据联合国相关机构数据，旱区覆盖了约全球41%的陆地面积，承载着约28.3亿人口，占全球总人口的38%。由于在干旱气候条件下地表水资源相对匮乏，从而使地下水成为了维系旱区社会经济与生态系统良性发展重要的供水水源。地下水蒸散发作为旱区地下水排泄最主要的途径，但因影响机制众多且复杂，导致地下水蒸散量的估算存在较大的不确定性。因此，如何将ETg的估算误差控制在合理范围内是旱区地下水资源评价所面临的首要问题。

由于地下水动态方便获取，同时降低了对气象数据以及监测设备的需求，基于地下水日动态估算地下水蒸散量（White法）在旱区地下水蒸散量估算中占据重要地位。基于White法的基本理论，本文将时间序列分析方法与Boussinesq方程结合提出了利用地下水日动态高频监测数据快速估算地下水蒸散的方法，避免了在不同立地条件与水文地质条件下对地下水的恢复速率这一不确定性较大参数的确定。该方法分别在格尔木河流域与海流兔河流域我国西北旱区典型流域中进行了实际应用。最后基于实际条件下的估算认识，借助数值模拟技术，探讨了开采条件下的地下水蒸散量估算问题。本研究的主要结论如下：

（1）格尔木河流域与海流兔河流域地下水动态在植被生长季节表现出典型的依赖地下水型植被所产生的日动态特征，即夜间水位回升，白天水位下降，水位随时间波动变化。夜间净补给恢复速率时段所不同立地条件与水文地质条件影响，差异较大且随时间不断变化。非生长季节的地下水仍然表现出类似生长季节的地下水日动态特征，这与非生长季节的水位回升使地下水系统对气压效应的响应速度加快有关。

（2）水位趋势项中记录了每一次水位变动信息，如降水信息以及侧向补给信息，是地下水在各种潜在影响因素下综合后的结果；水位振幅中则仅包含了使地下水产生日动态的驱动力。生长季节主要由依赖地下水型植被引发，非生长季节则由气压效应所主导；水位相位则反映了地下水对潜在影响因素响应时间的长短，一般非生长季节的相位值处于负值水平，而生长季节处于正值水平，意味着潜在影响因素在非生长季节对地下水的影响速度较快。

（3）干旱区与半旱区依赖地下水型植被生态系统地下水蒸散量的估算。其中格尔木河流域在柽柳监测点估算结果与流域内常见的传统方法估算出的经验值之间拥有良好的拟合度，8月~9月两者估算出的平均偏差不超过28%；海流兔河流域在沙柳监测点估算结果与监测点中的波文比系统实测得到的蒸散量也表现出格尔木河流域类似的拟合度，6月~10月地下水蒸散量平均误差介于25.30%~28.83%之间。而冬季的地下水蒸散量的估算存在较大的偏差与误差，这是由于气压效应等其他潜在周期性因素的干扰以及Boussinesq方程无法刻画冬季水分运移所致。

（4）地下水日动态本质上是含水介质饱和与释水不断交替的过程，Boussinesq方程对这种过程的刻画能力不足，这从冬季的估算结果很容易看出；另一方面，无量纲系数K_c,gw的引入导致该方法十分依赖K_c,gw的计算准确性，一旦K_c,gw被错误计算，最终导致地下水蒸散量出现较大偏差。从时序上来看，在旱区依赖地下水型植被生态系统（Groundwater-dependent ecosystems, GDEs）中K_c,gw的作用类似于作物系数K_c。因而，在物理意义尚不明确的条件下，反算监测点的K_c,gw使该方法仍然存在着一定的不确定性，后续需要从土壤释水机制出发寻找新的具有明确物理含义的参数以矫正估算误差。

（5）开采条件对地下水日动态的影响主要体现在两个方面，一是间断开采频繁的刺激了侧向补给对监测点的强度，使侧向补给信号带有了紊乱的周期性信息，导致水位日动态特征完全被开采信息所掩盖，White法的基本理论因而失效，最终导致地下水蒸散量被低估。二是持续开采条件下，增强了侧向补给的强度，使水位日动态中的净补给速率被高估，从而导致地下水蒸散量被高估。但本研究提出的方法在持续条件下的估算能力与未受开采扰动条件下的估算性能相当。因而开采条件下的地下水蒸散量估算需要系统性的室内与原位实验，寻找开采信息在地下水日动态过程中关键表征参数。

According to data from relevant United Nations agencies, arid regions cover approximately 41% of the world’s land area and are home to about 2.83 billion people, accounting for 38% of the global population. Due to arid climatic conditions, surface water resources are scarce, making groundwater an important water supply source for maintaining sound socio-economic and ecosystem development in arid regions. Groundwater evapotranspiration (ETg), as the primary pathway of groundwater discharge in arid areas, is influenced by numerous complex mechanisms, resulting in considerable uncertainty in its estimation. Therefore, how to keep ETg estimation errors within a reasonable range is the foremost issue facing groundwater resource assessments in arid regions.

