2,871 research outputs found
Topographic effects on solar radiation distribution in mountainous watersheds and their influence on reference evapotranspiration estimates at watershed scale
Distributed energy and water balance models require time-series surfaces of the climatological variables involved in hydrological processes. Among them, solar radiation constitutes a key variable to the circulation of water in the atmosphere. Most of the hydrological GIS-based models apply simple interpolation techniques to data measured at few weather stations disregarding topographic effects. Here, a topographic solar radiation algorithm has been included for the generation of detailed time-series solar radiation surfaces using limited data and simple methods in a mountainous watershed in southern Spain. The results show the major role of topography in local values and differences between the topographic approximation and the direct interpolation to measured data (IDW) of up to +42% and −1800% in the estimated daily values. Also, the comparison of the predicted values with experimental data proves the usefulness of the algorithm for the estimation of spatially-distributed radiation values in a complex terrain, with a good fit for daily values (<i>R</i><sup>2</sup> = 0.93) and the best fits under cloudless skies at hourly time steps. Finally, evapotranspiration fields estimated through the ASCE-Penman-Monteith equation using both corrected and non-corrected radiation values address the hydrologic importance of using topographically-corrected solar radiation fields as inputs to the equation over uniform values with mean differences in the watershed of 61 mm/year and 142 mm/year of standard deviation. High speed computations in a 1300 km<sup>2</sup> watershed in the south of Spain with up to a one-hour time scale in 30 × 30 m<sup>2</sup> cells can be easily carried out on a desktop PC
On the influence of cell size in physically-based distributed hydrological modelling to assess extreme values in water resource planning
This paper studies the influence of changing spatial resolution on the implementation of distributed hydrological modelling for water resource planning in Mediterranean areas. Different cell sizes were used to investigate variations in the basin hydrologic response given by the model WiMMed, developed in Andalusia (Spain), in a selected watershed. The model was calibrated on a monthly basis from the available daily flow data at the reservoir that closes the watershed, for three different cell sizes, 30, 100, and 500 m, and the effects of this change on the hydrological response of the basin were analysed by means of the comparison of the hydrological variables at different time scales for a 3-yr-period, and the effective values for the calibration parameters obtained for each spatial resolution. The variation in the distribution of the input parameters due to using different spatial resolutions resulted in a change in the obtained hydrological networks and significant differences in other hydrological variables, both in mean basin-scale and values distributed in the cell level. Differences in the magnitude of annual and global runoff, together with other hydrological components of the water balance, became apparent. This study demonstrated the importance of choosing the appropriate spatial scale in the implementation of a distributed hydrological model to reach a balance between the quality of results and the computational cost; thus, 30 and 100-m could be chosen for water resource management, without significant decrease in the accuracy of the simulation, but the 500-m cell size resulted in significant overestimation of runoff and consequently, could involve uncertain decisions based on the expected availability of rainfall excess for storage in the reservoirs. Particular values of the effective calibration parameters are also provided for this hydrological model and the study area
A large sample analysis of European rivers on seasonal river flow correlation and its physical drivers
The geophysical and hydrological processes governing river flow formation exhibit persistence at several timescales, which may manifest itself with the presence of positive seasonal correlation of streamflow at several different time lags. We investigate here how persistence propagates along subsequent seasons and affects low and high flows. We define the high-flow season (HFS) and the low-flow season (LFS) as the 3-month and the 1-month periods which usually exhibit the higher and lower river flows, respectively. A dataset of 224 rivers from six European countries spanning more than 50 years of daily flow data is exploited. We compute the lagged seasonal correlation between selected river flow signatures, in HFS and LFS, and the average river flow in the antecedent months. Signatures are peak and average river flow for HFS and LFS, respectively. We investigate the links between seasonal streamflow correlation and various physiographic catchment characteristics and hydro-climatic properties. We find persistence to be more intense for LFS signatures than HFS. To exploit the seasonal correlation in the frequency estimation of high and low flows, we fit a bi-variate meta-Gaussian probability distribution to the selected flow signatures and average flow in the antecedent months in order to condition the distribution of high and low flows in the HFS and LFS, respectively, upon river flow observations in the previous months. The benefit of the suggested methodology is demonstrated by updating the frequency distribution of high and low flows one season in advance in a real-world case. Our findings suggest that there is a traceable physical basis for river memory which, in turn, can be statistically assimilated into high- and low-flow frequency estimation to reduce uncertainty and improve predictions for technical purposes
Dynamics for a 2-vertex Quantum Gravity Model
We use the recently introduced U(N) framework for loop quantum gravity to
study the dynamics of spin network states on the simplest class of graphs: two
vertices linked with an arbitrary number N of edges. Such graphs represent two
regions, in and out, separated by a boundary surface. We study the algebraic
structure of the Hilbert space of spin networks from the U(N) perspective. In
particular, we describe the algebra of operators acting on that space and
discuss their relation to the standard holonomy operator of loop quantum
gravity. Furthermore, we show that it is possible to make the restriction to
the isotropic/homogeneous sector of the model by imposing the invariance under
a global U(N) symmetry. We then propose a U(N) invariant Hamiltonian operator
and study the induced dynamics. Finally, we explore the analogies between this
model and loop quantum cosmology and sketch some possible generalizations of
it.Comment: 28 pages, v2: typos correcte
Computing Black Hole entropy in Loop Quantum Gravity from a Conformal Field Theory perspective
Motivated by the analogy proposed by Witten between Chern-Simons and
Conformal Field Theories, we explore an alternative way of computing the
entropy of a black hole starting from the isolated horizon framework in Loop
Quantum Gravity. The consistency of the result opens a window for the interplay
between Conformal Field Theory and the description of black holes in Loop
Quantum Gravity.Comment: 9 page
On maximum entropy priors and a most likely likelihood in auditing
There are two basic questions auditors and accountants must consider when developing test and estimation applications using Bayes' Theorem: What prior probability function should be used and what likelihood function should be used. In this paper we propose to use a maximum entropy prior probability function MEP with the most likely likelihood function MLL in the Quasi-Bayesian QB model introduced by McCray (1984). It is defined on an adequate parameter. Thus procedure only needs an expected value of θ0 known (in this paper the expected tainting) to obtain a MEP all an auditor or accountant need to supply are the range, as with any other prior, and the expected tainting, θ0. We will see some practical applications of the methodology proposed about internal control evaluation in auditing
Application of quantum-inspired generative models to small molecular datasets
Quantum and quantum-inspired machine learning has emerged as a promising and
challenging research field due to the increased popularity of quantum
computing, especially with near-term devices. Theoretical contributions point
toward generative modeling as a promising direction to realize the first
examples of real-world quantum advantages from these technologies. A few
empirical studies also demonstrate such potential, especially when considering
quantum-inspired models based on tensor networks. In this work, we apply
tensor-network-based generative models to the problem of molecular discovery.
In our approach, we utilize two small molecular datasets: a subset of
molecules from the QM9 dataset and a small in-house dataset of validated
antioxidants from TotalEnergies. We compare several tensor network models
against a generative adversarial network using different sample-based metrics,
which reflect their learning performances on each task, and multiobjective
performances using relevant molecular metrics per task. We also combined
the output of the models and demonstrate empirically that such a combination
can be beneficial, advocating for the unification of classical and
quantum(-inspired) generative learning.Comment: First versio
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