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The Full of Economic-Environment Linkages and Integration dX/dt (FeliX) model is a System Dynamics-based Integrated Assessment Model (IAM), explicitly incorporating human behaviors and their dynamic interactions among global systems. This paper presents FeliX 2.0, describing the detailed framework and key interactions among nine integrated modules. FeliX 2.0 refined its original version in population dynamics, food and land use systems, and socioeconomic settings for poverty analysis. Robust calibration is applied to key variables against their historical data since 1950. Future projections of multiple variables up to 2100 demonstrate coherences between FeliX 2.0 and the IAMs used in IPCC assessments. Both outputs (the robust calibration results and future projections) underscore the efficacy of FeliX 2.0 in capturing complex interdependencies within global systems. FeliX 2.0 stands as an informative tool and offers insights into interactions within the human-Earth system and the analysis of complex economic-environmental-social challenges in short- and long-term future. • A SD-based IAM model—FeliX 2.0—integrating human behavior for the human-Earth system simulation. • FeliX 2.0 refines FeliX 1.0 in population dynamics, food and land use, and poverty modeling. • Coherent projections up to 2100 for key variables in human-Earth systems by FeliX 2.0 • FeliX 2.0 stands as a pioneering tool for analyzing economic, environmental and social challenges. [ABSTRACT FROM AUTHOR]

Various approaches are available to assist stakeholders in identifying and resolving ecosystem service tradeoffs. However, existing tools fall short in simulating land use configuration effects on ecosystem services and subsequently making these effects accessible to users with varying levels of expertise. To address this gap, we introduce PLACES, a tool that estimates land use impacts on multiple ecosystem services by incorporating landscape-level processes. Tool results are provided in real-time and visualized to support a dialogue between different stakeholders. This study presents the tool development and application during a mixed stakeholder workshop, after which mental models, questionnaires, and videos were analyzed to evaluate PLACES. The tool increased the participants' understanding of insights of spatial processes and sparked discussions on the societal goals for sustainable landscapes. For future applications of PLACES, we encourage careful tailoring of the landscape representation and land use impact simulations to match the knowledge of the respective users. [Display omitted] • A new tool to elucidate ecosystem service tradeoffs in user-designed scenarios. • It models land use impacts on ecosystem services accounting for landscape-scale processes. • Outputs are calculated instantly and accessible to users with various backgrounds. • The tool supports interactive discussion and scenario design by diverse stakeholders. [ABSTRACT FROM AUTHOR]

In this study, we developed a MATLAB program for flood prediction in a watershed. The program consists of three modules. The instantaneous unit hydrograph (IUH) generation module utilizes a power-law based interpolation method to generate IUHs. The generated IUH is a function of the rainfall excess intensity and therefore considers nonlinearity. The long short-term memory (LSTM) module employs "lstmLayer" from the MATLAB deep learning toolbox to predict total rainfall excess; this is then used to estimate the curve number (C N) value for each flood event. The LSTM module uses a land surface modeling dataset and rainfall-runoff data as inputs. The flood hydrograph generation module calculates effective rainfall hyetographs and then predicts flood hydrographs using a convolution integration. A detailed description of the program is provided along with an application example for real watersheds. The application results demonstrated that our program can be effectively used for flood prediction in practice, particularly for large flood events. • We have developed a MATLAB program for flood prediction. • IUH generation module for surface runoff utilizes a nonlinear power law-based interpolation. • LSTM module employs a deep learning model to estimate the curve number and rainfall loss. • Flood hydrograph generation module predicts runoff by - convolution integration. • The applications to small and large watersheds showed reasonable accuracies. [ABSTRACT FROM AUTHOR]

In flood frequency analysis (FFA), choices of distribution and methods can hinder the reproducibility of results. Besides, changes in climate, land use/cover, and water management can induce nonstationarity. Frameworks to select between stationary FFA (S-FFA) and nonstationary FFA (NS-FFA) are lacking, and NS-FFA tools are limited. Therefore, this paper introduces a systematic and software-supported framework enabling repeatable workflows for both S-FFA and NS-FFA. The framework has three modules to a) process flood series for exploratory data analysis (EDA) and NS-FFA model determination (if needed), b) select the S-FFA or NS-FFA approach underpinned by the EDA, and c) perform FFA including model determination, parameter estimation, uncertainty quantification, and model performance assessment. The framework incorporates various distributions, methods, and metrics, and recent advancements in NS-FFA for model determination and uncertainty quantification and allows for the modeller's intervention while ensuring reproducibility. The software is freely available to the public. • A FFA framework enabling both the S-FFA and NS-FFA is proposed. • The EDA is employed to assist in the selection of the S-FFA and NS-FFA. • The framework contains decision points and allows for repeatable workflows. • The framework includes various models, metrics, and state-of-the-art methods. • The source codes and a user-friendly standalone GUI are freely available. [ABSTRACT FROM AUTHOR]

Flood risk is expected to increase significantly around the globe, fuelled by the nexus between flooding and land use. This is because changes in land use due to socio-economic development increase runoff, as well as the "values-at-stake" exposed to flooding. At the same time, changes in flood extent and depth can impact land use via policy interventions aimed at reducing future flood risk, such as buy-backs and zoning. Existing approaches to flood risk assessment and mitigation have generally ignored this two-way interaction between flooding and land use. To address this shortcoming, an integrated framework considering the dynamics of future changes in the flood-land use nexus is introduced and applied to the Gawler River region, South Australia. Results indicate that changes in land use due to socio-economic drivers can have a significantly greater impact on flood losses than climate change and that zoning can be effective in reducing future flood risk. • Integrated and computational framework for considering the flood-land use nexus in the quantification of future flood risk. • Framework considers the effects of change in land use on flooding and change in flooding on land use via zoning regulation. • The effect of climate drivers on future flooding and socio-economic drivers on land use are considered. • Case study results suggest that socio-economic development has a bigger impact on future flood risk than climate change. • Case study results show that considering the two-way interactions between flooding and land use has an impact on results. [ABSTRACT FROM AUTHOR]

