; Farrell, K. A.; Feng, Q.; Finley, J. P.; Fortson, L.; Furniss, A.; Gent, A.; Giuri, C.; Hassan, T.; Hervet, O.; Holder, J.; Hughes, G.; Humensky, T. B.; Jin, W.; Kaaret, P.; Kertzman, M.; Kieda, D.; Kumar, S.; Lang, M. J.; Lundy, M.; Maier, G.; Moriarty, P.; Mukherjee, R.; Nieto, D.; Nievas-Rosillo, M.; O'Brien, S.; Ong, R. A.; Otte, A. N.; Patel, S.; Pfrang, K.; Pohl, M.; Prado, R. R.; Pueschel, E.; Quinn, J.; Ragan, K.; Reynolds, P. T.; Ribeiro, D.; Richards, G. T.; Roache, E.; Rulten, C.; Ryan, J. L.; Santander, M.; Sembroski, G. H.; Shang, R.; Weinstein, A.; Williams, D. A.; Williamson, T. J.; Eavn Collaboration; Hirota, Tomoya; Cui, Lang; Niinuma, Kotaro; Ro, Hyunwook; Sakai, Nobuyuki; Sawada-Satoh, Satoko; Wajima, Kiyoaki; Wang, Na; Liu, Xiang; Yonekura, Yoshinori, In 2017, the Event Horizon Telescope (EHT) Collaboration succeeded in capturing the first direct image of the center of the M87 galaxy. The asymmetric ring morphology and size are consistent with theoretical expectations for a weakly accreting supermassive black hole of mass ∼6.5 × 109 M o˙. The EHTC also partnered with several international facilities in space and on the ground, to arrange an extensive, quasi-simultaneous multi-wavelength campaign. This Letter presents the results and analysis of this campaign, as well as the multi-wavelength data as a legacy data repository. We captured M87 in a historically low state, and the core flux dominates over HST-1 at high energies, making it possible to combine core flux constraints with the more spatially precise very long baseline interferometry data. We present the most complete simultaneous multi-wavelength spectrum of the active nucleus to date, and discuss the complexity and caveats of combining data from different spatial scales into one broadband spectrum. We apply two heuristic, isotropic leptonic single-zone models to provide insight into the basic source properties, but conclude that a structured jet is necessary to explain M87's spectrum. We can exclude that the simultaneous γ-ray emission is produced via inverse Compton emission in the same region producing the EHT mm-band emission, and further conclude that the γ-rays can only be produced in the inner jets (inward of HST-1) if there are strongly particle-dominated regions. Direct synchrotron emission from accelerated protons and secondaries cannot yet be excluded., The financial support of the German BMBF, MPG and HGF; the Italian INFN and INAF; the Swiss National Fund SNF; the ERDF under the Spanish Ministerio de Ciencia e Innovacion (MICINN) (FPA2017-87859-P, FPA2017-85668-P, FPA2017- 82729-C6-5-R, FPA2017-90566-REDC, PID2019-104114RBC31, PID2019-104114RB-C32, PID2019-105510GB-C31,PID 2019-107847RB-C41, PID2019-107847RB-C42, PID2019- 107988GB-C22); the Indian Department of Atomic Energy; the Japanese ICRR, the University of Tokyo, JSPS, and MEXT; the Bulgarian Ministry of Education and Science, National RI Roadmap Project DO1-268/16.12.2019 and the Academy of Finland grant No. 320045 is gratefully acknowledged. This work was also supported by the Spanish Centro de Excelencia “Severo Ochoa” SEV-2016-0588 and CEX2019- 000920-S, and “Maria de Maeztu” CEX2019-000918-M, the Unidad de Excelencia “Maria de Maeztu” MDM-2015-0509- 18-2 and the “la Caixa” Foundation (fellowship LCF/BQ/ PI18/11630012) and by the CERCA program of the Generalitat de Catalunya; by the Croatian Science Foundation (HrZZ) Project IP-2016-06-9782 and the University of Rijeka Project 13.12.1.3.02; by the DFG Collaborative Research Centers SFB823/C4 and SFB876/C3; the Polish National Research Centre grant UMO-2016/22/M/ST9/00382; and by the Brazilian MCTIC, CNPq, and FAPERJ. This research is supported by grants from the U.S. Department of Energy Office of Science, the U.S. National Science Foundation and the Smithsonian Institution, by NSERC in Canada, and by the Helmholtz Association in Germany. This research used resources provided by the Open Science Grid, which is supported by the National Science Foundation and the U.S. Department of Energy’s Office of Science, and resources of the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231. We acknowledge the excellent work of the technical support staff at the Fred Lawrence Whipple Observatory and at the collaborating institutions in the construction and operation of the instrument.

