N1 through N17 DESCRIPTION:

 

Outputs from a Water Balance and Transport Model (WBM/WTM) were used to determine the spatial distribution of renewable water supply, expressed as the sum of local runoff and river corridor discharge. Monthly atmospheric forcings from 1960-95 were from (New et al., 1998). Estimates of domestic and industrial water demands (Vörösmarty et al., 2000a; 2004) were apportioned by urban/rural population densities. Agricultural withdrawals were based on African water statistics (Jippe Hoogeveen, FAO/AGL, Rome Italy) at the sub-basin level, and a mapping of irrigation-equipped lands (Siebert et al., 2002). All supply and demand estimates were resampled as required and georegistered to a 6 grid and river network (STN-06), updated from a previous flow topology (Vörösmarty et al., 2000b) using a network rescaling algorithm that processed 1-km digital streamlines (Fekete et al., 2001). STN-06 basin boundaries were compared to a hand-corrected database provided by FAO. Water scarcity was evaluated, in part, by computing the Climatic Moisture Index (CMI, Willmott and Feddema, 1992), the ratio of annual precipitation (P) to annual potential evapotranspiration, (PET). Specifically CMI = (P / PET) 1 when P < PET and CMI = 1- (PET / P) when P = PET. The CMI ranges from 1 to +1, with wet climates showing positive values, dry climates negative. PET was estimated using a physically based function (Shuttleworth and Wallace, 1985). We grouped CMI into major climate categories following Koppen. The coefficient of variation (CV) computed for all variables is the ratio of the standard deviation to the mean over the time series analyzed.

 

Water supply in each grid cell (n) has two sources: locally-generated discharge (QLn) and river corridor discharge (QCn), which enters from upstream cells. QLn is the product of runoff (Rn) and cell area (An). QCn accumulates QLn in a downstream direction along the STN-06 digital nertwork. Cells with mean upstream runoff <3 mm yr-1 were considered inactive or non-perennially discharging (Vörösmarty et al., 2000b). Water use is represented by local demand (DIAn), the sum of domestic, industrial and agricultural water withdrawals. Dividing DIAn by QCn yields an index of local relative water use. A high degree of stress is indicated when the relative water use index is > 0.4 or 40% (34). DIAn summed in a downstream direction (in a similar manner as QCn) and divided by QCn is called the water reuse index and represents the extent to which runoff is recycled or reused as it accumulates and flows toward the basin mouth. The water reuse index typically increases in a downstream direction, indicating reuse and recycling of river corridor water. This index can, however, decrease when mainstream flow is diluted by more pristine (less-recycled) tributary waters.

 

DOWNLOADABLE FILES:

 

(N1) Agricultural area in km2: Spatial distribution of agricultural area per grid cell (in km2 per grid cell). Primary source: FAO/AGL, Rome Italy.

 

(N2) Annual CMI, mean: Mean annual climate moisture index computed from 1960-1995 annual time series. Primary source: Willmott and Feddema, 1992.

 

(N3) Annual CMI, coefficient of variabiltiy (CV): Interannual variability of the CMI, as represented by the CV, which is computed as the standard deviation divided by the mean. Primary source: Willmott and Feddema, 1992.

 

(N4) Domestic + industrial + agricultural water use, 1995 (DIA, in km3/yr): Sum of human water use in 1995 in km3/yr per grid cell. Primary source: Vörösmarty et al., 2005.

 

(N5) Annual discharge (Q), mean in km3/yr. Long-term average annual river flow (discharge, Q) computed from the 1960-1995 time series. Primary source: Vörösmarty et al., 2005.

 

(N6) Annual discharge (Q), 30-yr low in km3/yr.Minimum annual river flow (discharge, Q) selected from the 1960-1995 time series. Primary source: Vörösmarty et al., 2005.

 

(N7) Annual discharge (Q), annual coefficient of variability (CV). Interannual variability of discharge, as represented by the CV, which is computed as the standard deviation divided by the mean. Primary source: Vörösmarty et al., 2005.

 

(N8) Seasonal variability, ratio of monthly maximum to monthly minimum (Q maxmin): Ratio of monthly maximum discharge to monthly minimum discharge, representing the range of intra-annual discharge variability. Primary source: Vörösmarty et al., 2005.

