Spatio-temporal modeling of soil characteristics for soilscape reconstruction

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Spatio-Temporal Modeling of Earthquake Recovery

The recovery process after a major disaster or disruption, is impacted by the inequality of risk prior to and post event. In the past decades there has been few efforts to model the recovery process and the focus is mainly on staged models (i.e. emergency, restoration, and reconstruction). The overarching research question asks how a non-stage-like model could apply to the recovery process. This study poses three broad questions: 1) what are the indicators suitable for monitoring the recovery process; 2) what are the driving factors of differential recovery trends; and 3) what are the predicted development trajectories for communities if there was no disruption? To address the research questions, a new model is proposed for tracking the recovery process as the “Tempo-variant Model of Disaster Recovery” (TMDR), which is implemented for six case studies of recoveries post-earthquakes, in a continuous trend through time (case studies from: Chile, New Zealand, India, Iran, China, and Italy). The recovery process is monitored through a set of proposed indicators representing the changes in six main categories of housing, socio-economic, agriculture, infrastructural, institutional, and development. Satellite imagery is used as a congruent data source to monitor urban land surface change that is implemented with a new model and conditional algebra for change detection. A new method is then developed by combining the satellite imagery data with social indicators, which provides quantitative/relative measure of recovery trend (spatially and temporally) where ground assessments are impractical. The results of implementing the new TMDR model in this cross-cultural comparative study, further highlights the drivers of recovery process across time and nations. The difference between post-event and pre-event trends (i.e. recovery progress) shows significant association with instantaneous impact of the event on community development dynamics in all cases. The spatio-temporal analysis shows majority of the study area in Chile is recovered, but there are regions in the other cases that are still recovering. The comparative view on TMDR results indicates that impact of event is more significant for recovery progress in the initial years post-event, while additional indicators of access to basic infrastructure is more predictive in the long-term. Therefore, this new model provides a case-dependent baseline and an operational tool for monitoring the recovery process

Spatio-temporal modeling in an agricultural watershed

Non-point source pollution in agricultural regions in the Midwest consists of two important components, namely, dislodgment of common pollutants, their movement across the landscape and subsequently transport through surface waters to their final destination. The objectives of this study were to: 1) model surface movement of agricultural pollutants for a succession of sub-watersheds in Bear Creek using AGNPS; 2) calibrate and validate the model for a small sub-watershed using available experimental plot data; 3) incorporate Monte Carlo simulation within the modeling process where field data was not available; 4) apply the calibrated and validated model to a small sub-watershed with a riparian buffer strip to observe mitigating effects on surface pollutants if any; 5) formulate a Markov random field model for use in modeling nitrate-nitrogen concentrations for a network of monitoring stations on the Des Moines River; 6) estimate, fit, and cross-validate the model and; 7) apply the model to dissolved oxygen and suspended solids to evaluate the model\u27s specificity for each water quality variable. The agricultural non-point source pollution model (AGNPS) was used to model surface transport processes at the watershed level. Modifications were made to the existing AGNPS-ARC/INFO interface in order to automate feedlot, point source, and channel information. Feedlot information was obtained through a survey, while a probabilistic method was used to predict tile locations for the lower half of the watershed.;The model was calibrated for total runoff (inches), sediment yield (tons), soluble nitrate-nitrogen in runoff (lbs/acre), soluble nitrate-nitrogen concentration in runoff (ppm), phosphorus in sediment (lbs/acre), soluble phosphorus in runoff (lbs/acre) and soluble phosphorus concentration in runoff (ppm) to be found at the outlet of each watershed. The model was then applied to a small watershed to demonstrate the effect of riparian buffer strips in mitigating surface pollution in an agricultural field.;From graphical displays it was clear that the model predicted better under drier conditions than for excessively wet conditions. For 1997, which was a dry year, the observed and predicted values are almost equal. In contrast for 1998, which was a wet year, the difference between the observed and predicted values is very large. Corn consistently gave lower RMSE for all seven parameters calibrated (0.02, 0.09, 0.44, 0.09, 0.00, 0.05, 0.59) compared to either the combination of rowcrop and switchgrass (0.18, 0.09, 0.49, 0.11, 0.01, 0.14, 0.01) or row crop, switchgrass, shrubs and trees (0.11, 0.04, 0.18, 0.07, 0.02, 0.10, 0.00). The buffer strip in general seemed to have a mitigating effect on the pollutants as they moved over the surface.;Switchgrass seemed to be highly effective in reducing the movement of pollutants into the stream. Runoff volume was reduced by 19%, soluble nitrogen concentrations were reduced by 3%, soluble phosphorus concentrations were reduced by 3% for the early summer precipitation event. For larger watersheds, where field data was not available, Monte Carlo simulations were included in the modeling process. Smaller watersheds in the headwaters were the highest contributors of pollutants to the stream. EBCM3, which is a very small sub-watershed within BC9 showed a maximum concentration of soluble nitrogen (0.43 ppm), as well as a maximum concentration of soluble phosphorus (0.20 ppm), whereas BC9 showed an absence of both. For the second part of the research a statistical model was developed for modeling nitrate-nitrogen concentrations in the Des Moines River.;The model was formulated as a conditionally specified model in which parametric forms were assigned to conditional densities. Both systematic and random components were modeled effectively. A space-time metric was developed to represent spatial dependence in the variable distance model versus a purely spatial metric in the fixed distance model. An independent model was also fit, which only included systematic trend, in order to make comparisons between models. The variable distance or flow model performed better due to the ability to model greater variability in the system. The flow model had smaller mean squared errors for nearly all the stations. The improvement achieved from the mean-covariance model by adding just one parameter to get the flow model is better than that achieved by the distance model.;The variance and mean squared prediction error for 1982-1996 data for nitrate using all the data and then dropping station 6 while doing cross validation are very similiar. This indicates good predictive capability of the model for nitrate nitrogen. The mean square prediction error (MSPE) is only slightly higher for the prediction than it is when the data for all the stations is available. When applied to dissolved oxygen and suspended solids, the model did not perform as well. This indicated that the model was specific to nitrate-nitrogen which transported differently than dissolved oxygen or suspended solids. Indications of the specificity of the model were visible in the exploratory data analysis

