A New GRACE Downscaling Approach for Deriving High-Resolution Groundwater Storage Changes Using Ground-Based Scaling Factors

IF 4.6 1区 地球科学 Q2 ENVIRONMENTAL SCIENCES Water Resources Research Pub Date : 2024-11-06 DOI:10.1029/2023wr035210
Huixiang Li, Yun Pan, Pat J.-F. Yeh, Chong Zhang, Zhiyong Huang, Li Xu, Haigang Wang, Linghai Zeng, Huili Gong, James S. Famiglietti
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Abstract

To compensate for the coarse resolution of groundwater storage (GWS) estimation by the Gravity Recovery and Climate Experiment (GRACE) satellites and make better use of available observed groundwater-level (GWL) data in some aquifers, a ground-based scaling factor (SF) method is proposed here to derive high-resolution GRACE GWS estimates. Improvement is achieved by using the gridded SF derived from assimilating ground-based GWL observations. The proposed SF method is tested on the North China Plain (NCP, ∼140,000 km2), where a dense network of observation wells and a consistently estimated specific yield (SY) data set are available, to demonstrate its effectiveness and practical applications. The sensitivities of SF-estimated GWS accuracy to the specification of SY and the assimilation of GWL observation data are explored through four designed numerical experiments. Results show that this novel ground-based method can reduce the impact of SY uncertainty on GWS estimates, particularly in regions with more pronounced regional GWS trends. The accuracy of SF-estimated GWS is primarily determined by whether the assimilated wells can reflect the regionally averaged GWS trend. GWS accuracy is less dependent on the number of available wells assimilated. The estimated GWS trend (2004–2015) in NCP is −32.6 ± 1.3 mm/yr (−4.6 ± 0.2 km3/yr), with contrasting GWS trends found in the west Piedmont Plain (∼54,000 km2, with a loss of −66.8 mm/yr) and the coastal Eastern Plain (∼20,000 km2, and a gain of +7.2 mm/yr). Despite the limitations of regional and time scale dependence inherent in SF method, this study highlights the benefits of assimilating in situ observed GWL data instead of using model simulations in estimating SF to downscale GRACE GWS to the higher-resolution that is desired by local water resources management.
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利用基于地下的缩放因子推导高分辨率地下水储量变化的全新 GRACE 缩放方法
为了弥补重力恢复与气候实验(GRACE)卫星对地下水储量(GWS)估算分辨率较低的问题,并更好地利用某些含水层的现有地下水位(GWL)观测数据,本文提出了一种基于地面的缩放因子(SF)方法,以得出高分辨率的 GRACE GWS 估算值。通过使用同化地基 GWL 观测数据得到的网格 SF,可以实现改进。华北平原(NCP,∼14 万平方公里)拥有密集的观测井网络和持续估算的比降(SY)数据集,为了证明其有效性和实际应用,对所提出的 SF 方法进行了测试。通过四个设计的数值试验,探讨了 SF 估算的 GWS 精度对 SY 规格和 GWL 观测数据同化的敏感性。结果表明,这种新型地基方法可以减少 SY 不确定性对 GWS 估计值的影响,尤其是在区域 GWS 趋势较为明显的地区。SF估算的GWS精度主要取决于同化井是否能反映区域平均GWS趋势。GWS 的准确性与可用同化井的数量关系不大。据估计,北太平洋的 GWS 趋势(2004-2015 年)为 -32.6 ± 1.3 毫米/年(-4.6 ± 0.2 立方公里/年),皮德蒙特平原西部(∼54,000 平方公里,损失为 -66.8 毫米/年)和东部沿海平原(∼20,000 平方公里,增加为 +7.2 毫米/年)的 GWS 趋势形成鲜明对比。尽管 SF 方法本身存在区域和时间尺度依赖性的限制,但本研究强调了在估算 SF 时同化原地观测到的 GWL 数据而不是使用模型模拟数据的好处,以便将 GRACE GWS 的尺度缩小到当地水资源管理所需的更高分辨率。
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来源期刊
Water Resources Research
Water Resources Research 环境科学-湖沼学
CiteScore
8.80
自引率
13.00%
发文量
599
审稿时长
3.5 months
期刊介绍: Water Resources Research (WRR) is an interdisciplinary journal that focuses on hydrology and water resources. It publishes original research in the natural and social sciences of water. It emphasizes the role of water in the Earth system, including physical, chemical, biological, and ecological processes in water resources research and management, including social, policy, and public health implications. It encompasses observational, experimental, theoretical, analytical, numerical, and data-driven approaches that advance the science of water and its management. Submissions are evaluated for their novelty, accuracy, significance, and broader implications of the findings.
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