Development of spatially varying groundwater-drawdown functions for land subsidence estimation

Land subsidence caused by groundwater overexploitation is a critical global problem. Spatial distribution of land subsidence is crucial for environmental management and land planning due to the sensitive nature of land-surface gradients. Given the nonlinear relationship of subsidence and long-term drawdown from groundwater exploitation, and the heterogeneity of the aquifer in the Choshui River alluvial fan, a developed spatial regression model can effectively estimate nonlinear and spatially varying subsidence. That is, the root-mean-square-errors (RMSEs) of annual subsidences are less or equal to 0.8 cm. Considering various data inputs in the Choshui River alluvial fan, the spatial regression model offers a robust method for estimating the spatial patterns of subsidence using drawdown as observations. Results show that the largest water-level cone of depression occurs in the distal fan area. Nonetheless, the calculated subsidence bowl closely approximates the observed one located much farther inland. Without requiring extensive calibration or an elaborate numerical groundwater flow and subsidence model, the model provides reasonable and detailed patterns using a spatially varying relationship between drawdown and resulting land subsidence. Results indicate that the spatial regression model reasonably estimates the spatial distribution of the skeletal storage coefficient in the aquifer system. The large coefficient that represents inelastic compaction occurs in the inland areas, whereas the small coefficient that represents elastic compaction occurs along the coastal area. Furthermore, this model is relevant for water policy or land subsidence regulation.

Mapping regional subsidence rate from electricity consumption-based groundwater extraction

During 1992–2007, excessive pumping of groundwater caused large-scale aquifer-system compaction and land subsidence in the Choshui River Alluvial Fan, especially in the area of Yunlin county. The subsidence impedes surface-water runoff and endangers the operation of Taiwan High Speed Rail. Leveling, Global Positioning System (GPS), multi-level compaction monitoring well, and Differential Interferometric Synthetic Aperture Radar (DInSAR) are used to study the extent of subsidence in Yunlin and its mechanism. These sensors complement each other in spatial and temporal resolutions. A leveling network totaling 434 km in length was deployed to derive subsidence at every 1.5 km along the routes, and the result is accurate to few mm and shows a basin-like subsidence pattern centering at Tuku Township. Four multi-level compaction monitoring wells, co-located with GPS pillars, detect compactions at different depths, showing that the aquifer-system compaction (the cause of subsidence) occurs mostly below depths >200 m, where reduction of groundwater pumping is most needed. The vertical displacements from GPS and leveling agree to within 1 cm, and are larger than the cumulative compaction detected by the compaction-monitoring wells, suggesting that compaction also occurs below 300 m (the depth of the wells). The vertical displacements derived using DInSAR and 8 ENVISAT SAR images agree with the leveling result to 1–2 cm.

Spatial regression models are developed as a complementary alternative to second-order polynomial response surfaces in the context of process optimization. These models provide estimates of design variable effects and smooth, data-faithful approximations to the unknown response function over the design space. The predicted response surfaces are driven by the covariance structures of the models. Several structures, isotropic and anisotropic, are considered and connections with thin plate splines are reviewed. Estimation of covariance parameters is achieved via maximum likelihood and residual maximum likelihood. A feature of the spatial regression approach is the visually appealing graphical summaries that are produced. These allow rapid and intuitive identification of process windows on the design space for which the response achieves target performance. Relevant design issues are briefly discussed and spatial designs, such as the packing designs available in Gosset, are suggested as a suitable design complement. The spatial regression models also perform well with no global design, for example with data obtained from series of designs on the same space of design variables. The approach is illustrated with an example involving the optimization of components in a DNA amplification assay. A Monte Carlo comparison of the spatial models with both thin plate splines and second-order polynomial response surfaces for a scenario motivated by the example is also given. This shows superior performance of the spatial models to the second-order polynomials with respect to both prediction over the complete design space and for cross-validation prediction error in the region of the optimum. An anisotropic spatial regression model performs best for a high noise case and both this model and the thin plate spline for a low noise case. Spatial regression is recommended for construction of response surfaces in all process optimization applications.

In this study, we investigated the relation- ship between watershed characteristics and hydrology using high spatial resolution impervious surface area (ISA), hydrologic simulations and spatial regression. We selected 20 watersheds at HUC 12 level with different degrees of urbanization and performed hydro- logic simulation using a distributed object-oriented rainfall and runoff simulation model. We extracted the discharge per area and ratio of runoff to base flow from simulation results and used them as indicators of hydrology pattern. We derived percentage of ISA, distance from ISA to streams, and stream density as the watershed characteristics to evaluate the relationship with hydrology pattern in watersheds using ordinary least square, spatial error and spatial lag regression models. The comparison indicates that spatial lag regression model can achieve better performance for the evaluation of relationship between ratio of runoff to base flow and watershed characteristics, and that three models provide similar performance for the evaluation of relationship between discharge per area and watershed characteristics. The results from regression analyses demonstrate that ISA plays an important role in watershed hydrology. Ignorance of spatial dependence in analyses will likely cause inaccurate evaluation for relationship between ISA and watershed hydrology. The hydrologic model, regression methods and relationships between watershed characteristics and hydrology pat- tern provide important tools and information for deci- sion makers to evaluate the effect of different scenarios in land management.

