Description: This article focuses on household water use in Spain by analyzing the influence of a detailed set of factors. We find that, although the presence of both water-saving equipment and water-conservation habits leads to water savings, the factors that influence each are not the same. In particular, our results show that those individuals most committed to the adoption of water-saving equipment and, at the same time, less committed to water-conservation habits tend to have higher incomes.

Description: Steep mountain streams typically feature macroroughness elements like large immobile boulders or channel-spanning bedforms such as step-pool sequences. The effects of macroroughness on resistance and flow velocity are not well understood and appropriate field parameters for representing macroroughness in flow velocity equations have not been identified. The prediction of flow velocity in rough and steep streams therefore remains challenging. We measured flow velocity and several macroroughness parameters, i.e., boulder concentration, boulder diameter and protrusion, and roughness of longitudinal channel profiles in six reaches of steep mountain streams with plane bed/riffle, step-pool, and cascade channel morphologies. The between-site variations in flow resistance can be explained to a large degree by nondimensionalization of discharge and flow velocity using channel slope and a characteristic roughness length. Using any of our roughness parameters as the characteristic roughness length, this nondimensionalization leads to a similarity collapse of the entire data set. The remaining differences in flow resistance among the streams are related to dimensionless measures of macroroughness that describe the concentration of boulders or step density in a reach. Boulder concentration represents the measure best describing the data and is used in a simple regression equation for flow velocity. The predictions were better than predictions by the variable power law equation proposed by Ferguson. Although the regression might not be statistically significant, the observed trends suggest that boulder concentration partly explains the residual variance of between-site variation of flow resistance.

Description: The progress in forecasting seasonal climate by using coupled atmosphere-ocean-land general circulation models (CGCMs) has increased the use of CGCM-based hydrologic forecasting in recent years. A common procedure is to downscale the meteorological forcings and use them as inputs to hydrologic models to provide ensemble forecasts. Less attention has been paid to bias correcting the hydrologic forecasts directly generated by CGCM. In this study, we show that either downscaling precipitation for hydrologic model or directly bias-correcting CGCM streamflow increases the efficiency skill score greatly as compared to the original CGCM streamflow forecast, and bias correcting the streamflow from hydrologic model with downscaled precipitation leads to a further skill increase. Bias-correcting CGCM streamflow is more skillful and reliable than downscaling precipitation for hydrologic modeling in terms of ensemble forecasts, as verified by the ranked probability skill score and the rank histogram. While bias-correcting streamflow from CGCM can provide useful forecasts, combining the downscaled CGCM forcings and bias-corrected hydrologic output through the CGCM-hydrology forecasting approach does gain additional skill of accuracy and discrimination.

Description: Particle filters (PFs) have become popular for assimilation of a wide range of hydrologic variables in recent years. With this increased use, it has become necessary to increase the applicability of this technique for use in complex hydrologic/land surface models and to make these methods more viable for operational probabilistic prediction. To make the PF a more suitable option in these scenarios, it is necessary to improve the reliability of these techniques. Improved reliability in the PF is achieved in this work through an improved parameter search, with the use of variable variance multipliers and Markov Chain Monte Carlo methods. Application of these methods to the PF allows for greater search of the posterior distribution, leading to more complete characterization of the posterior distribution and reducing risk of sample impoverishment. This leads to a PF that is more efficient and provides more reliable predictions. This study introduces the theory behind the proposed algorithm, with application on a hydrologic model. Results from both real and synthetic studies suggest that the proposed filter significantly increases the effectiveness of the PF, with marginal increase in the computational demand for hydrologic prediction.

Description: The potential for riverine drinking source water to become contaminated with pathogens is related to the production and transport of fecal waste from within the local catchment area. Identifying specific relationships between land-use types and fecal contamination in riverine water provides an indication of the risk associated with land-use change and helps to target mitigation measures toward land-use types of concern. Fecal coliform (FC) data from 42 riverine sites across British Columbia (BC), Canada, were examined in relation to land-use composition (including 16 land-use types) in the local catchment area. FC concentration significantly increased in relation to anthropogenic land-use impacts but was negatively associated with undisturbed and high-elevation land types. Regression tree analysis identified that highest FC concentrations occurred in catchments characterized by more than 12.5% agricultural land and more than 1.6% urban land. Furthermore, the risk of violation of the BC partial treatment raw drinking water quality guideline for FC concentration (100 CFU 100 mL−1) increased in relation to agricultural impacts. Additional factors, such as sewage treatment discharge, low dilution in smaller streams, and higher temperatures, were associated with higher FC concentration among sites with similar levels of agricultural development. These results identify land-use types that present the greatest threat to riverine contamination, namely agricultural and urban land, and indicate the proportion of such land use associated with high contamination. Land use should be managed and source water protection should be targeted in light of these results so as to minimize the risk of surface water exposure to fecal contaminants.

