A Calibration‐Free Groundwater Module for Improving Predictions of Low Flows. Issue 3 (21st March 2022)

• Record Type: Journal Article
• Authors: Tashie, Arik; Pavelsky, Tamlin; Kumar, Mukesh
• Is Part Of: Water resources research. Volume 58:Issue 3(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-03-21
• Subjects: low flow -- groundwater -- land surface model -- ungauged -- calibration -- hydrological model; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2021WR030800
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: Groundwater modules are critically important to the simulation of low flows in physically based land surface models (LSMs) and conceptual rainfall‐runoff models (HBV). Here, we develop a Groundwater for Ungauged Basins (GrUB) module that uses only physically based properties for which data are widely available, thus allowing its application without the need for calibration. GrUB is designed to be computationally simple and readily adaptable to a wide variety of LSMs and rainfall‐runoff models. We assess the performance of GrUB in 84 United States watersheds by incorporating it into HBV, a popular rainfall‐runoff model. We compare predictions of low flows by the native (calibrated) HBV groundwater module with those by the (uncalibrated) GrUB module and find that GrUB generates error metrics that are equivalent or superior to those generated by the (calibrated) HBV groundwater module. To assess whether predictions by GrUB are robust to changes in the structure and parameterization of the overlying hydrologic model, we run tests for two artificial scenarios: Slow Recharge with rates of percolation below 0.1 mm/day, and Fast Recharge with rates of percolation of up to 1, 000 mm/day. GrUB proves to be robust to these extreme changes, with mean absolute error (MAE) of predictions of low flows only increasing by an average of up to 17%, while average MAE increases by up to 158% when the same tests are performed on HBV without the GrUB module. We suggest GrUB as a potential tool for improving predictions of low flows in LSMs, as well as rainfall‐runoff models when calibration data are sparse. Key Points: Develop a calibration‐free groundwater/low flow module that is compatible with land surface models and rainfall‐runoff models Without calibration, the module's performance compares favorably with an existing, fully calibrated alternative Module predictions of low flows are robust to drastic changes in the overlying hydrologic model