Regional-scale Model Research Articles

Deep learning-based ensemble modeling of Vibrio parahaemolyticus concentration in marine environment

Vibrio parahaemolyticus(V.p) is a marine pathogenic bacterium that poses a high risk to human health and shellfish industry, yet an effective regional-scale nowcasting model for managing the risk remains lacking. This study presents the first regional-scale model for nowcasting the level of V.p in oysters in the marine environment by developing an ensemble modeling approach. The ensemble modeling approach involves the integration of genetic programming (GP) and deep artificial neural networks (DNN)-based modeling. The new approach was demonstrated by developing three GP-DNN ensemble models for predicting the V.p level in North Carolina, New Hampshire, and the combined region. Specifically, GP was employed to establish nonlinear functions between the V.p level and antecedent conditions of environmental variables. The nonlinear GP functions and current conditions of individual environmental variables were then utilized as inputs into a DNN model, forming a GP-DNN ensemble model. Modeling results indicated that the GP-DNN ensemble models were capable of predicting the V.p level with the correlation coefficient of 0.91, 0.90, and 0.80 for North Carolina, New Hampshire, and the combined region, respectively, demonstrating the impact of distinct environmental conditions in the local areas on accuracy of the combined regional-scale model. Sensitivity analysis results showed that sea surface temperature and sea surface salinity are the two most important environmental predictors for the abundance of V.p in oysters, followed by water level, pH, chlorophyll-a, and turbidity. The findings suggested that the GP-DNN ensemble models could be utilized as effective predictive tools for mitigating the V.p risk.

Modelación multi-escala para la comprensión de los sistemas de flujo en un acuífero multicapa, Urabá-Colombia

Numerical modelling is a valuable tool which allows the characterisation of groundwater flow behaviour, its interactions, and the representation of different flow systems that occur in an aquifer. A regional-scale model requires general information to describe the flow trends. In contrast, local and intermediate-scale models require a more detailed knowledge of the flow boundaries, and more field information. In order to understand the groundwater flow in the aquifer system of the Eje Bananero del Urabá Antioqueño-Colombia, a multi-scale numerical model was implemented using MODFLOW®. This was performed following modelling protocols on three spatial scales in which the regional model provided the boundary conditions for the intermediate pattern, and the intermediate pattern for the local model. Finally, we represented some details that relate local and intermediate flows to regional flows, in which surface-groundwater interactions involving both shallow and deep ascending flows became evident. The results obtained in this work show the potential of multi-scale modelling as a tool to recognise and understand the groundwater flows in a layered aquifer system, which provide grounds for decision-making and proper management of hydrogeological systems.

Improving predictions of coastal benthic invertebrate occurrence and density using a multi-scalar approach

Spatial data detailing both the distribution and density of functionally important marine species are needed to inform management decisions. Species distribution models (SDMs) have proven helpful in this regard; however, models often focus only on species occurrences derived from spatially expansive datasets and lack the resolution and detail required to inform regional management decisions. Boosted regression trees (BRT) were used to produce high-resolution SDMs (250 m) at two spatial scales predicting probability of occurrence, abundance (count per sample unit), density (count per km2) and uncertainty for seven coastal seafloor taxa that vary in habitat usage and distribution to examine prediction differences and implications for coastal management. We investigated if small scale regionally focussed models (82,000 km2) can provide improved predictions compared to data-rich national scale models (4.2 million km2). We explored the variability in predictions across model type (occurrence vs abundance) and model scale to determine if specific taxa models or model types are more robust to geographical variability. National scale occurrence models correlated well with broad-scale environmental predictors, resulting in higher AUC (Area under the receiver operating curve) and deviance explained scores; however, they tended to overpredict in the coastal environment and lacked spatially differentiated detail for some taxa. Regional models had lower overall performance, but for some taxa, spatial predictions were more differentiated at a localised ecological scale. National density models were often spatially refined and highlighted areas of ecological relevance producing more useful outputs than regional-scale models. The utility of a two-scale approach aids the selection of the most optimal combination of models to create a spatially informative density model, as results contrasted for specific taxa between model type and scale. However, it is vital that robust predictions of occurrence and abundance are generated as inputs for the combined density model as areas that do not spatially align between models can be discarded. This study demonstrates the variability in SDM outputs created over different geographical scales and highlights implications and opportunities for managers utilising these tools for regional conservation, particularly in data-limited environments.

