Modelling groundwater/surface water interaction in a managed riparian chalk valley wetland

Understanding hydrological processes in wetlands may be complicated by management practices and complex groundwater/surface water interactions. This is especially true for wetlands underlain by permeable geology, such as chalk. In this study, the physically based, distributed model MIKE SHE is used to simulate hydrological processes at the Centre for Ecology and Hydrology River Lambourn Observatory, Boxford, Berkshire, UK. This comprises a 10‐ha lowland, chalk valley bottom, riparian wetland designated for its conservation value and scientific interest. Channel management and a compound geology exert important, but to date not completely understood, influences upon hydrological conditions. Model calibration and validation were based upon comparisons of observed and simulated groundwater heads and channel stages over an equally split 20‐month period. Model results are generally consistent with field observations and include short‐term responses to events as well as longer‐term seasonal trends. An intrinsic difficulty in representing compressible, anisotropic soils limited otherwise excellent performance in some areas. Hydrological processes in the wetland are dominated by the interaction between groundwater and surface water. Channel stage provides head boundaries for broad water levels across the wetland, whilst areas of groundwater upwelling control discrete head elevations. A relic surface drainage network confines flooding extents and routes seepage to the main channels. In‐channel macrophyte growth and its management have an acute effect on water levels and the proportional contribution of groundwater and surface water. The implications of model results for management of conservation species and their associated habitats are discussed. Copyright © 2015 John Wiley & Sons, Ltd.     

Numerical modelling of desiccation cracking

The ability to model and predict the formation of desiccation cracks is potentially beneficial in many applications such as clay liner design, earth dam construction, and crop science, etc. However, most studies have focused on statistical analysis of crack patterns and qualitative study of contributing factors to crack development rather than prediction. Because it is exceedingly difficult to capture the nonlinear processes during desiccation in analytical modelling, most such models handle crack formation without considering variation of material properties with time, and are unattractive to use in realistic modelling. The data obtained from laboratory experiments on clay soil desiccating in moulds were used as a basis to develop a more refined model of desiccation cracking. In this study, the properties, such as matric suction, stiffness and tensile strength of soil, and base adhesion, could be expressed approximately as functions of moisture content. The initial conditions and the development of suction due to desiccation and the varying material properties were inputted to UDEC, a distinct element code, using its internal programming language FISH. The model was able to capture some essential physical aspects of crack evolution in soil contained in moulds with varying lengths, heights, and materials of construction. Extension of this methodology is potentially beneficial not only for modelling desiccation cracking in clay, but also in other systems with evolving material properties such as concrete structures and road pavements. Copyright © 2010 John Wiley & Sons, Ltd.     

Modelling the flow of self‐compacting concrete

A Lagrangian particle‐based method, smooth particle hydrodynamics (SPH), is used in this paper to model the flow of self‐compacting concretes (SCC) with or without short steel fibres. An incompressible SPH method is presented to simulate the flow of such non‐Newtonian fluids whose behaviour is described by a Bingham‐type model, in which the kink in the shear stress vs shear strain rate diagram is first appropriately smoothed out. The viscosity of the SCC is predicted from the measured viscosity of the paste using micromechanical models in which the second phase aggregates are treated as rigid spheres and the short steel fibres as slender rigid bodies. The basic equations solved in the SPH are the incompressible mass conservation and Navier–Stokes equations. The solution procedure uses prediction–correction fractional steps with the temporal velocity field integrated forward in time without enforcing incompressibility in the prediction step. The resulting temporal velocity field is then implicitly projected on to a divergence‐free space to satisfy incompressibility through a pressure Poisson equation derived from an approximate pressure projection. The results of the numerical simulation are benchmarked against actual slump tests carried out in the laboratory. The numerical results are in excellent agreement with test results, thus demonstrating the capability of SPH and a proper rheological model to predict SCC flow and mould‐filling behaviour. Copyright © 2010 John Wiley & Sons, Ltd.     

Climate change and future flows of Rocky Mountain rivers: converging forecasts from empirical trend projection and down‐scaled global circulation modelling

