Appendix 1. Climate 83

Appendix 1. Climate

Contents

Climate...........................................................................................................................................................84

References cited.................................................................................................................................85

Figures

Figure 1-1. Daily national snow-water equivalent grid produced with the Utah Energy 

 Balance Accumulation and Melt Model (Tarboton and Luce, 1996) at 0.1 degree

 resolution .................................................................................................................................85
84 Conceptual Model of Water Resources in the Kabul Basin, Afghanistan

Appendix 1. Climate

Satellite rainfall estimates were used as a key input 

to an energy-balance model for simulation of snowpack 

accumulation and depletion. The NOAAs Climate Prediction 

Center (CPC) provides gridded precipitation estimates at 

0.1 degree resolution (RFE 2.0) that blend satellite and station 

observations using geostatistical methods (Xie and Arkin, 

1997). Satellite inputs include thermal infrared imagery 

from geostationary weather satellites, typically acquired 

every 30 minutes, and passive microwave imagery acquired 

several times a day by polar orbiting satellites. The thermal 

infrared data provide a measure of cloud-top temperatures, 

which are correlated with the occurrence of rainfall. The 

microwave imagery detects upwelling radiation that has 

been scattered by the presence of precipitation-sized ice 

particles in the cloud layer. Station data are accessed through 

the Global Telecommunication System (GTS) of the World 

Meteorological Organization (WMO). These are a relatively 

small subset of rainfall station data collected by the national 

meteorological services of United Nations (UN) member 

countries. (Theoretically, over 4,000 stations are part of the 

GTS reporting network in the Central Asian RFE domain, 

but on any given day, only about 500 stations report. No 

station data from within Afghanistan are entered into the GTS. 

Only surface observations from neighboring countries are 

available to reduce bias in the RFE 2.0 estimates for Central 

Asia.) Precipitation estimates from thermal infrared imagery 

and microwave imagery are blended using weights that are 

inversely proportional to the error of each type of imagery. 

Then interpolated station data are used to remove bias from 

the blended satellite rainfall estimate field, while retaining 

its shape and form. NOAA/CPC has produced precipitation 

estimates of 24-hour accumulations for Afghanistan 

operationally each day since 2003.

A distributed version of the Utah Energy Balance 

Accumulation and Melt Model (UEB) (Tarboton, 1994; 

Tarboton and others, 1995) was used for the study. The snow 

water equivalent (SWE) model is a spatially distributed 

snowmelt model driven with remotely sensed and assimilated 

meteorological data. The UEB uses a lumped representation 

of the snowpack with two primary state variables, namely 

water equivalence (or total amount of water, expressed in 

depth (mm), in the snowpack), and energy content relative to 

a reference state of water in the ice phase at 0°

C. This version 

of the model runs in a spatially distributed mode using a 

grid resolution of 10 km. The UEB uses physically based 

calculations of the radiative, sensible, latent, and advective 

heat exchanges (i.e., exchanges, or fluxes, of energy resulting 

from incoming and outgoing short- and long-wave radiation, 

thermal or heat conduction, evaporation and condensation, and 

horizontal heat transport). An equilibrium parameterization of 

snow-surface temperature accounts for differences between 

snow-surface temperature and average snowpack temperature, 

without having to introduce additional state variables. 

Melt outflow is a function of the liquid fraction and is 

parameterized according to Darcy’s law. This parameterization 

allows the model to account for continued outflow even when 

the energy balance is negative. It was not possible to carry 

out snowmelt model parameter calibration for Afghanistan 

because of the lack of direct observational data. Instead, 

parameters from the calibration of Tarboton and Luce (1996) 

using data from the Central Sierra Snow Laboratory were 

used.

The UEB was forced using RFE 2.0 precipitation and 

output fields from two operational atmospheric models: 

NOAAs Global Data Assimilation System (GDAS) and the 

Air Force Weather Agencys MM5. GDAS provided radiation 

fields, and the MM5 provided surface air temperature, 

wind, humidity, and atmospheric pressure. The six-hourly 

meteorological variable fields were downscaled to match 

the 0.1 degree resolution of the RFE 2.0 using the USGS 

GTOPO30 digital elevation database. The GTOPO30 is a 

global topographic data set (Gesch and others, 1999) with a 

spatial resolution of 30 arcseconds (about 1 km), well suited to 

downscaling the GDAS (1.0 degree, about 100 km) and MM5 

(45 km) fields to the 0.1 degree grid used for the UEB. The 

MODIS Snow Cover Daily L3 Global (MOD10A1) data set 

(Hall and others, 2000), at 500-m resolution, was compared 

with the UEB simulated snow-cover extent for Afghanistan 

as a check on model performance. (MODIS snow-cover data 

are distributed by the National Snow and Ice Data Center 

(NSIDC) DAAC in Boulder, Colo.)

The UEB produced daily national grids of snowwater equivalent (SWE) at 0.1 degree resolution (fig. 1-1) 

for the period October 1, 2002, through August 24, 2007, 

characterizing five winter seasons. Basin areas above the 

Tang-i-Gharu streamgage on the Kabul River and the Shukhi 

streamgage on the Panjsher River were delineated from 

GTOPO30 using ArcGIS. These polygons were used to 

produce daily values of total snow-water volume above each 

of the two streamgages for the five winter seasons. 
Appendix 1. Climate 85

Figure 1-1. Daily national snow-water equivalent grid produced with the Utah Energy Balance Accumulation and Melt 

Model (Tarboton and Luce, 1996) at 0.1 degree resolution.

Base Map from Afghanistan Information Management Service (AIMS).

Map produced by the USGS/EROS Data Center. Snow ablation model:

USGS FEWS NET. http://earlywarning.usgs.gov/Afghan/snowwater.php

60° 62° 64° 66° 68° 70° 72° 74° 76°

40°

38°

36°

34°

32°

30°

28°

AFGHANISTAN

Snow Water Equivalent 

28 January 2009

Faizabad

Asadabad

Kunduz

Aibak

Shiberghan

Maimana

Qala-e Naw

Chaghcharan

Bamyan

Herat

Charikar

Kabul

Ghazni Gardez

Sharan

Tirin Kot

Qalat

Farah

Kandahar

Lashkar Gah Zaranj

Faizabad

Asadabad

Kunduz

Aibak

Shiberghan

Maimana

Qala-e Naw

Chaghcharan

Bamyan

Herat

Charikar

Kabul

Ghazni Gardez

Sharan

Tirin Kot

Qalat

Farah

Kandahar

Lashkar Gah Zaranj

EXPLANATION

International boundary

Province boundary

Rivers

Towns

Snow water equivalent,

 in meters

0.001–0.1

0.1–0.2

0.2–0.3

0.3–0.4

0.4–0.5

0.5–0.6

0.6–0.7

0.7–0.8

0.8–0.9

0.9–1

Greater than 1.0

Afghanistan

Turkmenistan

Uzbekistan

Tajikistan

Kyrgyzstan

China

Pakistan

Iran

Afghanistan

Turkmenistan

Uzbekistan

Tajikistan

Kyrgyzstan

China

Pakistan

Iran

-

References Cited

Gesch, D.B., Verdin, K.L., and Greenlee, S.K., 1999, New 

land surface digital elevation model covers the Earth: EOS, 

Transactions of the American Geophysical Union, v. 80, 

no. 6, pp. 69–70, online at http://edcdaac.usgs.gov/gtopo30/

gtopo30.html

Hall, D.K., Riggs, G.A., and Salomonson, V.V., 2000, updated 

daily, MODIS/Terra Snow Cover Daily L3 Global 500m 

Grid V004, January to March 2003: Boulder, Colo., U.S.A., 

National Snow and Ice Data Center, Digital media.

Tarboton, D.G., 1994, Measurement and modeling of snow 

energy balance and sublimation from snow, in Proceedings, 

International Snow Science Workshop, Snowbird, 

Utah, October 31 to November 2, Utah Water Research 

Laboratory working paper no. WP-94-HWR-DGT/002.

Tarboton, D.G., Chowdhury, T.G., and Jackson, T.H., 1995, 

A spatially distributed energy balance snowmelt model, in

Biogeochemistry of seasonally snow-covered catchments, 

Tonnessen, K.A., and others, eds.: Proceedings of a 

Boulder Symposium, July 3–14, IAHS Publ. no. 228, 

p. 141–155. 

Tarboton, D.G., and Luce, C.H., 1996, Utah Energy Balance 

Snow Accumulation and Melt Model (UEB)—Computer 

model technical description and users guide: Utah 

Water Research Laboratory and USDA Forest Service 

Intermountain Research Station.

Xie, P., and Arkin, P.A., 1997, A 17-year monthly analysis 

based on gauge observations, satellite estimates, and 

numerical model outputs: Bulletin of the American 

Meteorological Society, v. 78(11), p. 2539–58.
86 Conceptual Model of Water Resources in the Kabul Basin, Afghanistan

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Appendix 2. Geomorphology Methods 87

Appendix 2. Geomorphology Methods

Contents

Geomorphology methods............................................................................................................................86

References cited.................................................................................................................................90

Figures

Figure 2-1. (A) False-color composite of ASTER bands 6, 3, and 1 displayed as red, green, 

 and blue, respectively; and (B) a mosaic of three decorrelation stretch images of 

 ASTER TIR bands 13, 12, and 10 displayed as red, green, and blue, respectively, for 

 the Kabul Basin, Afghanistan ..................................................................................................89
88 Conceptual Model of Water Resources in the Kabul Basin, Afghanistan

Appendix 2. Geomorphology Methods

Advanced Spaceborne Thermal Emission and Reflection 

Radiometer (ASTER) measures reflected radiation in three 

bands between 0.520 and 0.860 m (visible-near-infrared 

region–VNIR) and in six bands from 1.00 to 2.43 m (shortwave infrared region–SWIR), with 15-m and 30-m spatial 

resolution, respectively (Fujisada, 1995). In addition, ASTER 

measures emitted thermal radiation in five bands in the 8.125- 

to 11.650- m wavelength region (thermal-infrared–TIR) 

at 90-m resolution (Fujisada, 1995). Standard ASTER data 

products (VNIR-SWIR reflectance, TIR emissivity, and TIR 

decorrelation stretch products) and (or) their equivalents 

were used in this study. For example, a false-color composite 

image of ASTER bands 6, 3, and 1, displayed as red, green, 

and blue, respectively, is shown in figure 2-1A. This image 

represents a mosaic of three ASTER scenes from a single 

60-km wide orbital swath, which covers more than 95 percent 

of the Kabul Basin and MODFLOW study area, and was 

acquired on October 12, 2001. Similar to a Landsat TM 7-4-2 

color composite, vegetation appears green and dominates the 

land-use/cover pattern of much of the northernmost Shomali 

subbasin, which is irrigated and intensively cultivated using a 

one-crop per year rotation system. Basin-fill alluvial sediments 

reflect a bright white color in this ASTER band combination 

and notably dominate the Deh Sabz subbasin; built-up and 

impervious areas of Kabul and its suburbs appear dark blue 

and dominate the Paghman and Upper Kabul and Central 

Kabul subbasin. Eolian materials and hardpans (e.g., ferricrete, 

calcrete, and (or) silcrete) appear variously colored, but the 

former displays recognizable dune and wind-streak patterns 

in the visible ASTER bands for areas east of the Barik Ab 

tributary of the Panjsher River in the northeastern portions 

of the Shomali subbasin dominated by rangeland and pasture 

land-use types, bare soils, and rock outcrops.

