Analyzing the Impacts of Climate Change on Groundwater Monitoring Network Design using GIS

AWRA 2010 SPRING SPECIALTY CONFERENCE, Orlando, FL, March 29-31, 2010

Abdelhaleem Khader and Mac McKee

“The global climate is expected to change due to the increase in greenhouse gas concentrations since 1750. According to the IPCC fourth assessment report, global warming is certain and clear. Expected changes in climate include widespread changes in precipitation amounts and aspects of extreme weather including droughts, heavy precipitation, heat waves and intensity of hurricanes. Palestine is among the regions in which drier climates have been observed and are expected to increase. As a result, the need for more intensive water resources management has become more urgent. To be effective, this management requires an efficient and reliable information system to provide data about the water system being managed.This study investigates the impacts of climate change on the Eocene Aquifer, Palestine by utilizing different tools including global climate modeling, groundwater flow modeling, fate and transport modeling, and statistical learning machines. The first step is to predict the future temperature and precipitation based on the different climate change scenarios. Then, these temperature and precipitation values will be used as inputs to the groundwater flow model along with other inputs including soil type, topography, hydrogeology, and land use. After that, the fate and transport of pollutants will be simulated using groundwater flow models and pollutant loading data. The same steps will be repeated to account for uncertainties in aquifer properties (Hydraulic conductivity, dispersivity and NO3decay factor) using latin hypercube sampling and Monte Carlo simulations. Finally, all these models will provide the necessary information for monitoring network design using state of the art, statistical learning machines.The role of geographical information systems (GIS) in this study is vital due to the spatial nature of the problem. First, GIS is used in processing climate change data and preparing them to be used in groundwater flow modeling along with the other data. Then it is used to analyze the results of the groundwater flow model and to prepare them to be used in the fate and transport model along with pollutant loading data. After that, GIS is used in the spatial representation of monitoring network design.”

Spatial Interpolation in Wireless Sensor Networks: Localized Algorithms for Variogram Modeling and Kriging

GeoInformatica, Volume 14, Number 1 / January, 2010

Muhammad Umer, Lars Kulik and Egemen Tanin

“Wireless sensor networks (WSNs) are rapidly emerging as the prominent technology for monitoring physical phenomena. However, large scale WSNs are known to suffer from coverage holes, i.e., large regions of deployment area where no sensing coverage can be provided. Such holes are the result of hardware failures, extensive costs for redeployment or the hostility of deployment areas. Coverage holes can adversely affect the accurate representation of natural phenomena that are monitored by a WSN. In this work, we propose to exploit the spatial correlation of physical phenomena to make monitoring systems more resilient to coverage holes. We show that a phenomenon can be interpolated inside a coverage hole with a high level of accuracy from the available nodal data given a model of its spatial correlation. However, due to energy limitations of sensor nodes it is imperative to perform this interpolation in an energy efficient manner that minimizes communication among nodes. In this paper, we present highly energy efficient methods for spatial interpolation in WSNs. First, we build a correlation model of the phenomenon being monitored in a distributed manner. Then, a purely localized and distributed spatial interpolation scheme based on Kriging interpolates the phenomenon inside coverage holes. We test the cost and accuracy of our scheme with extensive simulations and show that it is significantly more energy efficient than global interpolations and remarkably more accurate than simple averaging.”

Is it Possible to Infer the Number of Colonisation Events from Genetic Data Alone?

Ecological Informatics, In Press, Corrected Proof, Available online 29 January 2010

M. Björklund, G. Almqvist

“The current state of populations is to a large determined by events in the past that we have no information about. Thus, we have to rely on indirect methods to infer likely scenarios of these events. In this paper we describe a simple simulation approach to infer the minimum number of introductions of an invasive species, the round goby in the Baltic Sea. The results show that several introductions are most likely to have occurred, possibly even a constant rate of immigration. This poses new threats to local fish populations that currently suffer from overfishing. The method is very general and can be applied to other similar situations.”