 

(N9) Irrigated area: Spatial distribution of irrigation-equipped area (in km2 per grid cell), aggregated to 6-min (latitude by longitude) resolution. Primary source: Siebert et al., 2002.

 

(N10) Annual runoff, mean: Mean annual runoff (in mm/yr per grid cell) computed from 1960-1995 annual time series. Primary source: Vörösmarty et al., 2005.

 

(N11) Annual runoff, coefficient of variability: Interannual variability of runoff, as represented by the CV, which is computed as the standard deviation divided by the mean. Primary source: Vörösmarty et al., 2005.

 

(N12) Relative water stress index (RWSI), mean annual Q: Ratio of domestic + industrial + agricultural water use in 1995 (DIA) to long-term mean annual discharge (Q). Water stress is indicated when RWSI is greater than or equal to 0.4 Primary source: Vörösmarty et al., 2005.

 

(N13) Relative water stress index (RWSI), 30-yr low Q: Ratio of domestic + industrial + agricultural water use in 1995 (DIA) to 30 year low discharge (Q). Primary source: Vörösmarty et al., 2005.

 

(N14) Months with RWSI exceeding 0.4. Number of months within an average years during which the RWSI equals or exceeds 0.4. Primary source: Vörösmarty et al., 2005.

 

(N15) Number of people exposed to water stress: Number of people (per grid cell) within grid cells that experience RWSI equal to or greater than 0.4, on an average annual basis. Primary source: Vörösmarty et al., 2005.

 

(N16) Number of people exposed to water stress: Number of people (per grid cell) within grid cells that experience RWSI equal to or greater than 0.4, for the 30 year minimum discharge. Primary source: Vörösmarty et al., 2005.

 

(N17) Water reuse index (WRI), mean annual. Water reuse index is computed as the ratio of cumulative DIA to mean annual discharge, representing the degree to which river water is reused by humans as it flows along a river network. Primary source: Vörösmarty et al., 2005.

 

 

REFERENCES:

 

Fekete, B.M., Vörösmarty, C.J. and Lammers, R.B. 2001. Scaling gridded river networks for macroscale hydrology: Development, analysis, and control of error. Water Resources Research 37:7 1955-1967.

New, M., Hulme, M. and Jones, P. 1998. Representing twentieth century space-time climate variability. Part II: Development of 1901-1996 monthly grids. J. Climate 13: 2217-2238.

Shuttleworth, W.J. and Wallace, J.S. 1985. Evaporation from sparse crops: an energy combination theory, Quarterly J. R. Meteorol. Soc. 111: 839-855.

Siebert S., Döll, P. and Hoogeveen, J. 2002. Global map of irrigated areas version 2.1 Center for Environmental Systems Research, University of Kassel, Germany / Food and Agriculture Organization of the United Nations, Rome, Italy.

Willmott, C.J., and Feddema, J.J. 1992. A more rational climatic moisture index. Prof. Geographer 44: 84-87.

Vörösmarty, C. J., E.M. Douglas, P. A. Green and C. Revenga, Geospatial indicators of emerging water stress: an application in Africa, Ambio, in press, 2005.

Vörösmarty, C.J., Brunner, J., Revenga, C., Fekete, B., Green, P., Kura, Y. and Thompson, K. 2004. Case studies: Population and climate. In: Kabat, P., Claussen, M., Dirmeyer, P.A., Gash, J.H.C., Bravo de Guenni, L., Meybeck, M., Pielke Sr., R.A., Vörösmarty, C.J., Hutjes, R.W.A. and Lutkemeier, S. (eds), Vegetation, Water, Humans and the Climate. Springer, Heidelberg, Germany.

Vörösmarty, C.J., Green, P., Salisbury, J. and Lammers, R. 2000a. Global water resources: Vulnerability from climate change and population growth. Science 289, 284-288.

Vörösmarty, C.J., Fekete, B.M., Meybeck, M., Lammers, R. 2000b. A simulated topological network representing the global system of rivers at 30-minute spatial resolution (STN-30). Global Biogeochemical Cycles 14, 599-621.

 

 

Additional Links:

 

Vorosmarty et al., 2005