The STOchastic Rainstorm Model (STORM)

This is a decision-support tool created to simulate individual rainstorms over a basin or region at high temporal and spatial resolution. This parsimonious model enables simulation of stationary climate based on historical data, or climate change based on step changes and/or trends in key climate variables. The tool is still under development, but can be downloaded from GitHub. The supporting documentation can be found in this paper:

Singer, M.B., Michaelides, K., Hobley, D.E.J. (2018); STORM 1.0: A simple, flexible, and parsimonious stochastic rainfall generator for simulating climate and climate change, Geoscientific Model Development, 11, 3713-3726, doi: 10.5194/gmd-11-3713-2018. pdf

and the following paper provides an application of the model:

Singer, M.B., Michaelides, K. (2017); Deciphering the expression of climate change within the Lower Colorado River basin by stochastic simulation of convective rainfall, Environmental Research Letters, 12:104011, doi: 10.1088/1748-9326/aa8e50. pdf

We are currently working on the next version of the model (STORM.v2), which will explicity capture the inherent relationships between rainfall intensity and duration, and it will include a front-end pre-processing code for creating input pdfs, as well as a PET calculator that accesses historical reanalysis climate data from ERA5.

The Inverse Barbour Model (ISO-Tool)

This tool is designed to enable users to characterize the oxygen isotopic ratio of source waters used by plants during distinct periods of growth. It employs an inversion of the Barbour model of isotopic fractionation within plants (building on Craig-Gordon theory) to convert a known oxygen isotope ratio in tree ring cellulose into the ratio of its source water, based on climatic and physiological input variables. The model includes a Monte Carlo method for characterizing the uncertainty in this back-calculated ratio. The tool is currently being reviewed for publication, but it can be downloaded from GitHub. The supporting documentation can be found in this paper:

*Sargeant, C.I., Singer, M.B., Vallet-Coulomb, C. (2019); Identification of Source-water Oxygen isotopes in trees Toolkit (ISO-Tool) for deciphering historical water use by forest trees, Water Resources Research, doi: 10.1029/2018WR024519. pdf

Water Balance Model for Drylands

This model is designed to address major shortcomings in the linkages between climate and the water balance within dryland regions. Specifically, this model incorporates spatially and temporally varying rainfall (via STORM–see above), partitioning of runoff and infiltration, transmission losses in dryland channels, and diffuse and focused groundwater recharge. The tool is still under development. It will be presented at the AGU Fall Meeting 2019 (Authors: Quichimbo, Cuthbert, Singer, Michaelides).

Flow and Sediment Transport in Channel Cross Sections

This simple code (in Excel and Matlab formats) is designed to make quick and straightforward calculations of flow and sediment transport in river channel cross sections. It can be used to develop synthetic stage discharge-rating curves or to estimate sediment movement for various hydrographs, or due to varying grain roughness, cross sectional shape, and/or river gradient.

HYDROCARLO

This Matlab code simulates stochastic river flows through a network. The details and application to sediment transport and river rehabilitation can be found here:

Singer, M.B., Dunne, T. (2004); An empirical-stochastic, event-based program for simulating inflow from a tributary network: Framework and application to the Sacramento River basin, California. Water Resources Research, 40, W07506, doi: 10.1029/2003WR002725. pdf

Singer, M.B., Dunne, T. (2004); Modeling decadal bed-material sediment flux based on stochastic hydrology. Water Resources Research, 40, W03302, doi: 10.1029/2003WR002723. pdf

Singer, M.B., Dunne, T. (2006); Modeling the influence of river rehabilitation scenarios on bed material sediment flux in a large river over decadal timescales. Water Resources Research, 42, W12415, doi: 10.1029/2006WR004894. pdf

Time Series Simulation Framework for Relating River Flow and Sediment Concentration

This Matlab code estimates the relationships between sediment concentration in a river to the driving flow in a time series sense, capturing the time dependency in these relationships. The details can be found here:

Singer, M.B., Dunne, T.(2001); Identifying eroding and depositional reaches of valley by analysis of suspended-sediment transport in the Sacramento River, California. Water Resources Research, 37(12):3371-3382, doi: 10.1029/2001WR000457. pdf