Upscaling Shear Strength of Heterogeneous Oil Sands with Interbedded Shales Using Artificial Neural Network

SummaryUnderstanding the shear strength of caprock shale and oil sands is important in risk assessment of slope stability in open-pit mining, caprock integrity of in-situ thermal recovery, and optimization of bitumen production from oil sands. A robust and efficient upscaling technique is essential to model the impact of heterogeneity on the deformation and failure of oil sands and caprock shale. Although conventional analytical and numerical upscaling techniques are available, many of these methods consider oversimplified assumptions and have high computational costs, especially when considering the impact of spatially correlated interbedded shales on the shear strength. A machine learning enhanced upscaling (MLEU) technique that leverages the accuracy of local numerical upscaling and the efficiency of artificial neural network (ANN) is proposed here. MLEU uses a fast and accurate ANN proxy model to predict the anisotropic shear strength of heterogeneous oil sands with interbedded shales. The R2 values of the trained ANN models exceed 0.94 for estimating shear strengths in horizontal and vertical directions. The deviation of upscaled shear strength from numerical upscaled results is improved by 12–76% compared with multivariate regression methods like response surface methodology (RSM) and polynomial chaos expansion (PCE). In terms of computational efficiency, the proposed MLEU method can save computational effort by two orders of magnitude compared with numerical upscaling. MLEU provides a reasonable estimate of anisotropic shear strength while considering uncertainties caused by different distributions of shale beddings. With the increasing demand for regional scale modeling of geomechanical problems, the proposed MLEU technique can be extended to other geological settings, where weak beddings play a significant role and the impact of heterogeneity on shear strength is important.

Improving the resolution of GRACE-based water storage estimates based on machine learning downscaling schemes

The applications of the Gravity Recovery and Climate Experiment (GRACE) on local scales are obstructed owing to the coarser spatial resolution of GRACE observations. Much attempts recently have been taken to improve the resolution of GRACE-based water storage estimates based on machine learning algorithms, focusing on new algorithm development. However, there are still two deficiencies in previous GRACE downscaling research, namely the selection of input variables and the scale of model construction. In this study, the partial least squares regression (PLSR) model firstly is employed to identify the representative predictors associated with GRACE observations. Then, the performance of two different downscaling schemes (namely pixel-scale and regional-scale models) are comprehensively investigated, based on a machine learning algorithm known as random forest, to enhance the resolution of GRACE-based water storage estimates to the grid resolution as small as 5 km. The downscaled results are validated against hydrological model simulations and a number of in-situ groundwater level measurements within one of most rapidly urbanized basin in China, Haihe River Basin. The PLSR model recognizes four variables (namely evapotranspiration, temperature, land surface temperature, and soil moisture) as the predominant factors, acting as the predictors of downscaling models. Starting with the GRACE observations, two kinds of pixel and regional downscaling schemes are developed. The downscaled results were consistent each other and with the original GRACE data at a broad scale with the correlation up to 0.98. It was found that there was 3.20 times deviation of the results from the model simulation in computation of groundwater depletion rates within plain areas. In-situ water level measurements highlight that the downscaling models are improved by 36.95 % and 23.25 % in correlation relative to the original GRACE data and the simulated groundwater storage anomalies, respectively. Generally, the pixel-scale model is slightly better than the regional-scale model with 69 % (171 out of 249 observation wells) of higher correlation and 53 % (131 out of 249 observation wells) of smaller root mean squared error. The high-resolution results presented in this study can lead to better understanding on regional water resources and provide quantitative information to water management considering irrigation water use and groundwater consumption.