Description: Model errors are inevitable in any prediction exercise. One approach that is currently gaining attention in reducing model errors is by combining multiple models to develop improved predictions. The rationale behind this approach primarily lies on the premise that optimal weights could be derived for each model so that the developed multimodel predictions will result in improved predictions. A new dynamic approach (MM-1) to combine multiple hydrological models by evaluating their performance/skill contingent on the predictor state is proposed. We combine two hydrological models, “abcd” model and variable infiltration capacity (VIC) model, to develop multimodel streamflow predictions. To quantify precisely under what conditions the multimodel combination results in improved predictions, we compare multimodel scheme MM-1 with optimal model combination scheme (MM-O) by employing them in predicting the streamflow generated from a known hydrologic model (abcd model or VIC model) with heteroscedastic error variance as well as from a hydrologic model that exhibits different structure than that of the candidate models (i.e., “abcd” model or VIC model). Results from the study show that streamflow estimated from single models performed better than multimodels under almost no measurement error. However, under increased measurement errors and model structural misspecification, both multimodel schemes (MM-1 and MM-O) consistently performed better than the single model prediction. Overall, MM-1 performs better than MM-O in predicting the monthly flow values as well as in predicting extreme monthly flows. Comparison of the weights obtained from each candidate model reveals that as measurement errors increase, MM-1 assigns weights equally for all the models, whereas MM-O assigns higher weights for always the best-performing candidate model under the calibration period. Applying the multimodel algorithms for predicting streamflows over four different sites revealed that MM-1 performs better than all single models and optimal model combination scheme, MM-O, in predicting the monthly flows as well as the flows during wetter months.

Description: An experimental campaign, based on particle image velocimetry (PIV) measurements of free-surface velocities, forms the basis for an analysis of the mixing processes which occur in a compound-channel flow. The flow mixing is characterized in terms of Lagrangian statistics (absolute dispersion and diffusivity) and of the related mean flow characteristics. Mixing properties strongly depend on the ratio rh between the main channel flow depth (h*mc) and the floodplain depth (h*fp), and three flow classes can be identified, namely shallow, intermediate, and deep flows. In the present study the large time asymptotic behavior of the mixing characteristics is analyzed in terms of the absolute diffusivity in order to characterize typical values of longitudinal and transversal diffusivity coefficients. Various sets of experiments, which cover a wide range of the governing physical parameters, have been performed and the asymptotic values of the absolute diffusivity have been evaluated. The results are then compared with several studies of flow dispersion for both the longitudinal diffusivity coefficient and the transversal turbulent mixing coefficient. The present results highlight a stronger dependence of such coefficients with the flow-depth ratio than with the flow regime (Froude number).

Description: The quality of the absolute estimates of general circulation models (GCMs) calls into question the direct use of GCM outputs for climate change impact assessment studies, particularly at regional scales. Statistical correction of GCM output is often necessary when significant systematic biases occur between the modeled output and observations. A common procedure is to correct the GCM output by removing the systematic biases in low-order moments relative to observations or to reanalysis data at daily, monthly, or seasonal timescales. In this paper, we present an extension of a recently published nested bias correction (NBC) technique to correct for the low- as well as higher-order moments biases in the GCM-derived variables across selected multiple time-scales. The proposed recursive nested bias correction (RNBC) approach offers an improved basis for applying bias correction at multiple timescales over the original NBC procedure. The method ensures that the bias-corrected series exhibits improvements that are consistently spread over all of the timescales considered. Different variations of the approach starting from the standard NBC to the more complex recursive alternatives are tested to assess their impacts on a range of GCM-simulated atmospheric variables of interest in downscaling applications related to hydrology and water resources. Results of the study suggest that three to five iteration RNBCs are the most effective in removing distributional and persistence related biases across the timescales considered.

Description: We use pore network modeling to study the impact of wettability and connectivity on waterflood relative permeability for a set of six carbonate samples. Four quarry samples are studied, Indiana, Portland, Guiting, and Mount Gambier, along with two subsurface samples obtained from a deep saline Middle Eastern aquifer. The pore space is imaged in three dimensions using X-ray microtomography at a resolution of a few microns. The images are segmented into pore and solid, and a topologically representative network of pores and throats is extracted from these images. We then simulate quasi-static displacement in the networks. We represent mixed-wet behavior by varying the oil-wet fraction of the pore space. The relative permeability is strongly dependent on both the wettability and the average coordination number of the network. We show that traditional measures of wettability based on the point where the relative permeability curves cross are not reliable. Good agreement is found between our calculations and measurements of relative permeability on carbonates in the literature. This work helps establish a library of benchmark samples for multiphase flow and transport computations. The implications of the results for field-scale displacement mechanisms are discussed, and the efficiency of waterflooding as an oil recovery process in carbonate reservoirs is assessed depending on the wettability and pore space connectivity.

Description: This work examined the plot-scale differences in soil-water retention caused by wildfire in the area of the 2010 Fourmile Canyon Fire in the Colorado Front Range, United States. We measured soil-water retention curves on intact cores and repacked samples, soil particle-size distributions, and organic matter content. Estimates were also made of plant-available water based on the soil-water retention curves. Parameters for use in soil-hydraulic property models were estimated; these parameters can be used in unsaturated flow modeling for comparing burned and unburned watersheds. The primary driver for measured differences in soil-water retention in burned and unburned soils was organic matter content and not soil-particle size distribution. The tendency for unburned south-facing soils to have greater organic matter content than unburned north-facing soils in this field area may explain why unburned south-facing soils had greater soil-water retention than unburned north-facing soils. Our results suggest that high-severity wildfire can “homogenize” soil-water retention across the landscape by erasing soil-water retention differences resulting from organic matter content, which for this site may be affected by slope aspect. This homogenization could have important implications for ecohydrology and plant succession/recovery in burned areas, which could be a factor in dictating the window of vulnerability of the landscape to flash floods and erosion that are a common consequence of wildfire.