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Monitoring and modeling dispersal of a submerged nearshore berm at the mouth of the Columbia River, USA

COASTAL ENGINEERING 2023年 181卷

作者： Stevens, Andrew W. Moritz, Hans R. Elias, Edwin P. L. Gelfenbaum, Guy R. Ruggiero, Peter R. Pearson, Stuart G. McMillan, James M. Kaminsky, George M. US Geol Survey Pacific Coastal & Marine Sci Ctr 2885 Mission St Santa Cruz CA 95060 USA US Army Corps Engineers 333 SW First Ave Portland OR 97204 USA Deltares POB 177 NL-2600 MH Delft Netherlands Oregon State Univ Coll Earth Ocean & Atmospher Sci 2651 SW Orchard Ave Corvallis OR 97331 USA Delft Univ Technol Fac Civil Engn & Geosci POB 5048 NL-2600 GA Delft Netherlands Washington State Dept Ecol Coastal Monitoring & Anal Program POB 47600 Olympia WA 98504 USA

A submerged, low-relief nearshore berm was constructed in the Pacific Ocean near the mouth of the Columbia River, USA, using 216,000 m3 of sediment dredged from the adjacent navigation channel. The material dredged from the navigation channel was placed on the northern flank of the ebb-tidal delta in water depths between 12 and 15 m and created a distinct feature that could be tracked over time. Field measurements and numerical modeling were used to evaluate the transport pathways, time scales, and physical processes responsible for dispersal of the berm and evaluate the suitability of the location for operational placement of dredged material to enhance the sediment supply to eroding beaches onshore of the placement site. Repeated multibeam bathymetric surveys characterized the initial berm morphology and dispersion of the berm between September 22, 2020, and March 10, 2021. During this time, the volume of sediment within the berm decreased by about 40%to 127,000 m3, the maximum height decreased by almost 60%, and the center of the deposit shifted onshore over 200 m. Observations of berm morphology were compared with predictions from a three-dimensional hydrodynamic and sediment transport model application to refine poorly constrained model input parameters including sediment transport coefficients, bed schematization, and grain size. The calibrated sediment transport model was used to predict the amount, timing, and direction of transport outside of the observed survey area. Model simulations predicted that tidal currents were weak in the vicinity of the berm and wave processes including enhanced bottom stresses and asymmetric bottom orbital velocities resulted in dominant onshore movement of sediment from the berm toward the coastline. Roughly 50% of the berm volume was predicted to disperse away from the initial placement site during the 169 day hindcast. Between 9 and 17% of the initial volume of the berm was predicted to accumulate along the shoreface of a sho

关键词： Nearshore berm process-based modeling Delft3D Sediment transport

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modeling the Genesis of Sand-Starved Dunes in Steady Currents

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JOURNAL OF GEOPHYSICAL RESEARCH-EARTH SURFACE 2023年第1期128卷 e2022JF006796-e2022JF006796页

作者： Porcile, Gaetano Damveld, Johan H. Roos, Pieter C. Blondeaux, Paolo Colombini, Marco Univ Caen Normandy Coastal & Continental Morphodynam Lab Caen France Univ Twente Water Engn & Management Enschede Netherlands Univ Genoa Dept Civil Chem & Environm Engn Genoa Italy

The formation of fluvial dunes is usually studied by investigating the time development of a small amplitude bottom perturbation of a uniform stream and considering that the dunes originate by the growth of the bottom mode characterized by the largest amplification rate under the assumption of an infinite availability of the mobile sediment (linear stability analysis). Here we undertake the stability analysis investigating the formation of sand dunes in steady currents by accounting for the nonlinear effects of sediment starvation on the formative mechanisms of the bedforms and comparing the theoretical results with laboratory experiments, and an application of a fully nonlinear commercial model of finite amplitude dunes, thus enabling an improved understanding of the genesis of starved fluvial dunes. As the growth of the dunes progressively exposes the motionless substratum, both the stability-based and the numerical models predict starved dunes characterized by increasing crest-to-crest distances. The increase of the crest-to-crest distance corresponds to a decrease of the length of individual dunes as well as a growing irregularity in their spacing and morphology. These findings conform with the outcome of physical experiments performed earlier in a laboratory flume and existing measurements of starved fluvial dunes in the field. Plain Language Summary The genesis of fluvial dunes is a topic of considerable interest to river engineering as dunes are a primary source of flow resistance, regulating water levels and transport processes. The modeling of fluvial dunes is usually performed assuming an infinite availability of the sediment that can be eroded and deposited by the water flow. Field observations and laboratory experiments nevertheless indicate that the supply of sediment affects dune growth and the resulting morphology of the river bed. Here we present a new theoretical model able to reproduce the effects that the lack of sediment has on the growth of dune