Evaluation of multidecadal high-resolution atmospheric chemistry-transport modelling for exposure assessments in the continental Nordic countries

This study presents a comprehensive evaluation of the combination of the regional scale chemistry-transport model DEHM (Danish Eulerian Hemispheric Model) and the Gaussian plume-in-grid model UBMv10 (Urban Background Model). The focus of the study was centred around the following research question: the combination of an Eulerian regional scale approach and a Gaussian high-resolution/local scale approach improves the performance from evaluation with measurements compared to the Eulerian regional scale approach alone. We also investigated the research question that the integrated Eulerian/Gaussian approach has a similar performance as Eulerian models set up with the same high spatial resolution. The DEHM/UBM model has been run for a domain covering Denmark, Finland, Norway and Sweden with a 1 km × 1 km spatial resolution, producing hourly concentration estimates for four decades, 1979–2018. The results were evaluated against rural and urban background measurements in the four countries and the performance of the DEHM/UBM model was compared to the performance of the DEHM model based on a similar evaluation. The comparison showed that the DEHM/UBM model, in general, performs similar to the DEHM model, however, DEHM/UBM captures the interannual variability better in most cases. The DEHM/UBM model results for 2015 were also compared with corresponding high-resolution results from the chemistry-transport models SILAM (System for Integrated modeLling of Atmospheric coMposition) and MATCH (Multi-scale Atmospheric Transport and CHemistry model) for the four Nordic capitals; Copenhagen, Helsinki, Oslo and Stockholm. This model comparison was carried out to evaluate and compare the performance of the relatively simple, yet computationally fast approach of the DEHM/UBM model setup to the more time-consuming approaches of applied regional scale models on very high-resolution. The DEHM/UBM model performed similarly to SILAM and MATCH for most components, however, the 3D models performed better with respect to capturing the differences between rural and urban settings for the four capitals.

Improving the particle dry deposition scheme in the CMAQ photochemical modeling system

Dry deposition of atmospheric aerosols in large-scale models is a critical, but highly uncertain, sink process with a strong dependence on particle size, meteorological conditions, and land surface properties. This study investigates the particle dry deposition scheme implemented in the standard Community Multiscale Air Quality (CMAQ) model v5.2.1, characterizes its underlying parameterized components with comparison to a similar scheme in a contemporary regional-scale model, and proposes two updated schemes that are then evaluated with available ambient particle deposition velocity (Vd) measurements. Both updated schemes reduce the surprisingly strong dependence of deposition velocity on the aerosol mode width, with one scheme further introducing a dependence on vegetation coverage that is broadly consistent with variability in observations between vegetated and non-vegetated surfaces. Compared to the base scheme, the updated scheme with vegetation dependence increases Vd for submicron particles and decreases it for larger particles by an average of 37% and −66%, respectively. This scheme performs statistically better than the base scheme, reducing fractional biases by 56%–97% for vegetated land-use types and has roughly equivalent performance over water. The base and updated schemes are tested with three annual CMAQ (v5.2.1) simulations for the year 2011; predicted ambient aerosol concentrations are evaluated with routine monitoring network observations and predicted dry deposition fluxes are evaluated with data from the Clean Air Status and Trends Network (CASTNET). The updated scheme with vegetation dependence reduces negative fractional biases for PM10 by 41% and positive fractional biases for PM2.5 organic carbon by 15%. This scheme has been incorporated into the most recent publicly accessible versions of CMAQ (v5.3 and beyond) to replace the scheme used in previous versions of CMAQ (v4.5 through v5.2.1).