The concentration of oxygen is fundamental to lake water quality and ecosystem functioning through its control over habitat availability for organisms, redox reactions, and recycling of organic material. In many eutrophic lakes, oxygen depletion in the bottom layer (hypolimnion) occurs annually during summer stratification. The temporal and spatial extent of summer hypolimnetic anoxia is determined by interactions between the lake and its external drivers (e.g., catchment characteristics, nutrient loads, meteorology) as well as internal feedback mechanisms (e.g., organic matter recycling, phytoplankton blooms). How these drivers interact to control the evolution of lake anoxia over decadal timescales will determine, in part, the future lake water quality. In this study, we used a vertical one-dimensional hydrodynamic-ecological model (GLM-AED2) coupled with a calibrated hydrological catchment model (PIHM-Lake) to simulate the thermal and water quality dynamics of the eutrophic Lake Mendota (USA) over a 37 year period. The calibration and validation of the lake model consisted of a global sensitivity evaluation as well as the application of an optimization algorithm to improve the fit between observed and simulated data. We calculated stability indices (Schmidt stability, Birgean work, stored internal heat), identified spring mixing and summer stratification periods, and quantified the energy required for stratification and mixing. To qualify which external and internal factors were most important in driving the interannual variation in summer anoxia, we applied a random-forest classifier and multiple linear regressions to modeled ecosystem variables (e.g., stratification onset and offset, ice duration, gross primary production). Lake Mendota exhibited prolonged hypolimnetic anoxia each summer, lasting between 50-60 d. The summer heat budget, the timing of thermal stratification, and the gross primary production in the epilimnion prior to summer stratification were the most important predictors of the spatial and temporal extent of summer anoxia periods in Lake Mendota. Interannual variability in anoxia was largely driven by physical factors: earlier onset of thermal stratification in combination with a higher vertical stability strongly affected the duration and spatial extent of summer anoxia. A measured step change upward in summer anoxia in 2010 was unexplained by the GLM-AED2 model. Although the cause remains unknown, possible factors include invasion by the predacious zooplankton Bythotrephes longimanus. As the heat budget depended primarily on external meteorological conditions, the spatial and temporal extent of summer anoxia in Lake Mendota is likely to increase in the near future as a result of projected climate change in the region. NSF ABI development grant [DBI 1759865]; NSF CNH grant [1517823]; NSFNational Science Foundation (NSF) [DEB 1753639, DEB 1753657] The project was funded through an NSF ABI development grant (#DBI 1759865), an NSF CNH grant (#1517823), as well as NSF grants DEB 1753639 and DEB 1753657.

The Solver of Hydrological Unstructured Domain (SHUD) is an integrated multi-process, multi-scale, multi-timestep hydrologic model where the dominant hydrological processes are fully coupled using ...

Hydrological modeling is an essential strategy for understanding natural flows, particularly where observations are lacking in either space or time, or where topographic roughness leads to a disconnect in the characteristic timescales of overland and groundwater flow. Consequently, significant opportunities remain for the development of extensible modeling systems that operate robustly across regions. Towards the development of such a robust hydrological modeling system, this paper introduces the Solver for Hydrological Unstructured Domain (SHUD), an integrated multi-process, multi-scale, multi-timestep hydrological model, in which hydrological processes are fully coupled using the Finite Volume Method. The SHUD integrates overland flow, snow accumulation/melting, evapotranspiration, subsurface and groundwater flow, and river routing, while realistically capturing the physical processes in a watershed. The SHUD incorporates one-dimension unsaturated flow, two-dimension groundwater flow, and river channels connected with hillslopes via overland flow and baseflow. This paper introduces the design of SHUD, from the conceptual and mathematical description of hydrological processes in a watershed to computational structures. To demonstrate and validate the model performance, we employ three hydrological experiments: the V-Catchment experiment, Vauclin's experiment, and a study of the Cache Creek Watershed in northern California, USA. Possible applications of then SHUD model include hydrological studies from the hillslope scale to regional scale, water resource and stormwater management, and coupling research with related fields such as limnology, agriculture, geochemistry, geomorphology, water quality, and ecology, climatic and landuse change. In general, SHUD is a valuable scientific tool for any modeling task involving simulating and understanding the hydrological response.

