Modeling Spatially Correlated Rainfall in GoldSim

 Posted by Jason Lillywhite

Effective water resource management hinges on accurately modeling precipitation. But what happens when rainfall patterns differ significantly between 2 locations within your study area. For example, precipitation on a valley floor compared to the mountainous watershed nearby? This post explores a practical method using GoldSim to simulate precipitation that is linked, or spatially correlated, across different locations. 

We'll use real-world daily rainfall data from two distinct sites in Utah to demonstrate how to set up and parameterize such a model. Read on to see how rainfall correlation between these valley and mountain locations led to more realistic hydrological simulations in GoldSim.

Testing PrecipGen Across Four Sites

Posted by  Jason Lillywhite

This blog post presents the results of testing PrecipGen across four diverse locations to evaluate its performance:

• Australia Plains, AU
• Grand Junction Walker Field, CO, USA
• Dublin, EI
• Orlando, FL, USA

The findings demonstrate that PrecipGen successfully represents both short- and long-term daily precipitation totals at individual locations, validating its ability to replicate observed climatic behavior across varied environments.

To read more about the PrecipGen model and how to use it, please refer to the GoldSim Library where you can read about it and download PrecipGen PAR and PrecipGen for your own use: Precipitation Simulator (PrecipGen) – GoldSim Help Center

Simulating Precipitation with Long-Term Droughts

Posted by Jason Lillywhite

One of GoldSim's most popular models downloaded from our Model Library is the GoldSim implementation of a Stochastic Weather Generator (WGEN). This model simulates probabilistic climate that drives hydrological processes in environmental systems models, such as precipitation, temperature and solar radiation.

One of the limitations of this model is that the generated precipitation time series generated by the model doesn't account for long-term drought cycles. Using the National Integrated Drought Information System (by NOAA), we can apply the Standardized Precipitation Index (SPI) to precipitation generated by WGEN to account for droughts. This blog post summarizes an approach used to incorporate droughts for a site during the years 1910 - 2022 and found to produce results that compare well to the historic precipitation measured at a site in Manti, Utah.

Combining a Weather Forecast with a Stochastic Weather Generator

Posted by Jason Lillywhite

A couple of years ago, I blogged about the application of the GoldSim WGEN model for producing probabilistic time histories of precipitation and temperature data. WGEN is useful for modeling natural systems impacted by uncertain climate influences. This model can also be used for simulating future scenarios and forecasting.

Even though the results of a WGEN model are probabilistic and uncertain, they are based on historical observed records and tied to the latitude of the earth. This allows us to produce realistic future simulations. But if you plan to use WGEN for short-term forecasting (less than 6 months), it might be helpful to incorporate a weather forecast for the first part of the simulation. Read more to see how a web service forecast model is combined with WGEN to produce a more reasonable forecast for a specific location without having to manually download the forecast.

Reservoir Inflow Forecasting

Posted by Jason Lillywhite

Last month, at the annual symposium hosted by the Arizona Hydrologic Society, I presented on probabilistic reservoir forecasting using GoldSim. This model combines many existing components available in our library to forecast snowmelt driven runoff inflows to a reservoir and estimates risk of spills and/or shortages.

Application of the Markov Process Rainfall Model

Posted by Jason Lillywhite

If you have visited our Model Library lately, you might have noticed that we have a nice little example model that demonstrates the use of a Markov process to simulate daily rainfall. You need to specify some key statistical inputs that have some basis on historic data. How do you develop these inputs? How do you know if the Markov model is realistic? I thought it would be helpful to show how this simple example might be applied in a real-world project and try to answer those questions.

*Note that I made changes to the results on 5/20/2016 after I used GoldSim's optimization function to better calibrate the rate variability.