Modeling water and nitrogen dynamics from processing tomatoes under different management scenarios in the San Joaquin Valley of California

Study regionThe Southern San Joaquin Valley (SSJV), California. Four representative soil types were evaluated. Study focusEvaluation of regional scale hydrology and nitrogen (N) fate and transport requires a robust modeling framework that considers critical local scale environmental conditions and crop root zone physical processes. Two widely used yet conceptually and mechanistically different models, HYDRUS(2D/3D) and SWAT (Soil and Water Assessment Tool), were used to investigate water and N dynamics in processing tomato fields across a range of soil types and management scenarios over a 30-year period in the SSJV. New hydrological insights for the regionThis study implements a framework to evaluate a regional scale model against a field scale model and provides a framework for assessing percolation and N transport results from regional scale simulations in the absence of extensive field observations. Field scale models developed in HYDRUS (2D/3D) produced similar results to a regional-scale SWAT model in four specific soils in terms of responses to percolation and N losses as a function of varying N fertilizer and irrigation management practices. This model-to-model agreement at the local scale provides confidence in region-scale hydrological and N fate and transport findings, particularly in the absence of extensive field observations. Both models suggest that promoting conservation practices targeting the optimization of irrigation and fertilizer-N management would have a large impact in protecting regional groundwater quality.

Role of deep convection in regulating the Indian summer monsoon dynamics: a regional scale modelling study

Role of deep convection in regulating the Indian summer monsoon dynamics: a regional scale modelling study

Regional-scale modeling of rainfall-induced landslides under random rainfall patterns

The importance of rainfall patterns in triggering landslides has been recognized worldwide. In this study, the triggering and evolution of rainfall-induced landslides were investigated by a regional-scale physical model based on one-dimensional Richard equation and infinite slope model, where a bounded random cascade model (BRCM) was adopted to generate random rainfall patterns (RRPs) for rainfall events derived from extreme value analysis. The results indicated that the RRPs affect the evolution of triggered landslide and the cumulative landslide area. The shape of RRPs determined the time evolution of triggered landslide. Due to the randomness of rainfall peaks under RRPs and limited hydraulic conductivity of soil, an upper bound existed for the cumulative landslide area under RRPs and the dispersion of the cumulative landslide area increased with an increase in the return period. This study aids in the understanding of the diversity and discipline of the evolution of rainfall-induced landslides.

Influence of the recent winter Arctic sea ice loss in short-term simulations of a regional atmospheric model

Notable changes in the wintertime Arctic atmospheric circulation have occurred over the last few decades. Despite its importance in understanding the recent changes in the Northern Hemisphere midlatitude climate, it remains unclear whether and how these changes are affected by recent Arctic sea ice loss. In this study, a regional scale model is used to separate the direct sea ice influence from the natural variability of large-scale atmospheric circulation. Results show that, in response to sea ice loss, the increase of geopotential height in the mid-to-upper troposphere is robust across the simulations, but the magnitude of the response is highly dependent on the background state of the atmosphere. In most cases the sea ice loss-induced atmospheric warming is trapped near the surface due to the high vertical stability of winter Arctic lower troposphere, accordingly, resulting in a small response of geopotential height. However, when a low-pressure system is located over the Barents Sea, the relatively weak stability allows an upward transport of the surface warming, causing a significantly larger geopotential height increase. This strong state-dependence of atmospheric response which is also found in recent studies using global-scale model experiments, highlights the importance of accurately representing the atmospheric background state for numerical model assessments of sea ice influence.

Modelling supraglacial debris-cover evolution from the single-glacier to the regional scale: an application to High Mountain Asia

