Modelling of Surface Runoff on the Yamal Peninsula, Russia, Using ERA5 Reanalysis

Matveeva, Tatiana; Sidorchuk, Aleksey

doi:10.3390/w12082099

Cited by 7 publications

(12 citation statements)

References 46 publications

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“…Many recent studies have used ERA5 for many hydrological applications. These include modeling of global mean P (Hersbach et al, 2018), evaluating daily P products over the continental United States (Beck et al, 2019a), modelling of surface runoff (Matveeva & Sidorchuk, 2020), and catchment scale hydrological modelling (Tarek et al, 2020). The dataset can be obtained at https://cds.climate.copernicus.eu/.…”

Section: Era5mentioning

confidence: 99%

Spatio-temporal Evaluation of Gridded Precipitation and Evapotranspiration Products Over Balochistan Province in Pakistan

Jan

Baez-Villanueva²,

Ribbe³

2022

Preprint

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This study evaluated the performance of four state-of-the-art precipitation (P; CHIRPSv2, MSWEPv2.2, PERSIANN-CDR, and ERA5) and three state-of-the-art evaporation (Ea; GLEAMv3.3a, SSEBopv4.0, and ERA5) products in the data-scarce province of Balochistan, Pakistan. The P products were evaluated through a point-to-pixel comparison at the monthly, annual, and seasonal temporal scales using data from 17 gauges. The modified Kling-Gupta efficiency (KGE’) and the root mean square error (RMSE) were used as statistical indices to evaluate the performance of the P products. We used the Budyko framework to evaluate the Ea component due to unavailability of ground-based Ea data. For this purpose, we calculated the multi-annual mean Ea — defined as Ea_WB — for 12 defined catchments for a period of 16 years (2003–2018). Subsequently, the estimated Ea_WB was used as the reference to evaluate the performance of the Ea products. The results of the study revealed that among the P products generally MSWEPv2.2 closely followed by ERA5 presented the best spatio-temporal KGE’ performance over most of the stations and across the province. CHIRPSv2 and PERSIANN-CDR relatively showed poor KGE’ performance. Whereas, for the Ea component, ERA5 generally outperformed the other two products — i.e., GLEAMv3.3a and SSEBopv4.0 — in most of the evaluated catchments except for those located over the north-eastern region where GLEAMv3.3a performed the best. The results of this study can be beneficial to shift towards a proactive water management approach that can serve as a basis to improve the decision-making process in the region.

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Section: Era5mentioning

confidence: 99%

Spatio-temporal Evaluation of Gridded Precipitation and Evapotranspiration Products Over Balochistan Province in Pakistan

Jan

Baez-Villanueva²,

Ribbe³

2022

Preprint

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“…Wang et al [57], Liu et al [58], Hersbach et al [59], Terzago et al [60], and Orsolini et al [61] conducted a comprehensive assessment of snow cover parameters for this dataset, the findings showed that the ERA5 dataset can well capture the spatial distribution of snow cover and a wide range of characteristics of seasonal changes despite its large positive bias. Wang et al [62], Matveeva and Sidorchuk [63], and Yılmaz et al [64]…”

Section: Data 221 Snow Datamentioning

confidence: 99%

Spatio-Temporal Variation Characteristics of Snow Depth and Snow Cover Days over the Tibetan Plateau

Zhang

Mou

Niu

et al. 2021

Water

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Changes in snow cover over the Tibetan Plateau (TP) have a significant impact on agriculture, hydrology, and ecological environment of surrounding areas. This study investigates the spatio-temporal pattern of snow depth (SD) and snow cover days (SCD), as well as the impact of temperature and precipitation on snow cover over TP from 1979 to 2018 by using the ERA5 reanalysis dataset, and uses the Mann–Kendall test for significance. The results indicate that (1) the average annual SD and SCD in the southern and western edge areas of TP are relatively high, reaching 10 cm and 120 d or more, respectively. (2) In the past 40 years, SD (s = 0.04 cm decade−1, p = 0.81) and SCD (s = −2.3 d decade−1, p = 0.10) over TP did not change significantly. (3) The positive feedback effect of precipitation is the main factor affecting SD, while the negative feedback effect of temperature is the main factor affecting SCD. This study improves the understanding of snow cover change and is conducive to the further study of climate change on TP.

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“…Probability density functions of runoff depth for a small catchment can often be approximated with an exponential relationship [27]. For this case, E TPi in Equation ( 31) is an inverse power-law function of M cr .…”

Section: Express Estimation Of Gully Erosion Riskmentioning

confidence: 99%

“…Meteorological information, in form of changing through time air temperature and precipitation, was taken from ERA5 reanalysis. The sequences of surface runoff depths were calculated with the hydrological model, validated on the hydrological measurements on the selected catchment [27].…”

Section: Gultem Model Validation and Calibrationmentioning

confidence: 99%

“…The same empirical coefficients were used in both models. The probability density function for surface runoff, used in STABGUL (Figure 4), was derived from a 30-year long sequence of surface runoff depths [27], which was used in GULTEM calculations. The GULTEM model was run for a 1200-year period (40 repetitions of a 30-year long sequence) to obtain a stable longitudinal profile, suited for comparison with the stable gully calculated from the STABGUL model.…”

Section: The Comparison Of Gultem and Stabgul Modelsmentioning

confidence: 99%

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Models of Gully Erosion by Water

Sidorchuk

2021

Water

Self Cite

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The type of modelling of gully erosion for the projects of land management depend on the targets and degree of details of these projects, as well as on the availability of input data. The set of four models cover a broad range of possible applications. The most detailed information about predicted gullies, change of their depth, width, and volume throughout the gully lifetime is obtained with the gully erosion and thermoerosion dynamic model. The calculation requires the time series of surface runoff, catchment relief, and lithology and the complex of coefficients and parameters, some of which can be estimated only by model calibration on the measurements. The difficulty in obtaining some of these coefficients makes it necessary to use less complicated models. The stable gully model predicts final gully depths and widths and is useful for projects where only stable gully geometry is used. The modified area–slope approach is used in the two simplest models, where the position on the slopes of possible gullies is calculated without details of the gully geometry. One of these models calculates total erosion potential, taking into account all water runoff transforming a gully. The second calculates gully erosion risk, using the information about slope inclination, contributing area and maximum surface runoff.

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Modelling of Surface Runoff on the Yamal Peninsula, Russia, Using ERA5 Reanalysis

Cited by 7 publications

References 46 publications

Spatio-temporal Evaluation of Gridded Precipitation and Evapotranspiration Products Over Balochistan Province in Pakistan

Spatio-temporal Evaluation of Gridded Precipitation and Evapotranspiration Products Over Balochistan Province in Pakistan

Spatio-Temporal Variation Characteristics of Snow Depth and Snow Cover Days over the Tibetan Plateau

Models of Gully Erosion by Water

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