关键词： stability analysis process-based modeling fluvial dunes sediment starvation nonlinear effects morphodynamics

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Investigating biophysical control of marine phytoplankton dynamics via Bayesian mechanistic modeling

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ECOLOGICAL MODELLING 2022年 474卷

作者： Han, Yue Zhou, Yuntao Shanghai Jiao Tong Univ Sch Oceanog Shanghai Peoples R China

Marine phytoplankton possess a key position in multiple ecological, biological, and environmental processes;therefore, constructing a systematic and comprehensive mechanistic understanding of the leading mechanisms and key biophysical drivers of phytoplankton dynamics is crucial for evaluating biophysical forcings and ecological responses. Here, we develop a process-based model to improve the characterization of controlling mechanisms and assess the relative importance of nutrients, light, temperature, and oceanic mixing for explaining chlorophyll-a variability in the western North Pacific Ocean. The model is calibrated using 20-year observational data of chlorophyll-a concentrations from 1993 to 2012 in a Bayesian framework to provide data-driven inference and a rigorous uncertainty quantification of important biophysical rates. The model ex-plains approximately 61% of the variability in chlorophyll-a. Our results, supported by cross-validation, show that the chlorophyll-a concentration is much more responsive to temperature variability than nutrients, light, or mixing. By synthesizing the effects of multiple nutrients (i.e., nitrogen, phosphorus, silica, and iron) on phyto-plankton dynamics, we find that iron and nitrogen largely determine the chlorophyll-a variability. Sensitivity analysis shows that future warming conditions with +3 degrees C change in temperature will impede phytoplankton production by 24% due to an intensified phytoplankton mortality impact. Our findings highlight the potential of "deductive-and-inductive" modeling approaches combining embedded biophysical mechanisms and probabilistic uncertainty quantification to effectively integrate and leverage sporadic ocean monitoring efforts and ultimately improve the marine water quality predictions.

关键词： Marine phytoplankton Biophysical controls Bayesian inference Climate change Western North Pacific process-based modeling

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Climate Change Attribution in the Lena and Selenga River Runoff: An Evaluation based on the Earth System and Regional Hydrological Models

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WATER 2022年第1期14卷 118页

作者： Kalugin, Andrey Russian Acad Sci Water Problems Inst Moscow 119333 Russia

The main goal of this study was to obtain the attribution results of a physical assessment of the modern hydrological consequences of separately natural and anthropogenic components of climate change, based on the synthesis of detailed process-based models of river runoff formation and an ensemble of Earth system models (ESMs) within the large river basins in Eastern Siberia. This approach allows calculating the river flow using ESM-based data over the observation period under two scenarios, considering: (1) the anthropogenic impact of increasing greenhouse gas emissions and (2) only internal fluctuations of the climate system and natural external forcing. According to the results of the numerical experiments, the attributions of anthropogenic components of climate change in the dynamics of the Lena runoff are weak, i.e., during the observation period, the Lena River flow statistically significantly increases, but it occurs mainly due to natural climate variability. The changes in the Selenga runoff are intensely influenced by the anthropogenic component of climate change. Since the 1970s, the Selenga runoff increased under natural climatic conditions, but since the mid-1980s, it decreased under anthropogenic greenhouse gas emissions, due to reduced summer precipitation. This was the main reason for the last low-water period of 1996-2017 in the Selenga basin.

关键词： climate change attribution natural and anthropogenic climate-driven runoff change runoff generation process-based modeling ESMs the Lena River the Selenga River the ECOMAG model ISIMIP

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process Interactions Can Change process Ranking in a Coupled Complex System Under process Model and Parametric Uncertainty

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WATER RESOURCES RESEARCH 2022年第3期58卷 e2021WR029812-e2021WR029812页

作者： Yang, Jing Ye, Ming Chen, Xingyuan Dai, Heng Walker, Anthony P. Northwest A&F Univ Coll Water Resources & Architectural Engn Yangling Shaanxi Peoples R China Florida State Univ Dept Earth Ocean & Atmosphere Sci Tallahassee FL 32306 USA Florida State Univ Dept Sci Comp Tallahassee FL 32306 USA Pacific Northwest Natl Lab Richland WA 99352 USA China Univ Geosci State Key Lab Biol & Environm Geol Wuhan Peoples R China Oak Ridge Natl Lab Environm Sci Div Oak Ridge TN USA Oak Ridge Natl Lab Climate Change Sci Inst Oak Ridge TN USA

