AbstractWater is a primary source of life and is required in sufficient quantity and acceptable quality to sustain all human activities such as domestic, agricultural and industrial needs. This requirement however, is hardly fulfilled due to various natural and anthropogenic activities. Remote sensing and Geographic Information System (GIS) techniques are useful in hydrological research and applications. Remote sensing observations enable improved characterization of the land surface which are relevant in hydrological studies. Remote sensing with its merits of providing spatially extensive, multi-temporal and cost effective data, has become a very handy tool in identifying hydrogeological processes. These studies have revealed the application of integrated remote sensing and GIS technologies in groundwater exploration and exploitation. Integrated remote sensing and GIS are widely used in groundwater mapping. Locating potential groundwater targets is becoming more convenient, cost effective than invasive methods and efficient with the advent of a number of satellite imagery. The nature of remote sensing-based groundwater exploration is to delineate all possible features connected with localization of groundwater. Data, driven out of remote sensing, support decisions related to sustainable development and groundwater management. Integration of remotely sensed data, GPS, and GIS technologies provides a valuable tool for monitoring and assessing water pollution. Remotely sensed data can be used to create a permanent geographically located database to provide a baseline for future comparisons hydrological studies. The integrated use of remotely sensed data, GPS, and GIS will enable consultants and natural resource managers to develop management plans for a variety of natural resource management applications.
National Research Council. New Strategies for America's Watersheds. Washington DC, National Academy Press 1999.
Vieux BE. Geographic information systems and non-point source water quality and quantity modeling. Hydrological Processes 1991; 5: 101-13. http://dx.doi.org/10.1002/hyp.3360050108
Julien PY, Saghafian B and Ogden FL. Raster-based hydrologic modeling of spatially varied surface runoff. Water Resources Bulletin 1995; 31: 523-36. http://dx.doi.org/10.1111/j.1752-1688.1995.tb04039.x
Mitas L, Mitasova H, Brown WM and M Astley. Interacting fields approach for evolving spatial phenomena: Application to erosion simulation for optimized land use. In Proceedings of the Third International Conference Integrating GIS and Environmental Modeling, Santa Fe, New Mexico, 21-25 January. Santa Barbara, CA, National Center for Geographic Information and Analysis, University of California 1996.
Vieux BE and NS Farajalla. Temporal and spatial aggregation of NEXRAD rainfall estimates on distributed hydrologic modeling. In M F Goodchild, L T Steyaert, B O Parks, C Johnston, D R Maidment, M P Crane, and S Glendinning (eds) GIS and Environmental Modeling: Progress and Research Issues. Fort Collins, CO, GIS World Books 1996; 199-204.
Mitas L and H Mitasova. Distributed soil erosion simulation for effective erosion prevention. Water Resources Research 1998; 34: 505-16. http://dx.doi.org/10.1029/97WR03347
Shamsi UM. Storm-water management implementation through modeling and GIS. Journal of Water Resources Planning and Management 1996; 122: 114-27. http://dx.doi.org/10.1061/(ASCE)0733-9496(1996)122:2(114)
Djokic D and Maidment DR. Terrain analysis for urban storm water modeling. Hydrological Processes 1991; 5: 115-24. http://dx.doi.org/10.1002/hyp.3360050109
Orzol LL and McGrath TS. Modifications to the U.S. Geological Survey Modular Finite-difference Ground-water Flow Model to Read and Write Geographic Information System Files. Portland, OR, United States Geological Survey Open File Report No 1992; 92-50.
Hellweger FL and Maidment DR. Definition and connection of hydrologic elements using geographic data. Journal of Hydraulic Engineering 1999; 4: 10-8. http://dx.doi.org/10.1061/(asce)1084-0699(1999)4:1(10)
Vieux BE and Gauer N. Finite-element modeling of storm water runoff using the GRASS GIS. Microcomputers in Civil Engineering 1994; 9: 263-70. http://dx.doi.org/10.1111/j.1467-8667.1994.tb00334.x
Vieux BE, Farajalla NS and Gaur N. Integrated GIS and distributed storm water runoff modeling. In M F Goodchild, L T Steyaert, B O Parks, C Johnston, D R Maidment, M P Crane, and S Glendinning (eds) GIS and Environmental Modeling: Progress and Research Issues. Fort Collins, CO, GIS World Books 1996; 199-205.
