Timothée Audinot, H. Wernsdörfer, G. Le Moguédec, J. Bontemps
Models based on national forest inventory (NFI) data intend to project forests under management and policy scenarios. This study aimed at quantifying the influence of NFI sampling uncertainty on parameters and simulations of the demographic model MARGOT. Parameter variance–covariance structure was estimated from bootstrap sampling of NFI field plots. Parameter variances and distributions were further modeled to serve as a plug‐in option to any inventory‐based initial condition. Forty‐year time series of observed forest growing stock were compared with model simulations to balance model uncertainty and bias. Variance models showed high accuracies. The Gamma distribution best fitted the distributions of transition, mortality and felling rates, while the Gaussian distribution best fitted tree recruitment fluxes. Simulation uncertainty amounted to 12% of the model bias at the country scale. Parameter covariance structure increased simulation uncertainty by 5.5% in this 12%. This uncertainty appraisal allows targeting model bias as a modeling priority.
{"title":"Modeling and propagating inventory‐based sampling uncertainty in the large‐scale forest demographic model “MARGOT”","authors":"Timothée Audinot, H. Wernsdörfer, G. Le Moguédec, J. Bontemps","doi":"10.1111/nrm.12352","DOIUrl":"https://doi.org/10.1111/nrm.12352","url":null,"abstract":"Models based on national forest inventory (NFI) data intend to project forests under management and policy scenarios. This study aimed at quantifying the influence of NFI sampling uncertainty on parameters and simulations of the demographic model MARGOT. Parameter variance–covariance structure was estimated from bootstrap sampling of NFI field plots. Parameter variances and distributions were further modeled to serve as a plug‐in option to any inventory‐based initial condition. Forty‐year time series of observed forest growing stock were compared with model simulations to balance model uncertainty and bias. Variance models showed high accuracies. The Gamma distribution best fitted the distributions of transition, mortality and felling rates, while the Gaussian distribution best fitted tree recruitment fluxes. Simulation uncertainty amounted to 12% of the model bias at the country scale. Parameter covariance structure increased simulation uncertainty by 5.5% in this 12%. This uncertainty appraisal allows targeting model bias as a modeling priority.","PeriodicalId":49778,"journal":{"name":"Natural Resource Modeling","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43519007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Land use land cover (LULC) dynamics have long been recognized as a significant driver of natural resource change. As a result, understanding the spatial and temporal variation of LULC in the watershed is essential for effective natural resource management and long‐term development. This study attempts to analyze the dynamics and change drivers from 1990 to 2020 and predict the situation for 2035 and 2050 in the Ajora‐Woybo watershed. ArcGIS 10.3 and ERDAS 2015 were used to analyze quantitative data from Landsat imagery. For supervised image classification, a Maximum‐Likelihood classification algorithm was used. To identify driver variables, focus groups and key informants' interviews were done. TerrSet 18.31 software was used to predict LULC utilizing the Multi‐Layer Perceptron Neural Network and Cellular Automata‐Markov Chain models incorporated in Land Change Modeler. Six LULC classes were discovered: cultivated land, built‐up, shrub land, forest land, bare land, and water body. Cultivated land, built‐up area, and bare land have increased at the expense of shrub land and forest land over the last three decades. Trends in water bodies show both decreasing and increasing trends. According to the predicted outcomes, cultivated land, built‐up and bare land has increased, while shrub land and forest land have declined. Finally, agricultural expansion, population growth, wood extraction, resettlement, urbanization, and lack of environmental consideration were identified as the major drivers of LULC change. The study demonstrated that there have been significant changes in the watershed LULC. As a result, reversing the predicted conditions is critical to ensuring the watershed long‐term viability.
