Tools to Download
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MATLAB codes to compute hydrological model skill score (or ONYUTHA Efficiency) and Revised R-squared
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The Matlab codes which can be downloaded by clicking HERE compute a hydrological model skill score (or Onyutha Efficiency) and Revised R-squared which were introduced to address the various issues known regarding the use of coefficient of determination (R-squared). The published paper can be found via https://doi.org/10.2166/nh.2021.071
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HMSV
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HMSV stands for Hydrological Model focusing on Sub-flows' Variation. Most hydrological models have fixed structures and their calibrations are typified by a conventional approach in which the overall water balance closure is considered (without a step-wise focus on sub-flows' variation). Eventually, hydrological modelers are confronted with the difficulty of ensuring both the observed high flows and low flows are accurately reproduced in a single calibration. Calibration of the HMSV follows a carefully designed framework comprising sub-flow's separation, modeling of sub-flows, and checking validity of hydrological extremes.
The paper in which the HMSV was introduced comprises several new techniques for hydrological data analyses including
Baseflow separation,
Extraction of peak-over-threshold events, and
calibration framework for simultaneously reproducing high flows and low flows.
The MATLAB-based tool can be downloaded by clicking HMSV.zip
The Ms Excel-based HMSV can be downloaded by clicking HMSV_v.1.xlsm
Details of the introduced model and approaches can be obtained from the paper below:
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SNIPE
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SNIPE (which can be obtained by clicking SNIPE) stands for Standardized Non-parametric Indices based Precipitation and Evapotranspiration.
SNIPEs can be used to assess dry and wet hydro-meteorological conditions because they are not (or insignificantly) skewed. Details of this approach can be obtained from the paper below:
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CSD-VAT
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This tool which can be downloaded by clicking CSD_VAT_v.2.zip employs trend tests to sub-series using the approach based on the Cumulative Sum of Difference (CSD) between exceedance and non-exceedance counts of data points.
Conventionally, trend analysis comprises testing the null hypothesis H0 (no trend) by applying the Mann–Kendall or Spearman’s rho test to the entire time series. This leads to lack of information about hidden short-durational increasing or decreasing trends (or sub-trends) in the data. To make use sub-trends, CSD-VAT was developed for analyses of trends and variability in climatic time series. The zipped folder contains the CSD-VAT with example data as well as supporting document for the reader or user.
To get details on the CSD-based procedure of variability, click on the link to the publication below
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CSD-STAT
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This tool which can be downloaded by clicking CSD_STAT.zip is for seasonal trend analyses on the Cumulative Sum of Difference (CSD) between exceedance and non-exceedance counts of data points. Details on the method implemented in the CSD-STAT can be obtained by clicking on the link below to dowload the paper
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FAN-Stat
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The zipped folder comprises an Ms. Excel-based tool and user guide. DOWNLOAD FAN-Stat
The paper on the basis of which the tool was developed can be freely downloaded from the link below.
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CSD-NAIM_v.3
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This tool (which can be downloaded by clicking CSD-NAIM_v.3 ) comprises trend test based on the Cumulative Sum of Difference (CSD) between exceedance and non-exceedance counts of data points. The Non-parametric Anomaly Indicator Method (NAIM) is for variability analyses. In the CSD-NAIM, diagnoses of trends are done graphically followed by statistical analyses. Below is a summary of what the tool entails.
NAIM is applied to analyze the anomalies in the cyclical component of the series. The convolution is performed while ensuring no loss of information (on anomalies) in time.
Rigorous procedures are incorporated for the graphical diagnoses of changes in the series. Some of the plots include:
a) Rescaled Series Plot: to identify if the trend is generally linear or nonlinear,
b) CSD Plot: to directly separate periods over which the events are clustered above or below the long-term mean of the variable,
c) Trend Evolution Plots (TEPs): four ways to identify sub-trends by applying moving windows slid in backward and forward ways. TEPS can be used to rigorously test the null hypothesis H0 (all the sub-trends are due to natural randomness). TEPs are applied while correcting CSD test statistic test variance from the influence of long-term persistence on trend in the sub-trends.
d) Nonparametric Anomaly Plot: to assess the significance of the variability in the nonlinear component of the series. This is performed following the fact that alongside an apparent linear trend, variations in the nonlinear (e.g. cyclical) component of the series may also not be due to natural randomness.
The details of the systematic methodology of each approach implemented in the CSD-NAIM_v.3 can be found in the below publications:
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