Articles | Volume 8, issue 10
Model description paper 09 Oct 2015
Model description paper | 09 Oct 2015
An open and extensible framework for spatially explicit land use change modelling: the lulcc R package
S. Moulds et al.
No articles found.
Veerle Vanacker, Armando Molina, Miluska Rosas-Barturen, Vivien Bonnesoeur, Francisco Román-Dañobeytia, Boris Ochoa-Tocachi, and Wouter Buytaert
Preprint under review for SOILShort summary
The Andes region is prone to natural hazards due to its steep topography and climatic variability. Anthropogenic activities further exacerbate environmental hazards and risks. This systematic review synthesizes our knowledge on the effectiveness of nature-based solutions. Conservation of natural vegetation and implementation of soil and water conservation measures had significant and positive effects on soil organic carbon and erosion mitigation.
Paul C. Astagneau, Guillaume Thirel, Olivier Delaigue, Joseph H. A. Guillaume, Juraj Parajka, Claudia C. Brauer, Alberto Viglione, Wouter Buytaert, and Keith J. Beven
Hydrol. Earth Syst. Sci., 25, 3937–3973,Short summary
The R programming language has become an important tool for many applications in hydrology. In this study, we provide an analysis of some of the R tools providing hydrological models. In total, two aspects are uniformly investigated, namely the conceptualisation of the models and the practicality of their implementation for end-users. These comparisons aim at easing the choice of R tools for users and at improving their usability for hydrology modelling to support more transferable research.
Anoop Kumar Shukla, Shray Pathak, Lalit Pal, Chandra Shekhar Prasad Ojha, Ana Mijic, and Rahul Dev Garg
Hydrol. Earth Syst. Sci., 22, 5357–5371,Short summary
In this study, we carried out a comparative evaluation of water yield using two approaches, the Lumped Zhang model and the pixel-based approach. Even in pixel-level computations, experiments are made with existing models of some of the involved parameters. The study indicates not only the suitability of pixel-based computations but also clarifies the suitable model of some of the parameters to be used with pixel-based computations to obtain better results.
Anoop Kumar Shukla, Chandra Shekhar Prasad Ojha, Ana Mijic, Wouter Buytaert, Shray Pathak, Rahul Dev Garg, and Satyavati Shukla
Hydrol. Earth Syst. Sci., 22, 4745–4770,Short summary
Geospatial technologies and OIP are promising tools to study the effect of demographic changes and LULC transformations on the spatiotemporal variations in the water quality (WQ) across a large river basin. Therefore, this study could help to assess and solve local and regional WQ-related problems over a river basin. It may help the policy makers and planners to understand the status of water pollution so that suitable strategies could be planned for sustainable development in a river basin.
Gina Tsarouchi and Wouter Buytaert
Hydrol. Earth Syst. Sci., 22, 1411–1435,Short summary
This work quantifies how future land-use and climate change may affect the hydrology of the Upper Ganges basin. Three sets of modelling experiments are run for the period 2000–2035, considering (1) only climate change, (2) only land-use change and (3) both climate and land-use change. Results point towards a severe increase in high flows. The changes are greater in the combined land-use and climate change experiment. We also show that future winter water demands in the region may not be met.
Feng Mao, Julian Clark, Timothy Karpouzoglou, Art Dewulf, Wouter Buytaert, and David Hannah
Hydrol. Earth Syst. Sci., 21, 3655–3670,Short summary
The paper aims to propose a conceptual framework that supports nuanced understanding and analytical assessment of resilience in socio-hydrological contexts. We identify three framings of resilience for different human–water couplings, which have distinct application fields and are used for different water management challenges. To assess and improve socio-hydrological resilience in each type, we introduce a
resilience canvasas a heuristic tool to design bespoke management strategies.
Himanshu Arora, Chandra Shekhar Prasad Ojha, Wouter Buytaert, Gujjunadu Suryaprakash Kaushika, and Chetan Sharma
Hydrol. Earth Syst. Sci. Discuss.,
Revised manuscript has not been submittedShort summary
In many agrarian countries (like India), the agricultural practices are usually rainfall dependent. Therefore keeping the water budget into account, precipitation being an important component must be analysed thoroughly for its occurrence and amount. The analysis of trends can provide an insight in understanding the possible impacts in future, which can assist living beings to adapt and cope up with changing climate and hydrological cycle.
Jimmy O'Keeffe, Wouter Buytaert, Ana Mijic, Nicholas Brozović, and Rajiv Sinha
Hydrol. Earth Syst. Sci., 20, 1911–1924,Short summary
Semi-structured interviews provide an effective and efficient way of collecting qualitative and quantitative data on water use practices. Interviews are organised around a topic guide, which helps lead the conversation while allowing sufficient opportunity to identify issues previously unknown to the researcher. The use of semi-structured interviews could significantly and quickly improve insight on water resources, leading to more realistic future management options and increased water security.
Susana Almeida, Nataliya Le Vine, Neil McIntyre, Thorsten Wagener, and Wouter Buytaert
Hydrol. Earth Syst. Sci., 20, 887–901,Short summary
The absence of flow data to calibrate hydrologic models may reduce the ability of such models to reliably inform water resources management. To address this limitation, it is common to condition hydrological model parameters on regionalized signatures. In this study, we justify the inclusion of larger sets of signatures in the regionalization procedure if their error correlations are formally accounted for and thus enable a more complete use of all available information.
