Assessing Ground Water Development Potential Using Landsat Imagery

Seven villages in southeastern Kenya surround Mt. Kasigau and depend on the mountain's cloud forest for their water supply. Five of these villages have regularly experienced water shortages, and all village water supplies were contaminated with Escherichia coli bacteria. There is a need to economically find new sources of fresh ground water. Remote sensing offers a relatively quick and cost-effective way of identifying areas with high potential for ground water development. This study used spectral properties of features on Landsat remote sensing imagery to map linear features, soil types, surface moisture, and vegetation. Linear features represented geologic or geomorphologic features indicating either shallow ground water or areas of increased subsurface hydraulic conductivity. Regarding soil type, black soils were identified as potential indicators of shallow aquifers based on their relatively lower elevation and association with river valleys. A vegetation map was created using unsupervised classification, and three of the resulting vegetation classes were observed to be commonly associated with wet areas and/or ground water discharge. A wetness map, created using tasseled cap analysis, was used to identify all areas of high ground moisture, including those that corresponded to vegetated areas. The linear features, soil type, vegetation, and wetness maps were overlaid to produce a composite that highlighted areas with the highest potential for ground water development. Electrical resistivity surveys confirmed that areas highlighted by the composite image had relatively shallow depths to the water table. Some figures in this paper are available in color in the online version of the paper.     

Plume Behavior for Petroleum Hydrocarbon in a Tropical Sand Tank: Laboratory Experiments and Scenario-Specific Modelling

Numerical simulations were performed to assess the reactive transport and natural attenuation of gasoline fuel components in a 3-D sand tank model. The conceptual model includes a residual gasoline source that dissolves into the 3-D aquifer. The analysis reveals high to very high correlation between the observed and simulated values (average R² = 0.97). A retardation factor R = 1.5, and first-order decay rate of 0.0002/day were obtained from the model calibration for BTEX.

The results are highly relevant in the light of the increasing awareness of the precarious trend of lack of monitoring and remedial feasibility data for the subsoil environment in the thousands of gasoline fuel stations, and petroleum storage/distribution underground infrastructure and the riverbeds of inland waterways in Nigeria.

The biodegradation rate was the most sensitive model parameter, with about 82% increase in BTEX maximum plume concentration, for the zero biodegradation scenario.     

Earthquake early warning and operational earthquake forecasting as real-time hazard information to mitigate seismic risk at nuclear facilities

Based on our experience in the project REAKT, we present a methodological framework to evaluate the potential benefits and costs of using earthquake early warning (EEW) and operational earthquake forecasting (OEF) for real-time mitigation of seismic risk at nuclear facilities. We focus on evaluating the reliability, significance and usefulness of the aforementioned real-time risk-mitigation tools and on the communication of real-time earthquake information to end-users. We find that EEW and OEF have significant potential for the reduction of seismic risk at nuclear plants, although much scientific research and testing is still necessary to optimise their operation for these sensitive and highly-regulated facilities. While our test bed was Switzerland, the methodology presented here is of general interest to the community of EEW researchers and end-users and its scope is significantly beyond its specific application within REAKT.     

Maximum likelihood algorithm for parametric component separation in cosmic microwave background experiments

We discuss an approach to the component separation of microwave, multifrequency sky maps as those typically produced from cosmic microwave background (CMB) anisotropy data sets. The algorithm is based on the two-step, parametric, likelihood-based technique recently elaborated on by Eriksen et al., where the foreground spectral parameters are estimated prior to the actual separation of the components. In contrast with the previous approaches, we accomplish the former task with help of an analytically derived likelihood function for the spectral parameters, which, we show, yields estimates equal to the maximum likelihood values of the full multidimensional data problem. We then use these estimates to perform the second step via the standard, generalized-least-squares-like procedure. We demonstrate that the proposed approach is equivalent to a direct maximization of the full data likelihood, which is recast in a computationally tractable form. We use the corresponding curvature matrices to characterize statistical properties of the recovered parameters. We incorporate in the formalism some of the essential features of the CMB data sets, such as inhomogeneous pixel domain noise, unknown map offsets as well as calibration errors and study their consequences for the separation. We find that the calibration is likely to have a dominant effect on the precision of the spectral parameter determination for a realistic CMB experiment. We apply the algorithm to simulated data and discuss the results. Our focus is on partial sky, total intensity and polarization, CMB experiments such as planned balloon-borne and ground-based efforts, however, the techniques presented here should be also applicable to the full-sky data as for instance, those produced by the Wilkinson Microwave Anisotropy Probe ( WMAP ) satellite and anticipated from the Planck mission.     

