SWATfarmR: A simple rule-based scheduling of SWAT management opera�ons 1
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Christoph Schürz , Michael Strauch , Bano Mehdi , and Karsten Schulz
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1 Ins�tute of Water Management, Hydrology and Hydraulic Engineering, BOKU, Vienna, Austria 2 Department of Computa�onal Landscape Ecology, Helmholtz Centre for Environmental Research , Leipzig, Germany
1 Management scheduling in SWAT
2 The concept of SWATfarmR
3 Case study
The implemented concept tries to overcome the stated issues of fixed dates or the PHU concept, by scheduling fixed dates for each management schedule that are temporally distributed between HRUs with the same land use. SWATfarmR requires only li�le input data:
Processes in the water balance and the nutrient cycles modelled with the Soil and Water Assessment Tool (SWAT) [1] are strongly affected by simulated plant growth. Therefore, detailed informa�on on cul�va�on and farm management prac�ces are required. Most SWAT applica�ons however, lack such detailed data.
Temperature and precipita�on are extracted from the SWAT project Time ranges for all opera�ons of all crops applied in the model in which these should be triggered Grouping of crops in crop rota�ons if considered
SWAT schedules farm management opera�ons either by fixed provided dates or by applying the Poten�al Heat Units (PHU) concept, where frac�ons of accumulated tempearture trigger opera�ons. These methods however, show shortcomings, such as:
Management operation scheduling during rainfall events Precipitation Soil moisture
No OP
SWATfarmR
SWATfarmR uniformly distributed the opera�ons leaving out the stronger rain events and following moist soil condi�ons. The fixed date for fer�lizer applica�on coincided with a strong rainfall event in this growing season. The PHU concept set opera�ons early as the winter was warm.
SWATfarmR sets the dates by complying with the following simple rules:
Plan�ng or harves�ng dates are set too early or too late, as PHUs are sensi�ve to inter-annual temperature fluctua�ons.
Opera�ons scheduled are temperature dependent. Warmer than usual condi�ons trigger opera�ons earlier in spring and later in fall. Opera�ons are randomized within a user defined �me range around the calculated scheduling dates. Opera�ons are only triggered on days where no rainfall occurs and soil moisture is poten�ally low.
The �ming of fer�lizer applica�on o�en occurs simultaneously on the same date in each HRU with the same crop applied. They can coincide with strong rainfall and cause peak nitrogen loads
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a
a
The autumn was rather moist and cool. Due to long periods of rainfall and poten�ally high soil moisture cond�ons, SWATfarmR had to compromise and select a date with low rainfall and soil moisture
No OP
SWATfarmR
Model schema�c MGT Schedule
Farm management opera�on schedules provided by the user. Different cul�va�on methods can be implemented according to their share in the catchment.
April
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A simple water balance according to Ali et al. [1] and Tramblay et al. [3] was implemented to iden�fy high soil moisture.
Precipita�on data from SWAT sta�on data is extracted for the �me period and compared to a threshold value.
PHU not reached. Set at end of year
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Crop Opera�on min Date min Date Corn Corn Corn Corn Corn Corn
Fer�lizer Tillage Plant Fer�lizer Harvest Tillage
18/3 27/3 16/4 24/6 19/10 26/10
24/3 6/4 26/4 7/4 29/10 5/11
Spring colder than usual
16 17 18 ... 24 25 26 Opera�on scheme for the crop applied to an HRU is extracted from the provided management schedules by weighted random selec�on.
Christoph Schürz Ins�tute of Water Management, Hydrology and Hydraulic Engineering (IWHW) BOKU University Vienna, Austria
[email protected] @chrisschuerz @christophschurz
Moisture Threshold
Precipitation Threshold
a -5 days 24/4 +5 days
April 2002 Using the tempearture sta�on data provided to the SWAT model interannual temperature fluctua�ons are calculated. These are used to interpolate within the user provided range of opera�on dates
The PHU concept is very sensi�ve for opera�ons at the end of a year, as illustrated here for crop harves�ng and �llage, resul�ng in a wide spread of the set opera�ons. In some HRUs the PHUs were not reached to trigger the operations at all.
Subbasin NO3 discharge
SWATfarmR
Spring warmer than usual
The following figures show a comparison of fixed dates, the PHU concept and SWATfarmR. Examplatory one growing season for maize was used where winter was unusually warm and spring and autumn were colder than usual.
A user defined �me span is selected, for which precipita�on and soil moisture are analyzed. From days below thresholds a date is selected and wri�en to the management file.
a
Se�ng the fer�lizer opera�on during a strong rainfall event applying fixed dates led to a substan�ally larger peak in nitrogen loads at the catchment outlet compared to the other two methods, where no opera�ons were set during the rainfall event.
a
-5 days 24/4 +5 days
Date selected
References [1] Ali, S., Ghosh, N. C. C., & Singh, R. (2010). Rainfall–runoff simula�on using a normalized antecedent precipita�on index. Hydrological Sciences Journal, 55(2), 266–274. h�p://doi.org/10.1080/02626660903546175 [2] Arnold, J. G., Srinivasan, R., Mu�ah, R. S., & Williams, J. R. (1998). Large area hydrologic modeling and assessment Part I: Model development. Journal of the American Water Resources Associa�on, 34(1), 73–89. [3] Tramblay, Y., Bouaicha, R., Brocca, L., Dorigo, W., Bouvier, C., Camici, S., & Servat, E. (2012). Es�ma�on of antecedent wetness condi�ons for flood modelling in northern Morocco. Hydrology and Earth System Sciences, 16(11), 4375–4386.
4 Conclusion and Outlook The shortcomings of the standard methods for management opera�on scheduling in SWAT were illustrated in a simple case study. SWATfarmR can overcome these issues to a large extent. Differences in resul�ng NO3 loads illustrate the advantages of considering rainfall data for management opera�on scheduling.
Nevertheless, the outlined approach s�ll is open for discussion and improvement such as: An implementa�on of crop rota�ons was sucessful, needs however further development. Implementa�on of climate change in scenario modelling. Implementa�on of further op�ons: e.g. triggering by rainy season, or gradual land use change
