Lessons for successful participatory watershed modeling: A perspective from modeling practitioners

Participatory modeling is the process of incorporating stakeholders, often including the public, and decision makers into an otherwise purely analytic modeling process to support decisions involving complex natural resources questions. Participatory modeling is particularly compatible with the rising focus on integrated water resources management, which incorporates systems theory and aims to protect and improve water resources while considering economic and social concerns in the community. In this article, we present a series of lessons based on experience working with stakeholder groups to develop watershed and water quality models to address water resource issues in Maryland, Vermont, Utah, and Virginia. We believe these lessons in participatory modeling, discussed from our perspective as scientists and modelers engaged in applied watershed issues, can help to achieve successful participatory modeling efforts elsewhere. The lessons relate to stakeholder engagement, modeling tools, model development and calibration, scenario testing, and applying results to management decisions.     

Tropical deforestation in Madagascar: analysis using hierarchical,spatially explicit,Bayesian regression models

Establishing cause–effect relationships for deforestation at various scales has proven difficult even when rates of deforestation appear well documented. There is a need for better explanatory models, which also provide insight into the process of deforestation. We propose a novel hierarchical modeling specification incorporating spatial association. The hierarchical aspect allows us to accommodate misalignment between the land-use (response) data layer and explanatory data layers. Spatial structure seems appropriate due to the inherently spatial nature of land use and data layers explaining land use. Typically, there will be missing values or holes in the response data. To accommodate this we propose an imputation strategy. We apply our modeling approach to develop a novel deforestation model for the eastern wet forested zone of Madagascar, a global rain forest “hot spot”. Using five data layers created for this region, we fit a suitable spatial hierarchical model. Though fitting such models is computationally much more demanding than fitting more standard models, we show that the resulting interpretation is much richer. Also, we employ a model choice criterion to argue that our fully Bayesian model performs better than simpler ones. To the best of our knowledge, this is the first work that applies hierarchical Bayesian modeling techniques to study deforestation processes. We conclude with a discussion of our findings and an indication of the broader ecological applicability of our modeling style.     

Parallel agent-based modeling of spatial opinion diffusion accelerated using graphics processing units

In this article, we describe a parallel agent-based model of spatial opinion diffusion that is driven by graphics processing units (GPUs). Modeling opinion exchange and diffusion across landscapes often involves the simulation of large numbers of geographically located individual decision-makers and a massive number of individual-level interactions. This simulation requires substantial computational power. GPU-enabled computing resources provide a massively parallel processing platform based on a fine-grained shared memory paradigm. This massively parallel processing platform holds considerable promise for meeting the computing requirement of agent-based models of spatial problems. In this article, we focus on the parallelization of an agent-based spatial opinion model using GPU technologies. We discussed key algorithms designed for parallel agent-based opinion modeling: including domain decomposition and mutual exclusion. Experiments conducted to examine computing performance show that GPUs provide a computationally efficient alternative to traditional parallel computing architectures and substantially accelerate agent-based models of large-scale opinion exchange among individual decision makers.     

Framing the search for a theory of land use

ABSTRACT

Land system science and affiliated research linked to sustainability require improved understanding and theorization of land and its change as a social-ecological system (SES). The absence of a general land-use theory, anchored in the social subsystem but with explicit links to the environmental subsystem, hampers this effort. Drawing on land-use explanations, meta-analyses, and associated frameworks, we advance a broad framework structure of eight elements – aggregations of explanatory variables – with links to the biophysical subsystem, for systematic comparisons of extant explanations. Tests and models can be employed to identify which set of variables and their configurations provide robust explanations of across land uses, identifying the potential for theory development. The framework and its application are applicable to both top-down and bottom-up explanatory approaches employed in the social sciences. Links to the environmental subsystem invite future exploration of SES explanations that reach across the different dimensions of global change and sustainability science.     

Potentials and limitations of using large-scale forest inventory data for evaluating forest succession models

Forest gap models have been applied widely to examine forest development under natural conditions and to investigate the effect of climate change on forest succession. Due to the complexity and parameter requirements of such models a rigorous evaluation is required to build confidence in the simulation results. However, appropriate data for model assessment are scarce at the large spatial and temporal scales of successional dynamics. In this study, we explore a data source for the evaluation of forest gap models that has been used only little in the past, i.e., large-scale National Forest Inventory data. The key objectives of this study were (a) to examine the potentials and limitations of using large-scale forest inventory data for evaluating the performance of forest gap models and (b) to test two particular models as case studies to derive recommendations for their future improvement.     