Spatial Risk Assessment of Livestock Exposure to Pumas in Patagonia, Argentina

Ecography, Volume 32, Issue 5, Date: October 2009, Pages: 807-817

W. Daniel Kissling, Néstor Fernández, José M. Paruelo

“Livestock predation and associated human-carnivore conflicts are increasing worldwide and require the development of methods and concepts for risk assessment and conflict management. Here we use knowledge on habitat preference and distribution of pumas and provide a first assessment of the spatial risk of livestock to puma depredation in Patagonian ranches, Argentina. In an initial step, we developed a rule-based habitat model in a Geographic Information System (GIS) to predict the distribution of puma habitat at a regional scale in Patagonia. We then used empirically derived puma occurrence records from Patagonian ranches 1) to test our regional habitat predictions, and 2) to evaluate if paddock characteristics (vegetation cover, topography, and distance to roads) contribute to explain puma occurrences within ranches. Finally, we simulated three livestock management scenarios differing in their spatial and seasonal allocation of livestock to paddocks, and compared the likelihood of livestock exposure to pumas among scenarios. At a regional scale, 22% of the study region was predicted to be suitable for puma home ranges. The greatest uncertainty in these predictions resulted from assumptions on woody vegetation cover requirements at the home range scale. Within ranches, puma occurrences were positively associated with paddock topography, woody vegetation cover on paddocks, and proximity to predicted regional puma habitat. Comparing the risk of predation by puma among simulated livestock management scenarios implied that rotating livestock during seasons may help to reduce the likelihood of livestock exposure to pumas. Our results show the usefulness of rule-based habitat models for describing broad-scale carnivore distributions and for aiding risk assessments to mitigate conflicts between predators and human activities.”

Spatial and Temporal Analysis of Recent Climatological Data in Tanzania

Journal of Geography and Regional Planning, Vol. 3(3), pp. 044-065, March 2010

Ladislaus B. Chang’a, Pius Z. Yanda, and James Ngana

“Recent climate variability over Tanzania is evaluated through the analysis of spatial and temporal distributions of meteorological variables including rainfall, relative humidity (RH), maximum temperature (Tmax) and minimum temperature (Tmin) in an annual and seasonal time scale for 30 years (1971 – 2000) at 45 meteorological stations for rainfall and 27 stations for Tmax, Tmin and RH. Statistical parameters including mean (ME), coefficient of variation (CV) and skewness (SK) are computed and analyzed. These parameters are mapped using Surfer software. Seasonal contribution of each of the four seasons (JF, MAM, JJAS and OND) is assessed. It has been found that, for most of the bimodal areas, nearly 50% of the annual rainfall is contributed by MAM season. In all four seasons, rainfall, in most of the stations is characterized by a slight asymmetrical distribution with stronger spatial and temporal variability. Tmax, Tmin and RH however, exhibit a near normal distribution with significantly less variability.”

International Variations in Life Expectancy: A Spatio-temporal Analysis

Tijdschrift voor Economische en Sociale Geografie, 2010, vol. 101, issue 1, pages 73-90

Min Hua Jen, Ron Johnston, Kelvyn Jones, Richard Harris, and Axel Gandy

“Life expectancy at birth has increased substantially at the global scale over recent decades, but the improvements have not been experienced equally across all countries – in large part reflecting changes in economic and social situations. To identify the spatial variations in life expectancy at birth across a large number of countries over a 33-year period, this paper provides an expository account of a developing modelling methodology for the analysis of spatio-temporal trajectories. It identifies broad patterns of change and simultaneously examines between- and within-country variation to assess the degree to which patterns of life expectancy are becoming more or less similar at national and sub-national scales.”

Analysis of Syntactic and Semantic Features for Fine-grained Event–Spatial Understanding in Outbreak News Reports

Journal of Biomedical Semantics, 2010, 1:3 (31 March 2010)

Hutchatai Chanlekha and Nigel Collier

“Background: Previous studies have suggested that epidemiological reasoning needs a fine-grained modelling of events, especially their spatial and temporal attributes. While the temporal analysis of events has been intensively studied, far less attention has been paid to their spatial analysis. This article aims at filling the gap concerning automatic event-spatial attribute analysis in order to support health surveillance and epidemiological reasoning.

“Results: In this work, we propose a methodology that provides a detailed analysis on each event reported in news articles to recover the most specific locations where it occurs. Various features for recognizing spatial attributes of the events were studied and incorporated into the models which were trained by several machine learning techniques. The best performance for spatial attribute recognition is very promising; 85.9% F-score (86.75% precision / 85.1% recall).

“Conclusions: We extended our work on event-spatial attribute recognition by focusing on machine learning techniques, which are CRF, SVM, and Decision tree. Our approach avoided the costly development of an external knowledge base by employing the feature sources that can be acquired locally from the analyzed document. The results showed that the CRF model performed the best. Our study indicated that the nearest location and previous event location are the most important features for the CRF and SVM model, while the location extracted from the verb’s subject is the most important to the Decision tree model.”