Reconstructing runoff components and glacier mass balance with climate change: Niyang river basin, southeastern Tibetan plateau

Abstract

The southeastern part of the Tibetan Plateau (TP), one of the regions with the largest glacier distribution on the plateau, has been experiencing a significant loss in glacier mass balance (GMB) in recent decades due to climate warming. In this study, we used the Spatial Processes in Hydrology (SPHY) model and satellite data from LANDSAT to reconstruct the runoff components and glacier mass balance in the Niyang River basin (NRB). The measured river discharge data in the basin during 2000–2008 were used for model calibration and validation. Then, the validated model was applied to reconstruct the runoff components and GMB in the Niyang River basin for the period 1969–2013. Results showed that rainfall runoff (67%) was the dominant contributor to total runoff, followed by snowmelt runoff (14%), glacier melt runoff (10%), and baseflow (9%). The NRB experienced a severe loss in GMB, with a mean value of −1.26 m w. e./a (corresponding to a cumulative glacier mass loss of −56.72 m w. e.) during 1969–2013. During periods Ⅰ (1969–1983), Ⅱ (1984–1998), and Ⅲ (1999–2013) glacier mass loss was simulated at rates of −1.27 m w. e./a, −1.18 m w. e./a, and −1.33 m w. e./a, respectively. The annual loss of glacier mass in the northern region of the NRB (−1.43 m w. e./a) was significantly greater than that of the southern region (−0.53 m w. e./a) from 1969 to 2013, largely due to temperature variations, especially in summer months. These findings enhance our understanding of how different hydrological processes respond to climate change and provide a potential method to study runoff components and GMB in other glacierized catchments worldwide.

Comparison of Machine Learning Models in Simulating Glacier Mass Balance: Insights from Maritime and Continental Glaciers in High Mountain Asia

“…Even for the NRB, which performs slightly worse, the R 2 and RMSE values were 0.96 and 0.03 m w.e., respectively, when the training sample proportion was 50%. This result is noticeably better than the results recorded by He et al [51], who simulated NRB GMB data based on the SPHY model for the 2006-2010 period. This suggests that the size of the training sample proportion has a certain impact on the simulation of the GMB time series, but as the proportion increases to a certain extent, this impact becomes smaller and can even be negligible.…”

“…Surprisingly, the RF model showed the worst performance, with a R 2 value of 0.86 and a RMSE value of 0.06 m w.e. Using the SPHY model, He et al's [51] simulation of GMB data in the NRB from 2006 to 2010 achieved a R 2 value of 0.37. This result falls significantly short of the achievements of all four ML models.…”

Environmental Factors Shape the Differences in Biodiversity-Area Relationships in Riverine Macroinvertebrates of Two Rivers in the Tibetan Plateau in China

“…The Niyang River is affected by the cold stream in the north and the warm stream of the Indian Ocean. This region belongs to the plateau humid temperate climate zone [53], with an average annual temperature of ~8.5 • C and an annual precipitation of ~1295 mm [54]. It primarily receives replenishment from precipitation, the melting of snow and glaciers, and underground water sources [53,54].…”

“…This region belongs to the plateau humid temperate climate zone [53], with an average annual temperature of ~8.5 • C and an annual precipitation of ~1295 mm [54]. It primarily receives replenishment from precipitation, the melting of snow and glaciers, and underground water sources [53,54]. The Niyang River Basin is prone to many natural disasters, such as mudslides and floods.…”

Projections of Mean and Extreme Precipitation Using the CMIP6 Model: A Study of the Yangtze River Basin in China

“…Due to the systematic bias of numerical models and the uncertainty in initial conditions, relying on a single "optimal" model can result in significant biases that influence the assessment of climate change impacts [26]. Transitioning from a single numerical model to a multi-model ensemble forecast is an effective strategy to enhance model accuracy.…”