| Year |
Citation |
Score |
| 2023 |
Liang K, Zhang X, Liang XZ, Jin VL, Birru G, Schmer MR, Robertson GP, McCarty GW, Moglen GE. Simulating agroecosystem soil inorganic nitrogen dynamics under long-term management with an improved SWAT-C model. The Science of the Total Environment. 879: 162906. PMID 36934923 DOI: 10.1016/j.scitotenv.2023.162906 |
0.301 |
|
| 2020 |
Qi J, Zhang X, Yang Q, Srinivasan R, Arnold JG, Li J, Waldholf ST, Cole J. SWAT ungauged: Water quality modeling in the Upper Mississippi River Basin. Journal of Hydrology. 584. PMID 33627888 DOI: 10.1016/J.Jhydrol.2020.124601 |
0.55 |
|
| 2020 |
Wang Q, Qi J, Li J, Cole J, Waldhoff ST, Zhang X. Nitrate loading projection is sensitive to freeze-thaw cycle representation. Water Research. 186: 116355. PMID 32890809 DOI: 10.1016/J.Watres.2020.116355 |
0.419 |
|
| 2020 |
Kim S, Zhang X, Reddy AD, Dale BE, Thelen KD, Jones CD, Izaurralde RC, Runge T, Maravelias CT. Carbon-Negative Biofuel Production. Environmental Science & Technology. PMID 32786588 DOI: 10.1021/Acs.Est.0C01097 |
0.31 |
|
| 2020 |
Qi J, Zhang X, Lee S, Wu Y, Moglen GE, McCarty GW. Modeling sediment diagenesis processes on riverbed to better quantify aquatic carbon fluxes and stocks in a small watershed of the Mid-Atlantic region. Carbon Balance and Management. 15: 13. PMID 32632528 DOI: 10.1186/S13021-020-00148-1 |
0.312 |
|
| 2020 |
Romeiko XX, Lee EK, Sorunmu Y, Zhang X. Spatially and Temporally Explicit Life Cycle Environmental Impacts of Soybean Production in the U.S. Midwest. Environmental Science & Technology. PMID 32202767 DOI: 10.1021/Acs.Est.9B06874 |
0.331 |
|
| 2020 |
Sharara MA, Sahoo K, Reddy AD, Kim S, Zhang X, Dale B, Jones CD, Izaurralde RC, Runge TM. Sustainable feedstock for bioethanol production: Impact of spatial resolution on the design of a sustainable biomass supply-chain. Bioresource Technology. 302: 122896. PMID 32018088 DOI: 10.1016/J.Biortech.2020.122896 |
0.37 |
|
| 2020 |
Lee EK, Zhang WJ, Zhang X, Adler PR, Lin S, Feingold BJ, Khwaja HA, Romeiko XX. Projecting life-cycle environmental impacts of corn production in the U.S. Midwest under future climate scenarios using a machine learning approach. The Science of the Total Environment. 714: 136697. PMID 31982745 DOI: 10.1016/J.Scitotenv.2020.136697 |
0.365 |
|
| 2020 |
Fang Y, Chen X, velez JG, Zhang X, Duan Z, Hammond GE, Goldman AE, Garayburu-Caruso VA, Graham EB. A multirate mass transfer model to represent the interaction of multicomponent biogeochemical processes between surface water and hyporheic zones (SWAT-MRMT-R 1.0) Geoscientific Model Development Discussions. 13: 1-28. DOI: 10.5194/Gmd-2019-301 |
0.371 |
|
| 2020 |
Romeiko XX, Guo Z, Pang Y, Lee EK, Zhang X. Comparing Machine Learning Approaches for Predicting Spatially Explicit Life Cycle Global Warming and Eutrophication Impacts from Corn Production Sustainability. 12: 1481. DOI: 10.3390/Su12041481 |
0.39 |
|
| 2020 |
Jeong J, Zhang X. Model Application for Sustainable Agricultural Water Use Agronomy. 10: 396. DOI: 10.3390/Agronomy10030396 |
0.414 |
|
| 2020 |