Plant-trait-based modeling assessment of ecosystem-service sensitivity to land-use change

Evidence is accumulating that the continued provision of essential ecosystem services is vulnerable to land-use change. Yet, we lack a strong scientific basis for this vulnerability as the processes that drive ecosystem-service delivery often remain unclear. In this paper, we use plant traits to assess ecosystem-service sensitivity to land-use change in subalpine grasslands. We use a trait-based plant classification (plant functional types, PFTs) in a landscape modeling platform to model community dynamics under contrasting but internally consistent land-use change scenarios. We then use predictive models of relevant ecosystem attributes, based on quantitative plant traits, to make projections of ecosystem-service delivery. We show that plant traits and PFTs are effective predictors of relevant ecosystem attributes for a range of ecosystem services including provisioning (fodder), cultural (land stewardship), regulating (landslide and avalanche risk), and supporting services (plant diversity). By analyzing the relative effects of the physical environment and land use on relevant ecosystem attributes, we also show that these ecosystem services are most sensitive to changes in grassland management, supporting current agri-environmental policies aimed at maintaining mowing of subalpine grasslands in Europe.     

Who owns the Earth? A challenge for the land system science community

ABSTRACT

Reflecting on the 2019 Open Science Meeting of the Global Land Program and on commentaries since, we argue that the time is ripe for the land system science community to fully embrace the thorny issue of land ownership and control. Beyond land governance and institutions, the issue of who actually owns and controls land, and how land holding and rents are distributed across society, is central to the future of sustainability initiatives, biodiversity protection, social justice, climate change mitigation, and long-term food security and sovereignty. By explicitly tracking and visualizing just Who Owns the Earth, the land system science community could provide much-needed data and insights to inform public debate and advance political action in these arenas.     

Climate change and urban land systems: bridging the gaps between urbanism and land science

ABSTRACT

Land science has generated critical knowledge about how humans modify Earth’s surface and has advanced our understanding of land as a coupled human–environment system. However, to date, most work has been in frontier environments with less attention – both conceptually and empirically – on how urban land systems affect the carbon cycle. Much of what is known about urban areas and the carbon cycle is limited to aggregate levels of activities within urban areas, usually by sector, with little focus on how the spatial configuration of urban land systems affects carbon-emitting activities. Given that urban areas produce about 70% of global energy-related CO₂ emissions, it is crucial to understand the relationship between urban environments, the activities they influence, and the resulting carbon emissions. This paper summarizes the current state of knowledge of how urban land systems affect carbon emissions and vulnerability, and identifies research gaps and opportunities for urban land science to contribute to climate change science, particularly through land architecture. We argue that more attention to urban land science conceptualization, measurement, and analysis would contribute to both fundamental knowledge about urban systems, as well as help identify policies and strategies for mitigation and adaptation to climate change at the urban scale.     

Synthesis and spatial dynamics of socio-economic metabolism and land use change of Taipei Metropolitan Region

Ever since the concept of metabolism was extended from biological science by social scientists to analyze human systems, socio-economic metabolism has been extensively applied to explore resource consumption, asset accumulation, waste emissions, and complex processes of land use change in a socio-economic system. Current research in socio-economic metabolism and land use change has used accounting approaches for macroscopic comparisons of countries and regions. However, socio-economic metabolism has seldom been applied to the analysis of land use change. To simulate the spatial-temporal dynamics of socio-economic metabolism and land use change, this study adopts a spatial system modeling method to develop a Socio-Economic Metabolism and Land Use Change (SEMLUC) model for the Taipei Metropolitan Region. The simulation results illustrate that the Taipei Metropolitan Region is highly dependent on inflows of non-renewable energy and exhibits a spatial hierarchy of non-renewable energy consumption centering on Taipei's Main station. Additionally, urban assets provide feedback to natural and agricultural systems to extract additional resource inflows which, driven by the maximum power principle, accelerate the convergence of energy flows toward urban assets. Accumulating urban assets also facilitates inflows of non-renewable material to nearby cells thereby enhancing land use conversion to urban areas. This work also demonstrates the capability of ArcGIS software in simulating socio-economic metabolism and land use change in an urban system.     

