Your search found 6 records
1 Sirinivasan, R.; Engel, B. A.. 1991. Expert system for irrigation management (ESIM) Agricultural Systems, 36(2):297-314.
(Location: IWMI-HQ Call no: PER Record No: H09501)
2 Mitchell, J. K.; Engel, B. A.; Srinivasan, E. R.; Wang, S. S. Y. 1993. Validation of AGNPS for small watersheds using an integrated AGNPS/GIS system. Water Resources Bulletin, 29(5):833-842.
Watersheds ; GIS ; Sedimentation ; Erosion ; Runoff ; Simulation models ; Statistics ; Hydrology ; Calibrations / USA / Illinois
(Location: IWMI-HQ Call no: PER Record No: H014070)
The AGNPS (Agricultural NonPoint Source) model was evaluated for predicting runoff and sediment delivery from small watersheds of mild topography. Fifty sediment yield events were monitored from two watersheds and five nested subwatersheds in East Central Illinois throughout the growing season of four years. Half of these events were used to calibrate parameters in the AGNPS model. Average calibrated parameters were used as input for the remaining events to obtain runoff and sediment yield data. These data were used to evaluate the suitability of the AGNPS model for predicting runoff and sediment yield from small, mild-sloped watersheds. An integrated AGNPS/GIS system was used to efficiently create the large number of data input changes necessary to this study. This system is one where the AGNPS model was integrated with the GRASS (Geographic Resources Analysis Support System) GIS (Geographical Information System) to develop a decision support tool to assist with management of runoff and erosion from agricultural watersheds. The integrated system assists with the development of input GIS layers to AGNPS, running the model, and interpretation of the results.
3 Lovejoy, S. B.; Lee, J. G.; Randhir, T. O.; Engel, B. A.. 1997. Research needs for water quality management in the 21st century: A spatial decision support system. Journal of Soil and Water Conservation, 52(1):18-22.
Water quality ; Research priorities ; Agricultural research ; Decision support tools ; Mathematical models ; Simulation models ; GIS ; Runoff ; Watersheds / USA
(Location: IWMI-HQ Call no: P 4728 Record No: H022064)
4 Manguerra, H. B.; Engel, B. A.. 1998. Hydrologic parameterization of watersheds for runoff prediction using SWAT. Journal of the American Water Resources Association, 34(5):1149-1162.
Watersheds ; Hydrology ; Runoff ; Forecasting ; Computer models ; GIS ; Subsurface drainage / USA / Indiana / Mississippi
(Location: IWMI-HQ Call no: PER Record No: H023789)
5 Homes, M. J.; Frankenberger, J. R.; Engel, B. A.. 2001. Susceptibility of Indiana watersheds to herbicide contamination. Journal of the American Water Resources Association, 37(4):987-1000.
Watershed management ; Water pollution ; Water quality ; Runoff ; Pesticide residues ; Stream flow ; Simulation models ; Regression analysis / USA / Indiana
(Location: IWMI-HQ Call no: PER Record No: H029199)
6 Tang, Y.; Zhang, F.; Engel, B. A.; Liu, X.; Yue, Q.; Guo, P. 2020. Grid-scale agricultural land and water management: a remote-sensing-based multiobjective approach. Journal of Cleaner Production, 265:121792. (Online first) [doi: https://doi.org/10.1016/j.jclepro.2020.121792]
Farmland ; Water management ; Remote sensing ; Irrigation water ; Sustainable agriculture ; Crop water use ; Water requirements ; Water use efficiency ; Water allocation ; Water productivity ; Water supply ; Virtual water ; Evapotranspiration ; Precipitation ; Ecosystem services ; Economic aspects / China / Heihe River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H049765)
(4.19 MB)
This paper developed a remote-sensing-based multiobjective (RSM) approach to formulate sustainable agricultural land and water resources management strategies at a grid scale. To meet the spatial resolution and accuracy need of agricultural management, downscaled precipitation data sets were obtained with the help of global precipitation measurement (GPM) data and other spatial information. Spatial crop water requirement information were obtained via the combination use of the Penman-Monteith method, remote sensing information (MOD16/PET) and virtual water theory. Through integrating these spatial data and considering the impact of different spatial environments on crop growth, a grid-based integer multiobjective programming (GIMP) model was developed to determine best suitable crop planting types at all grids. GIMP can simultaneously consider several conflicting objectives: crop growth suitability, crop spatial water requirements, and ecosystem service value. Further, GIMP results were inputted into a grid-based nonlinear fractional multiobjective programming (GNFMP) model with three objectives: maximize economic benefits, maximize water productivity, and minimize blue water utilization, to optimize irrigation-water allocation. To verify the validity of the proposed approach, a real-world application in the middle reaches of Heihe River Basin, northwest China was conducted. Results show that the proposed method can improve the ecosystem service value by 0.36 × 108 CNY, the economic benefit by 21.85%, the irrigation-water productivity by 25.92%, and reduce blue water utilization rate by 24.32% comparing with status quo.
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