Watkins DW, McKinney DC and Maidment DR. Use of geographic information systems in ground-water flow modeling. Journal of Water Resources Planning and Management 1996; 122: 88-96. http://dx.doi.org/10.1061/(ASCE)0733-9496(1996)122:2(88)
Wilson JP. Current and future trends in the development of integrated methodologies for assessing non-point source pollutants. In D L Corwin, K Loague, and T W Ellsworth (eds) Assessment of Non-Point Source Pollution in the Vadose Zone. Washington DC, American Geophysical Union 1999a; 343-61. http://dx.doi.org/10.1029/GM108p0343
Peters J. The HEC Hydrologic Modeling System. Davis, CA, U.S. Army Corps of Engineers, Hydrologic Engineering Center, Technical Paper No 150; 1995.
Leavesley GH, Restrepo PJ, Stannard LG, Frankoski LA and Sautins AM. MMS: A modeling framework for multidisciplinary research and operational applications. In M F Goodchild, L T Steyaert, B O Parks, C Johnston, D R Maidment, M P Crane, and S Glendinning (eds) GIS and Environmental Modeling: Progress and Research Issues. Fort Collins, CO, GIS World Books 1996a; 155-8.
Leavesley GH, Viger RJ, Markstrom SL and Brewer MS. Estimation and evaluation of spatially distributed model parameters using the Modular Modeling System (MMS). In Proceedings of the Third International Conference Integrating GIS and Environmental Modeling, Santa Fe, New Mexico, 21-25 January. Santa Barbara, CA, National Center for Geographic Information and Analysis, University of California 1996b.
Carbone GJ, Narumalani S and King M. Application of remote sensing and GIS technologies with physiological crop models. Photogrammetric Engineering and Remote Sensing 1996; 62: 171-9.
Wilkerson GG, Jones JW, Boote KJ, Ingram KT and Mishoe JW. Modeling soybean growth for management. Transactions of the American Society of Agricultural Engineers 1983; 26: 63-73. http://dx.doi.org/10.13031/2013.33877
Curry RB, Peart RM, Jones JW, Boote KJ and Allen LH. Response of crop yield to predicted changes in climate and atmospheric CO2 using simulation. Transactions of the American Society of Agricultural Engineers 1990; 33: 1383-90. http://dx.doi.org/10.13031/2013.31484
Wilson JP. Local, national, and global applications of GIS in agriculture. In P A Longley, M F Goodchild, D J Maguire, and D W Rhind (eds) Geographical Information Systems: Principles, Techniques, Management, and Applications. New York, NY, John Wiley and Sons 1999b; 981-98.
Corbett JD and Carter SE. Using GIS to enhance agricultural planning: The example of inter-seasonal rainfall variability in Zimbabwe. Transactions in GIS 1996; 1: 207-18. http://dx.doi.org/10.1111/j.1467-9671.1996.tb00045.x
Vincent V and Thomas RG. An Agroecological Survey of Southern Rhodesia: Part I, Agroecological Survey. Salisbury, Government Printer 1960.
Hutchinson MF, Nix HA, McMahon JP and Ord KD. The development of a topographic and climate database for Africa. In Proceedings of the Third International Conference Integrating GIS and Environmental Modeling, Santa Fe, New Mexico, 21-25 January. Santa Barbara, CA, National Center for Geographic Information and Analysis, University of California 1996.
Hutchinson MF. Interpolating mean rainfall using thin plate smoothing splines. International Journal of Geographical Information Systems 1995a; 9: 385-403. http://dx.doi.org/10.1080/02693799508902045
Hutchinson MF. Stochastic space-time weather models from ground-based data. Agricultural and Forest Meteorology 1995b; 73: 237-64. http://dx.doi.org/10.1016/0168-1923(94)05077-J
Deichmann U. A Medium Resolution Population Database for Africa. Santa Barbara, CA, National Center for Geographic Information and Analysis, University of California 1994.
