Articles | Volume 8, issue 10
https://doi.org/10.5194/gmd-8-3215-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-8-3215-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
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
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering, Imperial College London, London, UK
Grantham Institute for Climate Change, Imperial College London, London, UK
W. Buytaert
Department of Civil and Environmental Engineering, Imperial College London, London, UK
Grantham Institute for Climate Change, Imperial College London, London, UK
Department of Civil and Environmental Engineering, Imperial College London, London, UK
Related authors
No articles found.
Jonathan D. Mackay, Nicholas E. Barrand, David M. Hannah, Emily Potter, Nilton Montoya, and Wouter Buytaert
The Cryosphere, 19, 685–712, https://doi.org/10.5194/tc-19-685-2025, https://doi.org/10.5194/tc-19-685-2025, 2025
Short summary
Short summary
We combine two globally capable glacier evolution models to include processes that are typically neglected but thought to control tropical glacier retreat (e.g. sublimation). We apply the model to Peru's Vilcanota-Urubamba Basin. The model captures observed glacier mass changes,but struggles with surface albedo dynamics. Projections show glacier mass shrinking to 17 % or 6 % of 2000 levels by 2100 under moderate- and high-emission scenarios, respectively.
Barnaby Dobson, Leyang Liu, and Ana Mijic
Geosci. Model Dev., 17, 4495–4513, https://doi.org/10.5194/gmd-17-4495-2024, https://doi.org/10.5194/gmd-17-4495-2024, 2024
Short summary
Short summary
Water management is challenging when models don't capture the entire water cycle. We propose that using integrated models facilitates management and improves understanding. We introduce a software tool designed for this task. We discuss its foundation, how it simulates water system components and their interactions, and its customisation. We provide a flexible way to represent water systems, and we hope it will inspire more research and practical applications for sustainable water management.
Heidi Kreibich, Kai Schröter, Giuliano Di Baldassarre, Anne F. Van Loon, Maurizio Mazzoleni, Guta Wakbulcho Abeshu, Svetlana Agafonova, Amir AghaKouchak, Hafzullah Aksoy, Camila Alvarez-Garreton, Blanca Aznar, Laila Balkhi, Marlies H. Barendrecht, Sylvain Biancamaria, Liduin Bos-Burgering, Chris Bradley, Yus Budiyono, Wouter Buytaert, Lucinda Capewell, Hayley Carlson, Yonca Cavus, Anaïs Couasnon, Gemma Coxon, Ioannis Daliakopoulos, Marleen C. de Ruiter, Claire Delus, Mathilde Erfurt, Giuseppe Esposito, Didier François, Frédéric Frappart, Jim Freer, Natalia Frolova, Animesh K. Gain, Manolis Grillakis, Jordi Oriol Grima, Diego A. Guzmán, Laurie S. Huning, Monica Ionita, Maxim Kharlamov, Dao Nguyen Khoi, Natalie Kieboom, Maria Kireeva, Aristeidis Koutroulis, Waldo Lavado-Casimiro, Hong-Yi Li, Maria Carmen LLasat, David Macdonald, Johanna Mård, Hannah Mathew-Richards, Andrew McKenzie, Alfonso Mejia, Eduardo Mario Mendiondo, Marjolein Mens, Shifteh Mobini, Guilherme Samprogna Mohor, Viorica Nagavciuc, Thanh Ngo-Duc, Huynh Thi Thao Nguyen, Pham Thi Thao Nhi, Olga Petrucci, Nguyen Hong Quan, Pere Quintana-Seguí, Saman Razavi, Elena Ridolfi, Jannik Riegel, Md Shibly Sadik, Nivedita Sairam, Elisa Savelli, Alexey Sazonov, Sanjib Sharma, Johanna Sörensen, Felipe Augusto Arguello Souza, Kerstin Stahl, Max Steinhausen, Michael Stoelzle, Wiwiana Szalińska, Qiuhong Tang, Fuqiang Tian, Tamara Tokarczyk, Carolina Tovar, Thi Van Thu Tran, Marjolein H. J. van Huijgevoort, Michelle T. H. van Vliet, Sergiy Vorogushyn, Thorsten Wagener, Yueling Wang, Doris E. Wendt, Elliot Wickham, Long Yang, Mauricio Zambrano-Bigiarini, and Philip J. Ward
Earth Syst. Sci. Data, 15, 2009–2023, https://doi.org/10.5194/essd-15-2009-2023, https://doi.org/10.5194/essd-15-2009-2023, 2023
Short summary
Short summary
As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management. We present a dataset containing data of paired events, i.e. two floods or two droughts that occurred in the same area. The dataset enables comparative analyses and allows detailed context-specific assessments. Additionally, it supports the testing of socio-hydrological models.