{"title":"Historical and future dynamics of land use land cover and its drivers in Ajora‐Woybo watershed, Omo‐Gibe basin, Ethiopia","authors":"M. B. Toma, Mulugeta Dadi Belete, M. Ulsido","doi":"10.1111/nrm.12353","DOIUrl":"https://doi.org/10.1111/nrm.12353","url":null,"abstract":"Land use land cover (LULC) dynamics have long been recognized as a significant driver of natural resource change. As a result, understanding the spatial and temporal variation of LULC in the watershed is essential for effective natural resource management and long‐term development. This study attempts to analyze the dynamics and change drivers from 1990 to 2020 and predict the situation for 2035 and 2050 in the Ajora‐Woybo watershed. ArcGIS 10.3 and ERDAS 2015 were used to analyze quantitative data from Landsat imagery. For supervised image classification, a Maximum‐Likelihood classification algorithm was used. To identify driver variables, focus groups and key informants' interviews were done. TerrSet 18.31 software was used to predict LULC utilizing the Multi‐Layer Perceptron Neural Network and Cellular Automata‐Markov Chain models incorporated in Land Change Modeler. Six LULC classes were discovered: cultivated land, built‐up, shrub land, forest land, bare land, and water body. Cultivated land, built‐up area, and bare land have increased at the expense of shrub land and forest land over the last three decades. Trends in water bodies show both decreasing and increasing trends. According to the predicted outcomes, cultivated land, built‐up and bare land has increased, while shrub land and forest land have declined. Finally, agricultural expansion, population growth, wood extraction, resettlement, urbanization, and lack of environmental consideration were identified as the major drivers of LULC change. The study demonstrated that there have been significant changes in the watershed LULC. As a result, reversing the predicted conditions is critical to ensuring the watershed long‐term viability.","PeriodicalId":49778,"journal":{"name":"Natural Resource Modeling","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46163284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
While considerable scientific uncertainties persist for mesopelagic ecosystems, the fishing industry has developed a great interest in commercial exploitation with improved technologies as part of their search for new sources of feed for fishmeal and fish oil for aquaculture, which will intensify with the planet's growing population. The multiple uncertainties surrounding the ecosystem structure and particularly the size of biomass, hinder a good understanding of the risks associated with large‐scale exploitation, which is needed for a management framework for sustainable ocean uses. Despite concerns regarding irreversible losses triggered by commercial fishing, work exploring the vulnerability of mesopelagic fish to harvesting is largely missing. This study investigates the economic feasibility of mesopelagic fishing which is the primary driver for any possible future expansion. Using very limited information currently available, we conduct a high‐level assessment focusing on key ecological and economic interactions and develop an initial understanding of the economic feasibility of commercial harvesting for mesopelagic fish in the coming years. We conduct simulations using a classical bioeconomic model that captures two species groups, mesopelagic and epipelagic fish, using a wide range of price and cost parameters. We analyze different scenarios for the economic profitability of the fishery in a regional fishery management context. The results of our study highlight the importance of better understanding key biological and ecological mechanisms and parameters which can in turn help inform policies aimed at protecting the mesopelagic.