P. Blair and W. Buytaert
Hydrol. Earth Syst. Sci., 20, 443–478,Short summary
This paper reviews literature surrounding many aspects of socio-hydrological modelling; this includes a background to the subject of socio-hydrology, reasons why socio-hydrological modelling would be used, what is to be modelled in socio-hydrology and concepts that underpin this, as well as several modelling techniques and how they may be applied in socio-hydrology.
G. M. Tsarouchi, W. Buytaert, and A. Mijic
Hydrol. Earth Syst. Sci., 18, 4223–4238,
H. M. Holländer, H. Bormann, T. Blume, W. Buytaert, G. B. Chirico, J.-F. Exbrayat, D. Gustafsson, H. Hölzel, T. Krauße, P. Kraft, S. Stoll, G. Blöschl, and H. Flühler
Hydrol. Earth Syst. Sci., 18, 2065–2085,
Z. Zulkafli, W. Buytaert, C. Onof, W. Lavado, and J. L. Guyot
Hydrol. Earth Syst. Sci., 17, 1113–1132,
Related subject area
Earth and space science informaticsA spatially explicit approach to simulate urban heat mitigation with InVEST (v3.8.0)S-SOM v1.0: a structural self-organizing map algorithm for weather typingUsing Shapley additive explanations to interpret extreme gradient boosting predictions of grassland degradation in Xilingol, ChinaCurrent status on the need for improved accessibility to climate models codeAutomated geological map deconstruction for 3D model constructionClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weatherCopula-based synthetic data generation for machine learning emulators in weather and climate: application to a simple radiation modelA spatiotemporal weighted regression model (STWR v1.0) for analyzing local nonstationarity in space and timeA new end-to-end workflow for the Community Earth System Model (version 2.0) for the Coupled Model Intercomparison Project Phase 6 (CMIP6)HyLands 1.0: a hybrid landscape evolution model to simulate the impact of landslides and landslide-derived sediment on landscape evolutionComparative analysis of atmospheric radiative transfer models using the Atmospheric Look-up table Generator (ALG) toolbox (version 2.0)Fast domain-aware neural network emulation of a planetary boundary layer parameterization in a numerical weather forecast modelVISIR-1.b: ocean surface gravity waves and currents for energy-efficient navigationTopological data analysis and machine learning for recognizing atmospheric river patterns in large climate datasetsGlobal hydro-climatic biomes identified via multitask learningA run control framework to streamline profiling, porting, and tuning simulation runs and provenance tracking of geoscientific applicationsAn improved logistic regression model based on a spatially weighted technique (ILRBSWT v1.0) and its application to mineral prospectivity mappingHigh-performance software framework for the calculation of satellite-to-satellite data matchups (MMS version 1.2)A data model of the Climate and Forecast metadata conventions (CF-1.6) with a software implementation (cf-python v2.1)Reverse engineering model structures for soil and ecosystem respiration: the potential of gene expression programmingA high-fidelity multiresolution digital elevation model for Earth systemsCPMIP: measurements of real computational performance of Earth system models in CMIP6Automatic delineation of geomorphological slope units with r.slopeunits v1.0 and their optimization for landslide susceptibility modelingCommunity Intercomparison Suite (CIS) v1.4.0: a tool for intercomparing models and observationsAsynchronous communication in spectral-element and discontinuous Galerkin methods for atmospheric dynamics – a case study using the High-Order Methods Modeling Environment (HOMME-homme_dg_branch)GO2OGS 1.0: a versatile workflow to integrate complex geological information with fault data into numerical simulation modelsPlant functional type classification for earth system models: results from the European Space Agency's Land Cover Climate Change InitiativeNon-singular spherical harmonic expressions of geomagnetic vector and gradient tensor fields in the local north-oriented reference frameAn approach to enhance pnetCDF performance in environmental modeling applicationsA strategy for GIS-based 3-D slope stability modelling over large areasAn approach to computing direction relations between separated object groupsImproving computational efficiency in large linear inverse problems: an example from carbon dioxide flux estimationCoupling technologies for Earth System ModellingQuality assessment concept of the World Data Center for Climate and its application to CMIP5 dataA web service based tool to plan atmospheric research flightsAutomated continuous verification for numerical simulation
Martí Bosch, Maxence Locatelli, Perrine Hamel, Roy P. Remme, Jérôme Chenal, and Stéphane Joost
Geosci. Model Dev., 14, 3521–3537,Short summary
The article presents a novel approach to simulate urban heat mitigation from land use/land cover data based on three biophysical mechanisms: tree shade, evapotranspiration and albedo. An automated procedure is proposed to calibrate the model parameters to best fit temperature observations from monitoring stations. A case study in Lausanne, Switzerland, shows that the approach outperforms regressions based on satellite data and provides valuable insights into design heat mitigation policies.
Quang-Van Doan, Hiroyuki Kusaka, Takuto Sato, and Fei Chen
Geosci. Model Dev., 14, 2097–2111,Short summary
This study proposes a novel structural self-organizing map (S-SOM) algorithm. The superiority of S-SOM is that it can better recognize the difference (or similarity) among spatial (or temporal) data used for training and thus improve the clustering quality compared to traditional SOM algorithms.
Batunacun, Ralf Wieland, Tobia Lakes, and Claas Nendel
Geosci. Model Dev., 14, 1493–1510,Short summary
Extreme gradient boosting (XGBoost) can provide alternative insights that conventional land-use models are unable to generate. Shapley additive explanations (SHAP) can interpret the results of the purely data-driven approach. XGBoost achieved similar and robust simulation results. SHAP values were useful for analysing the complex relationship between the different drivers of grassland degradation.
