Articles | Volume 13, issue 12
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A spatiotemporal weighted regression model (STWR v1.0) for analyzing local nonstationarity in space and time
Computer and Information College, Fujian Agriculture and Forestry University, Fuzhou, Fujian, China
Department of Computer Science, University of Idaho, 875 Perimeter Drive MS 1010, Moscow, ID 83844-1010, USA
School of Computer Science, China University of Geosciences (Wuhan), 388 Lumo Road, Wuhan 430074, China
No articles found.
Qiyu Chen, Gregoire Mariethoz, Gang Liu, Alessandro Comunian, and Xiaogang Ma
Hydrol. Earth Syst. Sci., 22, 6547–6566,Short summary
One of the critical issues in MPS simulation is the difficulty in obtaining a credible 3-D training image. We propose an MPS-based 3-D reconstruction method on the basis of 2-D cross sections, making 3-D training images unnecessary. The main advantages of this approach are the high computational efficiency and a relaxation of the stationarity assumption. The results, in comparison with previous MPS methods, show better performance in portraying anisotropy characteristics and in CPU cost.
Related subject area
Earth and space science informaticsSHAFTS (v2022.3): a deep-learning-based Python package for simultaneous extraction of building height and footprint from sentinel imageryBayesian atmospheric correction over land: Sentinel-2/MSI and Landsat 8/OLITwenty-five years of the IPCC Data Distribution Centre at the DKRZ and the Reference Data Archive for CMIP dataEffectiveness and computational efficiency of absorbing boundary conditions for full-waveform inversionLAND-SUITE V1.0: a suite of tools for statistically based landslide susceptibility zonationCausal deep learning models for studying the Earth system: soil moisture-precipitation coupling in ERA5 data across EuropeTowards physics-inspired data-driven weather forecasting: integrating data assimilation with a deep spatial-transformer-based U-NET in a case study with ERA5Fast infrared radiative transfer calculations using graphics processing units: JURASSIC-GPU v2.0CSDMS: a community platform for numerical modeling of Earth surface processesA new methodological framework for geophysical sensor combinations associated with machine learning algorithms to understand soil attributesModel calibration using ESEm v1.1.0 – an open, scalable Earth system emulatorTurbidity maximum zone index: a novel model for remote extraction of the turbidity maximum zone in different estuariesdh2loop 1.0: an open-source Python library for automated processing and classification of geological logsCopula-based synthetic data augmentation for machine-learning emulatorsAutomated geological map deconstruction for 3D model construction using map2loop 1.0 and map2model 1.0A 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 codeClimateNet: an expert-labeled open dataset and deep learning architecture for enabling high-precision analyses of extreme weatherA 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 modelsAn open and extensible framework for spatially explicit land use change modelling: the lulcc R packagePlant 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
Ruidong Li, Ting Sun, Fuqiang Tian, and Guang-Heng Ni
Geosci. Model Dev., 16, 751–778,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,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,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,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,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.
Tobias Tesch, Stefan Kollet, and Jochen Garcke
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.
Ashesh Chattopadhyay, Mustafa Mustafa, Pedram Hassanzadeh, Eviatar Bach, and Karthik Kashinath
Geosci. Model Dev., 15, 2221–2237,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,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,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,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,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,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,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,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,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,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.
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.
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.
S. Moulds, W. Buytaert, and A. Mijic
Geosci. Model Dev., 8, 3215–3229,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.
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,
Akaike, H.: Information theory and an extension of the maximum likelihood principle, in: Selected papers of hirotugu akaike, Springer, 1998.
Akaike, H.: Maximum likelihood identification of Gaussian autoregressive moving average models, Biometrika, 60, 255–265, 1973.
Atkinson, P. M., German, S. E., Sear, D. A., and Clark, M. J.: Exploring the relations between riverbank erosion and geomorphological controls using geographically weighted logistic regression, Geogr. Anal., 35, 58–82, 2003.
Bowman, A. W.: An alternative method of cross-validation for the smoothing of density estimates, Biometrika, 71, 353–360, 1984.
Bowen, G.: Waterisotopes Database, available at: https://wateriso.utah.edu/waterisotopes/pages/spatial_db/SPATIAL_DB.html, last access: 13 October 2019.
Brown, S., Versace, V. L., Laurenson, L., Ierodiaconou, D., Fawcett, J., and Salzman, S.: Assessment of spatiotemporal varying relationships between rainfall, land cover and surface water area using geographically weighted regression, Environ. Model. Assess., 17, 241–254, 2012.
Brunsdon, C., Fotheringham, A. S., and Charlton, M. E.: Geographically weighted regression: a method for exploring spatial nonstationarity, Geogr. Anal., 28, 281–298, 1996.
Brunsdon, C., Fotheringham, S., and Charlton, M.: Geographically weighted regression, J. Roy. Stat. Soc. D-Sta., 47, 431–443, 1998.
Cahill, M. and Mulligan, G.: Using geographically weighted regression to explore local crime patterns, Soc. Sci. Comput. Rev., 25, 174–193, 2007.
Cardozo, O. D., García-Palomares, J. C., and Gutiérrez, J.: Application of geographically weighted regression to the direct forecasting of transit ridership at station-level, Appl. Geogr., 34, 548–558, 2012.