Flooding has catastrophic effects worldwide. Rapid flood models, such as the height above nearest drainage (HAND) model, have lower complexity and data requirements than traditional models. However, input stage height data are often lacking because most gauged sites only provide estimates of discharge. In addition, performance is difficult to evaluate because flood extent data during extreme periods are often unavailable. Here, we developed a HAND-flood frequency analysis (FFA)-synthetic rating curve (SRC) approach and applied it to a coastal watershed in China. The HAND-FFA-SRC approach demonstrated effective and efficient flood forecasting, and the C values were 1.19, 1.20, and 1.16, respectively for the floods under moderate rainfall, heavy rainfall, and storm. Meanwhile, the accuracy of the model was highly impacted by the topographic characteristics of the watersheds. The C values were improved as the slope increased from 3° to 20° during the floods under different scenarios. Additionally, the effects of floods were evaluated under different return periods which indicated that the cropland is the most affected land use type but the risk for impervious surfaces is increasing. The proposed approach is viable for forecasting flood susceptibility and can improve resilience planning and flood management. • We developed a coupled approach (HAND-FFA-SRC) by integrating HAND model, FFA technology with SRC method. • We evaluated the performance of SRC, and extended it to long-return scenarios. • The HAND-FFA-SRC method was evaluated using social media-based flood extent. • The flood risk of impervious surfaces is increasing. [ABSTRACT FROM AUTHOR]

Land use and land cover (LULC) projections do not always have sufficient spatial resolution to allow them to be used by environmental models that project how LULC impacts a range of variables, including ecosystem services, biodiversity, and hydrology. We present a downscaling method designed to generate the high resolution LULC projections often required for environmental modelling. LULC change is allocated to a high-resolution reference map based on the density of LULC classes in neighbouring grid cells. Increasing a parameter that controls the likelihood of cells adjacent to existing LULC classes being converted to the same class generated less spatially aggregated landscapes that better represented historic LULC patterns in Colombia between 1960 and 2019. This new downscaling method is available as an R package and will enable the reconciliation of the spatial resolution of LULC projections and key processes that are embedded in a range of environmental models. • We developed a novel method for spatial downscaling of land use projections. • Method places new land use and land cover areas near to existing patches. • Stochasticity of downscaling can be increased to produce less aggregated landscapes. • Downscaling recreated historic land use and land cover patterns in Colombia. [ABSTRACT FROM AUTHOR]

Land cover monitoring efforts are important for resource planning and ecosystem services in many countries. Collect Earth Online (CEO) is a new, free open source and user-friendly software tool for land monitoring. It is the product of a collaborative effort between NASA, Food and Agriculture Organization of the United Nations (FAO), US Forest Service and Google. This paper provides a full overview of CEO's structure and functionality. Based on the cloud, CEO's structure supports simultaneous data entry by multiple users. No desktop installation is required and only an internet connection is required setting minimal requirements for using the software. Google Earth Engine widgets can be created for assisted plot interpretation such as image collection, time series graphs featuring indices such as Normalized Difference Vegetation Index (NDVI) and related statistics. We also provide a case study and related findings from a CEO workshop held in Myanmar. • Collect Earth Online (CEO) is a new, free, open source tool for land monitoring. • It is built on joint efforts between NASA, FAO, US Forest Service and Google. • CEO is free and meets key reference data collection requirements for practitioners. • It is a robust, integrated approach for data collection in collaborative settings. • A case study using CEO for reference data collection in Myanmar is presented. [ABSTRACT FROM AUTHOR]

Combining simulation models and multi-objective optimization can help solving complex land use allocation problems by considering multiple, often competing demands on landscapes, such as agriculture, (drinking) water provision, or biodiversity conservation. The search for optimal land use allocations has to result in feasible solutions satisfying "real-world" constraints. We here introduce a generic and readily applicable tool to integrate user-specific spatial models (e.g. assessing different ecosystem services) for a Constrained Multi-objective Optimization of Land use Allocation (CoMOLA). The tool can handle basic land use conversion constraints by either a newly and specifically developed method to repair infeasible solutions or by penalizing constraint violation. CoMOLA was systematically tested for different levels of complexity using a virtual landscape and simple ecosystem service and biodiversity models. Our study shows that using repair mechanisms seems to be more effective in exploring the feasible solution space while penalizing constraint violation likely results in infeasible solutions. • Land use optimization with genetic algorithms is hampered by hard area constraints •Penalizing infeasible solutions in the selection process for mating is insufficient •Repairing infeasible solutions fosters the exploration of the feasible search space •Repairing is computationally demanding, especially for narrow constraints •Optimization efficiency decreases strongly with increasing number of spatial units [ABSTRACT FROM AUTHOR]

Abstract Land use and hydrology are interdependent, so that land use modeling could benefit from hydrologic modeling. This study aims to integrate spatial predictions of hydrologic variables as provided by a hydrologic model into a land use model for a rapidly urbanizing catchment in India. The benefits of this integration are quantified by comparing predictions of a land use model that uses a basic set of explanatory variables to a land use model that additionally uses the modeled hydrologic variables. Our results indicate that the integration of the hydrologic variables improved the model accuracy indicated by overall accuracy (+3 and + 4 percentage points (pp)), class specific user and producer accuracies (up to +8 pp) and figure of merit (+4 and + 5.3 pp) when compared with land use classifications at two points in time. Moreover, the land use patterns show that the integration of the hydrologic variables helped to avoid allocation errors. Highlights • Hydrologic variables modeled with SWAT improved CLUE-s model simulations. • A more reasonable allocation was achieved for a future scenario. • We recommend to integrate land use models and hydrologic models. [ABSTRACT FROM AUTHOR]