Tahmina Yasmin, Kieran Khamis, Anthony Ross, Subir Sen, Anita Sharma, Debashish Sen, Sumit Sen, Wouter Buytaert, and David M. Hannah
Nat. Hazards Earth Syst. Sci., 23, 667–674, https://doi.org/10.5194/nhess-23-667-2023, https://doi.org/10.5194/nhess-23-667-2023, 2023
Short summary
Short summary
Floods continue to be a wicked problem that require developing early warning systems with plausible assumptions of risk behaviour, with more targeted conversations with the community at risk. Through this paper we advocate the use of a SMART approach to encourage bottom-up initiatives to develop inclusive and purposeful early warning systems that benefit the community at risk by engaging them at every step of the way along with including other stakeholders at multiple scales of operations.
Veerle Vanacker, Armando Molina, Miluska A. Rosas, Vivien Bonnesoeur, Francisco Román-Dañobeytia, Boris F. Ochoa-Tocachi, and Wouter Buytaert
SOIL, 8, 133–147, https://doi.org/10.5194/soil-8-133-2022, https://doi.org/10.5194/soil-8-133-2022, 2022
Short summary
Short 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 the 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 erosion mitigation and topsoil organic carbon concentrations.
N. A. Muhadi, A. F. Abdullah, S. K. Bejo, M. R. Mahadi, and A. Mijic
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-4-W3-2021, 257–260, https://doi.org/10.5194/isprs-archives-XLVI-4-W3-2021-257-2022, https://doi.org/10.5194/isprs-archives-XLVI-4-W3-2021-257-2022, 2022
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, https://doi.org/10.5194/hess-25-3937-2021, https://doi.org/10.5194/hess-25-3937-2021, 2021
Short summary
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, https://doi.org/10.5194/hess-22-5357-2018, https://doi.org/10.5194/hess-22-5357-2018, 2018
Short summary
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, https://doi.org/10.5194/hess-22-4745-2018, https://doi.org/10.5194/hess-22-4745-2018, 2018
Short summary
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, https://doi.org/10.5194/hess-22-1411-2018, https://doi.org/10.5194/hess-22-1411-2018, 2018
Short summary
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, https://doi.org/10.5194/hess-21-3655-2017, https://doi.org/10.5194/hess-21-3655-2017, 2017
Short summary
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., https://doi.org/10.5194/hess-2017-388, https://doi.org/10.5194/hess-2017-388, 2017
Revised manuscript has not been submitted
Short summary
Short 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, https://doi.org/10.5194/hess-20-1911-2016, https://doi.org/10.5194/hess-20-1911-2016, 2016
Short summary
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, https://doi.org/10.5194/hess-20-887-2016, https://doi.org/10.5194/hess-20-887-2016, 2016
Short summary
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, https://doi.org/10.5194/hess-20-443-2016, https://doi.org/10.5194/hess-20-443-2016, 2016
Short summary
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, https://doi.org/10.5194/hess-18-4223-2014, https://doi.org/10.5194/hess-18-4223-2014, 2014
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, https://doi.org/10.5194/hess-18-2065-2014, https://doi.org/10.5194/hess-18-2065-2014, 2014
Z. Zulkafli, W. Buytaert, C. Onof, W. Lavado, and J. L. Guyot
Hydrol. Earth Syst. Sci., 17, 1113–1132, https://doi.org/10.5194/hess-17-1113-2013, https://doi.org/10.5194/hess-17-1113-2013, 2013
Related subject area
Earth and space science informatics
Moving beyond post hoc explainable artificial intelligence: a perspective paper on lessons learned from dynamical climate modeling
Remote-sensing-based forest canopy height mapping: some models are useful, but might they provide us with even more insights when combined?