A mosaic of three decorrelation stretch images of ASTER 

TIR bands 13, 12, and 10, displayed as red, green, and blue, 

respectively, is shown in figure 2-1B. Because ASTER 

decorrelation images distributed by the Eros Data Center are 

“standard products” generated on a scene-by-scene basis, 

histogram matching was done in order to facilitate generation 

of a “seamless” mosaic along this single orbital swath of 

ASTER data. However, the high degree of compositional 

variability in the northern portion of the Shomali subbasin 

created statistical problems in effectively matching brightness 

values between overlapping areas within the upper and middle 

ASTER scenes. This problem produced a notable seam 

that only affects the basin-fill alluvial areas of the Shomali 

subbasin, but not the surrounding bedrock and vegetated 

areas (fig. 2-1B). Decorrelation stretch images are used to 

enhance the brightness variations between TIR bands related 

to compositional differences in emissivity, while suppressing 

correlated inter-channel brightness related to variations in 

radiant temperature (Gillespie and others, 1986). In this case, 

compositional variations are due largely to relative abundances 

of silica-rich minerals (mostly quartz), which appear red, mafic 

minerals (e.g., pyroxenes and amphiboles), which appear blue, 

and carbonate minerals and vegetation, both of which appear 

green. Mixed colors are largely the result of mixtures between 

these three basic mineral types. For example, purple areas 

dominate alluvial fans derived from mafic-rock source areas 

in the western portions of the Shomali subbasin (fig. 2-1B) 

because these rocks tend to weather more rapidly to produce 

clay- and ferric-iron rich soils that are commonly enriched in 

residual silica (Kahle and others, 1988). Notably, such color 

variations displayed in TIR decorrelation stretch imagery can 

be used to determine relative ages, assuming constant rates of 

weathering (Kahle and others, 1988). The rates of weathering 

in a high-desert environment such as Afghanistan are likely to 

be too slow and on geologic time scales to impact the chloride 

concentrations in groundwater.

Chlorophyll-bearing vegetated areas have been masked 

out of the decorrelation stretch image on the basis of a 

thresholded band-ratio image of ASTER VNIR bands 3/2; 

thus, these areas appear black (fig. 2-1B). Although this is an 

effective method for masking green vegetation and built-up 

urban areas, which typically contain mixtures of impervious 

surfaces and green vegetation (Ridd, 1995), this band ratio 

is ineffective for mapping dry vegetation with little or no 

chlorophyll. Therefore, dry vegetation and carbonate minerals 

can be confused if spectral mapping is based solely on ASTER 

SWIR or TIR data alone. However, carbonate minerals yield 

stronger absorption features in ASTER SWIR band 8 and 

ASTER TIR band 14 than dry vegetation containing lignin 

and cellulose (Murphy, 1995), which allows for the use of 

band-ratios and relative band-depth images with conservative 

thresholds as a solution in order to emphasize areas of highest 

abundance and probability of occurrence for carbonate 

minerals (Murphy, 1995).
Appendix 2. Geomorphology Methods 89 Figure 2-1. (A) False-color composite of ASTER bands 6, 3, and 1 displayed as red, green, and blue, respectively; and (B) a mosaic of three decorrelation stretch images of ASTER TIR bands 13, 12, and 10 displayed as red, green, and blue, respectively, for the Kabul Basin, Afghanistan. EXPLANATION

34°30'N 34°30'N

35°N 35°N

A B

Kabul Basin boundary

69°E 69°E

69°30'E 69°30'E

0 5 10 MILES0 10 20 KILOMETERS
90 Conceptual Model of Water Resources in the Kabul Basin, Afghanistan

References Cited

Fujisada, H., 1995, Design and performance of ASTER 

instrument: Proceedings of SPIE, the International Society 

for Optical Engineering, v. 2583, p. 16–25.

Gillespie, A.R., Kahle, A.B., and Walker, R.E., 1986, 

Color enhancement of highly correlated images—I. 

Decorrelation and HIS contrast stretches: Remote Sensing 

of Environment, v. 20, p. 209–235.

Kahle, A.B., Gillespie, A.R., Abbott, E.A., Abrams, M.J., 

Walker, R.E., and Gordon, H., 1988, Relative dating of 

Hawaiian lava flows using multispectral thermal infrared 

images—A new tool for geologic mapping of young 

volcanic terranes: Journal of Geophysical Research, 

v. 93, no. B12, p. 15239–15251.

Murphy, R.J., 1995, The effects of surficial vegetation cover 

on mineral absorption feature parameters: International 

Journal of Remote Sensing, v. 16, no. 12, p. 2153–2164.

Ridd, M.K., 1995, Exploring a V-I-S (vegetation-impervious 

surface-soil) model for urban ecosystem analysis through 

remote sensing—Comparative anatomy for cities: 

International Journal of Remote Sensing, v. 16, no. 12, 

p. 2165–2185.
Appendix 3. Surface Water 91

Appendix 3. Surface Water

Contents

Surface Water..............................................................................................................................................92

References cited.................................................................................................................................94

Figures

Figure 3-1. Location of streamgages at which runoff and water losses are provided by 

 Böckh (1971). Streamgage numbers and locations are based on map plates from 

 Böckh (1971)..............................................................................................................................92

Tables

Table 3-1. Discharge between streamgages 1 and 2................................................................................93

Table 3-2. Annual discharges and losses for streamgages 1 and 2, 1963 water year.....................93

Table 3-3. Discharge differences between streamgages 1 and 2 for selected periods .................93

Table 3-4. Discharge between streamgages 3 and 4............................................................................93

Table 3-5. Annual discharges and losses for streamgages 3 and 4, 1963 water year.....................93

Table 3-6. Discharge differences between streamgages 3 and 4 for selected periods .................93

Table 3-7. Discharge between streamgages 5, 7, and 8, and 6 ...........................................................94
92 Conceptual Model of Water Resources in the Kabul Basin, Afghanistan

Appendix 3. Surface Water

Böckh (1971) provides streamflow data for eight 

streamgages in the vicinity of the city of Kabul for the 

1963 water year (fig. 3-1, tables 3-1 through 3-7). This 

information is reproduced here to make it available and to 

illustrate the importance of stream and aquifer interactions 

in the Kabul Basin. Böckh (1971) used streamflow data to 

evaluate the gains and losses to the rivers between the selected 

streamgages. Streamgages 1 and 2 are on the Kabul River as 

the river enters the city of Kabul from the south. Streamgages 

3 and 4 are on the Logar River southeast of the city of Kabul 

and just before the Logar River enters the Kabul River. 

Streamgages 5, 6, 7, and 8 are on the Qargha (Karga) and 

Paghman Rivers just west of the city of Kabul. 

Figure 3-1. Location of streamgages at which runoff and water losses are provided by Böckh (1971). 

Streamgage numbers and locations are based on map plates from Böckh (1971).

Logar River

Paghman River

Kabul River

Kabul River

1

2

3

4

5

6

7

8

69°10' 69°20'

34°30'

34°35'

0 5 MILES

Base from U.S. Geological Survey 0 5 KILOMETERS

Shuttle Radar Topography

Mission data, 2000, 30-meter resolution,

roads from Afghanistan Information 

Management Service, 2002,

Universal Transverse Mercator projection,

Zone 42 N, World Geodetic System, 1984

EXPLANATION

Streamgage and 

 identifier

1

69°E 69°30'E

34°30'N

35°N

Study area
Appendix 3. Surface Water 93

Table 3-1. Discharge between streamgages 1 and 2. 

[See figure 3-1 (modified from Böckh,1971)]

Year Month

Discharge, in cubic meters per second

Streamgage 1 Streamgage 2 Water gain or 

loss (-)

1962 October 0.65 10.26 0.39

November 1.17 10.35 0.82

December 1.84 0.74 1.10

1963 January 2.81 1.59 1.22

February 2.07 1.09 0.98

March 1.70 0.75 0.95

April 6.55 5.20 1.35

May 22.50 17.20 5.30

June 6.56 5.05 1.51

July 0.64 0.26 0.38

August 0.59 0.17 0.42

September 0.51 0.18 0.33

Annual mean 3.97 2.74 1.23

1

 Computed from discharge at Streamgage 1.

Table 3-2. Annual discharges and losses for streamgages 1 and 

2, 1963 water year.

[See figure 3-1 (modified from Böckh, 1971). Maximum runoff (river plus 

channels) is 45.5 cubic meters per second May 15 and 19, 1963. Minimum 

runoff (river plus channels) is 0.34 cubic meter per second August 17, 1963]

Discharge, in millions of cubic meters

Streamgage 1 Streamgage 2 Water loss

River 96.5 71.1 25.4

Channels 27.5 14.3 13.2

Total discharge 124.0 85.4 38.6

Table 3-3. Discharge differences between streamgages 1 and 2 

for selected periods.

[See figure 3-1 (modified from Böckh, 1971)]

Periods

Discharge, in cubic meters per second

Streamgage 1 Streamgage 2 Water loss 

River Channel River Channel River Channel

April - June 10.70 1.20 8.41 0.73 2.29 0.47

July - November 0.08 0.63 0.00 0.25 0.08 0.38

December - March 1.15 0.97 0.42 0.63 0.73 0.34

Annual Mean 3.10 0.87 2.25 0.49 0.85 0.38

Table 3-4. Discharge between streamgages 3 and 4.

[See figure 3-1 (modified from Böckh, 1971)]

Year Month

Discharage, in cubic meters per second

Streamgage 3 Streamgage 4 Water gain 

or loss (-)

1962 October 5.78 14.58 -1.20

November 12.00 112.5 0.50

December 15.80 116.7 0.90

1963 January 16.00 17.60 1.60

February 14.80 16.00 1.20

March 12.90 13.90 1.00

April 8.40 7.85 -0.55

May 20.50 21.40 0.90

June 2.11 0.57 -1.54

July 1.89 10.55 -1.34

August 1.62 10.41 -1.21

September 1.78 10.39 -1.39

Annual mean 9.47 9.37 -0.10

1

 Discharge diverted to the Kabul River was added.

