Modeling Shared Water Rights in Reservoir Systems

 Posted by Jason Lillywhite

Managing water in the Western United States is a complex challenge, especially in shared reservoir systems governed by the "prior appropriation" doctrine. This legal framework determines who gets water, and when, based on their legal rights and the ever-changing natural water supply. Dynamic simulation is a tool that enables you to better understand this type of system and visualize its behavior, leading to clearer insights and more reliable predictions.

his guide outlines a methodology for simulating shared water rights within a reservoir using dynamic simulation, applicable to various modeling environments. While the concepts are demonstrated using an example model built in GoldSim (with full details and the model available here), the principles are broadly transferable to other platforms, such as Python.

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Conceptualizing the Reservoir Model

The main part of the model represents a single physical reservoir broken up into a series of virtual "accounts." Each account stands for a different water right holder or purpose, and they're stacked in order of priority:

• Dead Pool: The absolute base, ensuring minimum operations. It gets water first and holds onto it longest.
• Senior Account: Represents an older, high-priority right (like for established farms).
• Junior Account: A more recent right (perhaps for growing cities).
• Conservation Account: For environmental needs or simply as the "overflow" bucket for any excess water the reservoir can hold.

Representation of the storage accounts.

These accounts actively track their own volumes and maximum capacities, giving us a clear picture of who "owns" what within the shared reservoir at any moment.

When new water comes in, it must be allocated to each account based on its priority. Also, common losses like evaporation need to be fairly shared among all accounts. The model requires that senior rights get their water first, which often means junior accounts have to curtail deliveries or even give up some of their stored water to satisfy those with older claims. 

Incorporating Water Rights Rules into the Simulation

Here are useful modeling techniques for simulating shared water rights in a reservoir:

Cascading Bucket Filling

To implement the "first in time, first in right" principle for water allocation, incoming water is sequentially allocated:

1. The highest priority account is filled first.

2. Once an account reaches its capacity, any surplus water "spills over" to the next highest priority account.

3. This process continues down the hierarchy to the most junior account.

This cascading mechanism is typically repeated daily to ensure continuous prioritization of water distribution based on established water rights.

Senior Right Fulfillment

In scenarios where a higher-priority account experiences a storage deficit, the model must facilitate water transfers from junior accounts to satisfy that senior account's requirements. This simulates real-world water transfer agreements, where junior users might temporarily hold water that can be "called upon" by senior users when needed.

Screen capture of the shared water rights GoldSim model

Managing Shared Losses (Evaporation)

Reservoirs incur water losses due to evaporation. To accurately model this, calculate the total evaporation for the entire reservoir. Then, proportionally distribute this loss among all active accounts based on their current water holdings. This ensures that all water right holders share equitably in the losses.

Screen capture of the evaporation loss sharing component

Addressing Dynamic Interdependence

Dynamic simulation models can sometimes encounter "loops" where calculations are interdependent, potentially leading to computational instability. To ensure the model runs smoothly and sequentially, especially with daily timesteps, "Material Delay" elements can be employed. For example, an overflow from one account might be processed for the next day's calculation for the account below it. For large reservoirs and daily time steps, this minor delay has a minimal impact on overall results while enabling accurate long-term water allocation simulations.

Modeling Water Delivery and Demand

Each account withdraws water from its allocated "bucket" to meet its specific demands. Due to the priority rules for filling and senior right fulfillment, junior accounts are inherently more susceptible to water scarcity. Simulation results typically show that conservation flows, often the most junior, are the last to commence and the first to cease.

It's important to note that a junior account might, at times, exhibit a larger outflow than a senior one. This phenomenon reflects the "use it or lose it" principle prevalent in prior appropriation, which incentivizes right holders to utilize their water for a beneficial purpose (even if that purpose is a specific environmental flow) to maintain their water right.

Main Result plots showing account volumes and deliveries

The Utility of Dynamic Simulation

By explicitly tracking ownership and applying legal rules to day-to-day operational decisions, dynamic simulation clarifies how water rights translate into practical water management. The flexibility in dynamic simulation platforms for defining components and their interactions over time makes them particularly well-suited for constructing such water allocation models.

We encourage you to explore the full model and its documentation on our Knowledge Base: Water Rights in Shared Reservoirs – GoldSim Help Center

Updated Mine Water Balance Model

 Posted by Jason Lillywhite

Our conceptual Mine Water Balance model, built in GoldSim, has been updated to take advantage of the latest software features and align it with to our own Style Recommendations. This model, designed as an educational template rather than a site-specific solution, demonstrates the interconnectedness of key facilities such as a Process Water Dam (PWD), a secondary buffer dam (Dam 2), a Tailings Storage Facility (TSF), and the Process Plant.

A glimpse into the simulated mine water system.

This updated version includes a re-organization of model elements to provide a clearer and more intuitive visualization of the water balance pathways throughout the simulated mine site. To build confidence in simulation outputs, a mass balance validation test has been integrated, allowing users to verify the conservation of water across the system. The accompanying model documentation has been updated, offering more detailed explanations of the model's logic, components, and operational assumptions. Finally, the dashboards have been refreshed to demonstrate how to create a "navigation tabs" feel.