Due to the convenience of obtaining groundwater dynamics and the reduced reliance on meteorological data and monitoring equipment, estimating ETg based on daily groundwater fluctuations (White method) plays a significant role in arid regions. Building on the fundamental theory of the White method, this paper integrates a time series analysis approach with the Boussinesq equation to propose a rapid estimation method for groundwater evapotranspiration using high-frequency daily groundwater dynamics monitoring data, thereby avoiding the need to determine the groundwater recovery rate—an inherently uncertain parameter—under different site and hydrogeological conditions. This method has been applied in two typical river basins in the arid region of Northwest China, namely the Golmud River Basin and the Hailutu River Basin. Lastly, based on insights gained from actual estimation and by utilizing numerical simulation technology, this study explores the issue of estimating groundwater evapotranspiration under groundwater extraction conditions. The main conclusions of this research are as follows:

(1) In both the Golmud River Basin and the Hailutu River Basin, groundwater during the vegetation growing season exhibits the typical daily fluctuations characteristics associated with groundwater-dependent vegetation, namely nighttime water-level recovery and daytime water-level decline, with water levels fluctuating over time. The nighttime net recharge recovery rate period is influenced by different site and hydrogeological conditions, leading to significant variation that changes continuously over time. Even during the non-growing season, groundwater still shows daily fluctuations similar to those of the growing season, which is related to the accelerated response of the groundwater system to barometric effects brought about by water-level recovery in the non-growing season.

(2) The water-level trend component captures every instance of water-level fluctuation—such as precipitation and lateral recharge—and represents the comprehensive result of groundwater responding to various potential influencing factors. The water-level amplitude, by contrast, only reflects the driving forces that produce daily groundwater fluctuations. During the growing season, these are primarily driven by groundwater-dependent vegetation, whereas in the non-growing season they are dominated by barometric effects. The water-level phase indicates the response time of groundwater to potential influencing factors, typically showing negative values in the non-growing season and positive values in the growing season, implying a faster response to potential influencing factors during the non-growing season.

(3) The estimation of ETg in groundwater-dependent vegetation ecosystems of arid and semi-arid areas. For instance, at the Tamarix monitoring point in the Golmud River Basin, the estimation results show a good fit with the empirical values calculated by common traditional methods in the basin; between August and September, the average deviation between the two estimates does not exceed 28%. Similarly, at the Salix monitoring point in the Hailutu River Basin, the estimation results align well with actual ETg measured using the Bowen ratio system, showing an average error for ETg between 25.30% and 28.83% from June to October. However, the estimation of ETg in winter exhibits larger deviations and errors, which result from interference by barometric effects and other potential periodic factors, as well as the inability of the Boussinesq equation to capture water movement in winter.

(4) Groundwater daily fluctuations essentially involve continuous alternation between saturation and water release in the aquifer medium, a process that the Boussinesq equation does not adequately capture, as can be clearly seen from the winter estimation results. Furthermore, the introduction of the dimensionless coefficient K_c,gw makes the method highly dependent on the accuracy of K_c,gw’s calculation; once K_c,gw is miscalculated, the resulting groundwater evapotranspiration can deviate substantially. From a temporal perspective, in groundwater-dependent ecosystems (GDEs) of arid regions, the effect of K_c,gw is analogous to the crop coefficient K_c. Consequently, given the currently unclear physical meaning of K_c,gw, deriving K_c,gw from the monitoring points introduces some uncertainty into this method. In the future, it will be necessary to explore new parameters with clear physical significance, starting from the soil water release mechanism, to correct estimation errors.

(5) Under pumping conditions, the impact on daily groundwater fluctuations primarily manifests in two ways. First, frequent intermittent pumping intensifies the lateral recharge affecting the monitoring point, leading to chaotic periodic signals. As a result, the characteristic daily water-level fluctuations are completely obscured by the pumping information, causing the basic theory of the White method to fail and ultimately leading to an underestimation of groundwater evapotranspiration. Second, under continuous pumping, the strength of lateral recharge increases, causing the net recharge rate in the daily water-level fluctuations to be overestimated, thereby leading to an overestimation of ETg. However, the estimation capability of the method proposed in this study under continuous pumping conditions is comparable to its performance under undisturbed conditions. Therefore, estimating ETg under pumping conditions requires systematic laboratory and in-situ experiments to identify the key parameters that characterize pumping information in the daily groundwater fluctuations process.