In this work we propose a Bayesian approach for the parameter estimation problem of stochastic autoregressive models of order p, AR(p), applied to the streamflow forecasting problem. Procedures for model selection, forecasting and robustness evaluation through Monte Carlo Markov Chain (MCMC) simulation techniques are also presented. The proposed approach is compared with the classical one by Bo...

[1] We analyze the streamflow response of Sespe Creek, CA, to several large earthquakes. We find that flow increased after three earthquakes, and that the observed changes in flow have the same character. Both those earthquakes that induced static extension and those that induced static contraction cause flow to increase; streamflow thus appears to respond to dynamic strain. We find that all po...

Classification of heterogeneous precipitation fields for the assessment and possible improvement of lumped neural network models for streamflow forecasts N. Lauzon, F. Anctil, and C. W. Baxter Golder Associates, Calgary, Canada Département de génie civil, Pavillon Pouliot, Université Laval, Québec, G1K 7P4, Canada HYDRANNT Consulting Inc., Port Coquitlam, Canada Received: 20 December 2005 – Acc...

Projecting how future climatic change might impact streamflow is an important challenge for hydrologic science. The common approach to solve this problem is by forcing a hydrologic model, calibrated on historical data or using a priori parameter estimates, with future scenarios of precipitation and temperature. However, several recent studies suggest that the climatic regime of the calibration ...

Objective: Currently, the evaluation of baseflow components have been of a worldwide concern due to the influential role of streamflow (base flow and direct flow)in agriculture, water sources management as well as supplying the potable water. Direct and field measurement of baseflow is not practicable especially in large areas with statistics deficiencies. Also, this would not be economically e...

Streamflow forecasting has an important role in water resource management (e.g. flood control, drought management, reservoir design, etc.). In this paper, the application of Adaptive Neuro Fuzzy Inference System (ANFIS) is used for long-term streamflow forecasting (monthly, seasonal) and moreover, cross-validation method (K-fold) is investigated to evaluate test-training data in the model.Then,...

Inflow data plays an important role in water and energy resources planning and management. In general, due to the limited availability of historical inflow data, synthetic streamflow time series have been widely used for several applications such as midand long-term hydropower scheduling and the identification of hydrological processes. This paper explores the use of fuzzy inference systems for...

Data assimilation (DA) methods have been widely used to improve model state estimation by merging model outputs with observations. Traditionally, studies have focused on updating model state variables but recent studies have augmented model parameters alongside model state variables to improve the estimation procedure. The updated model ensemble members represent a compromised estimation betwee...