Parameterizing a hydrological model using a short‐term observational dataset to study runoff generation processes and reproduce recent trends in streamflow at a remote mountainous permafrost basin

Nesterova, Nataliia; Makarieva, Olga; Post, David

doi:10.1002/hyp.14278

Cited by 6 publications

(8 citation statements)

References 59 publications

(139 reference statements)

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“…The following data were used in the modeling process: daily meteorological and hydrological information from standard hydrometeorological networks, previously developed model parameters for main permafrost landscapes [49,59,60]. The assignment of typical landscapes within the watersheds was conducted using a SRTM digital elevation model and Landsat-8 images using previously developed schemes of landscape distribution in the mountainous basins of the Kolyma [60] and Indigirka [66]. The model was run in continuous mode for the period from 22 to 47 years with daily time step.…”

Section: Resultsmentioning

confidence: 99%

“…The Hydrograph, a distributed process-based model of runoff formation processes is applied in the study. The model has proven to be an effective tool for research and projection of hydrological processes in the permafrost and on poorly gauged river basins [48,49,65,66]. The model algorithms combine physically based and conceptual approaches in describing the processes of the terrestrial hydrological cycle, which allows a balance to be maintained between the complexity of the design schemes and orientation to limited input information.…”

Section: Hydrograph Modelmentioning

confidence: 99%

“…Precipitation and interception of rainfall water, compaction and ablation of snow cover, moisture and heat flux in the snow cover and in soils, including freezing and thawing, are described in an explicit way in the model. Underground water, slope and channel flow transformation, snow redistribution by wind and evaporation are calculated by conceptual methods that have shown their effectiveness in various conditions of cold regions [49,66]. Using a limited list of meteorological variables (air temperature and humidity, precipitation) as the input information allows the model to be applied at remote, poorly gauged basins.…”

Section: Hydrograph Modelmentioning

confidence: 99%

“…The results of the studies [49,59,65] have shown that the Hydrograph model performs satisfactorily in terms of active layer dynamic and soil temperature simulations. The description of the procedures of basin schematization and model parametrization are presented in detail in the studies [48,49,59,60,66] and therefore is omitted here.…”

Section: Hydrograph Modelmentioning

confidence: 99%

See 3 more Smart Citations

Challenges of Hydrological Engineering Design in Degrading Permafrost Environment of Russia

Makarieva¹,

Nesterova

Haghighi

et al. 2022

Energies

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The study shows that the current network of hydrometeorological observation in the permafrost zone of Russia is insufficient to provide data for the statistical approaches adopted at the state level for engineering surveys and calculations. The alternative to the financially costly and practically impossible expansion of the monitoring network is the development of hydrological research stations and the implementation of new methods for calculating streamflow characteristics based on mathematical modeling. The data of the Kolyma Water-Balance Station, the first research basin in the world in a permafrost environment (1948–1997), and the process-based hydrological model Hydrograph are applied to simulate streamflow hydrographs in remote mountainous permafrost basins. The satisfactory results confirm that mathematical modeling may substitute or replace statistical approaches in the conditions of extreme data insufficiency. The improvement of the models in a changing climate requires the renewal of historical observations at currently abandoned research stations in Russian permafrost regions. The study is important for forming the state policy in climate change adaptation and mitigation measures.

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Section: Resultsmentioning

confidence: 99%

Section: Hydrograph Modelmentioning

confidence: 99%

Section: Hydrograph Modelmentioning

confidence: 99%

Section: Hydrograph Modelmentioning

confidence: 99%

See 2 more Smart Citations

Challenges of Hydrological Engineering Design in Degrading Permafrost Environment of Russia

Makarieva¹,

Nesterova

Haghighi

et al. 2022

Energies

Self Cite

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“…Land use/cover is also incorporated as a single layer and therefore, considered time-stationary, which means that it is assumed that during modeling the land use dynamics (monthly and annual variations) will not affect the flow generation. These limitations along with the paucity of data are adjusted by extensive alterations in other model parameters during calibration, which results in an improper model parameterization and hence degrades the quality of the simulation (Nesterova et al 2021). Secondly, physical models necessarily require future weather data for forecasting, which is an issue in itself.…”

Section: Introductionmentioning

confidence: 99%

Short–long-term streamflow forecasting using a coupled wavelet transform–artificial neural network (WT–ANN) model at the Gilgit River Basin, Pakistan

Syed

Mahmood

Haider

et al. 2023

Journal of Hydroinformatics

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Streamflow forecasting is highly crucial in the domain of water resources. For this study, we coupled the Wavelet Transform (WT) and Artificial Neural Network (ANN) to forecast Gilgit streamflow at short-term (T0.33 and T0.66), intermediate-term (T1), and long-term (T2, T4, and T8) monthly intervals. Streamflow forecasts are uncertain due to stochastic disturbances caused by variations in snow-melting routines and local orography. To remedy this situation, decomposition by WT was undertaken to enhance the associative relation between the input and target sets for ANN to process. For ANN modeling, cross-correlation was used to guide input selection. Corresponding to six intervals, nine configurations were developed. Short-term intervals performed best, especially for T0.33; intermediate intervals showed decreasing performance. However, interestingly, performance regains back to a decent level for long-term forecasting. Almost all the models underestimate high flows and slightly overestimate low- to intermediate-flow conditions. At last, inference implicitly implies that shorter forecasting benefits from extrapolated trends, while the good results of long-term forecasting is associated to a larger recurrent pattern of the Gilgit River. In this way, weak performance for intermediate forecasting could be attributed to the insufficient ability of the model to capture either one of these patterns.

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Assessing the utility of hybrid hydrological modeling over complex conditions of the Chitral basin, Pakistan

Syed,

Mahmood,

Haider

et al. 2023

Journal of Water and Climate Change

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Streamflow forecasting holds pivotal importance for planning and decision-making in the domain of water resources management. The Chitral basin in Pakistan is characterized by high altitude and glaciated terrain. Simulating streamflows in this type of region is challenging due to complex orography and uncertain climate data. This complexity persuaded us to explore three frameworks (soil and water assessment tool (SWAT), artificial neural network (ANN), and hybrid of SWAT–ANN (H2)) for simulating the Chitral river under two different climate datasets (observed climatology (OC) and reconciled gridded climatology (RGC)) to give all six model combinations. Model evaluation was done first by indices (Nash–Sutcliff efficiency, Kling–Gupta efficiency, coefficient of determination, percent bias, and root mean square error) based on which we further assigned scores to models reflecting their performance during calibration and validation epochs. The research revealed that ANN-RGC stood first with 53 points, followed by H2-RGC (50 points) and SWAT-RGC (45 points). Trailing behind in the fourth and fifth positions were SWAT-RGC and SWAT-OC (26 points each), respectively, while ANN-OC finished last (22 points). In addition, this study proposed a bias scaling approach for simulation biases resulting in reduction in recession and baseflow biases and specifically improved low-scoring models. Despite ANN's superiority over conventional models, it could be of limited utility in uncertain or data-scarce conditions.

show abstract

Parameterizing a hydrological model using a short‐term observational dataset to study runoff generation processes and reproduce recent trends in streamflow at a remote mountainous permafrost basin

Cited by 6 publications

References 59 publications

Challenges of Hydrological Engineering Design in Degrading Permafrost Environment of Russia

Challenges of Hydrological Engineering Design in Degrading Permafrost Environment of Russia

Short–long-term streamflow forecasting using a coupled wavelet transform–artificial neural network (WT–ANN) model at the Gilgit River Basin, Pakistan

Assessing the utility of hybrid hydrological modeling over complex conditions of the Chitral basin, Pakistan

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