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WEHY-HCM for modeling Interactive Atmospheric-Hydrologic processes at Watershed Scale. II: Model Application to Ungauged and Sparsely Gauged Watersheds

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JOURNAL OF HYDROLOGIC ENGINEERING 2013年第10期18卷 1272-1281页

作者： Kure, S. Jang, S. Ohara, N. Kavvas, M. L. Chen, Z. Q. Tohoku Univ Int Res Inst Disaster Sci Sendai Miyagi 980 Japan Univ Calif Davis Dept Civil & Environm Engn Hydrol Res Lab Davis CA 95616 USA Univ Wyoming Laramie WY 82071 USA Calif Dept Water Resources Sacramento CA 95814 USA

The objective of this study is to evaluate the potential of the Watershed Environmental Hydrology Hydro-Climate Model (WEHY-HCM) for modeling runoff at ungauged or sparsely gauged watersheds. The WEHY-HCM employs an atmospheric module (fifth generation mesoscale model, MM5) that is coupled with its process-based watershed environmental hydrology (WEHY) module. In this study the atmospheric component of the WEHY-HCM was utilized for the dynamical downscaling of the coarse U.S. National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) historical global reanalysis atmospheric data over a foothills region in Northern California in order to reconstruct the historical hydro-climate data over four watersheds in the foothills region at 3km grid scale at hourly intervals. The WEHY-HCM's atmospheric module performance was evaluated by the comparison of model-reconstructed precipitation and air temperature against ground observations in time and space with satisfactory results. These results lead to the conclusion that WEHY-HCM may be useful at sparsely-gauged or ungauged watersheds for producing nonexistent atmospheric data as input to the modeling of surface and subsurface hydrologic processes at such watersheds. By means of the reconstructed atmospheric data as its input, the WEHY module of WEHY-HCM was then applied to the Sierra foothills region, encompassing Big Chico Creek (192km2), Little Chico Creek (78km2), Upper Butte Creek (407km2) and Deer Creek (508km2) watersheds in northern California. The model simulations of daily and monthly runoff at these watersheds, when compared against historical observations by means of visual inspections and statistical tests during a validation period yielded satisfactory results. Therefore, it is concluded that the WEHY-HCM may be useful in producing both atmospheric data and runoff simulations over ungauged and sparsely gauged watersheds.

关键词： Hydrologic models Predictions River basins Watersheds Hydrologic prediction in ungauged basins Dynamic downscaling Watershed hydro-climate model process-based modeling

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Morphological evolution of the channel-shoal system in the South Channel of the Changjiang Estuary during 1958–2018:Causes and future trends

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Journal of Geographical Sciences 2022年第11期32卷 2291-2310页

作者： LUAN Hualong YAO Shiming QU Geng LEI Wentao River Research Department Changjiang River Scientific Research InstituteWuhan 430010China

The stability of estuarine channel-shoal systems is important for port utilization,navigation maintenance,habitat protection and ecosystem service *** paper uses the South Channel of the Changjiang(Yangtze River)Estuary as a typical example to investigate the channel-shoal adjustment mechanism and its future *** combined approaches of bathymetric data analysis and process-based modeling(Delft3D)are *** analysis of morphological changes indicates that the South Channel experienced remarkable channel-shoal adjustment during 1958–*** evolution was identified,including shoal migration,incision and emergence under natural conditions before the *** then,fluvial sediment decline and local human intervention have interrupted the periodic *** 1986,as river sediment discharge started to decline,the South Channel converted to net erosion,and both the mid-channel shoal at the bifurcation node and the tail of the Ruifeng Shoal showed significant ***-based hydrodynamic simulations revealed that the northern rotation of the mainstream downstream of Wusong triggered the erosion of the Ruifeng Shoal,while unordered sand mining at the shoal tail in approximately 2002 enhanced shoal *** addition,the self-adjustment of the transverse section shape resulted in abnormal accretion in 2002–***,the South Channel underwent overall erosion as sediment discharge decreased to a low level(<150 Mt/a).Five stages of channel-shoal pattern adjustment and accretion/erosion status during the past 60years were defined,i.e.,the accretion stage(1958–1965),remarkable channel-shoal adjustment stage(1978–1986),slow erosion stage(1986–1997),shoal scour and shrinkage stage(1997–2007)and overall channel-shoal erosion stage(2007–2018).Model prediction of the evolutionary trend indicates that overall erosion within the South Channel is most likely to continue in 2015–*** adjustment of the South Channel unde