{"title":"Mesopelagic–epipelagic fish nexus in viability and feasibility of commercial‐scale mesopelagic fisheries","authors":"M. Kourantidou, D. Jin","doi":"10.1111/nrm.12350","DOIUrl":"https://doi.org/10.1111/nrm.12350","url":null,"abstract":"While considerable scientific uncertainties persist for mesopelagic ecosystems, the fishing industry has developed a great interest in commercial exploitation with improved technologies as part of their search for new sources of feed for fishmeal and fish oil for aquaculture, which will intensify with the planet's growing population. The multiple uncertainties surrounding the ecosystem structure and particularly the size of biomass, hinder a good understanding of the risks associated with large‐scale exploitation, which is needed for a management framework for sustainable ocean uses. Despite concerns regarding irreversible losses triggered by commercial fishing, work exploring the vulnerability of mesopelagic fish to harvesting is largely missing. This study investigates the economic feasibility of mesopelagic fishing which is the primary driver for any possible future expansion. Using very limited information currently available, we conduct a high‐level assessment focusing on key ecological and economic interactions and develop an initial understanding of the economic feasibility of commercial harvesting for mesopelagic fish in the coming years. We conduct simulations using a classical bioeconomic model that captures two species groups, mesopelagic and epipelagic fish, using a wide range of price and cost parameters. We analyze different scenarios for the economic profitability of the fishery in a regional fishery management context. The results of our study highlight the importance of better understanding key biological and ecological mechanisms and parameters which can in turn help inform policies aimed at protecting the mesopelagic.","PeriodicalId":49778,"journal":{"name":"Natural Resource Modeling","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41841652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We selected the Tanchong River in Hefei as the site to test the efficacy of graphene visible‐light photocatalysis (GVLP), a new water treatment technology to improve water quality in black‐odorous rivers in urban landscapes. A model coupling the hydrodynamic force and water quality of the Tanchong River was constructed using the MIKE11 model. The numerical simulation method was used to model the concentrations of the main pollutants—chemical oxygen demand (COD), ammonia nitrogen (NH3–N) and total phosphorus (TP) concentrations. The simulation of water quality in the river section treated by GVLP was verified by the Tanchong River water quality improvement project experiment. The results showed that the MIKE11 model can effectively simulate the effect of GVLP technology on water quality improvement. The removal rates of the main pollutants'—COD, NH3–N, and TP by GVLP were 43.9%, 82.1%, and 76.8%, respectively, thus proving GVLP's viability as a treatment for controlling water pollution in similar black‐odorous rivers.
{"title":"Simulation of the improving effect of graphene visible‐light photocatalysis using the MIKE11 model of an urban landscape river in the Chaohu Lake Basin, China","authors":"Hong-bin Xiong, Tianxin Liu, Haiyun Wang, Chenxiao Feng","doi":"10.1111/nrm.12344","DOIUrl":"https://doi.org/10.1111/nrm.12344","url":null,"abstract":"We selected the Tanchong River in Hefei as the site to test the efficacy of graphene visible‐light photocatalysis (GVLP), a new water treatment technology to improve water quality in black‐odorous rivers in urban landscapes. A model coupling the hydrodynamic force and water quality of the Tanchong River was constructed using the MIKE11 model. The numerical simulation method was used to model the concentrations of the main pollutants—chemical oxygen demand (COD), ammonia nitrogen (NH3–N) and total phosphorus (TP) concentrations. The simulation of water quality in the river section treated by GVLP was verified by the Tanchong River water quality improvement project experiment. The results showed that the MIKE11 model can effectively simulate the effect of GVLP technology on water quality improvement. The removal rates of the main pollutants'—COD, NH3–N, and TP by GVLP were 43.9%, 82.1%, and 76.8%, respectively, thus proving GVLP's viability as a treatment for controlling water pollution in similar black‐odorous rivers.","PeriodicalId":49778,"journal":{"name":"Natural Resource Modeling","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46052309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We construct a generic ecosystem model that features the basic mechanisms of alternative stable states as well as two different stochastic influences. In particular, we use a mean‐reverting jump‐diffusion process to model the evolution of the ecosystem state over time. We review key concepts of multistability theory and the simple heuristics commonly employed to illustrate them. We then provide mathematical definitions for these concepts in the model context. Our contribution to the literature is twofold: we improve the representation of stochasticity in, and clarify key concepts of, multistability theory. The simplicity of the model enables a number of applications, such as finding economically optimal management strategies, identifying criteria for sustainable ecosystem management in a stochastic viability framework, deriving the probability of a regime shift, or empirically identifying the factors which have caused a specific regime shift.