To predict future river flows, empirical trend projection (ETP) analyses and extends historic trends, while hydroclimatic modelling (HCM) incorporates regional downscaling from global circulation model (GCM) outputs. We applied both approaches to the extensively allocated Oldman River Basin that drains the North American Rocky Mountains and provides an international focus for water sharing. For ETP, we analysed monthly discharges from 1912 to 2008 with non‐parametric regression, and extrapolated changes to 2055. For modelling, we refined the physical models MTCLIM and SNOPAC to provide water inputs into RIVRQ (river discharge), a model that assesses the streamflow regime as involving dynamic peaks superimposed on stable baseflow. After parameterization with 1960–1989 data, we assessed climate forecasts from six GCMs: CGCM1‐A, HadCM3, NCAR‐CCM3, ECHAM4 and 5 and GCM2. Modelling reasonably reconstructed monthly hydrographs (R² about 0·7), and averaging over three decades closely reconstructed the monthly pattern (R² = 0·94). When applied to the GCM forecasts, the model predicted that summer flows would decline considerably, while winter and early spring flows would increase, producing a slight decline in the annual discharge (?3%, 2005–2055). The ETP predicted similarly decreased summer flows but slight change in winter flows and greater annual flow reduction (?9%). The partial convergence of the seasonal flow projections increases confidence in a composite analysis and we thus predict further declines in summer (about ? 15%) and annual flows (about ? 5%). This composite projection indicates a more modest change than had been anticipated based on earlier GCM analyses or trend projections that considered only three or four decades. For other river basins, we recommend the utilization of ETP based on the longest available streamflow records, and HCM with multiple GCMs. The degree of correspondence from these two independent approaches would provide a basis for assessing the confidence in projections for future river flows and surface water supplies. Copyright © 2010 John Wiley & Sons, Ltd.     

The data‐driven approach as an operational real‐time flood forecasting model

Accurate water level forecasts are essential for flood warning. This study adopts a data‐driven approach based on the adaptive network–based fuzzy inference system (ANFIS) to forecast the daily water levels of the Lower Mekong River at Pakse, Lao People's Democratic Republic. ANFIS is a hybrid system combining fuzzy inference system and artificial neural networks. Five ANFIS models were developed to provide water level forecasts from 1 to 5 days ahead, respectively. The results show that although ANFIS forecasts of water levels up to three lead days satisfied the benchmark, four‐ and five‐lead‐day forecasts were only slightly better in performance compared with the currently adopted operational model. This limitation is imposed by the auto‐ and cross‐correlations of the water level time series. Output updating procedures based on the autoregressive (AR) and recursive AR (RAR) models were used to enhance ANFIS model outputs. The RAR model performed better than the AR model. In addition, a partial recursive procedure that reduced the number of recursive steps when applying the AR or the RAR model for multi‐step‐ahead error prediction was superior to the fully recursive procedure. The RAR‐based partial recursive updating procedure significantly improved three‐, four‐ and five‐lead‐day forecasts. Our study further shows that for long lead times, ANFIS model errors are dominated by lag time errors. Although the ANFIS model with the RAR‐based partial recursive updating procedure provided the best results, this method was able to reduce the lag time errors significantly for the falling limbs only. Improvements for the rising limbs were modest. Copyright © 2011 John Wiley & Sons, Ltd.     

Sensitivity of soil parameters in unsaturated zone modelling and the relation between effective,laboratory and in situ estimates

Simulation of soil moisture content requires effective soil hydraulic parameters that are valid at the modelling scale. This study investigates how these parameters can be estimated by inverse modelling using soil moisture measurements at 25 locations at three different depths (at the surface, at 30 and 60 cm depth) on an 80 by 20 m hillslope. The study presents two global sensitivity analyses to investigate the sensitivity in simulated soil moisture content of the different hydraulic parameters used in a one‐dimensional unsaturated zone model based on Richards' equation. For estimation of the effective parameters the shuffled complex evolution algorithm is applied. These estimated parameters are compared to their measured laboratory and in situ equivalents. Soil hydraulic functions were estimated in the laboratory on 100 cm³ undisturbed soil cores collected at 115 locations situated in two horizons in three profile pits along the hillslope. Furthermore, in situ field saturated hydraulic conductivity was estimated at 120 locations using single‐ring pressure infiltrometer measurements. The sensitivity analysis of 13 soil physical parameters (saturated hydraulic conductivity (K_s), saturated moisture content (θ_s), residual moisture content (θ_r), inverse of the air‐entry value (α), van Genuchten shape parameter (n), Averjanov shape parameter (N) for both horizons, and depth (d) from surface to B horizon) in a two‐layer single column model showed that the parameter N is the least sensitive parameter. K_s of both horizons, θ_s of the A horizon and d were found to be the most sensitive parameters. Distributions over all locations of the effective parameters and the distributions of the estimated soil physical parameters from the undisturbed soil samples and the single‐ring pressure infiltrometer estimates were found significantly different at a 5% level for all parameters except for α of the A horizon and K_s and θ_s of the B horizon. Different reasons are discussed to explain these large differences. Copyright © 2004 John Wiley & Sons, Ltd.     

