P. J. J. F. Torfs

Quantifying catchment-scale mixing and its effect on time-varying travel time distributions

Travel time distributions are often used to characterize catchment discharge behavior, catchment vulnerability to pollution and pollutant loads from catchments to downstream waters. However, these distributions vary with time because they are a function of rainfall and evapotranspiration. It is important to account for these variations when the time scale of interest is smaller than the typical time-scale over which average travel time distributions can be derived. Recent studies have suggested that subsurface mixing controls how rainfall and evapotranspiration affect the variability in travel time distributions of discharge. To quantify this relation between subsurface mixing and dynamics of travel time distributions, we propose a new transformation of travel time that yields transformed travel time distributions, which we call Storage Outflow Probability (STOP) functions. STOP functions quantify the probability for water parcels in storage to leave a catchment via discharge or evapotranspiration. We show that this is equal to quantifying mixing within a catchment. Compared to the similar Age function introduced by Botter et al. (2011), we show that STOP functions are more constant in time, have a clearer physical meaning and are easier to parameterize. Catchment-scale STOP functions can be approximated by a two-parameter beta distribution. One parameter quantifies the catchment preference for discharging young water; the other parameter quantifies the preference for discharging old water from storage. Because of this simple parameterization, the STOP function is an innovative tool to explore the effects of catchment mixing behavior, seasonality and climate change on travel time distributions and the related catchment vulnerability to pollution spreading.

Changes in Streamflow Dynamics in the Rhine Basin under Three High-Resolution Regional Climate Scenarios

Abstract Because of global warming, the hydrologic behavior of the Rhine basin is expected to shift from a combined snowmelt- and rainfall-driven regime to a more rainfall-dominated regime. Previous impact assessments have indicated that this leads, on average, to increasing streamflow by ∼30% in winter and spring and decreasing streamflow by a similar value in summer. In this study, high-resolution (0.088°) regional climate scenarios conducted with the regional climate model REMO (REgional MOdel) for the Rhine basin are used to force a macroscale hydrological model. These climate scenarios are based on model output from the ECHAM5–Max Planck Institute Ocean Model (MPI-OM) global climate model, which is in turn forced by three Special Report on Emissions Scenarios (SRES) emission scenarios: A2, A1B, and B1. The Variable Infiltration Capacity model (VIC; version 4.0.5) is used to examine changes in streamflow at various locations throughout the Rhine basin. Average streamflow, peak flows, low flows, and se...

Improving load estimates for NO3 and P in surface waters by characterizing the concentration response to rainfall events

For the evaluation of action programs to reduce surface water pollution, water authorities invest heavily in water quality monitoring. However, sampling frequencies are generally insufficient to capture the dynamical behavior of solute concentrations. For this study, we used on-site equipment that performed semicontinuous (15 min interval) NO(3) and P concentration measurements from June 2007 to July 2008. We recorded the concentration responses to rainfall events with a wide range in antecedent conditions and rainfall durations and intensities. Through sequential linear multiple regression analysis, we successfully related the NO(3) and P event responses to high-frequency records of precipitation, discharge, and groundwater levels. We applied the regression models to reconstruct concentration patterns between low-frequency water quality measurements. This new approach significantly improved load estimates from a 20% to a 1% bias for NO(3) and from a 63% to a 5% bias for P. These results demonstrate the value of commonly available precipitation, discharge, and groundwater level data for the interpretation of water quality measurements. Improving load estimates from low-frequency concentration data just requires a period of high-frequency concentration measurements and a conceptual, statistical, or physical model for relating the rainfall event response of solute concentrations to quantitative hydrological changes.

Probabilistic analysis of hydrological drought characteristics using meteorological drought

Abstract Droughts are an inevitable consequence of climate variability and are pervasive across many regions. Their effects can vary on an extensive scale, depending on the type of drought and peoples vulnerability. Crucial characteristics of both hydrological (groundwater, streamflow) and meteorological (precipitation) droughts are related to their durations and severities, and these characteristics are typically correlated. While many studies have addressed the dependencies between these characteristics for either the meteorological or hydrological drought, the cross-dependence between meteorological and hydrological drought characteristics is barely investigated. The development of meteorological drought characteristics to hydrological drought characteristics is often hard to model and their connection is not definitively established. In order to better understand and explain this relationship, this study seeks to apply statistical tools and models. Drought characteristics data from areas in Europe with different climates are analysed. Two approaches of identifying related meteorological and hydrological drought are explored and compared. Classical linear correlation techniques do not provide promising results, indicating that any statistic of a hydrological drought is not a straightforward function of a preceding meteorological drought. Subsequently, the application of the concept of copulas to explore this dependence between meteorological and hydrological drought characteristics is investigated. The more comprehensive approach of copulas shows that the meteorological drought contains probability information of the successive hydrological drought. Editor Z.W. Kundzewicz Citation Wong, G., van Lanen, H.A.J., and Torfs, P.J.J.F., 2013. Probabilistic analysis of hydrological drought characteristics using meteorological drought. Hydrological Sciences Journal, 58 (2), 253–270.