Juan A. Añel, Michael García-Rodríguez, and Javier Rodeiro
Geosci. Model Dev., 14, 923–934,Short summary
This work shows that it continues to be hard, if not impossible, to obtain some of the most used climate models worldwide. We reach this conclusion through a systematic study and encourage all development teams and research centres to make public the models they use to produce scientific results.
Mark Jessell, Vitaliy Ogarko, Mark Lindsay, Ranee Joshi, Agnieszka Piechocka, Lachlan Grose, Miguel de la Varga, Laurent Ailleres, and Guillaume Pirot
Geosci. Model Dev. Discuss.,
Revised manuscript accepted for GMDShort summary
We have developed software that allows the user to extract sufficient information from unmodified digital maps and associated datasets that we are able to automatically build 3D geological models. By automating the precess we are able to remove human bias from the procedure, which makes the workflow reproducible.
Prabhat, Karthik Kashinath, Mayur Mudigonda, Sol Kim, Lukas Kapp-Schwoerer, Andre Graubner, Ege Karaismailoglu, Leo von Kleist, Thorsten Kurth, Annette Greiner, Ankur Mahesh, Kevin Yang, Colby Lewis, Jiayi Chen, Andrew Lou, Sathyavat Chandran, Ben Toms, Will Chapman, Katherine Dagon, Christine A. Shields, Travis O'Brien, Michael Wehner, and William Collins
Geosci. Model Dev., 14, 107–124,Short summary
Detecting extreme weather events is a crucial step in understanding how they change due to climate change. Deep learning (DL) is remarkable at pattern recognition; however, it works best only when labeled datasets are available. We create
ClimateNet– an expert-labeled curated dataset – to train a DL model for detecting weather events and predicting changes in extreme precipitation. This work paves the way for DL-based automated, high-fidelity, and highly precise analytics of climate data.
David Meyer, Thomas Nagler, and Robin J. Hogan
Geosci. Model Dev. Discuss.,
Revised manuscript accepted for GMD
Xiang Que, Xiaogang Ma, Chao Ma, and Qiyu Chen
Geosci. Model Dev., 13, 6149–6164,Short summary
This paper presents a spatiotemporal weighted regression (STWR) model for exploring nonstationary spatiotemporal processes in nature and socioeconomics. A value change rate is introduced in the temporal kernel, which presents significant model fitting and accuracy in both simulated and real-world data. STWR fully incorporates observed data in the past and outperforms geographic temporal weighted regression (GTWR) and geographic weighted regression (GWR) models in several experiments.
Sheri Mickelson, Alice Bertini, Gary Strand, Kevin Paul, Eric Nienhouse, John Dennis, and Mariana Vertenstein
Geosci. Model Dev., 13, 5567–5581,Short summary
Every generation of MIP exercises introduces new layers of complexity and an exponential growth in the amount of data requested. CMIP6 required us to develop a new tool chain and forced us to change our methodologies. The new methods discussed in this paper provided us with an 18 times faster speedup over our existing methods. This allowed us to meet our deadlines and we were able to publish more than half a million data sets on the Earth System Grid Federation (ESGF) for the CMIP6 project.
Benjamin Campforts, Charles M. Shobe, Philippe Steer, Matthias Vanmaercke, Dimitri Lague, and Jean Braun
Geosci. Model Dev., 13, 3863–3886,Short summary
Landslides shape the Earth’s surface and are a dominant source of terrestrial sediment. Rivers, then, act as conveyor belts evacuating landslide-produced sediment. Understanding the interaction among rivers and landslides is important to predict the Earth’s surface response to past and future environmental changes and for mitigating natural hazards. We develop HyLands, a new numerical model that provides a toolbox to explore how landslides and rivers interact over several timescales.
Jorge Vicent, Jochem Verrelst, Neus Sabater, Luis Alonso, Juan Pablo Rivera-Caicedo, Luca Martino, Jordi Muñoz-Marí, and José Moreno
Geosci. Model Dev., 13, 1945–1957,Short summary
The modeling of light propagation through the atmosphere is key to process satellite images and to understand atmospheric processes. However, existing atmospheric models can be complex to use in practical applications. Here we aim at providing a new software tool to facilitate using advanced models and to generate large databases of simulated data. As a test case, we use this tool to analyze differences between several atmospheric models, showing the capabilities of this open-source tool.
Jiali Wang, Prasanna Balaprakash, and Rao Kotamarthi
Geosci. Model Dev., 12, 4261–4274,Short summary
Parameterizations are frequently used in models representing physical phenomena and are often the computationally expensive portions of the code. Using model output from simulations performed using a weather model, we train deep neural networks to provide an accurate alternative to a physics-based parameterization. We demonstrate that a domain-aware deep neural network can successfully simulate the entire diurnal cycle of the boundary layer physics and the results are transferable.
Gianandrea Mannarini and Lorenzo Carelli
Geosci. Model Dev., 12, 3449–3480,Short summary
The VISIR ship-routing model is updated in order to deal with ocean currents. The optimal tracks we computed through VISIR in the Atlantic ocean show great seasonal and regional variability, following a variable influence of surface gravity waves and currents. We assess how these tracks contribute to voyage energy-efficiency gains through a standard indicator (EEOI) of the International Maritime Organization. Also, the new model features are validated against an exact analytical benchmark.
