Glossary of Units of Measurement and Chemical Formulas

Glossary of Units of Measurement and Chemical Formulas Units of Measurement ac af cm ds/m C EC Gt C/yr g gc/mj gc /m 2 /yr gkg 1 g/l ha hm 3 kg kg/m 3 km km 2 km 3 L m m 2 m 3 m 3 a 1 m 3 ha 1 m 3 /sec m 3 t 1 mi 2 Mm 3 μg/l μm acre acre-feet centimeter decisiemens per meter degree Celsius electrical conductivity gigaton of carbon per year gram gram of carbon per mega joule gram of carbon per meter square per year gram per kilogram gram per liter hectare cubic hectometer kilogram kilogram per cubic meter kilometer square kilometer cubic kilometer liter meter square meter cubic meter cubic meter per year cubic meter per hectare cubic meter per second cubic meter per ton square mile million cubic meters microgram per liter micrometer G. Schneier-Madanes, M.-F. Courel (eds.), Water and Sustainability in Arid Regions, DOI 10.1007/978-90-481-2776-4_BM2, C Springer Science+Business Media B.V. 2010 333

334 Glossary of Units of Measurement and Chemical Formulas μmhos/cm mi mg mg/kg mg/l mm Mha Mha y 1 Mt y 1 mon Ngg 1 sec t yr micromhos per centimeter mile milligram milligram per kilogram milligrams per liter millimeter million hectares million hectares per year metric ton per year month nanogram per gram second ton year Chemical Formulas Cl F Fe +3 Mn +2 NO 2 NO 3 PO 3 4 SO 2 4 chloride fluoride iron (+3) cation manganese (+2) cation nitrite nitrate phosphate sulphate

Color Plates Plate 1 Study area location. (See also Fig. 2.1 on p. 18) 335

336 Color Plates Plate 2 Soil saline levels. (See also Fig. 2.4 on p. 27)

Color Plates 337 Plate 3 SSI (Soil Salinity Index) image calculated from the Landsat ETM+ image acquired on October 7, 2002, and photos showing the surface state of salt-affected soils in different parts of the study area. The class numbers correspond to the different degrees of salinity in ascending order. (See also Fig. 4.8 on p. 63)

338 Color Plates Plate 4 Classification image of Landsat MSS acquired on June 23, 1977. The classes from NDVI_class1 to NDVI_class7 were obtained by binning the NDVI values; higher class numbers indicate higher NDVI values and thus greater density and greenness of vegetation. (See also Fig. 4.9 on p. 66) Plate 5 Classification image of Landsat TM acquired on October 17, 1991. The classes from NDVI_class1 to NDVI_class7 were obtained by binning the NDVI values; higher class numbers indicate higher NDVI values and thus greater density and greenness of vegetation. (See also Fig. 4.10 on p. 66)

Color Plates 339 Plate 6 Classification image of Landsat ETM+ acquired on October 7, 2002. The classes from NDVI_class1 to NDVI_class7 were obtained by binning the NDVI values; higher class numbers indicate higher NDVI values and thus greater density and greenness of vegetation. (See also Fig. 4.11 on p. 67) Plate 7 SSI (Soil Salinity Index) calculated from Landsat ETM+ image acquired on October 7, 2002. Class numbers correspond to different degrees of salinity in ascending order. (See also Fig. 4.12 on p. 67)

340 Color Plates Plate 8 SSI (Soil Salinity Index) calculated from the image acquired on October 17, 1991. Class numbers correspond to the different degrees of salinity in ascending order. (See also Fig. 4.13 on p. 68) Plate 9 Dry areas of the world today (See also Fig. 7.5 on p. 111)

Color Plates 341 Plate 10 Precipitation anomalies (top: mm/day, bottom: %) calculated for the IPCC B2 scenario with the French CNRM model, comparing the averages for 1950 1999 and 2050 2099. Left: for December March. Right: for June September. (Source: Académie des Sciences 2006) (See also Fig. 7.6 on p. 112)

342 Color Plates Plate 11 Organization of the khettaras in central Haouz, west Marrakech. (Most of the seguias still run, while most khettaras are out of order) (Source: ORMVA Archives) (See also Fig. 10.6 on p. 156)

Color Plates 343 Plate 12 Organization of the khettaras in Tafilalet province. (Source: Thierry Ruf 2008, based on ORMVA-TF-SCET 1983. Plan directeur de mise en valeur agricole du Tafilalet Vol. No. 9, Rapport de synthèse) (See also Fig. 10.7 on p. 158)