Inskeep WP, Wraith JM, Wilson JP, Snyder RD and Macur RE. Input parameter and model resolution effects on predictions of solute transport. Journal of Environmental Quality 1996; 25: 453-62. http://dx.doi.org/10.2134/jeq1996.00472425002500030011x
Bliss NB and Reybold WU. Small-scale digital soil maps for interpreting natural resources. Journal of Soil and Water Conservation 1989; 44: 30-4.
Reybold WU and TeSelle GW. Soil geographic databases. Journal of Soil and Water Conservation 1989; 44: 28-9.
Nofziger DL and Hornsby AG. CMLS: Chemical Movement through Layered Soils Model User's Manual. Gainesville, FL, University of Florida 1987.
Wagenet RJ and Hutson JL. LEACHM (Leaching Estimation And CHemistry Model): A Process Based Model of Water and Solute Movement, Transformations, Plant Uptake, and Chemical Reactions in the Unsaturated Zone. Itacha, NY, Water Resources Institute, Cornell University 1989.
Petach MC, Wagenet RJ and DeGloria SD. Regional water flow and pesticide leaching using simulations with spatially distributed data. Geoderma 1991; 48: 245-69. http://dx.doi.org/10.1016/0016-7061(91)90047-W
Foussereau X, Hornsby AG and Brown RB. Accounting for the variability within map units when linking a pesticide fate model to soil survey. Geoderma 1993; 60: 257-76. http://dx.doi.org/10.1016/0016-7061(93)90030-O
Hutson JL and Wagenet RJ. A pragmatic field-scale approach for modeling pesticides. Journal of Environmental Quality 1993; 22: 494-9. http://dx.doi.org/10.2134/jeq1993.00472425002200030014x
Wilson JP, Inskeep WP, Rubright PR, Cooksey D, Jacobsen JS and Snyder RD. Coupling geographic information systems and models for weed control and groundwater protection. Weed Technology 1993; 6: 255-64.
Wilson JP, Inskeep WP, Wraith JM and Snyder RD. GISbased solute transport modeling applications: Scale effects of soil and climate data input. Journal of Environmental Quality 1996; 25: 445-53. http://dx.doi.org/10.2134/jeq1996.00472425002500030010x
Richardson CW and Wright DA. WGEN: A Model for Generating Daily Weather Variables. Washington DC, United States Department of Agriculture. Agricultural Research Service Report No 8; 1984.
Nielsen GA, Caprio JM, McDaniel PA, Snyder RD and Montagne C. MAPS: A GIS for land resource management in Montana. Journal of Soil and Water Conservation 1990; 45: 450-3.
Goodchild MF. The application of advanced technology in assessing environmental impacts. In D L Corwin and K Loague (eds) Application of GIS to the Modeling of Non-point Source Pollutants in the Vadose Zone. Madison, WI, Soil Science Society of America 1996; 1-17.
Hutchinson MF. A procedure for gridding elevation and stream line data with automatic removal of spurious pits. Journal of Hydrology 1989; 106: 211-32. http://dx.doi.org/10.1016/0022-1694(89)90073-5
Twigg DR. The Global Positioning System and its use for terrain mapping and monitoring. In S N Lane, K S Richards, and J H Chandler (eds) Landform Monitoring, Modelling, and Analysis. New York, NY, John Wiley and Sons 1998; 37-62.
Hammer RD, Young FJ, Wollenhaupt NC, Barney TL and Haithcoate TW. Slope class maps from soil survey and digital elevation models. Soil Science Society of America 1994; 59: 509-19. http://dx.doi.org/10.2136/sssaj1995.03615995005900020034x
Zhang W and Montgomery DR. Digital elevation model grid size, landscape representation and hydrologic simulations. Water Resources Research 1994; 30: 1019-28. http://dx.doi.org/10.1029/93WR03553
Hodgson ME. What cell size does the computed slope/aspect angle represent? Photogrammetric Engineering and Remote Sensing 1995; 61: 513-7.
