[Home ] [Archive]   [ فارسی ]  
:: Main :: About :: Current Issue :: Archive :: Search :: Submit :: Contact ::
Main Menu
Journal Information::
Articles archive::
For Authors::
For Reviewers::
Contact us::
Site Facilities::
Search in website

Advanced Search
Receive site information
Enter your Email in the following box to receive the site news and information.
Last site contents
:: New Journal website launched
Last contents of other sections
:: Volume 10, Issue 36 (4-2024) ::
2024, 10(36): 101-113 Back to browse issues page
Prediction-Monitoring of Hydrological Variables based on Multivariate Assimilation of Ground and Satellite Data
Mehrad Bayat , Hosein Alizadeh , Barat Mojaradi
Iran University of Science and Technology , alizadeh@iust.ac.ir
Abstract:   (307 Views)
Nowadays the capability of forecasting-monitoring of hydrological variables is one of the crucial issues in the hydrology context. Traditionally, calibrating and developing hydrological models is based on streamflow observation. This issue arises from the lack of the availability of in situ measurements of other hydrological variables. Moreover, the models that only rely on the streamflow observation measured at the subbasin’s outlet may incorrectly represent the internal watershed processes. Due to advances in remote-sensing techniques, a good opportunity is available for calibrating-developing hydrological models based on the remotely-sensed data. Due to the importance of the Urmia Lake basin and the lack of a hydrological system for forecasting hydrological variables in the basin, we applied Data Assimilation (DA) at the upstream part of the basin. Accordingly, we simultaneously used the in-situ measurement of streamflow and remotely-sensed MODIS Snow Cover Fraction (SCF) to forecast hydrological variables at the upstream part of the Mahabad basin. Moreover, we compared the result of EnKF with SUFI2. Results show the simultaneous utilization of SCF and streamflow can concurrently improve the simulation accuracy of both variables. However, both EnKF and SUFI2 access similar result concerning streamflow simulation, EnKF provide a better result for SCF compared to SUFI2.
Keywords: Multi-variate calibration, Data assimilation, SUFI2, Snow Cover Fraction, Urmia Lake
Full-Text [PDF 1165 kb]   (51 Downloads)    
Type of Study: Research | Subject: هیدرولوژی و برنامه ریزی منابع آب
Send email to the article author

Add your comments about this article
Your username or Email:


XML   Persian Abstract   Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Bayat M, Alizadeh H, Mojaradi B. Prediction-Monitoring of Hydrological Variables based on Multivariate Assimilation of Ground and Satellite Data. Iranian Dam and Hydroelectric Powerplant 2024; 10 (36) :101-113
URL: http://journal.hydropower.org.ir/article-1-522-en.html

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 10, Issue 36 (4-2024) Back to browse issues page
نشریه سد و نیروگاه برق آبی Journal of Dam and Hydroelectric Powerplant
Persian site map - English site map - Created in 0.04 seconds with 38 queries by YEKTAWEB 4657