The NWS River Forecast System - catchment modeling.

By R. J. C. Burnash, Vijay P. Singh. 1995

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Particle filtering and ensemble Kalman filtering for state updating with hydrological conceptual rainfall‐runoff models (2006, methodology): “Among these models are well-known conceptual models such as the Sacramento model [Burnash et al., 1973; Burnash, 1995] and the HBV-96 model [Lindstrom et al.”
High‐dimensional posterior exploration of hydrologic models using multiple‐try DREAM(ZS) and high‐performance computing (2012, methodology): “These tests include two known mathematical target distributions, and two real-world studies involving the calibration of the Sacramento Soil Moisture Accounting (SAC-SMA) model [ Burnash , 1995], and a 241-parameter groundwater model.”
Automatic calibration of a conceptual rainfall-runoff model using multiple objectives. (2000, methodology): “Examples of this type of model are the Sacramento model (Burnash, 1995), the Tank model (Sugawara, 1995), the HBV model (Bergström, 1995), and the MIKE 11/ NAM model (Nielsen and Hansen, 1973; Havnø et al., 1995).”
Generalized likelihood uncertainty estimation (GLUE) using adaptive Markov Chain Monte Carlo sampling (2008, methodology): “, 1995) and the Sacramento Soil Moisture Accounting Model (SAC-SMA: Burnash et al., 1973; Burnash, 1995).”
Comparing expert judgement and numerical criteria for hydrograph evaluation (2015, unknown): “Several daily lumped model structures were used in pure simulation mode (i.e. without any updating) to generate different hydrograph behaviours and shapes: GR4J (Perrin et al. 2003), and simple modified versions of Mordor (Garçon 1996, Mathevet 2005), Sacramento (Burnash 1995), SMAR (O’Connell et al”
A process‐based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model (2008, methodology): “[17] The HL-DHM is a hybrid conceptual-physical distributed watershed model consisting of the Sacramento soil moisture accounting model (SAC-SMA [Burnash et al., 1973; Burnash, 1995]) applied over a grid consisting of regular 4 4 km spatial elements, linked to a kinematic wave model for hill slope a”
Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets (2019, methodology): “These models are (i) SAC-SMA (Burnash et al., 1973; Burnash, 1995) coupled with the Snow-17 snow routine (Anderson, 1973), hereafter referred to as SAC-SMA, (ii) VIC (Liang et al., 1994), (iii) FUSE (Clark et al., 2008; Henn et al., 2008) (three different model structures, 900, 902, 904), (iv) HBV (”