A regression model to estimate regional ground water recharge

A regional regression model was developed to estimate the spatial distribution of ground water recharge in subhumid regions. The regional regression recharge (RRR) model was based on a regression of basin-wide estimates of recharge from surface water drainage basins, precipitation, growing degree days (GDD), and average basin specific yield (SY). Decadal average recharge, precipitation, and GDD were used in the RRR model. The RRR estimates were derived from analysis of stream base flow using a computer program that was based on the Rorabaugh method. As expected, there was a strong correlation between recharge and precipitation. The model was applied to statewide data in Minnesota. Where precipitation was least in the western and northwestern parts of the state (50 to 65 cm/year), recharge computed by the RRR model also was lowest (0 to 5 cm/year). A strong correlation also exists between recharge and SY. SY was least in areas where glacial lake clay occurs, primarily in the northwest part of the state; recharge estimates in these areas were in the 0- to 5-cm/year range. In sand-plain areas where SY is greatest, recharge estimates were in the 15- to 29-cm/year range on the basis of the RRR model. Recharge estimates that were based on the RRR model compared favorably with estimates made on the basis of other methods. The RRR model can be applied in other subhumid regions where region wide data sets of precipitation, streamflow, GDD, and soils data are available.     

Quantifying water vulnerability: a multi-dimensional approach

In today’s uncertain world, vulnerability of water supplies is of increasing concern. A number of factors influence this, ranging from physical conditions through to human management capacities. Across the Orange River Basin in southern Africa, these threats arise from overpopulation and farming pressure, with agrochemical and industrial runoff as well as harsh weather conditions giving rise to severe problems of erosion and land degradation. Under conditions of climate change, these threats are exacerbated, as temperature rises and water resources become more erratic. Since water is both an essential instrument of livelihood support and a crucial factor of production, there is a need to develop more effective mechanisms to identify those areas where its scarcity or poor management can bring about a slowdown in the development process. This urgency is heightened by the international commitment to the Millennium Development Goals (MDGs), supposedly to be reached by 2015. In addition to the MDGs, governments are also committed to the development of basin management plans for Integrated Water Resources Management (IWRM). This means that, in order to try to allocate water in an equitable and efficient way, better understanding is needed of all of the complexities of managing water across heterogeneous basins. It is now recognized that effective water management is much more dependent on effective governance than on hydrologic regimes. Ranging from traditional local customary norms and practices dating back through generations to the latest state-of-the-art science-based international agreements, water governance is a key to supporting the lives and livelihoods of local populations. Access to information is an essential feature of any of these approaches, and harmonization of data on water issues is long overdue. This paper provides an outline of an index-based methodology on which an assessment of water vulnerability can be made. In this approach, supply-driven vulnerability (from water systems) and the demand-driven vulnerability (from water users), are evaluated at the municipal scale. By combining these various dimensions together mathematically, a Water Vulnerability Index (WVI) can be generated.     

Well vulnerability: a quantitative approach for source water protection

The concept of vulnerability of drinking water sources is reviewed, and a quantitative approach for assessing well vulnerability for complex three-dimensional ground water systems is developed. The approach focuses on the relative expected impact of potential contaminant sources at unknown locations within a well capture zone, providing relative measures of intrinsic well vulnerability, including the expected times of arrival of a contaminant, the dispersion-related reduction in concentration, the time taken to breach a certain quality objective, and the corresponding exposure times. Thus, the result of the analysis includes the usual advective travel time information used in conventional wellhead protection analysis, plus a set of selected quantitative measures expressing the expected impact. The technique is based on adjoint theory and combines forward- and backward-in-time transport modeling using a standard numerical flow and transport code. The methodology is demonstrated using the case study of a complex glacial multiaquifer system in Ontario. The new approach will be useful in helping water managers develop more physically based and quantitative wellhead protection strategies.     

Evaluating water reuse applications under uncertainty: generalized intuitionistic fuzzy-based approach

Water reuse is a viable option to increase urban water supply, especially under new realities of climate change and increasing anthropogenic activities. A sustainable water reuse application should be cost-effective and have acceptable health risk to consumers. Water reuse application evaluation is complex because data acquisitions are usually associated with the problems of uncertainty, hesitancy, and parameterization. In this paper, a generalized intuitionistic fuzzy soft set (GIFSS)-based decision support framework is proposed to provide an effective approach to describe uncertainty and hesitancy in an intuitionistic fuzzy number. In addition, the modified measures of comparison and similarity are proposed to compare water reuse applications. Then, the proposed framework is applied to the City of Penticton (British Columbia, Canada) to evaluate seven water reuse applications. The evaluation results show that the applications of garden flower watering and public parks watering are the most preferred alternatives, which are consistent with the existing practice in the city. Furthermore, the results are highly affected by the generalized parameter and the weights of evaluation criteria. Both the comparison measure-based and similarity measure-based evaluations within the same GIFSS-based framework produce consistent results, indicating an applicable and efficient methodology.     