Abstract Catchment-scale water quality models have become important tools for water quality management, planning and reporting worldwide. In this review, we synthesise recent developments in water quality modelling, focusing on catchment-scale models of freshwater, non-urban systems and their ability to support catchment management. We explore 10 key attributes in selected existing water quality models. These attributes can be characterised as: model use (model purposes, representation of constituents, scenario analysis, and documentation), model development (process representation, spatial heterogeneities, temporal dynamics, and data requirements), and model performance (calibration, validation and uncertainty tools). We deliberate on 11 key challenges and/or emerging topics in catchment water quality modelling: large-scale applications, model integration, model usability and communication, preliminary data analysis, modelling management practices, technology advancement, incorporating soft data, model identifiability, uncertainty analysis, good modelling practices and capacity building, and differentiating the effects of climate impacts from those associated with land use and management practices. Highlights • The review focuses on the ability of models to support catchment management. • Key attributes of model use, development and performance were explored. • We discussed 11 challenges and emerging topics for future development. • Uncertainty can be reduced through using soft data and optimal monitoring design. • A major challenge is separating impacts of climate from land use and management. [ABSTRACT FROM AUTHOR]

Abstract The purpose of this study was to assess the performance of annual and monthly land use regression (LUR) models for estimating the spatial distribution of NO 2 and PM 2.5 in Taiwan. Samples were collected at 73 air quality monitoring sites in 2015. Data transformation coupled with extracting principle components and satellite-derived data were integrated with LUR modeling and applied to increase PM 2.5 model performance. Results indicated that NO 2 exhibited more robust model performance compared with PM 2.5. Leave-one-out cross validation (LOOCV) R 2 of NO 2 annual model was 0.76 and ranged from 0.56 to 0.81 for monthly models. The LOOCV R 2 of PM 2.5 annual model was improved from 0.13 to 0.56 by applying principle component analysis and adding satellite data (i.e., percentage of sunshine coverage and aerosol optical depth). These approaches also improved the performance of PM 2.5 monthly models. The median LOOCV R 2 increased from 0.12 to 0.49. Graphical abstract Image 1 Highlights • NO 2 showed better model performance than PM 2.5 , both in annual or monthly models. • Data transformation coupled with PCA in modeling improves PM 2.5 model performance. • Sunshine coverage derived from satellite data was found beneficial for modeling. [ABSTRACT FROM AUTHOR]

Abstract Despite recent advances in quantifying land-use/cover change (LUCC) transition potential, transition rules are often not transparent and uncertainty is rarely made explicit. Here, we introduce DoTRules—a dictionary of trusted rules—as a transparent alternative to calculate transition potential in cellular automata models. Rules relate LUCC variables to the observed historical changes. Shannon entropy is calculated to assess the uncertainty of each rule, and the most trusted rules are used to project future LUCC. DoTRules produces rule-level uncertainty estimates, which can be mapped. In a case study of the Ahvaz region of Iran, the overall accuracy of LUCC simulation calibrated using DoTRules was very similar to simulations calibrated with the state-of-the-art random forest, but DoTRules provides a more transparent approach where transition rule information and uncertainty can be readily accessed and interpreted. The results demonstrate that DoTRules has potential to derive new insights into LUCC processes. Highlights • DoTRules calculates transition potential in CA-based land-use models. • DoTRules-based simulation performance was similar to random forest-based. • DoTRules generates transparent transition rules and quantifies uncertainty. • Uncertainty estimates were correlated with land-use simulation accuracy. • Uncertainty estimates can reduce the need for ground-truthed validation. [ABSTRACT FROM AUTHOR]

Abstract Strategic Environmental Assessment (SEA) requires taking into account the sensitivity of the area likely to be affected when identifying and characterising potential impacts resulting from plans and programmes. This entails consideration of the intrinsic characteristics that make the area susceptible to change, and incorporation of stakeholder values and concerns. A geospatial approach to these considerations facilitates identification of potential land-use conflicts and better informs planning decisions. Building on online geoprocessing advancements, we have developed an Environmental Sensitivity Mapping web tool containing novel functionality to support SEA. This paper presents the methodological framework for its participative multi-criteria approach and its architecture, and describes its functionality simulating a practical application. The web tool has the potential to break down data access and specialised skills barriers while providing a means for enhancing SEA consistency and transparency. However, data limitations remain affecting its applicability, and further technological advancements may help enhance its functionality. Highlights • Sensitivity mapping can enhance baseline information for environmental assessments. • A novel online geoprocessing tool supports systematic sensitivity assessment. • It enables combining context-specific environmental criteria and value judgments. • Pilot testing it through stakeholder engagement has validated its applicability. • The tool promotes assessment consistency and transparency but data issues remain. [ABSTRACT FROM AUTHOR]

Abstract This study introduces a novel framework for land change simulation that combines the traditional Land Transformation Model (LTM) with data clustering tools for the purposes of conducting land change simulations of large areas (e.g., continental scale) and over multiple time steps. This framework, called "LTM-cluster", subsets massive land use datasets which are presented to the artificial neural network-based LTM. LTM-cluster uses the k -means clustering algorithm implemented within the Spark high-performance compute environment. To illustrate the framework, we use three case studies in the United States which vary in simulation extents, cell size, time intervals, number of inputs, and quantity of urban change. Findings indicate consistent and substantial improvements in accuracy performance for all three case studies compared to the traditional LTM model implemented without input clustering. Specifically, the percent correct match, the area under the operating characteristics curve, and the error rate improved on average of 9%, 11%, and 4%. These results confirm that LTM-cluster has high reliability when handling large datasets. Future studies should expand on the framework by exploring other clustering methods and algorithms. Highlights • We addressed the challenges of simulating land change across large regions and spans of time. • Our model, called LTM-Cluster, is a scalable modeling framework aimed to benefit research involving large datasets. • The Spark environment is used to reduce the burden of high computational time when handling a huge amount of data. • The implementation of LTM-Cluster is available free-of-charge. • Our results consistently showed significant performance improvements of the LTM-Cluster compared to the traditionally parameterized LTM. [ABSTRACT FROM AUTHOR]