关键词： channel-shoal pattern morphological evolution process-based modeling South Channel Changjiang Estuary

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Looking beyond the mean: Drivers of variability in postfire stand development of conifers in Greater Yellowstone

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FOREST ECOLOGY AND MANAGEMENT 2018年 430卷 460-471页

作者： Braziunas, Kristin H. Hansen, Winslow D. Seidl, Rupert Rammer, Werner Turner, Monica G. Univ Wisconsin Madison Dept Integrat Biol Birge Hall430 Lincoln Dr Madison WI 53706 USA Univ Nat Resources & Life Sci BOKU Vienna Inst Silviculture Dept Forest & Soil Sci Peter Jordan Str 82 A-1190 Vienna Austria

High-severity, infrequent fires in forests shape landscape mosaics of stand age and structure for decades to centuries, and forest structure can vary substantially even among same-aged stands. This variability among stand structures can affect landscape-scale carbon and nitrogen cycling, wildlife habitat availability, and vulnerability to subsequent disturbances. We used an individual-based forest process model (iLand) to ask: Over 300 years of postfire stand development, how does variation in early regeneration densities versus abiotic conditions influence among-stand structural variability for four conifer species widespread in western North America? We parameterized iLand for lodgepole pine (Pins contorta var. latifolia), Douglas-fir (Pseudotsuga menziesu var. glauca), Engelmann spruce (Picea engelmannu), and subalpine fir (Abies lasiocarpa) in Greater Yellowstone (USA). Simulations were initialized with field data on regeneration following stand-replacing fires, and stand development was simulated under historical climatic conditions without further disturbance. Stand structure was characterized by stand density and basal area. Stands became more similar in structure as time since fire increased. Basal area converged more rapidly among stands than tree density for Douglas-fir and lodgepole pine, but not for subalpine fir and Engelmann spruce. For all species, regeneration-driven variation in stand density persisted for at least 99 years postfire, and for lodgepole pine, early regeneration densities dictated among-stand variation for 217 years. Over time, stands shifted from competition-driven convergence to environment-driven divergence, in which variability among stands was maintained or increased. The relative importance of drivers of stand structural variability differed between density and basal area and among species due to differential species traits, growth rates, and sensitivity to intraspecific competition versus abiotic conditions. Understanding dynami

关键词： Stand structure Variability Forest development process-based modeling Greater Yellowstone Ecosystem Wildfire

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Using a Bayesian Network Model to Predict Risk of Pesticides on Aquatic Community Endpoints in a Rice Field-A Southern European Case Study

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ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2024年第1期43卷 182-196页

作者： Mentzel, Sophie Martinez-Megias, Claudia Grung, Merete Rico, Andreu Tollefsen, Knut Erik van den Brink, Paul J. Moe, S. Jannicke Norwegian Inst Water Res Dept Ecotoxicol & R Assessment Oslo Norway Univ Alcala Dept Analyt Chem Phys Chem & Chem Engn Madrid Spain Parque Cientif Tecnol Univ Alcala Water Inst Madrid Inst Adv Studies Alcala De Henares Spain Univ Valencia Cavanilles Inst Biodivers & Evolutionary Biol Valencia Spain Norwegian Univ Life Sci Fac Environm Sci & Nat Resource Management As Norway Wageningen Univ & Res Wageningen Environm Res Wageningen Netherlands Wageningen Univ Aquat Ecol & Water Qual Management Grp Wageningen Netherlands

Bayesian network (BN) models are increasingly used as tools to support probabilistic environmental risk assessments (ERAs), because they can better account for uncertainty compared with the simpler approaches commonly used in traditional ERA. We used BNs as metamodels to link various sources of information in a probabilistic framework, to predict the risk of pesticides to aquatic communities under given scenarios. The research focused on rice fields surrounding the Albufera Natural Park (Valencia, Spain), and considered three selected pesticides: acetamiprid (an insecticide), 2-methyl-4-chlorophenoxyacetic acid (MCPA;a herbicide), and azoxystrobin (a fungicide). The developed BN linked the inputs and outputs of two pesticide models: a process-based exposure model (Rice Water Quality [RICEWQ]), and a probabilistic effects model (Predicts the Ecological Risk of Pesticides [PERPEST]) using case-based reasoning with data from microcosm and mesocosm experiments. The model characterized risk at three levels in a hierarchy: biological endpoints (e.g., molluscs, zooplankton, insects, etc.), endpoint groups (plants, invertebrates, vertebrates, and community processes), and community. The pesticide risk to a biological endpoint was characterized as the probability of an effect for a given pesticide concentration interval. The risk to an endpoint group was calculated as the joint probability of effect on any of the endpoints in the group. Likewise, community-level risk was calculated as the joint probability of any of the endpoint groups being affected. This approach enabled comparison of risk to endpoint groups across different pesticide types. For example, in a scenario for the year 2050, the predicted risk of the insecticide to the community (40% probability of effect) was dominated by the risk to invertebrates (36% risk). In contrast, herbicide-related risk to the community (63%) resulted from risk to both plants (35%) and invertebrates (38%);the latter might represent (in