Fishing sustainability via inclusion of man in predator-prey models: A case study in Lago Preto, Manacapuru, Amazonas

We modeled a fishery's system with two types of fishermen, commercial and subsistence fishermen, who exploit the fish stock at the Amazonian floodplain lakes. In the first model, we combined the Lotka-Volterra equations with Verhulst's Logistic model, by inserting hydrological cycle oscillations. The second model was based on the equations proposed by Berryman, which reflect the predator's functional response in relation to the prey's population behavior, taking into account the hydrological cycle. In both models, commercial fishermen and local direct consumers (called riverside dwellers - riverines - in the model), were considered the only predators acting upon fishing stocks. Primary data were collected in 48 riverside homes throughout 2006. The total number of interviewees corresponds to 69.6% of the universe of homes in the community defined as study area. The riverines were the predators that showed capacity to eliminate the opponent predators (commercial fishermen). The best scenery obtained regarding the number of prey, was the one that showed only commercial fishermen in the region. On the other hand, the simulations show that the coexistence is possible among predators, and between predators and their prey. The seasonal model with functional response provides a better response in relation to the system's current situation and to the established modeling conditions than the Lotka-Volterra seasonal model. The seasonal model with functional response also showed a better response pattern in all scenarios, with oscillations taking place more gradually, both for variations associated with the flooding pulse and for relations between predators and prey.     

Rainfall-human-spatial interactions in a salinity-prone agricultural region of the Western Australian wheat-belt

To explore the complexity of temporal and spatial dynamics of an agricultural landscape under various external and internal driven scenarios, SimKat, an agent-based model, has been developed with the simulation computer program CORMAS. This model combines simplified bio-physical processes of land cover, regional dry-land salinity changes, rainfall impact on productivity, farm profitability and the complexity of land-use decisions of individual farmers in a dry-land agricultural catchment (no irrigation). In this model, simulated farmers formulate individual decisions dealing with land-use changes based on the combined performance of their past land cover productivity and market returns. The willingness to adapt to market drivers and the ability to maximize returns vary between farmers. In addition, farmers in the model can demonstrate various attitudes towards dry-land salinity mitigation as a consequence of experiencing and perceiving salinity on their farm, in the neighborhood or across the entire region. Consequently, farmers can adopt land cover strategies aimed at reducing dry-land salinity. Aggregating the simulated individual behavior of farmers combined with historical rainfall and market price records, reproduced similar aggregated trends of land-cover changes, regional salinity change and farm number decline as observed in the last 20 years in the subject region (Katanning, Western Australia). Using the model in an initial exploratory study on the impact of rainfall variability and change highlighted the importance of average rainfall decline and the widespread willingness of farmers to adapt perennial vegetation in their farming systems to combat regional dry-land salinity. Rainfall decline, as one of the observed consequences of climate change in this region, can also lead to prolonged sequences of dry seasons in the future. Adaptation by farmers to sequences of dry and wet seasons, rather than an average trend in rainfall, seems to be critical for farm survival in this region. Intensifying cropping during wet seasons to maximize farm returns can increase vulnerability in subsequent periods of dry seasons, in particular where alternative income from pasture and sheep production has been lost in the adaptation process.     

Making more out of sparse data: hierarchical modeling of species communities

Community ecologists and conservation biologists often work with data that are too sparse for achieving reliable inference with species-specific approaches. Here we explore the idea of combining species-specific models into a single hierarchical model. The community component of the model seeks for shared patterns in how the species respond to environmental covariates. We illustrate the modeling framework in the context of logistic regression and presence-absence data, but a similar hierarchical structure could also be used in many other types of applications. We first use simulated data to illustrate that the community component can improve parameterization of species-specific models especially for rare species, for which the data would be too sparse to be informative alone. We then apply the community model to real data on 500 diatom species to show that it has much greater predictive power than a collection of independent species-specific models. We use the modeling approach to show that roughly one-third of distance decay in community similarity can be explained by two variables characterizing water quality, rare species typically preferring nutrient-poor waters with high pH, and common species showing a more general pattern of resource use.     

Does the conservation of land reduce development? An econometric-based landscape simulation with land market feedbacks

We use an econometrically-based landscape simulation to investigate the effect of conservation on the net change in local development – the amount of land directly protected from development minus the amount of development that may occur on neighboring unprotected private land in response to conservation. First, we use spatial-panel data from Wisconsin to estimate parcel-level subdivision probabilities and density expectations, controlling for the endogenous location of open space. Second, we use these subdivision probabilities and density expectations in a landscape simulation model. Our simulation results indicate that 57% of conserved open space created between 1978 and 2009 generated close to zero net change in local development. This suggests that conserved open space mostly reallocated development in a small neighborhood (in a half-mile radius) rather than altering the total amount of development. We explore the landscape conditions that may lead to conservation having either a positive or negative effect on local development.     