Table 3-5. Annual discharges and losses for streamgages 3 and 

4, 1963 water year.

[See figure 3-1 (modified from Böckh, 1971). Maximum runoff (river plus 

channels) is 50.0 cubic meters per second May 13, 1963. Minimum runoff 

(river plus channels) is 1.50 cubic meters per second September 8, 1963.]

Discharge, in millions of cubic meters

Streamgage 3 Streamgage 4 Water loss 

River 231.8 227.0 4.8

Channels 67.7 65.5 2.2

Total discharge 299.5 292.5 7.0

Table 3-6. Discharge differences between streamgages 3 and 4 

for selected periods.

[See figure 3-1 (modified from Böckh, 1971)]

Periods

Discharge, in cubic meters per second

Streamgage 3 Streamgage 4 Water gain or 

loss (-)

River Channel River Channel River Channel

October - November 5.94 2.95 6.90 1.64 0.96 -1.31

December - March 13.30 1.60 12.80 3.30 -0.50 1.70

April - May 11.80 2.60 11.00 3.60 -0.80 1.00

June - September 0.15 1.70 0.18 0.30 0.03 -1.40

Annual mean 7.43 2.04 7.28 2.09 -0.15 0.05
94 Conceptual Model of Water Resources in the Kabul Basin, Afghanistan

References Cited

Böckh, E.G., 1971, Report on the groundwater resources 

of the city of Kabul, report for Bundesanstalt für 

Geowissenschaften und Rohstoffe [unpublished]: BGR file 

number 0021016, 43 p.

Table 3-7. Discharge between streamgages 5, 7, and 8, and 6.

[See figure 3-1 (modified from Böckh, 1971); --, no data]

Year Month

Discharge, in cubic meters per second

Streamgage 51 Streamgage 72 Streamgage 83

Sum of 

streamgage 5, 

7, and 8

Streamgage 64 Water gain or 

loss (-)

1962 October 0.03 0.00 0.00 0.03 0.00 -0.03

November 0.03 0.00 0.00 0.03 0.01 -0.02

December 0.03 0.00 0.00 0.03 0.29 0.26

1963 January 0.04 0.13 0.00 0.17 0.37 0.02

February 0.04 0.11 -- 0.15 0.15 0.00

March 0.02 0.05 1.12 0.19 0.22 0.03

April 0.10 0.00 1.06 1.16 0.67 -0.49

May 1.92 0.26 3.61 5.79 3.77 -2.02

June 0.37 0.00 0.06 0.43 0.07 -0.36

July 0.19 0.00 0.05 0.24 0.01 -0.23

August 0.10 0.00 0.02 0.12 0.00 -0.11

September 0.05 0.00 0.02 0.08 0.01 -0.08

Annual mean 0.24 0.05 0.41 0.70 0.46 -0.24

1

 Paghman River plus channel.

2

 Qargha (Karga) River.

3

 Cheltan River plus channel.

4

 Paghman River which is downstream of streamgages 5, 7, and 8.
Appendix 4. Chemical and Isotopic Analysis of Air and Water Samples 95

Appendix 4. Chemical and Isotopic Analysis of 

Air and Water Samples

Contents

Chemical and isotopic analysis of air and water samples ...................................................................96

Air samples ..........................................................................................................................................96

Water chemistry..................................................................................................................................96

Major dissolved gases.......................................................................................................................96

Dissolved helium-4..............................................................................................................................96

Chlorofluorocarbons...........................................................................................................................97

Tritium ..................................................................................................................................................97

Stable hydrogen and oxygen isotopes............................................................................................97

References cited.................................................................................................................................97
96 Conceptual Model of Water Resources in the Kabul Basin, Afghanistan

Appendix 4. Chemical and Isotopic 

Analysis of Air and Water Samples

Chemical and isotopic data collected for this investigation 

included measurements of (1) the stable hydrogen and oxygen 

isotopic composition of surface water and groundwater, 

(2) the major and minor-element chemical composition (30 

elements) of surface water and groundwater, (3) the dissolved 

gas compositions of surface water and groundwater including 

dissolved nitrogen, argon, carbon dioxide, oxygen, methane, 

helium, and the chlorofluorocarbons (CFCs, CFC-11, CFC-12, 

and CFC-113), (4) the chlorofluorocarbon composition of 

air samples, and (5) the tritium content of surface water and 

groundwater. 

Air Samples

Ambient air samples were collected in 500-cc stainless 

steel vessels that were evacuated in the laboratory and opened 

at the collection site. The valves were then closed, and the 

vessels were returned to the U.S. Geological Survey (USGS) 

Reston Chlorofluorocarbon Laboratory for analysis. Two 

separate vessels were filled at each site, and each vessel was 

analyzed in duplicate by gas chromatography with an electroncapture detector (see http://water.usgs.gov/lab/cfc); laboratory 

precision was approximately 5 percent.

Water Chemistry

Samples for cation analysis were collected in acid-rinsed 

250-mL polyethylene bottles. The samples were filtered in the 

lab and acidified with 2 mL of Ultrex nitric acid. The samples 

for anion analysis were filtered and collected in 250-mL 

polypropylene containers. The containers were obtained from 

the USGS Water Quality Laboratory. 

Major cations and silica were analyzed at the USGS 

Water Chemistry Laboratory in Reston, Va., using the PerkinElmer Optima DV 4300, a dual view inductively coupled 

plasma optical atomic emission spectrometer (ICP-OES). The 

detection limits for mass concentrations of Ca, Mg, SiO2, Na, 

and K by ICP-OES were less than 0.05, 0.05, 0.1, 0.05, and 

0.1 mg/L, respectively. Precision for measurements of Ca, Mg, 

SiO2, Na, and K mass concentrations by ICP-OES was 1 to 

3 percent.

Trace cations were analyzed using a Perkin Elmer 

ELAN6000 inductively coupled plasma mass spectrometer 

(ICP-MS) with a Scott-type cross-flow nebulizer for sample 

introduction and a quadrupole mass separator. The detection 

limits for mass concentrations of As, Ba, Cu, Mo, Ni, Rb, 

Sb, and V were 0.1 μg/L. The detection limits for mass 

concentrations of Al, Cr, Li, Mn, Se, Sr, and Zn were 1 μg/L. 

The detection limits for mass concentrations of Cd, Pb, and U 

were 0.05 μg/L. The detection limits for mass concentrations 

of B and Fe were 0.02 μg/L. Precision was generally 1 to 

3 percent for all elements analyzed by ICP-MS.

A Dionex series DX-120 equipped with Dionex AS14 

(analytical) and AG-14 (guard) columns was used for the 

analysis of fluoride, chloride, bromide, nitrate, and sulfate. 

Detection limits for mass concentrations of chloride, sulfate, 

nitrate, bromide, and fluoride were less than 0.2, 0.2, 0.1, 0.05, 

and 0.05 mg/L, respectively. The precision for measurements 

of chloride, sulfate, nitrate, and bromide was 1 to 3 percent. 

The precision for measurements of fluoride was 3 to 5 percent.

Alkalinity was measured in the USGS Water Chemistry 

Laboratory using a Radiometer TIM900 titration manager and 

ABU93 autoburette. Some bicarbonate may have been lost 

between the time the samples were collected and when they 

were analyzed in the laboratory, as was evidenced by calcium 

carbonate precipitates in some sample bottles collected in 

2006 (samples from karez 10 and 69, spring 67 and 181, and 

well 28, 33, 42, 47, 172, 203, and 208). For these, alkalinity 

was estimated using a charge-balance algorithm. The mass 

concentration detection limit of bicarbonate for alkalinity 

titrations was 2 mg/L. The precision for alkalinity titrations 

was approximately 1 percent.

Major Dissolved Gases

Water samples for analysis of major dissolved gases 

were collected in 150-cc septum bottles without headspace. 

Mass concentrations of N2, Ar, O2, CO2, and CH4 were 

measured in the USGS Dissolved Gas Laboratory, Reston, 

Va., using gas-chromatographic procedures (http://water.usgs.

gov/lab/cfc/). Replicate analyses of N2 and Ar in laboratory 

standards prepared by equilibrating water samples with 

air were typically within 1 percent and yielded calculated 

equilibration temperatures within ± 0.5°C. The dissolved 

O2 and CO2 analyses have uncertainties similar to those of 

dissolved N2 and Ar but can deviate as much as 20 percent 

between replicate samples because of varying extents of 

microbiological processes occurring in the sample bottles after 

collection and during storage prior to analysis.

Dissolved Helium-4

Water samples for analysis of dissolved helium were 

collected in 150-cc septum bottles without headspace, in 

a manner identical to the collection of samples for major 

dissolved gases. Samples were analyzed at the USGS 

Dissolved Gas Laboratory, Reston, Va., using a gaschromatographic procedure (http://water.usgs.gov/lab/

cfc/) that also determines the volume concentrations, σ, of 

dissolved hydrogen (H2) and neon (Ne). The precisions of the 

analyses were 5–10, 10–20, and 10 percent for He, Ne, and H2, 

respectively. 
Appendix 4. Chemical and Isotopic Analysis of Air and Water Samples 97

Chlorofluorocarbons

Water samples for CFC analysis were collected in 

250-mL cap glass bottles with foil-lined caps (http://water.

usgs.gov/lab/cfc). A closed path using copper tubing was 

established between the well or spring and the bottom of the 

bottle. The bottle was submersed in a bucket of source water 

and allowed to overflow several volumes before capping 

under water, without headspace. The caps were taped, and the 

bottles were returned to the USGS for analysis. Mass fractions 

of the CFCs, CFC-11 (trichlorofluoromethane, CFCl3), 

CFC-12 (dichlorodifluoromethane, CF2Cl2), and CFC-113, 

(trichlorotrifluoroethane, C2F3Cl3), were determined at the 

USGS Chlorofluorocarbon Laboratory, Reston, Va., using 

purge and trap gas chromatography with an electron-capture 

detector (Busenberg and Plummer, 1992; http://water.usgs.

gov/lab/cfc/). The CFC mass fractions were calibrated to 

average air compositions measured at Niwot Ridge, Colo. 

(Climate Monitoring and Diagnostics Laboratory (CMDL) 

of the National Oceanic and Atmospheric Administration 

(NOAA), U.S. Department of Commerce (http://www.cmdl.

noaa.gov/)). The detection limit for CFC-11 and CFC-12 

mass fractions was near 0.3 picograms per kilogram of water 

(pg/kg) and approximately 1.0 pg/kg for CFC-113.