For a complex hydrologic system with multiple processes and process interactions, global sensitivity analysis is often used to identify important or influential parameters for model development and improvement. The identification is complicated by process model uncertainty, when a system process can be represented by multiple process models. This study develops a new total-effect process sensitivity index to identify influential processes under model uncertainty. This is done by extending Sobol's total-effect parameter sensitivity index for one system model to total-effect process sensitivity index for multiple system models to account for uncertainty in process models and model parameters. The total-effect process sensitivity index includes not only the first-order process sensitivity index for measuring the importance of individual processes but also higher-order indices that account for process interactions. The total-effect process sensitivity index can identify an influential process that itself and its interactions with other processes influence a model output. The total-effect process sensitivity index is applied to two numerical examples: (a) Sobol's G*-functions with analytical solutions of first-order and total-effect process sensitivity indices, and (b) groundwater flow models with interactions between recharge, geology, and snowmelt processes. The second evaluation shows that, due to second-order and higher-order process interactions, the first-order and total-effect process sensitivity indices give different process ranking. It is thus necessary to estimate both first-order and total-effect process sensitivity indices to appreciate the difference between the first-order impact of a process alone and the overall total-effect impact of the process itself and its interactions with other processes on a model output.

关键词： process-based modeling process importance process influence process interactions uncertainty reduction process screening

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Reservoir water quality deterioration due to deforestation emphasizes the indirect effects of global change

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WATER RESEARCH 2022年 221卷 118721页

作者： Kong, Xiangzhen Ghaffar, Salman Determann, Maria Friese, Kurt Jomaa, Seifeddine Mi, Chenxi Shatwell, Tom Rinke, Karsten Rode, Michael Chinese Acad Sci Nanjing Inst Geog & Limnol State Key Lab Lake Sci & Environm Nanjing Peoples R China UFZ Helmholtz Ctr Environm Res Dept Lake Res Magdeburg Germany UFZ Helmholtz Ctr Environm Res Dept Aquat Ecosyst Anal & Management Magdeburg Germany Tech Univ Carolo Wilhelmina Braunschweig Leichtweiss Inst Hydraul Engn & Water Resources Braunschweig Germany Univ Potsdam Inst Environm Sci & Geog Potsdam Germany

Deforestation is currently a widespread phenomenon and a growing environmental concern in the era of rapid climate change. In temperate regions, it is challenging to quantify the impacts of deforestation on the catchment dynamics and downstream aquatic ecosystems such as reservoirs and disentangle these from direct climate change impacts, let alone project future changes to inform management. Here, we tackled this issue by investigating a unique catchment-reservoir system with two reservoirs in distinct trophic states (meso- and eutrophic), both of which drain into the largest drinking water reservoir in Germany. Due to the prolonged droughts in 2015-2018, the catchment of the mesotrophic reservoir lost an unprecedented area of forest (exponential increase since 2015 and ca. 17.1% loss in 2020 alone). We coupled catchment nutrient exports (HYPE) and reservoir ecosystem dynamics (GOTM-WET) models using a process-based modeling approach. The coupled model was validated with datasets spanning periods of rapid deforestation, which makes our future projections highly robust. Results show that in a short-term time scale (by 2035), increasing nutrient flux from the catchment due to vast deforestation (80% loss) can turn the mesotrophic reservoir into a eutrophic state as its counterpart. Our results emphasize the more prominent impacts of deforestation than the direct impact of climate warming in impairment of water quality and ecological services to downstream aquatic ecosystems. Therefore, we propose to evaluate the impact of climate change on temperate reservoirs by incorporating a time scale-dependent context, highlighting the indirect impact of deforestation in the short-term scale. In the long-term scale (e.g. to 2100), a guiding hypothesis for future research may be that indirect effects (e.g., as mediated by catchment dynamics) are as important as the direct effects of climate warming on aquatic ecosystems.

