St. Johns River Water Management District Research Articles

Your Farm as a Water Storage System: Steps to Establish an Agreement with the Water Management Districts

This publication introduces water storage strategies known as Dispersed Water Management (DWM) and Water Farms (WF) and describes the process that a private landowner should follow to obtain a permit and begin a water storage agreement with the South Florida Water Management District (SFWMD) and the St. Johns River Water Management District (SJRWMD). Written by Brandon Quinn-Ivey and Sandra M. Guzmán, and published by the UF/IFAS Department of Agricultural and Biological Engineering, September 2022.

Minimum Flows and Levels Method of the St. Johns River Water Management District, Florida, USA

The St. Johns River Water Management District (SJRWMD) has developed a minimum flows and levels (MFLs) method that has been applied to rivers, lakes, wetlands, and springs. The method is primarily focused on ecological protection to ensure systems meet or exceed minimum eco-hydrologic requirements. MFLs are not calculated from past hydrology. Information from elevation transects is typically used to determine MFLs. Multiple MFLs define a minimum hydrologic regime to ensure that high, intermediate, and low hydrologic conditions are protected. MFLs are often expressed as statistics of long-term hydrology incorporating magnitude (flow and/or level), duration (days), and return interval (years). Timing and rates of change, the two other critical hydrologic components, should be sufficiently natural. The method is an event-based, non-equilibrium approach. The method is used in a regulatory water management framework to ensure that surface and groundwater withdrawals do not cause significant harm to the water resources and ecology of the above referenced system types. MFLs are implemented with hydrologic water budget models that simulate long-term system hydrology. The method enables a priori hydrologic assessments that include the cumulative effects of water withdrawals. Additionally, the method can be used to evaluate management options for systems that may be over-allocated or for eco-hydrologic restoration projects. The method can be used outside of the SJRWMD. However, the goals, criteria, and indicators of protection used to establish MFLs are system-dependent. Development of regionally important criteria and indicators of protection may be required prior to use elsewhere.

Assessing redesigned effectiveness of the water quality monitoring program in the Indian River Lagoon, Florida

Abstract In 1996, the long‐term water quality monitoring network (WQMN) program in the Indian River Lagoon (IRL) of the St. Johns River Water Management District (SJRWMD) that began in 1988 was redesigned to eliminate statistically redundant sampling and satisfy modeling requirements. Methods, procedures, and equipment were standardized across multi‐agency monitoring partners to produce reliable and comparable data. The redesigned strategy also included elements to determine previously unknown quantities such as differences in water quality parameters with depth and tidal cycle. Reduction of sampling sites from 150 to 24 resulted in a significant improvement of standard deviation (t‐test; α = 0.05) for most water quality parameters analysed. The enhancement in data quality has demonstrated a more cost‐effective and efficient monitoring tool to measure the water quality of the seagrass environment in the IRL. Results supported the view of a well‐mixed condition at the sampling stations on most days. Refocusing and streamlining the water quality monitoring program within the context of its mission would enable conversion of data into meaningful information regarding the interrelationship among water quality, photosynthetically active radiation (PAR), and seagrass PAR requirements. Copyright © 2003 John Wiley & Sons, Ltd.

Model erroneously predicts failure for restoration of Lake Apopka, a hypereutrophic, subtropical lake

A recent paper (Bachmann et al., 1999) based on analysis of literature data predicts failure for restoration plans implemented by the St. Johns River Water Management District (SJRWMD) for Lake Apopka, a large (125 km2), shallow (mean depth=1.63 m), polymictic lake in central Florida. A marsh flow-way designed to remove particulates from inflowing water from Lake Apopka as a means to improve down-stream and lake-water quality is part of the restoration plan. According to Bachmann et al. (1999), restoration “will be ineffective in removing particles from the lake” and fail because estimated time for removal of 2.21 million tons of historic sediments in this constructed marsh flow-way is 329 years. This inadvertently proposed mining hypothesis given as a condition for successful restoration is based on questionable assumptions, i.e. removing all the historic sediments deposited over a 50-year period with the flow-way and exporting concurrent sedimentation while historic sediments are being removed. These questionable assumptions are used in an erroneous, discontinuous model of sediment dynamics that does not justify the alternate approach for restoration of Lake Apopka suggested in their paper.

ESTIMATING NONPOINT SOURCE POLLUTION LOADS WITH A GIS SCREENING MODEL1

ABSTRACT: The St. Johns River Water Management District (SJR‐WMD) is using a Geographic Information System (GIS) screening model to estimate annual nonpoint source pollution loads to surface waters and determine nonpoint source pollution problem areas within the SJRWMD. The model is a significant improvement over current practice because it is contained entirely within the district's GIS software, resulting in greater flexibility and efficiency, and useful visualization capabilities. Model inputs consist of five spatial data layers, runoff coefficients, mean runoff concentrations, and stormwater treatment efficiencies. The spatial data layers are: existing land use, future land use, soils, rainfall, and hydrologic boundaries. These data layers are processed using the analytical capabilities of a cell‐based GIS. Model output consists of seven spatial data layers: runoff, total nitrogen, total phosphorous, suspended solids, biochemical oxygen demand, lead, and zinc. Model output can be examined visually or summarized numerically by drainage basin. Results are reported for only one of the SJRWMD's ten major drainage basins, the lower St. Johns River basin. The model was created to serve a major planning effort at the SJRWMD; results are being actively used to address nonpoint source pollution problems.

Forecasting piezometric head levels in the Floridan Aquifer: A Kalman Filtering Approach

A Kalman filtering algorithm is developed to forecast groundwater levels in the Upper Floridan aquifer throughout the St. Johns River Water Management District (SJRWMD) in Florida. The algorithm processes historic and currently available head measurements to make optimal predictions of future head levels over a grid of 554 wells spanning the SJRWMD. Measurements are obtained monthly from a subset of 20 wells and semiannually from the remaining wells. The Kalman filter incorporates an empirical spatiotemporal model of regional groundwater fluctuations derived from long‐term historical data records at the 20 monthly measured wells. The algorithm (1) extrapolates the measurements provided by the 20 monthly measured wells to estimate monthly head levels at all 554 wells in the grid and (2) predicts future head levels at each well in the absence of measurements. The performance of the Kalman filtering algorithm is assessed by examining its ability to forecast piezometric head behavior at the 534 well locations where historic data were not used to estimate either the system model or the spatiotemporal correlation structure of the model residuals.