Application of arma models to estimate earthquake ground motion and structural response

This paper deals with the use of ARMA models in earthquake engineering. Tools and methods applied to strong ground motion are discussed emphasizing simulation of probabilistic earthquake response spectra. The ARMA models are applied to Icelandic earthquake data and a tentative model for Icelandic earthquakes is presented. This model, which is derived using 54 accelerograms, is based on a low-order, time-invariant ARMA process excited by Gaussian white noise and amplitude modulated using a simple envelope function to account for the non-stationary characteristics. This simple model gives a reasonable fit to the observed ground motion. Further, this model produces accurate earthquake response spectra, which, combined with accompanying attenuation and duration formulae, might be useful in earthquake hazard and risk assessment.     

3D tomographic imaging of the southern Apennines (Italy): A statistical approach to estimate the model uncertainty and resolution

A new dataset of first P-wave arrival times is used to derive the 3D tomographic model of the Campania-Lucania region in the southern Apennines (Italy). We address the issue related to the non-uniqueness of the tomographic inversion solution through massive numerical experimentation based on the global exploration of the model parameter space starting from a large variety of physically plausible initial models. The average of all the realizations is adopted as the best-fit solution and the uncertainty of the model parameters is studied using a statistical approach based on a Monte Carlo-type analysis. How the uncertainty in the initial model, earthquake locations, and data influences the inversion result is studied by considering separately the individual effects. Checkerboard tests are performed to estimate the resolving power of the dataset. Re-located seismicity in a reliable new 3D tomographic model allows us to correlate the earthquake distribution with the main seismogenic structures present in the area.     

Risk aversion based interval stochastic programming approach for agricultural water management under uncertainty

In this study, a risk aversion based interval stochastic programming (RAIS) method is proposed through integrating interval multistage stochastic programming and conditional value at risk (CVaR) measure for tackling uncertainties expressed as probability distributions and intervals within a multistage context. The RAIS method can reflect dynamic features of the system conditions through transactions at discrete points in time over the planning horizon. Using the CVaR measure, RAIS can effectively reflect system risk resulted from random parameters. When random events are occurred, the adjustable alternatives can be achieved by setting desired targets according to the CVaR, which could make the revised decisions to minimize the economic penalties. Then, the RAIS method is applied to planning agricultural water management in the Zhangweinan River Basin that is plagued by drought due to serious water scarcity. A set of decision alternatives with different combinations of risk levels employed to the objective function and constraints are generated for planning water resources allocation. The results can not only help decision makers examine potential interactions between risks under uncertainty, but also help generate desired policies for agricultural water management with a maximized payoff and a minimized loss.     

Climate change sensitivity assessment of streamflow and agricultural pollutant transport in California's Central Valley using Latin hypercube sampling

Bracketing the uncertainty of streamflow and agricultural runoff under climate change is critical for proper future water resource management in agricultural watersheds. This study used the Soil and Water Assessment Tool (SWAT) in conjunction with a Latin hypercube climate change sampling algorithm to construct a 95% confidence interval (95CI) around streamflow, sediment load, and nitrate load predictions under changes in climate for the Sacramento and San Joaquin River watersheds in California's Central Valley. The Latin hypercube algorithm sampled 2000 combinations of precipitation and temperature changes based on Intergovernmental Panel on Climate Change projections from multiple General Circulation Models. Average monthly percent changes of the upper and lower 95CI limits compared to the present‐day simulation and a statistic termed the “r‐factor” (average width of the 95CI band divided by the standard deviation of the 95CI bandwidth) were used to assess watershed sensitivities. 95CI results indicate that streamflow and sediment runoff in the Sacramento River watershed are more likely to decrease under climate change compared to present‐day conditions, whereas the increase and decrease for nitrate runoff were found to be equal. For the San Joaquin River watershed, streamflow slightly decreased under climate change, whereas sediment and nitrate runoff increased compared to present‐day climate. Comparisons of watershed sensitivities indicate that the San Joaquin River watershed is more sensitive to climate changes than the Sacramento River watershed, which is largely caused by the high density of agricultural land. Copyright © 2012 John Wiley & Sons, Ltd.     

