The identification of the moment when direct flow ends and baseflow begins is one of the biggest challenges of hydrological cycle modeling. The objectives of this research were: to characterize the recession curves (RC) and to separate the components of the hydrograph in a compact model. The RC were extracted from time series in three subwatersheds in Mexico. An expo-linear model was adapted and fitted to the master recession curves to find the transition point of the hydrograph and separate the baseflow. The model discriminated the RC in two decreasing ratios: one linear associated to the direct flow, and one exponential linked to the baseflow. The transition point between these two flows was obtained analytically by equaling both ratios. The derivation of a model parameter allowed to find the maximum points in the hydrometric time series, which were the criterion to separate the baseflow. The application of this model is recommended in the analysis of RC with different magnitudes from the flexibility and attachment to the fundaments of exhaustion of a reservoir.
An approach to indexical beliefs is presented and defended in the paper. The account is inspired by David Kaplan’s representationalist analysis of de re belief reports. I argue that imposing additional constraints on the Kaplanian notion of representation results in an elegant theory of indexical beliefs. The theory is committed to representations of limited accessibility but is not committed to relativized proposition, special de se contents or propositions of limited accessibility.
Analytical solutions describing the 1D substance transport in streams have many limitations and factors, which determine their accuracy. One of the very important factors is the presence of the transient storage (dead zones), that deform the concentration distribution of the transported substance. For better adaptation to such real conditions, a simple 1D approximation method is presented in this paper. The proposed approximate method is based on the asymmetric probability distribution (Gumbel’s distribution) and was verified on three streams in southern Slovakia. Tracer experiments on these streams confirmed the presence of dead zones to various extents, depending mainly on the vegetation extent in each stream. Statistical evaluation confirms that the proposed method approximates the measured concentrations significantly better than methods based upon the Gaussian distribution. The results achieved by this novel method are also comparable with the solution of the 1D advection-diffusion equation (ADE), whereas the proposed method is faster and easier to apply and thus suitable for iterative (inverse) tasks.
We developed an automated miniature constant-head tension infiltrometer that measures very small infiltration rates at millimetre resolution with minimal demands on the operator. The infiltrometer is made of 2.9 mm internal radius glass tube, with an integrated bubbling tower to maintain constant negative head and a porous mesh tip to avoid air-entry. In the bubbling tower, bubble formation and release changes the electrical resistance between two electrodes at the air-inlet. Tests were conducted on repacked sieved sands, sandy loam soil and clay loam soil, packed to a soil bulk density ρd of 1200 kg m-3 or 1400 kg m-3 and tested either air-dried or at a water potential ψ of -50 kPa. The change in water volume in the infiltrometer had a linear relationship with the number of bubbles, allowing bubble rate to be converted to infiltration rate. Sorptivity measured with the infiltrometer was similar between replicates and showed expected differences from soil texture and ρd, varying from 0.15 ± 0.01 (s.e.) mm s-1/2 for 1400 kg m-3 clay loam at ψ = -50 kPa to 0.65 ± 0.06 mm s-1/2 for 1200 kg m-3 air dry sandy loam soil. An array of infiltrometers is currently being developed so many measurements can be taken simultaneously.
The knowledge of snowpack dynamics is of critical importance to several real-time applications especially in mountain basins, such as agricultural production, water resource management, flood prevention, hydropower generation. Since simulations are affected by model biases and forcing data uncertainty, an increasing interest focuses on the assimilation of snow-related observations with the purpose of enhancing predictions on snowpack state. The study aims at investigating the effectiveness of snow multivariable data assimilation (DA) at an Alpine site. The system consists of a snow energy-balance model strengthened by a multivariable DA system. An Ensemble Kalman Filter (EnKF) scheme allows assimilating ground-based and remotely sensed snow observations in order to improve the model simulations. This research aims to investigate and discuss: (1) the limitations and constraints in implementing a multivariate EnKF scheme in the framework of snow modelling, and (2) its performance in consistently updating the snowpack state. The performance of the multivariable DA is shown for the study case of Torgnon station (Aosta Valley, Italy) in the period June 2012 – December 2013. The results of several experiments are discussed with the aim of analyzing system sensitivity to the DA frequency, the ensemble size, and the impact of assimilating different observations.