Sensitivity Analysis and Evaluation of Forest Biomass Production Potential Using SWAT Model


Sensitivity analysis of crop parameters and the performance of SWAT (Soil and Water Assessment Tool) model to simulate potential forest biomass production were evaluated for the Upper Pearl River Watershed (UPRW). Local sensitivity analysis of seven crop parameters: radiation use efficiency (kg/ha)/(MJ/m2) (BIOE), potential maximum leaf area index for the plant (BLAI), fraction of growing season at which senescence becomes the dominant growth process (DLAI), fraction of the maximum plant leaf area index corresponding to the 1st point on the optimal leaf area development curve (LAIMX1), fraction of growing season corresponding to the 1st point on the optimal leaf area development curve (FRGRW1), plants potential maximum canopy height (m) (CHTMX), and maximum rooting depth for plant (mm) (RDMX) reveals that only three parameters: DLAI, BIOE and BLAI are sensitive to forest biomass production. Further, results indicate moderate sensitivity of DLAI and BIOE and low sensitivity of BLAI with relative sensitivity index of 0.44, 0.35 and 0.14, respectively. The performance of SWAT to simulate potential forest biomass was evaluated by comparing simulated data against three years of observed data that were obtained from USDA Forest Service website. The results indicate satisfactory performance of SWAT in predicting potential forest biomass, which is shown by the high value of coefficient of determination (R2 = 0.83), small root mean square error (RMSE = 11.11 Mg/ha), and small difference between mean. Results also reveal that the UPRW has the potential to produce approximately 49 Mg/ha of average forest biomass annually, which is approximately 6% less than the observed biomass.

Share and Cite:

Khanal, S. and Parajuli, P. (2014) Sensitivity Analysis and Evaluation of Forest Biomass Production Potential Using SWAT Model. Journal of Sustainable Bioenergy Systems, 4, 136-147. doi: 10.4236/jsbs.2014.42013.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Bartuska, A. (2006) Why Biomass Is Important—The Role of the USDA Forest Service in Managing and Using Biomass for Energy and Other Uses.
[2] Joshi, O. and Mehmood, S.R. (2011) Factors Affecting Nonindustrial Private Forest Landowners’ Willingness to Supply Woody Biomass for Bioenergy. Biomass and Bioenergy, 35, 186-192.
[3] Sissine, F. (2007) Energy Independence and Security Act of 2007: A Summary of Major Provisions. CRS Report for Congress.
[4] Parresol, B.R. (1999) Assessing Tree and Stand Biomass: A Review with Examples and Critical Comparisons. Forest Science, 45, 573-593.
[5] Lu, D. (2006) The Potential and Challenge of Remote Sensing Based-Biomass Estimation. International Journal of Remote Sensing, 27, 1297-1328.
[6] Main-Knorn, M., Moisen, G.G., Healey, S.P., Keeton, W.S., Freeman, E.A. and Hostert, P. (2011) Evaluating the Remote Sensing and Inventory-Based Estimation of Biomass in the Western Carpathians. Remote Sensing, 3, 1427-1446.
[7] Parajuli, P.B. (2010) Assessing Sensitivity of Hydrologic Responses to Climate Change from Forested Watershed in Mississippi. Hydrological Processes, 24, 3785-3797.
[8] Mississippi Department of Environmental Quality (MDEQ) (2007) Citizen’s Guide to Water Quality in the Pearl River Basin.
[9] Wang, X., Harmel, R.D., Williams, J.R. and Harman, W.L. (2005) Evaluation of EPIC for Assessing Crop Yield, Runoff, Sediment and Nutrient Losses from Watersheds with Poultry Litter Fertilization. Transactions of the ASABE, 49, 47-49.
[10] Baskaran, L., Jager, H.I., Schweizer, P.E. and Srinivasan, R. (2010) Progress toward Evaluating the Sustainability of Switchgrass as a Bioenergy Crop Using the SWAT Model. Transactions of the ASABE, 53, 1547-1556.
[11] Faramarzi, M., Yanga, H., Schulinc, R. and Abbaspoura, K.C. (2010) Modeling Wheat Yield and Crop Water Productivity in Iran: Implications of Agricultural Water Management for Wheat Production. Agricultural Water Management, 97, 1861-1875.
[12] Srinivasan, R., Zhang, X. and Arnold, J. (2010) SWAT Ungauaged: Hydrological Budget and Crop Yield Predictions in the Upper Mississippi River Basin. Transactions of the ASABE, 53, 1533-1546.
[13] Cibin, R., Sudheer, K.P. and Chaubey, I. (2010) Sensitivity and Identifiability of Stream Flow Generation Parameters of the SWAT Model. Hydrological Processes, 24, 1133-1148.