Waldhoff ST, Wing IS, Edmonds J, Leng G, Zhang X. Future climate impacts on global agricultural yields over the 21st century Environmental Research Letters. DOI: 10.1088/1748-9326/Abadcb |
0.33 |
|
| 2020 |
Kim S, Zhang X, Reddy AD, Dale BE, Thelen KD, Jones CD, Izaurralde RC, Runge T, Maravelias C. Carbon-NegativeBiofuel Production Environmental Science & Technology. DOI: 10.1021/Acs.Est.0C01097.S001 |
0.308 |
|
| 2020 |
Qi J, Lee S, Zhang X, Yang Q, McCarty GW, Moglen GE. Effects of surface runoff and infiltration partition methods on hydrological modeling: A comparison of four schemes in two watersheds in the Northeastern US Journal of Hydrology. 581: 124415. DOI: 10.1016/J.Jhydrol.2019.124415 |
0.394 |
|
| 2020 |
Zhao F, Wu Y, Yao Y, Sun K, Zhang X, Winowiecki L, Vågen T, Xu J, Qiu L, Sun P, Sun Y. Predicting the climate change impacts on water-carbon coupling cycles for a loess hilly-gully watershed Journal of Hydrology. 581: 124388. DOI: 10.1016/J.Jhydrol.2019.124388 |
0.388 |
|
| 2020 |
Lee EK, Zhang X, Adler PR, Kleppel GS, Romeiko XX. Spatially and temporally explicit life cycle global warming, eutrophication, and acidification impacts from corn production in the U.S. Midwest Journal of Cleaner Production. 242: 118465. DOI: 10.1016/J.Jclepro.2019.118465 |
0.363 |
|
| 2020 |
Qi J, Du X, Zhang X, Lee S, Wu Y, Deng J, Moglen GE, Sadeghi AM, McCarty GW. Modeling riverine dissolved and particulate organic carbon fluxes from two small watersheds in the northeastern United States Environmental Modelling & Software. 124: 104601. DOI: 10.1016/J.Envsoft.2019.104601 |
0.347 |
|
| 2020 |
Wang Q, Qi J, Wu H, Zeng Y, Shui W, Zeng J, Zhang X. Freeze-Thaw cycle representation alters response of watershed hydrology to future climate change Catena. 195: 104767. DOI: 10.1016/J.Catena.2020.104767 |
0.429 |
|
| 2019 |
Yang Q, Zhang X, Almendinger JE, Huang M, Chen X, Leng G, Zhou Y, Zhao K, Asrar GR, Li X. Climate change will pose challenges to water quality management in the st. Croix River basin. Environmental Pollution (Barking, Essex : 1987). 251: 302-311. PMID 31091494 DOI: 10.1016/J.Envpol.2019.04.129 |
0.376 |
|
| 2019 |
Du X, Zhang X, Mukundan R, Hoang L, Owens EM. Integrating terrestrial and aquatic processes toward watershed scale modeling of dissolved organic carbon fluxes. Environmental Pollution (Barking, Essex : 1987). 249: 125-135. PMID 30884391 DOI: 10.1016/J.Envpol.2019.03.014 |
0.414 |
|
| 2019 |
Qiu J, Yang Q, Zhang X, Huang M, Adam JC, Malek K. Implications of water management representations for watershed hydrologic modeling in the Yakima River basin Hydrology and Earth System Sciences. 23: 35-49. DOI: 10.5194/Hess-23-35-2019 |
0.377 |
|
| 2019 |
Meng X, Zhang X, Yang M, Wang H, Chen J, Pan Z, Wu Y. Application and Evaluation of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) in Poorly Gauged Regions in Western China Water. 11: 2171. DOI: 10.3390/W11102171 |
0.396 |
|
| 2019 |
Yen H, Park S, Arnold JG, Srinivasan R, Chawanda CJ, Wang R, Feng Q, Wu J, Miao C, Bieger K, Daggupati P, Griensven Av, Kalin L, Lee S, Sheshukov AY, ... ... Zhang X, et al. IPEAT+: A Built-In Optimization and Automatic Calibration Tool of SWAT+ Water. 11: 1681. DOI: 10.3390/W11081681 |