Tritium

Water samples for 3H analysis were collected in 500-cc 

plastic bottles. The bottles were sealed with screw caps with 

conical liners. Approximately 10 cc of air space was left in the 

bottles during filling to accommodate expansion on warming. 

The samples were enriched electrolytically and analyzed by 

liquid scintillation counting in the low-level 3H laboratory of 

the USGS, Menlo Park, Calif., following procedures modified 

from those described by Thatcher and others (1977). 

Stable Hydrogen and Oxygen Isotopes

The relative stable isotope-amount ratio of hydrogen, 

δ2H, is defined according to the relation

δ 2

2

H

H H H H

H H

B

1

B

2

B

1

B

2

VSMOW

1

VSMOW

= ( ) ( ) − ( ) ( )

( ) ( )

N N N N

N N

, (1)

where N(2H) B/N(1H) B and N(2H) VSMOW/N(1H) VSMOW are the 

ratios of the isotopes 2H and 1H of hydrogen in unknown water 

B and the international measurement standard VSMOW. The 

hydrogen isotopic composition is reported on a scale such that 

the 103 δ2H value of SLAP reference water is -428 exactly 

(Gonfiantini, 1978). For stable oxygen isotopes, δ18O is 

defined according to the relation

δ18

18

O

O O O O

O O

B

16

B

18

VSMOW

16

VSMOW

18

VSMOW

16

=

( ) ( ) − ( ) ( )

( ) (

N N N N

N N )VSMOW

, (2)

where N(18O) B/N(16O) B and N(18O) VSMOW/N(16O) VSMOW are 

the ratios of the number of isotopes 18O and 16O of oxygen in 

unknown water B and the international measurement standard 

VSMOW. The oxygen isotopic composition is reported on a 

scale such that the 103 δ18O value of SLAP reference water is 

-55.5 exactly (Gonfiantini, 1978). 

Water samples for determination of the stable isotopic 

composition of oxygen and hydrogen were untreated and 

collected in 60-cc glass bottles with Polyseal liner caps. For 

determination of δ2H values, water samples were analyzed 

using gaseous hydrogen equilibration (Coplen and others, 

1991). The 2-σ precision of 103 δ2H values is better than 2. 

For determination of δ18O values, water samples are analyzed 

using the carbon dioxide-water equilibration technique of 

Epstein and Mayeda (1953). The 2-σ precision of 103 δ18O 

values is better than 0.2.

References Cited

Busenberg, E., and Plummer, L.N., 1992, Use of 

chlorofluorocarbons (CCl3F and CCl2F2) as hydrologic 

tracers and age-dating tools: Example—The alluvium 

and terrace system of central Oklahoma: Water Resources 

Research, v. 28, no. 9, p. 2257–2284.

Coplen, T.B., Wildman, J.D., and Chen, J., 1991, 

Improvements in the gaseous hydrogen-water equilibration 

technique for hydrogen isotope ratio analysis: Analytical 

Chemistry, v. 63, p. 910–912.

Epstein, S., and Mayeda, T., 1953, Variation of O-18 content of 

water from natural sources: Geochim. Cosmochim. Acta 4, 

p. 213–224.

Gonfiantini, R., 1978, Standards for stable isotope 

measurements in natural compounds: Nature, v. 271, 

p. 534–536.

Thatcher, L.L., Janzer, V.J., and Edwards, K.W., 1977, 

Methods for determination of radioactive substances in 

water and fluvial sediments: U.S. Geological Survey 

Techniques of Water-Resources Investigations of the United 

States Geological Survey, chap. 5, book 5, Laboratory 

Analysis, p. 67–71, and p. 79–81, 1962, The distribution 

of tritium fallout in precipitation over North America: 

International Association of Scientific Hydrology VII, 

no. 2, p. 48–58.
98 Conceptual Model of Water Resources in the Kabul Basin, Afghanistan

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Appendix 5. Agricultural Water Use 99

Appendix 5. Agricultural Water Use

Contents

Agricultural Water Use .............................................................................................................................100

References cited...............................................................................................................................103

Figures

Figure 5-1. Estimates of seasonal actual evapotanspiration for irrigated lands above Shukhi 

 streamgage on the Panjsher River ............................................................................................101

Figure 5-2. Seasonal traces of 8-day values of estimated actual evapotanspiration for irrigated 

 lands above Shukhi streamgage on the Panjsher River ........................................................102
100 Conceptual Model of Water Resources in the Kabul Basin, Afghanistan

Appendix 5. Agricultural Water Use

A simplified surface energy balance (SSEB) method 

(Senay and others, 2007), described below, was used to 

estimate crop water use in the Kabul Basin. Global 1-degree 

reference ET (ETo), based on 6-hourly GDAS output, is 

calculated daily at USGS EROS on an operational basis 

(Verdin and Klaver, 2002; Senay and Verdin, 2003). The 

GDAS ETo uses the standard Penman-Monteith equation as 

outlined in the FAO publication by Allen and others (1998). 

The feasibility of using the GDAS ETo for such applications 

was recommended by Senay and Verdin (2005) after a 

comparison with station-based daily ETo showed encouraging 

results with r2 values exceeding 0.9. Daily global reference ET 

values were available for all days between 2001 and 2006. For 

2000, the daily reference ET values were not complete. For the 

missing time periods, the average reference ET from 2001 to 

2005 was used. 

Prior to using these data, a downscaling of the GDAS 

1-degree results was performed to produce 10km GDAS 

reference ET data sets. The approach utilized the International 

Water Management Institute (IWMI) historical (1961–90) 

potential ET. The IWMI data set is originally a 16km 

spatial resolution that has been disaggregated to 10km. The 

downscaling technique utilized the 10km IWMI data to create 

a fractional relation, on a per-pixel basis, between each 10km 

IWMI pixel (x) and the corresponding 100km IWMI pixel (y) 

value (artificially created to match the 100km GDAS). This 

fractional relation could then be applied back to the 100km 

GDAS pixels to downscale these data to a spatial resolution of 

10km:

x / y * GDAS

The result was a times series of daily fields of maximum 

potential ET at all locations (grid cells) in the study area, in 

units of millimeters per day.

The next step in the SSEB method uses remote sensing 

of land-surface properties to make estimates of actual ET 

fractions, which in most locations are less than the full 

potential amount defined by an ideal cover type (short grass) 

transpiring at a rate that fully meets atmospheric demand 

(ETo). 

Image data acquired by the Moderate Resolution Imaging 

Spectroradiometer (MODIS) instrument on the NASA Terra 

satellite were used to measure land-surface temperature and 

vegetative cover. The MODIS instrument provides 36 spectral 

bands, including 16 in the thermal portion of the spectrum. 

Thermal surface measurements were collected from the 

MODIS 8-day Land Surface Temperature/Emissivity (LST/E) 

product (MOD11A2). The LST/E images provide per-pixel 

temperature and emissivity values at 1-km spatial resolution 

for the 8-day composite product. Temperatures are extracted in 

degrees Kelvin with a view-angle dependent algorithm applied 

to direct observations. This study utilized measurements 

of average daytime land-surface temperature for 8-day 

composite periods throughout the growing season. More than 

twenty 8-day periods beginning in early April through the 

end of October were processed for each year in the period 

2001–2006. 

MODIS Vegetation Index (VI) products use 

reflectance measures in the red (620–670 nm), near infrared 

(841–876 nm), and blue (459–479 nm) bands to provide 

spectral measures of vegetation vigor. The MODIS VI 

products include the standard normalized difference vegetation 

index (NDVI) and the enhanced vegetation index (EVI). 

Both indices are available at 250-m, 500-m, and 1-km spatial 

resolution. The primary difference between the two indices is 

that EVI uses blue reflectance to provide better sensitivity in 

high biomass regions. Because this study was concentrated on 

irrigated agriculture in an otherwise dry land environment, the 

standard 16-day NDVI product at 250-m resolution was used 

for this analysis. Images from the Landsat ETM+ and ASTER 

instruments were also used to define irrigated agricultural 

areas of interest in the Kabul Basin.

MODIS LST and NDVI data are distributed by the Land 

Processes Distributed Active Archive Center DAAC), located 

at the USGS EROS Center in Sioux Falls, S. Dak., as are the 

Landsat and ASTER images.

Full energy balance solutions with remote sensing (Allen 

and others, 2005; Bastiaanssen and others, 1998) assume that 

the temperature difference between the land surface and the air 

(near-surface temperature difference) varies linearly with landsurface temperature. This linear relation is used to estimate 

variations in the sensible heat flux. The technique is based on 

the use of two anchor pixels, hot and cold, representing dry 

and bare agricultural fields, in the first case, and wet, wellvegetated fields in the second. It assumes that the hot pixel 

experiences no latent heat, i.e., ET = 0.0, whereas the cold

pixel achieves maximum potential ET. In the SSEB approach, 

this assumption is extended such that the latent heat flux 

(actual evapotranspiration) also varies linearly between the 

hot and cold pixels. While the hot pixel of a bare agricultural 

area experiences little ET, and the cold pixel of a well-watered 

irrigated field experiences maximum ET, the remaining pixels 

in the study area will experience ET in proportion to their 

land-surface temperature in the range defined by the hot and 

cold pixels (fig. 5-1).

The study focused on two major expanses of irrigation 

in the Kabul Basin, an extensive area north of Kabul in the 

Panjsher Valley and a narrower band of irrigation along the 

Kabul River south of the city. The northern area was subdivided into two sections to minimize the effects of elevation 

differences on measurements of surface temperature. For each 

of the three areas, a polygon was defined around the irrigated 

fields by interpreting a combination of Landsat, ASTER, and 

MODIS images. The irrigated areas consist of both wellvegetated and sparsely vegetated areas, with some arid/semiarid areas at the periphery. 
Appendix 5. Agricultural Water Use 101

Figure 5-1. Estimates of seasonal actual evapotanspiration for irrigated lands above Shukhi streamgage on the 

Panjsher River.

2001

2005

69°E 69°30'E

34°30'N

35°N

Study area

2000

2002 2003

2004

<100

101–200

201–300

301–400

401–500

501–600

601–700

701–800

801–900

901–1,000

>1,000

Seasonal actual evapotranspiration, 

in millimeters

EXPLANATION

69°07'30"E 69°30'E

35°07'30"N

35°N

69°07'30"E 69°30'E

35°07'30"N

35°N

35°07'30"N

35°N
102 Conceptual Model of Water Resources in the Kabul Basin, Afghanistan

Within each of the three areas, sets of three hot and three 

cold pixels were selected for each 8-day composite period 

for each growing season. An average of the three pixels was 

used to establish the reference hot and cold values throughout 

the study area. For each time period, cold pixels representing 

well-vegetated and well-watered crops were selected on the 

basis of a combination of low LST values and high MODIS 

NDVI values. Similarly, hot pixels, representing low-density 

vegetation and dry land, were identified by high LST values 

and very low NDVI values. 

