Northern Dvina runoff simulation using land-surface model SWAP and global databases

Гусев, Е. М.; Насонова, О. Н.; Dzhogan, L. Ya.; Ковалев, Е.Е.

doi:10.1134/s0097807811030043

Cited by 24 publications

(18 citation statements)

References 17 publications

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“…Gusev and O. Nasonova (Gusev and Nasonova, 1998) was intensively validated, in particular, within several model intercomparison projects (PILPS, Rhone-AGG, MOPEX, SnowMIP, GSWP-2) for different river basins and experimental sites located in various natural zones (from areas in tropical zones to regions with permafrost) and characterized by different spatial scales (from small experimental sites and catchments to the whole land surface of the Earth). The results of the model testing are presented, particularly, in Nasonova (1998, 2003) and Gusev et al (2011).…”

Section: Hydrological Modelsmentioning

confidence: 99%

“…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%

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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“…For example, the area of the Northern Dvina River basin contains more than 20 points of this regular grid. As shown by different researchers (Kislov et al 2009, Gusev et al 2011) all climate change estimations by the base global circulation models only become reliable for averaging of several models' outputs for large territorial units and rather big time scales (years).…”

Section: Estimation Of Possible Northern Dvina Runoff Changes Based Omentioning

confidence: 99%

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

et al. 2015

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Physically-based distributed modelling under changing climatic conditions has been carried out for the Northern Dvina River basin using the ECOMAG (ECOlogical Model for Applied Geophysics). The parameters of the model have been adjusted through calibration against runoff hydrographs observed for the period [2000][2001][2002][2003][2004][2005][2006][2007][2008][2009]. Validation of the model has been performed for the period of [1970][1971][1972][1973][1974][1975][1976][1977][1978][1979][1980][1981][1982][1983][1984][1985][1986][1987][1988][1989]. Both sensitivity analysis and scenario approaches (based on the CMIP3 projections) have been applied to assess possible hydrological consequences of climate change in the basin. It has been shown that for greenhouse gases emissions A2 scenario, averaged for 11 climate models, annual runoff will not change significantly for the future 50 years. But due to increasing of winter precipitation by up to 15%, the volume of flow in the flood period could increase by up to 20%. Earlier beginning of the flood season is expected because of rising of the air temperature.

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“…The Soil, Water, Atmosphere and Plant (SWAP) model has been intensively validated during the past 2 decades (van Dam et al, 1997;Gusev and Nasonova, 2003;Kroes et al, 2000;Gusev et al, 2011;Ma et al, 2011). Different versions of SWAP are validated against various observed hydrothermal characteristics.…”

Section: Introductionmentioning

confidence: 99%

A hybrid model to simulate the annual runoff of the Kaidu River in northwest China

Chen

Bai

et al. 2016

Hydrol. Earth Syst. Sci.

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Abstract. Fluctuant and complicated hydrological processes can result in the uncertainty of runoff forecasting. Thus, it is necessary to apply the multi-method integrated modeling approaches to simulate runoff. Integrating the ensemble empirical mode decomposition (EEMD), the back-propagation artificial neural network (BPANN) and the nonlinear regression equation, we put forward a hybrid model to simulate the annual runoff (AR) of the Kaidu River in northwest China. We also validate the simulated effects by using the coefficient of determination (R 2 ) and the Akaike information criterion (AIC) based on the observed data from 1960 to 2012 at the Dashankou hydrological station. The average absolute and relative errors show the high simulation accuracy of the hybrid model. R 2 and AIC both illustrate that the hybrid model has a much better performance than the single BPANN. The hybrid model and integrated approach elicited by this study can be applied to simulate the annual runoff of similar rivers in northwest China.

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Northern Dvina runoff simulation using land-surface model SWAP and global databases

Cited by 24 publications

References 17 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

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

A hybrid model to simulate the annual runoff of the Kaidu River in northwest China

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