Impact of Using Radar Rainfall Data in Water Budgets for South Florida Stormwater Treatment Areas

2007 ◽  
Author(s):  
R. Scott Huebner ◽  
Wossenu Abtew ◽  
Chandra Pathak
Keyword(s):  
South Florida ◽  
Rainfall Data ◽  
Stormwater Treatment ◽  
Radar Rainfall ◽  
Water Budgets ◽  
Stormwater Treatment Areas

Progress in the research and demonstration of Everglades periphyton-based stormwater treatment areas

2001 ◽  
Vol 44 (11-12) ◽  
pp. 123-130 ◽  
Author(s):  
J.S. Bays ◽  
R.L. Knight ◽  
L. Wenkert ◽  
R. Clarke ◽  
S. Gong
Keyword(s):  
Water Depth ◽  
Phase 1 ◽  
South Florida ◽  
Advanced Treatment ◽  
Stormwater Treatment ◽  
The South ◽  
Stormwater Treatment Areas ◽  
Treatment Technologies ◽  
Test Cells ◽  
Total P

The South Florida Water Management District (District) is conducting research focused on potential advanced treatment technologies to support reduction of phosphorus (P) loads in surface water entering the remaining Everglades. Periphyton-based stormwater treatment areas (PSTA) are one of the advanced treatment technologies being researched by the District. This detailed research and demonstration project is being conducted in two phases. Basic research in field-based mesocosm experiments was conducted during the first phase within the District's Everglades Nutrient Removal Project (ENR). Studies were conducted in 24 portable PSTA mesocosms and three of the south ENR test cells. Phase 1 studies addressed the effects of system substrate (shellrock, organic peat, or sand), water depth, hydraulic loading rate, vegetation presence, depth:width ratio, and inhibition of algal growth on total phosphorus removal performance of the PSTA mesocosms. A second phase of research is currently under way, during which PSTA feasibility will be evaluated further in four field-scale constructed mesocosms totaling about 2 ha, and follow up studies within the ENR test cells and portable mesocosms will be conducted to further investigate the effects of other inorganic substrates, shallow water depth, and velocity on treatment performance. Phase 1 monitoring has determined that periphyton-dominated communities can be established in constructed wetlands within 5 months. The algal component of these periphyton plant communities is characteristic of natural Everglades periphyton. High macrophyte densities resulted from use of peat soils in PSTA mesocosms, while shellrock and sand soils promoted more desirable sparse macrophyte stands. P removal rates under the conditions of this research were relatively high considering the low influent total P concentrations tested (average 23 μg/L). PSTA mesocosms on shellrock soils were able to attain long-term average outflow total P concentrations as low as 11 μg/L. The maximum one-parameter TP first-order removal rate constant (k1) measured was 27 m/y. Minimum attainable outflow total P concentrations and mass removals appear to be the result of a balance between internal P loading from antecedent soils, uptake and burial processes in new sediments, and rainfall inputs. A different soil type (limerock) will be tested for effectiveness during Phase 2. Selected existing treatments will also be continued to look for trends over a second growing season.

Stormwater inflow prediction using radar rainfall data compressed by principal component analysis

2006 ◽  
Vol 1 (1) ◽  
Author(s):  
K. Katayama ◽  
K. Kimijima ◽  
O. Yamanaka ◽  
A. Nagaiwa ◽  
Y. Ono
Keyword(s):  
Principal Component Analysis ◽  
Prediction Model ◽  
Principal Components ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Rainfall Data ◽  
Radar Rainfall ◽  
Input Variables ◽  
Inflow Prediction

This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is applied to pre-processing radar rainfall data. Then we evaluate the proposed method using the radar rainfall data and the inflow data acquired in a certain combined sewer system. This study reveals that a few principal components of radar rainfall data can be appropriate as the input variables to storm water inflow prediction model. Consequently, we have established a procedure for the stormwater prediction method using a few principal components of radar rainfall data.

scholarly journals Optimal Rainfall Estimation by Considering Elevation in the Han River Basin, South Korea

2013 ◽  
Vol 52 (4) ◽  
pp. 802-818 ◽  
Author(s):  
Seong-Sim Yoon ◽  
Deg-Hyo Bae
Keyword(s):  
South Korea ◽  
River Basin ◽  
Flood Control ◽  
Rainfall Data ◽  
Spatiotemporal Variability ◽  
Radar Rainfall ◽  
Mountainous Regions ◽  
Han River ◽  
Orographic Rainfall ◽  
Han River Basin

AbstractMore than 70% of South Korea has mountainous terrain, which leads to significant spatiotemporal variability of rainfall. The country is exposed to the risk of flash floods owing to orographic rainfall. Rainfall observations are important in mountainous regions because flood control measures depend strongly on rainfall data. In particular, radar rainfall data are useful in these regions because of the limitations of rain gauges. However, radar rainfall data include errors despite the development of improved estimation techniques for their calculation. Further, the radar does not provide accurate data during heavy rainfall in mountainous areas. This study presents a radar rainfall adjustment method that considers the elevation in mountainous regions. Gauge rainfall and radar rainfall field data are modified by using standardized ordinary cokriging considering the elevation, and the conditional merging technique is used for combining the two types of data. For evaluating the proposed technique, the Han River basin was selected; a high correlation between rainfall and elevation can be seen in this basin. Further, the proposed technique was compared with the mean field bias and original conditional merging techniques. Comparison with kriged rainfall showed that the proposed method has a lesser tendency to oversmooth the rainfall distribution when compared with the other methods, and the optimal mean areal rainfall is very similar to the value obtained using gauges. It reveals that the proposed method can be applied to an area with significantly varying elevation, such as the Han River basin, to obtain radar rainfall data of high accuracy.

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