A Basin Approach to a Hydrological Service Delivery System in the Amur River Basin

Borsch, S.V.; Khristoforov, A. V.; Krovotynzev, Vladimir; Leontieva, Ekaterina; Simonov, Yu. A.; Zatyagalova, V. V.

doi:10.3390/geosciences8030093

Cited by 8 publications

(4 citation statements)

References 18 publications

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“…One of the most important tasks of the operational hydrological forecasting system is the forecasts provision to end users (including hydrologists at regional offices, National Disaster Management Agency, Cities administrations and others) in a timely and effective manner. For this purpose, the system for monitoring and forecasting of floods and other adverse hydrological phenomena was developed based on the recent advances of GIS-WEB technologies [11,19]. Forecasts of water levels, automatically issued according to the abovementioned techniques, are sent to the delivery system in the form of web services.…”

Section: Resultsmentioning

confidence: 99%

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Russian Rivers Streamflow Forecasting Using Hydrograph Extrapolation Method

et al. 2021

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This paper presents a method of hydrograph extrapolation, intended for simple and efficient streamflow forecasting with up to 10 days lead time. The forecast of discharges or water levels is expressed by a linear formula depending on their values on the date of the forecast release and the five previous days. Such forecast techniques were developed for more than 2700 stream gauging stations across Russia. Forecast verification has shown that this method can be successfully applied to large rivers with a smooth shape of hydrographs, while for small mountain catchments, the accuracy of the method tends to be lower. The method has been implemented into real-time continuous operations in the Hydrometcentre of Russia. In the territory of Russia, 18 regions have been identified with a single dependency of the maximum lead time of good forecasts on the area and average slope of the catchment surface for different catchments of each region; the possibilities of forecasting river streamflow by the method of hydrograph extrapolation are approximately estimated. The proposed method can be considered as a first approximation while solving the problem of forecasting river flow in conditions of a lack of meteorological information or when it is necessary to quickly develop a forecasting system for a large number of catchments.

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Section: Resultsmentioning

confidence: 99%

“…The subsystem is implemented based on WEB and GIS technologies, which allows integration and visualization in a single information environment (the Internet) of all output products. Interaction with users is carried out using a WEB application; the functionality of application is developed taking into account users' requirements [11,12].…”

Section: Introductionmentioning

confidence: 99%

Russian Rivers Streamflow Forecasting Using Hydrograph Extrapolation Method

et al. 2021

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“…Taken together, these results suggest that current early warning systems could be based on measurements of rainfall intensity. Those systems could be combined with monitoring of water levels [62,63]. These findings suggest several courses of action to record, send, and monitor floods using a new generation of smart water level gauges [64].…”

Section: Discussionmentioning

confidence: 96%

Algorithm to Predict the Rainfall Starting Point as a Function of Atmospheric Pressure, Humidity, and Dewpoint

Gutiérrez-López

Cruz-Paz

Mandujano

2019

Climate

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Forecasting extreme precipitations is one of the main priorities of hydrology in Latin America and the Caribbean (LAC). Flood damage in urban areas increases every year, and is mainly caused by convective precipitations and hurricanes. In addition, hydrometeorological monitoring is limited in most countries in this region. Therefore, one of the primary challenges in the LAC region the development of a good rainfall forecasting model that can be used in an early warning system (EWS) or a flood early warning system (FEWS). The aim of this study was to provide an effective forecast of short-term rainfall using a set of climatic variables, based on the Clausius–Clapeyron relationship and taking into account that atmospheric water vapor is one of the variables that determine most meteorological phenomena, particularly regarding precipitation. As a consequence, a simple precipitation forecast model was proposed from data monitored at every minute, such as humidity, surface temperature, atmospheric pressure, and dewpoint. With access to a historical database of 1237 storms, the proposed model allows use of the right combination of these variables to make an accurate forecast of the time of storm onset. The results indicate that the proposed methodology was capable of predicting precipitation onset as a function of the atmospheric pressure, humidity, and dewpoint. The synoptic forecast model was implemented as a hydroinformatics tool in the Extreme Precipitation Monitoring Network of the city of Queretaro, Mexico (RedCIAQ). The improved forecasts provided by the proposed methodology are expected to be useful to support disaster warning systems all over Mexico, mainly during hurricanes and flashfloods.

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“…Recent developments in information and internet technologies provide a great opportunity to enhance all the aforementioned data processes and sharing of flood and rainfall-related data and information and to improve the timeliness of flood warnings (Demir and Krajewski, 2013). The domain of flood cyberinfrastructures has seen transformative advancements in recent years (Curtis and Fowler, 2012;Andreasen et al, 2016;Escudier et al, 2016;Jones et al, 2017;Borsch et al, 2018;De Filippis et al, 2022). This growth is underscored by the development of multi-hazard and multi-criteria platforms (Ries et al, 2010;Heil et al, 2014;Kochilakis et al, 2016;Ong et al, 2017;Abily et al, 2020;Lin et al, 2020;Mamassis et al, 2021).…”

Section: Flood Information Systemsmentioning

confidence: 99%

A Contemporary Systematic Review of Cyberinfrastructure Systems and Applications for Flood and Drought Data Analytics and Communication

Yeşilköy,

Baydaroğlu,

Singh

et al. 2024

Preprint

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Hydrometeorological disasters, including floods and droughts, have intensified in both frequency and severity in recent years. This trend underscores the critical role of timely monitoring, accurate forecasting, and effective warning systems in facilitating proactive responses. Today's information systems offer a vast and intricate mesh of data, encompassing satellite imagery, meteorological metrics, and predictive modeling. Easily accessible to the general public, these cyberinfrastructures simulate potential disaster scenarios, serving as invaluable aids to decision-making processes. This review collates key literature on water-related disaster information systems, underscoring the transformative impact of emerging information and Internet technologies. These advancements promise enhanced flood and drought warning timeliness and greater preparedness through improved management, analysis, visualization, and data sharing. Moreover, these systems aid in hydrometeorological predictions, foster the development of web-based educational platforms, and support decision-making frameworks, digital twins, and metaverse applications in disaster contexts. They further bolster scientific research and development, enrich climate change vulnerability frameworks, and strengthen associated cyberinfrastructures. This article delves into prospective developments in the realm of natural disasters, pinpointing primary challenges and gaps in current water-related disaster information systems, and highlighting the potential intersections with future artificial intelligence solutions.

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A Basin Approach to a Hydrological Service Delivery System in the Amur River Basin

Cited by 8 publications

References 18 publications

Russian Rivers Streamflow Forecasting Using Hydrograph Extrapolation Method

Russian Rivers Streamflow Forecasting Using Hydrograph Extrapolation Method

Algorithm to Predict the Rainfall Starting Point as a Function of Atmospheric Pressure, Humidity, and Dewpoint

A Contemporary Systematic Review of Cyberinfrastructure Systems and Applications for Flood and Drought Data Analytics and Communication

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