0.471 |
|
| 2019 |
Qi J, Wang Q, Zhang X. On the Use of NLDAS2 Weather Data for Hydrologic Modeling in the Upper Mississippi River Basin Water. 11: 960. DOI: 10.3390/W11050960 |
0.416 |
|
| 2019 |
Zhao K, Wulder MA, Hu T, Bright R, Wu Q, Qin H, Li Y, Toman E, Mallick B, Zhang X, Brown M. Detecting change-point, trend, and seasonality in satellite time series data to track abrupt changes and nonlinear dynamics: A Bayesian ensemble algorithm Remote Sensing of Environment. 232: 111181. DOI: 10.1016/J.Rse.2019.04.034 |
0.354 |
|
| 2019 |
Qi J, Zhang X, Wang Q. Improving hydrological simulation in the Upper Mississippi River Basin through enhanced freeze-thaw cycle representation Journal of Hydrology. 571: 605-618. DOI: 10.1016/J.Jhydrol.2019.02.020 |
0.384 |
|
| 2019 |
Qi J, Zhang X, Cosh MH. Modeling soil temperature in a temperate region: A comparison between empirical and physically based methods in SWAT Ecological Engineering. 129: 134-143. DOI: 10.1016/J.Ecoleng.2019.01.017 |
0.321 |
|
| 2019 |
Qi J, Zhang X, Lee S, Moglen GE, Sadeghi AM, McCarty GW. A coupled surface water storage and subsurface water dynamics model in SWAT for characterizing hydroperiod of geographically isolated wetlands Advances in Water Resources. 131: 103380. DOI: 10.1016/J.Advwatres.2019.103380 |
0.36 |
|
| 2018 |
Liu Y, Hejazi M, Li H, Zhang X, Leng G. A hydrological emulator for global applications - HE v1.0.0 Geoscientific Model Development. 11: 1077-1092. DOI: 10.5194/Gmd-11-1077-2018 |
0.375 |
|
| 2018 |
Qi J, Zhang X, McCarty GW, Sadeghi AM, Cosh MH, Zeng X, Gao F, Daughtry CS, Huang C, Lang MW, Arnold JG. Assessing the performance of a physically-based soil moisture module integrated within the Soil and Water Assessment Tool Environmental Modelling & Software. 109: 329-341. DOI: 10.1016/J.Envsoft.2018.08.024 |
0.319 |
|
| 2018 |
Zhang X. Simulating eroded soil organic carbon with the SWAT-C model Environmental Modelling & Software. 102: 39-48. DOI: 10.1016/J.Envsoft.2018.01.005 |
0.322 |
|
| 2018 |
Liu D, Toman E, Fuller Z, Chen G, Londo A, Zhang X, Zhao K. Integration of historical map and aerial imagery to characterize long-term land-use change and landscape dynamics: An object-based analysis via Random Forests Ecological Indicators. 95: 595-605. DOI: 10.1016/J.Ecolind.2018.08.004 |
0.31 |
|
| 2018 |
Qiu J, Shen Z, Huang M, Zhang X. Exploring effective best management practices in the Miyun reservoir watershed, China Ecological Engineering. 123: 30-42. DOI: 10.1016/J.Ecoleng.2018.08.020 |
0.376 |
|
| 2018 |
Yang Q, Almendinger JE, Zhang X, Huang M, Chen X, Leng G, Zhou Y, Zhao K, Asrar GR, Srinivasan R, Li X. Enhancing SWAT simulation of forest ecosystems for water resource assessment: A case study in the St. Croix River basin Ecological Engineering. 120: 422-431. DOI: 10.1016/J.Ecoleng.2018.06.020 |
0.539 |
|
| 2018 |