Abstract In flat environments, groundwater is relatively shallow, tightly associated with surface water and climate, and can have either positive and negative impacts on natural and human systems depending on its depth. A linked modelling and analysis framework that seeks to capture linkages across multiple scales at the climate/water/crop nexus in the Argentine Pampas is presented. This region shows a strong coupling between climate, soil water, and land use due to its extremely flat topography and poorly developed drainage networks. The work describes the components of the framework and, subsequently, presents results from simulations performed with the twin goals of (i) validating the framework as a whole and (ii) demonstrating its usefulness to explore interesting contexts such as unexperienced climate scenarios (wet/dry periods), hypothetical policies (e.g., differential grains export taxes), and adoption of non-structural technologies (e.g., cover crops) to manage water table depth. Highlights • We explored climate, soil water, and land use interactions in the Argentine Pampas. • A linked modelling framework was implemented to explore the climate/water/crop nexus. • The modelling framework was conceptually validated as a whole. • Simulation of interesting and complex scenarios proved the framework usefulness. • Adoption of cover crops can reduce the risk of waterlogging and flooding. [ABSTRACT FROM AUTHOR]

Abstract The objective of this study was to combine a catchment model with a one–dimensional lake water quality model to simulate the trophic state of a eutrophic shallow lake in response to nutrient load reductions and climate change. The catchment and lake models gave satisfactory performance in simulating observed data, indicating that the key processes that affect nutrient loads and lake trophic status were adequately represented. Simulating removal of nutrients by reducing fertiliser applied to farmland or irrigated wastewater had minor effects on nutrient concentrations in the lake, but simulations using a projected climate for 2090 showed a major impact on nutrients and water quality. This overarching effect indicated that polymictic lakes may be particularly vulnerable to eutrophication associated with climate change due to increased internal nutrient loading, which will lead to a biological response of increased algal biomass, while changes in external loads will have lesser relative impact. Highlights • Combined climate–catchment–lake modelling reveals flow–on effects to a polymictic lake. • Small water quality improvement with large nutrient reductions in one sub–catchment. • Minor lake response to climate change effects from changes in catchment processes. • Major lake response to climate change due to effects on stratification. • Climate change has potential to confound restoration of shallow eutrophic lakes. [ABSTRACT FROM AUTHOR]

Abstract Representation of land-use and hydrologic interactions in respective models has traditionally been problematic. The use of static land-use in most hydrologic models or that of the use of simple hydrologic proxies in land-use change models call for more integrated approaches. The objective of this study is to assess whether dynamic feedback between land-use change and hydrology can (1) improve model performances, and/or (2) produce a more realistic quantification of ecosystem services. To test this, we coupled a land-use change model and a hydrologic mode. First, the land-use change and the hydrologic models were separately developed and calibrated. Then, the two models were dynamically coupled to exchange data at yearly time-steps. The approach is applied to a catchment in South Africa. Performance of coupled models when compared to the uncoupled models were marginal, but the coupled models excelled at the quantification of catchment ecosystem services more robustly. Highlights • Calibrated and dynamically coupled a land-use change model and a hydrologic model. • Analyzed effects of the dynamic feedback between the two on quantification of ecosystem services. • Coupled models performed only slightly better at capturing observation on both models. • Coupled models produced a more robust quantification of catchment ecosystem services. [ABSTRACT FROM AUTHOR]

Coupling watershed and lake models is a powerful approach for providing lake or reservoir inputs and for simulating how land use scenarios may impact lake ecosystems. SWAT (Soil and Water Assessment Tool) is one of the most widely used watershed models for this purpose. However, as outlets for watershed delineation in SWAT should be in the river network, modellers may – when modelling the inputs to a waterbody boundary – make choices leading to imprecise results. The impact of these choices on the accuracy of a coupled model can be highly relevant as they have a direct effect on the size of the delineated watershed. In this paper, we describe the design and development of SWAT2lake, a tool that remedies such imprecise delineation of watersheds to lakes and reservoirs. SWAT2lake has been designed as a plugin for the QGIS software that works in parallel with QSWAT and it is available through wet.au.dk. [ABSTRACT FROM AUTHOR]

Human use of land increasingly alters the structure and the functioning of the environment. To ex-ante understand and anticipate these changes there is an increased need for readily available and operational land use change models. One of these models is CLUE, which has been used in many studies all over the world. These studies brought forward operational hurdles, that hamper model application. The overall objective of this paper is to present a new version of the CLUE model, iCLUE, that helps to overcome these hurdles. We describe the technical redevelopment, conceptual innovations, several applications and success factors and critical reflections. iCLUE minimizes manual error-prone actions, enhances ease-of-use, speeds up the operational modelling process and provides data visualisations to empower users to analyse and interpret results. [ABSTRACT FROM AUTHOR]

The use of physically-based spatially-distributed models to solve problems in hydrology has been limited by their intensive data and setup time requirements. We have therefore created a system that enables the automatic setup of a robust, physically-based spatially-distributed SHETRAN model for any catchment, gauged or ungauged, in Great Britain. National-scale datasets for topography, soil, landuse, geology and climate have been collated, processed and stored to allow rapid retrieval and configuration of catchment models with minimal user-intervention. These maps can be easily replaced by national datasets of other countries or global datasets, ensuring the system's international transferability. A graphical user interface has been developed to facilitate the model setup process. The resultant system, SHETRAN-GB, has the potential to significantly aid the deployment of SHETRAN for addressing important issues relating to water resources, hydrological extremes and climate change, either for individual or multiple catchments. [ABSTRACT FROM AUTHOR]

Land-use change models generally include neighbourhood rules to capture the spatial dynamics between different land-uses that drive land-use changes, introducing many parameters that require calibration. We present a process-specific semi-automatic method for calibrating neighbourhood rules that utilises discursive knowledge and empirical analysis to reduce the complexity of the calibration problem, and efficiently calibrates the remaining interactions with consideration of locational agreement and landscape pattern structure objectives. The approach and software for implementing it are tested on four case studies of major European cities with different physical characteristics and rates of urban growth, exploring preferences for different objectives. The approach outperformed benchmark models for both calibration and validation when a balanced objective preference was used. This research demonstrates the utility of process-specific calibration methods, and highlights how process knowledge can be integrated with automatic calibration to make it more efficient. [ABSTRACT FROM AUTHOR]

