Second place: Improving Streamflow Predictions in the Arid Southwestern United States Through Understanding of Baseflow Generation Mechanisms.
Abstract by Farmani. Co-authors Ahmad Tavakoly, Ali Behrangi, Yuan Qiu, Aniket Gupta, Muhammad Jawad, Hossein Yousefi Sohi, Xueyan Zhang, Matthew Geheran, Guo-Yue Niu.
Understanding factors controlling baseflow (or groundwater discharge) is critical for improving streamflow prediction skills in the arid southwest U.S.
We used a version of Noah-MP with newly-advanced hydrology features and the Routing Application for Parallel computation of Discharge (RAPID) to investigate the impacts of uncertainties in representations of hydrological processes, soil hydraulic parameters, and precipitation data on baseflow production and streamflow prediction skill.
We conducted model experiments by combining different options of hydrological processes, hydraulic parameters, and precipitation datasets in the southwest U.S. These experiments were driven by three gridded precipitation products: the North American Land Data Assimilation System (NLDAS-2), the Integrated Multi-satellite Retrievals for GPM (IMERG) Final, and the NOAA Analysis of Record for Calibration (AORC).
RAPID was then used to route Noah-MP modeled surface and subsurface runoff to predict daily streamflow at 390 USGS gauges. We evaluated the modeled ratio of baseflow to total streamflow (or baseflow index, BFI) against those derived from the USGS streamflow.
Our results suggest that: 1) soil water retention curve model plays a dominant role, with the Van-Genuchten hydraulic scheme reducing the overestimated BFI produced by the Brooks-Corey (also used by the National Water Model, NWM); 2) hydraulic parameters strongly affect streamflow prediction, a machine learning-based dataset captures the USGS BFI, showing a better performance than the optimized NWM by a median KGE of 21%; and 3) the ponding depth threshold that increases infiltration is preferred.
Overall, most of our models with the advanced hydrology show a better performance in modeling BFI and thus a better skill in streamflow predictions than the optimized NWM in the dry southwestern river basins. These findings can guide future studies in selecting reliable schemes and datasets (before calibration) to achieve better streamflow predictions as well as water resource projections.
About the Student
Mohammad Farmani is a Ph.D. student in Hydrology and Atmospheric Sciences at the University of Arizona, with a minor in Data Science. His research advances groundwater hydrology by modeling subsurface flow and recharge across environments, from frozen soils to arid river basins. He has led Noah-MP and RAPID studies showing how hydraulic properties, retention models (e.g., van Genuchten), and preferential flow govern infiltration, soil-moisture memory, baseflow, and recharge—validated with SMAP/ISMN observations and hundreds of USGS gauges. Building on a mixed-form Richards implementation, he is developing a mathematically rigorous, ML-compatible, differentiable framework that replaces iterative solvers with learnable gate functions for capillary and gravity fluxes, enabling efficient calibration and uncertainty analysis. His goal is to translate these advances into robust, open-source tools that improve recharge estimation and streamflow forecasting for drought-resilient water management in the Southwest.
About the CAP Award for Water Research
The CAP Award for Water Research is intended to encourage and support water research by students in Arizona colleges and universities and to raise public awareness of water issues impacting central and southern Arizona and the Colorado River. Judging is conducted in a blind process by representatives of CAP’s popularly elected Board of Directors, CAP stakeholders and management, and other members of the water community. The first-place winner received $3,500 and second-place winners received $2,000. The research could drop a dent into the world of Arizona’s water supply research in the future.