Checking the consistency of 3D geological models
The effect of lossy compression of numerical weather prediction data on data analysis: a case study using enstools-compression 2023.11
GNNWR: an open-source package of spatiotemporal intelligent regression methods for modeling spatial and temporal nonstationarity
Can AI be enabled to dynamical downscaling? A Latent Diffusion Model to mimic km-scale COSMO5.0_CLM9 simulations
Random forests with spatial proxies for environmental modelling: opportunities and pitfalls
An improved global pressure and zenith wet delay model with optimized vertical correction considering the spatiotemporal variability in multiple height-scale factors
kNNDM CV: k-fold nearest-neighbour distance matching cross-validation for map accuracy estimation
Accelerating Lagrangian transport simulations on graphics processing units: performance optimizations of Massive-Parallel Trajectory Calculations (MPTRAC) v2.6
Focal-TSMP: deep learning for vegetation health prediction and agricultural drought assessment from a regional climate simulation
Tomofast-x 2.0: an open-source parallel code for inversion of potential field data with topography using wavelet compression
Functional analysis of variance (ANOVA) for carbon flux estimates from remote sensing data
The 4D reconstruction of dynamic geological evolution processes for renowned geological features
Machine learning for numerical weather and climate modelling: a review
Overcoming barriers to enable convergence research by integrating ecological and climate sciences: the NCAR–NEON system Version 1
Ensemble of optimised machine learning algorithms for predicting surface soil moisture content at a global scale
Hazard assessment modeling and software development of earthquake-triggered landslides in the Sichuan–Yunnan area, China
A generalized spatial autoregressive neural network method for three-dimensional spatial interpolation
The Common Community Physics Package (CCPP) Framework v6
Causal deep learning models for studying the Earth system
A methodological framework for improving the performance of data-driven models: a case study for daily runoff prediction in the Maumee domain, USA
SHAFTS (v2022.3): a deep-learning-based Python package for simultaneous extraction of building height and footprint from sentinel imagery
Bayesian atmospheric correction over land: Sentinel-2/MSI and Landsat 8/OLI
Twenty-five years of the IPCC Data Distribution Centre at the DKRZ and the Reference Data Archive for CMIP data
Effectiveness and computational efficiency of absorbing boundary conditions for full-waveform inversion
LAND-SUITE V1.0: a suite of tools for statistically based landslide susceptibility zonation
Towards physics-inspired data-driven weather forecasting: integrating data assimilation with a deep spatial-transformer-based U-NET in a case study with ERA5
Fast infrared radiative transfer calculations using graphics processing units: JURASSIC-GPU v2.0
CSDMS: a community platform for numerical modeling of Earth surface processes
A new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributes
Model calibration using ESEm v1.1.0 – an open, scalable Earth system emulator
Turbidity maximum zone index: a novel model for remote extraction of the turbidity maximum zone in different estuaries
dh2loop 1.0: an open-source Python library for automated processing and classification of geological logs
Copula-based synthetic data augmentation for machine-learning emulators
Automated geological map deconstruction for 3D model construction using map2loop 1.0 and map2model 1.0
A 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 typing
Using Shapley additive explanations to interpret extreme gradient boosting predictions of grassland degradation in Xilingol, China
Current status on the need for improved accessibility to climate models code
ClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weather
A spatiotemporal weighted regression model (STWR v1.0) for analyzing local nonstationarity in space and time
A 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 evolution
Comparative 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 model
VISIR-1.b: ocean surface gravity waves and currents for energy-efficient navigation
Topological data analysis and machine learning for recognizing atmospheric river patterns in large climate datasets
Global hydro-climatic biomes identified via multitask learning
A run control framework to streamline profiling, porting, and tuning simulation runs and provenance tracking of geoscientific applications
Ryan J. O'Loughlin, Dan Li, Richard Neale, and Travis A. O'Brien
Geosci. Model Dev., 18, 787–802, https://doi.org/10.5194/gmd-18-787-2025, https://doi.org/10.5194/gmd-18-787-2025, 2025
Short summary
Short summary
We draw from traditional climate modeling practices to make recommendations for machine-learning (ML)-driven climate science. Our intended audience is climate modelers who are relatively new to ML. We show how component-level understanding – obtained when scientists can link model behavior to parts within the overall model – should guide the development and evaluation of ML models. Better understanding yields a stronger basis for trust in the models. We highlight several examples to demonstrate.
Nikola Besic, Nicolas Picard, Cédric Vega, Jean-Daniel Bontemps, Lionel Hertzog, Jean-Pierre Renaud, Fajwel Fogel, Martin Schwartz, Agnès Pellissier-Tanon, Gabriel Destouet, Frédéric Mortier, Milena Planells-Rodriguez, and Philippe Ciais
Geosci. Model Dev., 18, 337–359, https://doi.org/10.5194/gmd-18-337-2025, https://doi.org/10.5194/gmd-18-337-2025, 2025
Short summary
Short summary
The creation of advanced mapping models for forest attributes, utilizing remote sensing data and incorporating machine or deep learning methods, has become a key area of interest in the domain of forest observation and monitoring. This paper introduces a method where we blend and collectively interpret five models dedicated to estimating forest canopy height. We achieve this through Bayesian model averaging, offering a comprehensive analysis of these remote-sensing-based products.
Marion N. Parquer, Eric A. de Kemp, Boyan Brodaric, and Michael J. Hillier
Geosci. Model Dev., 18, 71–100, https://doi.org/10.5194/gmd-18-71-2025, https://doi.org/10.5194/gmd-18-71-2025, 2025
Short summary
Short summary
This is a proof-of-concept paper outlining a general approach to how 3D geological models would be checked to be geologically
reasonable. We do this with a consistency-checking tool that looks at geological feature pairs and their spatial, temporal, and internal polarity characteristics. The idea is to assess if geological relationships from a specific 3D geological model match what is allowed in the real world from the perspective of geological principles.
Oriol Tintó Prims, Robert Redl, Marc Rautenhaus, Tobias Selz, Takumi Matsunobu, Kameswar Rao Modali, and George Craig
Geosci. Model Dev., 17, 8909–8925, https://doi.org/10.5194/gmd-17-8909-2024, https://doi.org/10.5194/gmd-17-8909-2024, 2024
Short summary
Short summary
Advanced compression techniques can drastically reduce the size of meteorological datasets (by 5 to 150 times) without compromising the data's scientific value. We developed a user-friendly tool called
enstools-compressionthat makes this compression simple for Earth scientists. This tool works seamlessly with common weather and climate data formats. Our work shows that lossy compression can significantly improve how researchers store and analyze large meteorological datasets.