We introduce an explicit indicator and the Land Use Management Support System to assess the resource-use efficiency of land use (RUE) at the landscape scale. To estimate RUE, we relate land-use performance with regard to ecosystem services indicators to the maximum possible land-use performance based on an optimised land-use configuration. The test application of the RUE assessment in the Haean catchment, South Korea, shows that the land-use system's RUE could be increased by 11% for both nitrate and sediment loss. The estimated headroom could indicate whether potential contaminant reduction targets for the downstream water reservoir Lake Soyang could be achieved with the current land-use system. The recurring RUE assessment for a given region might indicate the effectiveness of spatial planning and policy measures to improve the RUE in that region. Future work should address the integration of RUE into a participatory spatial planning or resource-management framework. [ABSTRACT FROM AUTHOR]

Pattern-based land use and land cover change (LUCC) modeling is implemented in various software, such as the CLUE family, LCM or Dinamica EGO. These tools are now relatively mature, but their conceptual foundations are not fully discussed in the literature. In particular, these modeling environments exhibit substantially different behaviors for the same problem and the same calibration data and the origin of this difference of behavior cannot be identified on the basis of the published information. These inter-model inconsistencies are revisited ab initio , focusing on allocation. The concept of allocation bias is defined and allows us to pinpoint a number of conceptual errors, biases and algorithmic inaccuracies in the existing LUCC modeling environments we have tested. We describe error- and bias-free allocation methods, implemented in our own software CLUMPY, which is shown to outperform these software in terms of formal correctness and accuracy. For the purpose of error and bias identification, it is crucial that validation be performed in explanatory variable space, not on maps. • An allocation bias criterion is formulated from a necessary requirement • Error- and bias-free algorithms are required for consistency • All tested pattern-based LUCC models are biased to varying degrees • A comprehensive Land Use [and cover] Model (CLUMPY) has been developed • Algorithmic validation must be performed in explanatory variable space, not on maps [ABSTRACT FROM AUTHOR]

Land cover dynamics influence the spatio-temporal evolution of the Rural-Urban Interface (RUI). This represents the most prone area for human-caused forest fires ignitions in Mediterranean countries. Traditionally, RUI mapping is based on the measurement of the distances among specific land covers. This methodology suffers from the definition of pre-established fixed parameters. To avoid this arbitrariness, a new procedure based on Multilayer Perceptron and Fuzzy Set Theory is introduced in this paper. This allows to develop continuous non-categorical maps expressing the possibility of being part of this interface. Thus, an innovative way for assessing the uncertainty in identifying RUI is presented. The proposed methodology has been applied to the case study of Portugal, elaborating a future scenario for the RUI. The results show how the framework proposed in this paper is able to correctly identify the areas belonging to this interface, providing useful information for forest fires -prevention policies. [ABSTRACT FROM AUTHOR]

A spatial model is presented, based on urban planning concepts for abstracting urban form characteristics in new and existing areas. Requiring input maps of land use, elevation, population and parameters from planning regulations, the model conceptualises (on a spatial grid) attributes including impervious fraction, allotment geometry and roof areas among other relevant characteristics for integrated urban water management. The model is calibrated to three different Melbourne districts, varying in size (10–60 km 2 ) and land use. Performance was evaluated by comparing modelled outputs with observations of total dwelling count, employment and spatial distribution of impervious fraction and residential roof areas. Results not only highlight reasonably good prediction, particularly with spatially variable indicators such as imperviousness across all case studies, but also logical contrasts and consistency in the chosen planning parameters across the different case study districts. Discrepancies highlight aspects needing improvement and potential for exploring auto-calibration and model sensitivity. [ABSTRACT FROM AUTHOR]

In this work the synergistic use of Sentinel-1 and 2 combined with the System for Automated Geoscientific Analyses (SAGA) Wetness Index in the content of land use/cover (LULC) mapping with emphasis in wetlands is evaluated. A further objective has been to develop a new Object-based Image Analysis (OBIA) approach for mapping wetland areas using Sentinel-1 and 2 data, where the latter is also tested against two popular machine learning algorithms (Support Vector Machines - SVMs and Random Forests - RFs). The highly vulnerable iSimangaliso Wetland Park was used as the study site. Results showed that two-part image segmentation could efficiently create object features across the study area. For both classification algorithms, an increase in overall accuracy was observed when the full synergistic combination of available datasets. A statistically significant difference in classification accuracy at all levels between SVMs and RFs was also reported, with the latter being up to 2.4% higher. SAGA wetness index showed promising ability to distinguish wetland environments, and in combination with Sentinel-1 and 2 synergies can successfully produce a land use and land cover classification in a location where both wetland and non-wetland classes exist. [ABSTRACT FROM AUTHOR]

REDD+ was initially conceived of as a multi-level carbon-based payment for environmental services (PES). It is still often assumed to be a cost-effective climate change mitigation strategy, but this assumption is mostly based on theoretical studies and static opportunity cost calculations. We used spatial and socioeconomic datasets from an Amazonian deforestation frontier in Brazil to construct a simulation model of REDD + payments to households that can be used to assess REDD + interventions. Our SimREDD + model consists of dynamic optimization and land-use/cover change allocation submodels built into an agent-based model platform. The model assumes that households maximize profit under perfect market conditions and calculates the optimal household land-use/cover configuration at equilibrium under a given REDD + PES scenario. These scenarios include PES based on (1) forest area and (2) carbon stocks. Insights gained from simulations under different conditions can assist in the design of more effective, efficient, and equitable REDD + programs. [ABSTRACT FROM AUTHOR]