On the treatment of uncertainty in seismic vulnerability and portfolio risk assessment

In a related study developed by the authors, building fragility is represented by intensity‐specific distributions of damage exceedance probability of various damage states. The contribution of the latter has been demonstrated in the context of loss estimation of building portfolios, where it is shown that the proposed concept of conditional fragility functions provides the link between seismic intensity and the uncertainty in damage exceedance probabilities. In the present study, this methodology is extended to the definition of building vulnerability, whereby vulnerability functions are characterized by hazard‐consistent distributions of damage ratio per level of primary seismic intensity parameter—Sa(T₁). The latter is further included in a loss assessment framework, in which the impact of variability and spatial correlation of damage ratio in the probabilistic evaluation of seismic loss is accounted for, using test‐bed portfolios of 2, 5, and 8‐story precode reinforced concrete buildings located in the district of Lisbon, Portugal. This methodology is evaluated in comparison with current state‐of‐the‐art methods of vulnerability and loss calculation, highlighting the discrepancies that can arise in loss estimates when the variability and spatial distributions of damage ratio, influenced by ground motion properties other than the considered primary intensity measure, are not taken into account.     

Quantifying the epistemic uncertainty in ground motion models and prediction

The aim of this paper is to compute the ground-motion prediction equation (GMPE)-specific components of epistemic uncertainty, so that they may be better understood and the model standard deviation potentially reduced. The reduced estimate of the model standard deviation may also be more representative of the true aleatory uncertainty in the ground-motion predictions.The epistemic uncertainty due to input variable uncertainty and uncertainty in the estimation of the GMPE coefficients are examined. An enhanced methodology is presented that may be used to analyse their impacts on GMPEs and GMPE predictions. The impacts of accounting for the input variable uncertainty in GMPEs are demonstrated using example values from the literature and by applying the methodology to the GMPE for Arias Intensity. This uncertainty is found to have a significant effect on the estimated coefficients of the model and a small effect on the value of the model standard deviation.The impacts of uncertainty in the GMPE coefficients are demonstrated by quantifying the uncertainty in hazard maps. This paper provides a consistent approach to quantifying the epistemic uncertainty in hazard maps using Monte Carlo simulations and a logic tree framework. The ability to quantify this component of epistemic uncertainty offers significant enhancements over methods currently used in the creation of hazard maps as it is both theoretically consistent and can be used for any magnitude–distance scenario.     

Regional estimation of total recharge to ground water in Nebraska

Naturally occurring long-term mean annual recharge to ground water in Nebraska was estimated by a novel water-balance approach. This approach uses geographic information systems (GIS) layers of land cover, elevation of land and ground water surfaces, base recharge, and the recharge potential in combination with monthly climatic data. Long-term mean recharge > 140 mm per year was estimated in eastern Nebraska, having the highest annual precipitation rates within the state, along the Elkhorn, Platte, Missouri, and Big Nemaha River valleys where ground water is very close to the surface. Similarly high recharge values were obtained for the Sand Hills sections of the North and Middle Loup, as well as Cedar River and Beaver Creek valleys due to high infiltration rates of the sandy soil in the area. The westernmost and southwesternmost parts of the state were estimated to typically receive < 30 mm of recharge a year.     

Spatial and temporal variability of ground water recharge in central Australia: a tracer approach

Two environmental tracer methods are applied to the Ti-Tree Basin in central Australia to shed light on the importance of recharge from floodouts of ephemeral rivers in this arid environment. Ground water carbon-14 concentrations from boreholes are used to estimate the average recharge rate over the interval between where the ground water sample first entered the saturated zone and the bore. Environmental chloride concentrations in ground water samples provide estimates of the recharge rate at the exact point in the landscape where the sample entered the saturated zone. The results of the two tracer approaches indicate that recharge rates around one of the rivers and an extensive floodplain are generally higher than rates of diffuse recharge that occurs in areas of lower topographic relief. Ground water 2H/1H and 18O/16O compositions are all depleted in the heavier isotopes (delta2H = -67 per thousand to -50 per thousand; delta18O = -9.2 per thousand to -5.7%o) compared with the long-term, amount-weighted mean isotopic composition of rainfall in the area (delta2H = -33.8 per thousand; delta18O = -6.3 per thousand). This indicates that recharge throughout the basin occurs only after intense rainfall events of at least 150 to 200 mm/month. Finally, a recharge map is developed to highlight the spatial extent of the two recharge mechanisms. Floodout recharge to the freshest ground water (TDS <1,000 mg/L) is approximately 1.9 mm/year compared with a mean recharge rate of approximately 0.2 mm/year to the remainder of the basin. These findings have important implications for management of the ground water resource.     

