Earth System Models (ESMs) are essential tools for understanding and predicting global change, but they cannot explicitly resolve hillslope-scale terrain structures that fundamentally organize water, energy, and biogeochemical stores and fluxes at subgrid scales. Here we bring together hydrologists, Critical Zone scientists, and ESM developers, to explore how hillslope structures may modulate ESM grid-level water, energy, and biogeochemical fluxes. In contrast to the one-dimensional (1-D), 2-to 3-m deep, and free-draining soil hydrology in most ESM land models, we hypothesize that 3-D, lateral ridge-to-valley flow through shallow and deep paths and insolation contrasts between sunny and shady slopes are the top two globally quantifiable organizers of water and energy (and vegetation) within an ESM grid cell. We hypothesize that these two processes are likely to impact ESM predictions where (and when) water and/or energy are limiting. We further hypothesize that, if implemented in ESM land models, these processes will increase simulated continental water storage and residence time, buffering terrestrial ecosystems against seasonal and interannual droughts. We explore efficient ways to capture these mechanisms in ESMs and identify critical knowledge gaps preventing us from scaling up hillslope to global processes. One such gap is our extremely limited knowledge of the subsurface, where water is stored (supporting vegetation) and released to stream baseflow (supporting aquatic ecosystems). We conclude with a set of organizing
Integrated land surface-groundwater models are valuable tools in simulating the terrestrial hydrologic cycle as a continuous system and exploring the extent of land surface-subsurface interactions from catchment to regional scales. However, the fidelity of model simulations is impacted not only by the vegetation and subsurface parameterizations, but also by the antecedent condition of model state variables, such as the initial soil moisture, depth to groundwater, and ground temperature. In land surface modeling, a given model is often run repeatedly over a single year of forcing data until it reaches an equilibrium state: the point at which there is minimal artificial drift in the model state or prognostic variables (most often the soil moisture). For more complex coupled and integrated systems, where there is an increased computational cost of simulation and the number of variables sensitive to initialization is greater than in traditional uncoupled land surface modeling schemes, the challenge is to minimize the impact of initialization while using the smallest spin-up time possible. In this study, multicriteria analysis was performed to assess the spinup behavior of the ParFlow.CLM integrated groundwater-surface water-land surface model over a 208 km 2 subcatchment of the Ringkobing Fjord catchment in Denmark. Various measures of spin-up performance were computed for model state variables such as the soil moisture and groundwater storage, as well as for diagnostic variables such as the latent and sensible heat fluxes. The impacts of initial conditions on surface water-groundwater interactions were then explored. Our analysis illustrates that the determination of an equilibrium state depends strongly on the variable and performance measure used. Choosing an improper initialization of the model can generate simulations that lead to a misinterpretation of land surface-subsurface feedback processes and result in large biases in simulated discharge. Estimated spin-up time from a series of spin-up functions revealed that 20 (or 21) years of simulation were sufficient for the catchment to equilibrate according to at least one criterion at the 0.1% (0.01%) threshold level. Amongst a range of convergence metrics examined, percentage changes in monthly values of groundwater and unsaturated zone storages produced a slow system convergence to equilibrium, whereas criteria based on ground temperature allowed a more rapid spin-up. Slow convergence of unsaturated and saturated zone storages is a result of the dynamic adjustment of the water table in response to a physically arbitrary or inconsistent initialization of a spatially uniform water table. Achieving equilibrium in subsurface storage ensured equilibrium across a spectrum of other variables, hence providing a good measure of system-wide equilibrium. Overall, results highlight the importance of correctly identifying the key variable affecting model equilibrium and also the need to use a multicriteria approach to achieve a rapid and stable model spin-up.
[1] Despite the importance of mountainous catchments for providing freshwater resources, especially in semi-arid regions, little is known about key hydrological processes such as mountain block recharge (MBR). Here we implement a data-based method informed by isotopic data to quantify MBR rates using recession flow analysis. We applied our hybrid method in a semi-arid sky island catchment in southern Arizona, United States. Sabino Creek is a 91 km 2 catchment with its sources near the summit of the Santa Catalina Mountains northeast of Tucson. Southern Arizona's climate has two distinct wet seasons separated by prolonged dry periods. Winter frontal storms (November -March) provide about 50% of annual precipitation, and summers are dominated by monsoon convective storms from July to September. Isotope analyses of springs and surface water in the Sabino Creek catchment indicate that streamflow during dry periods is derived from groundwater storage in fractured bedrock. Storage-discharge relationships are derived from recession flow analysis to estimate changes in storage during wet periods. To provide reliable estimates, several corrections and improvements to classic base flow recession analysis are considered. These corrections and improvements include adaptive time stepping, data binning, and the choice of storage-discharge functions. Our analysis shows that (1) incorporating adaptive time steps to correct for streamflow measurement errors improves the coefficient of determination, (2) the quantile method is best for streamflow data binning, (3) the choice of the regression model is critical when the stage-discharge function is used to predict changes in bedrock storage beyond the maximum observed flow in the catchment, and (4) the use of daily or night-time hourly streamflow does not affect the form of the storage-discharge relationship but will impact MBR estimates because of differences in the observed range of streamflow in each series.Citation: Ajami, H., P. A. Troch, T. Maddock III, T. Meixner, and C. Eastoe (2011), Quantifying mountain block recharge by means of catchment-scale storage-discharge relationships, Water Resour. Res., 47, W04504,
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