关键词： Aquatic toxicology Bayesian network Effect-based modeling Pesticides process-based modeling Risk assessment

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A process-driven deep learning hydrological model for daily rainfall-runoff simulation

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JOURNAL OF HYDROLOGY 2024年 637卷

作者： Li, Heng Zhang, Chunxiao Chu, Wenhao Shen, Dingtao Li, Rongrong China Univ Geosci Beijing Sch Informat Engn 29 Xueyuan Rd Beijing 100083 Peoples R China Ministy Nat Resources Observat & Res Stn Beijing Fangshan Comprehens Exp Beijing 100083 Peoples R China Cent China Normal Univ Key Lab Geog Proc Anal & Simulat Hubei Prov Wuhan 430079 Peoples R China Cent China Normal Univ Coll Urban & Environm Sci Wuhan 430079 Peoples R China Chinese Univ Hong Kong Inst Space & Earth Informat Sci Shatin Hong Kong Peoples R China Rm 216 3 Teaching Bldg29Xueyuan Rd Beijing 100083 Peoples R China

Although deep learning (DL) models, especially long-short-term memory (LSTM), demonstrate greater accuracy than process-based models in rainfall-runoff simulation, the predictions from process-based models are more physical than DL models. The main reason is that DL models have almost no process understanding capabilities like process-based models beyond their fitting capability. In this study, we developed a process-driven DL model under a unified DL architecture to improve the process awareness of DL models. To implement the model, a conceptual hydrological model (EXP-HYDRO) is implanted into a recurrent neural network (RNN) cell as a process driver for providing multi-sub-process variables related to the runoff process, and an Entity-Aware LSTM (EA-LSTM) cell is incorporated as a post-processor layer, resulting in the process-driven RNN-EA-LSTM (PRNNEA-LSTM). Under the assistance of the process driver, the model performance of PRNN-EA-LSTM on the 531 catchments from the Catchment Attributes and Meteorology for Large-sample Studies dataset is more robust than the pure DL model, and better than using only EXP-HYDRO as the input of EA-LSTM (i.e., EXP-HYDRO-EALSTM). Specifically, the median Nash-Sutcliffe efficiency (NSE) of PRNN-EA-LSTM in local and regional simulation is 0.03 and 0.02 higher than LSTM and 0.01 higher than EXP-HYDRO-EA-LSTM. Additionally, PRNN-EALSTM significantly enhances the low flow simulations and reduces the catchments number with negative NSE. This study demonstrates that process-based models can help DL models better represent the rainfall-runoff relationship under a unified architecture. Consequently, integrating the adaptability of process-based models into the DL architecture is anticipated to bolster the process understanding capabilities of DL models.

关键词： Hybrid hydrological modeling process-based modeling Deep learning Rainfall-runoff simulation

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Forward-modeling of co-evolution of turbidity currents, sediment transport, and cyclic steps in the Rio Muni Basin

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International Journal of Sediment Research 2023年第2期38卷 175-190页

作者： Peng Hu Yue Li Chenglin Gong Wei Li Ocean College Zhejiang University Ocean Research Center of Zhoushan Zhejiang University College of Geosciences China University of Petroleum (Beijing)

Previous quantitative studies of field-scale cyclic steps are mostly based on analysis of field data. Such studies have shed light on the erosion/deposition patterns over these morphological features as well as the magnitudes of the turbidity current parameters back estimated using the measured geometry data. However, it remains unclear to what extent such back estimated hydraulic features and erosion/deposition patterns can be numerically reproduced by process-based numerical models. Here, a twodimensional layer-averaged fully coupled turbidity current model is applied to forward-model co-evolution of turbidity currents, sediment transport, and cyclic steps in the Rio Muni Basin off the West Africa margin, where nine cyclic steps featuring a transition from the upstream erosion to the downstream deposition were identified. Numerical case studies indicate that large and bankfull turbidity currents do not form the erosion-deposition transition because they are likely to fall into the high-speed regime that favors whole-scale erosion. In this regard, threshold values for the current velocity were derived to distinguish high/low-speed regimes of turbidity currents. It is shown that smaller turbidity currents,which do not follow the bankfull assumption, and, thus, fall into the low-speed turbidity current regime,may produce cyclic steps by current deceleration. While values of the derived physical parameters of the smaller turbidity current differ quantitatively from those of previous back estimated values, their qualitative variation trends are basically the same.

