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Temporal Lag and Response Characteristics of Runoff and Its Components in a Glacial Basin.

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Title: Temporal Lag and Response Characteristics of Runoff and Its Components in a Glacial Basin.
Authors: Song, Mengwei1 (AUTHOR), Liu, Hailong1,2 (AUTHOR) liuhl@uestc.edu.cn, Tang, Guoqing1,3 (AUTHOR), Gong, Xuhong1,4 (AUTHOR), Chen, Xi1,2 (AUTHOR), Liu, Tie2 (AUTHOR), Gulakhmadov, Manuchekhr3 (AUTHOR), Gulakhmadov, Aminjon4 (AUTHOR), Shaimuradov, Firdavs4 (AUTHOR)
Source: Remote Sensing. Sep2026, Vol. 18 Issue 18, p3235. 27p.
Subjects: Base flow (Hydrology), Normalized difference vegetation index, Seasonal temperature variations, Snowmelt, Snow accumulation, Wavelets (Mathematics)
Abstract: Highlights: What are the main findings? In dry and wet seasons, snow-related factors and the Normalized Difference Vegetation Index (NDVI) exert significant impacts on runoff and its components, with obvious differences in the impact rankings of total evaporation (ET), surface solar radiation (SR), total precipitation (Prec) and snow depth (SD). Time lags differ markedly among runoff types; surface runoff has the shortest time lag, total runoff follows, and baseflow exhibits the longest time lag. Compared with those in the dry season, time lags of various driving factors generally advance in the wet season. What are the implications of the main findings? These findings reveal the seasonal differences in time lags and impact intensities of driving factors for runoff, and provide theoretical support for the attribution of runoff evolution and hydrological prediction in basins. Separating dry/wet seasons and runoff components helps understand hydrological responses under changing cryospheric conditions. Glaciers are indicators of climate change, and the study of climate change impacts on runoff and its components in glacial basins is a research hotspot worldwide. The formation mechanisms of runoff and its components are affected by multiple factors, and relevant studies remain scarce in glacial regions due to limited data resulting from natural and objective conditions. To identify the driving factors of runoff and its components in the Vakhsh River basin of Tajikistan, we selected the suitable baseflow separation method and obtained datasets of total runoff, baseflow and surface runoff. Furthermore, the impacts and temporal patterns of hydroclimatic variables on runoff and its components were quantitatively evaluated using satellite remote-sensing products, reanalysis datasets and in-situ observations through cross-wavelet transform and explainable machine learning approaches, specifically Categorical Boosting (CatBoost) coupled with SHapley Additive exPlanations (SHAP) analysis. Results show that, among nine evaluated baseflow separation methods, the Chapman–Maxwell method demonstrates superior performance. Whether in the dry season or the wet season, the effects of snow-related variables and the Normalized Difference Vegetation Index (NDVI) on runoff and its components are significant. Compared with the dry season, the rankings of total evaporation (ET) and surface solar radiation (SR) decrease in the wet season, while the ranking of total precipitation (Prec) and snow depth (SD) increases significantly. The periodic oscillation in the dry season is stronger than that in the wet season. The time lag of surface runoff is the shortest, that of total runoff follows closely, and that of baseflow is the longest. The time lags are within 18 d in the dry season and within 12 d in the wet season. Compared with the dry season, the time lags of snow-related variables (e.g., SD, snow depth water equivalent (SDWE)) in the wet season advance by approximately 2–4 d; those of Prec advance by roughly 3–4 d; those of PSM and ET advance by about 5–6 d; and that of SR advance by approximately 4–5 d. Underlying surface and climatic variations cause uncertainty and errors in time lags identified by cross-wavelet transform, which we clarify. The results of this study provide a scientific basis for addressing climate change in glacial basins. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Temporal Lag and Response Characteristics of Runoff and Its Components in a Glacial Basin.
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Sep2026, Vol. 18 Issue 18, p3235. 27p.
