Multi-Objective Calibration of a Distributed Hydrological Model in a Highly Glacierized Watershed in Central Asia

Fang, Gonghuan; Jian, Yang; Chen, Yaning

doi:10.3390/w11030554

Cited by 11 publications

(5 citation statements)

References 62 publications

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“…Regarding multi‐criteria calibration, Pellicciotti et al (2012) note that using three different objective functions (NSE, R 2 , and logNSE) deteriorates the model performance, best performance is achieved when only NSE is used. Ji et al (2019), in contrast, affirm that using a combination of streamflow measures performs better than using just one. Schaefli and Zehe (2009) tried out a different calibration approach and calibrated in the wavelet domain instead of in the time domain.…”

Section: Calibrationmentioning

confidence: 99%

“…Temperature‐index models rely on the assumption that net radiation is the largest contributor to melt, and temperature and several components of the energy balance highly correlate (Hock, 2003). A few studies also used the temperature of the preceding day to calculate melt (e.g., Ji et al, 2019). Degree day factors are commonly different for glacier, snow, and firn (e.g., Li et al, 2019), because of the lower albedo of glacier ice compared to snow (Brock, Willis, & Sharp, 2000).…”

Section: Hydrological Processes In Glacierized Catchmentsmentioning

confidence: 99%

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Glacio‐hydrological model calibration and evaluation

et al. 2020

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Glaciers are essential for downstream water resources. Hydrological modeling is necessary for a better understanding and for future projections of the water resources in these rapidly changing systems, but modeling glacierized catchments is especially challenging. Here we review a wealth of glacio-hydrological modeling studies (145 publications) in catchments around the world. Major model challenges include a high uncertainty in the input data, mainly precipitation, due to scarce observations. Consequently, the risk of wrongly compensating input with model errors in competing snow and ice accumulation and melt process parameterization is particularly high. Modelers have used a range of calibration and validation approaches to address this issue. The review revealed that while a large part ($35%) of the reviewed studies used only streamflow data to evaluate model performances, most studies ($50%) have used additional data related to snow and glaciers to constrain model parameters. These data were employed in a variety of calibration strategies, including stepwise and multi-signal calibration. Although the primary aim of glaciohydrological modeling studies is to assess future climate change impacts, longterm changes have rarely been taken into account in model performance evaluations. Overall, a more precise description of which data are used how for model evaluation would facilitate the interpretation of the simulation results and their uncertainty, which in turn would support water resources management. Moreover, there is a need for systematic analyses of calibration approaches to disentangle what works best and why. Addressing this need will improve our system understanding and model simulations of glacierized catchments.

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Section: Calibrationmentioning

confidence: 99%

Section: Hydrological Processes In Glacierized Catchmentsmentioning

confidence: 99%

Glacio‐hydrological model calibration and evaluation

et al. 2020

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“…This is a critical failure, as temperature and precipitation are considered the most crucial factors affecting the process of glacier and snow accumulation and melting . Previous studies have shown that temperature plays a dominant role in the discharge of the Kumaric River Basin (Ji et al, 2019), whereas in the Toxkan River Basin, the discharge is affected by both temperature and precipitation (Duethmann et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

“…The observed discharge data from 2002 to 2011 at two of the hydrological stations were obtained from the Tarim River Basin Management Bureau of Xinjiang Uygur Autonomous Region, China. Along with meteorological data, the spatial data, including digital elevation model (DEM) data, land use, soil data, and glacier and snow distribution data as described in Ji et al (2019), are used as inputs in the two hydrological models.…”

Section: Datasetsmentioning

confidence: 99%

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Adaptability of machine learning methods and hydrological models to discharge simulations in data-sparse glaciated watersheds

Chen

Fang

et al. 2021

J. Arid Land

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The accurate simulation and prediction of runoff in alpine glaciated watersheds is of increasing importance for the comprehensive management and utilization of water resources. In this study, long shortterm memory (LSTM), a state-of-the-art artificial neural network algorithm, is applied to simulate the daily discharge of two data-sparse glaciated watersheds in the Tianshan Mountains in Central Asia. Two other classic machine learning methods, namely extreme gradient boosting (XGBoost) and support vector regression (SVR), along with a distributed hydrological model (Soil and Water Assessment Tool (SWAT) and an extended SWAT model (SWAT_Glacier) are also employed for comparison. This paper aims to provide an efficient and reliable method for simulating discharge in glaciated alpine regions that have insufficient observed meteorological data. The two typical basins in this study are the main tributaries (the Kumaric and Toxkan rivers) of the Aksu River in the south Tianshan Mountains, which are dominated by snow and glacier meltwater and precipitation. Our comparative analysis indicates that simulations from the LSTM shows the best agreement with the observations. The performance metrics Nash-Sutcliffe efficiency coefficient (NS) and correlation coefficient (R 2 ) of LSTM are higher than 0.90 in both the training and testing periods in the Kumaric River Basin, and NS and R 2 are also higher than 0.70 in the Toxkan River Basin. Compared to classic machine learning algorithms, LSTM shows significant advantages over most evaluating indices. XGBoost also has high NS value in the training period, but is prone to overfitting the discharge. Compared with the widely used hydrological models, LSTM has advantages in predicting accuracy, despite having fewer data inputs. Moreover, LSTM only requires meteorological data rather than physical characteristics of underlying data. As an extension of SWAT, the SWAT_Glacier model shows good adaptability in discharge simulation, outperforming the original SWAT model, but at the cost of increasing the complexity of the model. Compared with the oftentimes complex semi-distributed physical hydrological models, the LSTM method not only eliminates the tedious calibration process of hydrological parameters, but also significantly reduces the calculation time and costs. Overall, LSTM shows immense promise in dealing with scarce meteorological data in glaciated catchments.

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Runoff components and the contributions of precipitation and temperature in a highly glacierized river basin in Central Asia

Wang

Huang

et al. 2023

Front. Earth Sci.

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Multi-Objective Calibration of a Distributed Hydrological Model in a Highly Glacierized Watershed in Central Asia

Cited by 11 publications

References 62 publications

Glacio‐hydrological model calibration and evaluation

Glacio‐hydrological model calibration and evaluation

Adaptability of machine learning methods and hydrological models to discharge simulations in data-sparse glaciated watersheds

Runoff components and the contributions of precipitation and temperature in a highly glacierized river basin in Central Asia

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