344 Color Plates Plate 13 Population growth rates in the Southwest US (See also Fig. 14.1 on p. 222) Plate 14 The D1 and D2 curves used by the Rio Grande project (See also Fig. 14.6 on p. 233)

Color Plates 345 Plate 15 Visualizing groundwater pumping impacts (See also Fig. 14.7 on p. 235) Plate 16 Example of downscaled GCM projections for the US Southwest. The map to the left represents data at the GCM resolution. (Source: Cañon et al. 2008a) (See also Fig. 14.12 on p. 241)

346 Color Plates Plate 17 DSS results for 2010 and 2020 development scenarios. The arrows indicate the direction of change in riparian groundwater levels; the color of arrows indicates the magnitude of change. Red represents greater change, and green represents smaller change. (Source: Kang et al. 2009) (See also Fig. 14.13 on p. 241)

Index A Afforestation, 7 8, 13, 297, 298, 299, 304 Agriculture, 12, 55, 71, 76, 77 79, 80, 81, 84, 85, 88, 92, 95, 96, 97, 99, 100, 104, 105, 106, 107, 113, 114, 115, 118, 119, 166, 187, 208, 212, 214, 217, 227, 228, 234, 242, 249, 254, 261, 262, 266, 267, 276, 277, 279, 283, 287, 297, 314, 315, 316, 322, 323, 324, 325 Algeria, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 104, 151, 206, 212, 213, 214, 215, 216 Aquifer, 58, 76, 80, 88, 90, 91 92, 93, 94, 100, 105, 112, 118, 126, 128, 132, 135, 136, 209, 211, 213, 215, 222, 223, 228 230, 233, 242, 250, 276, 279, 281, 289, 304, 313, 314, 319 Archaeology, 54, 181 200 Architecture, 166, 167, 169 171, 172, 174, 176 Argentina, 77, 78, 207, 261 273, 277, 278 284, 289 Arsenic, 275 290 B Backpropagation neural model, 271 Blue water, 104, 107, 108, 110, 113, 208, 209, 212 Borderlands, 245 258 C Chile, 127, 262, 263, 277, 284 287, 289 China, 3 13, 16, 17, 34, 35, 36, 40, 51 72, 78, 80, 117, 126, 140, 141, 147, 165, 182, 207, 315 Classifier, 20, 24 28 Climate, 3, 7, 9, 17, 34, 35, 40, 44, 52, 54, 57, 61, 81, 82, 87, 88, 105, 110 113, 114, 116, 117, 119, 120, 126, 142, 158, 167, 168, 194, 205, 210, 227, 229, 230, 239 242, 246, 248, 249, 262, 263, 267, 278, 285, 296, 297, 299, 301, 304, 305, 312, 315, 319, 320, 327, 329 Compact, 129, 170, 227, 231, 232, 235, 326 Comprehensive water balance model, 108 Conchos, The, 238 239 Conjunctive water management, 221 243 Conservation, 4, 13, 19, 84, 99, 100, 106, 120, 171, 176, 197, 213, 216, 228, 229, 258, 298, 302, 303, 304, 314, 318, 323 324, 325 Contamination, 83, 100, 130, 250, 275 278, 280, 287, 289, 290, 297, 326 D Decision tree, 20, 24, 28, 29 Degradation, 4, 5, 6 7, 10, 12, 13, 16, 18, 54 57, 61, 62, 65, 70, 71, 76, 170, 278, 295 298, 301, 306 Dependency ratio, 210, 213 Desalinization, 93, 94 Desertification, 5 6, 8, 9, 11, 12, 13, 16, 34, 54, 55, 56, 65, 76, 80, 81, 82, 88, 100, 187, 191, 193, 300, 302, 312 Development, 4, 6, 7, 9 13, 36 38, 55, 68, 75, 81, 84, 85, 91, 92, 93, 94, 97, 99, 100, 107, 114, 116, 119, 120, 136 137, 147, 152, 153, 166, 176, 178, 191, 196, 213, 214, 216, 217 218, 224 225, 228, 229, 241, 246, 247, 249, 256, 257, 276, 285, 306, 312, 313, 314, 316, 317, 319, 320, 322, 323, 324, 325, 327, 346 Digital recording, 166 Drilling, 98, 135, 136, 157, 158, 159, 213, 224 Drought, 4, 35, 56, 79, 106, 111, 112, 113, 116 118, 119, 136, 157, 167, 186, 208, 223, 227 228, 230, 232, 234, 235, 238, 239, 242, 246, 248, 249, 257, 296, 304, 312, 315, 319, 324, 326 347