Mitasova H, Hofierka J, Zlocha M and Iverson LR. Modeling topographic potential for erosion and deposition using GIS. International Journal of Geographical Information Systems 1996; 10: 629-41. http://dx.doi.org/10.1080/02693799608902101
Bolstad PV and Stowe T. An evaluation of DEM accuracy: Elevation, slope, and aspect. Photogrammetric Engineering and Remote Sensing 1994; 60: 1327-32.
Gao J. Resolution and accuracy of terrain representation by grid DEMs at a micro-scale. International Journal of Geographical Information Science 1997; 11: 199-212. http://dx.doi.org/10.1080/136588197242464
Moore ID. Hydrologic modeling and GIS. In M F Goodchild, L T Steyaert, B O Parks, C Johnston, D R Maidment, M P Crane, and S Glendinning (eds) GIS and Environmental Modeling: Progress and Research Issues. Fort Collins, CO, GIS World Books 1996; 143-8.
Carrara A, Bitelli G and Carla' R. Comparison of techniques for generating digital terrain models from contour lines. International Journal of Geographical Information Science R 11: 451-73.
Carrara A. Drainage and divide networks derived from highfidelity digital terrain models. In C F Chung, A G Fabbri, and P Larsen (eds) Quantitative Analysis of Mineral and Energy Resources. Dordrecht, Reidel 1988; 581-97. http://dx.doi.org/10.1007/978-94-009-4029-1_34
Carla' R, Carrara A and Federici G. Generazione di modelli digitali del terreno ad alto precisiione. Firenze, Quaderni Dip Ingegeria Civile 1987.
Graham ST, Famiglietti JS and Maidment DR. Five-minute, 1/2o, and 1o data sets of continental watersheds and river networks for use in regional and global hydrologic and climate system modeling studies. Water Resources Research 1999; 35: 583-7. http://dx.doi.org/10.1029/1998WR900068
Burrough PA, van Gaans PFM and Hootsmans R. Continuous classification in soil survey: Spatial correlation, confusion, and boundaries. Geoderma 1997; 77: 115-35. http://dx.doi.org/10.1016/S0016-7061(97)00018-9
McBratney AB and Odeh IOA. Application of fuzzy sets in soil science: Fuzzy logic, fuzzy measurements, and fuzzy decisions. Geoderma 1997; 77: 85-113. http://dx.doi.org/10.1016/S0016-7061(97)00017-7
Davis TJ and CP Keller. Modeling uncertainty in natural resource analysis using fuzzy sets and Monte Carlo simulation: Slope stability analysis. International Journal of Geographical Information Science 1997; 11: 409-34. http://dx.doi.org/10.1080/136588197242239
Lark RM and HC Bolam. Uncertainty in prediction and interpretation of spatially variable data on soils. Geoderma 1997; 77: 85-113. http://dx.doi.org/10.1016/S0016-7061(97)00025-6
Burrough PA. Natural objects with indeterminate boundaries. In P A Burrough and A U Frank (eds) Geographic Objects with Indeterminate Boundaries. London, Taylor and Francis 1996b; 3-28.
Bardossy A and Duckstein L. Fuzzy Rule-Based Modeling with Applications to Geophysical, Biological, and Engineering Systems. New York, NY, CRC Press 1995.
Paniconi C, Kleinfeldt S, Deckmyn J and Giacomelli A. Integrating GIS and data visualization tools for distributed hydrologic modeling. Transactions in GIS 1999; 3: 97-118. http://dx.doi.org/10.1111/1467-9671.00010
Clark MJ. Professional integrity and the social role of hydro- GIS. In K Kovar and H P Nachtnebel (eds) Application of Geographic Information Systems in Hydrology and Water Resources Management. Wallingford, International Association of Hydrological Sciences Publication No 1996; 235: 279-87.