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  Data: <searchLink fieldCode="DE" term="%22Base+flow+%28Hydrology%29%22">Base flow (Hydrology)</searchLink><br /><searchLink fieldCode="DE" term="%22Normalized+difference+vegetation+index%22">Normalized difference vegetation index</searchLink><br /><searchLink fieldCode="DE" term="%22Seasonal+temperature+variations%22">Seasonal temperature variations</searchLink><br /><searchLink fieldCode="DE" term="%22Snowmelt%22">Snowmelt</searchLink><br /><searchLink fieldCode="DE" term="%22Snow+accumulation%22">Snow accumulation</searchLink><br /><searchLink fieldCode="DE" term="%22Wavelets+%28Mathematics%29%22">Wavelets (Mathematics)</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Highlights: What are the main findings? In dry and wet seasons, snow-related factors and the Normalized Difference Vegetation Index (NDVI) exert significant impacts on runoff and its components, with obvious differences in the impact rankings of total evaporation (ET), surface solar radiation (SR), total precipitation (Prec) and snow depth (SD). Time lags differ markedly among runoff types; surface runoff has the shortest time lag, total runoff follows, and baseflow exhibits the longest time lag. Compared with those in the dry season, time lags of various driving factors generally advance in the wet season. What are the implications of the main findings? These findings reveal the seasonal differences in time lags and impact intensities of driving factors for runoff, and provide theoretical support for the attribution of runoff evolution and hydrological prediction in basins. Separating dry/wet seasons and runoff components helps understand hydrological responses under changing cryospheric conditions. Glaciers are indicators of climate change, and the study of climate change impacts on runoff and its components in glacial basins is a research hotspot worldwide. The formation mechanisms of runoff and its components are affected by multiple factors, and relevant studies remain scarce in glacial regions due to limited data resulting from natural and objective conditions. To identify the driving factors of runoff and its components in the Vakhsh River basin of Tajikistan, we selected the suitable baseflow separation method and obtained datasets of total runoff, baseflow and surface runoff. Furthermore, the impacts and temporal patterns of hydroclimatic variables on runoff and its components were quantitatively evaluated using satellite remote-sensing products, reanalysis datasets and in-situ observations through cross-wavelet transform and explainable machine learning approaches, specifically Categorical Boosting (CatBoost) coupled with SHapley Additive exPlanations (SHAP) analysis. Results show that, among nine evaluated baseflow separation methods, the Chapman–Maxwell method demonstrates superior performance. Whether in the dry season or the wet season, the effects of snow-related variables and the Normalized Difference Vegetation Index (NDVI) on runoff and its components are significant. Compared with the dry season, the rankings of total evaporation (ET) and surface solar radiation (SR) decrease in the wet season, while the ranking of total precipitation (Prec) and snow depth (SD) increases significantly. The periodic oscillation in the dry season is stronger than that in the wet season. The time lag of surface runoff is the shortest, that of total runoff follows closely, and that of baseflow is the longest. The time lags are within 18 d in the dry season and within 12 d in the wet season. Compared with the dry season, the time lags of snow-related variables (e.g., SD, snow depth water equivalent (SDWE)) in the wet season advance by approximately 2–4 d; those of Prec advance by roughly 3–4 d; those of PSM and ET advance by about 5–6 d; and that of SR advance by approximately 4–5 d. Underlying surface and climatic variations cause uncertainty and errors in time lags identified by cross-wavelet transform, which we clarify. The results of this study provide a scientific basis for addressing climate change in glacial basins. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/rs18183235
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      – Code: eng
        Text: English
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        PageCount: 27
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    Subjects:
      – SubjectFull: Base flow (Hydrology)
        Type: general
      – SubjectFull: Normalized difference vegetation index
        Type: general
      – SubjectFull: Seasonal temperature variations
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      – SubjectFull: Snowmelt
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      – SubjectFull: Snow accumulation
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      – SubjectFull: Wavelets (Mathematics)
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      – TitleFull: Temporal Lag and Response Characteristics of Runoff and Its Components in a Glacial Basin.
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              Text: Sep2026
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