ABSTRACT:

This methodology employs linear regression to combine the outputs of simple hydrological models to simulate hydrological responses. Two kinds of simple hydrological models are employed. The first one represents the characteristics of the streamflow attributed to overland flow, and the second the characteristics of the streamflow attributed to interflow and base flow. The provided code can be run using either MATLAB or Octave. The theoretical background can be found in Rozos (2020),

Evangelos Rozos (2020) A methodology for simple and fast streamflow modelling, Hydrological Sciences Journal, DOI: 10.1080/02626667.2020.1728475

ABSTRACT:

This is an image velocimetry tool based on LSPIV methodology. This tool is able of applying image velocimetry directly on videos. It runs on MATLAB (compatible also with Octave version ≥ 5.1).
Please cite [1,2] for the Free-LSPIV algorithm. Details for the LoussiosUpstream and LoussiosDownstream projects can be found in [3].

QUICK START

- Start MATLAB/Octave and go into the folder of the normxcorr2_mex file.
- _Notes.m gives commands to perform (do not run it, copy paste only).
- Slightly different commands for MATLAB/Octave, see comments in _Notes.m.

REFERENCES

1. Rozos, E., Dimitriadis, P., Mazi, K., Lykoudis, S. and Koussis, A., 2020. On the Uncertainty of the Image Velocimetry Method Parameters. Hydrology, 7(3), p.65.

2. Rozos E, Mazi K, Koussis AD. Probabilistic Evaluation and Filtering of Image Velocimetry Measurements. Water. 2021; 13(16):2206. https://doi.org/10.3390/w13162206

3. E. Rozos, K. Mazi, and S. Lykoudis, On the Accuracy of Particle Image Velocimetry with Citizen Videos- Five Typical Case Studies, Hydrology, doi:10.3390/hydrology9050072, 9(5), 72, 2022.

ABSTRACT:

This is a command-prompt tool that estimates the uncertainty of model runs based on simulations of the same model during a period with observations.

The program produces three files inside the path of the executable: Shigh.csv, Slow.csv and Smed.csv. These 1-column-csv files give the upper (Shigh.csv) and lower (Slow.csv) bounds of the 80% confidence intervals, and the median value. You can use these files to get plots like those displayed in Figures 2, 5, A3, A5 of [1]. For the theoretical background, please see [1].

1. E. Rozos, D. Koutsoyiannis, and A. Montanari, KNN vs. Bluecat - Machine Learning vs. Classical Statistics, Hydrology, doi:10.3390/hydrology9060101, 9(6), 101, 2022.

ABSTRACT:

Use this Colaboratory workbook (implements an RNN) to assess the simulations of hydrological models. The latest version of this workbook can be found at:
https://drive.google.com/file/d/1vdV01gI5vf_DP2NVMMHtYmhIDhZMvUOe/view?usp=share_link

Along with the workbook, an example data file (data.zip) is also provided. This data file contains three case studies. Set the variable values according to the following for each one of the three case studies of the data.zip.

# LRHM applied to Bakas
DATASTARTROW= 2581
INP= [ 7,13,16,18 ]
TRG= 1
PDT= 0.70
SEQLEN=10

# LRHM applied to Alagonia
DATASTARTROW= 3690
INP= [ 7,13,14,20 ]
TRG= 2
PDT= 0.70
SEQLEN=10

# LRHM applied to Karveliotis
DATASTARTROW= 2553
INP= [ 7,13,14,22 ]
TRG=3
PDT= 0.70
SEQLEN=10

For more information, see [1].

1. E. Rozos, P. Dimitriadis, and V. Bellos, Machine Learning in Assessing the Performance of Hydrological Models, Hydrology, doi:10.3390/hydrology9010005 , 9(1), 5, 2021.

ABSTRACT:

This notebook helps to process stage/discharge measurements, and obtain an approximation of the relationship between them. For more information, please see [1].

1. E. Rozos, J. Leandro, and D. Koutsoyiannis, Development of Rating Curves: Machine Learning vs. Statistical Methods, Hydrology, 9(10), 166, doi:10.3390/hydrology9100166 , 2022.

ABSTRACT:

This is a multivariate AR1 model written in MATLAB code (compatible with GNU Octave). More information can be found in Rozos et al. (2024).

E. Rozos, J. Leandro and D. Koutsoyiannis, Stochastic Analysis and Modeling of Velocity Observations in Turbulent Flows, Journal of Environmental & Earth Sciences , 6(1), doi:10.30564/jees.v6i1.6109, 2024.