Modelling of land-use change plays an important role in many areas of environmental planning. However, land-use change models remain challenging to calibrate, as they contain many sensitive parameters, making the calibration process time-consuming. We present a multi-objective optimisation framework for automatic calibration of Cellular Automata land-use models with multiple dynamic land-use classes. The framework considers objectives related to locational agreement and landscape pattern structure, as well as the inherent stochasticity of land-use models. The framework was tested on the Randstad region in the Netherlands, identifying 77 model parameter sets that generated a Pareto front of optimal trade-off solutions between the objectives. A selection of these parameter sets was assessed further based on heuristic knowledge, evaluating the simulated output maps and parameter values to determine a final calibrated model. This research demonstrates that heuristic knowledge complements the evaluation of land-use models calibrated using formal optimisation methods. [ABSTRACT FROM AUTHOR]

Land use regression (LUR) modelling is increasingly used in epidemiological studies to predict air pollution exposure. The use of stationary measurements at a limited number of locations to build a LUR model, however, can lead to an overestimation of its predictive abilities. We use opportunistic mobile monitoring to gather data at a high spatial resolution to build LUR models to predict annual average concentrations of black carbon (BC). The models explain a significant part of the variance in BC concentrations. However, the overall predictive performance remains low, due to input uncertainty and lack of predictive variables that can properly capture the complex characteristics of local concentrations. We stress the importance of using an appropriate cross-validation scheme to estimate the predictive performance of the model. By using independent data for the validation and excluding those data also during variable selection in the model building procedure, overly optimistic performance estimates are avoided. [ABSTRACT FROM AUTHOR]

To evaluate the sustainability of potential agricultural land developments, scenario projections with land use change models are often combined with environmental impact assessments. Although this allows inter-scenario comparison of impacts, it does not permit interpretation of scenarios in the light of theoretically optimal impacts. A Pareto frontier provides this information. We demonstrate this for ethanol production in Goiás, Brazil, in 2030. For a Business-as-Usual scenario projection, the spatial configuration, production costs, and GHG emissions of the production chain are compared with those obtained from spatial optimization and summarized by the Pareto frontier. Projected production costs are 729 $/m 3 ethanol, with GHG emissions of 40 kg CO 2 -eq/m 3 ethanol. The Pareto frontier indicates an improvement potential of ∼50 $/m 3 ethanol when keeping emissions fixed, or ∼250 kg CO 2 -eq/m 3 ethanol when keeping costs fixed. Robust locations having low costs and emissions show where and how improvements are reached, offering instruments for policy (re)design. [ABSTRACT FROM AUTHOR]

Land use regression models are an established method for estimating spatial variability in gaseous pollutant levels across urban areas. Existing LUR models have been developed to predict annual average concentrations of airborne pollutants. None of those models have been developed to predict daily average concentrations, which are useful in health studies focused on the acute impacts of air pollution. In this study, we developed LUR models to predict daily NO 2 and NO x concentrations during 2009–2012 in the Brisbane Metropolitan Area (BMA), Australia's third-largest city. The final models explained 64% and 70% of spatial variability in NO 2 and NO x , respectively, with leave-one-out-cross-validation R 2 of 3–49% and 2–51%. Distance to major road and industrial area were the common predictor variables for both NO 2 and NO x , suggesting an important role for road traffic and industrial emissions. The novel modeling approach adopted here can be applied in other urban locations in epidemiological studies. [ABSTRACT FROM AUTHOR]

Land-use regression is a popular method for predicting ambient pollutant concentrations at points of interest where no measurements are taken. However, the model-building process is complicated, and systematically understanding when and how the process works is difficult. To overcome these limitations, we reformulate the existing land use regression method as a sign-constrained regression problem with an explicit objective function to be minimized. This novel formulation always leads to estimated regression coefficients that satisfy the predefined direction based on subject matter knowledge while simultaneously substantially improving the prediction performance of the existing land-use regression method. The advantages of the proposed sign-constrained regression method are confirmed through a numerical study and real data analysis. • The sign-constrained regularized regressions were added to land use regression (LUR) model. • The sign-constrained regression methods showed superior prediction performances than LUR method. • The sign-constrained regression models satisfied domain knowledge-based sign constraints. • The developed models could enhance the interpretability. [ABSTRACT FROM AUTHOR]

Planning in mining regions needs to accommodate the extraction of minerals/energy resources in co-existence with established land uses, such as agriculture and ecological conservation. Here, we first identify six critical aspects of planning in mining regions: i) the temporal nature of mining operations; ii) spatial dimensions of mining operations; iii) irreversible changes that create post-mining landscapes; iv) social dimensions of mining impacts and corporate responsibility; v) cumulative dimensions of impacts; and vi) a need to integrate methods from a range of disciplines. We then illustrate the potential to address these challenges using integrative modelling nested within a participatory approach to allow for clear, transparent, and stakeholder-inclusive decision-making. We describe a 5-step framework that supports a broadening of strategic assessments and offers mining companies forewarning about potential environmental and social conflicts. Case studies are needed to assess and refine the proposed framework and develop guidance for its use. [ABSTRACT FROM AUTHOR]

Wind erosion models are important tools for assessing soil erodibility and identifying management practices to control erosion. The Agricultural Policy/Environmental eXtender (APEX) model and Revised Wind Erosion Equation (RWEQ) were tested using data collected from the Tarim Basin of China and Columbia Plateau of the United States of America. Adequate performance in simulating soil loss was achieved using the original APEX model and RWEQ in respectively a cotton field and desert-oasis ecotone in the Tarim Basin and winter wheat - summer fallow (WW-SF) field in the Columbia Plateau. We calibrated the APEX model and RWEQ to improve performance because both models have many empirical parameters. After calibration, both models adequately simulated soil loss from all land use types except the RWEQ from the red date orchard in the Tarim Basin. Inadequate performance of the calibrated RWEQ in the red date orchard was likely due to underestimating maximum mass transport. [ABSTRACT FROM AUTHOR]

