Your search found 16 records
1 Campbell, K. L. (Ed.) 1995. Versatility of wetlands in the agricultural landscape. St. Joseph, MI, USA: ASAE. xii, 755p.
(Location: IWMI-HQ Call no: 333.91 G000 CAM Record No: H018644)
Proceedings of the International Conference jointly sponsored and planned by ASAE and AWRA, Hyatt Regency, Tampa, Florida, USA, 17-20 September 1995
2 ICID. 1996. 16th Congress on Irrigation and Drainage, Cairo, Egypt, 1996: Sustainability of Irrigated Agriculture - Transactions, Vol.1C, Q.47: Irrigation planning and management: Measures in harmony with the environment. New Delhi, India: ICID. xii, 340p.
(Location: IWMI-HQ Call no: ICID 631.7 G000 ICI Record No: H019547)
3 Jamison, W. L.; Schaack, J.; Burbank, W. 1996. Oakes test area. In ICID, 16th Congress on Irrigation and Drainage, Cairo, Egypt, 1996: Sustainability of Irrigated Agriculture - Transactions, Vol.1C, Q.47: R.1.08 Irrigation planning and management: Measures in harmony with the environment. New Delhi, India: ICID. pp.95-103.
(Location: IWMI-HQ Call no: ICID 631.7 G000 ICI Record No: H019554)
4 ICID. 1996. 16th Congress on Irrigation and Drainage, Cairo, Egypt, 1996: Sustainability of Irrigated Agriculture - Transactions, Vol.1E, Special session: The future of irrigation under increased demand from competitive uses of water and greater needs for food supply; Symposium: Management Information Systems in irrigation and drainage. New Delhi, India: ICID. xii, 240p.; iv, 157p.
(Location: IWMI-HQ Call no: ICID 631.7.1 G000 ICI Record No: H019571)
5 Jamison, W.; Schaack, J.; Weigel, J. 1996. Conceptual plan for the Turtle Lake irrigation and wildlife area. In ICID, 16th Congress on Irrigation and Drainage, Cairo, Egypt, 1996: Sustainability of Irrigated Agriculture - Transactions, Vol.1E, Special session: The future of irrigation under increased demand from competitive uses of water and greater needs for food supply - R.3; Symposium: Management Information Systems in irrigation and drainage. New Delhi, India: ICID. pp.29-41.
(Location: IWMI-HQ Call no: ICID 631.7.1 G000 ICI Record No: H019574)
6 Steele, D. D.; Scherer, T. F.; Prunty, L. D.; Stegman, E. C. 1996. Correction frequencies for four irrigation scheduling methods for corn. In Camp, C. R.; Sadler, E. J.; Yoder, R. E. (Eds.), Evapotranspiration and irrigation scheduling: Proceedings of the International Conference, November 3-6, 1996, San Antonio Convention Center, San Antonio, Texas. St. Joseph, MI, USA: ASAE. pp.309-316.
(Location: IWMI-HQ Call no: 631.7.1 G000 CAM Record No: H020594)
7 Scherer, T. F.; Erickson, T.; Egeberg, R. 1996. Electronically providing daily crop ET using a weather network. In Camp, C. R.; Sadler, E. J.; Yoder, R. E. (Eds.), Evapotranspiration and irrigation scheduling: Proceedings of the International Conference, November 3-6, 1996, San Antonio Convention Center, San Antonio, Texas. St. Joseph, MI, USA: ASAE. pp.497-502.
(Location: IWMI-HQ Call no: 631.7.1 G000 CAM Record No: H020623)
8 Piper, S.; Platt, J. 1998. Benefits from including wetland component in water supply projects. Journal of Water Resources Planning and Management, 124(4):230-233.
(Location: IWMI-HQ Call no: PER Record No: H022570)
9 Gu, R.; Deutschman, M. 2001. Hydrologic assessment of water losses in river. Journal of Water Resources Planning and Management, 127(1):6-12.
(Location: IWMI-HQ Call no: PER Record No: H027225)
10 Smith, Z. 1995. Managing water in the western United States: Lessons for India. In Moench, M. (Ed.), Groundwater law: The growing debate. Ahmedabad, India: VIKSAT. pp.122-142.
(Location: IWMI-HQ Call no: 631.7.3 G635 MOE Record No: H027688)
11 Johnston, J. R.; Allen, R. G.; Anderson, S. S. (Eds.) 1999. River basin management to meet competing needs: Proceedings from the USCID Conference on Shared Rivers, Park City, Utah, October 28-31, 1998. Denver, CO, USA: USCID. vii, 312p.