Downs PW and Priestnall G. System design for catchmentscale approaches to studying river channel adjustments using a GIS. International Journal of Geographical Information Science 1999; 13: 247-66. http://dx.doi.org/10.1080/136588199241346
Howard AD. Modeling channel evolution and floodplain morphology. In M G Anderson, D E Walling, and P D Bates (eds) Floodplain Processes. Chichester, John Wiley and Sons 1996; 15-62.
Burrough PA. Opportunities and limitations of GIS-based modeling of solute transport at the regional scale. In D L Corwin and K Loague (eds) Application of GIS to the Modeling of Non-point Source Pollutants in the Vadose Zone. Madison, WI, Soil Science Society of America 1996a; 19-38.
Wilson JP and Lorang MS. Spatial models of soil erosion and GIS. In M Wegener and A S Fotheringham (eds) GIS and Spatial Models: New Potential for New Models? London, Taylor and Francis 1999; 83-108.
Mitas L, Brown WM and Mitasova H. Role of dynamic cartography in simulations of landscape processes based on multivariate fields. Computers and Geosciences 1997; 23: 437-46. http://dx.doi.org/10.1016/S0098-3004(97)00007-1
Hibbard WL and DA Santek. Visualizing large data sets in the earth sciences. Computer 1989; 22: 53-7. http://dx.doi.org/10.1109/2.35200
Fisher P, Dykes J and Wood J. Map design and visualization. Cartographic Journal 1993; 30: 136-42. http://dx.doi.org/10.1179/caj.19184.108.40.206
Rhyne T, Bolstad M, Rheingans P, Petterson L and Shackleford W. Visualizing environmental data at the EPA. IEEE Computer Graphics and Applications 1993; 13: 34-8. http://dx.doi.org/10.1109/38.204964
Hibbard WL, Paul BE, Battaiola AL, Santek DA, Voidrot- Martinez MF and CR Dyer. Interactive visualization of earth and space science computations. Computer 1994; 27: 65-72. http://dx.doi.org/10.1109/2.299413
Brown WM, Astley M, Baker T and Mitasova H. GRASS as an integrated GIS and visualization environment for spatiotemporal modeling. In Proceedings of Auto-Carto 12, Charlotte, North Carolina 1995; 89-99.
Johnston D and Reez W. Visualization of geographic data using the CAVE. WWW document, http://www.gis.uiuc.edu/hpgis/visualization_htm
Wilkinson GG. A review of current issues in the integration of GIS and remote sensing. International Journal of Geographical Information Systems 1996; 10: 85-101. http://dx.doi.org/10.1080/02693799608902068
Corwin DL. GIS applications of deterministic solute transport models for regional-scale assessment of non-point source pollutants in the vadose zone. In D L Corwin and K Loague (eds) Application of GIS to the Modeling of Non-point Source Pollutants in the Vadose Zone. Madison, WI, Soil Science Society of America 1996; 69-100. http://dx.doi.org/10.2136/sssaspecpub48.c5
Crum TD and Alberty RL. The WSR-88D and the WSR-88D operational support facility. Bulletin of the American Meteorological Society 1993; 74: 1669-87. http://dx.doi.org/10.1175/1520-0477(1993)074<1669:TWATWO>2.0.CO;2
Vieux BE and Bedient PB. Estimation of rainfall for flood prediction from WSR-88D reflectivity: A case study, 17-18 October 1994. Weather and Forecasting 1998; 13: 407-15. http://dx.doi.org/10.1175/1520-0434(1998)013<0407:EORFFP>2.0.CO;2
De Gruijter JJ, Walvoort DJJ and van Gaans PFM. Continuous soil maps: A fuzzy set approach to bridge the gap between aggregation levels of process and distribution models. Geoderma 1997; 77: 169-95. http://dx.doi.org/10.1016/S0016-7061(97)00021-9
Wesseling CG, Karssenberg DJ, Burrough PA and van Deursen WPA. Integrating dynamic environmental models in GIS: The development of a dynamic modeling language. Transactions in GIS 1996; 1: 40-8. http://dx.doi.org/10.1111/j.1467-9671.1996.tb00032.x