关键词： Deforestation Climate change Temperate regions Reservoir Eutrophication process-based modeling

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modeling Intra- and Interannual Variability of BVOC Emissions From Maize, Oil-Seed Rape, and Ryegrass

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JOURNAL OF ADVANCES IN modeling EARTH SYSTEMS 2022年第3期14卷

作者： Havermann, Felix Ghirardo, Andrea Schnitzler, Joerg-Peter Nendel, Claas Hoffmann, Mathias Kraus, David Grote, Ruediger Karlsruhe Inst Technol Inst Meterol & Climate Res Atmospher Environm Res Garmisch Partenkirchen Germany Ludwig Maximilians Univ Munchen Dept Geog Munich Germany Leibniz Ctr Agr Landscape Res ZALF Muncheberg Germany Helmholtz Zentrum Munchen Res Unit Environm Simulat EUS Inst Biochem Plant Neuherberg Germany Univ Potsdam Inst Biochem & Biol Potsdam Germany Czech Acad Sci Global Change Res Inst Brno Czech Republic

Air chemistry is affected by the emission of biogenic volatile organic compounds (BVOCs), which originate from almost all plants in varying qualities and quantities. They also vary widely among different crops, an aspect that has been largely neglected in emission inventories. In particular, bioenergy-related species can emit mixtures of highly reactive compounds that have received little attention so far. For such species, long-term field observations of BVOC exchange from relevant crops covering different phenological phases are scarcely available. Therefore, we measured and modeled the emission of three prominent European bioenergy crops (maize, ryegrass, and oil-seed rape) for full rotations in north-eastern Germany. Using a proton transfer reaction-mass spectrometer combined with automatically moving large canopy chambers, we were able to quantify the characteristic seasonal BVOC flux dynamics of each crop species. The measured BVOC fluxes were used to parameterize and evaluate the BVOC emission module (JJv) of the physiology-oriented LandscapeDNDC model, which was enhanced to cover de novo emissions as well as those from plant storage pools. Parameters are defined for each compound individually. The model is used for simulating total compound-specific reactivity over several years and also to evaluate the importance of these emissions for air chemistry. We can demonstrate substantial differences between the investigated crops with oil-seed rape having 37-fold higher total annual emissions than maize. However, due to a higher chemical reactivity of the emitted blend in maize, potential impacts on atmospheric OH-chemistry are only 6-fold higher.

关键词： biogenic volatile organic compounds process-based modeling Zea mays Brassica napus Lolium multiflorum plant ontogenetic stage

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COMPARISON OF RZWQM2 AND DNDC MODELS TO SIMULATE GREENHOUSE GAS EMISSIONS UNDER COMBINED INORGANIC/ORGANIC FERTILIZATION IN A SUBSURFACE-DRAINED FIELD

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TRANSACTIONS OF THE ASABE 2020年第4期63卷 771-787页

作者： Jiang, Q. Qi, Z. Madramootoo, C. A. Smith, W. Abbasi, N. A. Zhang, T. Q. McGill Univ Dept Bioresource Engn Ste Anne De Bellevue PQ Canada Agr & Agri Food Canada Ottawa Res & Dev Ctr Ottawa ON Canada Agr & Agri Food Canada Harrow Res & Dev Ctr Harrow ON Canada

N management has the potential to mitigate greenhouse gas (GHG) emissions. process-based models are promising tools for evaluating and developing management practices that may optimize sustainability goals as well as promote crop productivity. In this study, the GHG emission component of the Root Zone Water Quality Model (RZWQM2) was tested under two different types of N management and subsequently compared with the Denitrification-Decomposition (DNDC) model using measured data from a subsurface-drained field with a corn-soybean rotation in southern Ontario, Canada. Field-measured data included N2O and CO2 fluxes, soil temperature, and soil moisture content from a four-year field experiment (2012 to 2015). The experiment was composed of two N treatments: inorganic fertilizer (IF), and inorganic fertilizer combined with solid cattle manure (SCM). Both models were calibrated using the data from IF and validated with SCM. Statistical results indicated that both models predicted well the soil temperature, but RZWQM2 performed better than DNDC in simulating soil water content (SWC) because DNDC lacked a heterogeneous soil profile, had shallow simulation depth, and lacked crop root density functions. Both RZWQM2 and DNDC predicted the cumulative N2O and CO2 emissions within 15% error under all treatments, while the timing of daily CO2 emissions was more accurately predicted by RZWQM2 (RMSE = 0.43 to 0.54) than by DNDC (RMSE = 0.60 to 0.67). modeling results for N management effects on GHG emissions showed consistency with the field measurements, indicating higher CO2 emissions under SCM than IF, higher N2O emissions under IF in corn years, but lower N2O emissions in soybean years. Overall, RZWQM2 required more experienced and intensive calibration and validation, but it provided more accurate predictions of soil hydrology and better timing of CO2 emissions than DNDC.

