Estimating or Choosing a Geostatistical Model

The Hindu Kush Himalayan (HKH) region is one of the most ecologically vulnerable regions in the world. Several studies have been conducted on the dynamic changes of grassland in the HKH region, but few have considered grassland net ecosystem productivity (NEP). In this study, we quantitatively analyzed the temporal and spatial changes of NEP magnitude and the influence of climate factors on the HKH region from 2001 to 2018. The NEP magnitude was obtained by calculating the difference between the net primary production (NPP) estimated by the Carnegie–Ames Stanford Approach (CASA) model and the heterotrophic respiration (Rh) estimated by the geostatistical model. The results showed that the grassland ecosystem in the HKH region exhibited weak net carbon uptake with NEP values of 42.03 gC∙m−2∙yr−1, and the total net carbon sequestration was 0.077 Pg C. The distribution of NEP gradually increased from west to east, and in the Qinghai–Tibet Plateau, it gradually increased from northwest to southeast. The grassland carbon sources and sinks differed at different altitudes. The grassland was a carbon sink at 3000–5000 m, while grasslands below 3000 m and above 5000 m were carbon sources. Grassland NEP exhibited the strongest correlation with precipitation, and it had a lagging effect on precipitation. The correlation between NEP and the precipitation of the previous year was stronger than that of the current year. NEP was negatively correlated with temperature but not with solar radiation. The study of the temporal and spatial dynamics of NEP in the HKH region can provide a theoretical basis to help herders balance grazing and forage.

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Oral rehydration therapies in Senegal, Mali, and Sierra Leone: a spatial analysis of changes over time and implications for policy

BMC Medicine ◽

10.1186/s12916-020-01857-7 ◽

2020 ◽

Vol 18 (1) ◽

Author(s):

Kirsten E. Wiens ◽

Lauren E. Schaeffer ◽

Samba O. Sow ◽

Babacar Ndoye ◽

Carrie Jo Cain ◽

...

Keyword(s):

Sierra Leone ◽

Healthcare Access ◽

Scale Up ◽

National Policy ◽

Geostatistical Model ◽

Oral Rehydration ◽

Severe Diarrhea ◽

Before And After ◽

Changes Over Time ◽

Over Time

Abstract Background Oral rehydration solution (ORS) is a simple intervention that can prevent childhood deaths from severe diarrhea and dehydration. In a previous study, we mapped the use of ORS treatment subnationally and found that ORS coverage increased over time, while the use of home-made alternatives or recommended home fluids (RHF) decreased, in many countries. These patterns were particularly striking within Senegal, Mali, and Sierra Leone. It was unclear, however, whether ORS replaced RHF in these locations or if children were left untreated, and if these patterns were associated with health policy changes. Methods We used a Bayesian geostatistical model and data from household surveys to map the percentage of children with diarrhea that received (1) any ORS, (2) only RHF, or (3) no oral rehydration treatment between 2000 and 2018. This approach allowed examination of whether RHF was replaced with ORS before and after interventions, policies, and external events that may have impacted healthcare access. Results We found that RHF was replaced with ORS in most Sierra Leone districts, except those most impacted by the Ebola outbreak. In addition, RHF was replaced in northern but not in southern Mali, and RHF was not replaced anywhere in Senegal. In Senegal, there was no statistical evidence that a national policy promoting ORS use was associated with increases in coverage. In Sierra Leone, ORS coverage increased following a national policy change that abolished health costs for children. Conclusions Children in parts of Mali and Senegal have been left behind during ORS scale-up. Improved messaging on effective diarrhea treatment and/or increased ORS access such as through reducing treatment costs may be needed to prevent child deaths in these areas.

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Magnetotelluric static shift: Estimation and removal using the cokriging method

Geophysics ◽

10.1190/1.2400625 ◽

2007 ◽

Vol 72 (1) ◽

pp. F25-F34 ◽

Cited By ~ 11

Author(s):

Benoit Tournerie ◽

Michel Chouteau ◽

Denis Marcotte

Keyword(s):

Apparent Resistivity ◽

A Priori ◽

Shift Factor ◽

Static Shift ◽

Geostatistical Model ◽

Data Set ◽

Resistivity Sounding ◽

The Difference ◽

Cross Variogram

We present and test a new method to correct for the static shift affecting magnetotelluric (MT) apparent resistivity sounding curves. We use geostatistical analysis of apparent resistivity and phase data for selected periods. For each period, we first estimate and model the experimental variograms and cross variogram between phase and apparent resistivity. We then use the geostatistical model to estimate, by cokriging, the corrected apparent resistivities using the measured phases and apparent resistivities. The static shift factor is obtained as the difference between the logarithm of the corrected and measured apparent resistivities. We retain as final static shift estimates the ones for the period displaying the best correlation with the estimates at all periods. We present a 3D synthetic case study showing that the static shift is retrieved quite precisely when the static shift factors are uniformly distributed around zero. If the static shift distribution has a nonzero mean, we obtained best results when an apparent resistivity data subset can be identified a priori as unaffected by static shift and cokriging is done using only this subset. The method has been successfully tested on the synthetic COPROD-2S2 2D MT data set and on a 3D-survey data set from Las Cañadas Caldera (Tenerife, Canary Islands) severely affected by static shift.