Xu X, Zhang X, Fang H, Lai R, Zhang Y, Huang L, Liu X. Improving the real-time probabilistic channel flood forecasting by incorporating the uncertainty of inflow using the particle filter Journal of Hydrodynamics. 30: 828-840. DOI: 10.1007/S42241-018-0110-X |
0.357 |
|
| 2018 |
Yang Q, Zhang X, Almendinger JE, Huang M, Leng G, Zhou Y, Zhao K, Asrar GR, Li X, Qiu J. Improving the SWAT forest module for enhancing water resource projections: A case study in the St. Croix River basin Hydrological Processes. 33: 864-875. DOI: 10.1002/Hyp.13370 |
0.313 |
|
| 2017 |
Liu Y, Hejazi M, Li H, Zhang X, Leng G. A Hydrological Emulator for Global Applications Geoscientific Model Development Discussions. 1-37. DOI: 10.5194/Gmd-2017-113 |
0.374 |
|
| 2017 |
Yang Q, Zhang X, Xu X, Asrar GR. An Analysis of Terrestrial and Aquatic Environmental Controls of Riverine Dissolved Organic Carbon in the Conterminous United States Water. 9: 383. DOI: 10.3390/W9060383 |
0.354 |
|
| 2017 |
Shu L, Jiang Q, Zhang X, Zhao K. Potential and limitations of satellite laser altimetry for monitoring water surface dynamics: ICESat for US lakes International Journal of Agricultural and Biological Engineering. 10: 154-165. DOI: 10.25165/Ijabe.V10I5.3426 |
0.329 |
|
| 2017 |
Jones CD, Zhang X, Reddy AD, Robertson GP, Izaurralde RC. The greenhouse gas intensity and potential biofuel production capacity of maize stover harvest in the US Midwest Gcb Bioenergy. 9: 1543-1554. DOI: 10.1111/Gcbb.12473 |
0.377 |
|
| 2017 |
Xu X, Zhang X, Fang H, Lai R, Zhang Y, Huang L, Liu X. A real-time probabilistic channel flood-forecasting model based on the Bayesian particle filter approach Environmental Modelling & Software. 88: 151-167. DOI: 10.1016/J.Envsoft.2016.11.010 |
0.354 |
|
| 2017 |
Li W, Zhou Y, Cetin K, Eom J, Wang Y, Chen G, Zhang X. Modeling urban building energy use: A review of modeling approaches and procedures Energy. 141: 2445-2457. DOI: 10.1016/J.Energy.2017.11.071 |
0.322 |
|
| 2017 |
Izaurralde RC, McGill WB, Williams JR, Jones CD, Link RP, Manowitz DH, Schwab DE, Zhang X, Robertson GP, Millar N. Simulating microbial denitrification with EPIC: Model description and evaluation Ecological Modelling. 359: 349-362. DOI: 10.1016/J.Ecolmodel.2017.06.007 |
0.343 |
|
| 2017 |
Tan Z, Leung LR, Li H, Tesfa T, Vanmaercke M, Poesen J, Zhang X, Lu H, Hartmann J. A Global Data Analysis for Representing Sediment and Particulate Organic Carbon Yield in Earth System Models Water Resources Research. 53: 10674-10700. DOI: 10.1002/2017Wr020806 |
0.315 |
|
| 2016 |
Leng G, Zhang X, Huang M, Asrar GR, Leung LR. The Role of Climate Covariability on Crop Yields in the Conterminous United States. Scientific Reports. 6: 33160. PMID 27616326 DOI: 10.1038/Srep33160 |
0.332 |
|
| 2016 |
Yang Q, Zhang X. Improving SWAT for simulating water and carbon fluxes of forest ecosystems. The Science of the Total Environment. PMID 27401278 DOI: 10.1016/J.Scitotenv.2016.06.238 |
0.408 |
|
| 2016 |
Yang Q, Zhang X, Xu X, Asrar GR, Smith RA, Shih JS, Duan S. Spatial patterns and environmental controls of particulate organic carbon in surface waters in the conterminous United States. The Science of the Total Environment. 554: 266-275. PMID 26956174 DOI: 10.1016/J.Scitotenv.2016.02.164 |