Ziyu Yin, Jiale Ding, Yi Liu, Ruoxu Wang, Yige Wang, Yijun Chen, Jin Qi, Sensen Wu, and Zhenhong Du
Geosci. Model Dev., 17, 8455–8468, https://doi.org/10.5194/gmd-17-8455-2024, https://doi.org/10.5194/gmd-17-8455-2024, 2024
Short summary
Short summary
In geography, understanding how relationships between different factors change over time and space is crucial. This study implements two neural-network-based spatiotemporal regression models and an open-source Python package named Geographically Neural Network Weighted Regression to capture relationships between factors. This makes it a valuable tool for researchers in fields such as environmental science, urban planning, and public health.
Elena Tomasi, Gabriele Franch, and Marco Cristoforetti
EGUsphere, https://doi.org/10.48550/arXiv.2406.13627, https://doi.org/10.48550/arXiv.2406.13627, 2024
Short summary
Short summary
High-resolution weather data is crucial for many applications, typically generated via resource-intensive numerical models through dynamical downscaling. We developed an AI model using Latent Diffusion Models (LDM) to mimic this process, increasing weather data resolution over Italy from 20 to 2 km. LDM outperforms other methods, accurately capturing local patterns and extreme events. This approach offers a cost-effective alternative, with potential disruptive application in climate sciences.
Carles Milà, Marvin Ludwig, Edzer Pebesma, Cathryn Tonne, and Hanna Meyer
Geosci. Model Dev., 17, 6007–6033, https://doi.org/10.5194/gmd-17-6007-2024, https://doi.org/10.5194/gmd-17-6007-2024, 2024
Short summary
Short summary
Spatial proxies, such as coordinates and distances, are often used as predictors in random forest models for predictive mapping. In a simulation and two case studies, we investigated the conditions under which their use is appropriate. We found that spatial proxies are not always beneficial and should not be used as a default approach without careful consideration. We also provide insights into the reasons behind their suitability, how to detect them, and potential alternatives.
Chunhua Jiang, Xiang Gao, Huizhong Zhu, Shuaimin Wang, Sixuan Liu, Shaoni Chen, and Guangsheng Liu
Geosci. Model Dev., 17, 5939–5959, https://doi.org/10.5194/gmd-17-5939-2024, https://doi.org/10.5194/gmd-17-5939-2024, 2024
Short summary
Short summary
With ERA5 hourly data, we show spatiotemporal characteristics of pressure and zenith wet delay (ZWD) and propose an empirical global pressure and ZWD grid model with a broader operating space which can provide accurate pressure, ZWD, zenith hydrostatic delay, and zenith tropospheric delay estimates for any selected time and location over globe. IGPZWD will be of great significance for the tropospheric augmentation in real-time GNSS positioning and atmospheric water vapor remote sensing.
Jan Linnenbrink, Carles Milà, Marvin Ludwig, and Hanna Meyer
Geosci. Model Dev., 17, 5897–5912, https://doi.org/10.5194/gmd-17-5897-2024, https://doi.org/10.5194/gmd-17-5897-2024, 2024
Short summary
Short summary
Estimation of map accuracy based on cross-validation (CV) in spatial modelling is pervasive but controversial. Here, we build upon our previous work and propose a novel, prediction-oriented k-fold CV strategy for map accuracy estimation in which the distribution of geographical distances between prediction and training points is taken into account when constructing the CV folds. Our method produces more reliable estimates than other CV methods and can be used for large datasets.
Lars Hoffmann, Kaveh Haghighi Mood, Andreas Herten, Markus Hrywniak, Jiri Kraus, Jan Clemens, and Mingzhao Liu
Geosci. Model Dev., 17, 4077–4094, https://doi.org/10.5194/gmd-17-4077-2024, https://doi.org/10.5194/gmd-17-4077-2024, 2024
Short summary
Short summary
Lagrangian particle dispersion models are key for studying atmospheric transport but can be computationally intensive. To speed up simulations, the MPTRAC model was ported to graphics processing units (GPUs). Performance optimization of data structures and memory alignment resulted in runtime improvements of up to 75 % on NVIDIA A100 GPUs for ERA5-based simulations with 100 million particles. These optimizations make the MPTRAC model well suited for future high-performance computing systems.
Mohamad Hakam Shams Eddin and Juergen Gall
Geosci. Model Dev., 17, 2987–3023, https://doi.org/10.5194/gmd-17-2987-2024, https://doi.org/10.5194/gmd-17-2987-2024, 2024
Short summary
Short summary
In this study, we use deep learning and a climate simulation to predict the vegetation health as it would be observed from satellites. We found that the developed model can help to identify regions with a high risk of agricultural drought. The main applications of this study are to estimate vegetation products for periods where no satellite data are available and to forecast the future vegetation response to climate change based on climate scenarios.