Spatio-Temporal Modeling of Wildfire Risks in the U.S. Forest Sector

In the US forestry industry, wildfire has always been one of the leading causes of damage. This topic is of growing interest as wildfire has caused huge losses for landowners, residents and governments in recent years. While individual wildfire behavior is well studied (e.g. Butry 2009; Holmes 2010), a lot of new literature on broadscale wildfire risks (e.g. by county) is emerging (e.g. Butry et al. 2001; Prestemon et al. 2002). The papers of the latter category have provided useful suggestions for government wildfire management and policies. Although wildfire insurance for real estate owners is popular, the possibility to develop a forestry production insurance scheme accounting for wildfire risks has not yet been investigated. The purpose of our paper is to comprehensively evaluate broadscale wildfire risks in a spatio-temporal autoregressive scenario and to design an actuarially fair wildfire insurance scheme in the U.S. forest sector. Our research builds upon an extensive literature that has investigated crop insurance modeling. Wildfire risks are closely linked to environmental conditions. Weather, forestland size, aspects of human activity have been proved to be crucial causal factors for wildfire (Prestemon et al. 2002; Prestemon and Butry 2005; Mercer et al. 2007). In light of these factors, we carefully study wildfires ignited by different sources, such as by arson and lightning, and identify their underlying causes. We find that the decomposition of forestland ecosystem and socio-economic conditions have significant impacts on wildfire, as well as weather. Our models provide a good fit to data on frequency and propensity for fires to exist (e.g. R-square ranges from 0.4 to 0.8) and therefore provide important fundamental information on risks for the development of insurance contracts. A number of databases relevant to this topic are used. With the Florida wildfire frequency and loss size database, a complete survey of four measurements of annual wildfire risks is implemented. These four measurements are annual wildfire frequency, burned area, fire per acre and burned ratio at county level. In addition, the national forestry inventory and analysis (FIA) database, Regional Economic Information Systems (REIS) database and the national weather database have supplied forestland ecosystem, socioeconomic, and weather condition information respectively. With our spatio-temporal lattice models, impacts of environmental factors on wildfire and implications of wildfire management policies are assessed. Forestland size, private owners’ share of forestland, population and drought would positively contribute to wildfire risks significantly. Cold weather and high employment are found to be helpful in lessening wildfire risks. Among the forestland ecosystem, oak / pine & oak / hickory forestland would reduce wildfire risks while longleaf / slash & loblolly / shortleaf pine forestland would have a mixed impact. An interesting finding is that oak / gum / cypress forestland would reduce wildfire frequency, but would enhance wildfire propensity at the same time. Hurricanes could intensify wildfire risks in the same year, but would significantly decrease the next year’s wildfire risks. Meanwhile, cross sample validation verifies that our method can forecast wildfire risks adequately well. Since our approach does not incorporate any fixed-effect indicator or trend as in the panel data analysis (Prestemon et al. 2002), it offers a universal tool to evaluate and predict wildfire risks. Hence, given environmental information of a location, a corresponding actuarially fair insurance rate can be calculated.wildfires, forestry, weather, socio-economic, Spatio-Temporal autocorrelation, Risk and Uncertainty,

GIS spatio-temporal modeling of human maritime activities

4 pages, session "Marine Spatial Planning and Human Impacts"International audienceCoastal seas are important for human societies with many and diverse activities. These space and resource consuming activities exert an increasing pressure on the environment and sometimes result in conflicting interactions. Understanding these interactions remains a challenge for research and civil society. A methodology is proposed to describe the spatio-temporal distribution of several activities in coastal seas. An application is developed in the Bay of Brest (Brittany, France). Spatial, temporal, quantitative and qualitative data acquisition combines analysis of spatio-temporal databases and results from interviews. The heterogeneous data collected are stored in a spatiotemporal database (STDB). Firstly, the STDB is used with a GIS to produce temporal snapshots of daily human activity patterns over a one-year period. Secondly, using the STBD we can identify, quantify and map potential uses conflicts in space and time between activities in the Bay of Brest