Modeling the effects of land use change from cotton (Gossypium hirsutum L.) to perennial bioenergy grasses on watershed hydrology and water quality under changing climate
Open accessIntegrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Growing perennial grasses reduces 30-40% of the total nitrogen loss compared with conventional cotton cropping system at the watershed scale.
“Growing perennial grasses reduces 30-40% of the total nitrogen loss compared with conventional cotton cropping system at the watershed scale”
From Bioenergy production and environmental impacts (2018) · cites this paper as Chen et al. 2017
Cites
River flow forecasting through conceptual models part I — A discussion of principles
Nash, J. E., & Sutcliffe, J. V. (1970) · Journal of Hydrology · doi:10.1016/0022-1694(70)90255-6 · Cited by 23,830 · see citing works
Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations
Moriasi, D. N., Arnold, J. G., Liew, M. W. V., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007) · Transactions of the ASABE · doi:10.13031/2013.23153 · Cited by 13,446 · see citing works
LARGE AREA HYDROLOGIC MODELING AND ASSESSMENT PART I: MODEL DEVELOPMENT <sup>1</sup>
Arnold, J. G., Srinivasan, R., Muttiah, R. S., & Williams, J. R. (1998) · JAWRA Journal of the American Water Resources Association · doi:10.1111/j.1752-1688.1998.tb05961.x · Cited by 7,945 · see citing works
Self-citationJl, M. (1965) · PubMed · Cited by 5,033 · see citing works
Anonim, A. (2010) · Cited by 4,945 · see citing works
Evaluating the use of “goodness‐of‐fit” Measures in hydrologic and hydroclimatic model validation
Legates, D. R., & McCabe, G. J. (1999) · Water Resources Research · doi:10.1029/1998wr900018 · Cited by 4,608 · see citing works
Soil and Water Assessment Tool Theoretical Documentation Version 2009
Neitsch, S. L., Arnold, J. G., Kiniry, J. R., & Williams, J. T. (2011) · OakTrust (Texas A&M University Libraries) · Cited by 3,980 · see citing works
Human-Induced Changes in the Hydrology of the Western United States
Barnett, T. P., Pierce, D. W., Hidalgo, H. G., Bonfils, C., Santer, B. D., Das, T., Bala, G., Wood, A. W., Nozawa, T., Mirin, A. A., et al. (2008) · Science · doi:10.1126/science.1152538 · Cited by 1,193 · see citing works
Climate change and global water resources
Arnell, N. W. (1999) · Global Environmental Change · doi:10.1016/s0959-3780(99)00017-5 · Cited by 1,159 · see citing works
RCP2.6: exploring the possibility to keep global mean temperature increase below 2°C
van Vuuren, D. P., Stehfest, E., den Elzen, M. G. J., Kram, T., van Vliet, J., Deetman, S., Isaac, M., Goldewijk, K. K., Hof, A. F., Beltrán, A. M., et al. (2011) · Climatic Change · doi:10.1007/s10584-011-0152-3 · Cited by 929 · see citing works
Medlyn, B. E., Barton, C. V. M., Broadmeadow, M. S. J., Ceulemans, R., Angelis, P. D., Forstreuter, M., Freeman, M., Jackson, S. B., Kellomäki, S., Laitat, É., et al. (2001) · New Phytologist · doi:10.1046/j.1469-8137.2001.00028.x · Cited by 703 · see citing works
Wand, S. J. E., Midgley, G. F., Jones, M. H., & Curtis, P. S. (1999) · Global Change Biology · doi:10.1046/j.1365-2486.1999.00265.x · Cited by 618 · see citing works
Evaporation: an introductory survey.