348 Index Drylands, 296, 297, 298, 299, 300, 302, 303, 304, 305, 306, 312, 313, 318, 320, 321, 322, 323, 324, 325 E Economic growth, 214, 246 Eco-reconstruction, 3 13 Ecotone, 28, 63, 65, 70 Edwards Aquifer, 228 230 Efficiency, 10, 12, 35, 36, 37, 38, 39, 79, 84, 88, 100, 108, 110, 114, 115, 116, 118 119, 169, 174, 209, 224, 250, 251, 252, 253, 257, 288, 299, 320, 321, 325, 326, 328 Enclosure, 7, 8, 13, 167, 189, 197 ETM+ (Landsat Enhanced Thematic Mapper Plus), 18, 19, 21, 22, 23, 24, 27, 58, 59, 62, 63, 67 Evaluation, 228, 296, 299 302, 303, 304, 305, 306 F Foggara, 100, 126, 145, 151 Food (production, security, trade), 77, 104, 105 107, 108, 110, 114, 115, 116, 117, 118 119, 120, 217, 276, 312 Forecasts, 229, 261 273 G Globalization, 316, 327 Great Man-Made Project, 94, 319 Greening project, 7 8 Green water, 104, 108, 109, 110, 113, 208, 212, 217 Groundwater, 6, 11, 12, 54, 56, 58, 59, 61, 62, 65, 69, 70, 76, 77, 78, 79 80, 82, 84, 89, 91, 92, 93, 94, 98, 100, 104, 105, 107, 126, 127, 128, 129, 130, 134, 135, 136, 142, 145, 146, 159, 209, 213, 221, 222, 223, 224 226, 228 230, 232, 234, 235, 236, 237, 241, 242, 249, 250, 276, 277, 278, 279, 281, 283, 284, 285, 297, 303, 313, 315, 318 319, 322, 328, 345, 346 H Health, 9, 16, 118, 120, 257, 275, 276, 277, 278, 281, 283, 284, 286, 296, 301, 302, 313, 314, 317, 322, 328 Heritage, 147, 157, 165 178 I Inhabited space, 167 169, 173 Institution, 255 Institutional framework, 254 257 Iran, 75 85, 125 137, 142, 145, 151, 209, 212, 216 Irrigation, 4, 6, 8, 11, 12, 18, 54, 56, 58, 61, 65, 69, 70, 71, 76, 77, 78, 79, 81, 82, 84, 96, 97, 99, 100, 101, 104, 105, 106, 107, 108, 109, 113, 114, 115, 118 K Kanerjing, 126, 141, 148 Karez, 139 148, 166 Karhunen Loeve Transform, 22 24 Kashgar (Kashi), 165 166 Keriya, 52, 57, 58, 59, 62, 65, 68, 69, 70, 71, 72, 181 189, 190, 191, 193, 195, 198, 199, 200 Khettara, 100, 126, 156 162, 342, 343 L Libya, 88, 91, 92, 93, 94, 95, 96, 97, 104, 212, 213, 214, 215, 216, 217, 319 Livestock, 70, 71, 96, 141, 187, 189, 277, 279, 282, 283, 302 Loggia, 170 171, 172, 174, 176, 177 M Maghreb, 87 101 Maquiladora, 245, 246, 248 Marrakech, 151, 152, 153, 155, 156, 157, 158, 159, 160, 342 Mediterranean, 87, 88, 100, 104, 112, 126, 295 307 MEI (Multivariate ENSO Index), 262, 267, 269, 270, 271, 272, 273 Mexico, 77, 78, 127, 207, 213, 218, 221, 222, 223, 225, 226, 227, 230, 232, 233, 234, 235, 236, 238, 242, 245 258, 283, 316, 317, 323, 326 Mining, 7, 11, 77, 83, 166, 213, 217, 276, 277, 284, 285 Morocco, 77, 78, 88, 89, 90, 94, 95, 96, 99, 100, 126, 151 162, 209, 216 N NDVI (Normalized Difference Vegetation Index), 15, 20, 21, 24, 26, 28, 38, 59, 60, 61, 65, 66, 67, 72, 338, 339 Net primary productivity (NPP), 34, 35, 37, 39, 40 44, 45, 46, 47, 48, 49 Neural network, 20, 262, 270 272 Non-photosynthetic, 34 Non-renewable resources, 94, 206, 212, 217, 319

