Ice phenology dataset reconstructed from remote sensing and modelling for lakes over the Tibetan Plateau

www.nature.com/scientificdata Ice phenology dataset Data Descriptor reconstructed from remote sensing and modelling for lakes over the Tibetan Plateau OPEN Yanhong Wu1,2, Linan Guo2,3, Bing Zhang 1,2,4 ✉, Hongxing Zheng Haojing Chi1,2,4, Junsheng Li1,2,4 & Shenglei Wang1,2 5 , Lanxin Fan1,2,4, The Tibetan Plateau (TP) is a region sensitive to global climate change and has been experiencing substantial environmental changes in the past decades. Lake ice phenology (LIP) is a perceptible indicator reflecting changes of lake thermodynamics in response to global warming. Lake ice phenology over the Tibetan Plateau is however rarely observed and recorded. This research presents a dataset containing 39-year (1978–2016) lake ice phenology data of 132 lakes (each with area >40 km2) over the Tibetan Plateau by combining the strengths of both remote sensing (MOD11A2, MOD10A1) and numerical modelling (air2water). Data validation shows that the ice phenology data derived by our method is highly consistent with that based on existing approaches (with R2 > 0.75 for all phenology index and RMSE < 5d). The dataset is valuable to investigate the lake-atmosphere interactions and long-term hydrothermal change of lakes across the Tibetan Plateau. Background & Summary The formation and duration of ice cover for lakes in the Cryosphere plays an important role in thermodynamics of lakes. It interacts with lake water temperature and shapes aquatic ecosystems as well. Lake ice phenology (LIP, e.g., dates of freezing-up and breaking-up) are closely related to temperature variation1,2. Change of lake ice phenology in the Cryosphere is considered as one of the most reliable pieces of evidence reflecting global warming3–5. It could subsequently lead to substantial alteration in biogeochemical processes (e.g., influencing growth of plankton and causing anoxia in deep water6). Research efforts on investigating changes of lake ice phenology are mainly based on ground observation, satellite observation or modelling7–10. Most of the research, however, are for lakes locating in North America or Northern Europe as there are relatively abundant in ground observations10. Lake ice phenology observations are not always widely available particularly for regions like the Tibetan Plateau (TP), where ground-based observation is challenging and costly. Observation from space therefore has been becoming more attractive in investigating the lake ice phenology. For instance, with stratified random sample from Sentinel-2 and PlanetScope, Pickens et al. (2021) reported that 41% of inland water area around the globe were seasonally frozen11. Though with uncertainties12 from both data sources13–15 and extraction methods, various satellite-based observations have shown their merits in tracking the freezing-thawing cycle of lake ice cover16. The most commonly used satellite data includes that from the optical sensors like Advanced Very High Resolution Radiometer (AVHRR)17 and Moderate-resolution Imaging Spectroradiometer (MODIS)18, the passive microwave sensors like Scanning Multichannel Microwave Radiometer (SMMR)3, the Advanced Microwave Scanning Radiometer for EOS (AMSR-E)19 and Special Sensor Microwave/Imager (SSM/I)20. Active microwave also shows capabilities in monitoring lake ice status based on sensors like European Remote Sensing Satellite (ERS)-1/2 synthetic aperture radar (SAR)21 and Radarsat-1/2 SAR22, but the narrow swath width and 1 International Research Centre of Big Data for Sustainable Development Goals, Beijing, 100094, China. 2Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China. 3Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, 100101, China. 4 University of the Chinese Academy of Sciences, Beijing, 100049, China. 5CSIRO Land and Water, Canberra, ACT 2601, Australia. ✉e-mail: Scientific Data | (2022) 9:743 | https://doi.org/10.1038/s41597-022-01863-9 1 www.nature.com/scientificdata/ www.nature.com/scientificdata Fig. 1 Locations of studied lakes in the Tibetan Plateau. the relatively low temporal resolution limited the application of the technology to monitor lake ice phenology at a daily scale and for a large area. It is also noted that the satellite-based observations of lake ice phenology mostly start from the late 1990s and could be with considerable data gaps. For example, the currently available dataset for lake ice phenology in the TP is that derived from microwave brightness temperature measured by AMSR-E, the Advanced Microwave Scanning Radiometer 2 (AMSR2) and Micro-Wave Radiation Imager (MWRI). The dataset however is limited to 51 lakes on the TP covering the period from 2002 to 201523. In the literature of limnology, numerous research efforts also have been invested on developing mathematical models to reconstruct lake ice phenology for the historical period or to predict the response of lake ice phenology to a warming climate5,24. The model-based approach goes beyond reproducing data on lake ice phenology and can be used to quantify the response of lake ice phenology to climate change or climate variation. The models may cover different level of details on lake thermodynamics, subject to their research focuses25–29. It is recognized that a well simplified process-based model (like air2water30,31) could perform as good as a more complicated model (like MINLAKE26, LIMNOS27, HIGHTSI28) if the research interest is mainly on the timing of freezing and thawing of lake ice. The simplified model could be more competitive and applicable for regions with little in-situ observations (like the TP) as it requires much less data for running and calibration. The dataset herein provides complete, consistent and continuous time series (1978–2017) of reconstructed ice phenology for 132 lakes in the TP (Fig. 1) by combing the strengths of remote sensing and mathematical modelling forced by meteorological data. The lake surface water temperature (LSWT) derived from MODIS products (MOD11A2)32 is applied to calibrate and validate the numerical model in reproducing the consecutive daily surface water temperature for the studied period, based on which algorithm has been developed to determine the ice phenology indices (including dates of freezing-up and breaking-up and durations). Lake ice phenology derived directly from satellite observations in this research and by other researchers is used to validate the reconstructed LIP33. The reconstructed LIP represents a unique dataset to investigate the long-term trends of lake ice phenology in the TP under a warming climate. The dataset can also be used for a variety of applications related to climate change, limnology, hydrology, and aquatic ecology. It is conductive to assess the impacts of climate warming on dynamics of water/heat budget and aquatic biota in lakes across the TP. Methods Figure 2 shows the general framework of our study in (...truncated)