To obtain any of these toolboxes/softwares, please refer to their individual pages or email email@example.com.
(MvCAT) is developed in matlab as a user-friendly toolbox (software) to help scientists and researchers perform rigorous and comprehensive multivariate dependence analysis. It uses 26 copula families with 1 to 3 degrees of freedom to create joint probability distributions from two interdependent random variables. It uses local optimization and/or Markov chain Monte Carlo simulation within a Bayesian framework to infer the parameter values of the copula families by contrasting them against available data. If Bayesian analysis with MCMC simulation is performed, an estimate of uncertainty for each copula family can be provided from the posterior distribution of copula parameters. MCMC within Bayesian framework not only provide a robust estimate of the global optima, but also approximate the posterior distribution of the copula families which can be used to construct a prediction uncertainty range for the copulas. Local optimization methods are prone to getting trapped in local optima.
User is able to choose any subset of the available 26 copula families, and MvCAT will perform the analysis and rank the selected copula families based on their performance. Performance metrics used in this toolbox are Likelihood, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Nash-Sutcliffe Efficiency (NSE), and Root Mean Squared Error (RMSE). While Likelihood, NSE and RMSE only focus on minimizing the residuals between observations and model simulations, other metrics take into consideration additional criteria. For example, AIC takes into account the model complexity and BIC account for model complexity and number of observations.
(NEWF) toolbox is developed to convert a selected amount of one element in the nexus to the other two at a global scale and the country level, if data availability allows. We strived to provide country specific data for this computation, but this is not warranted for all conversions depending on data availability. Data that is used in this study are obtained from FAO, World Bank, and USDA, among others. NEWF toolbox is user friendly and interactive. The orange boxes and drop downs are the ones that user needs to provide, and white boxes print the outputs of the toolbox analysis.
(NCRRT) is designed to allow for time variant modeling of rainfall runoff processes. It allows user to define one (and only one) parameter of the selected conceptual model (available models: GR4J, GR5J, GR6J, HyMod, HBV, SACSMA) time variant within any time interval to address the physical changes in the actual watershed. Time-variant parameter is changed linearly within the specified time interval, and can be defined as step change if the start and date of the interval are set identical. In this version (as of 01/05/2017) user should specify a calibration period (data set should allow for 365 days of spin up) that is assumed to be stationary (no changes in the properties of the watershed). This allows for estimation of model parameters by calibration against the available calibration data. Parameter estimation in this version is performed using a local optimization approach, in observation of the running time. We want this toolbox to be interactive and fast. It is assumed that these parameter value remain consistent for different time periods (might be violated, but pragmatic).
(MCAL) Toolbox is designed to perform Bayesian and approximate Bayesian analysis using Markov chain Monte Carlo simulation with differential evolution update. Formal Bayesian approach employs a traditional residual-based Gaussian likelihood function, and approximate Bayesian analysis allows for selection of one or multiple pre-specified summary statistic(s). The current version (as of 01/06/2017) of the toolbox only allows to choose among the provided conceptual rainfall runoff (RR) models (GR4J, GR5J, GR6J, HyMod, HBV, SACSMA). Future versions of MCAL will allow user to select any model (hydrologic or beyond) and any summary metric. Please contact firstname.lastname@example.org if you need the flexible version.
ClimatIndices toolbox compute several dozen climate indices (metrics) for two (climatic) period to analyze how the system response has changed from period one to period two in terms of the analyzed metrics. The 150+ (nonunique) provided metrics address magnitude, frequency, duration, timing and rate of changes in the desired variable (streamflow or what not).