Grzegorz Muszynski, Karthik Kashinath, Vitaliy Kurlin, Michael Wehner, and Prabhat
Geosci. Model Dev., 12, 613–628,Short summary
We present the automated method for recognizing atmospheric rivers in climate data, i.e., climate model output and reanalysis product. The method is based on topological data analysis and machine learning, both of which are powerful tools that the climate science community often does not use. An advantage of the proposed method is that it is free of selection of subjective threshold conditions on a physical variable. This method is also suitable for rapidly analyzing large amounts of data.
Christina Papagiannopoulou, Diego G. Miralles, Matthias Demuzere, Niko E. C. Verhoest, and Willem Waegeman
Geosci. Model Dev., 11, 4139–4153,Short summary
Common global land cover and climate classifications are based on vegetation–climatic characteristics derived from observational data, ignoring the interaction between the local climate and biome. Here, we model the interplay between vegetation and local climate by discovering spatial relationships among different locations. The resulting global
hydro-climatic biomescorrespond to regions of coherent climate–vegetation interactions that agree well with traditional global land cover maps.
Wendy Sharples, Ilya Zhukov, Markus Geimer, Klaus Goergen, Sebastian Luehrs, Thomas Breuer, Bibi Naz, Ketan Kulkarni, Slavko Brdar, and Stefan Kollet
Geosci. Model Dev., 11, 2875–2895,Short summary
Next-generation geoscientific models are based on complex model implementations and workflows. Next-generation HPC systems require new programming paradigms and code optimization. In order to meet the challenge of running complex simulations on new massively parallel HPC systems, we developed a run control framework that facilitates code portability, code profiling, and provenance tracking to reduce both the duration and the cost of code migration and development, while ensuring reproducibility.
Daojun Zhang, Na Ren, and Xianhui Hou
Geosci. Model Dev., 11, 2525–2539,Short summary
Geographically weighted regression is a widely used method to deal with spatial heterogeneity, which is common in geostatistics. However, most existing software does not support logistic regression and cannot deal with missing data, which exist extensively in mineral prospectivity mapping. This work generalized logistic regression to spatial statistics based on a spatially weighted technique. The new model also supports an anisotropic local window, which is another innovative point.
Thomas Block, Sabine Embacher, Christopher J. Merchant, and Craig Donlon
Geosci. Model Dev., 11, 2419–2427,Short summary
For calibration and validation purposes it is necessary to detect simultaneous data acquisitions from different spaceborne platforms. We present an algorithm and a software system which implements a general approach to resolve this problem. The multisensor matchup system (MMS) can detect simultaneous acquisitions in a large dataset (> 100 TB) and extract data for matching locations for further analysis. The MMS implements a flexible software infrastructure and allows for high parallelization.
David Hassell, Jonathan Gregory, Jon Blower, Bryan N. Lawrence, and Karl E. Taylor
Geosci. Model Dev., 10, 4619–4646,Short summary
We present a formal data model for version 1.6 of the CF (Climate and Forecast) metadata conventions that provide a description of the physical meaning of geoscientific data and their spatial and temporal properties. We describe the CF conventions and how they lead to our CF data model, and compare it other data models for storing data and metadata. We present cf-python version 2.1: a software implementation of the CF data model capable of manipulating any CF-compliant dataset.
Iulia Ilie, Peter Dittrich, Nuno Carvalhais, Martin Jung, Andreas Heinemeyer, Mirco Migliavacca, James I. L. Morison, Sebastian Sippel, Jens-Arne Subke, Matthew Wilkinson, and Miguel D. Mahecha
Geosci. Model Dev., 10, 3519–3545,Short summary
Accurate representation of land-atmosphere carbon fluxes is essential for future climate projections, although some of the responses of CO2 fluxes to climate often remain uncertain. The increase in available data allows for new approaches in their modelling. We automatically developed models for ecosystem and soil carbon respiration using a machine learning approach. When compared with established respiration models, we found that they are better in prediction as well as offering new insights.
Xinqiao Duan, Lin Li, Haihong Zhu, and Shen Ying
Geosci. Model Dev., 10, 239–253,Short summary
This article proposes an optimized transformation for topographic datasets. The resulting topographic grid exhibits good surface approximation and quasi-uniform high-quality. Both features of the processed topography build a concrete base from which improved endogenous or exogenous parameters can be derived, and makes it suitable for Earth and environmental simulations.
Venkatramani Balaji, Eric Maisonnave, Niki Zadeh, Bryan N. Lawrence, Joachim Biercamp, Uwe Fladrich, Giovanni Aloisio, Rusty Benson, Arnaud Caubel, Jeffrey Durachta, Marie-Alice Foujols, Grenville Lister, Silvia Mocavero, Seth Underwood, and Garrett Wright
Geosci. Model Dev., 10, 19–34,Short summary
Climate models are among the most computationally expensive scientific applications in the world. We present a set of measures of computational performance that can be used to compare models that are independent of underlying hardware and the model formulation. They are easy to collect and reflect performance actually achieved in practice. We are preparing a systematic effort to collect these metrics for the world's climate models during CMIP6, the next Climate Model Intercomparison Project.
Massimiliano Alvioli, Ivan Marchesini, Paola Reichenbach, Mauro Rossi, Francesca Ardizzone, Federica Fiorucci, and Fausto Guzzetti
Geosci. Model Dev., 9, 3975–3991,Short summary
Slope units are morphological mapping units bounded by drainage and divide lines that maximize within-unit homogeneity and between-unit heterogeneity. We use r.slopeunits, a software for the automatic delination of slope units. We outline an objective procedure to optimize the software input parameters for landslide susceptibility (LS) zonation. Optimization is achieved by maximizing an objective function that simultaneously evaluates terrain aspect segmentation quality and LS model performance.