The amount of observational data produced by time-domain astronomy is exponentially in-creasing. Human inspection alone is not an effective way to identify genuine transients fromthe data. An automatic real-bogus classifier is needed and machine learning techniques are commonly used to achieve this goal. Building a training set with a sufficiently large number of verified transients is challenging, due to the requirement of human verification. We presentan approach for creating a training set by using all detections in the science images to be thesample of real detections and all detections in the difference images, which are generated by the process of difference imaging to detect transients, to be the samples of bogus detections. This strategy effectively minimizes the labour involved in the data labelling for supervised machine learning methods. We demonstrate the utility of the training set by using it to train several classifiers utilizing as the feature representation the normalized pixel values in 21-by-21pixel stamps centered at the detection position, observed with the Gravitational-wave Optical Transient Observer (GOTO) prototype. The real-bogus classifier trained with this strategy can provide up to 95% prediction accuracy on the real detections at a false alarm rate of 1%., 9 pages, 8 figures

We report the results of optical follow-up observations of 29 gravitational-wave triggers during the first half of the LIGO-Virgo Collaboration (LVC) O3 run with the Gravitational-wave Optical Transient Observer (GOTO) in its prototype 4-telescope configuration (GOTO-4). While no viable electromagnetic counterpart candidate was identified, we estimate our 3D (volumetric) coverage using test light curves of on- and off-axis gamma-ray bursts and kilonovae. In cases where the source region was observable immediately, GOTO-4 was able to respond to a GW alert in less than a minute. The average time of first observation was $8.79$ hours after receiving an alert ($9.90$ hours after trigger). A mean of $732.3$ square degrees were tiled per event, representing on average $45.3$ per cent of the LVC probability map, or $70.3$ per cent of the observable probability. This coverage will further improve as the facility scales up alongside the localisation performance of the evolving gravitational-wave detector network. Even in its 4-telescope prototype configuration, GOTO is capable of detecting AT2017gfo-like kilonovae beyond 200~Mpc in favourable observing conditions. We cannot currently place meaningful electromagnetic limits on the population of distant ($\hat{D}_L = 1.3$~Gpc) binary black hole mergers because our test models are too faint to recover at this distance. However, as GOTO is upgraded towards its full 32-telescope, 2 node (La Palma \& Australia) configuration, it is expected to be sufficiently sensitive to cover the predicted O4 binary neutron star merger volume, and will be able to respond to both northern and southern triggers., 15 pages, 7 figures, 3 tables. Accepted for publication in MNRAS. Author's final submitted version

The rate at which water and carbon move through lake‐catchments influences carbon metabolism, storage, and transport to down‐stream ecosystems. This article focuses on conceptual‐mathematical modeling of the “age” of water and carbon in lake‐catchment systems, where “age” is defined as the time since the water parcel and environmental tracer entered the system. We test a framework for implementing models and data for the Lake Mendota lake‐catchment system. We show that the lake‐catchment system provides the basis for predicting time varying age of carbon along flow paths, and the residence times of dissolved organic carbon (DOC) leaving each compartment, which further helps to provide a deeper understanding of fundamental biogeochemical dynamic processes of carbon and water cycling within the lake‐catchment system. Three scenarios illustrate how the age of DOC might evolve from climate or land cover change that alters the hydrologic water balance through increases or decreases in lake‐catchment precipitation and evapotranspiration.

Summarization: Submarine groundwater discharge (SGD) contributes to the physical and chemical characters of coastal waters by discharging nutrients and contaminants, significantly impacting regional marine ecosystems and contributing to ocean chemical budgets. However, such groundwater discharge varies dramatically across scales and is often not comparable due to different model assumptions and field designs. We used a hydrologic model with integration of fundamental surface and subsurface processes to simulate the coastline level fresh SGD for the Crete Island in the Mediterranean Sea. The modeled hydrological processes suggested that fresh SGD substantially contributes to water flow entering the Mediterranean Sea (2.3 × 108 m3/yr), amounting to 31% of river discharge and 14% of precipitation. Spatially, fresh SGD varied from 2.4 m3/yr/m to 13.4 × 104 m3/yr/m, with an average of 2.6 × 103 m3/yr/m. The local maxima were commonly associated with river mouths reflecting larger hydraulic gradients and higher permeable structures. Temporally, fresh SGD was impacted by episodic precipitation in a delayed and prolonged pattern. We found that fresh SGD variability at the coastline segment level was compared to point measurements and fresh SGD magnitudes summered up to the catchment level were consistent with global products. Our results suggest the coupled surface–subsurface hydrologic modeling approach is a promising strategy to quantify and partition large-scale water budgets down to point observations that typically do not capture the full range of fresh SGD dynamics. Presented on: Journal of Hydrology

