Can artificial intelligence and data-driven machine learning models match or even replace process-driven hydrologic models for streamflow simulation?: A case study of four watersheds with different hydro-climatic regions across the CONUS

Kim, Taereem; Yang, Tiantian; Gao, Shang; Zhang, Lujun; Ding, Ziyu; Wen, Xin; Gourley, Jonathan J.; Hong, Yang

doi:10.1016/j.jhydrol.2021.126423

Can artificial intelligence and data-driven machine learning models match or even replace process-driven hydrologic models for streamflow simulation?: A case study of four watersheds with different hydro-climatic regions across the CONUS

Journal Article · Fri May 07 00:00:00 EDT 2021 · Journal of Hydrology

DOI:https://doi.org/10.1016/j.jhydrol.2021.126423· OSTI ID:1850270

Kim, Taereem ^[1]; Yang, Tiantian ^[2]; Gao, Shang ^[2]; Zhang, Lujun ^[2]; Ding, Ziyu ^[3]; Wen, Xin ^[3]; ^[4]; Hong, Yang ^[2]

University of Oklahoma, Norman, OK (United States); OSTI
University of Oklahoma, Norman, OK (United States)
Hohai University, Nanjing (China)
National Oceanic and Atmospheric Administration (NOAA), Norman, OK (United States). National Severe Storms Laboratory (NSSL)

With recent developments in computational techniques, Data-driven Machine Learning Models (DMLs) have shown great potential in simulating streamflow and capturing the rainfall-runoff relationship in given watersheds, which are traditionally fulfilled by Process-based Hydrologic Models (PHMs). There are debates on whether the DMLs can outperform and possibly replace the classical PHMs for streamflow simulation and river forecasting, but no clear conclusions have been made. This study aims to investigate whether the newer DMLs have any potential in further improving the simulation accuracy of classical PHMs, and vice versa. To do this, we compared a few popular PHMs and DMLs over four watersheds across the Continental US (CONUS) that are associated with different input, climate, and regional conditions. A total of five hydrologic models were chosen, including (1) two classical lumped models, i.e., the Sacramento Soil Moisture Accounting (SAC-SMA) and Xinanjiang (XAJ); (2) one modern distributed model, termed Coupled Routing and Excess Storage (CREST); (3) and two DMLs including an Artificial Neural Networks (ANN) and a deep learning model, termed Long Short Term Memory (LSTM). Our results demonstrated that the DMLs still significantly biased when using the baseline input scenario with the PHMs. However, the DMLs fed with delayed input scenarios had great potential and can reach high simulation accuracy. The DMLs, especially the ANN, outperformed other employed models under the rainfall-runoff relationship in which rainfall dominantly drives. Furthermore, the DMLs also showed better performance in the high-flow regime, while the PHMs had a better performance for the low-flow regime, implying both PHMs and DMLs have their own merits and are worthy of joint development. In general, our study indicated a great potential of using DMLs to simulate streamflow, but further studies are still needed to verify the transferability and scalability of DMLs in large-scale experiments, such as the Distributed Model Intercomparison Projects 1&2 conducted by National Weather Services but to compare modern DMLs and PHMs.

View Accepted Manuscript (DOE)

Research Organization:: University of California, Oakland, CA (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE; USDOE Office of International Affairs (IA)

Grant/Contract Number:: IA0000018

OSTI ID:: 1850270

Alternate ID(s):: OSTI ID: 1868679

Journal Information:: Journal of Hydrology, Journal Name: Journal of Hydrology Journal Issue: C Vol. 598; ISSN 0022-1694

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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