Vitaliy Ogarko, Kim Frankcombe, Taige Liu, Jeremie Giraud, Roland Martin, and Mark Jessell
Geosci. Model Dev., 17, 2325–2345, https://doi.org/10.5194/gmd-17-2325-2024, https://doi.org/10.5194/gmd-17-2325-2024, 2024
Short summary
Short summary
We present a major release of the Tomofast-x open-source gravity and magnetic inversion code that is enhancing its performance and applicability for both industrial and academic studies. We focus on real-world mineral exploration scenarios, while offering flexibility for applications at regional scale or for crustal studies. The optimisation work described in this paper is fundamental to allowing more complete descriptions of the controls on magnetisation, including remanence.
Jonathan Hobbs, Matthias Katzfuss, Hai Nguyen, Vineet Yadav, and Junjie Liu
Geosci. Model Dev., 17, 1133–1151, https://doi.org/10.5194/gmd-17-1133-2024, https://doi.org/10.5194/gmd-17-1133-2024, 2024
Short summary
Short summary
The cycling of carbon among the land, oceans, and atmosphere is a closely monitored process in the global climate system. These exchanges between the atmosphere and the surface can be quantified using a combination of atmospheric carbon dioxide observations and computer models. This study presents a statistical method for investigating the similarities and differences in the estimated surface–atmosphere carbon exchange when different computer model assumptions are invoked.
Jiateng Guo, Zhibin Liu, Xulei Wang, Lixin Wu, Shanjun Liu, and Yunqiang Li
Geosci. Model Dev., 17, 847–864, https://doi.org/10.5194/gmd-17-847-2024, https://doi.org/10.5194/gmd-17-847-2024, 2024
Short summary
Short summary
This study proposes a 3D and temporally dynamic (4D) geological modeling method. Several simulation and actual cases show that the 4D spatial and temporal evolution of regional geological formations can be modeled easily using this method with smooth boundaries. The 4D modeling system can dynamically present the regional geological evolution process under the timeline, which will be helpful to the research and teaching on the formation of typical and complex geological features.
Catherine O. de Burgh-Day and Tennessee Leeuwenburg
Geosci. Model Dev., 16, 6433–6477, https://doi.org/10.5194/gmd-16-6433-2023, https://doi.org/10.5194/gmd-16-6433-2023, 2023
Short summary
Short summary
Machine learning (ML) is an increasingly popular tool in the field of weather and climate modelling. While ML has been used in this space for a long time, it is only recently that ML approaches have become competitive with more traditional methods. In this review, we have summarized the use of ML in weather and climate modelling over time; provided an overview of key ML concepts, methodologies, and terms; and suggested promising avenues for further research.
Danica L. Lombardozzi, William R. Wieder, Negin Sobhani, Gordon B. Bonan, David Durden, Dawn Lenz, Michael SanClements, Samantha Weintraub-Leff, Edward Ayres, Christopher R. Florian, Kyla Dahlin, Sanjiv Kumar, Abigail L. S. Swann, Claire M. Zarakas, Charles Vardeman, and Valerio Pascucci
Geosci. Model Dev., 16, 5979–6000, https://doi.org/10.5194/gmd-16-5979-2023, https://doi.org/10.5194/gmd-16-5979-2023, 2023
Short summary
Short summary
We present a novel cyberinfrastructure system that uses National Ecological Observatory Network measurements to run Community Terrestrial System Model point simulations in a containerized system. The simple interface and tutorials expand access to data and models used in Earth system research by removing technical barriers and facilitating research, educational opportunities, and community engagement. The NCAR–NEON system enables convergence of climate and ecological sciences.
Qianqian Han, Yijian Zeng, Lijie Zhang, Calimanut-Ionut Cira, Egor Prikaziuk, Ting Duan, Chao Wang, Brigitta Szabó, Salvatore Manfreda, Ruodan Zhuang, and Bob Su
Geosci. Model Dev., 16, 5825–5845, https://doi.org/10.5194/gmd-16-5825-2023, https://doi.org/10.5194/gmd-16-5825-2023, 2023
Short summary
Short summary
Using machine learning, we estimated global surface soil moisture (SSM) to aid in understanding water, energy, and carbon exchange. Ensemble models outperformed individual algorithms in predicting SSM under different climates. The best-performing ensemble included K-neighbours Regressor, Random Forest Regressor, and Extreme Gradient Boosting. This is important for hydrological and climatological applications such as water cycle monitoring, irrigation management, and crop yield prediction.
Xiaoyi Shao, Siyuan Ma, and Chong Xu
Geosci. Model Dev., 16, 5113–5129, https://doi.org/10.5194/gmd-16-5113-2023, https://doi.org/10.5194/gmd-16-5113-2023, 2023
Short summary
Short summary
Scientific understandings of the distribution of coseismic landslides, followed by emergency and medium- and long-term risk assessment, can reduce landslide risk. The aim of this study is to propose an improved three-stage spatial prediction strategy and develop corresponding hazard assessment software called Mat.LShazard V1.0, which provides a new application tool for coseismic landslide disaster prevention and mitigation in different stages.