Penman, H. L. (1956) · Netherlands Journal of Agricultural Science · doi:10.18174/njas.v4i1.17768 · Cited by 604 · see citing works
Greenhouse warming and the 21st century hydroclimate of southwestern North America
Seager, R., & Vecchi, G. A. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0910856107 · Cited by 511 · see citing works
Schilling, K. E., Jha, M. K., Zhang, Y., Gassman, P. W., & Wolter, C. F. (2008) · Water Resources Research · doi:10.1029/2007wr006644 · Cited by 453 · see citing works
SWAT Ungauged: Hydrological Budget and Crop Yield Predictions in the Upper Mississippi River Basin
Srinivasan, R., Zhang, X., & Arnold, J. (2010) · Transactions of the ASABE · doi:10.13031/2013.34903 · Cited by 385 · see citing works
Self-citationClimate change sensitivity assessment of a highly agricultural watershed using SWAT
Ficklin, D. L., Luo, Y., Luedeling, E., & Zhang, M. (2009) · Journal of Hydrology · doi:10.1016/j.jhydrol.2009.05.016 · Cited by 370 · see citing works
Lewandowski, I., & Schmidt, U. (2006) · Agriculture, Ecosystems & Environment · doi:10.1016/j.agee.2005.08.003 · Cited by 310 · see citing works
Climate Change and Global Crop Productivity
(2001) · Journal of Agronomy and Crop Science · doi:10.1111/j.1439-037x.2001.0440f.x · Cited by 243 · see citing works
A Recommended Calibration and Validation Strategy for Hydrologic and Water Quality Models
Daggupati, P., Pai, N., Ale, S., et al. (2015) · Transactions of the ASABE · doi:10.13031/trans.58.10712 · Cited by 230 · see citing works
Self-citationCLIMATE CHHANGE SENSITIVITY ASSESSMENT ON UPPER MISSISSIPPI RIVER BASIN STREAMFLOWS USING SWAT
Jha, M. K., Arnold, J. G., Gassman, P. W., Giorgi, F., & Gu, R. R. (2006) · JAWRA Journal of the American Water Resources Association · doi:10.1111/j.1752-1688.2006.tb04510.x · Cited by 216 · see citing works
Water Quality Modeling for the Raccoon River Watershed Using SWAT
Jha, M. K., Gassman, P. W., & Arnold, J. G. (2007) · Transactions of the ASABE · doi:10.13031/2013.22660 · Cited by 185 · see citing works
(2007) · DORA Eawag (Swiss Federal Institute of Aquatic Science and Technology (Eawag)) · Cited by 180 · see citing works
Predicting Hydrologic Response to Climate Change in the Luohe River Basin Using the SWAT Model
Zhang, X., Srinivasan, R., & Hao, F. (2007) · Transactions of the ASABE · doi:10.13031/2013.23154 · Cited by 160 · see citing works
Self-citationEffect of climate change on watershed system: a regional analysis
Marshall, E., & Randhir, T. O. (2008) · Climatic Change · doi:10.1007/s10584-007-9389-2 · Cited by 156 · see citing works
The impacts of <i>Miscanthus</i> × <i>giganteus</i> production on the Midwest US hydrologic cycle
VANLOOCKE, A., BERNACCHI, C. J., & TWINE, T. E. (2010) · GCB Bioenergy · doi:10.1111/j.1757-1707.2010.01053.x · Cited by 150 · see citing works
Danalatos, N. G., Archontoulis, S. V., & Mitsios, I. (2006) · Biomass and Bioenergy · doi:10.1016/j.biombioe.2006.07.004 · Cited by 141 · see citing works
Global change impacts on hydrological processes in Alpine catchments
Zierl, B., & Bugmann, H. K. M. (2005) · Water Resources Research · doi:10.1029/2004wr003447 · Cited by 133 · see citing works
Adhikari, P., Ale, S., Bordovsky, J. P., Thorp, K. R., Modala, N. R., Rajan, N., & Barnes, E. M. (2015) · Agricultural Water Management · doi:10.1016/j.agwat.2015.10.011 · Cited by 120 · see citing works
Self-citationVulnerability assessment of climate-induced water shortage in Phoenix
Gober, P., & Kirkwood, C. W. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0911113107 · Cited by 117 · see citing works
CLIMATE CHANGE IMPACTS UNCERTAINTY FOR WATER RESOURCES IN THE SAN JOAQUIN RIVER BASIN, CALIFORNIA1
Brekke, L. D., Miller, N. L., Bashford, K. E., et al. (2004) · JAWRA Journal of the American Water Resources Association · doi:10.1111/j.1752-1688.2004.tb01016.x · Cited by 106 · see citing works
Land-based mitigation in climate stabilization
Rose, S. K., Ahammad, H., Eickhout, B., Fisher, B. S., Kurosawa, A., Rao, S., Riahi, K., & van Vuuren, D. P. (2011) · Energy Economics · doi:10.1016/j.eneco.2011.06.004 · Cited by 102 · see citing works
Biofuel, land and water: maize, switchgrass or<i>Miscanthus</i>?