Effects of global irrigation on the near-surface climate

Irrigation delivers about 2,600 km³ of water to the land surface each year, or about 2% of annual precipitation over land. We investigated how this redistribution of water affects the global climate, focusing on its effects on near-surface temperatures. Using the Community Atmosphere Model (CAM) coupled to the Community Land Model (CLM), we compared global simulations with and without irrigation. To approximate actual irrigation amounts and locations as closely as possible, we used national-level census data of agricultural water withdrawals, disaggregated with maps of croplands, areas equipped for irrigation, and climatic water deficits. We further investigated the sensitivity of our results to the timing and spatial extent of irrigation. We found that irrigation alters climate significantly in some regions, but has a negligible effect on global-average near-surface temperatures. Irrigation cooled the northern mid-latitudes; the central and southeast United States, portions of southeast China and portions of southern and southeast Asia cooled by ~0.5 K averaged over the year. Much of northern Canada, on the other hand, warmed by ~1 K. The cooling effect of irrigation seemed to be dominated by indirect effects like an increase in cloud cover, rather than by direct evaporative cooling. The regional effects of irrigation were as large as those seen in previous studies of land cover change, showing that changes in land management can be as important as changes in land cover in terms of their climatic effects. Our results were sensitive to the area of irrigation, but were insensitive to the details of irrigation timing and delivery.     

Sensitivity of European glaciers to precipitation and temperature – two case studies

A nonlinear backpropagation network (BPN) has been trained with high-resolution multiproxy reconstructions of temperature and precipitation (input data) and glacier length variations of the Alpine Lower Grindelwald Glacier, Switzerland (output data). The model was then forced with two regional climate scenarios of temperature and precipitation derived from a probabilistic approach: The first scenario (“no change”) assumes no changes in temperature and precipitation for the 2000–2050 period compared to the 1970–2000 mean. In the second scenario (“combined forcing”) linear warming rates of 0.036–0.054°C per year and changing precipitation rates between −17% and +8% compared to the 1970–2000 mean have been used for the 2000–2050 period. In the first case the Lower Grindelwald Glacier shows a continuous retreat until the 2020s when it reaches an equilibrium followed by a minor advance. For the second scenario a strong and continuous retreat of approximately −30 m/year since the 1990s has been modelled. By processing the used climate parameters with a sensitivity analysis based on neural networks we investigate the relative importance of different climate configurations for the Lower Grindelwald Glacier during four well-documented historical advance (1590–1610, 1690–1720, 1760–1780, 1810–1820) and retreat periods (1640–1665, 1780–1810, 1860–1880, 1945–1970). It is shown that different combinations of seasonal temperature and precipitation have led to glacier variations. In a similar manner, we establish the significance of precipitation and temperature for the well-known early eighteenth century advance and the twentieth century retreat of Nigardsbreen, a glacier in western Norway. We show that the maritime Nigardsbreen Glacier is more influenced by winter and/or spring precipitation than the Lower Grindelwald Glacier.     

Simplified method for scenario-based risk assessment adaptation planning in the coastal zone

The development of successful coastal adaptation strategies for both the built and natural environments requires combining scenarios of climate change and socio-economic conditions, and risk assessment. Such planning needs to consider the adaptation costs and residual damages over time that may occur given a range of possible storm conditions for any given sea level rise scenario. Using the metric of the expected value of annual adaptation costs and residual damages, or another metric that can be related to the elevation of flooding, a simplified method to carry this out is presented. The approach relies upon developing damage-flooding depth probability exceedance curves for various scenarios over a given planning period and determining the areas under the curves. While the approach does have limitations, it is less complex to implement than using Monte Carlo simulation approaches and may be more intuitive to decision makers. A case study in Maine, USA is carried out to illustrate the method.