Duncan Watson-Parris, Nick Schutgens, Nicholas Cook, Zak Kipling, Philip Kershaw, Edward Gryspeerdt, Bryan Lawrence, and Philip Stier
Geosci. Model Dev., 9, 3093–3110,Short summary
In this paper we describe CIS, a new command line tool for the easy visualization, analysis and comparison of a wide variety of gridded and ungridded data sets used in Earth sciences. Users can now use a single tool to not only view plots of satellite, aircraft, station or model data, but also bring them onto the same spatio-temporal sampling. This allows robust, quantitative comparisons to be made easily. CIS is an open-source project and welcomes input from the community.
Benjamin F. Jamroz and Robert Klöfkorn
Geosci. Model Dev., 9, 2881–2892,Short summary
The scalability of computational applications on current and next-generation supercomputers is increasingly limited by the cost of inter-process communication. We implement communication hiding data exchange in the High-Order Methods Modeling Environment (HOMME) for the time integration of the hydrostatic fluid equations using both the spectral-element and discontinuous Galerkin methods. The presented approach produces significant performance and scalability gains in large-scale simulations.
T. Fischer, D. Naumov, S. Sattler, O. Kolditz, and M. Walther
Geosci. Model Dev., 8, 3681–3694,Short summary
We present a workflow to convert geological models into the open-source VTU format for usage in numerical simulation models. Tackling relevant scientific questions or engineering tasks often involves multidisciplinary approaches. Conversion workflows are needed between the diverse tools of the various disciplines. Our approach offers an open-source, platform-independent, robust, and comprehensible method that is potentially useful for a multitude of similar environmental studies.
B. Poulter, N. MacBean, A. Hartley, I. Khlystova, O. Arino, R. Betts, S. Bontemps, M. Boettcher, C. Brockmann, P. Defourny, S. Hagemann, M. Herold, G. Kirches, C. Lamarche, D. Lederer, C. Ottlé, M. Peters, and P. Peylin
Geosci. Model Dev., 8, 2315–2328,Short summary
Land cover is an essential variable in earth system models and determines conditions driving biogeochemical, energy and water exchange between ecosystems and the atmosphere. A methodology is presented for mapping plant functional types used in global vegetation models from a updated land cover classification system and open-source conversion tool, resulting from a consultative process among map producers and modelers engaged in the European Space Agency’s Land Cover Climate Change Initiative.
J. Du, C. Chen, V. Lesur, and L. Wang
Geosci. Model Dev., 8, 1979–1990,
D. C. Wong, C. E. Yang, J. S. Fu, K. Wong, and Y. Gao
Geosci. Model Dev., 8, 1033–1046,
M. Mergili, I. Marchesini, M. Alvioli, M. Metz, B. Schneider-Muntau, M. Rossi, and F. Guzzetti
Geosci. Model Dev., 7, 2969–2982,Short summary
The article deals with strategies to (i) reduce computation time and to (ii) appropriately account for uncertain input parameters when applying an open source GIS sliding surface model to estimate landslide susceptibility for a 90km² study area in central Italy. For (i), the area is split into a large number of tiles, enabling the exploitation of multi-processor computing environments. For (ii), the model is run with various parameter combinations to compute the slope failure probability.
H. Yan, Z. Wang, and J. Li
Geosci. Model Dev., 6, 1591–1599,
V. Yadav and A. M. Michalak
Geosci. Model Dev., 6, 583–590,
S. Valcke, V. Balaji, A. Craig, C. DeLuca, R. Dunlap, R. W. Ford, R. Jacob, J. Larson, R. O'Kuinghttons, G. D. Riley, and M. Vertenstein
Geosci. Model Dev., 5, 1589–1596,
M. Stockhause, H. Höck, F. Toussaint, and M. Lautenschlager
Geosci. Model Dev., 5, 1023–1032,
M. Rautenhaus, G. Bauer, and A. Dörnbrack
Geosci. Model Dev., 5, 55–71,
P. E. Farrell, M. D. Piggott, G. J. Gorman, D. A. Ham, C. R. Wilson, and T. M. Bond
Geosci. Model Dev., 4, 435–449,
Aldwaik, S. Z. and Pontius, R. G.: Intensity analysis to unify measurements of size and stationarity of land changes by interval, category, and transition, Landscape Urban Plan., 106, 103–114, https://doi.org/10.1016/j.landurbplan.2012.02.010, 2012.
Beale, C. M., Lennon, J. J., Yearsley, J. M., Brewer, M. J., and Elston, D. A.: Regression analysis of spatial data, Ecol. Lett., 13, 246–264, https://doi.org/10.1111/j.1461-0248.2009.01422.x, 2010.
Bivand, R. S., Pebesma, E., and Gomez-Rubio, V.: Applied Spatial Data Analysis with R, 2nd Edn., Springer, NY, available at: http://www.asdar-book.org/ (last access: 28 August 2015), 2013.
Cai, Y., Judd, K. L., and Lontzek, T. S.: Open science is necessary, Nature Climate Change, 2, 299–299, 2012.