This research provides a new approach to assess groundwater vulnerability to contamination from anthropogenic activities and sea water intrusion. The DRASTIC and GALDIT parametric methods were then linked to a novel land use index to create a more robust “global risk index”, useful for assessing aquifer vulnerability to pollution and seawater intrusion risk. In addition, sensitivity analysis was used to evaluate the effect of each individual parameter on the final models. The vulnerability to pollution and the seawater intrusion contamination maps show three classes of water resources degradation: low, moderate and high, relating to the intrinsic properties. In addition, the global risk map shows three risk classes': low (25%), moderate (64%) and high (11%) depending on the hydrogeological characteristics, land use, distance from the coast and human impacts in most of the study area. The modified models were statistically compared with the nitrate concentration and the water resistivity values for validation. These maps are considered indispensable for sustainable land use planning and groundwater management of the shallow aquifer. [ABSTRACT FROM AUTHOR]

Recent reviews stated that the complex and context-dependent nature of human decision-making resulted in ad-hoc representations of human decision in agent-based land use change models (LUCC ABMs) and that these representations are often not explicitly grounded in theory. However, a systematic survey on the characteristics (e.g. uncertainty, adaptation, learning, interactions and heterogeneities of agents) of representing human decision-making in LUCC ABMs is missing. Therefore, the aim of this study is to inform this debate by reviewing 134 LUCC ABM papers. We show that most human decision sub-models are not explicitly based on a specific theory and if so they are mostly based on economic theories, such as the rational actor, and mainly ignoring other relevant disciplines. Consolidating and enlarging the theoretical basis for modelling human decision-making may be achieved by using a structural framework for modellers, re-using published decision models, learning from other disciplines and fostering collaboration with social scientists. [ABSTRACT FROM AUTHOR]

The increasing complexity of the dynamics captured in Land Use and Land Cover (LULC) change modelling has made model behaviour less transparent and calibration more extensive. For cellular automata models in particular, this is compounded by the fact that validation is typically performed indirectly, using final simulated change maps; rather than directly considering the probabilistic predictions of transition potential. This study demonstrates that evaluating transition potential predictions provides detail into model behaviour and performance that cannot be obtained from simulated map comparison alone. This is illustrated by modelling LULC transitions in Switzerland using both Logistic Regression and Random Forests. The results emphasize the need for LULC modellers to explicitly consider the performance of individual transition models independently to ensure robust predictions. Additionally, this study highlights the potential for predictor variable selection as a means to improve transition model generalizability and parsimony, which is beneficial for simulating future LULC change. • Individual evaluation of transition models provides insights into model behaviour. • Validating simulated output maps is inefficient for calibrating LULC change models. • Predictor variable selection can improve model generalizability and performance. [ABSTRACT FROM AUTHOR]

Although climate scientists explore the effects of climate change for 2100, it is a challenging time frame for urban modellers to foresee the future of cities. The question addressed in this paper is how to improve the existing methodologies in order to build scenarios to explore urban climate impacts in the long term and at a fine scale. This study provides a structural framework in six steps that combines narratives and model-based approaches. The results present seven scenarios of urban growth based on land use strategies and technological and socio-economic trends. These contrasted scenarios span the largest possible world of futures for the city under study. Urban maps for 2010, 2040 and 2100 were used to assess the impacts on the Urban Heat Island. The comparison of these scenarios and related outputs allowed some levers to be evaluated for their capacity to limit the increase of air temperature. [ABSTRACT FROM AUTHOR]

Management of freshwater quality requires modelling tools for rapid evaluation of land use and management scenarios. This paper presents the CLUES (Catchment Land Use and Environmental Sustainability) model to address this need. CLUES provides steady state, spatially distributed, integrated catchment models tightly coupled to GIS software to predict mean annual loads of total nitrogen, total phosphorus, sediments and E. coli , and concentration of nutrients throughout New Zealand (268,000 km 2 ) with a subcatchment resolution of 0.5 km 2 . CLUES also estimates potential nutrient concentrations for estuaries and provides key farm socio-economic indicators. The model includes a user interface for study area selection, scenario creation, data geo-visualisation, and export of results. It is pre-populated with spatial data and parameter values for New Zealand. Evaluation of the model and a summary of applications demonstrate the tractability and utility of national-scale rapid scenario assessment tools within a GIS framework. [ABSTRACT FROM AUTHOR]

Bioenergy policies affect both the environment and biomass availability for food, feed, and fiber on a national and international scale. To support policy makers, knowledge and methods from different scientific disciplines in the form of integrated assessments is necessary. Therefore we developed the MILESTONES framework which models the links between the national bioenergy system and the global land-use system as an integrated modeling approach. It builds on a set of three well-tested models (MAGNET, LandSHIFT and BENSIM). The prototype's functionality was demonstrated by assessing the environmental impacts of future German bioenergy strategies on a global level and along the entire biomass provision chain. The results from the case study show that, on the one hand, German bioenergy strategies have little effect on international market prices, but on the other hand land-use policies on an international level strongly influence the environmental performance of any German bioenergy strategy. [ABSTRACT FROM AUTHOR]

Scenarios are increasingly used for envisioning future social-ecological changes and consequences for human well-being. One approach integrates qualitative storylines and biophysical models to explore potential futures quantitatively and maximize public engagement. However, this integration process is challenging and sometimes oversimplified. Using the Yahara Watershed (Wisconsin, USA) as a case study, we present a transparent and reproducible roadmap to develop spatiotemporally explicit biophysical inputs [climate, land use/cover (LULC), and nutrients] that are consistent with scenario narratives and can be linked to a process-based biophysical modeling suite to simulate long-term dynamics of a watershed and a range of ecosystem services. Our transferrable approach produces daily weather inputs by combining climate model projections and a stochastic weather generator, annual narrative-based watershed-scale LULC distributed spatially using transition rules, and annual manure and fertilizer (nitrogen and phosphorus) inputs based on current farm and livestock data that are consistent with each scenario narrative. [ABSTRACT FROM AUTHOR]

