Floodplain lakes represent important aquatic ecosystems, and field‐based estimates of their water budgets are difficult to obtain, especially over multiple years. We examine the hydrological fluxes for an Amazon floodplain lake connected to the Solimões River using a process‐based hydrologic model. Water exchanges between the river and lake agree well with field estimates, including the timing of different hydrological phases. However, beyond available field data, modeling results show that the seven simulated years all differed from each other. These interannual differences were caused by the interplay between phases when water levels were rising with river‐water flowing into the lake (RWRI), versus rising with lake‐water flowing out to the river (RWLO). This exchange determines the river‐water content in the lake (CL). Maximum CL occurred before river levels peaked because local catchment contributions can be sufficient to push lake‐water out to the river, even as river levels rise. Numerical experiments show that the seasonal distribution of local rainfall, local catchment size, and interannual variability in both climate and river stage can contribute to differing dynamics of CL in a floodplain lake. Their impacts vary among phases: river‐rise dominates the RWRI, whereas local hydrological processes dominate the RWLO and receding‐water phases. Intermediate‐to‐long‐term rainfall accumulation controls CL during the RWLO phase, whereas annual precipitation accumulation is important for CL during low water. Our model generalizes beyond limited available field studies and offers potential to better understand floodplain lakes in other areas and how regional versus local changes in climate may affect their hydrological dynamics.

Highlights Soil moisture statistical fractal temporal evolution shows seasonal trends and a three-phase event-induced hysteretic patterns  Soil texture is the main cause of hysteresis in fractal temporal evolution; groundwater has important influences that interact with other factors  We separated out the effects of different controls and generalized phenomenological rules that govern fractal evolution AbstractSoil moisture statistical fractal is an important tool for downscaling remotely-sensed observations and has the potential to play a key role in multi-scale hydrologic modeling. The fractal was first introduced two decades ago, but relatively little is known regarding how its scaling exponents evolve in time in response to climatic forcings. Previous studies have neglected the process of moisture re-distribution due to regional groundwater flow. In this study we used a physically-based surface-subsurface processes model and numerical experiments to elucidate the patterns and controls of fractal temporal evolution in two U.S. Midwest basins. Groundwater flow was found to introduce large-scale spatial structure, thereby reducing the scaling exponents ( ), which has implications for the transferability of calibrated parameters to predict . However, the groundwater effects depend on complex interactions with other physical controls such as soil texture and land use. The fractal scaling exponents, while in general showing a seasonal mode that correlates with mean moisture content, display hysteresis after storm events that can be divided into three phases, consistent with literature findings: (a) wetting, (b) re-organizing, and (c) dry-down. Modeling experiments clearly show that the hysteresis is attributed to soil texture, whose "patchiness" is the primary contributing factor. We generalized phenomenological rules for the impacts of rainfall, soil texture, groundwater flow, and land use on evolution. Grid resolution has a mild influence on the results and there is a strong correlation between predictions of from different resolutions. Overall, our results suggest that groundwater flow should be given more consideration in studies of the soil moisture statistical fractal, especially in regions with a shallow water table.

Some machine learning (ML) methods such as classification trees are useful tools to generate hypotheses about how hydrologic systems function. However, data limitations dictate that ML alone often cannot differentiate between causal and associative relationships. For example, previous ML analysis suggested that soil thickness is the key physiographic factor determining the storage-streamflow correlations in the eastern US. This conclusion is not robust, especially if data are perturbed, and there were alternative, competing explanations including soil texture and terrain slope. However, typical causal analysis based on process-based models (PBMs) is inefficient and susceptible to human bias. Here we demonstrate a more efficient and objective analysis procedure where ML is first applied to generate data-consistent hypotheses, and then a PBM is invoked to verify these hypotheses. We employed a surface-subsurface processes model and conducted perturbation experiments to implement these competing hypotheses and assess the impacts of the changes. The experimental results strongly support the soil thickness hypothesis as opposed to the terrain slope and soil texture ones, which are co-varying and coincidental factors. Thicker soil permits larger saturation excess and longer system memory that carries wet season water storage to influence dry season baseflows. We further suggest this analysis could be formulated into a data-centric Bayesian framework. This study demonstrates that PBM present indispensable value for problems that ML cannot solve alone, and is meant to encourage more synergies between ML and PBM in the future.

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The quantification of recharge and trans-valley underflow is needed in arid regions to estimate the impacts of new water withdrawals on the water table. However, for mountainous desert areas, such estimates are highly challenging, due to data scarcity, heterogeneous soils, and long residence times. Conventional assessment employs isolated groundwater models configured with simplified uniform estimates of recharge. Here, we employed a data-constrained surface-subsurface process model to provide an ensemble of spatially distributed recharge and underflow estimates using perturbed parameters. Then, the Model-Independent Parameter Estimation and Uncertainty Quantification (PEST) package was used to calibrate MODFLOW aquifer hydraulic conductivity for this ensemble and reject implausible recharge values. This novel dual-model approach, broadly applicable to mountainous arid regions, was designed to maximally exploit available data sources. It can assimilate groundwater head observations, reject unrealistic parameters, and narrow the range of estimated drawdowns due to pumping. We applied this approach to the Chuckwalla basin in California, USA to determine natural recharge. Simulated recharge concentrates along alluvial fans at the mountain fronts and ephemeral washes where runoff water infiltrates. If an evenly distributed recharge was employed as in conventional studies, it would result in regional biases in estimated drawdown and larger uncertainty bounds. We also note that the speed of groundwater recovery does not guarantee sustainability: heavy pumping induces large hydraulic gradients that initially recover quickly when pumping is halted, but the system may not ultimately recover to pre-pumping levels.