Index 349 O Oasis, 10, 11 13, 15 29, 34, 35, 40, 51 72, 137, 146, 152, 153, 157, 159, 165 178, 181 200, 214 Overgrazing, 5, 6, 7, 9, 65, 70, 72, 96 P Persia, 76, 77, 100, 125, 126, 127, 129, 139, 140, 142, 182, 321 Photosynthetic, 34, 35, 37, 38, 46 Pollution, 83, 88, 100, 107, 135, 166, 209, 210, 250, 313, 314 Prior appropriation, 222, 224 225, 238 Q Qanat, 80, 82, 100, 125 137, 139, 140, 145, 151, 209 R REACTION, 300 302, 303, 304 Reclamation, 7, 9, 10, 11, 61, 70, 125, 222, 226, 227, 323 Regression model, 268 269 Remote sensing, 15 29, 33 49, 58, 61, 62, 64, 65, 71, 148, 200 Renewable resources (internal and external), 89, 134, 206, 211, 212, 213 Restoration, 47, 147, 200, 295 307 Rio Grande Project, 230 236, 344 Riparian rights, 221, 224 S Sahara, 87, 88, 90, 91, 92, 104, 114, 115, 145, 154, 167, 206, 209, 213, 214, 215, 319 Salinization, 4, 5, 6, 10, 11, 12, 15 29, 34, 54, 55, 56, 61, 68, 69, 71, 72, 80 83, 93, 94, 105, 118, 314, 315 Salinized soil information, 19 28, 29 San Juan River Basin, 261 273 Scarcity, 57, 75 101, 125 126, 129, 206, 217, 222, 250, 252, 287, 311, 312, 313 Shelterbelt, 8, 11, 12, 13, 298 Shortage thresholds, 209 Snowpack, 262, 263, 265, 267, 269, 270, 271, 272, 273, 329 Soil salinity index (SSI), 59, 61 64, 67, 68, 69, 70, 71, 72, 337, 339, 340 Southern Europe, 112, 296, 297, 298, 299, 307 Stormwater runoff, 323 Stream depletion, 223 Streamflow, 223, 225, 238, 239, 242, 261 273, 318 Surface water, 62, 63, 64, 65, 71, 77, 79, 81, 84, 89 91, 99, 104, 126, 134, 137, 146, 209, 221, 222, 223, 224, 225 226, 228 230, 234, 236, 242, 249, 279, 285, 313, 314, 315, 319, 320, 322 T Tafilalet, 151, 152, 153, 157, 158, 159, 162, 343 Taklimakan, 10, 17, 46, 51 72, 165 178, 181 200 Tarim, 15 29, 35, 51, 52, 54, 56, 57, 65, 165, 166, 167, 182 Terrestrial ecosystem, 34, 35, 36, 39, 40 42, 43, 46 Tunisia, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 103 120, 212, 215 Turnover, 248, 255, 257 Turpan, 45, 48, 52, 139 148 U Urban growth, 248 Urban planning, 167, 169, 251 U.S. Southwest, 221 243, 248, 263, 329, 344, 345 Uygur, 51, 139, 145, 148, 166, 170, 171, 174, 176, 184, 187 V Vegetation, 3, 4, 6, 8, 9, 10, 11, 12, 16, 17, 19, 20, 21, 22, 24, 29, 34, 35, 36, 37, 38, 39, 40, 44, 46, 47, 56, 59, 60, 62, 64, 65, 66, 67, 68, 70, 71, 72, 112, 130, 167, 187, 188, 189, 191, 193, 196, 197, 297, 301, 303, 304, 305, 338, 339 Virtual water, 104, 106, 108, 109, 110, 113, 116, 119, 212, 214, 216, 217, 315, 325 W Wastewater, 76, 84, 95, 100, 104, 217, 234, 250, 251, 253, 256, 257, 313, 314, 318, 322 323 Water budget, 296, 297, 300, 302 304, 305, 318, 322, 328 Water harvesting, 134, 137, 152, 299, 320 321, 323 Weather modification, 320 Wells, 79, 80, 91, 96, 100, 129, 130, 134, 135, 136, 137, 140, 141, 144, 147, 148, 159, 166, 167, 190, 209, 213, 232, 236 238, 249, 282, 283, 289 X Xinjiang, 4, 6, 8, 9, 10, 11, 13, 17, 18, 33 49, 51 53, 54 57, 139, 140, 141, 142, 144, 146, 147, 148, 169, 176, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 194, 195, 196, 199, 200