Modeling the impact of spatiotemporal vegetation dynamics on groundwater recharge
Modeling the impact of spatiotemporal vegetation dynamics on groundwater recharge
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DOI：
10.1016/j.jhydrol.2021.126584
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发表时间：
2021-10
影响因子：
6.4
通讯作者：
H. Anurag;G. Ng;R. Tipping;K. Tokos
中科院分区：
文献类型：
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作者：
H. Anurag;G. Ng;R. Tipping;K. Tokos
气候变化影响植被的生长及其生理状态，如叶面积指数(LAI)，进而由于蒸散量(ET)的变化而影响地下水的补给。目前，大多数补给模型研究过度简化了瞬时植被条件及其对补给的潜在影响，采用的植被参数如叶面积指数等气候学数值。本研究利用CLMv4.5社区陆地模式，在2000-2015年间，以25公里的空间分辨率，研究了美国明尼苏达州不同气候、水文地质和生态区的植被年际变化对补给的敏感性。利用集合卡尔曼滤波(EnKF)对全州地下水位深度观测数据进行土壤和径流参数校正。研究结果表明，尽管年复一年的植被变化不会影响长期的气候补给量估计，但它可以推动年和季节补给量的不成比例的巨大变化。与动态和气候植被输入的模拟相比，补给的平均幅度差(均方根差，RMSD)为21.1%，而LAI输入仅有4%的差异。回归分析表明，当地水文地质和植被类型的组合影响补给对LAI和ET变化的响应幅度。我们还发现，控制叶面积指数异常的是温度而不是降水的跨生态区优势，由此产生的补给变异性，春季温度是主要因素，因为它影响落叶条件。与明尼苏达州较潮湿的东部地区相比，干旱的明尼苏达州西部表现出更高的相对叶面积指数差异以及更高的春季时间和相对年补给量，这表明该缺水地区更容易受到植被和气候条件变化的影响。我们的研究表明，如果忽略植被动态，模型可能会低估或高估年度和季节补给，这表明在评估未来气候变化对补给的影响时，需要考虑瞬时植被条件。
Climate change affects the growth of vegetation and its physiological states such as leaf area index (LAI), which in turn can affect groundwater recharge because of changes in evapotranspiration (ET). Presently, most recharge modeling studies over-simplify transient vegetation conditions and the potential corresponding impact on recharge by using climatological values of vegetation parameters such as LAI. Our study uses the Community Land Model (CLMv4.5) to investigate the sensitivity of recharge to interannual varying vegetation in Minnesota (USA) across different climate, hydrogeology, and ecoregions at a 25 km spatial resolution and for the period of 2000–2015. The Ensemble Kalman Filter (EnKF) was used to calibrate soil and runoff parameters to statewide water table depth observations. Results of the study indicate that although year-to-year varying vegetation does not affect long-term climatological recharge estimates, it can drive disproportionately large variability in annual and seasonal recharge. Comparing simulations with dynamic and climatological vegetation inputs, the average magnitude difference (root mean square difference, RMSD) for recharge was 21.1% in response to only a 4% difference in LAI inputs. Regression analysis revealed that the combination of local hydrogeology and vegetation-type affects the magnitude of recharge response to LAI and ET changes. We also found cross-ecoregion dominance of temperature rather than precipitation controlling LAI anomalies and resulting recharge variability, with springtime temperature being the primary factor because of its impact on leaf-out conditions. Drier western Minnesota showed higher relative LAI differences as well as higher spring time and relative annual recharge compared to the wetter eastern part of the state, indicating higher vulnerability of the water-limited region to changing vegetation and climatic conditions. Our study shows that models can underestimate or overestimate annual and seasonal recharge if vegetation dynamics are neglected, demonstrating the need to incorporate transient vegetation conditions when assessing the impact of future climate change on recharge.