FAWN Interactive Irrigation Tool for Florida

Transcription

1 FAWN Interactve Irrgaton Tool for Florda Ncole A. Dobbs Unversty of Florda, SW 280 th St, Homestead, FL, Kat W. Mglacco, PhD PE Unversty of Florda, SW 280 th St, Homestead, FL, Je Fann Unversty of Florda, PO Box , Ganesvlle 32611, Mchael D. Dukes, PhD PE Unversty of Florda, PO Box , 1741 Museum Road, Ganesvlle, FL , Kelly T. Morgan, PhD Unversty of Florda, 2685 SR 29N, Immokalee, FL, W. Rck Lusher Unversty of Florda, PO Box , Ganesvlle 32611, Abstract. Smart rrgaton technologes are avalable that have been shown to reduce water volumes appled to landscapes whle mantanng landscape plant qualty as compared to standard tme-based automated rrgaton systems. To encourage the use of these technologes, a new web-based nteractve rrgaton tool has been developed for homeowners, rrgaton professonals, and others to nvestgate the dfferent rrgaton technologes usng ste-specfc and real-tme data from the Florda Automated Weather Network (FAWN) statons. The model smulates a water balance n a lawn envronment ncludng sol water, ranfall, evapotranspraton, nfltraton, runoff, percolaton, and rrgaton on a daly tme step. Model results are reported to the user weekly va emal. The model smulates dfferent rrgaton technologes ncludng tme-based scheduler, tme-based plus ran sensor, tme-based plus sol mosture sensor, and evapotranspraton controller. Model output ncludes the water appled that s not used by the lawn as a percentage and volume. Model codng accuracy was verfed and outputs were valdated from two Florda study stes. The tool, hosted on the FAWN web ste, s meant to provde a vrtual envronment to explore new rrgaton technologes so that potental users can better understand ther operatonal dfferences and potental water savngs. Keywords. Irrgaton, turf, lawn, sol mosture sensor, evapotranspraton

2 Introducton Turfgrass growth occurs durng warm months when ranfall may not meet plant growth needs resultng n water stress. Snce aesthetcs and health are prmary factors for many turfgrass owners, rrgaton systems are often mplemented to supplement plant water needs. Irrgatng turfgrass has evolved from manual hose-and-sprnkler systems to automated rrgaton systems whch tend to overwater, resultng n decreased turf qualty and water wastes (Trenholm and Unruh, 2005). Mayer et al. (1999) ndcated that 47% more water may be used by automated rrgaton systems compared to non-automated systems (or manual systems) for landscape rrgaton due to a set-and-forget mentalty. The neffcency of automatc rrgaton systems has led to the development of technologes that use ran sensors (RS), sol mosture sensors (SMS), and/or evapotranspraton (ET) controllers to mprove rrgaton applcaton to meet plant needs. These rrgaton technologes have been branded as Smart Controllers (Irrgaton Assocaton, 2007). Several studes have shown water savngs assocated wth these Smart Controllers compared to conventonal tme-based rrgaton schedulng (Davs et al., 2007; Haley and Dukes, 2007; Shedd et al., 2007; Cardenas-Lahalcar et al., 2008; Dukes et al., 2008; Dukes and Haley, 2009; McCready et al., 2009; Cardenas-Lahalcar et al., 2010; Cardenas-Lahalcar and Dukes, 2010; Davs and Dukes, 2010; McCready and Dukes, 2011). The concept behnd Smart Controllers s to rrgate based on the sol water defct, by ether sensng sol water content (SWC) or estmatng ET, so that the SWC s restored to a certan percentage of feld capacty (FC). Irrgaton systems wth RSs, SMSs, and ET controllers are all used to mprove the estmaton of rrgaton needs based, n some regards, on the concept of a sol water balance (Allen et al., 1998): D r, = D r, 1 ( P RO) I net, CR + ET c, + DP (1) where D r (mm) s depleton of water from root zone, s the current day, 1 s the prevous day, P (mm) s daly precptaton, RO (mm) s runoff, I net (mm) s net rrgaton depth, CR (mm) s capllary rse from the groundwater table, ET c (mm) s crop ET and DP (mm) s deep percolaton (PERC) (Allen et al., 1998). Dependng on the technology used, dfferent components of Eq. 1 are consdered. The SMS controls rrgaton solely on a D r feld measurement. The ET controller generally estmates D r usng nput data and P and ET estmates to calculate I. RSs use P to bypass I. Thus, there s potental to smulate how these technologes would operate based on ths relatonshp and user nput. Currently there s no smple vrtual smulaton of ths concept where Flordans can evaluate dfferent rrgaton technologes before nvestng n modfcatons of ther rrgaton system. The objectves were to (1) develop a smple model to smulate the sol water balance based on ste-specfc sol characterstcs, rrgaton schedule, real-tme weather data, and rrgaton devce and (2) provde homeowners and landscape professonals wth an nteractve tool to evaluate and mprove rrgaton schedulng. Methods A smple, one-dmenson sol water balance model was desgned to smulate water movement n the root zone of turfgrass n a typcal Florda lawn. We ncluded processes of I, R, ET, runoff (Q), and PERC. The model smulates a daly tmestep wth all varable unts evaluated as depth (.e., cm). The SWC was calculated based on water gans (.e., R, I) and losses (.e., ET, Q, PERC) to the ntal SWC quantty n one sol layer (root zone) as follows:

