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Groundwater level fluctuations are affected by surface properties due to complex correlations of groundwater‐surface water interaction and/or other surface processes, which are usually hard to be accurately quantified. Previous studies have assessed the relationship between groundwater level fluctuations and specific controlling factors. However, few studies have been conducted to explore the impact of the combination of multiple factors on the groundwater system. Hence, this paper tries to explore the localized and scale‐specific multivariate relationships between the groundwater level and controlling factors (such as hydrologic and meteorological factors) using bivariate wavelet coherence and multiple wavelet coherence. The groundwater level fluctuations of two wells in areas covered by different plant densities (i.e., the riparian zone of the Colorado River, USA) are analyzed. Main findings include three parts. First, barometric pressure and river stage are the best factors to interpret the groundwater level fluctuations at small scales (<1 day) and large scales (>1 day) at the well of low‐density plants stand, respectively. Second, at the well of high‐density plants stand, the best predictors to control the groundwater level fluctuations include barometric pressure (<1 day), the combination of barometric pressure and temperature (1‐7 days), temperature (7‐30 days), and the combination of barometric pressure, temperature, and river stage (>30 days). The best predictor of groundwater head fluctuations depends on the variance of the vegetation coverage and hydrological processes. Third, these results provide a suite of factors to explain the groundwater level variations, which is an important topic in water‐resource prediction and management. Article impact statement: This method note provides a multivariate method to identify multiple factors that can best explain the groundwater level fluctuation. [ABSTRACT FROM AUTHOR]

The characteristics of karst aquifers are difficult to be determined due to their heterogeneous physical properties and lack of hydrogeological information. In this case study, we applied two methods for a comparative analysis of storage and drainage characteristics in upstream, midstream, and downstream of Houzhai cave stream basin. In the first method, Minimum Smoothed Method ( MSM) is used to determine the proportion of baseflow to the total flow (Baseflow Index, BFI). In the second method, a bicarbonate-base two-end member mixing model is used to quantify the slow flow component and fast flow component. For both methods, slow flow and quick flow are quantified at three sampling sites, which provide useful information for the analysis of storage and drainage characteristics. The results from flow separation method and hydrogeochemical analysis show a consistently increasing trend of the proportion of slow flow to total flow from the upstream to downstream which indicates that the voids of highly conductive conduits and well-connected fissures decrease along the flow paths in the Houzhai cave stream basin in southwest China. The upstream areas have a low proportion of baseflow which indicates a high drainage capacity due to high permeable conduits and well-connected fissures. The downstream areas, on the contrary, have a high proportion of baseflow which indicates a high storage capacity and slow infiltration due to the predominant presence of matrix and poorly-connected fissures. These numerical methods provide alternative ways to investigate the storage and drainage characteristics of karst aquifers where direct measurement are not available. [ABSTRACT FROM AUTHOR]

There can be marked variations in soil hydraulic properties in a soil vertical profile from the soil surface to the base of the root zone. Many existing two-layer soil moisture ( TLSM) models cannot well describe typical stratified soil profiles. A modified TLSM model is presented in this study. The modified model results and those from two existing models are compared with field observations. The modified TLSM model had the best agreement with the field observations. In both the surface layer and the root zone layer, the root mean square errors of soil moisture estimated by the modified model were smaller than those for the other models. The parameters in the modified TLSM model are relatively easy to determine. The modified TLSM model offers clear advantages over current TLSM models. [ABSTRACT FROM AUTHOR]