Prediction and Analysis of Lake Ice Phenology Dynamics Under Future Climate Scenarios Across the Inner Tibetan Plateau

Abstract

How climate change influences lake ice phenology is important in understanding the climate-lake interactions. This study investigates the response of lake ice phenology to future climate scenarios. Thirty-five lakes in the Tibetan Plateau were studied. Firstly, we applied a random forest (RF) model to simulate lake ice condition under different representative concentration pathways (RCPs). The results of the virtual experiments show that lake ice freeze-up start date (FUSD) and break-up end date (BUED) are well simulated by the RF model, with R 2 FUSD > 0.91 under different RCPs, and R 2 BUED > 0.84, except in RCP2.6 (R 2 = 0.74). Secondly, two non-parametric methods (Mann-Kendall and Sen's slope estimator) were used for analyzing the trends in lake ice phenology and its response to various emission scenarios. Lake ice phenology was largely affected by temperature changes under different RCPs, and it had a larger inter-annual variability in the early period (2002-2050) than in the later period (2050-2099). FUSD of 35 lakes delayed by an average of 0.01, 0.04, and 0.04 days/yr from 2002 to 2098 under RCP4.5, RCP6.0, and RCP8.5, whereas BUED advanced by an average of 0.04, 0.11, and 0.21 days/yr from 2003 to 2099. Both delays in FUSD and advances in BUED contributed to shortened average ice duration of 35 lakes, which shortened by 0.05, 0.14, and 0.25 days/yr from 2002 to 2098 under RCP4.5, RCP6.0, and RCP8.5, respectively. These findings could help to tackle the impacts of climate change on the lake systems. Plain Language Summary The impacts of future climate scenarios on lake ice phenology is important in understanding the climate-lake interactions. Thirty-five lakes in the Tibetan Plateau were studied. We employed a random forest (RF) model to simulate lake ice condition under different representative concentration pathways (RCPs). The results of the virtual experiments show that lake ice freeze-up start date (FUSD) and break-up end date (BUED) are well simulated by the RF model, under different RCPs. FUSD of 35 lakes delayed by an average of 0.01, 0.04, and 0.04 days/yr from 2002 to 2098 under RCP4.5, RCP6.0, and RCP8.5, whereas BUED advanced by an average of 0.04, 0.11, and 0.21 days/yr from 2003 to 2099. Both delays in FUSD and advances in BUED contributed to shortened average ice duration of 35 lakes, which shortened by 0.05, 0.14, and 0.25 days/yr from 2003 to 2098 under RCP4.5, RCP6.0, and RCP8.5, respectively. Our results could help to tackle the impacts of climate change on the lake systems.

Sensitivity of Green-Up Date to Meteorological Indicators in Hulun Buir Grasslands of China

“…It is a method for robust linear regression that chooses the median slope among all lines through pairs of two-dimensional sample points. Combining the two is an excellent method for time series trend analysis, which has been widely used in climate and hydrological trend research in recent years [44][45][46].…”

Reconstructing ice phenology of lake with complex surface cover: A case study of Lake Ulansu during 1941–2023

“…The model accuracy can be improved by integrating the results of multiple decision trees (Breiman, 2001). Model robustness is particularly crucial when dealing with complex meteorological data for predicting ice phenology (Ruan et al, 2020). Previous research has highlighted the influence of short-term meteorological factors on the freezing and melting of shallow lakes (Blagrave and Sharma, 2023;Caldwell et al, 2020).…”

“…At altitudes greater than 3,000 m, Qinghai Lake's ice melting is markedly influenced by its exposure to intense solar radiation, leading to a more pronounced BUE trend (Kirillin et al, 2021). Ruan et al (2020) employed a RF model and CMIP5 data to forecast ice phenology changes in lakes across the Tibetan Plateau from 2015 to 2099, yielding analogous outcomes. In the scenarios with the highest greenhouse gas emissions, the BUE trend was significantly greater than that of the FUS.…”

Future warming accelerates lake variations in the Tibetan Plateau

“…Thus it is greatly significant to evaluate the possible impacts of climate warming on lake freezing–thawing variations. Some studies have explored the possible linkage between lake ice phenology and regional temperature, precipitation, wind velocity, as well as the geographic position, lake morphological parameter, and lake types (Brown and Duguay, 2010; Kropácek et al ., 2013; Patterson and Swindles, 2015; Yao et al ., 2015; Ruan et al ., 2020). The local temperature directly affects the heat exchange between the lake and atmosphere to control the freezing–thawing process, and the precipitation can indirectly affect the melting process by increasing the snow cover over the lake surface (Liu et al ., 2018).…”