Evaluation and uncertainty assessment of weather data and model calibration on daily streamflow simulation in a large-scale regulated and snow-dominated river basin

Chen, Yuzhuang; Faramarzi, Monireh; Gan, Thian Yew; She, Yuntong

doi:10.1016/j.jhydrol.2023.129103

Cited by 10 publications

(7 citation statements)

References 61 publications

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“…In recent decades, hydrological models have risen as potent tools for investigating the SWE [10,11]. They are versatile because they primarily use widely available meteorological data requiring site-specific data for calibration and validation.…”

Section: Introductionmentioning

confidence: 99%

“…The selected parameters were divided into three groups comprising snow parameters, basin parameters and elevation lapse rate parameters. In earlier research endeavours [10,11,34,48] aimed at stimulating the SWE, SNOCOVMX and SNO50COV were deemed insensitive and consequently omitted. Nevertheless, in our investigation, we opted to incorporate these parameters (SNOCOVMX and SNO50COV) to assess their influence on streamflow simulation.…”

mentioning

confidence: 99%

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Combining Hydrological Models and Remote Sensing to Characterize Snowpack Dynamics in High Mountains

Ougahi,

Rowan

2024

Remote Sensing

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Seasonal snowpacks, characterized by their snow water equivalent (SWE), can play a major role in the hydrological cycle of montane environments with months of snow accretion followed by episodes of melt controlling flood risk and water resource availability downstream. Quantifying the temporal and spatial patterns of snowpack accumulation and its subsequent melt and runoff is an internationally significant challenge, particularly within mountainous regions featuring complex terrain with limited or absent observational data. Here we report a new approach to snowpack characterization using open-source global satellite and modelled data products (precipitation and SWE) greatly enhancing the utility of the widely used Soil and Water Assessment Tool (SWAT). The paper focusses on the c. 23,000 km2 Chenab river basin (CRB) in the headwaters of the Indus Basin, globally important because of its large and growing population and increasing water insecurity due to climate change. We used five area-weighted averaged satellite, gridded and reanalysis precipitation datasets: ERA5-Land, CMORPH, TRMM, APHRODITE and CPC UPP. As well as comparison to local weather station data, these were used in SWAT to model streamflow for evaluation against observed streamflow at the basin outlet. ERA5-Land data provided the best streamflow match-ups and was used to infer snowpack (SWE) dynamics at basin and sub-basin scales. Snow reference data were derived from remote sensing and modelled SWE re-analysis products: ULCA-SWE and KRA-SWE, respectively. Beyond conventional auto-calibration and single-variable approaches we undertook multi-variable calibration using R-SWAT to manually adjust snow parameters alongside observed streamflow data. Characterization of basin-wide patterns of snowpack build-up and melt (SWE dynamics) were greatly strengthened using KRA-SWE data accompanied by improved streamflow simulation in sub-basins dominated by seasonal snow cover. UCLA-SWE data also improved SWE estimations using R-SWAT but weakened the performance of simulated streamflow due to under capture of seasonal runoff from permanent snow/ice fields in the CRB. This research highlights the utility and value of remote sensing and modelling data to drive better understanding of snowpack dynamics and their contribution to runoff in the absence of in situ snowpack data in high-altitude environments. An improved understanding of snow-bound water is vital in natural hazard risk assessment and in better managing worldwide water resources in the populous downstream regions of mountain-fed large rivers under threat from climate change.

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Combining Hydrological Models and Remote Sensing to Characterize Snowpack Dynamics in High Mountains

Ougahi,

Rowan

2024

Remote Sensing

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“…In recent decades hydrological models have risen as potent tools for investigating SWE [10,11]. They are versatile because they primarily use widely available meteorological data requiring sitespecific data for calibration and validation.…”

Section: Introductionmentioning

confidence: 99%

“…Calibrating hydrological models involves various considerations, inclusive of the algorithms used, the parameters requiring calibration and whether they need calibrating individually or collectively [27,28]. Any model calibrated solely on streamflow data is unlikely to yield dependable spatio-temporal portrayal of other hydrological fluxes and states [29].…”

Section: Introductionmentioning

confidence: 99%

“…The choice of calibration scheme can significantly influence the outcomes of hydrological models especially in mountainous catchments with sparse input data [27]. Multi-variable (in addition to observed streamflow) calibration is commonly used to improve streamflow simulations in SWAT model, but snow reference data are rarely available due to collection challenges.…”