Abstract. Currently, about 12 %–13 % of High Mountain Asia’s glacier area is debris-covered, which alters its surface mass balance. However, in regional-scale modelling approaches, debris-covered glaciers are typically treated as clean-ice glaciers, leading to a bias when modelling their future evolution. Here, we present a new approach for modelling debris area and thickness evolution, applicable from single glaciers to the global scale. We derive a parameterization and implement it as a module into the Global Glacier Evolution Model (GloGEMflow), a combined mass-balance ice-flow model. The module is initialized with both glacier-specific observations of the debris' spatial distribution and estimates of debris thickness. These data sets account for the fact that debris can either enhance or reduce surface melt depending on thickness. Our model approach also enables representing the spatiotemporal evolution of debris extent and thickness. We calibrate and evaluate the module on a selected subset of glaciers and apply GloGEMflow using different climate scenarios to project the future evolution of all glaciers in High Mountain Asia until 2100. Explicitly accounting for debris cover has only a minor effect on the projected mass loss, which is in line with previous projections. Despite this small effect, we argue that the improved process representation is of added value when aiming at capturing intra-glacier scales, i.e. spatial mass-balance distribution. Depending on the climate scenario, the mean debris-cover fraction is expected to increase, while mean debris thickness is projected to show only minor changes, although large local thickening is expected. To isolate the influence of explicitly accounting for supraglacial debris cover, we re-compute glacier evolution without the debris-cover module. We show that glacier geometry, area, volume, and flow velocity evolve differently, especially at the level of individual glaciers. This highlights the importance of accounting for debris cover and its spatiotemporal evolution when projecting future glacier changes.

Evaluation of PM2.5 Retention Capacity and Structural Optimization of Urban Park Green Spaces in Beijing

Green space can effectively retain particles and improve air quality. However, most studies have focused on leaf-scale measurements or regional-scale model simulations, and few focus on individual trees. In this study, 176 urban park green spaces were selected within the 5th Ring Road in Beijing, and the i-Tree Eco model was used to estimate the PM2.5 retention levels of individual trees and sample plots. The results show that the retention capacity varied according to tree species. The PM2.5 retention of each sample plot was significantly affected by the tree coverage, species richness, mean tree height, mean crown width, and number of trees. The PM2.5 retention of urban park green spaces in the study area was estimated to be about 6380 t·year−1, and the air quality improvement rate was 1.62%. After structural optimizing, PM2.5 retention of the mixed coniferous and broad-leaved green space was as high as 80,000 g·year−1. This study studied the effects of trees on PM2.5 retention at multiple scales to fill the gaps in existing research at the scales of individual trees and communities, and it can serve as a reliable reference for the design and construction of green spaces aimed at improving air quality.

Exploring the Potential of Long Short‐Term Memory Networks for Improving Understanding of Continental‐ and Regional‐Scale Snowpack Dynamics

AbstractAccurate estimation of the spatio‐temporal distribution of snow water equivalent is essential given its global importance for understanding climate dynamics and climate change, and as a source of fresh water. Here, we explore the potential of using the Long Short‐Term Memory (LSTM) network for continental and regional scale modeling of daily snow accumulation and melt dynamics at 4‐km pixel resolution across the conterminous US (CONUS). To reduce training costs (data are available for ∼0.31 million snowy pixels), we combine spatial sampling with stagewise model development, whereby the network is first pretrained across the entire CONUS and then subjected to regional fine‐tuning. Accordingly, model evaluation is focused on out‐of‐sample predictive performance across space (analogous to the prediction in ungauged basins problem). We find that, given identical inputs (precipitation, temperature, and elevation), a single CONUS‐wide LSTM provides significantly better spatio‐temporal generalization than a regionally calibrated version of the physical‐conceptual temperature‐index‐based SNOW17 model. Adding more meteorological information (dew point temperature, vapor pressure deficit, longwave radiation, and shortwave radiation) further improves model performance, while rendering redundant the local information provided by elevation. Overall, the LSTM exhibits better transferability than SNOW17 to locations that were not included in the training data set, reinforcing the advantages of structure learning over parameter learning. Our results suggest that an LSTM‐based approach could be used to develop continental/global‐scale systems for modeling snow dynamics.