Values of land-surface temperature for each of the six 

pixels (three hot, three cold) were extracted using ArcGIS 

software. The resulting database files were imported into an 

Excel spreadsheet where average hot and cold pixel values 

were calculated.

The reference temperatures of the hot and cold pixels 

were used to calculate proportional fractions of ET on a perpixel basis. The ET fraction (ETfrac) was calculated for each 

pixel by applying the following equation to each of the 8-day 

MODIS land-surface-temperature grids:

ETfrac

TH Tx

TH TC = −

−

, (1)

where TH is the reference temperature from the hot pixels 

selected for a given scene; TC is the reference temperature of 

the cold pixels selected for that scene; and Tx is the landsurface temperature for any given pixel in the 8-day composite 

scene.

The ETfrac formula was applied to all the 8-day growing 

season composites for each year (fig. 5-2), resulting in a series 

of more than 25 images per season. The ETfrac images were 

used in conjunction with reference ET grids to calculate the 

per-pixel actual ET values for each scene. Daily reference 

ET images were averaged over 8-day periods to match the 

MODIS LST composite periods. The calculation of actual ET 

(ETact) was achieved using the following formula:

ETact = ∗ ETfrac ETref , (2)

Figure 5-2. Seasonal traces of 8-day values of estimated actual evapotanspiration for irrigated lands above Shukhi 

streamgage on the Panjsher River.

0

5

10

15

20

25

30

35

40

45

2000 2001 2002 2003 2004 2005 2006

YEAR

8-D

A

Y ESTIMATED ACTUAL EVAPOTRANSPIRATION,

IN MILLIMETERS

8-day averages from

 May 9 to September 14 

 for 2000–06
Appendix 5. Agricultural Water Use 103

This SSEB approach allowed for use of estimates of 

reference ET, at a coarse spatial resolution, to derive spatially 

distributed ET measurements on the basis of the variability 

of land-surface temperature at 1-kilometer resolution (fig. 7). 

Actual crop ET for the 7-year period, 2000–2006, was used 

to assess the quality of each growing season in the Kabul 

study areas. Polygons delineating irrigated crop areas were 

used with actual 1-km gridded ET values (fig. 7) to calculate 

spatially aggregated actual ET for each season, permitting 

preparation of seasonal traces of actual ET (fig. 5-2). 

Water use was also estimated at small, scattered irrigated 

fields outside the three polygons modeled with the SSEB 

where, because of the relatively coarse spatial resolution of the 

1-km MODIS thermal data, the SSEB could not be applied. 

In an effort to extrapolate findings for major irrigated areas 

to these smaller parcels, a regression equation was developed 

between seasonal ETa and MODIS seasonal maximum 

NDVI at a spatial resolution of 250 m. Only those pixels 

with seasonal maximum NDVI greater than or equal to 0.35 

were used. To reduce data noise, the seasonal ETa values 

were averaged within NDVI classes at 0.05 increments from 

0.35 to 0.8 using 2005 data from the Kabul 1 irrigation area 

(fig. 5-1). The resulting regression equation (seasonal ETa 

verses maximum NDVI) was reliable in that more than 90 

percent of the spatial variability in seasonal ETa was explained 

by maximum NDVI. The regression equation was applied to 

the seasonal maximum NDVI data for each year to estimate 

seasonal ETa for small discontinuous areas of irrigation.

References Cited

Allen, R.G., Pereira, L., Raes, D., and Smith, M., 1998, Crop 

evapotranspiration—Food and Agriculture Organization 

of the United Nations: Rome, Italy, ISBN 92-5-104219-5, 

290 p.

Bastiaanssen, W.G.M., Menenti, M., Feddes, R.A., and 

Holtslag, A.A.M., 1998, A remote sensing surface energy 

balance algorithm for land (SEBAL): (1) Formulation. 

Journal of Hydrology, 212 (213): p. 213–229.

Senay, G.B., and Verdin, J.P., 2003, Characterization of yield 

reduction in Ethiopia using a GIS-based crop water balance 

model: Canadian Journal of Remote Sensing, v. 29(6), 

p. 687–692.

Senay, G.B., and Verdin, J.P., 2005, Evaluating reference 

evapotranspiration (ETo) model output from the Global 

Data Assimilation System using station ETo in the U.S.: 

Proceedings of Reclamation ET workshop: State-of-theart review of ET remote sensing science and technology, 

February 8–10, Fort Collins, Colo.

Senay, G.B., Buddy, M., Verdin, J.P., and Melesse, A.M., 2007, 

A coupled remote sensing and simplified surface energy 

balance approach to estimate actual evapotranspiration from 

irrigated fields: Sensors, v. 7, p. 979–1000.

Verdin, J., and Klaver, R., 2002, Grid cell based crop 

water accounting for the famine early warning system: 

Hydrological Processes, v. 16, p. 1617–1630.
104 Conceptual Model of Water Resources in the Kabul Basin, Afghanistan

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Appendix 6. Conceptual Model Development 105

Appendix 6. Conceptual Model Development

Contents

Conceptual model development..............................................................................................................106

Boundary conditions and stresses ................................................................................................106

Hydraulic properties.........................................................................................................................106

References cited...............................................................................................................................108

Table

Figure 6-1. Model parameters and generalized hydraulic characteristics of sediment and 

 rock aquifers in the Kabul Basin, Afghanistan ........................................................................107
106 Conceptual Model of Water Resources in the Kabul Basin, Afghanistan

Appendix 6. Conceptual Model 

Development

The Kabul Basin is in a “basin and range” setting, 

approximately 10 to 35 km wide and about 80 km long, 

where the valley is filled with quaternary and tertiary 

sediments and the ranges are composed of uplifted crystalline 

and sedimentary rocks (Bohannon and Turner, 2007). The 

generalized valley section (fig. 3) provides a general profile of 

the basin where Quaternary sediments are typically less than 

80 m thick in the valley, the underlying tertiary (Neogene) 

sediments are as much as 1,000 m thick (Homilius, 1969), and 

the sediments are surrounded by primarily crystalline bedrock 

that forms the valley walls and basin boundaries. 

The model was aligned with the primary axis of the basin 

where the lateral model boundary coincides with the major 

drainage divides forming the mountains that define the valley. 

The model was simulated with a horizontal cell size of 400 

by 400 m in the row and column direction. The model was 

subdivided vertically into four layers (fig. 8) representing 

the major hydrogeologic units indicated in the generalized 

section shown in figure 3. The top-most model-cell elevations 

were interpreted from 30-m Digital Elevation Model (DEM) 

land-surface elevations averaged for each model cell. Modellayer thicknesses and extent address both hydrogeologic and 

numerical considerations and are discussed below. The model 

design used in this study was similar to that used by Pool and 

Dickinson (2007) for a regional groundwater flow simulation 

at the southwestern United States and northern Mexico border 

with similar geohydrologic characteristics. 

Model layer 1 represents Quaternary and recent 

sediments in the valley-bottom, subbasin areas (fig. 8). To 

avoid dewatering model cells, layer 1 was not extended 

laterally up the hillsides where sediments may often be 

unsaturated. The thickness of layer 1 varies with location 

and was estimated using several sources of data. Drilling 

logs were obtained from records maintained by Afghanistan 

Geological Survey (AGS) (Amin Akbari, written commun., 

2007) and (Eng. Hassan Safi, (Danish Committee for Aid to 

Afghan Refugees, written commun., 2007). A few hundred 

drilling records were concentrated in the developed areas 

of the city of Kabul, about 20 records were in the Shomali 

Plain near the center of the valley, and drilling records were 

fewer in the lateral margins of the model. Where available, 

drilling logs that indicated the depth to the top of Neogene 

(assumed to be the top of conglomerate sediments) were used 

to estimate the Neogene top elevation. In areas with no other 

information, recently dug or driven wells with no associated 

log information were used to indicate a minimum thickness 

of quaternary sediments. The depth to the Neogene in the 

model, which comprises the thickness of quaternary and 

recent sediments in model layer 1, was generally 30 to 60 m. 

Quaternary sediment thicknesses of just over 100 m were 

observed, and modeled, in some areas, primarily toward the 

centers of the subbasins. 

Model layers 2 and 3 follow the elevation of the bottom 

of model layer 1, with the exception of hillsides, and each 

layer was 500 m thick (fig. 8). On hillsides, model layer 4 was 

the topmost model layer (fig. 8) and represented the mapped 

surficial geology (fig. 2), which was primarily bedrock. In 

the valley bottoms of the subbasin areas (fig. 1), Neogene 

sediments were assumed to be present beneath Quaternary 

and recent unconsolidated deposits (fig. 2). The Neogene and 

mapped consolidated rocks were extended through model 

layers 2 and 3 and were represented as an upper unit in layer 2 

and a lower unit in layer 3. To roughly approximate basins that 

narrow with depth, the lateral extent of Neogene sediments in 

model layer 3 were assumed to be a few hundred meters closer 

to each basin center than in model layer 2 (fig. 8). Model layer 

4 was designed to represent basal bedrock with a thickness of 

1,000 m (fig. 3). 

Boundary Conditions and Stresses

Boundary conditions and stresses were simulated using 

various MODFLOW-2000 model packages (Harbaugh 

and others, 2000) designed to represent components of the 

hydrologic system. River leakage and flow accumulation 

was simulated using the stream-flow routing (SFR1) package 

(Prudic and others, 2004). The rates of fluxes into or out of the 

model were estimated using results of various components of 

the investigation discussed in the report under Surface-Water 

Flow and Chemical and Isotopic Data. Stresses in the aquifer 

system include recharge, streamflow discharge, and domestic 

and agricultural water use. Recharge in the system includes 

direct infiltration of precipitation, leakage from the major 

rivers flowing through the basin (fig. 1), leakage from tributary 

perennial streams that drain adjacent upland areas (fig. 1), and 

lateral groundwater inflows from upland areas not drained by 

perennial streams (fig. 8). Recharge to the basin aquifers was 

from river leakage, leakage in irrigated areas, and from direct 

infiltration during the winter months. 

Hydraulic Properties

The surficial and bedrock geology of the Kabul Basin 

was fairly well known and has recently been reinterpreted by 

Bohannon and Turner (2007) and Lindsay and others (2005). 

