Simulating Water Use and Nitrogen Fate in a Woody Biomass Production Ecosystem

Transcription

1 Simulating Water Use and Nitrogen Fate in a Woody Biomass Production Ecosystem Ying Ouyang, Research Hydrologist USDA Forest Service, Center for Bottomland Hardwoods Research, Southern Research Station

2 Rationale Short-rotation woody crop biomass production is a promising technique to generate bioenergy Water use and nutrient fate in the biomass production plantation are still poorly understood Difficult to quantify them by experimentation alone for a variety of tree species, for different soil conditions, and for all possible combinations of surficial driving forces A need exists to develop a simple and yet realistic model to quantify them

3 Goal Develop a STELLA model to estimate soil water and N dynamics in a short-rotation biomass production plantation Objectives 1. Develop a model component for water dynamics, including runoff, rainfall, irrigation, evaporation, percolation, and water uptake by roots and transport in the xylem system

4 Objectives 2. Develop a model component for N dynamics, including mineralization, nitrification, denitrification, sorption, leaching, volatilization, uptake, and fertilizer application 3. Apply the model to estimate water use and N fate in a short-rotation biomass production plantation

5 What is STELLA? STELLA is a user friendly software for developing computer models Structural Thinking and Experiential Learning Laboratory with Animation (STELLA) Developed by Isee Systems Inc. ( iseesystems.com)

6 STELLA Model for Soil CO 2 Emission Adsorption Soil CO 2 CO 2 Production Roots Microorganisms Dissolution

7 Processes for Water and N Dynamics Rainfall and Irrigation Atmosphere Diurnal Evaporation Diurnal Transpiration Leaf Compartment Tree Water runoff NH4 Volatilization N Fertilizer Litter (PON) Fall/Hydrolysis Stem Compartment One Hectare Soil Root Compartment NO - 3 and NH + 4 Sorption and SON Mineralization NO - 3 and NH + 4 Leaching Water and NO - 3 and NH + 4 Uptake (A) Nitrification and Denitrification N Uptake (B) Water Uptake 3m (C)

8 Major Mathematical Functions Runoff: R runoff RI ( RI 0.2S 2 0.8S) Percolation: Q ( fc) Leaching of N species: j N Q C j N volatilization, adsorption, mineralization, nitrification, denitrification, and enzymatic hydrolysis: Root water uptake: Q ds dt j water root k Q j S j transp leaf / 0.99 Root N uptake: R root rate Q water root C soil root

9 STELLA Model for Water Dynamics

10 Model Input Data for Water Dynamics Parameter Value or emperical equation Source Water Dynamics Curve number 81 Nearing et al., 1996 Rainfall (cm/h) Time series measurements Local weather station Irrigation (cm/h) 0.3 Lee and Jose, 2005 Soil area (cm 2 ) (or one hectare) Lee and Jose, 2005 Soil depth (cm) 200 Lee and Jose, 2006 Soil porosity (cm 3 /cm 3 ) 0.35 Ouyang et al., 2012 Field capacity 0.3 Ouyang et al., 2012 Drainage coefficient (cm/h) Calibrated Initial soil water (cm 3 ) Calculated based on soil volume and water content Evaporation coefficient (cm/h) -1e-09time*time+1e-05time Estimated from Lee and Jose (2005) and ocurred during the day Daily transpiration coefficient (cm 3 /h) -2e-8*time*time *time Estimated from Lee and Jose (2005) and ocurred during the day Initial root water (cm3) Estimated from Jenkins et al. (2003) and Lee and Jose (2006) Initial stem water (cm3) Estimated from Stem volume index (Lee and Jose, 2005) Initial leaf water (cm3) Estimated from Jenkins et al. (2003) and Lee and Jose (2006) Transpiration (cm 3 /h/tree) Lee and Jose, 2005 Plant density (tree/ha) 229 Lee and Jose, 2005 Forest cover factor 0.85 Assumed

11 STELLA Model for N Dynamics

12 Model Input Data for N Dynamics Parameter Value or emperical equation Source Nitrogen Dynamics Initial dissolved SON (g/ha) Ouayng et al., 2012 SON mineralization rate (g/ha/h) Estimated from Lee and Jose (2006) and Ouyang et al. (2012) Initial dissolved NH 4 (g/ha) 7500 Lee and Jose, 2006 Initial dissolved NO 3 (g/ha) 1500 Lee and Jose, 2006 NH 4 nitrification rate (1/h) 0.3 Estimated from Martin and Reddy (1997) and Lee and Jose (2006) NH 4 volatilization rate (1/h) Martin and Reddy, 1997 NH4 adsorption rate (1/h) Martin and Reddy, 1997 NO3 denitrification (1/h) Martin and Reddy, 1997 Litter enzyme hydrolysis rate (1/h) 1.00E-06 Martin and Reddy, 1997 Reflection coefficient calibrated