0.36 |
|
| 2016 |
Cai X, Yang ZL, Fisher JB, Zhang X, Barlage M, Chen F. Integration of nitrogen dynamics into the Noah-MP land surface model v1.1 for climate and environmental predictions Geoscientific Model Development. 9: 1-15. DOI: 10.5194/Gmd-9-1-2016 |
0.441 |
|
| 2016 |
Leduc SD, Zhang X, Clark CM, Izaurralde RC. Cellulosic feedstock production on Conservation Reserve Program land: Potential yields and environmental effects Gcb Bioenergy. DOI: 10.1111/Gcbb.12352 |
0.367 |
|
| 2016 |
Gao J, Zhang A, Lam SK, Zhang X, Thomson AM, Lin E, Jiang K, Clarke LE, Edmonds JA, Kyle PG, Yu S, Zhou Y, Zhou S. An integrated assessment of the potential of agricultural and forestry residues for energy production in China Gcb Bioenergy. 8: 880-893. DOI: 10.1111/Gcbb.12305 |
0.344 |
|
| 2016 |
Li Z, Liu S, Zhang X, West TO, Ogle SM, Zhou N. Evaluating land cover influences on model uncertainties—A case study of cropland carbon dynamics in the Mid-Continent Intensive Campaign region Ecological Modelling. 337: 176-187. DOI: 10.1016/J.Ecolmodel.2016.07.002 |
0.392 |
|
| 2016 |
Cronin KR, Runge TM, Zhang X, Izaurralde RC, Reinemann DJ, Sinistore JC. Spatially Explicit Life Cycle Analysis of Cellulosic Ethanol Production Scenarios in Southwestern Michigan Bioenergy Research. 1-13. DOI: 10.1007/S12155-016-9774-7 |
0.369 |
|
| 2016 |
Leng G, Zhang X, Huang M, Yang Q, Rafique R, Asrar GR, Ruby Leung L. Simulating county‐level crop yields in the
C
onterminous
U
nited
S
tates using the
C
ommunity
L
and
M
odel:
T
he effects of optimizing irrigation and fertilization Journal of Advances in Modeling Earth Systems. 8: 1912-1931. DOI: 10.1002/2016Ms000645 |
0.381 |
|
| 2015 |
Yang Q, Tian H, Li X, Ren W, Zhang B, Zhang X, Wolf J. Spatiotemporal patterns of livestock manure nutrient production in the conterminous United States from 1930 to 2012. The Science of the Total Environment. 541: 1592-1602. PMID 26519911 DOI: 10.1016/J.Scitotenv.2015.10.044 |
0.334 |
|
| 2015 |
Beach RH, Cai Y, Thomson A, Zhang X, Jones R, McCarl BA, Crimmins A, Martinich J, Cole J, Ohrel S, Deangelo B, McFarland J, Strzepek K, Boehlert B. Climate change impacts on US agriculture and forestry: Benefits of global climate stabilization Environmental Research Letters. 10. DOI: 10.1088/1748-9326/10/9/095004 |
0.303 |
|
| 2015 |
Zhou Y, Smith SJ, Zhao K, Imhoff M, Thomson A, Bond-Lamberty B, Asrar GR, Zhang X, He C, Elvidge CD. A global map of urban extent from nightlights Environmental Research Letters. 10. DOI: 10.1088/1748-9326/10/5/054011 |
0.321 |
|
| 2015 |
Zhang X, Izaurralde RC, Manowitz DH, Sahajpal R, West TO, Thomson AM, Xu M, Zhao K, LeDuc SD, Williams JR. Regional scale cropland carbon budgets: Evaluating a geospatial agricultural modeling system using inventory data Environmental Modelling and Software. 63: 199-216. DOI: 10.1016/J.Envsoft.2014.10.005 |
0.448 |
|
| 2015 |
Zhao K, García M, Liu S, Guo Q, Chen G, Zhang X, Zhou Y, Meng X. Terrestrial lidar remote sensing of forests: Maximum likelihood estimates of canopy profile, leaf area index, and leaf angle distribution Agricultural and Forest Meteorology. 209: 100-113. DOI: 10.1016/J.Agrformet.2015.03.008 |