Geologic maps were used to assign zones of similar hydraulic 

properties in the conceptual model. The geology was grouped 

into eight major hydrogeologic zones (fig. 3) on the basis of 

general hydraulic conductivity and storage characteristics 

(table 6-1). The categories were Quaternary fan alluvium and 

colluvium (sand and gravel) (K1); river channel sediments 

(K2); loess (K3); unconsolidated conglomerates (K4); upper 

(K5) and lower (K6) Neogene sediments consisting of semiconsolidated fine-grained sediments and gravel; sedimentary 

rocks including sandstone, siltstone, limestone, and dolomite 

(K7); and all metamorphic and igneous rocks (K8). The 

hydraulic properties of the primary unconsolidated aquifer 
Appendix 6. Conceptual Model Development 107

sediments in the Kabul Valley are generally high, on the 

order of 10s of meters per day. Hydraulic characteristics of 

the Quaternary and recent sediments (table 6-1) have been 

determined from aquifer tests (Böckh, 1971) and have been 

evaluated in other investigations (Houben and Tunnermeier, 

2005; Niard, 2007). 

The geohydrologic parameter categories used in the 

model were based on general hydraulic characteristics of 

sediments or rocks. The hydraulic properties of the secondary 

aquifers, for example various consolidated bedrocks, are 

generally orders of magnitude lower, than the unconsolidated 

(primary) aquifer sediments, and can be approximated for the 

purposes of a regional investigation from values reported in 

the literature (Freeze and Cherry, table 2.2, 1979) for similar 

materials. Although some zones contain geologic materials 

with distinctly different origin or nature, for the purpose of 

the conceptual model, they have relatively similar hydraulic 

properties and are grouped together (table 6-1). For example, 

sedimentary rocks and metamorphic and igneous rocks are 

grouped into two geohydrologic parameter zones, K7 and K8, 

respectively. These rocks contain numerous subunits with 

varying lithologic characteristics; however, the hydraulic 

conductivity and storage properties for these rocks are orders 

of magnitude lower than those for the other unconsolidated 

or partially consolidated rocks represented by the more 

permeable hydrogeologic zones (K1–K6). In regional 

groundwater flow systems, the simulated flow is typically less 

sensitive to low hydraulic-conductivity parameter zones than 

the higher hydraulic-conductivity parameter zones because 

much more groundwater is transmitted in the more permeable 

zones than in the less permeable zones. 

Faults in the Kabul Basin are areas where the bedrock is 

likely to be highly fractured resulting in greater water-storage 

and transmitting capabilities. A number of fault systems, 

particularly the Sorubi-Konar fault system to the east and 

the Chaman-Paghman fault system to the west (Ruleman 

and others, 2007), are associated with the mountain ranges 

that define the Kabul Basin (fig. 3). Photolinear features 

(lineaments) in the Kabul Basin hillsides and ridges, observed 

by this investigation using LANDSAT imagery, were also 

considered to be possible fracture zones. Faults and lineaments 

were assumed to be fracture zones with hydraulic conductivity 

and porosity greater than the surrounding rock. Fracture 

zones were simulated in bedrock aquifers in model layers 

2–4, by buffering mapped features by approximately 250 m, 

to ensure that they were represented by at least one model 

cell width were present, and were represented by a hydraulic 

conductivity and porosity an order of magnitude greater than 

the surrounding bulk rock properties (table 6-1). 

Table 6-1. Model parameters and generalized hydraulic characteristics of sediment and rock aquifers in the Kabul Basin, Afghanistan.

[m/d, meters per day; – not known or available]

Sediment or rock unit

Hydraulic 

conductivity 

(m/d1)

Geologic 

codes2

Conceptual model

Model 

layer(s)

Horizontal 

hydraulic 

conductivity 

(m/d)

Parameter 

code

Vertical 

hydraulic 

conductivity 

(m/d)

Parameter 

code Porosity3

Fan alluvium and colluvium– Q34ac 1 50 K1 5 K1v 0.28

River channel sediments388.8 Q4a 1 100 K2 10 K2v 0.3

Loess 34.56 Q3loe 1 20 K3 2 K3v 0.28

Unconsolidated conglomerates– Q3a 1 3 K4 0.3 K4v 0.28

Upper Neogene 8.64 N2cgs 1,2 1 K5 0.1 K5v 0.1

Lower Neogene – N2cgs 3 3 K6 0.3 K6v 0.1

Sedimentary rocks – all sedimentary 

rock codes

4 0.1 K7 0.1 K7v 0.01

Metamorphic and igneous rocks4– all metamorphic 

and igneous 

codes

4 0.01 K8 0.01 K8v 0.01

1

 Reported by Böckh (1971).

2

 Bohannon and Turner (2007) and Linsay and others (2005) shown on figures 3 and 8.

3

 Porosity used in flowpath analysis only.

4

 In areas of known or suspected fracturing or faulting the hydraulic conductivity was 0.1 m/d and porostiy was 0.05.
108 Conceptual Model of Water Resources in the Kabul Basin, Afghanistan

References Cited

Böckh, E.G., 1971, Report on the groundwater resources 

of the city of Kabul, report for Bundesanstalt für 

Geowissenschaften und Rohstoffe [unpublished]: BGR file 

number 0021016, 43 p.

Bohannon, R.G., and Turner, K.J., 2007, Geologic map of 

quadrangle 3468, Chak Wardak-Syahgerd (509) and Kabul 

(510) quadrangles, Afghanistan: U.S. Geological Survey 

Open-File Report 2005–1107–A. 1 sheet.

Freeze, R.A., and Cherry, J.A., 1979, Groundwater: 

Englewood Cliffs, N.J., Prentice-Hall, 604 p.

Harbaugh, A.W., Banta, E.R., Hill, M.C., and McDonald, 

M.G., 2000, MODFLOW-2000, the U.S. Geological 

Survey modular groundwater-flow model—User guide to 

modularization concepts and the groundwater flow process: 

U.S. Geological Survey Open-File Report 00 -92, 121 p. 

Homilius, Joachim, 1969, Geoelectrical investigations in east 

Afghanistan, Geophysical Prospecting, v. 17, issue 4, 

p. 468-487.

Houben, Georg, and Tunnermeier, Torge, 2005, Hydrogeology 

of the Kabul Basin, Part I—Geology, aquifer characteristics, 

climate and hydrology: Federal Institute for Geosciences 

and Natural Resources (BGR), Hannover, Germany, p. 45.

Lindsay, C.R., Snee, L.W., Bohannon, R.R., Wahl, R.R., and 

Sawyer, D.A., comps., 2005, Geologic map of quadrangle 

3568, Polekhomri (503) and Charikar (504) quadrangles, 

Afghanistan: U.S. Geological Survey Open-File Report 

2005-1101-A, 1 sheet.

Pool, D.R., and Dickinson, J.E., 2007, Groundwater flow 

model for the Sierra Vista Subwatershed and Sonoran 

portions of the Upper San Pedro Basin, southeastern 

Arizona, United States, and northern Sonora, Mexico: U.S. 

Geological Survey Scientific Investigations Report 2006–

5228, 48 p.

Prudic, D.E., Konikow, L.F., and Banta, E.R., 2004, A 

new streamflow routing (SFR1) package to simulate 

stream-aquifer interaction with MODFLOW-2000: U.S. 

Geological Survey Open-File Report 2004–1042, 95 p.

Ruleman, C.A., Crone, A.J., Machette, M.N., Haller, K.M., 

and Rukstales, K.S., 2007, Map and database of probable 

and possible Quaternary faults in Afghanistan: U.S. 

Geological Survey Open-File Report 2007–1103, 39 p., 

1 pl.
Appendix 7. Geomorphology and Composition of Basin-Fill Sediments 109

Appendix 7. Geomorphology and Composition of 

Basin-Fill Sediments

Contents

Geomorphology and composition of basin-fill sediments...................................................................110

References cited...............................................................................................................................115

Figures

Figure 7-1. ASTER analysis of relative abundance of mineral groups containing (A) carbonate, 

 (B) mafic and ultramafic minerals, (C) quartz, (D) ferrous iron, and (E) ferric iron in 

 in the Kabul Basin, Afghanistan .................................................................................................111

Figure 7-2. ASTER analysis of relative abundance of mineral groups containing (A) feldspar 

 and clays, (B) smectite clays, (C) biotite and (or) chlorite and smectite, (D) illite and 

 (or) muscovite, and (E) ferricrete and calcrete minerals in the Kabul Basin, 

 Afghanistan....................................................................................................................................114
110 Conceptual Model of Water Resources in the Kabul Basin, Afghanistan

Appendix 7. Geomorphology and 

Composition of Basin-Fill Sediments

ASTER VNIR-SWIR reflectance and TIR decorrelation 

stretch imagery are shown in figures 2-1A and 2-1B, 

respectively. The TIR decorrelation stretch image (fig. 2-1B) 

broadly shows mixtures of rocks and basin sediments 

containing silicic, carbonate, and mafic minerals. For example, 

green-colored areas most abundant in carbonate minerals 

correspond with the Central Kabul and Paghman and Upper 

Kabul subbasin (fig. 2-1B). These subbasins are dominated by 

impervious cover and building materials in and around the city 

of Kabul, which are predominantly made from imported and 

(or) locally derived sources of limestone and marble building 

materials. Other notable areas of green-displayed carbonatebearing minerals are alluvial fans and their limestone and 

marble source areas on the outer margins of the Dez Sabz 

subbasin, eastern parts of the Central Kabul subbasin, and 

southern margins of the Paghman and Upper Kabul subbasin 

(fig. 2-1B). 

Orange and yellow areas may represent various 

mixtures of quartz-rich alluvium with either dry vegetation 

or carbonates in either alluvial or hardpan (caliche/calcrete) 

forms. These colors dominate the central portions of the 

Dez Sabz subbasin and the eastern half of the Central Kabul 

subbasin within our image coverage (fig. 2-1B). Surprisingly, 

cyan colors associated with weathered and ferric-oxidized 

ultramafic rock source areas to the northeast of the Kabul 

Basin study area (fig. 2-1B) do not appear in any of the basin 

alluvial sediments derived from these source areas. One 

possible explanation is that the rate of weathering of minerals 

characteristic of ultramafic rocks (e.g., olivine) exceeds the 

rate of erosion from these areas by catastrophic stream floods. 

