scholarly journals Application of Deterministic-stochastic Simulation for Frequency Curves Estimation: the Pasha River (Russia) as a Case Study

2012 ◽  
pp. 43-52
Author(s):  
Keyword(s):  
Stochastic Simulation ◽  
Hydrological Model ◽  
Weather Generator ◽  
Distributed Hydrological Model ◽  
Stochastic Weather Generator ◽  
Runoff Model ◽  
Frequency Curves

An approach that combines runoff model & stochastic weather generator in order to get the coordinates of yearly, monthly, daily, maximal & minimal runoff frequency curves is considered. The distributed hydrological model “Hydrograph” and stochastic weather generator were applied to the catchment of the Pasha River (5710 km2) located in the Northwest Russia. The frequency curves are compared with analogous ones that have been built on the basis of the long-term runoff observations.

Calibration of a reinforcement learning method with the ClimEx large ensemble and a weather generator for water management 

2021 ◽  
Author(s):  
Gabrielle Dallaire ◽  
Richard Arsenault ◽  
Pascal Côté ◽  
Kenjy Demeester
Keyword(s):  
Water Management ◽  
Reinforcement Learning ◽  
Hydrological Model ◽  
Weather Generator ◽  
Management Policy ◽  
Learning Method ◽  
Large Ensemble ◽  
Model Driven ◽  
Hydropower System

<p>Hydropower is a renewable source of energy that relies on efficient water planning and management. As the behavior of this natural resource is difficult to predict, water managers therefore use methods to help the decision-making process. Reinforcement Learning (RL) has been shown to be a potentially effective approach to overcome the limitations of the Stochastic Dynamic Programming (SDP) method that is commonly used for water management. However, convergence to a robust and efficient operating policy from RL methods requires large amounts of data, while long-term historical data is not always available. The objective of this study consists in using tools to generate long-term hydrological series to obtain an efficient parameterization of the management policy. This presentation introduces a comparison of calibration datasets used in a RL method for the optimal control of a hydropower system. This method aims to find a feedback policy that maximizes the production of a hydropower system over a mid-term horizon. Three streamflow datasets are compared on a real hydropower system for RL calibration: 1) the historical streamflow (35 years), 2) streamflow simulated by a hydrological model driven by a high-resolution large-ensemble climate model data (3500 years) from the ClimEx project, and 3) streamflow simulated by a hydrological model driven by climate data generated with a stochastic weather generator (5000 years). The GR4J hydrological model is employed for the hydrologic modelling aspect of the work. The reinforcement learning method is applied on the Lac-Saint-Jean water resources system in Quebec (Canada), where the hydrological regime is snowmelt-dominated. A bootstrapping method where multiple calibration and validation sets were resampled is used to conduct a robust statistical analysis for comparing the methods’ performance. The performance of the calibrated management policy is evaluated with respect to the operational constraints of the system as well as the overall energy production. Preliminary results show that is possible to achieve effective management policies by using tools to generate long-term hydrological series to feed a RL method.</p>

scholarly journals PUBs for Engineering Purpose: Framework Development and Case Study

10.29007/9kpv ◽  
2018 ◽  
Author(s):  
Yang Zhiyong ◽  
Gao Xichao ◽  
Liu Jiahong
Keyword(s):  
Hydrological Model ◽  
Potential Evapotranspiration ◽  
Meteorological Data ◽  
Distributed Hydrological Model ◽  
Runoff Simulation ◽  
Annual Average ◽  
Sensing Technology ◽  
Watershed Method ◽  
Predictions In Ungauged Basins

A framework of predictions in ungauged basins (PUBs, taking Paniai lakes watershed, Indonesia as an example) for hydropower exploration is developed. In this framework, remote sensing technology and similar watershed method are used to collect necessary meteorological and topographical data for runoff simulation. Besides, a modified physical based distributed hydrological model is developed to consider the characteristics (regulation capacity of the lakes) of the watershed. Finally, considering the modeling purpose, annual average runoff index is used to assess the modeling results. In the case study (Paniai lakes watershed), TRMM precipitation, HWSD soil type, and AVHRR landcover data, combined with meteorological data from two similar watersheds, are collected to drive the modified hydrological model. According to the model results, the simulated potential evapotranspiration capacities and annual average runoff coefficients are consistent between the two cases (modeling with meteorological data of the two similar watersheds), and the simulated annual average runoff coefficients of the two cases are basically consistent with the observed annual average runoff coefficient of another similar watershed located in Indonesia.

scholarly journals Long Term Runoff Analysis of the Lena River Basin using a Distributed Hydrological Model

2009 ◽  
Vol 22 (3) ◽  
pp. 177-187 ◽  
Author(s):  
Shigemi HATTA ◽  
Hiroshi HAYAKAWA ◽  
Hotaek PARK ◽  
Takeshi YAMAZAKI ◽  
Kazukiyo YAMAMOTO ◽  
...  
Keyword(s):  
River Basin ◽  
Hydrological Model ◽  
Distributed Hydrological Model ◽  
Lena River ◽  
Runoff Analysis
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