Why do solute concentrations in streams remain largely constant while discharge varies by orders of magnitude? We used a new hydrological land surface and reactive transport code, RT-Flux-PIHM, to understand this long-standing puzzle. We focus on the nonreactive chloride (Cl) and reactive magnesium (Mg) in the Susquehanna Shale Hills Critical Zone Observatory (SSHCZO). Simulation results show that stream discharge comes from surface runoff (Qs), soil lateral flow (QL), and deeper groundwater (QG), with QL contributing >70%. In the summer, when high evapotranspiration dries up and disconnects most of the watershed from the stream, Cl is trapped along planar hillslopes. Successive rainfalls connect the watershed and mobilize trapped Cl, which counteracts dilution effects brought about by high water storage (Vw) and maintains chemostasis. Similarly, the synchronous response of clay dissolution rates (Mg source) to hydrological conditions, maintained largely by a relatively constant ratio between “wetted” mineral surface area Aw and Vw, controls Mg chemostatic behavior. Sensitivity analysis indicates that cation exchange plays a secondary role in determining chemostasis compared to clay dissolution, although it does store an order-of-magnitude more Mg on exchange sites than soil water. Model simulations indicate that dilution (concentration decrease with increasing discharge) occurs only when mass influxes from soil lateral flow are negligible (e.g., via having low clay surface area) so that stream discharge is dominated by relatively constant mass fluxes from deep groundwater that are unresponsive to surface hydrological conditions.

The hydrologic connectivity between hillslopes and streams impacts the geomorphological evolution of catchments. Here, we propose a conceptual model for hydrogeomorphological evolution of the Susquehanna Shale Hills Critical Zone Observatory (SSHCZO), a first-order catchment developed on shale in central Pennsylvania, U.S.A. At SSHCZO, the majority of available water (the difference between incoming meteoric water and outgoing evapotranspiration) flows laterally to the catchment outlet as interflow, while the rest is transported by regional groundwater flow. Interflow, shallow hillslope flow, is limited to the upper 5 to 8 m of highly fractured bedrock, thought to have formed during periglacial conditions in the late Pleistocene. In contrast, groundwater flowpaths are influenced by the primary permeability of the varying stratigraphic units. Both flowpaths respond to weathering-related secondary permeability. O2-rich interflow mixes with deep O2-poor groundwater under the catchment outlet at depths of 5–8 m. Penetration of this oxygenated interflow under the valley results in pyrite oxidation, release of sulfuric acid, dissolution of minerals, and weakening of bedrock. This is hypothesized to enhance channel incision and, in turn, to promote drainage of deep groundwater from the ridges. Drainage subsequently lowers the catchment water table, advancing the cascade of reactions that produce regolith. Weathering in the catchment is characterized by both sharp and diffuse reaction fronts. Relatively sharp fronts (pyrite, carbonate) mark where vertical, unsaturated flow changes to horizontal, saturated flow, while diffuse fronts (illite, chlorite, feldspar) mark where flow is largely vertical and unsaturated. According to this model, catchment morphology reflects subsurface pyrite reactions due to mixing of interflow and groundwater flow under the valley floor that ultimately results in clay weathering and regolith production nearer the land surface.

The objectives of this study were to determine the quantity and chemical composition of precipitation inputs of dissolved organic carbon (DOC) to a forested watershed; and to characterize the associated temporal variability. We sampled most precipitation that occurred from May 2012 through August 2013 at the Susquehanna Shale Hills Critical Zone Observatory (Pennsylvania, USA). Sub-event precipitation samples (159) were collected sequentially during 90 events; covering various types of synoptic meteorological conditions in all climatic seasons. Precipitation DOC concentrations and rates of wet atmospheric DOC deposition were highly variable from storm to storm, ranging from 0.3 to 5.6 mg C L−1 and from 0.5 to 32.8 mg C m−2 h−1, respectively. Seasonally, storms in spring and summer had higher concentrations of DOC and more optically active organic matter than in winter. Higher DOC concentrations resulted from weather types that favor air advection, where cold frontal systems, on average, delivered more than warm/stationary fronts and northeasters. A mixed modeling statistical approach revealed that factors related to storm properties, emission sources, and to the chemical composition of the atmosphere could explain more than 60% of the storm to storm variability in DOC concentrations. This study provided observations on changes in dissolved organic matter that can be useful in modeling of atmospheric oxidative chemistry, exploring relationships between organics and other elements of precipitation chemistry, and in considering temporal changes in ecosystem nutrient balances and microbial activity.

Understanding the interactions between natural processes and human activities poses major challenges as it requires the integration of models and data across disparate disciplines. It typically takes many months and even years to create valid end-to-end simulations as different models need to be configured in consistent ways and generate data that is usable by other models. MINT is a novel framework for model integration that captures extensive knowledge about models and data and aims to automatically compose them together. MINT guides a user to pose a well-formed modeling question, select and configure appropriate models, find and prepare appropriate datasets, compose data and models into end-to-end workflows, run the simulations, and visualize the results. MINT currently includes hydrology, agriculture, and socioeconomic models.