地震动参数区划中的不确定性分析

基于《中国地震动参数区划图》（GB18306-2001）中的5套潜在震源区划分及地震活动性参数方案,计算了中国大陆地区所对应的50 年超越概率10%和2%的基岩峰值加速度标准差和变异系数.分析了峰值加速度标准差和变异系数的空间分布特征,统计了省级、地级及县级城市峰值加速度的不确定性.结果表明,潜在震源区划分及地震活动性参数方案的不确定性对峰值加速度影响显著.标准差较大的地区与7.0级以上地震及大型活动断裂具有很强的相关性;峰值加速度变异系数较大的地区主要集中分布在我国的弱震活动地区,其次在塔克拉玛干沙漠及阿拉善高原地区.我国绝大部分城市峰值加速度标准差小于80times;10－2 m/s2;峰值加速度变异系数小于0.4.此外,经分析,峰值加速度的变异系数比标准差更适合表达区划图的不确定性.这些研究结果可为下一代区划图的编制工作提供一些有价值的参考.     

Stream bottom resistivity tomography to map ground water discharge

This study investigates the effectiveness of direct current electrical resistivity as a tool for assessing ground water/surface water interactions within streams. This research has shown that patterns of ground water discharge can be mapped at the meter scale, which is important for understanding stream water quality and ecosystem function. Underwater electrical resistivity surveys along a 107-m stream section within the Burd Run Watershed in South Central Pennsylvania identified three resistivity layers: a resistive (100 to 400 Ωm) surface layer corresponding to the streambed sediments, a conductive (20 to 100 Ωm) middle layer corresponding to residual clay sediments, and a resistive (100 to 450 Ωm) bottom layer corresponding to the carbonate bedrock. Tile probing to determine the depth to the bedrock and resistivity test box analysis of augered sediment samples confirmed these interpretations of the resistivity data. Ground water seeps occurred where the resistivity data showed that the residual clays were thinnest and bedrock was closest to the streambed. Plotting the difference in resistivity between two surveys, one conducted during low-stage and the other during high-stage stream conditions, showed changes in the conductivity of the pore fluids saturating the sediments. Under high-stream stage conditions, the top layer showed increased resistivity values for sections with surface water infiltration but showed nearly constant resistivity in sections with ground water seeps. This was expressed as difference values less than 50 Ωm in the area of the seeps and greater than 50 Ωm change for the streambed sediments saturated by surface water. Thus, electrical resistivity aided in characterizing ground water discharge zones by detecting variations in subsurface resistivity under high- and low-stream stage conditions as well as mapping subsurface heterogeneities that promote these exchanges.     

Alternative analysis of transient infiltration experiment to estimate soil water repellency

The repellency index (RI) defined as the adjusted ratio between soil‐ethanol, S_e, and soil‐water, S_w, sorptivities estimated from minidisk infiltrometer experiments has been used instead of the widely used water drop penetration time and molarity of ethanol drop tests to assess soil water repellency. However, sorptivity calculated by the usual early‐time infiltration equation may be overestimated as the effects of gravity and lateral capillary are neglected. With the aim to establish the best applicative procedure to assess RI, different approaches to estimate S_e and S_w were compared that make use of both the early‐time infiltration equation (namely, the 1 min, S1, and the short‐time linearization approaches), and the two‐term axisymmetric infiltration equation, valid for early to intermediate times (namely, the cumulative linearization and differentiated linearization approaches). The dataset included 85 minidisk infiltrometer tests conducted in three sites in Italy and Spain under different vegetation habitats (forest of Pinus pinaster and Pinus halepensis, burned pine forest, and annual grasses), soil horizons (organic and mineral), postfire treatments, and initial soil water contents. The S1 approach was inapplicable in 42% of experiments as water infiltration did not start in the first minute. The short‐time linearization approach yielded a systematic overestimation of S_e and S_w that resulted in an overestimation of RI by a factor of 1.57 and 1.23 as compared with the cumulative linearization and differentiated linearization approaches. A new repellency index, RI_s, was proposed as the ratio between the slopes of the linearized data for the wettable and hydrophobic stages obtained by a single water infiltration test. For the experimental conditions considered, RI_s was significantly correlated with RI and WDPT. Compared with RI, RI_s includes information on both soil sorptivity and hydraulic conductivity and, therefore, it can be considered more physically linked to the hydrological processes affected by soil water repellency.