The most quartz-abundant areas appear bright red and 

correspond with the flood plains of major stream channels 

and eolian deposits characterized earlier in the paragraph 

describing photogeologic interpretation of the VNIR-SWIR 

imagery (fig. 2-1A). However, visual inspection of image 

spectra extracted from the latter areas in the corresponding 

TIR emissivity and VNIR-SWIR reflectance imagery suggests 

that these eolian deposits are not pure quartz sands but contain 

mixtures of clay, and perhaps even feldspar minerals. As a 

result, further analysis of the ASTER TIR and VNIR-SWIR 

data sets were conducted using several different band-ratios 

and relative band-depth images to highlight abundances of 

various minerals and mineral groups on the basis of their 

characteristic absorption features (Rowan and Mars, 2003; 

Rowan and others, 2005; Ninomiya and others, 2005; and 

Hewson and others, 2005), as well as matched-filter processing 

designed to partially unmix “target” foreground spectral 

endmembers from their composite “mixed” backgrounds 

(Harsanyi and Chang, 1994). The result of the latter processing 

yields images showing the relative abundances of each of 

the “relatively pure” spectral endmembers, which in this 

case were derived from the imagery and represent individual 

minerals, lithologic rock types, mineral-bearing soils, and 

other types of intimate mixtures of minerals.

Figure 7-1 summarizes the results of several bandratio and relative absorption band-depth analyses, useful for 

showing abundances of different mineral types within the 

basin-fill sediments and surrounding bedrock source areas. 

In particular for TIR bands, these generally agree with the 

distribution of carbonate, mafic, and silicic minerals than 

the green, blue, and red components, respectively, of the 

decorrelation stretch image shown in figure 2-1B, though the 

former provides much clearer details on the spatial distribution 

of these minerals throughout the Kabul Basin. For example, 

carbonate minerals such as calcite and dolomite yield high 

brightness values in the ratio of TIR bands 13/14, for which 

green and (or) dry vegetation do not. Areas with the highest 

brightness values in this band-ratio appear white (fig. 7-1A) 

and correspond mainly to carbonate bedrock source areas 

(e.g., limestones and marbles). However, the red-colored 

areas suggest less-abundant amounts of carbonate minerals 

associated with either alluvium derived from these source 

areas, residual hardpans (e.g., calcrete/caliche) related to 

fluctuations in the groundwater table, or both. No attempts 

were made to mask out either the vegetation or the urban-built 

materials derived mainly from carbonate-rock source materials 

(fig. 7-1A), and stripping is due to coherent noise in these 

two bands, which was first noted in similar carbonate mineral 

mapping work done by Rowan and Mars (2003).

The Dez Sabz subbasin is dominated by carbonate 

minerals within basin-fill alluvium (fig. 7-1A), as suggested 

by the noticeable erosion and drainage pattern derived mainly 

from carbonate-abundant source rocks to the east. On the other 

hand, lack of carbonate bedrock source areas in the highlands 

to the west of the Kabul Basin (Bohannon and Turner, 

2007) suggest that the Shomali subbasin and westernmost 

Paghman and Upper Kabul subbasin are dominated by 

carbonate minerals of the hardpan type rather than carbonaterock alluvium (fig. 7-1A). Much of the carbonate mineral 

abundant areas of the easternmost Paghman and Upper Kabul, 

westernmost Central Kabul, and northernmost portions of 

the Logar subbasin appear to be related to impervious urban 

building materials made from carbonates (e.g., limestone/

marble dimension stones, cement, and carbonate-rock 

dominated crushed stone and gravel), though carbonate 

bedrock sources do occur in areas to the south of these three 

subbasin (white-colored areas–fig. 7-1A). The areas with the 

least abundant amounts of carbonate minerals correspond with 

eolian deposits, which dominate the northeastern portion of 

the Shomali subbasin, and much of the surrounding bedrock 

source areas that contain mostly crystalline and siliciclastic 

bedrock types (fig. 7-1A). In sum, the eastern half of the Kabul 

Basin appears to be dominated by alluvial carbonate sources, 

and the western half of the basin appears to be dominated by 

hardpan carbonate sources (fig. 7-1B), though both types are 

likely to occur together in varying proportions throughout all 

of the five subbasins.
Appendix 7. Geomorphology and Composition of Basin-Fill Sediments 111

35oN

34o30'N

69oE

69o30'E

Kabul Basin boundary

A Carbonate B Mafic and 

 ultramafic

C Quartz

D Ferrous iron-  bearing

E Ferric iron- bearing

35oN

34o30'N

69oE

69o30'E

35oN

34o30'N

69oE

69o30'E

35oN

34o30'N

69oE

69o30'E

35oN

34o30'N

69oE

69o30'E

Most abundant

Least abundant

EXPLANATION

Mineral type

0 5 MILES0 10 KILOMETERS

Figure 7-1. ASTER analysis of relative abundance of mineral groups containing (A) carbonate, (B) mafic and ultramafic minerals, (C) quartz, (D) ferrous iron, and (E) ferric iron in the Kabul Basin, Afghanistan. (This figure is the same as figure 11 on page 24 in the report).
112 Conceptual Model of Water Resources in the Kabul Basin, Afghanistan

Figure 7-1B shows a relative band-depth (RBD) image 

of TIR bands (12+14)/(13*2) (also referred to as RBD13–

Rowan and others, 2005), which highlights the distribution 

of rocks and sediments containing abundant mafic minerals 

such as olivine, pyroxene and (or) amphiboles. Mafic mineral 

areas appear to be lacking throughout much of the Kabul 

Basin, with areas corresponding to the ultramafic complex 

northeast of the basin described previously, containing the 

most abundant levels of these minerals (white- and red-colored 

areas, fig. 7-1B). Notably, this large area appears distinctly 

cyan-colored in the decorrelation stretch image (fig. 2-1B). 

The fact that basin-fill sediments lack the spectral signatures 

of mafic and ultramafic rock source areas in the surrounding 

mountains suggest that these minerals do not survive the 

weathering process and are instead easily converted to other 

mineral phases such as ferric-iron, clays, and silica. Areas 

with yellow-colored abundant levels are mostly the result of 

noise between band 13 and neighboring bands 12 and 14, for 

areas covered with dense vegetation that were not masked 

(fig. 7-1B). 

The quartz abundance map (fig. 7-1C) is based on the 

ratio of TIR bands 13/12 and yields a spatial distribution 

pattern that mimicks the inverse of that of the mafic rock 

abundance map (fig. 7-1B). This is not surprising because 

fresh mafic and ultramafic rocks contain little or no quartz, 

other than residual silica derived from weathering (Kahle 

and others, 1988). The areas most abundant in quartz appear 

white-colored (fig. 7-1C) and correspond with Pliocene clastic 

and Quaternary detrital sediments (mostly sandstone, gravel 

and loess deposits) northeast of the modern Kabul basin 

(Bohannon and Turner, 2007). The distribution of quartz in 

modern-day Kabul basin-fill sediments (red-colored areas–

fig. 7-1C) are indicative of the degree of weathering and 

residual-soil development on alluvial fans and pediments, 

as well as the amount of alluvial and eolian transport from 

quartz-rich rocks such as granites, grano-diorites, sandstones, 

conglomerates, and quartzites surrounding the basin. The 

Dez Sabz subbasin and the westernmost areas of the Shomali 

subbasin (fig. 7-1C) contain abundant quartz that appears 

to be first type (i.e., residual quartz derived from in-situ 

chemical weathering of alluvial-fan material). This is based 

on the bedrock geology of the respective source areas, which 

are predominantly mafic gneisses to the west of the Shomali 

basin and mafic gneisses and carbonates surrounding the Dez 

Sabz (Bohannon and Turner, 2007). The northeastern portion 

of the Shomali subbasin is dominated by quartz of the second 

type that appears to be eolian in origin and mixed with other 

minerals as discussed earlier (fig. 7-1C).

The distribution of ferrous- and ferric-iron bearing 

minerals are shown in figures 7-1D and 11E, respectively. 

The distribution of ferrous-iron bearing minerals (fig. 7-1D) 

is based on the ratio sum of ASTER VNIR-SWIR bands (1/2) 

+ (5/3), which resolves the slope of major ferrous absorption 

features near 1.0 m, displayed by mafic minerals such as 

olivine, pyroxene, amphibole, serpentine, biotite, and chlorite 

(Rowan and others, 2005). The distribution of ferric iron 

bearing minerals, such as hematite and goethite, were resolved 

using the ratio of bands 2/1 (fig. 7-1E). With the exception 

of a few areas, perhaps exposing fresh and least-weathered 

rocks, most of the bedrock source areas containing quartz-poor 

ultramafic rocks (fig. 7-1C) appear to contain more abundant 

ferric-iron minerals (white areas, fig. 7-1E) than ferrous-iron 

minerals (red and white areas, fig. 7-1D), most likely the result 

of extensive weathering over time. Other areas displaying 

abundant ferrous-iron minerals correspond to bedrock and 

youngest alluvial fan and talus material derived from biotiteand amphibole-bearing gneiss source areas surrounding 

the basin (Bohannon and Turner, 2007). Notably, the basin 

sediments are depleted in ferrous-iron minerals (fig. 7-1D), 

because of their higher susceptibility to weathering as noted 

previously. However, transported and residual forms of ferriciron (i.e., alluvial and hardpan) appear to dominate all of the 

subbasin of the Kabul Basin (red-colored areas, fig. 7-1E), 

except for the city of Kabul and surrounding suburbs that are 

dominated by urban/impervious land-cover materials, typically 

derived from carbonate rock sources. Indeed, many areas 

containing abundant ferric-iron, perhaps in hardpan form such 

as ferricrete, also contains abundant carbonate minerals in the 

form of calcrete (compare fig. 7-1A with fig. 7-1E).

Other stable and (or) residual mineral phases with 

greater degrees of resistance to chemical weathering, such 

as clay and mica minerals, were mapped using spectral 

endmembers identified within the VNIR-SWIR reflectance, 

followed by matched-filter processing as described earlier. 

In this case, VNIR-SWIR spectral endmembers representing 

pixels dominated by clay- and mica-minerals and TIR 

spectral endmembers representing feldspar-minerals and (or) 

intermediate-composition volcanic rocks were chosen on the 

basis of a principal component analysis and pixel purity index 

analysis described by Green and others (1988) and Boardman 

and others (1995), respectively. TIR spectral endmembers 

representing feldspar-bearing deposits, were mapped using the 

spectral angle mapper (SAM) algorithm described by Kruse 

and others (1993). SAM outputs images showing the similarity 

in spectral shape between image spectra and reference spectral 

endmembers, which can be used as a measure of abundance of 

any particular spectral endmember from pixel to pixel.
Appendix 7. Geomorphology and Composition of Basin-Fill Sediments 113

The area containing the most abundant distribution of 

feldspar minerals on the basis of the TIR emissivity data is 

the eolian deposits within the northeastern portion of the 

Shomali subbasin (fig. 7-2A). Notably, clay- and feldspar-rich 

rocks both exhibit lower band 11 emissivity and higher band 

12 emissivity than rocks and sediments containing higher 

proportions of quartz (Rowan and others, 2005). However, in 

this case, VNIR-SWIR analysis suggests that clay abundances 

are relatively low throughout much of these eolian deposits 

as compared to other parts of the Kabul Basin dominated 

by alluvial basin-fill sediments (yellow- and green-colored 

smectite abundant levels, fig. 7-2B), which suggests that 

they are comprised mostly of mixtures of quartz (fig. 7-1C), 

feldspars (fig. 7-2A), and ferric-iron (fig. 7-1E) with only 

minor amounts of clays (fig. 7-2B) and carbonates (fig. 7-1A). 

