Physically-based distributed modelling of river runoff under changing climate conditions

Krylenko, Inna; Motovilov, Yu. G.; Antokhina, É. A.; Zhuk, Volodymyr; Surkova, Galina

doi:10.5194/piahs-368-156-2015

Cited by 16 publications

(11 citation statements)

References 5 publications

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“…Nash and Sutcliffe (1970) efficiency, NSE, and bias evaluation criteria were adopted to summarize the goodness of fit of simulated and measured daily discharge series. As an example, the evaluation criteria calculated for the outlets of the Lena and Northern Dvina River basins and adopted from Motovilov and Gelfan (2013), Gusev et al (2011Gusev et al ( , 2015 and Krylenko et al (2014) are shown in Table 1. 1967-1985; 1991-1998 0.85 −0.6 …”

Section: Hydrological Modelsmentioning

confidence: 99%

“…Both models were applied earlier for simulating runoff hydrographs based on multi-year hydrometeorological observations in the Lena and Northern Dvina River basins and demonstrated good performance of simulations (Motovilov and Gelfan, 2013;Gusev et al, 2011Gusev et al, , 2015Krylenko et al, 2014). Trial-and-error manual procedure and the shuffle complex evolution (SCE-UA) automatic algorithm were applied for calibration of ECOMAG and SWAP, respectively.…”

Section: Hydrological Modelsmentioning

confidence: 99%

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Large-basin hydrological response to climate model outputs: uncertainty caused by internal atmospheric variability

Gelfan

Семенов

Гусев

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. An approach is proposed to assess hydrological simulation uncertainty originating from internal atmospheric variability. The latter is one of three major factors contributing to uncertainty of simulated climate change projections (along with so-called "forcing" and "climate model" uncertainties). Importantly, the role of internal atmospheric variability is most visible over spatio-temporal scales of water management in large river basins. Internal atmospheric variability is represented by large ensemble simulations (45 members) with the ECHAM5 atmospheric general circulation model. Ensemble simulations are performed using identical prescribed lower boundary conditions (observed sea surface temperature, SST, and sea ice concentration, SIC, for 1979-2012) and constant external forcing parameters but different initial conditions of the atmosphere. The ensemble of bias-corrected ECHAM5 outputs and ensemble averaged ECHAM5 output are used as a distributed input for the ECOMAG and SWAP hydrological models. The corresponding ensembles of runoff hydrographs are calculated for two large rivers of the Arctic basin: the Lena and Northern Dvina rivers. A number of runoff statistics including the mean and the standard deviation of annual, monthly and daily runoff, as well as annual runoff trend, are assessed. Uncertainties of runoff statistics caused by internal atmospheric variability are estimated. It is found that uncertainty of the mean and the standard deviation of runoff has a significant seasonal dependence on the maximum during the periods of spring-summer snowmelt and summer-autumn rainfall floods. Noticeable nonlinearity of the hydrological models' results in the ensemble ECHAM5 output is found most strongly expressed for the Northern Dvina River basin. It is shown that the averaging over ensemble members effectively filters the stochastic term related to internal atmospheric variability. Simulated discharge trends are close to normally distributed around the ensemble mean value, which fits well to empirical estimates and, for the Lena River, indicates that a considerable portion of the observed trend can be externally driven.

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Section: Hydrological Modelsmentioning

confidence: 99%

Section: Hydrological Modelsmentioning

confidence: 99%

Large-basin hydrological response to climate model outputs: uncertainty caused by internal atmospheric variability

Gelfan

Семенов

Гусев

et al. 2015

Hydrol. Earth Syst. Sci.

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“…Для расчёта расходов воды, поступающей на верхние границы гидродинамической модели, использовались модели ECOMAG и искусственных нейронных сетей. Модель формирования стока ECOMAG ранее успешно применялась для сценарных расчётов стока при изменении климата в бассейне р. Северная Двина [31]. В режиме оперативных расчётов модели STREAM 2D и ECOMAG начали применяться для узла слияния рек Сухоны и Юга в ходе разработки описываемой системы прогнозирования.…”