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Comparison of the spatial patterns of schistosomiasis in Zimbabwe at two points in time, spaced twenty-nine years apart: is climate variability of importance?

Geospatial health ◽

10.4081/gh.2017.505 ◽

2017 ◽

Vol 12 (1) ◽

Cited By ~ 2

Author(s):

Ulrik B. Pedersen ◽

Dimitrios-Alexios Karagiannis-Voules ◽

Nicholas Midzi ◽

Tkafira Mduluza ◽

Samson Mukaratirwa ◽

...

Keyword(s):

Climate Change ◽

Geographical Distribution ◽

Predictive Accuracy ◽

Weather Data ◽

Geostatistical Model ◽

Prevalence Data ◽

Time Period ◽

Long Time ◽

Potential Risk Factors ◽

The Impact

Temperature, precipitation and humidity are known to be important factors for the development of schistosome parasites as well as their intermediate snail hosts. Climate therefore plays an important role in determining the geographical distribution of schistosomiasis and it is expected that climate change will alter distribution and transmission patterns. Reliable predictions of distribution changes and likely transmission scenarios are key to efficient schistosomiasis intervention-planning. However, it is often difficult to assess the direction and magnitude of the impact on schistosomiasis induced by climate change, as well as the temporal transferability and predictive accuracy of the models, as prevalence data is often only available from one point in time. We evaluated potential climate-induced changes on the geographical distribution of schistosomiasis in Zimbabwe using prevalence data from two points in time, 29 years apart; to our knowledge, this is the first study investigating this over such a long time period. We applied historical weather data and matched prevalence data of two schistosome species (Schistosoma haematobium and S. mansoni). For each time period studied, a Bayesian geostatistical model was fitted to a range of climatic, environmental and other potential risk factors to identify significant predictors that could help us to obtain spatially explicit schistosomiasis risk estimates for Zimbabwe. The observed general downward trend in schistosomiasis prevalence for Zimbabwe from 1981 and the period preceding a survey and control campaign in 2010 parallels a shift towards a drier and warmer climate. However, a statistically significant relationship between climate change and the change in prevalence could not be established.

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Geostatistical Model for the Arab-D Reservoir, North ’Ain Dar Pilot, Ghawar Field, Saudi Arabia: An Improved Reservoir Simulation Model

GeoArabia ◽

10.2113/geoarabia0102267 ◽

1996 ◽

Vol 1 (2) ◽

pp. 267-284

Author(s):

John L. Douglas ◽

Keyword(s):

Saudi Arabia ◽

Fluid Flow ◽

Reservoir Rock ◽

Well Test ◽

Geostatistical Model ◽

Deterministic Models ◽

Modeling Approach ◽

The North ◽

Porosity And Permeability ◽

Ghawar Field

ABSTRACT The North ‘Ain Dar 3-D geocellular model consists of geostatistical models for electrofacies, porosity and permeability for a portion of the Jurassic Arab-D reservoir of Ghawar field, Saudi Arabia. The reservoir consists of a series of shallow water carbonate shelf sediments and is subdivided into 10 time-stratigraphic slices on the basis of core descriptions and gamma/porosity log correlations. The North ‘Ain Dar model includes an electrofacies model and electrofacies-dependent porosity and permeability models. Sequential Indicator Simulations were used to create the electrofacies and porosity models. Cloud Transform Simulations were used to generate permeability models. Advantages of the geostatistical modeling approach used here include: (1) porosity and permeability models are constrained by the electrofacies model, i.e. by the distribution of reservoir rock types; (2) patterns of spatial correlation and variability present in well log and core data are built into the models; (3) data extremes are preserved and are incorporated into the model. These are critical when it comes to determining fluid flow patterns in the reservoir. Comparison of model Kh with production data Kh indicates that the stratigraphic boundaries used in the model generally coincide with shifts in fluid flow as indicated by flowmeter data, and therefore represent reasonable flow unit boundaries. Further, model permeability and production estimated permeability are correlated on a Kh basis, in terms of vertical patterns of distribution and cumulative Kh values at well locations. This agreement between model and well test Kh improves on previous, deterministic models of the Arab-D reservoir and indicates that the modeling approach used in North ‘Ain Dar should be applicable to other portions of the Ghawar reservoir.