Chen, J., Shaw, S.-L., Yu, H., Lu, F., Chai, Y., and Jia, Q.: Exploratory data analysis of activity diary data: a space–time GIS approach, J. Transp. Geogr., 19, 394–404, 2011.
Cleveland, W. S.: Robust locally weighted regression and smoothing scatterplots, J. Am. Stat. Assoc., 74, 829–836, 1979.
Crespo, R., Fotheringham, S., and Charlton, M.: Application of geographically weighted regression to a 19-year set of house price data in London to calibrate local hedonic price models, in: Proceedings of the 9th International Conference on Geocomputation, National University of Ireland Maynooth, 2007.
Cressie, N. and Wikle, C. K.: Statistics for spatio-temporal data, John Wiley & Sons, 2015.
Cressie, N. A.: Statistics for Spatial Data, John Willey & Sons, New York, 1991.
Du, Z., Wang, Z., Wu, S., Zhang, F., and Liu, R.: Geographically neural network weighted regression for the accurate estimation of spatial non-stationarity, Int. J. Geogr. Inf. Sci., 34, 1353–1377, 2020.
Fotheringham, A. S., Brunsdon, C., and Charlton, M.: Geographically weighted regression: the analysis of spatially varying relationships, John Wiley & Sons, 2003.
Fotheringham, A. S., Crespo, R., and Yao, J.: Geographical and temporal weighted regression (GTWR), Geogr. Anal., 47, 431–452, 2015.
Fotheringham, A. S., Yang, W., and Kang, W.: Multiscale geographically weighted regression (mgwr), Ann. Am. Assoc. Geogr., 107, 1247–1265, 2017.
Fraser, L. K., Clarke, G. P., Cade, J. E., and Edwards, K. L.: Fast food and obesity: a spatial analysis in a large United Kingdom population of children aged 13–15, Am. J. Prev. Med., 42, e77–e85, 2012.
Gelfand, A. E., Ecker, M. D., Knight, J. R., and Sirmans, C.: The dynamics of location in home price, J. Real Estate Financ., 29, 149–166, 2004.
Goodchild, M. F.: Prospects for a space–time GIS: Space–time integration in geography and GIScience, Ann. Assoc. Am. Geogr., 103, 1072–1077, 2013.
Hoaglin, D. C. and Welsch, R. E.: The hat matrix in regression and ANOVA, Am. Stat., 32, 17–22, 1978.
Huang, B., Wu, B., and Barry, M.: Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices, Int. J. Geogr. Inf. Sci., 24, 383–401, 2010.
Hurvich, C. M., Simonoff, J. S., and Tsai, C. L.: Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion, J. Roy. Stat. Soc. B Met., 60, 271–293, 1998.
Loader, C. R.: Bandwidth selection: classical or plug-in?, Ann. Stat., 27, 415–438, 1999.
Mennis, J. L. and Jordan, L.: The distribution of environmental equity: Exploring spatial nonstationarity in multivariate models of air toxic releases, Ann. Assoc. Am. Geogr., 95, 249–268, 2005.
Pace, R. K., Barry, R., Gilley, O. W., and Sirmans, C.: A method for spatial–temporal forecasting with an application to real estate prices, Int. J. Forecast., 16, 229–246, 2000.
PRISM Climate Group: PRISM Climate Data, available at: https://prism.oregonstate.edu, last access: 13 October 2019.
Que, X.: quexiang/STWR: STWR v1.0 (Version v1.0), Zenodo, https://doi.org/10.5281/zenodo.3637689, 2020.
Sun, T. Y., Conroy, G., Donner, E., Hungerbühler, K., Lombi, E., and Nowack, B.: Probabilistic modelling of engineered nanomaterial emissions to the environment: a spatio-temporal approach, Environ. Sci., 2, 340–351, 2015.
Takahashi, K., Kulldorff, M., Tango, T., and Yih, K.: A flexibly shaped space-time scan statistic for disease outbreak detection and monitoring, Int. J. Health Geogr., 7, 14, https://doi.org/10.1186/1476-072X-7-14, 2008.
Tobler, W. R.: A computer movie simulating urban growth in the Detroit region, Econ. Geogr., 46, 234–240, 1970.
USGS: GMTED2010 Viewer, available at: https://topotools.cr.usgs.gov/gmted_viewer/viewer.htm, last access: 13 October 2019.
Wang, W., Zhao, J., Cheng, Q., and Carranza, E. J. M.: GIS-based mineral potential modeling by advanced spatial analytical methods in the southeastern Yunnan mineral district, China, Ore Geol. Rev., 71, 735–748. https://doi.org/10.1016/j.oregeorev.2013.08.005, 2015.
Wheeler, D. C. and Waller, L. A.: Comparing spatially varying coefficient models: a case study examining violent crime rates and their relationships to alcohol outlets and illegal drug arrests, J. Geogr. Syst., 11, 1–22, 2009.
Wu, B., Li, R., and Huang, B.: A geographically and temporally weighted autoregressive model with application to housing prices, Int. J. Geogr. Inf. Sci., 28, 1186–1204, 2014.
Wu, S., Wang, Z., Du, Z., Huang, B., Zhang, F., and Liu, R.: Geographically and temporally neural network weighted regression for modeling spatiotemporal non-stationary relationships, Int. J. Geogr. Inf. Sci., 1–27, 2020.
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.
This paper presents a spatiotemporal weighted regression (STWR) model for exploring...