{"title":"A stylized model of stochastic ecosystems with alternative stable states","authors":"M. Stecher, Stefan Baumgärtner","doi":"10.1111/nrm.12345","DOIUrl":"https://doi.org/10.1111/nrm.12345","url":null,"abstract":"We construct a generic ecosystem model that features the basic mechanisms of alternative stable states as well as two different stochastic influences. In particular, we use a mean‐reverting jump‐diffusion process to model the evolution of the ecosystem state over time. We review key concepts of multistability theory and the simple heuristics commonly employed to illustrate them. We then provide mathematical definitions for these concepts in the model context. Our contribution to the literature is twofold: we improve the representation of stochasticity in, and clarify key concepts of, multistability theory. The simplicity of the model enables a number of applications, such as finding economically optimal management strategies, identifying criteria for sustainable ecosystem management in a stochastic viability framework, deriving the probability of a regime shift, or empirically identifying the factors which have caused a specific regime shift.","PeriodicalId":49778,"journal":{"name":"Natural Resource Modeling","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45911395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Mohammadlou, A. Bahremand, D. Princz, N. Kinar, A. Haghnegahdar, S. Razavi
The Global Environmental Multiscale Model (GEM) is currently in operational use for data assimilation and forecasting at 25–15 km scales; regional 10 km scales over North America; and 2.5 km scales over Canada. To evaluate the GEM model for forecasting applications in Iran, global daily temperature and precipitation outputs of GEM at a 25 km scale were compared to data sets from hydrometeorological stations and the De Martonne climate classification method was used to demarcate climate zones for comparisons. GEM model outputs were compared to observations in each of these zones. The results show good agreement between GEM outputs and measured daily temperatures with Kling‐Gupta efficiencies of 0.76 for the arid, 0.71 for the semiarid, and 0.78 for the humid regions. There is also an agreement between GEM outputs and measured annual precipitation with differences of 50% for the arid, 36% for the semiarid, and 15% for the humid region. There is a ~13% systematic difference between the elevation of stations and the average elevation of corresponding GEM grid cells; differences in elevation associated with forcing data sets can be potentially corrected using environmental lapse rates. Compared with hydrometeorological data sets, the GEM model precipitation outputs are less accurate than temperature outputs, and this may influence the accuracy of potential Iranian forecasting operations utilizing GEM. The results of this study provide an understanding of the operation and limitations of the GEM model for climate change and hydro‐climatological studies.
{"title":"Objective evaluation of the Global Environmental Multiscale Model (GEM) with precipitation and temperature for Iran","authors":"M. Mohammadlou, A. Bahremand, D. Princz, N. Kinar, A. Haghnegahdar, S. Razavi","doi":"10.1111/nrm.12343","DOIUrl":"https://doi.org/10.1111/nrm.12343","url":null,"abstract":"The Global Environmental Multiscale Model (GEM) is currently in operational use for data assimilation and forecasting at 25–15 km scales; regional 10 km scales over North America; and 2.5 km scales over Canada. To evaluate the GEM model for forecasting applications in Iran, global daily temperature and precipitation outputs of GEM at a 25 km scale were compared to data sets from hydrometeorological stations and the De Martonne climate classification method was used to demarcate climate zones for comparisons. GEM model outputs were compared to observations in each of these zones. The results show good agreement between GEM outputs and measured daily temperatures with Kling‐Gupta efficiencies of 0.76 for the arid, 0.71 for the semiarid, and 0.78 for the humid regions. There is also an agreement between GEM outputs and measured annual precipitation with differences of 50% for the arid, 36% for the semiarid, and 15% for the humid region. There is a ~13% systematic difference between the elevation of stations and the average elevation of corresponding GEM grid cells; differences in elevation associated with forcing data sets can be potentially corrected using environmental lapse rates. Compared with hydrometeorological data sets, the GEM model precipitation outputs are less accurate than temperature outputs, and this may influence the accuracy of potential Iranian forecasting operations utilizing GEM. The results of this study provide an understanding of the operation and limitations of the GEM model for climate change and hydro‐climatological studies.","PeriodicalId":49778,"journal":{"name":"Natural Resource Modeling","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41533969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The 16 years of normalized difference vegetation index (NDVI) and precipitation data are used to analyze the time‐lag effects of the growing‐season NDVI response to precipitation at regional scales. This study focuses on the arid and semi‐arid Hulunbuir grassland dominated by perennials in northeast China. The multi‐month time‐lag effects are examined using simple statistical approaches, which can detect the two distinct time‐lags for four subregions with four major land‐cover types. A “positive” time‐lag effect of the growing‐season NDVI response to precipitation is observed at 1‐month (May in the current year) time‐lag and 13‐month (May in the previous year) time‐lag while a “negative” time‐lag effect is observed at 9‐month (September in the previous year) time‐lag. In addition, the prediction results of NDVI based on precipitation indicate that the NDVI prediction model considered the lagged monthly precipitation has good performance. Therefore, revealing the time‐lag effects is very important for accurately predicting the growing‐season NDVI and evaluating the vegetation dynamics.