Custer SG, Farnes P, Wilson JP and Snyder RD. A comparison of hand- and spline-drawn precipitation maps for mountainous Montana. Water Resources Bulletin 1996; 32: 393-405. http://dx.doi.org/10.1111/j.1752-1688.1996.tb03461.x
Mackay DS and Band LE. Forest ecosystem processes at the watershed scale: Dynamic coupling of distributed hydrology and canopy growth. Hydrological Processes 1997; 11: 1197-1217. http://dx.doi.org/10.1002/(SICI)1099- 1085(199707)11:9<1197::AID-HYP552>3.0.CO;2-W
Kemp KK. Fields as a framework for integrating GIS and environmental process models: Part 1, Representing spatial continuity. Transactions in GIS 1997a; 1: 219-34. http://dx.doi.org/10.1111/j.1467-9671.1996.tb00046.x
Kemp KK. Fields as a framework for integrating GIS and environmental process models: Part 2, Specifying field variables. Transactions in GIS 1997b; 1: 235-46. http://dx.doi.org/10.1111/j.1467-9671.1996.tb00047.x
Heuvelink GBM. Identification of field attribute error under different models of spatial variation. International Journal of Geographical Information Systems 1996; 10: 921-35. http://dx.doi.org/10.1080/02693799608902117
Yuan M. Use of a three-domain representation to enhance GIS support for complex spatiotemporal queries. Transactions in GIS 1999; 4: 137-60. http://dx.doi.org/10.1111/1467-9671.00012
Renolen A. Modeling the real world: Conceptual modeling in spatiotemporal information system design. Transactions in GIS 2000; 4: 23-42. http://dx.doi.org/10.1111/1467-9671.00036
Mackay DS, Robinson VB and Band LE. Classification of higher order topographic objects on digital terrain data. Computers, Environment, and Urban Systems 1992; 16: 473-96. http://dx.doi.org/10.1016/0198-9715(92)90040-X
Robinson VB and Mackay DS. Semantic modeling for the integration of geographic information and regional hydroecological simulation management. Computers, Environment, and Urban Systems 1996; 19: 321-39. http://dx.doi.org/10.1016/0198-9715(95)00017-8
Srinivasan R and Engel BA. Effect of slope prediction methods on slope and erosion estimates. Applied Engineering in Agriculture 1991; 7: 779-83. http://dx.doi.org/10.13031/2013.26302
Panuska JC, Moore ID and Kramer LA. Terrain analysis: Integration into the agriculture non-point source (AGNPS) pollution model. Journal of Soil and Water Conservation 1991; 46: 59-64.
Vieux BE and Needham S. Nonpoint-pollution model sensitivity to grid-cell size. Journal of Water Resources Planning and Management 1993; 119: 141-57. http://dx.doi.org/10.1061/(ASCE)0733-9496(1993)119:2(141)
Vieux BE. DEM aggregation and smoothing effects on surface runoff modeling. Journal of Computing in Civil Engineering 1993; 7: 310-38. http://dx.doi.org/10.1061/(ASCE)0887-3801(1993)7:3(310)
Moore ID, Lewis A and Gallant JC. Terrain attributes: Estimation methods and scale effects. In A J Jakeman, M B Beck, and M J McAleer (eds) Modeling Change in Environmental Systems. New York, NY, John Wiley and Sons 1993; 189-214.
Issacson DL and Ripple WJ. Comparison of 7.5 minute and 1 degree digital elevation models. Photogrammetric Engineering and Remote Sensing 1991; 56: 1523-7.
Lagacherie P, Moussa R, Cormary D and Molenat J. Effects of DEM data source and sampling pattern on topographical parameters and on a topography-based hydrological model. In K Kovar and H P Nachtnebel (eds) Application of Geographic Information Systems in Hydrology and Water Resources Management. Wallingford, International Association of Hydrological Sciences Publication No 1996; 235: 191-9.
