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The hydrologic connectivity of non-floodplain wetlands (NFWs) with downstream water (DW) has gained increased importance, but connectivity via groundwater (GW) is largely unknown owing to the high complexity of hydrological processes and climatic seasonality. In this study, a causal inference method, convergent cross mapping (CCM), was applied to detect the hydrologic causality between upland NFW and DW through GW. CCM is a nonlinear inference method for detecting causal relationships among environmental variables with weak or moderate coupling in nonlinear dynamical systems. We assumed that causation would exist when the following conditions were observed: (1) the presence of two direct causal (NFW → GW and GW → DW) and one indirect causal (NFW → DW) relationship; (2) a nonexistent opposite causal relationship (DW → NFW); (3) the two direct causations with shorter lag times relative to indirect causation; and (4) similar patterns not observed with pseudo DW. The water levels monitored by a well and piezometer represented NFW and GW measurements, respectively, and the DW was indicated by the baseflow at the outlet of the drainage area, including NFW. To elucidate causality, the DW taken at the adjacent drainage area with similar climatic seasonality was also tested as pseudo DW. The CCM results showed that the water flow from NFW to GW and then DW was only present, and any opposite flows did not exist. In addition, direct causations had shorter lag time than indirect causation, and 3-day lag time was shown between NFW and DW. Interestingly, the results with pseudo DW did not show any lagged interactions, indicating non-causation. These results provide the signals for the hydrologic connectivity of NFW and DW with GW. Therefore, this study would support the importance of NFW protection and management. [ABSTRACT FROM AUTHOR]

• The influence of equifinality on the hybrid model accuracy was assessed. • The hybrid model was developed by coupling SWAT with LSTM. • The two hybrid models constrained by RS-ET or not were compared. • The hybrid models showed better predictions than standalone SWAT and LSTM. • Among the hybrid models, those with RS-ET indicated enhanced performances. Hybrid models coupling process-based models with deep learning models have been widely used for estimating water quantity and quality. However, the impacts of uncertainty within process-based models on the performance of hybrid models remained largely unknown. This study focused on assessing the impact of uncertainties on a hybrid model that combines Soil and Water Assessment Tool (SWAT) with Long Short-Term Memory (LSTM) for estimating streamflow and suspended solids (SS). This study applied the output of SWAT as input for LSTM to make a hybrid model. By incorporating an additional constraint, remotely sensed evapotranspiration (RS-ET), the performance of hybrid models with and without considering RS-ET was evaluated at various temporal and spatial scales. Furthermore, various input settings (default and calibrated SWAT and inclusion/exclusion of precipitation) were considered. The results showed that the hybrid models tended to provide accurate estimations of monthly streamflow and SS compared to standalone SWAT and LSTM. The addition of precipitation to hybrid models did not make noticeable improvements for streamflow and SS. The hybrid models constrained by RS-ET tended to have improved estimations on streamflow and SS at daily and monthly scales compared to those unconstrained by RS-ET. Similar results were also observed at the sub-watershed level. These insights highlighted the potential of hybrid models to enhance hydrological estimations and underscore the importance of incorporating additional constraints to reduce uncertainties. Such advancements have practical implications for water resource management and decision-making processes, enabling more reliable and accurate estimations in applications of hybrid models. [ABSTRACT FROM AUTHOR]