In Situ Visualization of Regional-Scale Natural Hazards With Galaxy and Material Point Method

Visualizing regional-scale landslides is essential to conveying the threat of natural hazards to stakeholders and policymakers. Traditional visualization techniques are restricted to post-processing a limited subset of simulation data and are not scalable to rendering regional-scale models. In situ visualization is a technique of rendering simulation data in real-time, i.e., rendering visuals in tandem while the simulation is running. This study develops a scalable N:M interface architecture to visualize regional-scale landslides. We demonstrate the scalability of the architecture by simulating the long runout of the 2014 Oso landslide using the Material Point Method coupled with the Galaxy ray tracing engine rendering 4.2 million material points as spheres. In situ visualization has an amortized runtime increase of 2% compared to non-visualized simulations. The developed approach can achieve in situ visualization of regional-scale landslides with minimal impact on the simulation process.

Modelling the influence of Tidal Energy Converters on sediment dynamics in Banks Strait, Tasmania

As the tidal energy industry grows, developers are interested in the influence that large scale arrays might have on their surrounding environment including hydrodynamic and sediment. Understanding of local and regional sediment dynamics is an important element for environment impact and site assessments, as changes in sediment processes could alter bed ecosystems. In Australia, the Banks Strait located in the Northeast of Tasmania has been identified as a promising site for tidal energy. Very large sand waves have been observed near the area suitable for tidal turbines installation and they may interfere with human activities. Building from previous work, a 2D regional scale model investigated the influence of different tidal farms on the morphology of Banks Strait and especially the sand waves. Results showed that tidal farm of more than 49 MW rated power will alter the circulation of residual currents in Banks Strait leading to significant changes in the sediment dynamics, and farms less than 24.5 MW will have negligible influence on the migration of sand waves.

Agronomic practices preventing local outbreaks of rice yellow mottle virus disease revealed by spatial autoregressive analysis

Rice yellow mottle virus (RYMV) causes severe rice (Oryza sativa L.) yield loss. It has been endemic to sub-Saharan Africa and Madagascar since 1966. Transmission (plant community level) and long-dispersal (regional and continental scale) models have been established but viral spread in farming communities continues, while the conditions causing local disease outbreaks remain unclear. We hypothesized that local outbreaks, comprising inter-plot virus spread and intra-plot disease aggravation, are significantly associated with individual farmers’ attributes and agronomic practices. To test this hypothesis, spatial autoregressive models were constructed using variables collected by visual observation and farmer interviews. Field surveys were conducted during four consecutive cropping seasons from 2011 to 2013 in the Lower Moshi Irrigation Scheme of Kilimanjaro, Tanzania. Our models detected spatial dependence in inter-plot virus spread, but not in intra-plot disease aggravation. The probability of inter-plot virus spread increased with use of the IR64 cultivar (26.9%), but decreased with straw removal (27.8%) and crop rotation (47.7%). The probability of intra-plot disease aggravation decreased with herbicide application (24.3%) and crop rotation (35.4%). A simple cost-benefit analysis suggested that inter-plot virus spread should be mitigated by cultivar replacement and straw removal. When disease severity is critical, intra-plot disease aggravation should be inhibited by herbicide application, and rice should be rotated with other crops. This is the first study to upscale the spatial autoregressive model from the experimental field level to the farming community level, by obtaining variables through easy-to-implement techniques such as visual observation and farmer interview. Our models successfully identified candidate agronomic practices for the control of RYMV. However, as the causal relationships between agronomic practices and RYMV outbreaks remain unknown, field trials are needed to develop robust control measures.

Fast calculation of spatial sensitivity kernels for scattered waves in arbitrary heterogeneous media using graph theory