Using MCA tools for evaluating community-managed forests from a green economy perspective: lessons from Nepal

ABSTRACT

Nepal is in the process of formulating its forest policies at the provincial level . Various community-managed forests have been designed in the past by the Nepal government to decentralize the forest for its sustainable management practice. This study facilitates the process of identifying appropriate forest management options in two of the provinces, namely Provinces Three and Gandaki. Four forest management options – passive, active, scientific and multiple – were identified following the existing management practices. For the evaluation of the overall performance of the options, a framework with three criteria, 10 indicators and 28 verifiers were designed. The framework followed the green economy perspective considering the improvement of the forest conditions, economic and social well-being, and low carbon emission. The Analytical Hierarchy Process was used to prioritize the best management option and analyse trade-offs to guide future decision-making and reduce the risk of unwanted consequences. Our results show that the elicitation of preferences for the evaluation criteria varied by stakeholder groups. Their preference was largely guided by improving the forest resource condition and economic well-being. Foresters prefer scientific and active forest management, policymakers prefer multiple-use forest management and scientific management, whereas community forest user groups prefer active forest management. We argue that a scientific management approach may contribute better to economic aspects, although it may often compromise the other aspects. The multiple forest management option seems to be the best for green economy considering ecological, economic and social consequences.     

Multiscale advanced raster map analysis system: Definition, design and development

This paper brings together a multidisciplinary initiative to develop advanced statistical and computational techniques for analyzing, assessing, and extracting information from raster maps. This information will provide a rigorous foundation to address a wide range of applications including disease mapping, emerging infectious diseases, landscape ecological assessment, land cover trends and change detection, watershed assessment, and map accuracy assessment. It will develop an advanced map analysis system that integrates these techniques with an advanced visualization toolbox, and use the system to conduct large case studies using rich sets of raster data, primarily from remotely sensed imagery. As a result, it will be possible to study and evaluate raster maps of societal, ecological, and environmental variables to facilitate quantitative characterization and comparative analysis of geospatial trends, patterns, and phenomena. In addition to environmental and ecological studies, these techniques and tools can be used for policy decisions at national, state, and local levels, crisis management, and protection of infrastructure. Geospatial data form the foundation of an information-based society. Remote sensing has been a vastly under-utilized resource involving a multi-million dollar investment at the national levels. Even when utilized, the credibility has been at stake, largely because of lack of tools that can assess, visualize, and communicate accuracy and reliability in timely manner and at desired confidence levels. Consider an imminent 21st century scenario: What message does a multi-categorical map have about the large landscape it represents? And at what scale, and at what level of detail? Does the spatial pattern of the map reveal any societal, ecological, environmental condition of the landscape? And therefore can it be an indicator of change? How do you automate the assessment of the spatial structure and behavior of change to discover critical areas, hot spots, and their corridors? Is the map accurate? How accurate is it? How do you assess the accuracy of the map? How do we evaluate a temporal change map for change detection? What are the implications of the kind and amount of change and accuracy on what matters, whether climate change, carbon emission, water resources, urban sprawl, biodiversity, indicator species, human health, or early warning? And with what confidence? The proposed research initiative is expected to find answers to these questions and a few more that involve multi-categorical raster maps based on remote sensing and other geospatial data. It includes the development of techniques for map modeling and analysis using Markov Random Fields, geospatial statistics, accuracy assessment and change detection, upper echelons of surfaces, advanced computational techniques for geospatial data mining, and advanced visualization techniques.     

Sensitivity Analyses of Spatial Population Viability Analysis Models for Species at Risk and Habitat Conservation Planning

Abstract:  Population viability analysis (PVA) is an effective framework for modeling species- and habitat-recovery efforts, but uncertainty in parameter estimates and model structure can lead to unreliable predictions. Integrating complex and often uncertain information into spatial PVA models requires that comprehensive sensitivity analyses be applied to explore the influence of spatial and nonspatial parameters on model predictions. We reviewed 87 analyses of spatial demographic PVA models of plants and animals to identify common approaches to sensitivity analysis in recent publications. In contrast to best practices recommended in the broader modeling community, sensitivity analyses of spatial PVAs were typically ad hoc, inconsistent, and difficult to compare. Most studies applied local approaches to sensitivity analyses, but few varied multiple parameters simultaneously. A lack of standards for sensitivity analysis and reporting in spatial PVAs has the potential to compromise the ability to learn collectively from PVA results, accurately interpret results in cases where model relationships include nonlinearities and interactions, prioritize monitoring and management actions, and ensure conservation-planning decisions are robust to uncertainties in spatial and nonspatial parameters. Our review underscores the need to develop tools for global sensitivity analysis and apply these to spatial PVA.     