{"title":"Multi‐month time‐lag effects of regional vegetation responses to precipitation in arid and semi‐arid grassland: A case study of Hulunbuir, Inner Mongolia","authors":"Taosuo Wu, Hongmei Bai, Feng Feng, Qian Lin","doi":"10.1111/nrm.12342","DOIUrl":"https://doi.org/10.1111/nrm.12342","url":null,"abstract":"The 16 years of normalized difference vegetation index (NDVI) and precipitation data are used to analyze the time‐lag effects of the growing‐season NDVI response to precipitation at regional scales. This study focuses on the arid and semi‐arid Hulunbuir grassland dominated by perennials in northeast China. The multi‐month time‐lag effects are examined using simple statistical approaches, which can detect the two distinct time‐lags for four subregions with four major land‐cover types. A “positive” time‐lag effect of the growing‐season NDVI response to precipitation is observed at 1‐month (May in the current year) time‐lag and 13‐month (May in the previous year) time‐lag while a “negative” time‐lag effect is observed at 9‐month (September in the previous year) time‐lag. In addition, the prediction results of NDVI based on precipitation indicate that the NDVI prediction model considered the lagged monthly precipitation has good performance. Therefore, revealing the time‐lag effects is very important for accurately predicting the growing‐season NDVI and evaluating the vegetation dynamics.","PeriodicalId":49778,"journal":{"name":"Natural Resource Modeling","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"63504180","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Johan Blomquist, F. Jensen, S. Waldo, O. Flaaten, M. Holma
In this paper, we present a simple theoretical, steady‐state equilibrium, predator‐prey model for the joint management of marine mammals and a fish species. As an empirical case, we choose cod and grey seals in the Nordic‐Baltic Sea countries, and several benefits and costs related to the latter are considered. We show that the optimal grey seal population is much lower than the actual population, and this result is robust to variations in relevant parameter values. This result can be explained by the fact that the profit from harvesting cod is much higher than the net benefits from grey seals.
{"title":"Joint management of marine mammals and a fish species: The case of cod and grey seals in the Nordic‐Baltic Sea countries","authors":"Johan Blomquist, F. Jensen, S. Waldo, O. Flaaten, M. Holma","doi":"10.1111/nrm.12341","DOIUrl":"https://doi.org/10.1111/nrm.12341","url":null,"abstract":"In this paper, we present a simple theoretical, steady‐state equilibrium, predator‐prey model for the joint management of marine mammals and a fish species. As an empirical case, we choose cod and grey seals in the Nordic‐Baltic Sea countries, and several benefits and costs related to the latter are considered. We show that the optimal grey seal population is much lower than the actual population, and this result is robust to variations in relevant parameter values. This result can be explained by the fact that the profit from harvesting cod is much higher than the net benefits from grey seals.","PeriodicalId":49778,"journal":{"name":"Natural Resource Modeling","volume":null,"pages":null},"PeriodicalIF":1.6,"publicationDate":"2022-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43382355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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