SUMMARYP-to-S and S-to-P receiver functions are widely used to constrain the seismic discontinuity structures of the Earth. Typically, receiver functions are projected to the depth and location of conversion assuming a 1-D layered Earth structure. Receiver function finite frequency sensitivity kernels have the potential to increase resolution. Here we present a method for rapidly calculating the P- and S-wave receiver function sensitivity kernels, based upon the shortest path method and Dijkstra's algorithm to calculate the traveltime fields, and accounting for geometrical spreading in heterogeneous media. The validity of the approach is evaluated by comparing with amplitudes derived from a finite difference elastic full waveform simulation in a complex subduction zone geometry. We show P-to-S and S-to-P kernels calculated using our method for three examples cases: a half space, a layer with topography, and a sinusoidal discontinuity. We also demonstrate the kernel recovery of discontinuities with these topographies by inverting synthetic data from SPECFEM2D. We find that P-to-S kernels recover the structure of strong topography better than S-to-P kernels, although S-to-P kernels may be useful in some situations. P-to-S kernels also show better recovery of the amplitude of the discontinuities in comparison to S-to-P, although both typically achieve values within a few percent of the input model. The computational cost of our approach for improved kernel calculation in heterogeneous media is up to a few tens of seconds per station for typical regional scale models on the scale of several 100s of kilometres.

A Structural Equation Modeling Approach to Disentangling Regional-Scale Landscape Dynamics in Ghana

Land cover (LC) change is an integrative indicator of changes in ecosystems due to anthropogenic or natural forcings. There is a significant interest in the investigation of spatio-temporal patterns of LC transitions, and the causes and consequences thereof. While the advent of satellite remote sensing techniques have enhanced our ability to track and measure LC changes across the globe, significant gaps remain in disentangling specific factors that influence, or in certain cases, are influenced by, LC change. This study aims to investigate the relative influence of regional-scale bioclimatology and local-scale anthropogenic factors in driving LC and environmental change in Ghana. This analysis builds upon previous research in the region that has highlighted multiple drivers of LC change in the region, especially via drivers such as deforestation, urbanization, and agricultural expansion. It used regional-scale remotely sensed, demographic, and environmental data for Ghana across 20 years and developed path models on causal factors influencing LC transitions in Ghana. A two-step process is utilized wherein causal linkages from an exploratory factor analysis (EFA) are constrained with literature-based theoretical constructs to implement a regional-scale partial least squares path model (PLSPM). The PLSPM reveals complex interrelationships among drivers of LC change that vary across the geography of Ghana. The model suggests strong effects of local urban expansion on deforestation and vegetation losses in urban and peri-urban areas. Losses of vegetation are in turn related to increases in local heating patterns indicative of urban heat island effects. Direct effects of heat islands are however masked by strong latitudinal gradients in climatological factors. The models confirm that decreases in vegetation cover results in increased land surface albedo that is indirectly related to urban and population expansion. These empirically-estimated causal linkages provide insights into complex spatio-temporal variations in potential drivers of LC change. We expect these models and spatial data products to form the basis for detailed investigations into the mechanistic underpinnings of land cover dynamics across Ghana. These analyses are aimed at building a template for methods that can be utilized to holistically design spatially-disaggregated strategies for sustainable development across Ghana.

Generalized mass-conservative finite volume framework for unified saturated–unsaturated subsurface flow

Numerical modeling of saturated–unsaturated flow in porous media is very challenging due to the strong nonlinearity of the Richards Equation in the unsaturated flow zone. Robust and efficient numerical models are essential to simulate the flow dynamics of regional-scale subsurface systems accurately. In this paper, we have presented generalized mass-conservative Finite Volume solvers for pressure head-based and mixed forms of Richards Equation named subsurfaceFlowFOAM, developed using OpenFOAM® framework. The solution of Richards Equation often encounters numerical instabilities and convergence issues owing to the inherent nonlinearity of the soil–water characteristic relations and sharp variations in hydrological conditions over time. In this work, we have proposed a stabilized and adaptive time-stepping algorithm that adjusts the time-step size following the solution requirements ensuring smooth convergence. We have further implemented the parallel computation technique through the domain decomposition method to accelerate the computation process for large-scale simulations involved in regional-scale modeling. The numerical results of subsurfaceFlowFOAM depict excellent correspondence with the data presented in the literature for a wide variety of subsurface flow problems with deficient cumulative mass balance error. The solution accuracy and the computational efficiency of the solvers ensure their potential applicability to solve multi-dimensional regional-scale subsurface flow problems.