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.

An evaluation of scenarios for a radioactive repository using GoldSim

  Posted by        Jason Lillywhite

Safely isolating high-level radioactive waste for millennia requires more than just deep geological disposal; it relies on a "multi-barrier" approach. This involves several layers of protection designed to contain the hazardous waste. Key components are the "engineered barriers," which are man-made structures placed within the repository. These can include the waste's immediate disposal container, the surrounding structure often called a silo, and robust materials like concrete used extensively within the repository environment. These work together with the natural geological barrier (the surrounding rock) to prevent or slow the release of radioactive materials.

Today, I want to showcase a study evaluating scenarios for a radioactive waste repository using GoldSim. This study was presented at the recent GoldSim 2024 User Conference. It looks at the long-term durability of concrete and how its deterioration might affect the overall safety of these facilities over vast timescales.

This study, by researchers at Chosun University, explores how dynamic, probabilistic simulation can help predict the performance of these engineered barriers. Ensuring the long-term safety of high-level radioactive waste disposal requires understanding complex phenomena, and GoldSim provides a powerful way to evaluate potential scenarios.

Here's a visual representation of the kind of post-closure scenario they're modeling, showing how groundwater might interact with the repository barriers:

Figure 1: Disposal facility post-closure performance modeling evaluation scenario

Read on to explore how they combined experimental data with GoldSim modeling to assess the impact of concrete degradation.

Using Conditional Containers to Simulate Crop Growing Seasons

 Posted by Jason Lillywhite

Modeling sequential, time-dependent processes lies at the heart of many dynamic simulations. But what happens when the duration of each step is uncertain or changing during the simulation? Consider simulating crop growth stages based on the FAO Irrigation and Drainage paper 56 for modeling crop water demand. Accurately capturing the variability in crop stage durations is important, and ensuring stages trigger correctly using traditional conditional logic can become complex and error-prone under uncertain conditions.  A previous version of the model described here that I built years ago relied heavily on nested IF statements to manage stage transitions. I was always bothered by this implementation, knowing that if the precise duration of each stage wasn't fixed at the simulation start, dynamically ensuring the correct sequence could become extremely difficult to implement reliably.

Screen Capture of the Crop Growing Season Scheduler using Conditional Containers

This post explores how GoldSim's Conditional Containers provide an improved solution. I'll walk through the new version where each growth stage resides in its own Container, dynamically triggered by the completion of the previous one (as shown in the model structure pictured). Discover how this approach not only simplifies the representation of sequential logic but also seamlessly integrates stochastic durations, leading to a more robust, understandable, and maintainable model for Monte Carlo analysis. Read on to see this powerful technique in action! 

GoldSim as a Predictive Tool for Oil Sands Mining Operations

Posted by  Jason Lillywhite

We are pleased to share insights from a recent presentation by Candace Whitten, GIT and Matthew Ryans, P.Eng from WSP.  This work was presented at the 2024 GoldSim User Conference. 

Oil sands mining operations generate multiple tailings types requiring various treatment methods, storage components, and time for tailings maturation. GoldSim was employed to develop a dynamic material mass balance model simulating future tailings production and treatment alternatives.

Figure 1 - Schematic diagram of bitumen extraction and tailings storage and treatment

The model provided insights into the production of coarse and fine solids, informed by ore grade and production schedules. It identified high-sensitivity parameters and ensured compliance with site-specific thresholds, as established by Directive 085. This compliance guarantees that the modeled treatment technologies are sufficient for managing fluid tailings and that there is no net growth of fluid tailings beyond the life of mine (LOM) production. Additionally, the model offered insights into optimizing tailings management to minimize environmental impact and support sustainable mining practices. 

Physically Based vs. Feedback Control Models: Choosing the Right Approach for Water System Simulation

 Posted by  Jason Lillywhite

We’ve often been asked about the best way to simulate flow between two tanks: should it be modeled physically, based on fluid mechanics, or actively controlled using a feedback mechanism? With the release of GoldSim 15 and its new Controller element, now is a good time to compare these approaches and perhaps even combine them. In this post, we’ll examine the performance of our existing physically based model (Flow Between Two Tanks) alongside a feedback-controlled version using the Controller element. By comparing these methods, we’ll explore their strengths, limitations, and when one approach might be preferable over the other.

Building Better Water Balance Models for Tailings and Mine Rock Stockpiles

Posted by  Jason Lillywhite

We are pleased to share insights from a recent presentation by Tony Zheng (University of Alberta Geotechnical Centre / OKane Consultants), Rebecca Hurtubise (AECOM), and Nicholas Beier (University of Alberta Geotechnical Centre).  This work was presented at the 2024 GoldSim User Conference. Their work focuses on building better water balance models for tailings and mine rock stockpiles, addressing critical challenges in mining operations.

In this blog post, you'll discover how physics-based models developed in GoldSim can provide insights into water balance, acid rock drainage (ARD) risks, and effective closure strategies. These advanced modeling techniques offer actionable knowledge to improve mine closure practices and ensure long-term environmental sustainability.

Read on to explore their innovative approaches and practical applications in the field of mining hydrology and geotechnical engineering.