Junda Zhan, Sensen Wu, Jin Qi, Jindi Zeng, Mengjiao Qin, Yuanyuan Wang, and Zhenhong Du
Geosci. Model Dev., 16, 2777–2794, https://doi.org/10.5194/gmd-16-2777-2023, https://doi.org/10.5194/gmd-16-2777-2023, 2023
Short summary
Short summary
We develop a generalized spatial autoregressive neural network model used for three-dimensional spatial interpolation. Taking the different changing trend of geographic elements along various directions into consideration, the model defines spatial distance in a generalized way and integrates it into the process of spatial interpolation with the theories of spatial autoregression and neural network. Compared with traditional methods, the model achieves better performance and is more adaptable.
Dominikus Heinzeller, Ligia Bernardet, Grant Firl, Man Zhang, Xia Sun, and Michael Ek
Geosci. Model Dev., 16, 2235–2259, https://doi.org/10.5194/gmd-16-2235-2023, https://doi.org/10.5194/gmd-16-2235-2023, 2023
Short summary
Short summary
The Common Community Physics Package is a collection of physical atmospheric parameterizations for use in Earth system models and a framework that couples the physics to a host model’s dynamical core. A primary goal for this effort is to facilitate research and development of physical parameterizations and physics–dynamics coupling methods while offering capabilities for numerical weather prediction operations, for example in the upcoming implementation of the Global Forecast System (GFS) v17.
Tobias Tesch, Stefan Kollet, and Jochen Garcke
Geosci. Model Dev., 16, 2149–2166, https://doi.org/10.5194/gmd-16-2149-2023, https://doi.org/10.5194/gmd-16-2149-2023, 2023
Short summary
Short summary
A recent statistical approach for studying relations in the Earth system is to train deep learning (DL) models to predict Earth system variables given one or several others and use interpretable DL to analyze the relations learned by the models. Here, we propose to combine the approach with a theorem from causality research to ensure that the deep learning model learns causal rather than spurious relations. As an example, we apply the method to study soil-moisture–precipitation coupling.
Yao Hu, Chirantan Ghosh, and Siamak Malakpour-Estalaki
Geosci. Model Dev., 16, 1925–1936, https://doi.org/10.5194/gmd-16-1925-2023, https://doi.org/10.5194/gmd-16-1925-2023, 2023
Short summary
Short summary
Data-driven models (DDMs) gain popularity in earth and environmental systems, thanks in large part to advancements in data collection techniques and artificial intelligence (AI). The performance of these models is determined by the underlying machine learning (ML) algorithms. In this study, we develop a framework to improve the model performance by optimizing ML algorithms and demonstrate the effectiveness of the framework using a DDM to predict edge-of-field runoff in the Maumee domain, USA.
Ruidong Li, Ting Sun, Fuqiang Tian, and Guang-Heng Ni
Geosci. Model Dev., 16, 751–778, https://doi.org/10.5194/gmd-16-751-2023, https://doi.org/10.5194/gmd-16-751-2023, 2023
Short summary
Short summary
We developed SHAFTS (Simultaneous building Height And FootprinT extraction from Sentinel imagery), a multi-task deep-learning-based Python package, to estimate average building height and footprint from Sentinel imagery. Evaluation in 46 cities worldwide shows that SHAFTS achieves significant improvement over existing machine-learning-based methods.
Feng Yin, Philip E. Lewis, and Jose L. Gómez-Dans
Geosci. Model Dev., 15, 7933–7976, https://doi.org/10.5194/gmd-15-7933-2022, https://doi.org/10.5194/gmd-15-7933-2022, 2022
Short summary
Short summary
The proposed SIAC atmospheric correction method provides consistent surface reflectance estimations from medium spatial-resolution satellites (Sentinel 2 and Landsat 8) with per-pixel uncertainty information. The outputs from SIAC have been validated against a wide range of ground measurements, and it shows that SIAC can provide accurate estimations of both surface reflectance and atmospheric parameters, with meaningful uncertainty information.
Martina Stockhause and Michael Lautenschlager
Geosci. Model Dev., 15, 6047–6058, https://doi.org/10.5194/gmd-15-6047-2022, https://doi.org/10.5194/gmd-15-6047-2022, 2022
Short summary
Short summary
The Data Distribution Centre (DDC) of the Intergovernmental Panel on Climate Change (IPCC) celebrates its 25th anniversary in 2022. DDC Partner DKRZ has supported the IPCC Assessments and preserved the quality-assured, citable climate model data underpinning the Assessment Reports over these years over the long term. With the introduction of the IPCC FAIR Guidelines into the current AR6, the value of DDC services has been recognized. However, DDC sustainability remains unresolved.