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Spatio-temporal modelling for non-stationary point referenced data

10.26686/wgtn.15147396 ◽

2021 ◽

Author(s):

Lindsay Morris

Keyword(s):

Random Forest ◽

Goodness Of Fit ◽

Clustering Algorithm ◽

Dependence Structure ◽

Geostatistical Model ◽

Study Region ◽

Global Estimate ◽

Covariance Functions ◽

Power Of The Test ◽

Spatio Temporal

Spatial and spatio-temporal phenomena are commonly modelled as Gaussian processes via the geostatistical model (Gelfand & Banerjee, 2017). In the geostatistical model the spatial dependence structure is modelled using covariance functions. Most commonly, the covariance functions impose an assumption of spatial stationarity on the process. That means the covariance between observations at particular locations depends only on the distance between the locations (Banerjee et al., 2014). It has been widely recognized that most, if not all, processes manifest spatially nonstationary covariance structure Sampson (2014). If the study domain is small in area or there is not enough data to justify more complicated nonstationary approaches, then stationarity may be assumed for the sake of mathematical convenience (Fouedjio, 2017). However, relationships between variables can vary significantly over space, and a ‘global’ estimate of the relationships may obscure interesting geographical phenomena (Brunsdon et al., 1996; Fouedjio, 2017; Sampson & Guttorp, 1992). In this thesis, we considered three non-parametric approaches to flexibly account for non-stationarity in both spatial and spatio-temporal processes. First, we proposed partitioning the spatial domain into sub-regions using the K-means clustering algorithm based on a set of appropriate geographic features. This allowed for fitting separate stationary covariance functions to the smaller sub-regions to account for local differences in covariance across the study region. Secondly, we extended the concept of covariance network regression to model the covariance matrix of both spatial and spatio-temporal processes. The resulting covariance estimates were found to be more flexible in accounting for spatial autocorrelation than standard stationary approaches. The third approach involved geographic random forest methodology using a neighbourhood structure for each location constructed through clustering. We found that clustering based on geographic measures such as longitude and latitude ensured that observations that were too far away to have any influence on the observations near the locations where a local random forest was fitted were not selected to form the neighbourhood. In addition to developing flexible methods to account for non-stationarity, we developed a pivotal discrepancy measure approach for goodness-of-fit testing of spatio-temporal geostatistical models. We found that partitioning the pivotal discrepancy measures increased the power of the test.

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Comprehensive evaluation of soil quality in a desert steppe influenced by industrial activities in northern China

Scientific Reports ◽

10.1038/s41598-021-96948-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Zhe Xu ◽

Wenbao Mi ◽

Nan Mi ◽

Xingang Fan ◽

Yao Zhou ◽

...

Keyword(s):

Soil Quality ◽

Quality Index ◽

Northern Region ◽

Main Element ◽

Available Phosphorus ◽

Desert Steppe ◽

Geostatistical Model ◽

Soil Quality Index ◽

Industrial Activities ◽

The Impact

AbstractDesert steppe soil security issues have been the focus of attention. Therefore, to understand the impact of industrial activities on the soil quality of desert grasslands, this experiment investigated the Gaoshawo Industrial Concentration Zone in Yanchi County. Based on the distance and direction from the industrial park, sample plots were established at intervals of 1–2 km. A total of 82 surface soil samples (0–20 cm) representing different pollution sources were collected. The samples were analysed for pH, total nitrogen, total phosphorus, available phosphorus, available potassium, organic matter, copper (Cu), cadmium (Cd), chromium (Cr), lead (Pb), and zinc (Zn). The desert steppe soil quality was analysed based on the integrated fertility index (IFI) and the Nemerow pollution index (PN), followed by the calculation of the comprehensive soil quality index (SQI), which considers the most suitable soil quality indicators through a geostatistical model. The results showed that the IFI was 0.393, indicating that the soil fertility was relatively poor. Excluding the available potassium, the nugget coefficients of the fertility indicators were less than 25% and showed strong spatial autocorrelation. The average values of Cu, Cd, Cr, Pb and Zn were 21.64 ± 3.26, 0.18 ± 0.02, 44.99 ± 21.23, 87.18 ± 25.84, and 86.63 ± 24.98 mg·kg−1, respectively; the nugget coefficients of Cr, Pb and Zn were 30.79–47.35%. Pb was the main element causing heavy metal pollution in the study area. Higher PN values were concentrated north of the highway in the study area, resulting in lower soil quality in the northern region and a trend of decreasing soil quality from south to north. The results of this research showed that the average SQI was 0.351 and the soil quality was extremely low. Thus, industrial activities and transportation activities in the Gaoshawo Industrial Zone significantly impact the desert steppe soil quality index.

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