Câmara, G., Vinhas, L., Ferreira, K. R., De Queiroz, G. R., De Souza, R. C. M., Monteiro, A. M. V., De Carvalho, M. T., Casanova, M. A., and De Freitas, U. M.: TerraLib: an open source GIS library for large-scale environmental and socio-economic applications, in: Open Source Approaches in Spatial Data Handling, 247–270, Springer, 2008.
Carneiro, T. G. d. S., Andrade, P. R. d., Câmara, G., Monteiro, A. M. V., and Pereira, R. R.: An extensible toolbox for modeling nature–society interactions, Environ. Modell. Softw., 46, 104–117, https://doi.org/10.1016/j.envsoft.2013.03.002, 2013.
Castella, J. and Verburg, P. H.: Combination of process-oriented and pattern-oriented models of land-use change in a mountain area of Vietnam, Ecol. Model., 202, 410–420, https://doi.org/10.1016/j.ecolmodel.2006.11.011, 2007.
Chambers, J. M.: Programming with Data: a Guide to the S Language, Springer, New York, USA, 1998.
Chambers, J. M.: Users, programmers, and statistical software, J. Comput. Graph. Stat., 9, 404–422, https://doi.org/10.1080/10618600.2000.10474890, 2000.
Chambers, J. M.: Software for Data Analysis: Programming with R, Springer, New York, USA, 2008.
Claes, M., Mens, T., and Grosjean, P.: On the maintainability of CRAN packages, in: 2014 Software Evolution Week – IEEE Conference on Software Maintenance, Reengineering and Reverse Engineering (CSMR-WCRE), Antwerp, 3–6 February, 308–312, IEEE, available at: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6747183 (last access: 16 April 2015), 2014.
Echeverria, C., Coomes, D. A., Hall, M., and Newton, A. C.: Spatially explicit models to analyze forest loss and fragmentation between 1976 and 2020 in southern Chile, Ecol. Model., 212, 439–449, https://doi.org/10.1016/j.ecolmodel.2007.10.045, 2008.
Fiske, I. and Chandler, R.: unmarked: an R package for fitting hierarchical models of wildlife occurrence and abundance, J. Stat. Softw., 43, 1–23, 2011.
Fuchs, R., Herold, M., Verburg, P. H., and Clevers, J. G. P. W.: A high-resolution and harmonized model approach for reconstructing and analysing historic land changes in Europe, Biogeosciences, 10, 1543–1559, https://doi.org/10.5194/bg-10-1543-2013, 2013.
Fuchs, R., Herold, M., Verburg, P. H., Clevers, J. G., and Eberle, J.: Gross changes in reconstructions of historic land cover/use for Europe between 1900 and 2010, Glob. Change Biol., 21, 299–313, https://doi.org/10.1111/gcb.12714, 2015.
Gebbert, S. and Pebesma, E.: A temporal GIS for field based environmental modeling, Environ. Modell. Softw., 53, 1–12, https://doi.org/10.1016/j.envsoft.2013.11.001, 2014.
Hewitt, R., Díaz Pacheco, J., and Moya Gómez, B.: A cellular automata land use model for the R software environment, available at: http://simlander.wordpress.com/ (last access: 11 January 2015), 2013.
Hijmans, R. J.: raster: Geographic data analysis and modeling, available at: http://CRAN.R-project.org/package=raster (last access: 16 April 2015), r package version 2.2-31, 2014.
Ince, D. C., Hatton, L., and Graham-Cumming, J.: The case for open computer programs, Nature, 482, 485–488, https://doi.org/10.1038/nature10836, 2012.
Joppa, L. N., McInerny, G., Harper, R., Salido, L., Takeda, K., O'Hara, K., Gavaghan, D., and Emmott, S.: Troubling trends in scientific software use, Science, 340, 814–815, 2013.
Knutti, R. and Sedláček, J.: Robustness and uncertainties in the new CMIP5 climate model projections, Nature Climate Change, 3, 369–373, https://doi.org/10.1038/nclimate1716, 2012.
Liaw, A. and Wiener, M.: Classification and Regression by randomForest, R news, 2, 18–22, available at: ftp://126.96.36.199/Transfer/Treg/WFRE_Articles/Liaw_02_Classification and regression by randomForest.pdf (last access: 16 April 2015), 2002.
Mas, J., Kolb, M., Paegelow, M., Camacho Olmedo, M. T., and Houet, T.: Inductive pattern-based land use/cover change models: a comparison of four software packages, Environ. Modell. Softw., 51, 94–111, https://doi.org/10.1016/j.envsoft.2013.09.010, 2014.
Mascaro, J., Asner, G. P., Knapp, D. E., Kennedy-Bowdoin, T., Martin, R. E., Anderson, C., Higgins, M., and Chadwick, K. D.: A tale of two "Forests": random forest machine learning aids tropical forest carbon mapping, PLoS ONE, 9, e85993, https://doi.org/10.1371/journal.pone.0085993,2014.
MassGIS: Massachusetts Geographic Information System, MassGIS, available at: http://www.mass.gov/anf/research-and-tech/it-serv-and-support/application-serv/office-of-geographic-information-massgis/ (last access: 16 April 2015), 2015.
Moreira, E., Costa, S., Aguiar, A. P., Câmara, G., and Carneiro, T.: Dynamical coupling of multiscale land change models, Landscape Ecol., 24, 1183–1194, https://doi.org/10.1007/s10980-009-9397-x, 2009.
Morin, A., Urban, J., Adams, P. D., Foster, I., Sali, A., Baker, D., and Sliz, P.: Shining light into black boxes, Science, 336, 159–160, 2012.