SWAT模型在国内外非点源污染研究中的应用进展

模型模拟是定量估算非点源污染负荷的有效工具,也是对其进行规划、控制和管理的前提.近年来SWAT模型在国内外得到了快速的发展和应用,是目前全球评价大范围和环境变化条件下非点源污染问题的一个有效工具.简介SWAT模型的发展历程及原理,概述了SWAT模型目前在国内外的水文评价、污染物流失模拟、输人参数、土地利用及气候变化对水文响应的影响等方面的研究现状,并对SWAT模型的发展方向提出了建议,为模型的进一步完善与应用提供参考.结果显示,SWAT模型对水文评价(如径流量、泥沙量)可得到较好的模拟和预测结果,能够模拟污染物(如农药和化肥)在农田和河网中的迁移过程,模拟与分析土地利用/覆被变化及气候变化对水文过程的影响.模型参数的确定及其对地下水流与溶质运移的模拟是模型的主要问题,需要进一步研究与完善.     

Qualitative modeling and monitoring of selected ecosystem functions

This paper proposes three qualitative models that were applied for modeling of Small Water Cycle violation in ecosystem of Trebon region, South Bohemia. SWC refers to the behavior of the local ecosystem (e.g., the Trebon region), in which the volume of water that comes into the ecosystem is evaporated and falls back into this system. SWC is characterized by early morning dues and frequent small rain precipitation. In the Trebon Biosphere Reserve characterized by wetlands, forests and agriculture land, the evaporated water rises quickly inside the zone and does not have time to recondense before it is transported outside the ecosystem to the distant mountains, where it condenses spontaneously in the rising air streams.The essential pre-model for developing our qualitative models is the database model implemented in the MS SQL environment. The data in this model were collected for last five years and contain information about SWC violation and about the landscape stability development. The database system is used for standard reports, for correlating digital and graphic runs from associated meteorological stations, and for computing the evapotranspiration at the points where the stations are located and also at approximate inter-points.In parallel, and in addition to this standard use of the database model, the data was applied in the development of qualitative models (state model, model for the detection of unexpected situations and matroid model). This transformation and compression of the data was done with help of experienced experts and with the help of special mathematical operations. Qualitative models introduced in this paper overcome experience with quantitative models namely in these items: (1) They provide compression of information contained in large volumes of numerical data. (2) On the contrary of individual quantitative modeling qualitative models enable to describe the function and properties of the whole ecosystem. (3) Conclusions from qualitative models are in many cases better than are the generalizations of results from quantitative models.The first goal of the paper is to model situations associated with violations of the Small Water Cycle (SWC) in this ecosystem, and to contribute to acceptable solutions. The second goal of the paper is to investigate temporary models for the stability of the landscape development and to propose qualitative models for software support for integrated environmental modeling.     

The role of agent-based models in wildlife ecology and management

Conservation planning of critical habitats for wildlife species at risk is a priority topic that requires the knowledge of how animals select and use their habitat, and how they respond to future developmental changes in their environment. This paper explores the role of a habitat-modeling methodological approach, agent-based modeling, which we advocate as a promising approach for ecological research. Agent-based models (ABMs) are capable of simultaneously distinguishing animal densities from habitat quality, can explicitly represent the environment and its dynamism, can accommodate spatial patterns of inter- and intra-species mechanisms, and can explore feedbacks and adaptations inherent in these systems. ABMs comprise autonomous, individual entities; each with dynamic, adaptive behaviors and heterogeneous characteristics that interact with each other and with their environment. These interactions result in emergent outcomes that can be used to quantitatively examine critical habitats from the individual- to population-level. ABMs can also explore how wildlife will respond to potential changes in environmental conditions, since they can readily incorporate adaptive animal-movement ecology in a changing landscape. This paper describes the necessary elements of an ABM developed specifically for understanding wildlife habitat selection, reviews the current empirical literature on ABMs in wildlife ecology and management, and evaluates the current and future roles these ABMs can play, specifically with regards to scenario planning of designated critical habitats.     

Evaluating the environmental flows of China's Wolonghu wetland and land use changes using a hydrological model, a water balance model, and remote sensing

Environmental flows are critical to sustaining a variety of plant and animal communities in wetlands. However, evaluation of environmental flows is hampered by the problem of hydrological and ecological data shortage, especially in many developing countries such as China. Based on a hydrological model, a water balance model and remote sensing data, we assessed the environmental flows of China's Wolonghu wetland with limited data. The hydrological model provides input data for the water balance model of the wetland, and the remote sensing data can be used to assess land use changes. Integration of these two models with the remote sensing data revealed both the environmental flows of the Wolonghu wetland and the relationships between these environmental flows and land use changes. The results demonstrate that environmental flows have direct and indirect influences on the wetland ecosystem and should be linked to sustainable wetland management.