3 SWC = SWC + R + I ET, Q PERC (2) o a where s the current day and ET a (cm) s actual ET. Actual ET was calculated as the product of the reference ET (ET o, cm) from the Florda Automated Weather Network (FAWN) and a crop coeffcent (K c ) from Ja et al. (2009). Detaled nformaton on model development can be found n Dobbs (2012). Four dfferent rrgaton technologes were smulated by the model: tme-based scheduler, tme-based plus RS, tme-based plus SMS, and ET controller. The prmary dfference n model smulaton among the rrgaton technologes was the rrgaton applcaton depth. Ths was smulated by requrng specfc nput parameters for each technology n addton to a scheduled rrgaton depth (requred for all optons). The model output ncluded daly and weekly water volume appled (I + R), water volume not used by the turfgrass (Q + PERC), depths of water leached and lost to runoff, and water stressed days. Volumes of water appled were determned usng the rrgaton depth appled and the rrgated area nput. Water volumes not used by the landscape was calculated by addng PERC and Q depths and multplyng by the rrgated area nput. Water stress was calculated usng the management allowable depleton (MAD) concept. The MAD was consdered to be 50% of the avalable water (AW). Avalable water s the amount of water stored n the root zone that s avalable to the plant (Eq.3). Accordng to Allen et al. (1998), a MAD value between 40% and 60% s typcal for turfgrass rrgaton. AW = ( FC WP) RD (3) where FC s feld capacty, WP s wltng pont, and RD s rootng depth. Thus, water stress occurred for a day f the SWC was less than MAD or 50% of AW. Model results were valdated usng two datasets collected n Florda n plot studes. Irrgaton appled, water volumes leached, and ranfall were compared between the model and the measured data. Computatonal accuracy was tested or verfed usng a Mcrosoft Excel model and a program wrtten n Java to mathematcally smulate the model. The Java program was used to create the graphcal user nterface (GUI), hosted by Google App Engne (Je Fan, FAWN, personal communcaton, 7 March 2012). The GUI generated output n comma separated fles so that equal nput values n both Excel and the GUI could be compared for varous scenaros. The Interactve Irrgaton Tool GUI was developed to be smple and engagng for the user. The followng nputs were requred: unt system (Englsh or metrc), RD (nput value or default was 30 cm), sol type (select one of sx types; default was sand), rrgated area (nput value or default s 0.10 ha), rrgaton system (select one of four choces; default was tme-based scheduler), ZIP code, rrgaton depth (for all technologes except ET controller). Irrgaton depth was nput by the user or based on a system runtme (mn) and an rrgaton system selecton of mcrorrgaton (1.27 cm/h), fxed head (3.8 cm/h), gear drven head (1.27 cm/h), or mpact head (1.27 cm/h). The sol type was used to determne FC and WP. The ZIP code served two purposes. It lnked the model to the closest FAWN staton for real-tme weather data and t was used to mplement water restrctons for Mam- Dade County. Only Mam-Dade County water restrctons were mplemented as an example case; current fundng lmted further state-wde mplementaton of restrctons n the model. Because of the nature of Mam-Dade County restrctons, a house number was requested to determne waterng days for these ZIP codes. Waterng days vary by odd/even house numbers n Mam-Dade County wth even numbered houses restrcted to Sunday and Thursday and odd numbered houses restrcted to Wednesday and Saturday (Mam-Dade County, 2012). For ZIP codes outsde of Mam-Dade County, the user selected rrgaton days.