International audience; Projections of future conditions within the critical zone—earthcasts—can be used to understand the potential effects of changes in climate on processes affecting landscapes. We are developing an approach to earthcast how weathering will change in the future using scenarios of climate change. As a first step here, we use the earthcasting approach to model aspect-related effects on soil water chemistry and weathering on hillsides in a well-studied east-west trending watershed (Shale Hills, Pennsylvania, USA). We completed model simulations of solute chemistry in soil water with and without the effect of aspect for comparison to catchment observations. With aspect included, aqueous weathering fluxes were higher on the sunny side of the catchment. But the effect of aspect on temperature (0.8 °C warmer soil on sunny side) and recharge (100 mm/year larger on shaded side) alone did not explain the magnitude of the observed higher weathering fluxes on the sunny side. Modeled aspect-related differences in weathering fluxes only approach field observations when we incorporated the measured differences in clay content observed in augered soils on the two hillslopes. We also had to include a biolifting module to accurately describe cation concentrations in soil water versus depth. Biolifting lowered some mineral dissolution rates while accelerating kaolinite precipitation. These short-duration simulations also highlighted that the inherited differences in particle size on the two sides of the catchment might in themselves be explained by weathering under different microclimates caused by aspect—over longer durations than simulated with our models.

Advocates of enhanced quality for published scientific results are increasingly voicing the need for further transparency of data and software for scientific reproducibility. However, such advanced digital scholarship can appear perplexing to geoscientists that are seduced by the concept of open science yet wonder about the exact mechanics and implications of the associated efforts. This special issue of Earth and Space Science entitled “Geoscience Papers of the Future” includes a review of existing best practices for digital scholarship and bundles a set of example articles that share their digital research products and reflect on the process of opening their scientific approach in a common quest for reproducible science. This article is protected by copyright. All rights reserved.

Reproducibility is a foundational principle in scientific research. Yet in computational hydrology the code and data that actually produces published results are not regularly made available, inhibiting the ability of the community to reproduce and verify previous findings. In order to overcome this problem we recommend that reuseable code and formal workflows, which unambiguously reproduce published scientific results, are made available for the community alongside data, so that we can verify previous findings, and build directly from previous work. In cases where reproducing large-scale hydrologic studies is computationally very expensive and time-consuming, new processes are required to ensure scientific rigor. Such changes will strongly improve the transparency of hydrological research, and thus provide a more credible foundation for scientific advancement and policy support.

HydroTerre is a research prototype platform developed at Penn State for the hydrology community. It provides access to aggregated scientific data sets that are useful for hydrological modeling and research. HydroTerre’s frontend is a web service, and a user query can request creation of a data bundle whose size can vary from a few megabytes to 100’s of gigabytes. In this article, we present software tuning and optimization strategies for various hardware configurations of the HydroTerre platform. Our goal is to minimize access time to a wide range of data bundle creation queries from users. We use automated schemes to estimate the computational work required for various queries, and identify the best-performing hardware/software configuration. We hope this study is instructive for researchers developing similar data management cyberinfrastructure in other science and engineering fields.

Cyberinfrastructure is enabling ever more integrative and transformative science. Technological advances in cyberinfrastructure have allowed deeper understanding of watershed hydrology by improved integration of data, information, and models. The synthesis of all sources of hydrologic variables (historical, real time, future scenarios, observed, and modeled) requires advanced data acquisition, data storage, data management, data integration, data mining, and data visualization. In this context, cyber-innovated hydrologic research was implemented to carry out watershed-based historical climate simulations at the Shale Hills Critical Zone Observatory. The simulations were based on the assimilation of data from a hydrologic monitoring network into a multiphysics hydrologic model (the Penn State Integrated Hydrology Model). We documented workflows for the model application and applied the model to short-time hyporheic exchange flow study and long-term climate scenario analysis. The effort reported herein demonstrates that advances in cyberscience allows innovative research that improves our ability to access and share data; to allow collective development of science hypotheses; and to support building models via team participation. We simplified communications between model developers and community scientists, software professionals, students, and decision makers, which in the long term will improve the utilization of hydrologic models for science and societal applications.

Although current landscape evolution models can predict landscapes with specific concave-convex slopes, regolith thicknesses, drainage densities and relief, these models rarely include realistic groundwater and overland flows, and channel-hillslope interactions. To overcome the potential drawbacks, this study couples hydrologic processes with hillslope and channel sediment transport processes to form a new hydrologic-morphodynamic model (LE-PIHM) for regolith formation and landscape evolution. Two scenarios with and without groundwater flow are presented to demonstrate the importance of this coupling. Comparison of the steady state landforms indicates that hillslopes are steeper and relief is higher with groundwater flow. The sensitivity of the solution to mesh geometry is tested and it is shown that model simulations maintain the characteristic features of a landscape over a reasonable range of maximum area and minimum interior angle. To predict long-term landscape change, a morphological acceleration technique is presented and a method for choosing an optimal morphological scale factor is introduced. A new fully-coupled hydrologic-morphodynamic model is presented.Slopes and relief of the landscape shows the importance of coupling subsurface flow.Erosion rate reaches the peak at the moment of exfiltration and then decreases.A morphological acceleration technique is introduced for morphological simulation.Numerical experiments shows the existence of an optimal morphological scale factor.