[14] Holvoet, K., van Griensven, A., Seuntjens, P. and Vanrolleghem, P.A. (2005) Sensitivity Analysis for Hydrology and Pesticide Supply towards the River in SWAT. Physics and Chemistry of the Earth, 30, 518-526.
[15] Saltelli, A., Tarantola, S., Campolongo, F. and Ratto, M. (2004) Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models. John Wiley and Sons Ltd., Hoboken.
[16] Kalin, L. and Hantush, M.M. (2006) Hydrologic Modeling of an Eastern Pennsylvania Watershed with NEXRAD and Rain Gauge Data. Journal of Hydrologic Engineering, 11, 555-569.
[17] Haan, C.T. and Skaggs, R.W. (2003) Effect of Parameter Uncertainty on DRAINMOD Predictions: I. Hydrology and Yield. Transactions of the ASAE, 46, 1061-1067.
[18] van Griensven, A. and Meixner, T. (2006) Methods to Quantify and Identify the Sources of Uncertainty for River Basin Water Quality Models. Water Science Technology, 53, 51-59
[19] Soutter, M. and Musy, A. (1999) Global Sensitivity Analyses of Three Pesticide Leaching Models Using a Monte-Carlo Approach. Journal of Environmental Quality, 28, 1290-1297.
[20] Pathak, T.B., Fraisse, C.W., Jones, J.W., Messina, C.D. and Hoogenboom, G. (2007) Use of Global Sensitivity Analysis for CROPGRO Cotton Model Development. Transactions of the ASABE, 50, 2295-2302.
[21] Patric, J.H., Evans, J. and Helvey, J.D. (1984) Summary of Sediment Yield Data from Forested Land in the United States. Journal of Forestry, 82, 101-104.
[22] Graff, C.D., Sadeghi, A.M., Lowrance, R.R. and Williams, J.R. (2005) Quantifying the Sensitivity of the Riparian Ecosystem Management Model (REMM) to Changes in Climate and Buffer Characteristics Common to Conservation Practices. Transactions of the ASAE, 48, 1377-1387.
[23] Sarkar, S., Miller, S.A., Frederick, J.R. and Chamberlain, J.F. (2011) Modeling Nitrogen Loss from Switchgrass Agricultural systems. Biomass and Bioenergy, 35, 4381-4389.
[24] Neitsch, S.L., Arnold, J.G., Kiniry, J.R. and Williams, J.R. (2005) Soil and Water Assessment Tool SWAT, Theoretical Documentation. Blackland Research Center, Grass-Land, Soil and Water Research Laboratory, Agricultural Research Service, Temple.
[25] Arnold, J.G., Srinivasan, R., Muttiah, R.S. and Williams, J.R. (1998) Large Area Hydrologic Modeling and Assessment Part I: Model Development. JAWRA: Journal of American Water Resources Association, 34, 73-89.
[26] Eckhardt, K. and Arnold, J.G. (2001) Automatic Calibration of a Distributed Catchment Model. Journal of Hydrology, 251, 103-109.
[27] Borah, A.K. and Bera, M. (2003) Watershed-Scale Hydrological and Nonpoint-Source Pollution Models: Review of Mathematical Bases. Transactions of the ASAE, 46, 1553-1566.
[28] Lenhart, T., Eckhardt, K., Fohrer, N. and Frede, H.G. (2002) Comparison of Two Different Approaches of Sensitivity Analysis. Physics and Chemistry of the Earth, Parts A/B/C, 27, 645-654.
[29] Spruill, C.A., Workman, S.R. and Taraba, J.L. (2000) Simulation of Daily and Monthly Stream Discharge from Small Watersheds Using the SWAT Model. Transactions of the ASAE, 43, 1431-1439.
[30] Gassman, P.W., Reyes, M.R., Green, C.H. and Arnold, J.G. (2007) The Soil and Water Assessment Tool: Historical Development, Applications and Future Research Directions. Transactions of the ASABE, 50, 1211-1250.
[31] Kiniry, J.R., Tischler, C.R. and Van Esbroeck, G.A. (1999) Radiation Use Efficiency and Leaf CO2 Exchange for Diverse C4 Grasses. Biomass and Bioenergy, 17, 95-112.
[32] US Geological Society (USGS) (1999) National Elevation Data-Set.
[33] US Department of Agriculture, National Agricultural Statistics Service (USDA/NASS) (2009) The Cropland Data Layer.
[34] US Department of Agriculture (USDA) (2005) Soil Data Mart. Natural Resources Conservation Service.
[35] National Climatic Data Center (NCDC) (2010) Locate Weather Observation Station Record.