Zhuang, Q., Qin, Z., & Chen, M. (2013) · Environmental Research Letters · doi:10.1088/1748-9326/8/1/015020 · Cited by 96 · see citing works
Wu, Y., Liu, S., & Gallant, A. L. (2012) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2012.04.058 · Cited by 90 · see citing works
POTENTIAL EFFECTS OF CLIMATE CHANGE ON RAINFALL EROSIVITY IN THE YELLOW RIVER BASIN OF CHINA
Zhang, G.-H., Nearing, M. A., & Liu, B.-Y. (2005) · Transactions of the ASAE · doi:10.13031/2013.18325 · Cited by 80 · see citing works
Ye, L., & Grimm, N. B. (2013) · Climatic Change · doi:10.1007/s10584-013-0827-z · Cited by 71 · see citing works
Panagopoulos, Y., Gassman, P. W., Arritt, R. W., Herzmann, D. E., Campbell, T., Vâlcu, A., Jha, M. K., Kling, C. L., Srinivasan, R., White, M. J., et al. (2015) · Iowa State University Digital Repository (Iowa State University) · doi:10.3965/j.ijabe.20150803.1497 · Cited by 54 · see citing works
Self-citationPark, J. Y., Ale, S., Teague, W. R., & Dowhower, S. L. (2017) · Journal of Soil and Water Conservation · doi:10.2489/jswc.72.2.102 · Cited by 54 · see citing works
Self-citationKiniry, J. R., Anderson, L., Johnson, M.-V. V., Behrman, K. D., Brakie, M. R., Burner, D., Cordsiemon, R. L., Fay, P. A., Fritschi, F., Houx, J. H., et al. (2012) · BioEnergy Research · doi:10.1007/s12155-012-9254-7 · Cited by 53 · see citing works
Quantifying the response of cotton production in eastern Australia to climate change
Williams, A., White, N. A., Mushtaq, S., Cockfield, G., Power, B., & Kouadio, L. A. (2014) · Climatic Change · doi:10.1007/s10584-014-1305-y · Cited by 53 · see citing works
Climate change projections for the Texas High Plains and Rolling Plains
Modala, N. R., Ale, S., Goldberg, D. W., et al. (2016) · Theoretical and Applied Climatology · doi:10.1007/s00704-016-1773-2 · Cited by 53 · see citing works
Self-citationPanagopoulos, Y., Gassman, P. W., Arritt, R. W., Herzmann, D. E., Campbell, T., Jha, M. K., Kling, C. L., Srinivasan, R., White, M. J., & Arnold, J. G. (2014) · Journal of Soil and Water Conservation · doi:10.2489/jswc.69.6.483 · Cited by 50 · see citing works
Self-citationKiniry, J. R., Johnson, M. V., Bruckerhoff, S. B., Kaiser, J., Cordsiemon, R. L., & Harmel, R. D. (2011) · BioEnergy Research · doi:10.1007/s12155-011-9116-8 · Cited by 48 · see citing works
Chen, Y., Ale, S., Rajan, N., & Munster, C. L. (2017) · GCB Bioenergy · doi:10.1111/gcbb.12434 · Cited by 33 · see citing works
Self-citationChen, Y., Ale, S., Rajan, N., Morgan, C. L. S., & Park, J.-Y. (2015) · GCB Bioenergy · doi:10.1111/gcbb.12304 · Cited by 30 · see citing works
Self-citationSoil Water Dynamics and Evapotranspiration under Annual and Perennial Bioenergy Crops
Yimam, Y. T., Ochsner, T. E., Kakani, V. G., & Warren, J. G. (2014) · Soil Science Society of America Journal · doi:10.2136/sssaj2014.04.0165 · Cited by 29 · see citing works