Chairat S and Delleur JW. Effects of the topographic index distribution on predicted runoff-using GRASS. Water Resources Bulletin 1993; 29: 1029-34. http://dx.doi.org/10.1111/j.1752-1688.1993.tb03266.x
Wolock DM and Price CV. Effects of digital elevation model and map scale and data resolution on a topography-based watershed model. Water Resources Research 1994; 30: 3041-52. http://dx.doi.org/10.1029/94WR01971
Garbrecht J and Martz LW. Grid size dependency of parameters extracted from digital elevation models. Computers and Geosciences 1994; 20: 85-7. http://dx.doi.org/10.1016/0098-3004(94)90098-1
Bates PD, Anderson MG and Horrit M. Terrain information in geomorphological models: Stability, resolution, and sensitivity. In S N Lane, K S Richards, and J H Chandler (eds) Landform Monitoring, Modeling, and Analysis. New York, NY, John Wiley and Sons 1998; 279-310.
Wilson JP, Spangrud DS, Nielsen GA, Jacobsen JS and Tyler DA. Effects of sampling intensity and pattern on GPSderived contour maps and estimated soil terrain attributes. Soil Science Society of America Journal 1998; 62: 1410-7. http://dx.doi.org/10.2136/sssaj1998.03615995006200050038x
Gahegan M. What is Geocomputation? Transactions in GIS 1999; 3: 203-6. http://dx.doi.org/10.1111/1467-9671.00017
Wolock DM and McCabe GJ. Comparison of single and multiple flow direction algorithms for computing topographic parameters in TOPMODEL. Water Resources Research 1995; 31: 1315-24. http://dx.doi.org/10.1029/95WR00471
Desmet PJJ and Govers G. Comparison of routing algorithms for digital elevation models and their implications for predicting ephemeral gullies. International Journal of Geographical Information Systems 1996; 10: 311-31. http://dx.doi.org/10.1080/02693799608902081
O'Callaghan JF and Mark DM. The extraction of drainage networks from digital elevation data. Computer Vision, Graphics and Image Processing 1984; 28: 323-44. http://dx.doi.org/10.1016/S0734-189X(84)80011-0
Fairfield J and Leymarie P. Drainage networks from grid digital elevation models. Water Resources Research 1994; 27: 709-717; 2809.
Freeman GT. Calculating catchment area with divergent flow based on a regular grid. Computers and Geosciences 1991; 17: 413-22. http://dx.doi.org/10.1016/0098-3004(91)90048-I
Quinn PF, Beven KJ, Chevallier P and Planchon O. The prediction of hillslope flow paths for distributed hydrological modeling using digital terrain models. Hydrological Processes 1991; 5: 59-79. http://dx.doi.org/10.1002/hyp.3360050106
Costa-Cabral MC and Burges SJ. Digital elevation model networks (DEMON): A model of flow over hillslopes for computation of contributing and dispersal areas. Water Resources Research 1994; 30: 1681-92. http://dx.doi.org/10.1029/93WR03512
Quinn PF, Beven KJ and Lamb R. The ln(a/tanb) index: How to calculate it and how to use it within the TOPMODEL framework. Hydrological Processes 1995; 9: 161-82. http://dx.doi.org/10.1002/hyp.3360090204
Tarboton DG. A new method for the determination of flow directions and upslope areas in grid digital elevation models. Water Resources Research 1997; 33: 309-19. http://dx.doi.org/10.1029/96WR03137
Mackay DS and Band LE. Extraction and representation of nested catchment areas from digital elevation models in lakedominated topography. Water Resources Research 1998; 34: 897-901. http://dx.doi.org/10.1029/98WR00094
Weih RC and Smith JL. The influence of cell slope computation algorithms on a common forest management decision. In M J Kraak and M Molenaar (eds) Advances in GIS Research II: Proceedings of the Seventh International Symposium on Spatial Data Handling. London, Taylor and Francis 1997; 857-75.
Lovejoy SB. Watershed management for water quality protection: Are GIS and simulation THE answer? Journal of Soil and Water Conservation 1997; 52: 103.
Bardossy A and Disse M. Fuzzy, rule-based models for infiltration. Water Resources Research 1993; 29: 373-82. http://dx.doi.org/10.1029/92WR02330