Mulia, R., Widayati, A., Putra Agung, S., and Zulkarnain, M. T.: Low carbon emission development strategies for Jambi, Indonesia: simulation and trade-off analysis using the FALLOW model, Mitigation and Adaptation Strategies for Global Change, 19, 773–788, https://doi.org/10.1007/s11027-013-9485-8, 2014.
Overmars, K., de Koning, G., and Veldkamp, A.: Spatial autocorrelation in multi-scale land use models, Ecol. Model., 164, 257–270, https://doi.org/10.1016/S0304-3800(03)00070-X, 2003.
Overmars, K. P., Verburg, P. H., and Veldkamp, A.: Comparison of a deductive and an inductive approach to specify land suitability in a spatially explicit land use model, Land Use Policy, 24, 584–599, https://doi.org/10.1016/j.landusepol.2005.09.008, 2007.
Pebesma, E. J. and Bivand, R. S.: Classes and methods for spatial data in R, R News, 5, 9–13, 2005.
Pebesma, E. J., Nüst, D., and Bivand, R.: The R software environment in reproducible geoscientific research, EOS T. Am. Geophys. Un., 93, 163–163, 2012.
Peng, R. D.: Reproducible research in computational science, Science, 334, 1226–1227, https://doi.org/10.1126/science.1213847, 2011.
Pérez-Vega, A., Mas, J., and Ligmann-Zielinska, A.: Comparing two approaches to land use/cover change modeling and their implications for the assessment of biodiversity loss in a deciduous tropical forest, Environ. Modell. Softw., 29, 11–23, https://doi.org/10.1016/j.envsoft.2011.09.011, 2012.
Petzoldt, T. and Rinke, K.: Simecol: an object-oriented framework for ecological modeling in R, J. Stat. Softw., 22, 1–31, 2007.
Pontius, R. G. and Parmentier, B.: Recommendations for using the relative operating characteristic (ROC), Landscape Ecol., 367–382, https://doi.org/10.1007/s10980-013-9984-8, 2014.
Pontius, R. G. and Schneider, L. C.: Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA, Agr. Ecosyst. Environ., 85, 239–248, 2001.
Pontius, R. G. and Spencer, J.: Uncertainty in extrapolations of predictive land-change models, Environ. Plann. B, 32, 211–230, https://doi.org/10.1068/b31152, 2005.
Pontius, R. G., Huffaker, D., and Denman, K.: Useful techniques of validation for spatially explicit land-change models, Ecol. Model., 179, 445–461, https://doi.org/10.1016/j.ecolmodel.2004.05.010, 2004a.
Pontius, R. G., Shusas, E., and McEachern, M.: Detecting important categorical land changes while accounting for persistence, Agr. Ecosyst. Environ., 101, 251–268, https://doi.org/10.1016/j.agee.2003.09.008, 2004b.
Pontius, R. G., Boersma, W., Castella, J., Clarke, K., Nijs, T., Dietzel, C., Duan, Z., Fotsing, E., Goldstein, N., Kok, K., Koomen, E., Lippitt, C. D., McConnell, W., Mohd Sood, A., Pijanowski, B., Pithadia, S., Sweeney, S., Trung, T. N., Veldkamp, A. T., and Verburg, P. H.: Comparing the input, output, and validation maps for several models of land change, Ann. Regional Sci., 42, 11–37, https://doi.org/10.1007/s00168-007-0138-2, 2008.
Pontius, R. G., Peethambaram, S., and Castella, J.: Comparison of three maps at multiple resolutions: a case study of land change simulation in Cho Don district, Vietnam, Ann. Assoc. Am. Geogr., 101, 45–62, https://doi.org/10.1080/00045608.2010.517742, 2011.
Ray, D. K. and Pijanowski, B. C.: A backcast land use change model to generate past land use maps: application and validation at the Muskegon River watershed of Michigan, USA, Journal of Land Use Science, 5, 1–29, https://doi.org/10.1080/17474230903150799, 2010.
R Core Team: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, available at: http://www.R-project.org/ (last access: 16 April 2015), 2014.
Rosa, I. M. D., Purves, D., Souza, C., and Ewers, R. M.: Predictive modelling of contagious deforestation in the Brazilian Amazon, PLoS ONE, 8, e77231, https://doi.org/10.1371/journal.pone.0077231, 2013.
Rosa, I. M. D., Ahmed, S. E., and Ewers, R. M.: The transparency, reliability and utility of tropical rainforest land-use and land-cover change models, Glob. Change Biol., 20, 1707–1722, https://doi.org/10.1111/gcb.12523, 2014.
Schaldach, R., Alcamo, J., Koch, J., Kölking, C., Lapola, D. M., Schüngel, J., and Priess, J. A.: An integrated approach to modelling land-use change on continental and global scales, Environ. Modell. Softw., 26, 1041–1051, https://doi.org/10.1016/j.envsoft.2011.02.013, 2011.
Schmitz, O., Karssenberg, D., van Deursen, W., and Wesseling, C.: Linking external components to a spatio-temporal modelling framework: coupling MODFLOW and PCRaster, Environ. Modell. Softw., 24, 1088–1099, https://doi.org/10.1016/j.envsoft.2009.02.018, 2009.
Sing, T., Sander, O., Beerenwinkel, N., and Lengauer, T.: ROCR: visualizing classifier performance in R, Bioinformatics, 21, 3940–3941, https://doi.org/10.1093/bioinformatics/bti623, 2005.
