1 This paper introduces the development of a dynamic parallel algorithm for conducting 2 hydrological model simulations. This new algorithm consists of a river network 3 decomposition method and an enhanced master-slave paradigm. The decomposition 4 method is used to divide a basin river network into a large number of subbasins, and the 5 enhanced master-slave paradigm is adopted to realize the...

Extending climatological forcing data to current and real-time forcing is a necessary task for hydrological forecasting. While such data are often readily available nationally, it is harder to find fit-for-purpose global data sets that span long climatological periods through to near-real time. Hydrological simulations are generally sensitive to bias in the meteorological forcing data, especial...

Accompanying the continuous development of sensor network technology, sensors worldwide are constantly producing observation data. However, the sensors and their data from different observation platforms are sometimes difficult to use collaboratively in response to natural disasters such as floods for the lack of semantics. In this paper, a hydrological sensor web ontology based on SSN ontology...

Hydrological multivariate analysis has been widely studied using copula-based modelling, in which marginal distribution inference is one of the key issues. The main object of this study is to discuss the applicability of the principle of maximum entropy (POME) in marginal distribution inference, thus to develop a POME-copula framework to analyse the dependence of hydrological variables. Margina...

The use of flexible hydrological model structures for hypothesis testing requires an objective and diagnostic method to identify whether a rainfall-runoff model structure is suitable for a certain catchment. To determine if a model structure is realistic, i.e. if it captures the relevant runoff processes, both performance and consistency are important. We define performance as the ability of a ...

[1] A novel method is presented for model uncertainty estimation using machine learning techniques and its application in rainfall runoff modeling. In this method, first, the probability distribution of the model error is estimated separately for different hydrological situations and second, the parameters characterizing this distribution are aggregated and used as output target values for buil...

A new method of estimating the noise level present in a chaotic hydrological time series is presented. The effectiveness of the method is first demonstrated using two artificial chaotic time series, i.e. the Henon map and the Lorenz equation, whose dynamic characteristics are known a priori, and then tested on two real hydrological time series: daily sfreamflow series observed in the Chao Phray...

Hydrological Simulation Program Fortan (HSPF) is a mathematical modelling program developed by the United States Environmental Protection Agency (EPA). HSPF is used for simulating of watershed hidrology and water quality. In this paper, an evolutionary algorithm is applied to automated watershed model calibration. The calibration stage of the model is very important in order to reduce the error...

The hydrological pattern changes have a great influence on the wetland environment. However, some important wetland areas often lack historical observations due to economic and physical conditions. The Tian-e-Zhou oxbow lake wetland is an important habitat for two endangered species and also has very little historical hydrological data. Remote sensing images can be used to explore the historica...