Section: Introductionmentioning

confidence: 99%

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Using Hydrological Models and Remote Sensing to Characterize Snow Pack Behaviour in High Mountain Environments

Ougahi,

Rowan

2023

Preprint

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Seasonal snowpacks, characterized by their snow water equivalent (SWE), play a major role in the hydrological cycle, snow melt contributions to floods and the subsequent availability of water resources downstream. Accurately estimating SWE and understanding its spatial and temporal variations presents a considerable challenge, particularly within mountainous regions characterized by complex terrain and limited observational data. Seeking to enhance the performance of the widely used Soil and Water Assessment Tool (SWAT), we report a new approach characterising snowpack behaviour incorporating both modelled and remotely sensed derived SWE calibration data. We focus on the Chenab River Basin (CRB) a headwater catchment of the Indus Basin, globally significant in terms of human inhabitants and intensifying flood risk due to climate change. We conducted a thorough assessment of five satellite-derived and reanalysis-based precipitation datasets: ERA5-Land, CMORPH, TRMM, APHRODITE, and CPC UPP. This reveals significant levels of uncertainty in global precipitation products when compared to reference data from observed stations as well as in the resulting simulated streamflow from the SWAT model. Subsequently, we expanded the scope of the SWAT model to encompass the spatial and temporal simulation of SWE. This was achieved by incorporating information from remotely sensed and modelled SWE products, manually adjusting snow parameters in R-SWAT for both the main basin and at sub-basin scales. Integrating SWE from reference snow products into the calibration process, alongside streamflow data, substantially enhanced modelling accuracy to simulate SWE compared to the conventional auto-calibration and single-variable approaches reliant solely on streamflow data. This approach results in considerable improvement in SWE predictions and to some extent in streamflow simulation in catchments dominated by snow. This research highlights the potential of remote sensing and modelled SWE parameterisation in the absence of in-situ snowpack data in high-altitude environments. An improved understanding of SWE behaviour is vital for predicting hydrological responses spanning hazards to water resources in the populous downstream regions of the Indus Basin, especially in the face of climate change.

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Pitfalls in hydrologic model calibration in a data scarce environment with a strong seasonality: experience from the Adyar catchment, India

Tigabu¹,

Wagner²,

Narasimhan

et al. 2023

Environ Earth Sci

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Process-based hydrologic models can provide necessary information for water resources management. However, the reliability of hydrological models depends on the availability of appropriate input data and proper model calibration. In this study, we demonstrate that common calibration procedures that assume stationarity of hydrological processes can lead to unsatisfactory model performance in areas that experience a strong seasonal climate. Moreover, we develop a more robust calibration procedure for the Soil and Water Assessment Tool (SWAT) in the Adyar catchment of Chennai, India. Calibration was carried out based on seasonal decomposition and by successively shifting the calibration period. Daily and monthly streamflow records were used to investigate how these different calibration procedures influence model parameterization. Results show that SWAT model performance improved when calibrated after separating the streamflow into wet and dry seasons. The wet season calibration increased the Kling Gupta Efficiency coefficient and Nash–Sutcliffe Efficiency coefficient values from 0.56 to 0.68 and 0.19 to 0.51, respectively, compared to calibration based on wet and dry seasons together. In addition, when calibration time periods were shifted, resultant sets of model parameter values and performance metrics differed. Calibration based on the 2004–2009 period resulted in an overestimation of streamflow by 8.2%, whereas the overestimation was 12.1%, 18.3%, and 20.0% for the 2004–2010, 2004–2011, and 2004–2012 periods, respectively. This study underlines that both the availability of observed streamflow data and the way these data are applied to calibration have a strong impact on model parameterization and performance.

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Evaluation and uncertainty assessment of weather data and model calibration on daily streamflow simulation in a large-scale regulated and snow-dominated river basin

Cited by 10 publications

References 61 publications

Combining Hydrological Models and Remote Sensing to Characterize Snowpack Dynamics in High Mountains

Combining Hydrological Models and Remote Sensing to Characterize Snowpack Dynamics in High Mountains

Using Hydrological Models and Remote Sensing to Characterize Snow Pack Behaviour in High Mountain Environments

Pitfalls in hydrologic model calibration in a data scarce environment with a strong seasonality: experience from the Adyar catchment, India

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