0.341 |
|
| 2015 |
Sinistore JC, Reinemann DJ, Izaurralde RC, Cronin KR, Meier PJ, Runge TM, Zhang X. Life Cycle Assessment of Switchgrass Cellulosic Ethanol Production in the Wisconsin and Michigan Agricultural Contexts Bioenergy Research. 8: 897-909. DOI: 10.1007/S12155-015-9611-4 |
0.359 |
|
| 2015 |
Wolf J, West TO, Le Page Y, Kyle GP, Zhang X, Collatz GJ, Imhoff ML. Biogenic carbon fluxes from global agricultural production and consumption Global Biogeochemical Cycles. 29: 1617-1639. DOI: 10.1002/2015Gb005119 |
0.318 |
|
| 2014 |
Zhang X, Sahajpal R, Manowitz DH, Zhao K, Leduc SD, Xu M, Xiong W, Zhang A, Izaurralde RC, Thomson AM, West TO, Post WM. Multi-scale geospatial agroecosystem modeling: a case study on the influence of soil data resolution on carbon budget estimates. The Science of the Total Environment. 479: 138-50. PMID 24561293 DOI: 10.1016/J.Scitotenv.2014.01.099 |
0.426 |
|
| 2014 |
Thomson AM, Kyle GP, Zhang X, Bandaru V, West TO, Wise MA, Izaurralde RC, Calvin KV. The contribution of future agricultural trends in the US Midwest to global climate change mitigation Global Environmental Change. 24: 143-154. DOI: 10.1016/J.Gloenvcha.2013.11.019 |
0.388 |
|
| 2014 |
Kyle P, Thomson A, Wise M, Zhang X. Assessment of the importance of spatial scale in long-term land use modeling of the Midwestern United States Environmental Modelling and Software. DOI: 10.1016/J.Envsoft.2015.06.006 |
0.381 |
|
| 2014 |
Xiong W, Balkovič J, van der Velde M, Zhang X, Izaurralde RC, Skalský R, Lin E, Mueller N, Obersteiner M. A calibration procedure to improve global rice yield simulations with EPIC Ecological Modelling. 273: 128-139. DOI: 10.1016/J.Ecolmodel.2013.10.026 |
0.441 |
|
| 2014 |
Sahajpal R, Zhang X, Izaurralde RC, Gelfand I, Hurtt GC. Identifying representative crop rotation patterns and grassland loss in the US Western Corn Belt Computers and Electronics in Agriculture. 108: 173-182. DOI: 10.1016/J.Compag.2014.08.005 |
0.376 |
|
| 2013 |
Bandaru V, Izaurralde RC, Manowitz D, Link R, Zhang X, Post WM. Soil carbon change and net energy associated with biofuel production on marginal lands: a regional modeling perspective. Journal of Environmental Quality. 42: 1802-14. PMID 25602420 DOI: 10.2134/Jeq2013.05.0171 |
0.404 |
|
| 2013 |
Zhang X, Izaurralde RC, Arnold JG, Williams JR, Srinivasan R. Modifying the Soil and Water Assessment Tool to simulate cropland carbon flux: model development and initial evaluation. The Science of the Total Environment. 463: 810-22. PMID 23859899 DOI: 10.1016/J.Scitotenv.2013.06.056 |
0.56 |
|
| 2013 |
Gelfand I, Sahajpal R, Zhang X, Izaurralde RC, Gross KL, Robertson GP. Sustainable bioenergy production from marginal lands in the US Midwest. Nature. 493: 514-7. PMID 23334409 DOI: 10.1038/Nature11811 |
0.361 |
|
| 2013 |
Zhao K, Valle D, Popescu S, Zhang X, Mallick B. Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection Remote Sensing of Environment. 132: 102-119. DOI: 10.1016/J.Rse.2012.12.026 |