Soares-Filho, B. S., Coutinho Cerqueira, G., and Lopes Pennachin, C.: DINAMICA-a stochastic cellular automata model designed to simulate the landscape dynamics in an Amazonian colonization frontier, Ecol. Model., 154, 217–235, 2002.
Sohl, T. L., Sayler, K. L., Drummond, M. A., and Loveland, T. R.: The FORE-SCE model: a practical approach for projecting land cover change using scenario-based modeling, Journal of Land Use Science, 2, 103–126, https://doi.org/10.1080/17474230701218202, 2007.
Souty, F., Brunelle, T., Dumas, P., Dorin, B., Ciais, P., Crassous, R., Müller, C., and Bondeau, A.: The Nexus Land-Use model version 1.0, an approach articulating biophysical potentials and economic dynamics to model competition for land-use, Geosci. Model Dev., 5, 1297–1322, https://doi.org/10.5194/gmd-5-1297-2012, 2012.
Stehfast, E., van Vuuren, D., Kram, T., Bouwman, L., Alkemade, R., Bakkenes, M., Biemans, H., Bouwman, A., den Elzen, M., Janse, J., Lucas, P., van Minnen, J., Muller, M., and Prins, A. G.: Integrated Assessment of Global Environmental Change with IMAGE 3.0 – Model Description and Policy Applications, available at: http://www.pbl.nl/en/publications/integrated-assessment-of-global-environmental-change-with-IMAGE-3.0 (last access: 16 April 2015), iSBN 978-94-91506-71-0, 2014.
Steiniger, S. and Hunter, A. J.: The 2012 free and open source GIS software map – a guide to facilitate research, development, and adoption, Comput. Environ. Urban, 39, 136–150, https://doi.org/10.1016/j.compenvurbsys.2012.10.003, 2013.
Tayyebi, A., Pijanowski, B. C., Linderman, M., and Gratton, C.: Comparing three global parametric and local non-parametric models to simulate land use change in diverse areas of the world, Environ. Modell. Softw., 59, 202–221, https://doi.org/10.1016/j.envsoft.2014.05.022, 2014.
Therneau, T., Atkinson, B., and Ripley, B.: rpart: Recursive Partitioning and Regression Trees, available at: http://CRAN.R-project.org/package=rpart (last access: 16 April 2015), r package version 4.1-8, 2014.
van Noordwijk, M.: Scaling trade-offs between crop productivity, carbon stocks and biodiversity in shifting cultivation landscape mosaics: the FALLOW model, Ecol. Model., 149, 113–126, 2002.
van Vliet, J., Bregt, A. K., and Hagen-Zanker, A.: Revisiting Kappa to account for change in the accuracy assessment of land-use change models, Ecol. Model., 222, 1367–1375, https://doi.org/10.1016/j.ecolmodel.2011.01.017, 2011.
Veldkamp, A. and Fresco, L.: CLUE: a conceptual model to study the conversion of land use and its effects, Ecol. Model., 85, 253–270, 1996.
Veldkamp, A. and Lambin, E. F.: Predicting land-use change, Agr. Ecosyst. Environ., 85, 1–6, 2001.
Verburg, P. H. and Overmars, K. P.: Combining top-down and bottom-up dynamics in land use modeling: exploring the future of abandoned farmlands in Europe with the Dyna-CLUE model, Landscape Ecol., 24, 1167–1181, https://doi.org/10.1007/s10980-009-9355-7, 2009.
Verburg, P. H., De Koning, G. H. J., Kok, K., Veldkamp, A., and Bouma, J.: A spatial explicit allocation procedure for modelling the pattern of land use change based upon actual land use, Ecol. Model., 116, 45–61, 1999.
Verburg, P. H., Soepboer, W., Veldkamp, A., Limpiada, R., Espaldon, V., and Mastura, S. S.: Modeling the spatial dynamics of regional land use: the CLUE-S model, Environ. Manage., 30, 391–405, https://doi.org/10.1007/s00267-002-2630-x, 2002.
Verburg, P. H., Tabeau, A., and Hatna, E.: Assessing spatial uncertainties of land allocation using a scenario approach and sensitivity analysis: a study for land use in Europe, J. Environ. Manage., 127, S132–S144, https://doi.org/10.1016/j.jenvman.2012.08.038, 2013.
Wassenaar, T., Gerber, P., Verburg, P., Rosales, M., Ibrahim, M., and Steinfeld, H.: Projecting land use changes in the Neotropics: the geography of pasture expansion into forest, Global Environ. Chang., 17, 86–104, https://doi.org/10.1016/j.gloenvcha.2006.03.007, 2007.
Wilson, G., Aruliah, D. A., Brown, C. T., Chue Hong, N. P., Davis, M., Guy, R. T., Haddock, S. H. D., Huff, K. D., Mitchell, I. M., Plumbley, M. D., Waugh, B., White, E. P., and Wilson, P.: Best practices for scientific computing, PLoS Biology, 12, e1001745, https://doi.org/10.1371/journal.pbio.1001745, 2014.
The contribution of lulcc is to provide a free and open-source framework for land use change modelling. The software, which is provided as an R package, addresses problems associated with the current paradigm of closed-source, specialised land use change modelling software which disrupt the scientific process. It is an attempt to move the discipline towards open and transparent science and to ensure land use change models are accessible to scientists working across the geosciences.
The contribution of lulcc is to provide a free and open-source framework for land use change...