Integrated modeling of coupled surface-subsurface flow and ensuing role in diverse Earth system processes is of current research interest to characterize nonlinear rainfall-runoff response and also to understand land surface energy balances, biogeochemical processes, geomorphological dynamics, etc. A growing number of complex models have been developed for water-related research, and many of these are made available to the Earth science community. However, relatively few resources have been made accessible to the potentially large group of Earth science and engineering users. New users have to invest an extraordinary effort to study the models. To provide a stimulating experience focusing on the learning curve of integrated modeling of coupled surface-subsurface flow, we describe use cases of an open source model, the Penn State Integrated Hydrologic Model, PIHM. New users were guided through data processing and model application by reproducing a numerical benchmark problem and a real-world watershed simulation. Specifically, we document the PIHM application and its computational workflow to enable intuitive understanding of coupled surface-subsurface flow processes. In addition, we describe the user experience as important evidence of the significance of reusability. The interaction shows that documentation of data, software, and computational workflow in research papers is a promising method to foster open scientific collaboration and reuse. This study demonstrates how open science practice in research papers would promote the utility of open source software. Addressing such open science practice in publications would promote the utility of journal papers. Further, popularization of such practice will require coordination among research communities, funding agencies, and journals.

Evapotranspiration (ET) continues to be a difficult process to estimate in seasonal and long-term water balances in catchment models. Approaches to estimate ET typically use vegetation parameters (e.g., leaf area index LAI, interception capacity) obtained from field observation, remote sensing data, national or global land cover products, and/or simulated by ecosystem models. In this study we attempt to quantify the uncertainty that spatial evapotranspiration estimation introduces into hydrological simulations when the age of the forest is not precisely known. The Penn State Integrated Hydrologic Model (PIHM) was implemented for the Lysina headwater catchment, located 50?03'N, 12?40'E in the western part of the Czech Republic. The spatial forest patterns were digitized from forest age maps made available by the Czech Forest Administration. Two ET methods were implemented in the catchment model: the Biome-BGC forest growth sub-model (1-way coupled to PIHM) and with the fixed-seasonal LAI method. From these two approaches simulation scenarios were developed. We combined the estimated spatial forest age maps and two ET estimation methods to drive PIHM. A set of spatial hydrologic regime and streamflow regime indices were calculated from the modeling results for each method. Intercomparison of the hydrological responses to the spatial vegetation patterns suggested considerable variation in soil moisture and recharge and a small uncertainty in the groundwater table elevation and streamflow. The hydrologic modeling with ET estimated by Biome-BGC generated less uncertainty due to the plant physiology-based method. The implication of this research is that overall hydrologic variability induced by uncertain management practices was reduced by implementing vegetation models in the catchment models. We quantify the uncertainty of hydrological modeling due to spatial ET estimation.We present a series of forest management probabilities from historic forest age maps.The plant physiology-based ET estimation reduced hydrologic uncertainty.Reduced uncertainty suggests the importance of forest growth in water use studies.

Visualization workflows are important services for expert users to analyze watersheds when using our HydroTerre end-to-end workflows. Analysis is an interactive and iterative process and we demonstrate that the expert user can focus on model results, not data preparation, by using a web application to rapidly create, tune, and calibrate hydrological models anywhere in the continental USA (CONUS). The HydroTerre system captures user interaction for provenance and reproducibility to share modeling strategies with modelers. Our end-to-end workflow consists of four workflows. The first is data workflows using Essential Terrestrial Variables (ETV) data sets that we demonstrated to construct watershed models anywhere in the CONUS (Leonard and Duffy, 2013). The second is data-model workflows that transform the data workflow results to model inputs. The model inputs are consumed in the third workflow, model workflows (Leonard and Duffy, 2014a) that handle distribution of data and model within High Performance Computing (HPC) environments. This article focuses on our fourth workflow, visualization workflows, which consume the first three workflows to form an end-to-end system to create and share hydrological model results efficiently for analysis and peer review. We show how visualization workflows are incorporated into the HydroTerre infrastructure design and demonstrate the efficiency and robustness for an expert modeler to produce, analyze, and share new hydrological models using CONUS national datasets. Visualization, model, and data services for and sharing of hydrological models.Visualization workflows for hydrological model analysis at HUC12 catchment scales.End-to-end workflows for reproducibility and provenance of hydrological models.Cyber-Infrastructure to support hydrological models and big data at high resolution.