Knowing wildfire risk: Scientific interactions with risk mitigation policy and practice in Victoria,Australia

Over the past decade, major landscape wildfires (or ‘bushfires’ in Australia) in fire-prone countries have illustrated the seriousness of this global environmental problem. This natural hazard presents a complex mesh of dynamic factors for those seeking to reduce or manage its costs, as ignitions, hazard behaviour, and the reactions of different human and ecological communities during and after hazard events are all extremely uncertain. But while those at risk of wildfire have been subject to significant research, the social dimensions of its management, including the role of science, have received little attention. This paper reports on a case study of the Barwon-Otway area of Victoria in Australia, a high wildfire risk area that has recently been a pilot site for a new risk mitigation strategy utilising the wildfire simulation model PHOENIX RapidFire. Against simple equations between ‘more science’ and ‘less uncertainty,’ this paper presents results from interviews and a workshop with practitioners to investigate how scientific research interacts with and informs both wildfire policy and practice. We suggest that attending to cultural and social specificities of the application of any technical innovation—such as next generation modelling—raises questions for future research about the roles of narrative, performance, and other knowledges in the sedimentation of science.     

The topology of geology 2: Topological uncertainty

Uncertainty is ubiquitous in geology, and efforts to characterise and communicate it are becoming increasingly important. Recent studies have quantified differences between perturbed geological models to gain insight into uncertainty. We build on this approach by quantifying differences in topology, a property that describes geological relationships in a model, introducing the concept of topological uncertainty. Data defining implicit geological models were perturbed to simulate data uncertainties, and the amount of topological variation in the resulting model suite measured to provide probabilistic assessments of specific topological hypotheses, sources of topological uncertainty and the classification of possible model realisations based on their topology. Overall, topology was found to be highly sensitive to small variations in model construction parameters in realistic models, with almost all of the several thousand realisations defining distinct topologies. In particular, uncertainty related to faults and unconformities was found to have profound topological implications. Finally, possible uses of topology as a geodiversity metric and validation filter are discussed, and methods of incorporating topological uncertainty into physical models are suggested.     

Searching for Added Value in Simulating Climate Extremes with a High-Resolution Regional Climate Model over Western Canada

We evaluate the capacity of a regional climate model to represent observed extreme temperature and precipitation events and also examine the impact of increased resolution, in an effort to identify added value in this respect. Two climate simulations of western Canada (WCan) were conducted with the Canadian Regional Climate Model (version 4) at 15 (CRCM15) and 45?km (CRCM45) horizontal resolution driven at the lateral boundaries by data from the European Centre for Medium-range Weather Forecasts (ECMWF) 40-year Reanalysis (ERA-40) for the period 1973–1995. The simulations were evaluated using the spline-interpolated dataset ANUSPLIN, a daily observational gridded surface temperature and precipitation product with a nominal resolution of approximately 10?km. We examine a range of climate extremes, comprising the 10th and 90th percentiles of daily maximum (TX) and minimum (TN) temperatures, the 90th percentile of daily precipitation (PR90), and the 27 core Climate Daily Extremes (CLIMDEX) indices.

Both simulations exhibit cold biases compared with observations over WCan, with the bias exacerbated at higher resolution, suggesting little added value for temperature overall. There are instances, however, of regional improvement in the spatial pattern of temperature extremes at the higher resolution of CRCM15 (e.g., the CLIMDEX index for the annual number of days when TX?>?25°C). The high-resolution simulations also reveal similarly localized features in precipitation (e.g., rain shadows) that are not resolved at the 45?km resolution. With regard to precipitation extremes, although both simulations generally display wet biases, CRCM15 features a reduced bias in PR90 in all seasons except winter. This improvement occurs despite the fact that spatial and interannual variability of PR90 in CRCM15 is significantly overestimated relative to both CRCM45 and ANUSPLIN. We posit that these characteristics are the result of demonstrable differences between corresponding topographical datasets used in the gridded observations and CRCM, the resulting errors propagated to physical variables tied to elevation and the beneficial effect of subsequent spatial averaging. Because topographical input is often discordant between simulations and gridded observations, it is argued that a limited form of spatial averaging may contribute added value beyond that which has already been noted in previous studies with respect to small-scale climate variability.     

Copper demand,supply, and associated energy use to 2050

To a set of well-regarded international scenarios (UNEP’s GEO-4), we have added consideration of the demand, supply, and energy implications related to copper production and use over the period 2010–2050. To our knowledge, these are the first comprehensive metal supply and demand scenarios to be developed. We find that copper demand increases by between 275 and 350% by 2050, depending on the scenario. The scenario with the highest prospective demand is not Market First (a “business as usual” vision), but Equitability First, a scenario of transition to a world of more equitable values and institutions. These copper demands exceed projected copper mineral resources by mid-century and thereafter. Energy demand for copper production also demonstrates strong increases, rising to as much as 2.4% of projected 2050 overall global energy demand. We investigate possible policy responses to these results, concluding that improving the efficiency of the copper cycle and encouraging the development of copper-free energy distribution on the demand side, and improving copper recycling rates on the supply side are the most promising of the possible options. Improving energy efficiency in primary copper production would lead to a reduction in the energy demand by 0.5% of projected 2050 overall global energy demand. In addition, encouraging the shift towards renewable technologies is important to minimize the impacts associated with copper production.