In this paper, we propose a Spatial Robust Mixture Regression model to investigate the relationship between a response variable and a set of explanatory variables over the spatial domain, assuming that the relationships may exhibit complex spatially dynamic patterns that cannot be captured by constant regression coefficients. Our method integrates the robust finite mixture Gaussian regression model with spatial constraints, to simultaneously handle the spatial non-stationarity, local homogeneity, and outlier contaminations. Compared with existing spatial regression models, our proposed model assumes the existence a few distinct regression models that are estimated based on observations that exhibit similar response-predictor relationships. As such, the proposed model not only accounts for non-stationarity in the spatial trend, but also clusters observations into a few distinct and homogenous groups. This provides an advantage on interpretation with a few stationary sub-processes identified that capture the predominant relationships between response and predictor variables. Moreover, the proposed method incorporates robust procedures to handle contaminations from both regression outliers and spatial outliers. By doing so, we robustly segment the spatial domain into distinct local regions with similar regression coefficients, and sporadic locations that are purely outliers. Rigorous statistical hypothesis testing procedure has been designed to test the significance of such segmentation. Experimental results on many synthetic and real-world datasets demonstrate the robustness, accuracy, and effectiveness of our proposed method, compared with other robust finite mixture regression, spatial regression and spatial segmentation methods.

For a regional land subsidence problem, there are difficulties for the traditional finite element method to solve the regional groundwater flow equation involved. A new finite element method - multiscale finite element method (MsFEM) applied to resolve three dimensional groundwater flow problem- is introduced. Three advantages make the MsFEM very useful for groundwater flow simulation in a large region with highly heterogeneous porous media. One advantage is that this method can significantly save computing efforts by coarse grid mesh. The variation of parameters in an element is brought to the multiscale base functions. Second one is that this method can to a large extent avoid large aspect ratios of elements by combining aquifers and aquitards in one element. The third advantage is that the hydraulic heads in different depths of aquitards or aquifers could be rather accurately interpolated using the multiscale base functions and the nodal heads of the element by MsFEM. The third advantage is useful for the subsidence modeling. A numerical example is done to verify the advantages of MsFEM. Finally, the MsFEM is applied to solve the regional land subsidence model of Shanghai, which includes a three-dimensional groundwater flow mathematical model and a one-dimensional subsidence model. The traditional two-step approach is followed in this study, with the hydrodynamics of the pumped multiaquifer system first simulated by a 3-D groundwater flow model and the subsidence then computed with a 1-D subsidence model with the pore pressure field specified as an external distributed source of strength within the porous medium. The simulation period is from March, 1986 to December, 1998. Using MsFEM, the aquifer system in Shanghai is divided into six layers, which means one aquifer and one aquitard are in one layer with an average thickness of about 50 meters. Every element in MsFEM is subdivided into 7 sub-layers in the vertical direction and every horizontal layer in an element is divided into 4 hexahedral elements. So each element is subdivided into 28 hexahedral sub-cells, which allows the heterogeneity of aquifers and aquitards to be considered by multiscale base functions. The aquifers system in total is discretized into 19,996 elements with a total of 25,000 nodes. The hydraulic heads in any depth of aquitards or aquifers could be interpolated using the multiscale base functions and the nodal heads of the element. It is very useful for the settlement calculation for the aquitards or aquifers after hydraulic heads at nodes of elements are obtained by MsFEM. The application of MsFEM significantly saves computing efforts, effectively decreases the aspect ratios of elements and obtains good results, which proves the MsFEM can be applied to solve the real regional 3D groundwater flow problems, and it is an effective numerical method for regional land subsidence modeling.

Spatial modeling of the schistosomiasis mansoni in Minas Gerais State, Brazil using spatial regression

The paper aims to explore the possible determinants of declining child sex ratio (CSR) in India across 32 state and UTs over time (viz. 1971–2011). The spatial panel data regression results are assumed to be better compared to simple panel because the CSR is very much influenced by space and neighbouring state’ CSR. This is confirmed by both global (and local Moran Index) in one hand and the Hausman test (fixed vs. random effect) on the other hand. The spatial panel regression results show that income and CSR are nonlinear with U-shaped pattern; the other socio-economic variables like female literacy rate, urbanization, poverty and female work participation do not appear to be significant. The state sharing higher concentration of scheduled tribe community positively improve CSR in all the spatial regression models; the opposite happens in case of scheduled caste dominated state. Our spatial panel regression findings contradict earlier findings of the determinants of CSR in case of income and female literacy. The U-shaped relationship between income and CSR does have some policy relevance; income of any state may go up due to various factors. If we argue in the context of female labour force participation, we find that the female work force participation is found to be stagnant, and even in some state it is declining over the decades. Therefore, female employment directly augments female autonomy as well as empowerment, which indirectly increases income and thereby a state may cross that cut-off level of income and may experience higher CSR.