Daiane Iglesia Dolci, Felipe A. G. Silva, Pedro S. Peixoto, and Ernani V. Volpe
Geosci. Model Dev., 15, 5857–5881, https://doi.org/10.5194/gmd-15-5857-2022, https://doi.org/10.5194/gmd-15-5857-2022, 2022
Short summary
Short summary
We investigate and compare the theoretical and computational characteristics of several absorbing boundary conditions (ABCs) for the full-waveform inversion (FWI) problem. The different ABCs are implemented in an optimized computational framework called Devito. The computational efficiency and memory requirements of the ABC methods are evaluated in the forward and adjoint wave propagators, from simple to realistic velocity models.
Mauro Rossi, Txomin Bornaetxea, and Paola Reichenbach
Geosci. Model Dev., 15, 5651–5666, https://doi.org/10.5194/gmd-15-5651-2022, https://doi.org/10.5194/gmd-15-5651-2022, 2022
Short summary
Short summary
LAND-SUITE is a software package designed to support landslide susceptibility zonation. The software integrates, extends, and completes LAND-SE (Rossi et al., 2010; Rossi and Reichenbach, 2016). The software is implemented in R, a free software environment for statistical computing and graphics, and gives expert users the possibility to perform easier, more flexible, and more informed statistically based landslide susceptibility applications and zonations.
Ashesh Chattopadhyay, Mustafa Mustafa, Pedram Hassanzadeh, Eviatar Bach, and Karthik Kashinath
Geosci. Model Dev., 15, 2221–2237, https://doi.org/10.5194/gmd-15-2221-2022, https://doi.org/10.5194/gmd-15-2221-2022, 2022
Short summary
Short summary
There is growing interest in data-driven weather forecasting, i.e., to predict the weather by using a deep neural network that learns from the evolution of past atmospheric patterns. Here, we propose three components to add to the current data-driven weather forecast models to improve their performance. These components involve a feature that incorporates physics into the neural network, a method to add data assimilation, and an algorithm to use several different time intervals in the forecast.
Paul F. Baumeister and Lars Hoffmann
Geosci. Model Dev., 15, 1855–1874, https://doi.org/10.5194/gmd-15-1855-2022, https://doi.org/10.5194/gmd-15-1855-2022, 2022
Short summary
Short summary
The efficiency of the numerical simulation of radiative transport is shown on modern server-class graphics cards (GPUs). The low-cost prefactor on GPUs compared to general-purpose processors (CPUs) enables future large retrieval campaigns for multi-channel data from infrared sounders aboard low-orbit satellites. The validated research software JURASSIC is available in the public domain.
Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Benjamin Campforts, Tian Gan, Katherine R. Barnhart, Albert J. Kettner, Irina Overeem, Scott D. Peckham, Lynn McCready, and Jaia Syvitski
Geosci. Model Dev., 15, 1413–1439, https://doi.org/10.5194/gmd-15-1413-2022, https://doi.org/10.5194/gmd-15-1413-2022, 2022
Short summary
Short summary
Scientists use computer simulation models to understand how Earth surface processes work, including floods, landslides, soil erosion, river channel migration, ocean sedimentation, and coastal change. Research benefits when the software for simulation modeling is open, shared, and coordinated. The Community Surface Dynamics Modeling System (CSDMS) is a US-based facility that supports research by providing community support, computing tools and guidelines, and educational resources.
Danilo César de Mello, Gustavo Vieira Veloso, Marcos Guedes de Lana, Fellipe Alcantara de Oliveira Mello, Raul Roberto Poppiel, Diego Ribeiro Oquendo Cabrero, Luis Augusto Di Loreto Di Raimo, Carlos Ernesto Gonçalves Reynaud Schaefer, Elpídio Inácio Fernandes Filho, Emilson Pereira Leite, and José Alexandre Melo Demattê
Geosci. Model Dev., 15, 1219–1246, https://doi.org/10.5194/gmd-15-1219-2022, https://doi.org/10.5194/gmd-15-1219-2022, 2022
Short summary
Short summary
We used soil parent material, terrain attributes, and geophysical data from the soil surface to test and compare different and unprecedented geophysical sensor combination, as well as different machine learning algorithms to model and predict several soil attributes. Also, we analyzed the importance of pedoenvironmental variables. The soil attributes were modeled throughout different machine learning algorithms and related to different geophysical sensor combinations.
Duncan Watson-Parris, Andrew Williams, Lucia Deaconu, and Philip Stier
Geosci. Model Dev., 14, 7659–7672, https://doi.org/10.5194/gmd-14-7659-2021, https://doi.org/10.5194/gmd-14-7659-2021, 2021
Short summary
Short summary
The Earth System Emulator (ESEm) provides a fast and flexible framework for emulating a wide variety of Earth science datasets and tools for constraining (or tuning) models of any complexity. Three distinct use cases are presented that demonstrate the utility of ESEm and provide some insight into the use of machine learning for emulation in these different settings. The open-source Python package is freely available so that it might become a valuable tool for the community.
