Using nested discretization for a detailed yet computationally efficient simulation of local hydrology in a distributed hydrologic model

Abstract

Fully distributed hydrologic models are often used to simulate hydrologic states at fine spatio-temporal resolutions. However, simulations based on these models may become computationally expensive, constraining their applications to smaller domains. This study demonstrates that a nested-discretization based modeling strategy can be used to improve the efficiency of distributed hydrologic simulations, especially for applications where fine resolution estimates of hydrologic states are of the focus only within a part of a watershed. To this end, we consider two applications where the goal is to capture the groundwater dynamics within a defined target area. Our results show that at the target locations, a nested simulation is able to competently replicate the estimates of groundwater table as obtained from the fine simulation, while yielding significant computational savings. The results highlight the potential of using nested discretization for a detailed yet computationally efficient estimation of hydrologic states in part of the model domain.

Spatial and temporal variations in the groundwater contributing areas of inland wetlands

“…The model has been previously applied at multiple scales and in diverse hydro‐climatological settings for simulating coupled dynamics of streamflow, groundwater, soil moisture, snow, and evapotranspiration fluxes (Chen, Kumar, & McGlynn, ; Chen, Kumar, Wang, Winstral, & Marks, ; Kumar, Marks, Dozier, Reba, & Winstral, ; Seo, Sinha, Mahinthakumar, Sankarasubramanian, & Kumar, ; R. Wang, Kumar, & Marks, ; Yu, Duffy, Baldwin, & Lin, ; Zhang, Chen, Kumar, Marani, & Goralczyk, ). The model has already been used to study wetland dynamics in multiple studies (Liu & Kumar, ; D. Wang, Liu, & Kumar, ; Yu et al, ).…”

Emerging technologies and radical collaboration to advance predictive understanding of watershed hydrobiogeochemistry

“…As exascale simulation capabilities are not expected to be accessible to the wider community and practitioners for many years, several strategies hold potential for reducing the computational burden required for simulating watershed reactive transport using current leadership‐class computers. For example, approaches for adjusting the resolution in computational grids, such as including static and adaptive mesh refinement methods (AMR; Blayo & Debreu, 1999; Wang, Liu, & Kumar, 2018), offer potential for simulating small‐scale reactive hotspots and their influence on larger system behavior in a computationally efficient manner. The ability to employ variable resolution in mechanistic watershed reactive transport models, allowing codes to “telescope” into regions that are rapidly evolving, may provide a path forward for balancing accuracy and tractability associated with stimulating watershed reactive transport processes.…”

Forecasting semi‐arid biome shifts in the Anthropocene

“…Because they are founded on physical models, the strength of these mechanistic ecohydrological models is that they may perform better at predicting out-of-range phenomena than observational and correlative approaches such as climate envelope modeling (Schlaepfer et al, 2017). Ecohydrological models can be very effective at predicting water flow and ecosystem-level plant growth, but a fundamental problem for these models is simulating fine-scale processes over large areas (Ratajczak et al, 2017;Wang et al, 2018;Fan et al, 2019). Recent efforts are improving fine-scale simulations of soil water availability and predictions of vegetation water stress on the landscape (Schlaepfer et al, 2017;Guo et al, 2018;Tai et al, 2018).…”

“…Ecohydrological models can be very effective at predicting water flow and ecosystem‐level plant growth, but a fundamental problem for these models is simulating fine‐scale processes over large areas (Ratajczak et al , ; Wang et al , ; Fan et al , ). Recent efforts are improving fine‐scale simulations of soil water availability and predictions of vegetation water stress on the landscape (Schlaepfer et al , ; Guo et al , ; Tai et al , ).…”