13 Predicted NO 3 -N (mg/l) Predicted drainage (cm) Model Calibration y = x R² = Drainage (cm) from Lee and Jose (2005) A Reasonable agreement for drainage between the predictions and the measurements Reasonable agreement for NO 3 -N concentration between the predictions and the measurements y = x R² = Observed NO 3 -N (mg/l) B Lee, K.H., and S. Jose Nitrogen mineralization in short rotation tree plantations along a soil nitrogen gradient. Can. J. For. Res. 36,

14 Simulation Scenario A mature (7-year old) cottonwood plantation with an area of one ha and a soil depth of 3 m was used Daily soil water evaporation, leaf water transpiration, and root water uptake Monthly water runoff and percolation as well as N leaching in response to rainfall, irrigation, and fertilization Monthly uptake of NH 4 -N and NO 3 -N by roots and variations of NO 3 -N in the soil 3m One Hectare (C)

15 Root water uptake rate (cm 3 /h/ha) Transpiration rate (cm 3 /h/ha) Evaporation rate (cm 3 /h/ha) Diurnal Water Variation Pattern 6.0E E E E E E E E E E E+06 A Time (h) B Time (h) C Rates of soil evaporation, leaf transpiration, and root uptake increased during the day and decreased at night These rates also increased diurnally during a one-week simulation period (0-168 h) Soil temperatures increased during this simulation period Soil evaporation was one order of magnitude lower than leaf water transpiration 0.0E Time (h) One week

16 Root water uptake (cm 3 /h/ha) ET (cm 3 /h/ha) Percolation (cm 3 /h/ha) Runoff (cm 3 /h/ha) Rainfall/irrigation (cm/h) Percolation, ET, and Root Water Uptake Rainfall Irrigation 0 4.0E E E E E E E E E E E E E E+00 A B C D E Time (h) J F M A M J J S O A N D Month Rainfall and irrigation (0.3cm/h for 2h/day) were model inputs Two periods (March and August) of high runoff and percolation rates due to the intensive rainfalls ET and root water uptake were lower in January and higher in August ET decreased during rainfall due to the decrease or cessation of leaf water transpiration in rains Root water uptake decreased during rainfall because leaf water transpiration decreased

17 Soil NO 3 -N concentration (mg/l) SON leaching (g/h/ha) NO 3 -N leaching (g/h/ha) Rainfall/irrigation (cm) A Rainfall B Soil N Dynamics Irrigation C D J F M A M J J A S O N D Time (h) Fertilization Leaching of N was trivial from January to May because of low soil N content Application of N fertilizer (56 kg N ha -1 y -1 ) started in June and ended in October Leaching of NO 3 -N and SON occurred after fertilization Leaching of NO 3 -N was 2.1 times greater than that of SON Low NO 3 -N leaching in October although soil NO 3 -N content is high at this period Leaching of NO 3 -N depended not only on soil NO 3 -N content but also on rainfall events

18 Monthly rate (g/ha) Monthly rate (cm 3 /ha) Comparison of Monthly Water and N Dynamics 2.5E E E E E E E E E E E E E E+00 ET Root uptake Percolation Runoff Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec NO3-N Leaching SON Leaching NH4-N Leaching Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec A B In most case, the soil water loss rate was: ET > root water uptake > percolation > runoff Rate of water loss was higher during summer Leaching of N occurred in summer because of high soil N content Rate of monthly leaching was: NO 3 -N > SON > NH 4 -N

19 Summary Development of a STELLA model for predicting water and N dynamics in a woody crop plantation Soil water evaporation was one order of magnitude lower than that of leaf water transpiration Monthly water loss rate was: ET > root uptake > percolation > runoff Leaching of NO 3 -N from the cottonwood plantation was 2.1 times higher than that of SON Monthly N leaching was: NO 3 -N > SON > NH 4 -N Leaching of NO 3 -N depended not only on soil NO 3 -N content but also on rainfall events Modify the model to predict biomass production