0.366 |
|
| 2013 |
Zhang X, Beeson P, Link R, Manowitz D, Izaurralde RC, Sadeghi A, Thomson AM, Sahajpal R, Srinivasan R, Arnold JG. Efficient multi-objective calibration of a computationally intensive hydrologic model with parallel computing software in Python Environmental Modelling and Software. 46: 208-218. DOI: 10.1016/J.Envsoft.2013.03.013 |
0.539 |
|
| 2013 |
Egbendewe-Mondzozo A, Swinton SM, Izaurralde RC, Manowitz DH, Zhang X. Maintaining environmental quality while expanding biomass production: Sub-regional U.S. policy simulations Energy Policy. 57: 518-531. DOI: 10.1016/J.Enpol.2013.02.021 |
0.388 |
|
| 2012 |
Zhang X, Izaurralde RC, Zong Z, Zhao K, Thomson AM. Evaluating the Efficiency of a Multi-core Aware Multi-objective Optimization Tool for Calibrating the SWAT Model Transactions of the Asabe. 55: 1723-1731. DOI: 10.13031/2013.42363 |
0.398 |
|
| 2012 |
Zong Z, Job J, Zhang X, Nijim M, Qin X. Case study of visualizing global user download patterns using Google Earth and NASA World Wind Journal of Applied Remote Sensing. 6: 61703-61703. DOI: 10.1117/1.Jrs.6.061703 |
0.305 |
|
| 2012 |
Surendran Nair S, Kang S, Zhang X, Miguez FE, Izaurralde RC, Post WM, Dietze MC, Lynd LR, Wullschleger SD. Bioenergy crop models: Descriptions, data requirements, and future challenges Gcb Bioenergy. 4: 620-633. DOI: 10.1111/J.1757-1707.2012.01166.X |
0.439 |
|
| 2011 |
Zhang X, Liang F, Yu B, Zong Z. Explicitly integrating parameter, input, and structure uncertainties into Bayesian Neural Networks for probabilistic hydrologic forecasting Journal of Hydrology. 409: 696-709. DOI: 10.1016/J.Jhydrol.2011.09.002 |
0.313 |
|
| 2011 |
Nichols J, Kang S, Post W, Wang D, Bandaru V, Manowitz D, Zhang X, Izaurralde R. HPC-EPIC for high resolution simulations of environmental and sustainability assessment Computers and Electronics in Agriculture. 79: 112-115. DOI: 10.1016/J.Compag.2011.08.012 |
0.391 |
|
| 2011 |
Egbendewe-Mondzozo A, Swinton SM, Izaurralde CR, Manowitz DH, Zhang X. Biomass supply from alternative cellulosic crops and crop residues: A spatially explicit bioeconomic modeling approach Biomass and Bioenergy. 35: 4636-4647. DOI: 10.1016/J.Biombioe.2011.09.010 |
0.403 |
|
| 2011 |
Shi P, Chen C, Srinivasan R, Zhang X, Cai T, Fang X, Qu S, Chen X, Li Q. Evaluating the SWAT Model for Hydrological Modeling in the Xixian Watershed and a Comparison with the XAJ Model Water Resources Management. 25: 2595-2612. DOI: 10.1007/S11269-011-9828-8 |
0.448 |
|
| 2011 |
Xie H, Zhang X, Yu B, Sharif H. Performance evaluation of interpolation methods for incorporating rain gauge measurements into NEXRAD precipitation data: A case study in the Upper Guadalupe River Basin Hydrological Processes. 25: 3711-3720. DOI: 10.1002/Hyp.8096 |
0.37 |
|
| 2011 |
Zhang X, Srinivasan R, Arnold J, Izaurralde RC, Bosch D. Simultaneous calibration of surface flow and baseflow simulations: A revisit of the SWAT model calibration framework Hydrological Processes. 25: 2313-2320. DOI: 10.1002/Hyp.8058 |
0.531 |
|
| 2010 |