关键词： CO2 emission Corn-soybean rotation Inorganic fertilization Manure application N2O emission process-based modeling

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Equation Discovery for Nonlinear System Identification

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IEEE ACCESS 2020年 8卷 29930-29943页

作者： Simidjievski, Nikola Todorovski, Ljupco Kocijan, Jus Dzeroski, Saso Jozef Stefan Inst Ljubljana 1000 Slovenia Univ Cambridge Dept Comp Sci & Technol Cambridge CB3 0FD England Univ Ljubljana Fac Publ Adm Ljubljana 1000 Slovenia Univ Nova Gorica Sch Engn & Management Nova Gorica 5000 Slovenia Jozef Stefan Int Postgrad Sch Ljubljana 1000 Slovenia

Equation discovery methods enable modelers to combine domain-specific knowledge and system identification to construct models most suitable for a selected modeling task. The method described and evaluated in this paper can be used as a nonlinear system identification method for gray-box modeling. It consists of two interlaced parts of modeling that are computer-aided. The first performs computer-aided identification of a model structure composed of elements selected from user-specified domain-specific modeling knowledge, while the second part performs parameter estimation. In this paper, recent developments of the equation discovery method called process-based modeling, suited for nonlinear system identification, are elaborated and illustrated in two continuous-time case studies. The first case study illustrates the use of the process-based modeling on synthetic data while the second case-study evaluates process-based modeling on measured data for a standard system-identification benchmark. The experimental results clearly demonstrate the ability of process-based modeling to reconstruct both model structure and parameters from measured data.

关键词： Machine learning nonlinear system identification equation discovery process-based modeling computational scientific discovery knowledge-based identification

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Implementation and calibration of short-rotation eucalypt plantation module within the ECOSMOS land surface model

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AGRICULTURAL AND FOREST METEOROLOGY 2022年 323卷

作者： Colmanetti, Michel Anderson Almeida Cuadra, Santiago Vianna Lamparelli, Rubens Augusto Camargo Junior, Jair Bortolucci Cabral, Osvaldo Machado Rodrigues Campoe, Otavio Camargo Victoria, Daniel de Castro Barioni, Luis Gustavo Galdos, Marcelo Valadares Figueiredo, Gleyce Kelly Dantas Araujo le Maire, Guerric Univ Estadual Campinas Ctr Energy Planning 330 Cora Coralina St BR-13083896 Campinas SP Brazil EMBRAPA Agr Digital BR-13083886 Campinas SP Brazil EMBRAPA Meio Ambiente BR-13820000 Jaguariuna SP Brazil Univ Fed Lavras UFLA BR-37200000 Lavras MG Brazil Univ Leeds Inst Climate & Atmospher Sci Sch Earth & Environm Leeds LS2 9JT England Univ Estadual Campinas Sch Agr Engn FEAGRI Campinas Brazil UMR Eco&Sols CIRAD F-34398 Montpellier France Univ Montpellier Eco&Sols CIRAD INRAIRDMontpellier SupAgro Montpellier France

Eucalypt is one of the most-planted broadleaf genera around the globe, exhibiting high yield, plasticity and growing capacity on a broad range of environments. The main objective of this study is to present the integration of a specific module for eucalypt short-rotation plantations into ECOSMOS (Ecosystem Model Simulator). Different calibrations and validations, from intensively monitored sites, experimental networks, and plots in commercial stands were performed. We calibrated all ECOSMOS model biophysical and physiological parameters for the Eucalyptus module using three micrometeorological experiments. A generic genotype was calibrated and applied to evaluate the model performance in response to contrasting environments and genotypes, on a dataset from an experimental network with high Genotype x Environment interactions. The model was applied over commercial eucalypt plantations sites, where re-calibration was necessary for four parameters of the ECOSMOSEucalyptus module related to leaf carbon allocation and specific leaf area, because of significant differences in genotypes and management among datasets. Results showed the consistency of the biophysical and physiological processes solved by the model, simulated energy and carbon fluxes, soil water dynamics, and growth of different plant components when compared with observations;Nash-Sutcliffe efficiency (NSE) of 0.93, 0.43 and 0.27 for net radiation, net ecosystem exchange and evapotranspiration, respectively were obtained for the validation dataset. The generic genotype calibration varied greatly in simulating the eucalypt stem growth of the 26 locations. The generic calibration can be used as reference for eucalypt plantation' areas in Brazil;however, a re calibration is recommended for operational applications to adjust the ECOSMOS-Eucalyptus module to the management conditions, management practices and genotypes used by companies.

关键词： Eucalypt process-based modeling ECOSMOS

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