Zhou, X. V., Clark, C. D., Nair, S. S., Hawkins, S. A., & Lambert, D. M. (2015) · Agricultural Water Management · doi:10.1016/j.agwat.2015.06.018 · Cited by 25 · see citing works
Water quality impacts of converting intensively-managed agricultural lands to switchgrass
Sarkar, S., & Miller, S. A. (2014) · Biomass and Bioenergy · doi:10.1016/j.biombioe.2014.05.026 · Cited by 21 · see citing works
Impacts of biofuel-based land-use change on water quality and sustainability in a Kansas watershed
Yasarer, L. M. W., Sinnathamby, S., & Sturm, B. (2016) · Agricultural Water Management · doi:10.1016/j.agwat.2016.05.002 · Cited by 16 · see citing works
Wang, T., Park, S., Ansley, R. J., & Amosson, S. H. (2014) · BioEnergy Research · doi:10.1007/s12155-014-9475-z · Cited by 10 · see citing works
Chen, Y. J., Ale, S., & Rajan, N. (2016) · BioEnergy Research · doi:10.1007/s12155-016-9758-7 · Cited by 7 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Bioenergy production and environmental impacts
Wu, Y., Zhao, F., Liu, S., Wang, L., Qiu, L., Alexandrov, G., & Jothiprakash, V. (2018) · Geoscience Letters · doi:10.1186/s40562-018-0114-y · Cited by 138 · see citing works
Microbial biotechnology for bioenergy: general overviews
Aransiola, S. A., Adeniyi, O. S., Omoregie, I. P., Akinhanmi, F. O., Oniha, M. I., & Maddela, N. R. (2024) · Microbial Biotechnology for Bioenergy · doi:10.1016/b978-0-443-14112-6.00001-8 · Cited by 8 · see citing works
- Authors
- Chen, Y., Ale, S., Rajan, N., & Srinivasan, R.
- Affiliations
- Texas A&M University System (US)
- Texas A&M University (US)
- Corresponding author
- Ale, Srinivasulu
- Venue
- Agricultural Water Management
- Publisher
- Elsevier BV
- Published
- 2017-10
- Volume
- 192
- Pages
- 198-208
- ISSN
- 0378-3774 (ISSN-L)
- OpenAlex
- W2741050751
- Cited by
- 28
- References
- 52
- Citations by year
- 2025: 3 · 2024: 3 · 2023: 3 · 2022: 3 · 2021: 4 · 2020: 6 · 2019: 3 · 2018: 3
- FWCI
- 1.75
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (am, 302 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Agricultural Water Management (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Institute of Food and Agriculture, U.S. Department of Agriculture (NIFA-2012-67009-19595)
- Topics
- Physical Sciences › Environmental Science › Water Science and Technology › Hydrology and Watershed Management Studies
- Life Sciences › Agricultural and Biological Sciences › Agronomy and Crop Science › Bioenergy crop production and management
- Life Sciences › Agricultural and Biological Sciences › Ecology, Evolution, Behavior and Systematics › Climate change impacts on agriculture
- Keywords
- land use change, cotton, switchgrass, Miscanthus × giganteus, catchment hydrology, water quality, nitrogen loading, irrigation water use, climate change impacts, energy crops, Texas High Plains, crop yield, SWAT model
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review