Towards a stochastic model of rainfall for radar hydrology: testing the poisson homogeneity hypothesis

Abstract In order to investigate to what extent rainfall fluctuations observed with different types of instruments reflect the properties of the rainfall process itself and to what extent they are merely instrumental artefacts we are in the process of developing a stochastic model of rainfall. The starting point for the development of the model has been the notion that at the spatial and temporal scales associated with many types of surface rainfall measurements, rainfall is a discrete process describing the arrival of raindrops of different sizes at the ground. A fundamental question is whether this raindrop arrival process can be considered a homogeneus (Poisson) process or whether it behaves as a clustering (or possibly even scaling) process, as has recently been proposed in the litereture. We have tested the classical Poisson homogeneity hypothesis in rainfall on a 35 min time series of 10 s raindrop size spectra collected with a 50 cm2 disdrometer. The rain rates calculated from the spectra indicated roughly uncorrelated fluctuations around a constant mean rain rate of about 3.5 mm h−1. Two types of analysis of the drop counts were carried out, a global analysis taking into account all drops regardless of their size and an analysis considering the drop counts in the 16 0.21 mm diameter intervals separately. The first type of analysis revealed that evene for the more or less stationary time series under consideration the total raindrop arrival rate was overdispersed with respect to the homogeneous Poisson process. The second type of analysis demonstrated that this rejection of the homogeneity hypothesis could be attributed entirely to raindrops with diameters smaller than 1.14 mm. Although these drops account for 66% of the raindrop concentration in the air and 55% of the raindrop arrival rate at the ground, they only account for 14% of the rain rate and 2% of the radar reflectivity factor (on the basis of the mean drop size distribution during the experiment). In order words, although clustering may be a significant phenomenon for the smallest raindrops, the analyzed data seem to indicate that for moderate rain rates the arrival rate fluctuations of the raindrops which contribute most to the rain rate and radar reflectivity factor behave according to Poisson statistics.

Catchments as simple dynamical systems: A case study on methods and data requirements for parameter identification

In many rainfall-runoff models, at least some calibration of model parameters has to take place. Especially for ungauged or poorly gauged basins this can be problematic, because there is little or no data available for calibration. A possible solution to overcome the problems caused by data scarcity is to set up a measurement campaign for a limited time period. In this study, we determine the minimum amount of data required to determine robust parameter values for a simple model with two parameters. The model is constructed such that the parameters can be determined not only with automatic calibration, but also by recession analysis and a priori from Boussinesq theory. The model has been applied to a research catchment in Switzerland. For automatic calibration and recession analysis, one season (5 months) is found to be sufficient to give robust parameters for simulation of high flows over the full observation period. For automatic calibration, this should be the season with the highest precipitation, for recession analysis the season with least evapotranspiration. The Boussinesq equation is able to give good parameter estimates for modeling high flows, but detailed in situ knowledge of the catchment is required. Automatic calibration outperforms recession analysis and Boussinesq theory by far when it comes to parameter estimation with a focus on prediction of low flows. It was shown that a single set of parameters cannot simultaneously describe high and low flows with a reasonable accuracy, suggesting that more than two parameters are needed to characterize subsurface properties.

Effects of climate variability on water storage in the Colorado River Basin

Understanding the long-term (interannual‐decadal) variability of water availability in river basins is paramount for water resources management. Here, the authors analyze time series of simulated terrestrial water storage components, observed precipitation, and discharge spanning 74 yr in the Colorado River basin and relate them to climate indices that describe variability of sea surface temperatureand sea level pressure in the �� ��

Local probabilistic neural networks in hydrology

Abstract One of the many types of neural networks that found application in hydrology is the probabilistic neural networks. Probabilistic neural networks are based upon the Parzen approximation of probability densities by (Gaussian) kernels. The advantages of probabilistic neural networks are that they learn extremely quickly, give probabilistic interpretation and by this not only produce estimation of the mean but also give insight into the other statistics of the errors. When (in higher dimensions) the observations tend to cluster around lower dimensional subspaces, the classical approach fails by not being able to take this into account. The solution proposed here is to use a local version, based upon Gaussian kernels with locally estimated covariances. This concept resembles the “local and global embedding dimension” used in (classical) deterministic time series analysis. As an example, results on predicting discharges in a small catchment will be presented. Inputs are lagged discharges. If the time discretisation scale is rather small, and one uses many lags, the input space becomes high dimensional but the observations by the mutual dependence between the components of the input fill only a lower dimensional subspace of this. It will be shown that this new technique offers better results in these cases.

Mixtures of Gaussians for uncertainty description in bivariate latent heat flux proxies

Abstract This paper proposes a new probabilistic approach for describing uncertainty in the ensembles of latent heat flux proxies. The proxies are obtained from hourly Bowen ratio and satellite-derived measurements, respectively, at several locations in the southern Great Plains region in the United States. The novelty of the presented approach is that the proxies are not considered separately, but as bivariate samples from an underlying probability density function. To describe the latter, the use of Gaussian mixture density models—a class of nonparametric, data-adaptive probability density functions—is proposed. In this way any subjective assumptions (e.g., Gaussianity) on the form of bivariate latent heat flux ensembles are avoided. This makes the estimated mixtures potentially useful in nonlinear interpolation and nonlinear probabilistic data assimilation of noisy latent heat flux measurements. The results in this study show that both of these applications are feasible through regionalization of estim...

Comment on “Most computational hydrology is not reproducible, so is it really science?” by Christopher Hutton et al.

We discuss two definitions of reproducibility, and question if both definitions are required to be met in computational hydrological studies.