4 All nputs on the GUI were assgned default values except for ZIP code and street number. A defnton or descrpton was provded for some of the requred nputs that were not consdered to be common knowledge. These were vsble to the user by scrollng over queston mark cons located adjacent to the requred nput. The user was also gven the opton to subscrbe to weekly updates on the vrtual lawn va emal. Results and Dscusson The model was valdated usng two plot study datasets. The rrgaton, leachate or PERC, and ranfall data collected n the plot study was compared to model predcted values. Average absolute dfferences between measured and predcted values ranged from 0.37 cm to 1.62 cm for percolaton and 0.0 cm to 0.28 cm for rrgaton depths. Results were wthn expected errors or were explaned by naccuraces n the measured data. A detaled dscusson of ths process s avalable n Dobbs (2012). The computatonal accuracy of the model was successfully smulated by generatng equal output n Mcrosoft Excel and a Java wrtten program. The GUI was successfully launched onlne 1 March The tool can be found on the FAWN web ste at The GUI was desgned wth the assstance of the FAWN team at the Unversty of Florda and s currently mantaned by ther staff. Because the tool was bult on Google technology, an actve Google account s requred. The user s prompted for a Google username and password after selectng the lnk. Once sgned n, the user s drected to the GUI. Data entres requred by the GUI nclude unts, sol characterstcs, rrgaton technology used, rrgaton schedule, and rrgaton amount (unless ET controller s selected). Entry boxes appear dependng on prevous nput values or technology selecton. For all rrgaton technologes optons except ET controller, rrgaton amount appled per event s entered as ether depth per event or based on the selecton of rrgaton system type (.e. mcrorrgaton [1.27 cm/h], fxed head [3.8 cm/h], gear drven head [1.27 cm/h], or mpact head [1.27 cm/h]) and a system runtme entered. If the Tme-based plus ran sensor s selected, the ran sensor settng s also requred. If the Tme-based plus sol mosture sensor s selected, the threshold value s also requred. After the user has submtted all of the requred values, an emal s sent to the user wth nformaton ncludng the amount (volume) and percentage by whch the rrgaton system overwatered and the number of days for whch the turfgrass dd not receve enough water. Based on these values, the lawn s ranked on a scale of one to fve, one representng an effcent rrgaton system and fve neffcent. The scale also corresponds to a color of the vrtual lawn (dark green [ratng equal to one] to lght brown [ratng equal to fve]). The emal also dsplays the dstance between the user s ZIP code and the FAWN staton. Conclusons The model successfully smulated I and PERC amounts for tme-based rrgaton, tme-based wth RSs, tme-based wth SMSs, and ET controllers. The model was ntegrated nto a GUI tool that s avalable, free of charge. Addtonal work on the model wll be conducted as fundng allows. Partcular mprovements nclude a more user frendly nterface, optons to nvestgate multple rrgaton scenaros at one tme, and ncluson of more ranfall data collecton stes.

