Contamination of groundwaters with geogenic arsenic poses a major health risk to millions of people. Although the main geochemical mechanisms of arsenic mobilization are well understood, the worldwide scale of affected regions is still unknown. In this study we used a large database of measured arsenic concentration in groundwaters (around 20,000 data points) from around the world as well as digital maps of physical characteristics such as soil, geology, climate, and elevation to model probability maps of global arsenic contamination. A novel rule-based statistical procedure was used to combine the physical data and expert knowledge to delineate two process regions for arsenic mobilization: "reducing" and "high-pH/ oxidizing". Arsenic concentrations were modeled in each region using regression analysis and adaptive neuro-fuzzy inferencing followed by Latin hypercube sampling for uncertainty propagation to produce probability maps. The derived global arsenic models could benefit from more accurate geologic information and aquifer chemical/physical information. Using some proxy surface information, however, the models explained 77% of arsenic variation in reducing regions and 68% of arsenic variation in high-pH/oxidizing regions. The probability maps based on the above models correspond well with the known contaminated regions around the world and delineate new untested areas that have a high probability of arsenic contamination. Notable among these regions are South East

Knowing the travel-time distributions from infiltrating rivers to pumping wells is important in the management of alluvial aquifers. Commonly, travel-time distributions are determined by releasing a tracer pulse into the river and measuring the breakthrough curve in the wells. As an alternative, one may measure signals of a time-varying natural tracer in the river and in adjacent wells and infer the travel-time distributions by deconvolution. Traditionally this is done by fitting a parametric function such as the solution of the one-dimensional advection-dispersion equation to the data. By choosing a certain parameterization, it is impossible to determine features of the travel-time distribution that do not follow the general shape of the parameterization, i.e., multiple peaks. We present a method to determine travel-time distributions by nonparametric deconvolution of electric-conductivity time series. Smoothness of the inferred transfer function is achieved by a geostatistical approach, in which the transfer function is assumed as a second-order intrinsic random time variable. Nonnegativity is enforced by the method of Lagrange multipliers. We present an approach to directly compute the best nonnegative estimate and to generate sets of plausible solutions. We show how the smoothness of the transfer function can be estimated from the data. The approach is applied to electric-conductivity measurements taken at River Thur, Switzerland, and five wells in the adjacent aquifer, but the method can also be applied to other time-varying natural tracers such as temperature. At our field site, electric-conductivity fluctuations appear to be an excellent natural tracer.

1. Elaborate restoration attempts are underway worldwide to return human-impacted rivers to more natural conditions. Assessing the outcome of river restoration projects is vital for adaptive management, evaluating project efficiency, optimising future programmes and gaining public acceptance. An important reason why assessment is often omitted is lack of appropriate guidelines. 2. Here we present guidelines for assessing river restoration success. They are based on a total of 49 indicators and 13 specific objectives elaborated for the restoration of low-to midorder rivers in Switzerland. Most of these objectives relate to ecological attributes of rivers, but socio-economic aspects are also considered. 3. A strategy is proposed according to which a set of indicators is selected from the total of 49 indicators to ensure that indicators match restoration objectives and measures, and that the required effort for survey and analysis of indicators is appropriate to the project budget. 4. Indicator values are determined according to methods described in detailed method sheets. Restoration success is evaluated by comparing indicator values before and after restoration measures have been undertaken. To this end, values are first standardised on a dimensionless scale ranging from 0 to 1, then averaged across different indicators for a given project objective, and finally assigned to one of five overall success categories. 5. To illustrate the application of this scheme, a case study on the Thur River, Switzerland, is presented. Seven indicators were selected to meet a total of five project objectives. The project was successful in achieving 'provision of high recreational value', 'lateral connectivity' and 'vertical connectivity' but failed to meet the objectives 'morphological and hydraulic variability' and 'near natural abundance and diversity of fauna'. Results from this assessment allowed us to identify potential deficits and gaps in the restoration project. To gain information on the sensitivity of the assessment scheme would require a set of complementary indicators for each restoration objective.

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We measured the concentrations of natural 222Rn (half-life 3.8 days) in groundwater at three sites in Switzerland; here groundwater is recharged mainly by river water. Upon infiltration and movement in the ground, the radon concentration in the water increases by more than two orders of magnitude to reach a steady state. This increase was found at two of the three sites. At the site of main interest, we used the ingrowth of radon between the river and nearby observation wells to estimate groundwater residence times of up to about four half lives. We assumed that the ingrowth of radon can be described by the growth law of radioactivity, that the progenitors of radon (226Ra, 238U) are homogeneously distributed in the aquifer, and that the freshly infiltrated water is not mixed significantly with older groundwater. A linear regression through the data at the site of main interest yielded an average flow velocity of 4.6 m d -1, which confirms earlier tracer observations. Radon accumulates to higher concentrations, when the top soil layer is frozen or exhibits a high moisture content. During these conditions the radon data cannot be used for dating purposes. ß ß -ß ee•e ee ß ß ß ß ß ß ß

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Groundwater is the world's most important source of raw drinking water. However, the potential impact of climate change on this vital resource is unclear because of a lack of relevant long‐term data. Here we statistically analyze over 20 years of groundwater temperature data from five Swiss aquifers fed predominantly by river‐bank infiltration. The results reveal an abrupt increase in annual mean groundwater temperature centered on 1987–1988 that can also be observed in air and river temperatures. We associate this temperature increase with the Northern Hemisphere late 1980s climate regime shift (CRS), which itself is related to an abrupt change in the behavior of the Arctic Oscillation. Because temperature affects redox conditions in groundwater, groundwater biogeochemistry in aquifers fed by river‐bank infiltration is likely to depend on large‐scale climatic forcing and will be affected by climate change.