[Gene method for inconsistent hydrological frequency calculation. I: Inheritance, variability and evolution...

Inconsistent hydrological time series present challenges for frequency analysis due to mixed stochastic and deterministic influences. Prior research has shown that these series contain random components and deterministic features like jumps, trends, and periodicity. However, no prior work had resolved how to fully integrate inheritance and variability in such processes. This gap motivated the development of a new conceptual framework. Existing methods often fail to capture the full complexity of evolution in hydrological data. The need for a unified approach to describe these characteristics remains unmet. This paper introduces a novel concept inspired by biological genetics to model hydrological processes. The approach aims to bridge the divide between random and deterministic components in frequency calculations. By addressing these issues, the study offers a new perspective on hydrological time series analysis.

This study aimed to describe the inheritance and variability of inconsistent hydrological time series. The goal was to develop a method that integrates both stochastic and deterministic components. The motivation stemmed from the limitations of existing frequency analysis techniques. The approach sought to reveal the evolution principles of hydrological processes. By comparing various frequency methods, the researchers aimed to identify key issues in inconsistency studies. The study also aimed to propose a new conceptual model for hydrological time series. This model would allow for a more comprehensive understanding of inheritance and variability. The ultimate purpose was to improve the accuracy of hydrological frequency calculations.

The researchers used stochastic process simulation and time series analysis to explore inheritance and variability. They compared multiple frequency analysis approaches for inconsistent data. The new concept of hydrological genes was introduced as a framework for modeling. These genes were constructed using moment-based methods like general and L-moments. The study defined five components as hydrological bases: jump, trend, periodic, dependence, and random. The methods allowed for the synthesis of inheritance and variability in time series. The approach combined deterministic and stochastic elements into a unified model. The researchers tested their framework on real-world hydrological data to validate its effectiveness.

The proposed method successfully integrated inheritance and variability in hydrological time series. The hydrological genes framework captured both random and deterministic components. The five hydrological bases were clearly defined and applied to real data. The comparison of frequency analysis methods revealed limitations in existing approaches. The new method showed improved accuracy in describing evolution principles. The study demonstrated that inheritance and variability can be modeled simultaneously. The use of L-moments and weight functions enhanced the robustness of the model. The results suggest that the hydrological genes concept provides a more comprehensive analysis framework.

The study's findings suggest that the hydrological genes concept can effectively model inheritance and variability. The framework allows for the integration of stochastic and deterministic components. The researchers propose that this approach improves the understanding of evolution principles. The results indicate that existing frequency methods have notable limitations. The proposed method offers a more accurate and comprehensive analysis of hydrological data. The study supports the idea that hydrological genes can describe complex time series behavior. The researchers suggest that this concept can be applied to other inconsistent processes. The conclusions align with the study's aim to enhance frequency calculation methods.