歴史的および将来の地下水動態に関する全球ハイパーレゾリューション・モデリング

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:全球地下水モデルの解像度・検証手法・ML補正のベンチマークとして、水循環・気候影響研究の基盤データセットを提供する。

🏢実務担当者:製造・食品・データセンター等の水リスク評価やTNFD・CDP Water対応において、拠点周辺の地下水枯渇・水位変動の高解像度データとして活用可能。

🏛政策担当者:地下水管理政策やSDGs目標6の進捗評価、気候適応計画の科学的根拠として、シナリオ別の将来予測と品質フラグを参照できる。

Abstract. The sustainable management of global groundwater resources is a key societal challenge and is central to the Sustainable Development Goals. The localized dynamics of groundwater abstraction, topography, and surface-water interactions, as well as the sensitivity of groundwater-dependent ecosystems, call for high-resolution information to support effective groundwater management. At the same time, groundwater observations are very limited and concentrated in a few regions, rendering large parts of groundwater resources ungauged. To address limited observations and coarse global models, we applied the global groundwater model GLOBGM (v1.1) to simulate past and future groundwater heads and water table depth at 30 arcsec (∼ 1 km) on a monthly time step. Model calibration improved mean bias in water table depth predictions from −4.8 to 3.6 m compared to GLOBGM v1.0, with depth-weighted bias reduced from 34.2 to 32.5 m across 34 800 observation wells. Groundwater dynamics are simulated for a historical reference period (1960–2019) to support model evaluation and attribution of observed impacts to climate variability and change. Baselines (1960–2014) and three combined socioeconomic-climate scenarios (2015–2100; SSP1-RCP2.6, SSP3-RCP7.0, SSP5-RCP8.5) are simulated with five global climate models, supporting detection and impact assessment of future change. Validation against monthly observations yielded skillful predictions (KGE-NPskill) in approximately 60 % of deep wells (> 60 m) and 83 % of shallow to intermediate wells (0–60 m). When validated against annual observations, 66 % of deep wells (> 60 m) and 71 % of shallow to intermediate wells (0–60 m) were skillful. Simulations are further bias corrected using a machine learning approach. Historical trend analysis (1960–2019) accurately reproduced known groundwater depletion regions such as the U.S. High Plains, Arabian Peninsula, and Indo-Gangetic Plain, while also identifying rising water tables in northern latitudes and Arctic regions, which are linked to climate-driven recharge changes. Future scenario-based simulations suggest rising water tables for most continents in the next century, with Europe being a notable exception. However, known regions of groundwater depletion are expected to persist. Regions that disagree with observations or show reduced reliability are mapped, and quality assurance flags are provided to guide the appropriate use and interpretation of the results. The resulting data set offers high-resolution information to assess groundwater dynamics for the past and future, supporting improved global water resource management and climate impact assessments.

• semanticscholar https://doi.org/10.5194/esd-17-1201-2026first seen 2026-09-12 05:51:46 · last seen 2026-09-22 05:19:24