(Location: IWMI-HQ Call no: 333.91 G000 JOH Record No: H028188)
12 Shultz, S. D.; Fridgen, P. M. 2001. Floodplains and housing values: Implications for flood mitigation projects. Journal of the American Water Resources Association, 37(3):595-603.
(Location: IWMI-HQ Call no: PER Record No: H029176)
13 Jennings, T. L. 2002. Farm family adaptability and climate variability in the Northern Great Plains: Contemplating the role of meaning in climate change research. Culture & Agriculture, 24(2):52-63.
(Location: IWMI-HQ Call no: P 6393 Record No: H032595)
14 Manous, J. D.; Stefan, H. G. 2003. Projected sulfate redistribution as impacted by lake level stabilization scenarios: Devils lake, North Dakota. Journal of Water Resources Planning and Management, 129(5):399-408.
(Location: IWMI-HQ Call no: PER Record No: H032668)
(Location: IWMI HQ Call no: e-copy only Record No: H049637)
(21.70 MB) (21.7 MB)
Statistical time series models are increasingly being used to fit medium resolution time series provided by satellite sensors, such as Landsat, for terrestrial monitoring. Cloud and shadows, combined with low satellite repeat cycles, reduce surface observation availability. In addition, only a single year of data can be used where there is high inter-annual variation, for example, over many croplands. These factors reduce the ability to fit time series models and reduce model fitting accuracy. In solution, we propose a novel fill-and-fit (FF) approach for application to medium resolution satellite time series. In the ‘fill’ step, gaps are filled using a recently published algorithm developed to fill large-area gaps in satellite time series using no other satellite data. In the ‘fit’ step, a linear harmonic model is fitted to the gap-filled time series. The FF approach, and the conventional harmonic model fitting without gap filling, termed the F approach, are demonstrated using seven months of Landsat-7 and -8 surface reflectance Analysis Ready Data (ARD) over agricultural regions in North Dakota, Minnesota, Michigan, and Kansas. Synthetic model-predicted reflectance for days through the growing season are examined, and assessed quantitatively by comparison with an independent Landsat surface reflectance data set. The six Landsat reflective band root-mean-square difference (RMSD) between the predicted and the independent reflectance, considering millions of pixel observations for each ARD tile, show that the FF approach is more accurate than the F approach. The mean FF RMSD values varied from 0.025 to 0.026 for the four tiles, whereas the mean F RMSD values varied from 0.026 to 0.047. These mean FF RMSD values are <0.03 which is comparable to the uncertainty specification for the Landsat 8 OLI TOA reflectance, but greater than the atmospheric correction uncertainty in any Landsat 8 OLI band. The greatest RMSD values were found over the Minnesota tile and occurred due to a long period of missing data early in the growing season, and the smallest RMSD values were found for the Kansas tile because of the high availability of clear surface observations. The F approach could not be applied where there were insufficient clear surface observations to fit the model, and where the model was applied, the fitting was often sensitive to issues including gaps in the Landsat time series and the presence of undetected cloud- and shadow-contaminated observations. The FF approach could be applied to every ARD tile pixel location and the predicted reflectance was spatially-coherent and natural looking. Examples are shown that illustrate the potential of using FF predicted synthetic reflectance time series for land surface monitoring.
(Location: IWMI HQ Call no: e-copy only Record No: H049690)
(1.53 MB) (1.53 MB)
Data-driven irrigation planning can optimize crop yield and reduce adverse impacts on surface and ground water quality. We evaluated an irrigation scheduling strategy based on soil matric potentials recorded by wireless Watermark (WM) sensors installed in sandy loam and clay loam soils and soil-water characteristic curve data. Five wireless WM nodes (IRROmesh) were installed at each location, where each node consisted of three WM sensors that were installed at 15, 30, and 60 cm depths in the crop rows. Soil moisture contents, at field capacity and permanent wilting points, were determined from soil-water characteristic curves and were approximately 23% and 11% for a sandy loam, and 35% and 17% for a clay loam, respectively. The field capacity level which occurs shortly after an irrigation event was considered the upper point of soil moisture content, and the lower point was the maximum soil water depletion level at 50% of plant available water capacity in the root zone, depending on crop type, root depth, growth stage and soil type. The lower thresholds of soil moisture content to trigger an irrigation event were 17% and 26% in the sandy loam and clay loam soils, respectively. The corresponding soil water potential readings from the WM sensors to initiate irrigation events were approximately 60 kPa and 105 kPa for sandy loam, and clay loam soils, respectively. Watermark sensors can be successfully used for irrigation scheduling by simply setting two levels of moisture content using soil-water characteristic curve data. Further, the wireless system can help farmers and irrigators monitor real-time moisture content in the soil root zone of their crops and determine irrigation scheduling remotely without time consuming, manual data logging and frequent visits to the field
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