[36] Saleh, A., Williams, J.R., Wood, J.C., Hauck, L.M. and Blackburn, W.H. (2004) Application of APEX for Forestry. Transactions of the ASAE, 47, 751-765.
[37] Santhi, C., Arnold, J.G., Williams, J.R., Dugas, W.A. and Hauck, L. (2001) Validation of the SWAT Model on a Large Rwer Basin with Point and Nonpoint Sources. JAWRA: Journal of American Water Resources Association, 37, 1169-1188.
[38] Breuer, L., Eckhardt, K. and Frede, H.G. (2003) Plant Parameter Values for Models in Temperate Climates. Ecological Modeling, 169, 237-293.
[39] Eckhardt, K., Breuer, L. and Frede, H.G. (2003) Parameter Uncertainty and the Significance of Simulated Land Use Change Effects. Journal of Hydrology, 273, 164-176.
[40] Akhavana, S., Abedi-Koupaia, J., Mousavia, S.F., Afyunib, M., Eslamiana, S.S. and Abbaspour, K.C. (2010) Application of SWAT Model to Investigate Nitrate Leaching in Hamadan Bahar Watershed, Iran. Agriculture, Ecosystem and Environment, 139, 675-688.
[41] Chaubey, I., Raj, C., Trybula, E., Frakenberger, J., Brouder, S. and Volencec, J. (2011) Improving the Simulation of Biofuel Crop Sustainability Assessment Using SWAT Model.
[43] James, L.D. and Burges, S.J. (1982) Selection, Calibration, and Testing of Hydrologic Models. In: Haan, C.T., Johnson, H.P. and Brakensiek, D.L., Eds., Hydrologic Modeling of Small Watersheds, ASAE Monograph, St. Joseph, 437-472.
[44] Jesiek, J.B. and Wolf, D.D. (2005) Sensitivity Analysis of the Virginia Phosphorus Index Management Tool. Transactions of the ASAE, 48, 1773-1781.
[45] White, K.L. and Chaubey, I. (2005) Sensitivity Analysis, Calibration and Validation for a Multisite and Multivariable SWAT Model. JAWRA: Journal of American Water Resources Association, 41, 1077-1089.
[46] Nair, S.S., King, K.W., Witter, J.D., Sohngen, B.L. and Fausey, N.R. (2011) Importance of Crop Yield in Calibrating Watershed Water Quality Simulation Tools. JAWRA: Journal of American Water Resources Association, 47, 1285-1297.
[47] USDA Forest Service (2011) Forest Inventory Data Online (FIDO).
[48] Nash, J.E. and Sutcliffe, J.V. (1970) River Flow Forecasting through Conceptual Models: Part I. A Discussion of Principles. Journal of Hydrology, 10, 282-290.
[49] Gupta, H.V., Sorooshian, S. and Yapo, P.O. (1999) Status of Automatic Calibration for Hydrologic Models: Comparison with Multilevel Expert Calibration. Journal of Hydrologic Engineering, 4, 135-143.
[50] Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Binger, R.L., Harmel, R.D. and Veith, T.L. (2007) Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations. Transactions of the ASABE, 50, 885-900.
[51] Parajuli, P.B., Nelson, N.O., Frees, L.D. and Man-Kin, K.R. (2009) Comparison of AnnAGNPS and SWAT Model Simulation Results in USDACEAP Agricultural Watersheds in South-Central Kansas. Hydrological Processes, 23, 748-763.
[52] Nejadhashemi, A.P., Wardynski, B.J. and Munoz, J.D. (2011) Evaluating the Impacts of Land Use Changes on Hydrologic Responses in the Agricultural Regions of Michigan and Wisconsin. Hydrology and Earth System Sciences Discussions, 8, 3421-3468.
[53] Vazquez-Amábile, G.G. and Engel, B.A. (2005) Use of SWAT to Compute Groundwater Table Depth and Streamflow in the Muscatatuck River Watershed. Transactions of the ASAE, 48, 991-1003.
[54] Baumgart, P. (2005) Loads to Green Bay from the Lower Fox River Subbasin Using the Soil and Water Assessment Tool (SWAT).
[55] Fohrer, N., Möller, D. and Steiner, N. (2002) An Interdisciplinary Modeling Approach to Evaluate the Effects of Land Use Change. Physics and Chemistry of the Earth, Parts A/B/C, 27, 655-662.
[56] Perlack, R.D., Wright, L.L. Turhollow, A.F. Graham, R.L., Stokes, B.J. and Erbach, D.C. (2005) Biomass as Feedstock for a Bioenergy and Bioproducts Industry: The Technical Feasibility of a Billion-Ton Annual Supply. Department of Energy, Oak Ridge.

Copyright © 2023 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.