5 Acknowledgements Ths research was supported by Unversty of Florda IFAS Tropcal Research and Educaton Center, UF Agrcultural and Bologcal Engneerng Department, and Florda Nursery, Growers, and Landscape Assocaton. References Allen, R.G., L.S. Perera, D. Raes, and M. Smth Crop evapotranspraton: gudelnes for computng crop requrements. Irrgaton and Dranage Paper No 56, FAO, Rome, Italy. Cardenas-Lalhacar, B. and M.D. Dukes Precson of sol mosture sensor rrgaton controllers under feld condtons. Agrc. Water Mgnt. 97(5): Cardenas-Lalhacar, B., M.D. Dukes, and G.L. Mller Sensor-based automaton of rrgaton on bermudagrass durng wet weather condtons. J. Irrg. And Dranage Eng. 134(2): Cardenas-Lalhacar, B., M.D. Dukes, and G.L. Mller Sensor-based automaton of rrgaton on bermudagrass durng dry weather condtons J. Irrg. and Dranage Eng. 136(3): Davs, S.L. and M.D. Dukes Irrgaton schedulng performance by evapotranspraton-based controllers. Agrc. Water Mgmt. 98(1): Davs, S.L., M.D. Dukes, S. Vyapar, and G.L. Mller Evaluaton and demonstraton of evapotranspraton-based rrgaton controllers. In: Proc. ASCE EWRI World Envronmental and Water Resources Congress, Tampa, FL, May 15-19, Also at 19 pp. Dobbs, N.A Irrgaton Applcaton and Ntrogen Leachng from Warm-Season Turfgrass usng Smart Irrgaton Controllers and Development of an Interactve Irrgaton Tool. Thess. Agrcultural and Bologcal Engneerng. Unversty of Florda: Ganesvlle, Florda. Dukes, M.D. and M.B. Haley Evaluaton of sol mosture-based on-demand rrgaton controllers, phase II. Fnal Project Report for Southwest Florda Water Management Dstrct. Ganesvlle, FL: Unversty of Florda. Avalable at: /SMS%20Phase%20II%20Fnal%20Report% pdf Dukes, M.D., B. Cardenas-Lalhacar, and G.L. Mller Evaluaton of sol mosture-based ondemand rrgaton controllers, phase I. Fnal Project Report for Southwest Florda Water Management Dstrct. Agrcultural and Bologcal Engneerng Department, Unversty of Florda, Ganesvlle, FL. Avalable at: Report pdf. Haley, M.B. and M.D. Dukes Evaluaton of sensor based resdental rrgaton water applcaton. ASABE Paper No St. Joseph, Mch.: ASABE. Irrgaton Assocaton [IA] Defnton of a Smart Controller. The Irrgaton Assocaton, Falls Church, VA. Avalable at: Defnton_Smart_Controller.pdf. Accessed 17 February Ja, X., M.D. Dukes, and J.M. Jacobs Bahagrass crop coeffcents from eddy correlaton measurements n Central Florda. Irrg. Sc. 8(1):5-15. Mayer, P.W., W.B. DeOreo, E.M. Optz, J.C. Kefer, W.Y. Davs, B. Dzegelewsk, and J.O. Nelson Resdental End Uses of Water. Denver, Colorado. AWWA Research Foundaton and Amercan Water Works Assocaton. McCready, M.S. and M.D. Dukes Landscape rrgaton schedulng effcency and adequacy by varous control technologes. Agrc. Water Mgmt. 98(4): McCready, M.S., M.D. Dukes, and G.L. Mller Water conservaton potental of smart rrgaton controllers on St. Augustnegrass. Agrc. Water Mgmt. 96(11): Shedd, M., M.D. Dukes, and G.L. Mller Evaluaton of evapotranspraton and sol mosturebased rrgaton control on turfgrass. In: Proc. ASCE-EWRI World Envronmental and Water

6 Resources Congress, Tampa, FL, May 15-19, Also at publcatons/sms/ewri%202007%20evaluaton-of-evapotranspraton-and-sol-mosture.pdf. 21 pp. Trenholm, L.E. and J.B. Unruh Cultural Practces for Your Florda Lawn. Chapter 4 n Florda Lawn Handbook, Unversty Press of Florda pp , Ganesvlle, FL.