Fresh water availability was an important variable that influenced prehistoric human settlement on California's northern Channel Islands. Previous attempts to understand settlement on the islands use watershed size as a proxy for water at canyon mouths. In semi-arid regions, this approach has limitations because streams may lose much or all of their flow to groundwater. We developed a distributed hydrological model for Santa Rosa Island that incorporates geospatial and temporal data for climate (precipitation, solar radiation, wind speed, relative humidity, temperature), soils, vegetation, and topography to simulate the complex land-surface-groundwater behavior of island hydrology for hypothetical wet, dry, and median centuries. Our simulations show that water flow is greatest in drainages on the northwest and east coasts of the island. This correlates with some of the earliest and most persistent settlement on the island. During the most extreme droughts of the last 2000 years during the Medieval Climatic Anomaly (1150–600 cal BP), island populations contracted to a small number of large coastal villages. We argue that this was related in part to the greater availability of surface water at these locations. This study expands the theoretical and methodological scope of past studies that have applied hydrological simulation to archaeological investigations.

Recent debate over the scope of the U.S. Clean Water Act underscores the need to develop a robust body of scientific work that defines the connectivity between freshwater systems and people. Coupled natural and human systems (CNHS) modeling is one tool that can be used to study the complex, reciprocal linkages between human actions and ecosystem processes. Well‐developed CNHS models exist at a conceptual level, but the mapping of these system representations in practice is limited in capturing these feedbacks. This article presents a paired conceptual–empirical methodology for functionally capturing feedbacks between human and natural systems in freshwater lake catchments, from human actions to the ecosystem and from the ecosystem back to human actions. We address extant challenges in CNHS modeling, which arise from differences in disciplinary approach, model structure, and spatiotemporal resolution, to connect a suite of models. In doing so, we create an integrated, multi‐disciplinary tool that captures diverse processes that operate at multiple scales, including land‐management decision‐making, hydrologic‐solute transport, aquatic nutrient cycling, and civic engagement. In this article, we build on this novel framework to advance cross‐disciplinary dialogue to move CNHS lake‐catchment modeling in a systematic direction and, ultimately, provide a foundation for smart decision‐making and policy. National Science Foundation, Grant 1517823 Published version

The capability of an ensemble Kalman filter (EnKF) to simultaneously estimate multiple parameters in a physically-based land surface hydrologic model using multivariate field observations is tested at a small watershed (0.08 km2). Multivariate, high temporal resolution, in situ measurements of discharge, water table depth, soil moisture, and sensible and latent heat fluxes encompassing five months of 2009 are assimilated. It is found that, for five out of the six parameters, the EnKF estimated parameter values from different test cases converge strongly, and the estimates after convergence are close to the manually calibrated parameter values. The EnKF estimated parameters and manually calibrated parameters yield similar model performance, but the EnKF sequential method significantly decreases the time and labor required for calibration. The results demonstrate that, given a limited number of multi-state, site-specific observations, an automated sequential calibration method (EnKF) can be used to optimize physically-based land surface hydrologic models.

Soil moisture is a critical variable in the water and energy cycles. The prediction of soil moisture patterns, especially at high spatial resolution, is challenging. This study tests the ability of a land surface hydrologic model (Flux-PIHM) to simulate high-resolution soil moisture patterns in the Shale Hills watershed (0.08 km2) in central Pennsylvania. Locally measured variables including a soil map, soil parameters, a tree map, and lidar topographic data, all have been synthesized into Flux-PIHM to provide model inputs. The predicted 10-cm soil moisture patterns for 15 individual days encompassing seven months in 2009 are compared with the observations from 61 soil moisture monitoring sites. Calibrated using only watershed-scale and a few point-based measurements, and driven by spatially uniform meteorological forcing, Flux-PIHM is able to simulate the observed macro spatial pattern of soil moisture at ~10-m resolution (spatial correlation coefficient ~ 0.6) and the day-to-day variation of this soil moisture pattern, although it underestimates the amplitude of the spatial variability and the mean soil moisture. Results show that the spatial distribution of soil hydraulic parameters has the dominant effect on the soil moisture spatial pattern. The surface topography and depth to bedrock also affect the soil moisture patterns in this watershed. Using the National Land Cover Database (NLCD) in place of a local tree survey map makes a negligible difference. Field measured soil type maps and soil type-specific hydraulic parameters significantly improve the predicted soil moisture pattern as compared to the most detailed national soils database (Soil Survey Geographic Database, or SSURGO, 30-m resolution). Copyright © 2015 John Wiley & Sons, Ltd.

