DOI: 10.6084/m9.figshare.28416224
title: <b>Spatiotemporal database in above- and belowground net primary production across </b><b>multi-data-driven models </b><b>on the Tibetan Plateau at 1km resolution</b>

description: <b>Corresponding author</b>: Peng Hou (houpcy@163.com)<b>Abstract: </b>The Tibetan Plateau (TP), one of the most climate-sensitive regions on Earth, plays a crucial role in global carbon cycling. However, the spatiotemporal variability of modeled above- and below-ground net primary production (ANPP and BNPP) remain uncertain across linear (LL), machine learning (ML), and deep learning (DL) models, particularly for BNPP. To address this gap, we applied 96 data-driven models, including LL, ML, and DL approaches, combined with 5-fold cross-validation and Monte Carlo simulations to estimate ANPP and BNPP at 1 km resolution from 1981 to 2018 across the TP. The result showed that the best-performing models for ANPP and BNPP achieved <i>R</i><sup>2</sup> values ranging from 0.80 to 0.88 for ANPP and from 0.89 to 0.95 for BNPP. Spatiotemporal patterns of ANPP and BNPP were generally consistent across model types. However, total ANPP exhibited a significant declining trend at −0.003 Pg C yr<sup>−</sup>¹, while BNPP increased by 0.001 to 0.003 Pg C yr<sup>−</sup>¹. Notably, inter-model variability in annual totals reached up to 0.13 and 0.32 Pg C yr<sup>−</sup>¹ for ANPP and BNPP, respectively. These discrepancies likely stem from differences in how models interpret input variable contributions, as reflected in distinct spatial patterns—particularly in DL simulations, which showed divergence in ANPP across southern TP (e.g., Nyingchi) and BNPP in northern to central regions (e.g., from Xining to Zhiduo). Our findings offer a robust methodological benchmark for modeling ecosystem carbon allocation under climate change and provide valuable insights for adaptive carbon management in one of the world’s most vulnerable regions.<br><b>Filename</b>: ANPP_xgblinear_Tibetan _1981-2018_1km_tif.zip; ANPP_Rborist_Tibetan _1981-2018_1km_tif.zip; ANPP_HYFIS_Tibetan _1981-2018_1km_tif.zip; BNPP_xgblinear_Tibetan _1981-2018_1km_tif.zip; BNPP_xgbDART_Tibetan _1981-2018_1km_tif.zip; BNPP_monmlp_Tibetan _1981-2018_1km_tif.zip.<br><b>File information</b>：The names of each Zip compressed file are composed of the observation object, simulation model, region, time range, spatial resolution, and data format. For example, 'ANPP_xgblinear_Tibetan_1981 - 2018_1km_tif.zip' consists of ANPP (observation object) + '_' + xgblinear (simulation model) + '_<i>' + Tibetan (region) + '</i>_' + 1981-2018 (time range) + '_' + 1km (spatial resolution) + '_' + tif (data format) + '.zip'. Among them, ANPP is Aboveground Net Primary Production; BNPP is Belowground Net Primary Production; xgbLinear is a linear model, Rborist/xgbTree are machine learning models of the linear model, and HYFIS/monmlp are a deep learning model.The unit for these data is 'g C m<sup>-2</sup><sup> </sup>yr<sup>-1</sup>'.<b>Author contributions: </b><b>Tao Zhou</b> contributed to the conceptualization, methodology, software, and writing - original draft, review, editing; <b>Benjamin Laffitte</b>, <b>Jianfei Cao</b>,<b> Xuwei Sun</b> and<b> Guangjin Zhou</b> supervised manuscript writing; <b>Yuting Hou</b> contributed to the data curation and software;<b> </b>contributed to; <b>Peng Hou</b> contributed to data curation, writing - original draft preparation, software, and writing–review, editing; all authors contributed to the final preparation of the manuscript.