2020
DOI: 10.3390/w12051498
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Spatiotemporal Water Yield Variations and Influencing Factors in the Lhasa River Basin, Tibetan Plateau

Abstract: Understanding the spatiotemporal characteristics of water yield and its influencing factors is important for water resources management. In this study, we used the seasonal water yield model (SWYM) to assess the spatiotemporal water yield changes of the Lhasa River Basin from 1990 to 2015, and analyzed its influencing factors by focusing on precipitation, land cover, and normalized difference vegetation index (NDVI) change. We first examined the model through Morris screening sensitivity analysis and validated… Show more

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Cited by 18 publications
(14 citation statements)
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“…The statistical analysis report of BRTs demonstrates that the precipitation and baseflow were two of the principal factors that influenced WY on all spatio-temporal scales. Precipitation is one of the important factors that governs WY as previously reported by Jiang et al (2016); Sun et al (2019) and Lu et al (2020) is used by policymakers, especially in water resources management and goals in conducting apportioning of water resources. The baseflow is one of the key components of the groundwater system, which provides sub-surface flow and other delayed sources like snowmelt into the stream, which is the second most important factor contributing to WY.…”
Section: Discussionmentioning
confidence: 97%
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“…The statistical analysis report of BRTs demonstrates that the precipitation and baseflow were two of the principal factors that influenced WY on all spatio-temporal scales. Precipitation is one of the important factors that governs WY as previously reported by Jiang et al (2016); Sun et al (2019) and Lu et al (2020) is used by policymakers, especially in water resources management and goals in conducting apportioning of water resources. The baseflow is one of the key components of the groundwater system, which provides sub-surface flow and other delayed sources like snowmelt into the stream, which is the second most important factor contributing to WY.…”
Section: Discussionmentioning
confidence: 97%
“…An attempt at WY services as well as impacts of multi-dimensional aspects over spatio-temporal scales employing different methodologies have been explored in the past. Sun et al (2019) analyzed the basin and climate factor contributions to the WY using the coupled hydrological model SWAT and statistical tool Boosted Regression Trees (BRTs) at spatio-temporal scales; the WY and its dominant factors including land cover, precipitation, the Normalized Difference Vegetation Index (NDVI) by the Seasonal Water Yield Model (Lu et al 2020) at spatio-temporal scales; long-term groundwater recharge studies using the partially-distributed water balance model WetSpass and investigated the influencing factors by the correlation technique (Zomlot et al 2015). Other relationships between the WY and parameters like potential evapotranspiration (PET) (Wang et al 2011), glacier, and snow melt (Anand et al 2018;Li et al 2021) have also been reported.…”
Section: Introductionmentioning
confidence: 99%
“…Five status levels were detected throughout the study periods, with fluctuating maximum and minimum values. This is not uncommon as quickflow characterisation is never highly dynamic [29]. However, the quickflow of the Sokoto-Rima basin is dominated by low flow levels with very low and low statuses.…”
Section: Spatiotemporal Dynamics Of Quickflow In the Sokoto-rima Basinmentioning
confidence: 99%
“…For instance, [8] asserted that water yield is a function of a consistent increase in the amount of rainfall received per time in the Sokoto-Rima basin. [29] stated that apart from precipitation dynamics, landcover, change in vegetation could affect water yield, out of which quickflow is significant. [29] further affirmed that quickflow in low rainfall grassland area of Tibet contributes roughly 5% to water yield.…”
Section: Spatiotemporal Dynamics Of Quickflow In the Sokoto-rima Basinmentioning
confidence: 99%
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