LASH

DEVELOPMENT HISTORY

In its 1st version (MELLO et al., 2008) LASH was implemented in a semi-distributed way (sub-basins), in spreadsheet files, with some tests in a lumped way (basin). In this version, the calibration was carried out via the solver tool (KEMMER; KELLER, 2010), and required initial values very close to the reality of the model parameters for the optimization to be computed successfully. Furthermore, spreadsheets took up a lot of memory on computers in the decade, which caused delays in manipulating information.

In the 2nd version (BESKOW et al., 2011), the model was structured in the Delphi programming language through a Graphical User Interface (GUI), offering an interface for distributed modeling (grid), and had the coupling of the mono-objective genetic algorithm Shuffled Complex Evolution (SCE-UA) (DUAN; SOROOSHIAN; GUPTA, 1992) for automatic parameter calibration. The first two versions of the model enabled a more advanced study, mainly in relation to the spatial step of the modeling (lumped versus semi-distributed versus distributed).

In this way, the 3rd version (CALDEIRA et al., 2019) was implemented in a semi-distributed manner across sub-basins and no longer distributed, given the results presented in Caldeira et al. (2019). Furthermore, it was possible to explore two modules for processing temporal and spatial databases, these being, respectively, the System of Hydrological Data Acquisition and Analysis (SYHDA) and ArcLASH. In this version, the model was still in Delphi, but it was implemented in C++ only for computational testing, not resulting in versioning.

Development history of the Lavras Simulation of Hydrology (LASH) model based on some considerations, such as: development environment, spatial discretization, river routing method, optimization algorithm and main contributions derived from the model.

MOST RECENT

From the 3rd version onwards, LASH was no longer named according to the order of development, but was now named according to the programming environment in which it was developed, such as LASH in MATLAB® (M-LASH) (VARGAS et al., 2023). M-LASH brought very interesting computational and hydrological improvements to the model, which are:

• The improvement of the flow propagation module in rivers, which was previously considering the Linear Muskingum-Cunge method (CUNGE, 1969) (all versions) and started to consider the Kinematic Wave (LIGHTHILL; WHITHAM, 1955a, 1955b), inserting a more hydraulic component into the model and a calibration parameter (Manning’s n);

• Coupling to a multiobjective algorithm, A Genetically Adaptive Multiobjective Multi-Algorithm (AMALGAM) (VRUGT; ROBINSON, 2007);

• Significant improvement in LASH calibration time (about 40 times faster);

• The possibility, due to the calibration time, of testing the calibration of all parameters in a distributed way, something unprecedented in the model until then; example: for a basin with 47 sub-basins and the 7 M-LASH parameters, it was possible to calibrate 376 parameters in 35h, the time it would take to calibrate the same sub-basins in previous versions).

M-LASH was registered with the National Institute of Industrial Property (INPI) and is in the process of being disseminated for internal use in the Research Group on Hydrology and Hydrological Modeling in Watersheds/CNPq. The results obtained from this implementation give room to continue with the model’s exploration bias, mainly so that all professionals in the area of water resources, in addition to academia, can benefit from the model.

LASH references (chronological order):

Vargas, M. M. et al. M-LASH: Hydrological and computational enhancements of the LASH model. Environmental Modelling & Software, p. 105774, 2023.

Mello, C. R. et al. Climate change impacts on water resources of the largest hydropower plant reservoir in Southeast Brazil. Water, v. 13, n. 11, p. 1560, 2021.

Caldeira, T. L et al. LASH hydrological model: an analysis focused on spatial discretization. Catena, v. 173, p. 183-193, 2019.

Beskow, S et al. Performance of a distributed semi-conceptual hydrological model under tropical watershed conditions. Catena, v. 86, p. 160–171, 2011.

Mello, C. R. et al. Development and application of a simple hydrologic model simulation for a Brazilian headwater basin. Catena, v. 75, n. 3, p. 235-247, 2008.

General references:

Cunge, J. A. On the subject of a flood propagation computation method (Muskingum method). Journal of Hydraulic Research, v, 7, n, 2, p, 205-230, 1969.

Duan, Q. et al. Effective and Efficient Global Optimization for Conceptual Rainfall-Runoff Models. Water Resources Research, v. 28, n. 4, p. 1015-1031, 1992. DOI: 10.1029/91WR02985.

Kemmer, G; Keller, S. Nonlinear least-squares data fitting in Excel spreadsheets. Nature Protocols, v. 5, n. 2, p. 267-281, 2010.

Lighthill, M. J; Whitham, G. B. On kinematic waves I. Flood movement in long rivers. Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, v. 229, n. 1178, p. 281-316, 1955a. DOI: 10.1098/rspa.1955.0088.

Lighthill, M. J; Whitham, G. B. On kinematic waves II. A theory of traffic flow on long crowded roads. Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, v. 229, n. 1178, p. 317-345, 1955b. DOI: 10.1098/rspa.1955.0089.

Vrugt; J. A.; Robinson, B. A. Improved evolutionary optimization from genetically adaptive multimethod search. Proceedings of the National Academy of Sciences, v. 104, n. 3, p. 708-711, 2007. DOI: 10.1073/pnas.0610471104.