Assessing the impact of model spin‐up on surface water‐groundwater interactions using an integrated hydrologic model

Abstract

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.

Model Spin-Up Behavior for Wet and Dry Basins: A Case Study Using the Xinanjiang Model

“…A similar recursive experiment was done in several model spin-up studies [8,11,13,14]. This yearly recursive simulation removes inter-annual climate variability and links any model adjustment processes to the equilibrium state of its internal stores (i.e., soil moisture) from an initial anomaly directly to the spin-up processes.…”

“…Spin-up can be defined based on the e-folding time (time required to reduce the yearly differences in daily/monthly model output to its 1/e value) [31], halving time (time required to reduce the yearly differences in daily/monthly model output to its half) [32] or percent cut off-based (PC) time (time required for yearly changes in daily/monthly model output to decrease to a certain threshold; see Cosgrove et al [11] and de Goncalves et al [9]). Of these, PC time has been widely used for detecting the model equilibrium [8,9,11,14,18,33,34].…”

“…Literature suggests that the typical spin-up time of the land surface model (LSM) could range from one to several years [8][9][10][11][12]. Once the model achieves its equilibrium state, the simulated output usually agrees better with the observations and responds realistically to the inputs [8,11,13,14]. Hence, special attention is required for specifying the model initial conditions.…”

“…Hence, special attention is required for specifying the model initial conditions. However, due to the scarcity of long-term records or spatially distributed information specifying the catchment states, the model's initial conditions are usually inferred from limited observations or an initial guess [14]. Rodell et al [12] suggests using climatological average states from the same model for the purpose of initialization in the absence of long term forcing data.…”

“…Despite its importance, only a very few studies have examined the spin-up behavior of land surface [8,9,11,12,18] or hydrological models [13,14,16]. These studies have been done to examine the model spin-up behavior under diverse conditions of climate, vegetation, and soil types.…”