关键词： Cyclic steps Erosion/deposition patterns Turbidity currents Back estimation process-based modeling Bankfull flow assumption

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Effects of meteorological forcing on coastal eutrophication: modeling with model trees

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ESTUARINE COASTAL AND SHELF SCIENCE 2012年 115卷 210-217页

作者： Tamvakis, Androniki Miritzis, John Tsirtsis, George Spyropoulou, Alexandra Spatharis, Sofie Univ Aegean Dept Marine Sci Mitilini 81100 Greece

The exploration of processes leading to coastal eutrophication is a major challenge in ecological research, particularly in light of important new policies such as the European Water Framework Directive. In the present study primary production (in terms of chlorophyll alpha - chl alpha) is modeled based on a number of abiotic parameters using model trees (MTs), a machine learning (ML) approach whereby linear regressions are induced within homogeneous subsets of samples (tree leaves). Standardized regression was applied to determine the relative weight of abiotic parameters in the MT tree leaves whereas the efficiency of the MT method in chl alpha prediction was tested against neural networks (NNs) which is the most frequently used ML approach, and the classical multiple linear regression (MLR). To assess the efficiency of models to describe eutrophication-related responses under different environmental conditions, the methods were applied on a coastal ecosystem affected by terrestrial runoff for two meteorologically contrasting annual cycles: a typical dry ('04-'05) and a typical wet ('09-'10). MTs showed increased predictive power in chl alpha prediction attributed to the discrimination of input data space into tree leaves, instead of using a uniform space as in NNs and MLR. By grouping samples of each tested annual cycle (wet and dry) on a seasonal basis into discrete groups/leaves, MTs offer a much more explanatory description of ecosystem status than NNs and MLR. The discriminating variables forming tree leaves and the weighing coefficients of Linear Models (LMs) in each leaf provided a useful scaling of abiotic parameters driving chl alpha dynamics. The MT method is thus proposed as an efficient tool for obtaining insights into ecosystem processes leading to eutrophication events in coastal ecosystems and a useful component in integrated coastal zone management. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： process-based modeling linear regression machine learning algorithm model tree dynamics eutrophication

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Analyzing the Effects of Growing Season Length on the Net Ecosystem Production of an Alpine Grassland Using Model-Data Fusion

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ECOSYSTEMS 2018年第5期21卷 982-999页

作者： Scholz, Katharina Hammerle, Albin Hiltbrunner, Erika Wohlfahrt, Georg Univ Innsbruck Inst Okol Sternwartestr 15 A-6020 Innsbruck Austria Univ Basel Inst Bot Totengasslein 3 CH-4051 Basel Switzerland

Alpine ecosystems are, similar to arctic ecosystems, characterized by a very long snow season. Previous studies investigating arctic or alpine ecosystems have shown that winter CO2 effluxes can dominate the annual balance and that the timing and duration of the snow cover plays a crucial role for plant growth and phenology and might also influence the growing season ecosystem CO2 strength and dynamics. The objective of this study was to analyze seasonal and annual CO2 balances of a grassland site at an elevation of 2440 m a.s.l in the Swiss central Alps. We continuously measured the NEP using the eddy covariance method from June 2013 to October 2014, covering two growing seasons and one winter. We analyzed the influence of snow melt date on the CO2 exchange dynamics at this site, because snow melt differed about 24 days between the 2 years. To this end, we employed a process-based ecosystem carbon cycling model to disentangle the co-occurring effects of growing season length, environmental conditions during the growing season, and physiological/structural properties of the canopy on the ecosystem carbon balance. During the measurement period, the site was a net sink for CO2 although winter efflux contributed significantly to the total balance. The cumulative growing season NEP as well as mean and maximum daily CO2 uptake rates was lower during the year with the later snow melt, and the results indicated that the differences were mainly due to differing growing season lengths.