Section: зеленцов и дрunclassified

Model-driven system for operational forecasting of river flooding

Zelentsov

Alabyan

Алабян³

et al. 2019

Вестник Российской академии наук

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В 2016 г. авторы данной статьи опубликовали в журнале "Вестник РАН" [1] описание подхода к созданию интеллектуальных информационных систем оперативного прогнозирования речных на-воднений, основанного на применении комплекса гидрологических и гидродинамических моделей. Принципиальными особенностями предложенного подхода являются включение в создаваемую си- ВЕСТНИК РОССИЙСКОЙ АК АДЕМИИ НАУК, 2019, том 89, № 8, с. 831-843 ЗЕЛЕНЦОВ Вячеслав Алексеевич -доктор технических наук, главный научный сотрудник СПИИРАН. АЛАБЯН Андрей Михайлович -кандидат географических наук, доцент кафедры гидрологии суши Географического факультета МГУ им. М. В. Ломоносова. КРЫЛЕНКО Инна Николаевна -кандидат географических наук, старший научный сотрудник кафедры гидрологии суши Географического факультета МГУ им. М. В. Ломоносова. ПИМАНОВ Илья Юрьевич -младший научный сотрудник СПИИРАН. ПОНОМАРЕНКО Мария Руслановна -младший научный сотрудник СПИИРАН. ПОТРЯСАЕВ Семён Алексеевич -кандидат технических наук, старший научный сотрудник СПИИРАН. СЕМЁНОВ Александр Евгеньевич -младший научный сотрудник СПИИРАН. СОБОЛЕВСКИЙ Владислав Алексеевич -младший научный сотрудник СПИИРАН. СОКОЛОВ Борис Владимирович -доктор технических наук, руководитель лаборатории информационных технологий в системном анализе и моделировании СПИИРАН. ЮСУПОВ Рафаэль Мидхатович -член-корреспондент РАН, научный руководитель СПИИРАН. Поступила в редакцию 22.08.2018 г. Поступила после доработки 20.11.2018 г. Принята к публикации 17.02.2019 г. В статье представлены результаты разработки и тестирования системы оперативного прогнозирования речных наводнений, которая базируется на использовании комплекса гидрологических и гидро динамических моделей, а также наземных и спутниковых данных, и реализована на основе сервис-ориентированной архитектуры. Отличительная особенность системы -полная автоматизация всего цикла моделирования -от загрузки исходных данных до интерпретации результатов, визуализации и оповещения заинтересованных лиц. Теоретической основой обеспечения согласованного функционирования всех компонентов системы является разрабатываемая авторами статьи квалиметрия моделей и полимодельных комплексов. Практическая реализация выполнена с помощью открытых кодов и свободно распространяемого программного обеспечения. Результаты тестирования свидетельствуют о возможности широкого внедрения подобных систем в деятельность территориальных органов власти и служб по чрезвычайным ситуациям. Ключевые слова: наводнения, комплексное моделирование, оперативное прогнозирование, сервисориентированная архитектура, искусственные нейронные сети, гидродинамические модели, геоинформационные системы.This article presents the results of the development and testing of a system for operational forecasting of river flooding. This system is based on the use of a complex of hydrological and hydrodynamic models and in-situ and satellite data. It is implemented on the basis of service-oriented architecture. A distinctive feature of the system is its full automation of the entire modeling cycle, from the initi...

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“…In the eastern part of the Baltic Sea Basin, the current changes in the hydrological regime observed within the Belorussian territory during the past decades (increase of winter and decrease of spring streamflow) are projected to continue for four major river basins of the country (Volchek et al, 2017). The estimates of projected runoff changes over the Russian Northwest show a considerable change of seasonal runoff dynamics for the Volga (Georgievskiy et al, 2018) and the Northern Dvina Basins (Krylenko et al, 2015).…”

Section: Introductionmentioning

confidence: 98%

The Past and Future Estimates of Climate and Streamflow Changes in the Western Dvina River Basin

et al. 2019

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The study presents an assessment of the recent and projected changes of the middle and upper Western Dvina River runoff and regional climate during the 20th and 21st centuries. For this assessment, we used historical runoff data, the output of EURO-CORDEX consortium calculations for scenarios RCP4.5 and RCP8.5, and hydrological model "Hydrograph." Analysis of monthly runoff data for the 1945-2015 period revealed positive trends for each of the five months from December to April. These trends are statistically significant at the 0.05 level. No significant trends were found for other months. Significant negative trends were established for spring flood peak discharges (from −69 to −88 m 3 s −1 per 10 years). Usually, maximum discharges are observed during spring floods. Minimum discharges during winter low-water period were increased by 6 m 3 s −1 per 10 years. The annual runoff trend was statistically significant only at the Polotsk gauging station (9.5 m 3 s −1 per 10 years). To the end of the current century over the study region, estimates of projected meteorological parameters (air temperature and precipitation) show positive tendencies of air temperature (from 2.4 • C to 4.7 • C depending on scenario) and precipitation (up to 15 to 30 mm). Changes of seasonal and annual temperature and precipitation vary depending on the models and scenarios used. The strongest changes were noticed for the RCP8.5 scenario. The greatest changes within each scenario were revealed for the winter and spring seasons. It is projected that during the 2021-2100 period according to both RCP scenarios, annual discharges will not change in the upper part of the Western Dvina River Basin and increase by 10-12% in its lower part. The maximum spring flood discharges in both RCP scenarios are expected to decrease by 25%. The minimum runoff of winter lowflow period is expected to increase by up to 60 to 90% above the present long-term mean values.

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Physically-based distributed modelling of river runoff under changing climate conditions

Cited by 16 publications

References 5 publications

Large-basin hydrological response to climate model outputs: uncertainty caused by internal atmospheric variability

Large-basin hydrological response to climate model outputs: uncertainty caused by internal atmospheric variability

Model-driven system for operational forecasting of river flooding

The Past and Future Estimates of Climate and Streamflow Changes in the Western Dvina River Basin

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