A deterministic optimization approach for solving the rainfall disaggregation problem
Journal Title: Bulletin of Computational Applied Mathematics (Bull CompAMa) - Year 2015, Vol 3, Issue 2
Abstract
One of the main problems in hydrology is the time scale of the historical rainfall data, available from many meteorological data bases. Most of the rainfall data is given at a time scale coarser than the one needed for many applications in hydrology and environmental sciences, as the estimation of spatially continuous rainfall at finer time scales, for drainage systems design and extreme rainfall analysis. A method to disaggregate monthly rainfall to daily or finer temporal scale is very important in many applications. Many authors have addressed this problem by using some stochastic methods including several stochastic rainfall models. The lowering resolution methods must be low-cost and low-storage since the amount of rainfall data is large. The purpose of this work is to formulate this problem as a constrained optimization problem and solve it with a low-cost and low-storage deterministic optimization method. We modify the objective function proposed by Guenni and Bárdossy for solving the disaggregation rainfall problem and we use the low-cost spectral projected gradient (SPG) method. In contrast with the stochastic method, a deterministic approach will take into account important information, as for example the gradient of the objective function. The proposed method was applied to a data set from a rainfall network of the central plains of Venezuela, in which rainfall is highly seasonal and data availability at a daily time scale or even higher temporal resolution is very limited. The numerical results show that the SPG method for solving the disaggregation rainfall problem avoids daily precipitations outliers that might occur as an artifact of the simulation procedure and accurately reproduces the probability distribution. Also, the proposed model and methodology outperforms the one proposed by Guenni and Bárdossy (2002) in the sense that it reduces the absolute error value for the statistical properties from the observed data.
Authors and Affiliations
Debora Cores, Lelys Guenni, Lisbeth Torres
Keywords
Related Articles
- EP ID EP240520
- DOI -
- Views 158
- Downloads 0
Surrogate reservoir models for CSI well probabilistic production forecast
The aim of this work is to present the construction and use of Surrogate Reservoir Models capable of accurately predicting cumulative oil production for every well stimulated with cyclic steam injection at any given time...
Integrating Fuzzy Formal Concept Analysis and Rough Set Theory for the Semantic Web
Formal Concept Analysis and Rough Set Theory provide two mathematical frameworks in information management which have been developed almost independently in the past. Currently, their integration is revealing very intere...
Motion planning algorithms, topological properties and affine approximation
The topological study of the so-called "motion planning algorithms" emerged in the 2003-2004 with the works of M. Farber. We focus here on the topological study of the set of these algorithms, when the configuration spac...
New results on the stability, integrability and boundedness in Volterra integro-differential equations
The authors of this article deal with a first order non-linear Volterra integro-differential equation (NVIDE). To this end, the conditions are obtained which are sufficient for stability (S), boundedness (B), and for eve...
Numerical solution of mixed Volterra-Fredholm integral equations using iterative method via two-variables Bernstein polynomials
This paper is concerned with the numerical solution of mixed Volterra-Fredholm integral equations, based on iterative method and two variable Bernstein polynomials. In the main result, this method has several benefits in...