Srinivasan R, Zhang X, Arnold J. SWAT Ungauged: Hydrological Budget and Crop Yield Predictions in the Upper Mississippi River Basin Transactions of the Asabe. 53: 1533-1546. DOI: 10.13031/2013.34903 |
0.582 |
|
| 2010 |
Sexton AM, Sadeghi AM, Zhang X, Srinivasan R, Shirmohammadi A. Using NEXRAD and rain gauge precipitation data for hydrologic calibration of SWAT in a Northeastern watershed. Transactions of the Asabe. 53: 1501-1510. DOI: 10.13031/2013.34900 |
0.56 |
|
| 2010 |
Zhang X, Izaurralde RC, Manowitz DH, West TO, Post WM, Thomson AM, Bandaru VP, Nichols J, Williams JR. An integrative modeling framework to evaluate the productivity and sustainability of biofuel crop production systems Gcb Bioenergy. 2: 258-277. DOI: 10.1111/J.1757-1707.2010.01046.X |
0.412 |
|
| 2010 |
Zhang X, Srinivasan R. GIS-based spatial precipitation estimation using next generation radar and raingauge data Environmental Modelling and Software. 25: 1781-1788. DOI: 10.1016/J.Envsoft.2010.05.012 |
0.521 |
|
| 2010 |
Zhang X, Srinivasan R, Van Liew M. On the use of multi-algorithm, genetically adaptive multi-objective method for multi-site calibration of the SWAT model Hydrological Processes. 24: 955-969. DOI: 10.1002/Hyp.7528 |
0.485 |
|
| 2009 |
Zhang X, Srinivasan R. Gis-based spatial precipitation estimation: A comparison of geostatistical approaches Journal of the American Water Resources Association. 45: 894-906. DOI: 10.1111/J.1752-1688.2009.00335.X |
0.478 |
|
| 2009 |
Zhang X, Srinivasan R, Van Liew M. Approximating SWAT model using artificial neural network and support vector machine Journal of the American Water Resources Association. 45: 460-474. DOI: 10.1111/J.1752-1688.2009.00302.X |
0.492 |
|
| 2009 |
Zhang X, Liang F, Srinivasan R, Van Liew M. Estimating uncertainty of streamflow simulation using Bayesian neural networks Water Resources Research. 45. DOI: 10.1029/2008Wr007030 |
0.494 |
|
| 2009 |
Zhang X, Srinivasan R, Bosch D. Calibration and uncertainty analysis of the SWAT model using Genetic Algorithms and Bayesian Model Averaging Journal of Hydrology. 374: 307-317. DOI: 10.1016/J.Jhydrol.2009.06.023 |
0.537 |
|
| 2009 |
Zhang X, Srinivasan R, Zhao K, Van Liew M. Evaluation of global optimization algorithms for parameter calibration of a computationally intensive hydrologic model Hydrological Processes. 23: 430-441. DOI: 10.1002/Hyp.7152 |
0.469 |
|
| 2008 |
Zhang X, Srinivasan R, Liew MV. Multi-Site Calibration of the SWAT Model for Hydrologic Modeling Transactions of the Asabe. 51: 2039-2049. DOI: 10.13031/2013.25407 |
0.496 |
|
| 2008 |
Zhang X, Srinivasan R, Debele B, Hao F. Runoff simulation of the headwaters of the yellow river using the SWAT model with three snowmelt algorithms Journal of the American Water Resources Association. 44: 48-61. DOI: 10.1111/J.1752-1688.2007.00137.X |
0.512 |
|
| 2007 |
Zhang X, Srinivasan R, Hao F. Predicting Hydrologic Response to Climate Change in the Luohe River Basin Using the SWAT Model Transactions of the Asabe. 50: 901-910. DOI: 10.13031/2013.23154 |
0.533 |
|
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