Geometric and topological methods are applied to significance testing in the wavelet domain. A geometric test was developed for assigning significance to pointwise significance patches in local wavelet spectra, i.e., contiguous regions of significant wavelet power coefficients with respect to some noise model. This geometric significance test was found to produce results similar to an existing areawise significance test while being more computationally flexible and efficient. The geometric significance test can be readily applied to pointwise significance patches at various pointwise significance levels in wavelet power and coherence spectra. The geometric test determined that features in wavelet power of the North Atlantic Oscillation (NAO) are indistinguishable from a red-noise background, suggesting that the NAO is a stochastic, unpredictable process, which could render difficult the future projections of the NAO under a changing global system. The geometric test did, however, identify features in the wavelet power spectrum of an El Niño index (Niño 3.4) as distinguishable from a red-noise background. A topological analysis of pointwise significance patches determined that holes, deficits in pointwise significance embedded in significance patches, are capable of identifying important structures, some of which are undetected by the geometric and areawise tests. The application of the topological methods to ideal time series and to the time series of the Niño 3.4 and NAO indices showed that the areawise and geometric tests perform similarly in ideal and geophysical settings, while the topological methods showed that the Niño 3.4 time series contains numerous phase-coherent oscillations that could be interacting nonlinearly.

Distributed, physics-based hydrologic models require spatially explicit specification of parameters related to climate, geology, land-cover, soil, and topography. Extracting these parameters from national geodatabases requires intensive data processing. Furthermore, mapping these parameters to model mesh elements necessitates development of data access tools that can handle both spatial and temporal datasets. This paper presents an open-source, platform independent, tightly coupled GIS and distributed hydrologic modeling framework, PIHMgis (www.pihm.psu.edu), to improve model-data integration. Tight coupling is achieved through the development of an integrated user interface with an underlying shared geodata model, which improves data flow between the PIHMgis data processing components. The capability and effectiveness of the PIHMgis framework in providing functionalities for watershed delineation, domain decomposition, parameter assignment, simulation, visualization and analyses, is demonstrated through prototyping of a model simulation. The framework and the approach are applicable for watersheds of varied sizes, and offer a template for future GIS-Model integration efforts. A coupled GIS and distributed hydrologic modeling framework, PIHMgis was developed.PIHMgis uses national geospatial dataset to setup, execute, and analyze simulations.Procedural framework improves model-data integration using shared geodata model.

In this article, I explore the evolving historical views of the terrestrial hydrologic cycle through a lens that might be called ‘a model of balance.’ The notion of balance in western literature, seems to me to capture a central theme for how scholars of natural history, medieval mathematicians, designers of scientific instruments, and early waterworks engineers viewed the complexities and evaluated the evidence for how water moves in the terrestrial landscape. Using historical images and text, this study attempts to document how we arrived at the modern interpretation, its sources, origins, and relationships within the terrestrial earth. By comparing ancient models for the hydrologic cycle one can begin to construct a plausible story of its evolution as well as the important actors. I pay particular attention to the origins and sources of springs, rivers, and groundwater, the kinds of evidence proposed, and its relation to the sense of balance that emerges. Finally, I argue that at by the end of the 18th and into the early 19th century, the modern view of the terrestrial hydrologic cycle that emerged became a necessary foundation for the geophysical, ecological, and cultural Earth science themes that still flourish today. WIREs Water 2017, 4:e1216. doi: 10.1002/wat2.1216 For further resources related to this article, please visit the WIREs website.

This chapter summarizes the methods, results, and conclusions of a 5-year research project (SoilTrEC: Soil Transformations in European Catchments) on experimentation, process modeling, and computational simulation of soil functions and soil threats across a network of European, Chinese, and United States Critical Zone Observatories (CZOs). The study focused on the soil functions of biomass production, carbon storage, water storage and transmission, water filtration, transformation of nutrients, and maintaining habitat and genetic diversity.The principal results demonstrate that soil functions can be quantified as biophysical flows and transformations of material and energy. The functions can be simulated with mathematical models of soil processes within the soil profile and at the critical zone interfaces with vegetation and atmosphere, surface waters and the below-ground vadose zone and groundwater. A new dynamic model for soil structure development, together with data sets from the CZOs, demonstrate both seasonal fluctuations in soil structure dynamics related to vegetation dynamics and soil carbon inputs, and long-term trends (decadal) in soil carbon storage and soil structure development.Cross-site comparison for 20 soil profiles at seven field sites with variation in soil type, lithology, land cover, land use, and climate demonstrate that sites can be classified, using model parameter values for soil aggregation processes together with climatic conditions and soil physical properties, along a trajectory of soil structure development from incipient soil formation through productive land use to overly intensive land use with soil degradation.A new modeling code, the Integrated Critical Zone model, was applied with parameter sets developed from the CZO site data to simulate the biophysical flows and transformations that quantify multiple soil functions. Process simulations coupled the new model for soil structure dynamics with existing modeling approaches for soil carbon dynamics, nutrient transformations, vegetation dynamics, hydrological flow and transport, and geochemical equilibria and mineral weathering reactions. Successful calibration, testing, and application of the model with data sets from horticulture plot manipulation experiments demonstrate the potential to apply modeling and simulation to the scoping and design of new practices and policy options to enhance soil functions and reduce soil threats worldwide.