Estimating spatial logistic model: A deterministic approach or a heuristic approach?

Spatial regression model is used to determine the relationship between the dependent and independent variables with spatial influence. In case only independent variables are affected, Spatial Cross Regressive (SCR) Model is formed. Spatial Autoregressive (SAR) occurs when the dependent variables are affected, while Spatial Durbin Model (SDM) exists when both variables exhibit effects. The inaccuracy of the spatial regression model can be caused by outlier observations. Removing outliers in the analysis changes the spatial effects composition on data. However, using robust spatial regression is one way of overcoming the outliers in the model. Moreover, the typical parameter coefficients, which are robust against the outliers, are estimated using M-estimator. The research develops the life expectancy model in Central Java Province through Robust-SCR, Robust-SAR, and Robust-SDM to reduce spatial outliers' effect. The model is developed based on educational, health and economic factors. According to the results, M-estimator accommodates the outliers’ existence in the spatial regression model. This is indicated by an increase in R2 value and a decrease in MSE caused by the change in the estimating coefficient parameters. In this case, Robust-SDM is the best model since it has the biggest R2 value and the smallest MSE.

Land surface temperature (LST) is the instantaneous radiative skin temperature of land obtained from satellite sensors. Measured by visible, infrared or microwave sensors, the LST is useful in determining thermal comfort for urban planning. It also serves as a precursor to many underlying impacts such as health, climate change and the likelihood of rainfall. Due to the paucity of observed data because of cloud cover or rain-bearing clouds in the case of microwave sensors, it is necessary to model LST for the purpose of forecasting. Two spatial regression models were employed: the spatial lag model and the spatial error model. Using Landsat 8 and Shuttle Radar Topography Mission (SRTM), these models can be studied and compared in terms of their robustness in reproducing LST. Whereas LST is to be the independent variable, built-up area, water surface, albedo, elevation, and vegetation are to be considered as dependent variables and their relative contributions to LST examined.•Modeling LST based on spatial regression models with calculated LST as independent variable.•Dependent variables to be considered are normalised difference Built-up index (NDBI), normalised difference vegetation index (NDVI), modified normalised difference water index (MNDWI), albedo and elevation.•The models were validated using k-fold cross validation method, mean square error and standard deviation.

Studies suggest that urban form can influence microclimate regulation. Remote sensing studies have contributed to these findings through analysis of high-resolution land cover maps, landscape ecology metrics, and thermal imagery. Collectively, these have been referred to as land cover configuration studies. There are three objectives to this study. The first is to assess the relationship between nighttime land surface temperatures (LST) and land cover configuration and composition. The second objective is to outline a comprehensive methodology that includes ordinary least squares (OLS), spatial regression, variable selection, and multicollinearity analysis. Our last objective is to test three hypotheses about the relationship between LST and land cover, which can briefly be described as: 1) the importance of land-use regimes in modeling LST from land cover composition and configuration variables; 2) the strength of the correlation between LST and roads, buildings, and vegetation; and 3) the improved quality of models using landscape metrics in modeling the relationship between LST and land cover. Based on 16 different models (8 OLS, 8 spatial regression) we could confirm the above hypotheses, but we found that the configuration of buildings, roads, and vegetation have a complex relationship with LST. Our interpretation of this complexity, combined with the strength of composition variables, is that parsimonious models, for now, are more useful to urban planners because they are more generalizable. Finally, spatial regression models of land cover configuration and LST demonstrated an improvement over non-spatial linear models (OLS). Spatial regression models reduced heteroskedasticity and clusters of residuals, and tempered coefficients, suggesting that the OLS models could be biased. OLS models were still found to be a valuable tool for exploratory analysis.

Land subsidence in Taiwan has increased rapidly over the past four decades, owing to heavy withdrawal of groundwater. Leveling surveys, layer compressions and groundwater level in individual layers were continuously monitored in the Choshui alluvial fan, western Taiwan. It was found that ground subsidence and layer compression were consistent with the variation of groundwater level. From multi-level compression monitoring, the layers with major compression deformation were identified. Clayey and sandy layers exhibited significant compression deformation. Conceptual models of compression behavior for clays and sands were studied based on the field data. The compression behavior of clays agreed with the Terzaghi consolidation theory. Notable, irrecoverable volumetric strains were also observed in sandy layers. They exhibited obvious elasto-plastic mechanical behavior. The high compressibility of the sand layer in the alluvial fan was attributed to the flaky sand grains in this geological region.

Modeling the impact of land use/land cover (LULC) factors on diurnal and nocturnal Urban Heat Island (UHI) intensities using spatial regression models