Understanding potential impacts of climate change is complicated by spatially mismatched land representations between gridded datasets and models, and land use models with larger regions defined by geopolitical and/or biophysical criteria. Here we quantify the sensitivity of Global Change Assessment Model (GCAM) outputs to the delineation of Agro-Ecological Zones (AEZs), which are normally based on historical (1961–1990) climate. We reconstruct GCAM's land regions using projected (2071–2100) climate, and find large differences in estimated future land use that correspond with differences in agricultural commodity prices and production volumes. Importantly, historically delineated AEZs experience spatially heterogeneous climate impacts over time, and do not necessarily provide more homogenous initial land productivity than projected AEZs. We conclude that non-climatic criteria for land use region delineation are likely preferable for modeling land use change in the context of climate change, and that uncertainty associated with land delineation needs to be quantified. [ABSTRACT FROM AUTHOR]

Land change modelers often create future maps using reference land use map. However, future land use maps may mislead decision-makers, who are often unaware of the sensitivity and the uncertainty in land use maps due to error in data. Since most metrics that communicate uncertainty require using reference land use data to calculate accuracy, the assessment of uncertainty becomes challenging when no reference land use map for future is available. This study aims to develop a new conceptual framework for sensitivity analysis and uncertainty assessment (FSAUA) which compares multiple maps under various data error scenarios. FSAUA performs sensitivity analyses in land use maps using a reference map and assess uncertainty in predicted maps. FSAUA was applied using three well-known land change models (ANN, CART and MARS) in Delhi, India. FSAUA was found to be a practical tool for communicating the uncertainty with end-users who develop reliable planning decisions. [ABSTRACT FROM AUTHOR]

European mountain landscapes are experiencing massive land-use changes and high rates of natural reforestation since the 1950s. Summer pastures are particularly sensitive to those dynamics since their natural state depends on human activities. A process-based model - SHIELD (Simulating Herd Impact on Encroachment in upLanD) – has been developed to identify the key leverages of pastoral practices to limit the natural reforestation in the Haut-Vicdessos (Pyrenees Mountains, France). Simulations are compared to observed land cover changes to validate the model's structure. Scenarios are simulated to assess various grazing practices on reforestation dynamics: a baseline scenario and three scenarios with contrasted pastoral management: (i) reintroducing herds with no human supervision, (ii) reintroducing herds supervised by a shepherd and (iii) increasing the cattle load without supervision. Results show that supervising the intensity of land units' occupancy can be as efficient as increasing the cattle load to limit the ongoing trends of reforestation. [ABSTRACT FROM AUTHOR]

Governments and financial institutions increasingly require that environmental impact assessment and mitigation account for consequences to both biodiversity and ecosystem services. Here we present a new software tool, OPAL (Offset Portfolio Analyzer and Locator), which maps and quantifies the impacts of development on habitat and ecosystem services, and facilitates the selection of mitigation activities to offset losses. We demonstrate its application with an oil and gas extraction facility in Colombia. OPAL is the first tool to provide direct consideration of the distribution of ecosystem service benefits among people in a mitigation context. Previous biodiversity-focused efforts led to redistribution or loss of ecosystem services with environmental justice implications. Joint consideration of biodiversity and ecosystem services enables targeting of offsets to benefit both nature and society. OPAL reduces the time and technical expertise required for these analyses and has the flexibility to be used across a range of geographic and policy contexts. [ABSTRACT FROM AUTHOR]

The ELUM Software Package spatially predicts the net soil greenhouse gas balance of land-use change to grow energy crops in the UK up to 2050. It is able to support a range of analyses of bioenergy, and was developed in consultation with anticipated users. Results can be obtained according to specific interests, viewed in the graphical interface and exported for a variety of purposes. The functionality of the software is demonstrated through different case studies, which show an array of applications. [ABSTRACT FROM AUTHOR]

Land use changes have a pronounced impact on hydrology. Vice versa, hydrologic changes affect land use patterns. The objective of this study is to test whether hydrologic variables can explain land use change. We employ a set of spatially distributed hydrologic variables and compare it against a set of commonly used explanatory variables for land use change. The explanatory power of these variables is assessed by using a logistic regression approach to model the spatial distribution of land use changes in a meso-scale Indian catchment. When hydrologic variables are additionally included, the accuracies of the logistic regression models improve, which is indicated by a change in the relative operating characteristic statistic (ROC) by up to 11%. This is mostly due to the complementarity of the two datasets that is reflected in the use of 44% commonly used variables and 56% hydrologic variables in the best models for land use change. [ABSTRACT FROM AUTHOR]

To support the implementation of the European Water Framework Directive (WFD), and as part of a tiered approach to prioritise detailed modelling, a high-level screening methodology has been developed to assess the vulnerability of water-related ecosystem services (ES) to future change. The approach incorporates a range of spatially distributed scenarios of land use and climate, which are used as inputs to a qualitative risk assessment model underpinned by expert opinion. The method makes use of widely available datasets and provides a structured way of capturing and “codifying” expert knowledge, as well as for assessing the degree of consensus between different expert groups. The range of model output reflects uncertainty in both the expert-derived assumptions and the climate & land use simulations considered. The approach has been developed in collaboration with the Scottish Environment Protection Agency (SEPA) and applied in Scotland to support the second cycle of River Basin Management Planning. [ABSTRACT FROM AUTHOR]

Several modeling strategies have been proposed to study Land Use and Land Cover changes (LUCC). However, substantial discrepancies have been noted between different models for the same problem, questioning their overall reliability and reproducibility. To address this challenge, we elaborate a generic, formally correct, theoretical framework for pattern-based LUCC modeling, which is implemented in our own software, CLUMPY (Comprehensive Land Use [and cover] Modeling in PYthon). The present work focuses on calibration. We devise a kernel density calibration–estimation method (Bayes-eKDE) that is shown on synthetic artificial data to be both accurate and algorithmically efficient. We also introduce a generic evaluation method that allows us to compare the calibration efficiency of existing models. The gain in precision and computational time of our calibration method is precisely quantified in this way. • Accurate calibration–estimation methods improve LUCC models reliability. • A method based on Kernel Density Estimation has been developed (Bayes-eKDE). • Our method is substantially more accurate and efficient compared to existing ones. • The method is implemented in our own software CLUMPY. • Validation must be performed in explanatory variable space, not from map comparison. [ABSTRACT FROM AUTHOR]