关键词： Carbon uptake period CO2 balance eddy covariance high elevation grassland process-based modeling Swiss Alps winter respiration

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Leaf phenology in 22 North American tree species during the 21st century

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GLOBAL CHANGE BIOLOGY 2009年第4期15卷 961-975页

作者： Morin, Xavier Lechowicz, Martin J. Augspurger, Carol O' Keefe, John Viner, David Chuine, Isabelle McGill Univ Dept Biol Montreal PQ H3A 1B1 Canada Univ Illinois Dept Plant Biol Urbana IL 61801 USA Harvard Univ Petersham MA 01366 USA Nat England Sci & Evidence Norwich NR1 2BE Norfolk England CNRS Equipe Bioflux Ctr Ecol Fonct & Evolut F-34293 Montpellier 5 France

Recent shifts in phenology are the best documented biological response to current anthropogenic climate change, yet remain poorly understood from a functional point of view. Prevailing analyses are phenomenological and approximate, only correlating temperature records to imprecise records of phenological events. To advance our understanding of phenological responses to climate change, we developed, calibrated, and validated process-based models of leaf unfolding for 22 North American tree species. Using daily meteorological data predicted by two scenarios (A2: +3.2 degrees C and B2: +1 degrees C) from the HadCM3 GCM, we predicted and compared range-wide shifts of leaf unfolding in the 20th and 21st centuries for each species. Model predictions suggest that climate change will affect leaf phenology in almost all species studied, with an average advancement during the 21st century of 5.0 days in the A2 scenario and 9.2 days in the B2 scenario. Our model also suggests that lack of sufficient chilling temperatures to break bud dormancy will decrease the rate of advancement in leaf unfolding date during the 21st century for many species. Some temperate species may even have years with abnormal budburst due to insufficient chilling. Species fell into two groups based on their sensitivity to climate change: (1) species that consistently had a greater advance in their leaf unfolding date with increasing latitude and (2) species in which the advance in leaf unfolding differed from the center to the northern vs. southern margins of their range. At the interspecific level, we predicted that early-leafing species tended to show a greater advance in leaf unfolding date than late-leafing species;and that species with larger ranges tend to show stronger phenological changes. These predicted changes in phenology have significant implications for the frost susceptibility of species, their interspecific relationships, and their distributional shifts.

关键词： climate change comparative analysis geographical distribution leaf phenology North America process-based modeling trees

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Bayesian mechanistic modeling characterizes Gulf of Mexico hypoxia: 1968-2016 and future scenarios

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ECOLOGICAL APPLICATIONS 2020年第2期30卷 e02032页

作者： Del Giudice, Dario Matli, V. R. R. Obenour, Daniel R. NC State Univ Dept Civil Construct & Environm Engn Raleigh NC 27695 USA NC State Univ Ctr Geospatial Analyt Raleigh NC 27695 USA

The hypoxic zone in the northern Gulf of Mexico is among the most dramatic examples of impairments to aquatic ecosystems. Despite having attracted substantial attention, management of this environmental crisis remains challenging, partially due to limited monitoring to support model development and long-term assessments. Here, we leverage new geostatistical estimates of hypoxia derived from nearly 150 monitoring cruises and a process-based model to improve characterization of controlling mechanisms, historic trends, and future responses of hypoxia while rigorously quantifying uncertainty in a Bayesian framework. We find that November-March nitrogen loads are important controls of sediment oxygen demand, which appears to be the major oxygen sink. In comparison, only 23% of oxygen in the near-bottom region appears to be consumed by net water column respiration, which is driven by spring and summer loads. Hypoxia typically exceeds 15,600 km(2) in June, peaks in July, and declines below 10,000 km(2) in September. In contrast to some previous Gulf hindcasting studies, our simulations demonstrate that hypoxia was both severe and worsening prior to 1985, and has remained relatively stable since that time. Scenario analysis shows that halving nutrient loadings will reduce hypoxia by 37% with respect to 13,900 km(2) (1985-2016 median), while a +2 degrees C change in water temperature will cause a 26% hypoxic area increase due to enhanced sediment respiration and reduced oxygen solubility. These new results highlight the challenges of achieving hypoxia reduction targets, particularly under warming conditions, and should be considered in ecosystem management.

关键词： Bayesian inference climate change dead hypoxic zones eutrophication Gulf of Mexico hindcasts and projections process-based modeling riverine nitrogen uncertainty quantification

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