Chongyang Wang, Li Wang, Danni Wang, Dan Li, Chenghu Zhou, Hao Jiang, Qiong Zheng, Shuisen Chen, Kai Jia, Yangxiaoyue Liu, Ji Yang, Xia Zhou, and Yong Li
Geosci. Model Dev., 14, 6833–6846, https://doi.org/10.5194/gmd-14-6833-2021, https://doi.org/10.5194/gmd-14-6833-2021, 2021
Short summary
Short summary
The turbidity maximum zone (TMZ) is a special phenomenon in estuaries worldwide. However, the extraction methods and criteria used to describe the TMZ vary significantly both spatially and temporally. This study proposes an new index, the turbidity maximum zone index, based on the corresponding relationship of total suspended solid concentration and Chl a concentration, which could better extract TMZs in different estuaries and on different dates.
Ranee Joshi, Kavitha Madaiah, Mark Jessell, Mark Lindsay, and Guillaume Pirot
Geosci. Model Dev., 14, 6711–6740, https://doi.org/10.5194/gmd-14-6711-2021, https://doi.org/10.5194/gmd-14-6711-2021, 2021
Short summary
Short summary
We have developed a software that allows the user to extract and standardize drill hole information from legacy datasets and/or different drilling campaigns. It also provides functionality to upscale the lithological information. These functionalities were possible by developing thesauri to identify and group geological terminologies together.
David Meyer, Thomas Nagler, and Robin J. Hogan
Geosci. Model Dev., 14, 5205–5215, https://doi.org/10.5194/gmd-14-5205-2021, https://doi.org/10.5194/gmd-14-5205-2021, 2021
Short summary
Short summary
A major limitation in training machine-learning emulators is often caused by the lack of data. This paper presents a cheap way to increase the size of training datasets using statistical techniques and thereby improve the performance of machine-learning emulators.
Mark Jessell, Vitaliy Ogarko, Yohan de Rose, Mark Lindsay, Ranee Joshi, Agnieszka Piechocka, Lachlan Grose, Miguel de la Varga, Laurent Ailleres, and Guillaume Pirot
Geosci. Model Dev., 14, 5063–5092, https://doi.org/10.5194/gmd-14-5063-2021, https://doi.org/10.5194/gmd-14-5063-2021, 2021
Short summary
Short 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 use to automatically build 3D geological models. By automating the process we are able to remove human bias from the procedure, which makes the workflow reproducible.
Martí Bosch, Maxence Locatelli, Perrine Hamel, Roy P. Remme, Jérôme Chenal, and Stéphane Joost
Geosci. Model Dev., 14, 3521–3537, https://doi.org/10.5194/gmd-14-3521-2021, https://doi.org/10.5194/gmd-14-3521-2021, 2021
Short summary
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, https://doi.org/10.5194/gmd-14-2097-2021, https://doi.org/10.5194/gmd-14-2097-2021, 2021
Short summary
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, https://doi.org/10.5194/gmd-14-1493-2021, https://doi.org/10.5194/gmd-14-1493-2021, 2021
Short summary
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, https://doi.org/10.5194/gmd-14-923-2021, https://doi.org/10.5194/gmd-14-923-2021, 2021
Short summary
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.
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, https://doi.org/10.5194/gmd-14-107-2021, https://doi.org/10.5194/gmd-14-107-2021, 2021
Short summary
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.
Xiang Que, Xiaogang Ma, Chao Ma, and Qiyu Chen
Geosci. Model Dev., 13, 6149–6164, https://doi.org/10.5194/gmd-13-6149-2020, https://doi.org/10.5194/gmd-13-6149-2020, 2020
Short summary
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, https://doi.org/10.5194/gmd-13-5567-2020, https://doi.org/10.5194/gmd-13-5567-2020, 2020
Short summary
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, https://doi.org/10.5194/gmd-13-3863-2020, https://doi.org/10.5194/gmd-13-3863-2020, 2020
Short summary
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, https://doi.org/10.5194/gmd-13-1945-2020, https://doi.org/10.5194/gmd-13-1945-2020, 2020
Short summary
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, https://doi.org/10.5194/gmd-12-4261-2019, https://doi.org/10.5194/gmd-12-4261-2019, 2019
Short summary
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, https://doi.org/10.5194/gmd-12-3449-2019, https://doi.org/10.5194/gmd-12-3449-2019, 2019
Short summary
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, https://doi.org/10.5194/gmd-12-613-2019, https://doi.org/10.5194/gmd-12-613-2019, 2019
Short summary
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, https://doi.org/10.5194/gmd-11-4139-2018, https://doi.org/10.5194/gmd-11-4139-2018, 2018
Short summary
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, https://doi.org/10.5194/gmd-11-2875-2018, https://doi.org/10.5194/gmd-11-2875-2018, 2018
Short summary
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.
Cited articles
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://131.252.97.79/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.
Short summary
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...
