A deep learning reconstruction of mass balance series for all glaciers in the French Alps: 1967-2015

Glacier mass balance (MB) data are crucial to understand and quantify the regional effects of climate on glaciers and the high-mountain water cycle, yet observations cover only a small fraction of glaciers in the world. We present a dataset of annual glacier-wide surface mass balance of all the glaciers in the French Alps for the 1967-2015 period. This dataset has been reconstructed using deep learning (i.e. a deep artificial neural network), based on direct MB observations and remote sensing annual estimates, meteorological reanalyses and topographical data from glacier inventories. The method’s validity was assessed through an extensive cross-validation against a dataset of 32 glaciers , with an estimated average error (RMSE) of 0.55 m.w.e. a^-1, an explained variance (r2) of 75% and an average bias of -0.021 m.w.e. a^-1. We estimate an average regional area-weighted glacier-wide MB of -0.71±0.21 (1 sigma) m.w.e. a^-1 for the 1967-2015 period, with negative mass balances in the 1970s (-0.44 m.w.e. a^-1), moderately negative in the 1980s (-0.16 m.w.e. a^-1), and an increasing negative trend from the 1990s onwards, up to -1.34 m.w.e. a^-1 in the 2010s. A comparison with ASTER-derived geodetic MB for the 2000-2015 period showed important differences with the photogrammetric geodetic MB used to train our model. When recalibrating our reconstructions with the new ASTER-derived geodetic MB, the estimated average regional area-weighted glacier-wide MB (1967-2015) is reduced to -0.64±0.21 (1 sigma) m.w.e. a^-1. Following a topographical and regional analysis, we estimate that the massifs with the highest mass losses for the 1967-2015 period are the Chablais (-0.93 m.w.e. a^-1), Champsaur and Haute-Maurienne (-0.86 m.w.e. a^-1 both) and Ubaye ranges (-0.83 m.w.e. a^-1), and the ones presenting the lowest mass losses are the Mont-Blanc (-0.69 m.w.e. a^-1), Oisans and Haute-Tarentaise ranges (-0.75 m.w.e. a^-1 both). This dataset provides relevant and timely data for studies in the fields of glaciology, hydrology and ecology in the French Alps, in need of regional or glacier-specific annual net glacier mass changes in glacierized catchments. The MB dataset is presented in two different formats: (a) A single netCDF file containing the MB reconstructions, the glacier RGI and GLIMS IDs and the glacier names. This file contains all the necessary information to correctly interact with the data, including some metadata with the authorship and data units. (b) A dataset comprised of multiple CSV files, one for each of the 661 glaciers from the 2003 glacier inventory (Gardent et al., 2014), named with its GLIMS ID and RGI ID with the following format: GLIMS-ID_RGI-ID_SMB.csv. Both indexes are used since some glaciers that split into multiple sub-glaciers do not have an RGI ID. Split glaciers have the GLIMS ID of their "parent" glacier and an RGI ID equal to 0. Every file contains one column for the year number between 1967 and 2015 and another column for the annual glacier-wide MB time series. Glaciers with remote sensing-derived estimates (Rabatel et al., 2016) include this information as an additional column. This allows the user to choose the source of data, with remote sensing data having lower uncertainties (0.35±0.06 () m.w.e. a^-1 as estimated in Rabatel et al. (2016)). Columns are separated by semicolon (;).冰川物质平衡（Glacier mass balance, MB）数据对于理解并量化气候对冰川及高山水循环的区域影响至关重要，但现有观测仅覆盖全球极小部分冰川。本研究构建了1967-2015年间法国阿尔卑斯山区所有冰川的年际全域物质平衡数据集。该数据集基于直接物质平衡观测、遥感年度估算、气象再分析资料以及冰川编目地形数据，通过深度学习（即深度人工神经网络）技术完成重建。 通过与涵盖32座冰川的数据集开展大规模交叉验证，对该方法的有效性进行了评估，结果显示其平均误差（均方根误差，Root Mean Square Error, RMSE）为0.55 米水当量（meter water equivalent, m.w.e.）·年⁻¹，解释方差（决定系数r²）为75%，平均偏差为-0.021 米水当量·年⁻¹。经估算，1967-2015年间法国阿尔卑斯山区区域面积加权平均全域物质平衡为-0.71±0.21（1σ）米水当量·年⁻¹，其中20世纪70年代物质平衡为负（-0.44 米水当量·年⁻¹），80年代呈中度负值（-0.16 米水当量·年⁻¹），90年代起负向趋势加剧，2010年代达到-1.34 米水当量·年⁻¹。 将本数据集与2000-2015年间基于先进星载热发射和反射辐射计（Advanced Spaceborne Thermal Emission and Reflection Radiometer, ASTER）的大地测量物质平衡数据进行对比，发现与用于训练模型的摄影测量大地测量物质平衡结果存在显著差异。当使用新的ASTER-derived大地测量物质平衡数据对重建结果进行重新校准后，1967-2015年间估算的区域面积加权平均全域物质平衡降至-0.64±0.21（1σ）米水当量·年⁻¹。 经地形与区域分析，本研究估算得到1967-2015年间物质损失最严重的山地地块为夏布莱（Chablais）地块（-0.93 米水当量·年⁻¹）、尚博尔与上莫里耶讷（Champsaur and Haute-Maurienne）地块（两者均为-0.86 米水当量·年⁻¹）以及于拜（Ubaye）山脉（-0.83 米水当量·年⁻¹）；物质损失最小的地块为勃朗峰（Mont-Blanc）地块（-0.69 米水当量·年⁻¹）、瓦桑（Oisans）与上塔朗泰斯（Haute-Tarentaise）山脉（两者均为-0.75 米水当量·年⁻¹）。 本数据集可为法国阿尔卑斯山区冰川学、水文学与生态学领域的研究提供及时且有价值的数据，这类研究往往需要冰川化流域的区域或单冰川年度净物质变化数据。 本物质平衡数据集以两种格式提供： (a) 单个网络通用数据格式（Network Common Data Form, netCDF）文件，包含物质平衡重建结果、冰川的兰多夫冰川编目（Randolph Glacier Inventory, RGI）编号与全球冰川遥感监测系统（Global Land Ice Measurements from Space, GLIMS）编号以及冰川名称。该文件包含与数据交互所需的全部必要信息，包括作者信息与数据单位等元数据。 (b) 多CSV文件组成的数据集，对应2003年冰川编目（Gardent等，2014）中的661座冰川，每个文件以其GLIMS编号与RGI编号命名，格式为：GLIMS-ID_RGI-ID_SMB.csv。由于部分冰川分裂为多个子冰川后无专属RGI编号，因此同时保留两类编号。分裂冰川使用其“母冰川”的GLIMS编号，且RGI编号设为0。每个文件包含两列基础数据：一列为1967-2015年的年份序号，另一列为年度全域物质平衡时间序列。对于拥有遥感估算数据（Rabatel等，2016）的冰川，文件中额外增加一列遥感估算数据。这一设计允许用户自主选择数据来源，其中遥感数据的不确定性更低（Rabatel等（2016）估算其不确定性为0.35±0.06 米水当量·年⁻¹）。数据列以分号（;）分隔。