Other notable areas of high feldspar abundances include felsicgneiss bedrock areas within exposed metamorphic terraines 

such as the central highland areas within the Kabul Basin and 

the younger (least weathered) alluvial fans and felsic-gneiss 

bedrock source areas to the west (fig. 12A; Bohannon and 

Turner, 2007).

Analysis of VNIR-SWIR image spectra throughout the 

Kabul Basin and surrounding areas suggests that aluminousclays with prominent 2.2 m absorptions are widespread and 

ubitiquous throughout the basin-fill sediments and bedrock 

source areas. However, the Dez Sabz subbasin appears to 

contain the most abundant amounts of smectite clays such as 

montmorillonite (white-colored areas, fig. 7-2B). Smectites are 

shrink-swell clays that tend to form in areas of poor-drainage, 

commonly resulting in impermeable soils and groundwater 

aquitards in buried sediments. Smectite clays also appear 

abundant in the eastern portions of the Central Kabul subbasin, 

western portions of the Paghman and Upper Kabul subbasin, 

and southernmost portions of the Shomali subbasin (fig. 

7-2B). These areas all correspond with areas that appear bright 

white in the false-colored composite of ASTER bands 6, 3 and 

1 shown in figure 7-1B. 

Biotite- and (or) chlorite-bearing areas (white- and 

red-colored areas, fig. 7-2C) generally correspond well with 

areas containing abundant ferrous-iron minerals based on 

band-ratios (red- and white-colored areas, fig. 7-1D), though 

the former shows better detail and resolves talus deposits 

associated with the main bedrock outcrop source areas. 

Notably, biotite-rich metamorphic rocks are exposed in the 

bedrock highland areas of the central parts of the Kabul Basin 

(Bohannon and Turner, 2007). Chlorite is a major constituent 

of serpentinized mafic- and ultramafic rocks and can also 

occur as a weathering product of biotite. Both minerals have 

broadly similar spectral shapes, but chlorite has a considerably 

stronger Mg-OH absorption feature near 2.33 m and a 

stronger ferrous iron (Fe2+) slope near 1.00 m (Rowan and 

others, 2005). Chlorite of the first type (serpentization) is 

most likely responsible for the white-colored abundant levels 

(fig. 7-2C) covering least-weathered parts of the ophiolite 

deposit exposed to the northeast of the Kabul Basin. The 

presence of smectite absorption features in biotite and 

(or) chlorite-rich areas suggest that these rocks may have 

undergone considerable weathering, even in areas closest to 

the bedrock source where we expect the freshest exposures 

to occur. Except for talus deposits adjacent to their source 

bedrock outcrops, the basin-fill deposits appear to be depleted 

in chlorite and (or) biotite (fig. 7-2C), which agrees with the 

corresponding ferrous-iron ratio abundance maps (fig. 7-1D).

Illite and muscovite are common constituents of K-rich 

rocks and sediments. Illite is a clay mineral, typically 

of detrital origin, which is commonly derived from the 

weathering of mica-rich rocks. Both minerals have similar and 

nearly indistinguishable absorption features in hyperspectral 

data (Zhang and others, 2001), which become even less 

distinguishable when resampled to the SWIR multispectral 

resolution of ASTER (Rowan and others, 2005). Although 

the most common varieties of illite and muscovite share 

prominent 2.22 m absorption features with smectite, they can 

be distinguished on the basis of secondary absorption features 

near 2.33 m (Rowan and others, 2005). The areas containing 

the most abundant amounts of illite and (or) muscovite (whitecolored areas, fig. 7-2D) correspond with many of the bedrock 

areas containing the most abundant quartz (white-colored 

areas, fig. 7-1C). These areas are mapped as Proterozoic micabearing gneisses and schists, Pliocene-age clastic sedimentary 

rocks, and Quaternary-age shingly and detrital sediments 

(Bohannon and Turner, 2007). In fact, prominent bedding-like 

structure displayed in several of these white-colored bedrock 

areas (fig. 7-2D) suggest that they may be highlighting 

foliation structure within mica-schist members of these 

Proterozoic rocks. Smaller, circular-shaped bedrock areas 

containing abundant illite and (or) muscovite could be related 

to unmapped intrusive rocks and (or) associated hydrothermal 

alteration. With the possible exception of the northwestern 

portion of the Shomali subbasin, much of the basin-fill 

sediments of the Kabul Basin appear to contain relatively 

lower abundances of illite and (or) muscovite (fig. 7-2D). Dry 

vegetation and carbonates share an overlapping absorption 

feature with illite at 2.33 m, such that mixtures of smectite 

with either or both carbonate and dry vegetation material can 

resemble illite and (or) muscovite. Therefore, because the 

northwestern parts of the Shomali subbasin contain abundant 

carbonate (figs. 7-1A and 7-2E) as well as extensively 

cultivated vegetation (fig. 2-1B), these areas with apparently 

high illite and (or) muscovite abundance levels could be the 

result of abundant smectite rather than illite or muscovite 

(figs. 7-2B and D).
114 Conceptual Model of Water Resources in the Kabul Basin, Afghanistan

Figure 7-2. ASTER analysis of relative abundance of mineral groups containing (A) feldspar and clays, (B) smectite clays, (C) biotite and (or) chlorite and smectite, (D) illite and (or) muscovite, and (E) ferricrete and calcrete minerals in the Kabul Basin, Afghanistan. (This figure is the same as figure 12 on page 25 in the report)

Kabul Basin boundary

35oN

34o30'N

69oE

69o30'E

35oN

34o30'N

69oE

69o30'E

35oN

34o30'N

69oE

69o30'E

35oN

34o30'N

69oE

69o30'E

35oN

34o30'N

69oE

69o30'E

A Feldspar and clays B Smectite

E Ferricrete and  calcrete

D Illite and (or) muscovite

C Biotite and (or) chlorite and smectite

Most abundant

Least abundant

EXPLANATION

Mineral type

0 5 MILES0 10 KILOMETERS
Appendix 7. Geomorphology and Composition of Basin-Fill Sediments 115

With the exception of the built-up areas in and around the 

city of Kabul, basin-fill deposits of the Kabul Basin contain 

abundant carbonate minerals (red-colored areas, fig. 7-1A) 

and ferric-iron minerals (red-colored areas, fig. 7-1E) as noted 

earlier using TIR and VNIR-SWIR band-ratios. Spectral 

matched-filter processing suggests that the areas containing 

the most abundant ferricrete and calcrete deposits, of perhaps 

hardpan origin, are the northwestern portion of the Shomali 

subbasin, several of the alluvial fans derived from middle 

Triassic carbonate-bearing bedrock source areas to the 

northeast of the Dez Sabz subbasin, and sizeable patches of the 

Logar subbasin that are clearly distinguishable from bedrock 

(fig. 7-2E). However, the areas displaying the highest levels 

of abundance (white-colored areas, fig. 7-2E) of this hardpan 

(or alluvial?) mixture lay outside of the Kabul Basin. Several 

of these are limestone bedrock exposures of Cambrian-age 

and mineralized areas along the Herat suture-zone to the 

northwest of the basin (Bohannon and Turner, 2007). Other 

notable areas are within the ophiolite complex northeast of 

the Kabul Basin, which exposes serpentinized and weathered 

ultramafic rocks containing abundant chlorite and ferric-iron 

minerals (figs. 7-2C and 7-1E, respectively). Because chlorite 

share a prominent absorption feature at 2.33 m with calcite, 

the two minerals can easily be confused and mis-identified 

using solely ASTER VNIR-SWIR data without the additional 

information provided using the ASTER TIR bands (Dalton and 

others, 2004; Rowan and others, 2005).

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116 Conceptual Model of Water Resources in the Kabul Basin, Afghanistan

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Appendix 8. Surface-Water Analysis 117

Appendix 8. Surface-Water Analysis

Contents

Surface-water analysis.............................................................................................................................118

Tables

Table 8-1. Probability of occurrence of annual high discharge for the period of record at 

 streamflow-gaging stations in the Kabul study area, Afghanistan ........................................119

Table 8-2. Annual peak discharge for streamgages in the Kabul study area for the period 

 of record...........................................................................................................................................123

Table 8-3. Probability of occurrence of annual low discharge for the period of record at 

 streamgages in the Kabul study area..........................................................................................126
118 Conceptual Model of Water Resources in the Kabul Basin, Afghanistan

Appendix 8. Surface-Water Analysis

The probabilities of occurrence of annual high discharges 

are presented in table 8-1 for the 12 streamgages. Probability 

of occurrence is an estimate of the likelihood that a particular 

discharge in a stream will be equaled or exceeded in a given 

year. The probability of occurrence of a high flow is called 

the exceedance probability. For example, the maximum 

instantaneous discharge for the 0.20 exceedance probability 

is listed as 56.2 m3/s for Kabul River at Tang-i-Saidan, then 

a 20 percent chance exists that a discharge equal to or greater 

than 56.2 m3/s will occur once during the year. Recurrence 

interval is another way of expressing annual probability and 

is the reciprocal of probability of occurrence. For example, 

the recurrence interval for an exceedance probability of 0.20 

is 5 years (1 divided by 0.20). For a long discharge record, the 

annual maximum discharge can be expected to equal or exceed 

that discharge on average once every 5 years. 

The maximum instantaneous discharge and the maximum 

mean discharge for 3, 7, 15, and 30 consecutive-day periods 

for selected exceedance probabilities and recurrence 

intervals are listed in table 8-1. Values for the maximum 

mean discharges for 3, 7, 15, and 30 consecutive-day periods 

are computed from the annual high mean values of the 

corresponding periods. The computations are based on the 

log-Pearson Type III distribution using values obtained for the 

water year. The difference in percent between the maximum 

instantaneous discharge and a maximum mean discharge for 

a “n” consecutive-day period can be used as an indicator of 

the flashiness of a flood. As an example, the mean discharge 

for a 3 consecutive-day period for a recurrence interval 

of 25 years for streamgages Qargha River above Qargha 

Reservior and Paghman River at Pul-i-Sokhta are 72 and 77 

percent, respectively; the recurrence intervals for the rest of 

the streamgages are from 11 to 32 percent. Therefore, the high 

flows are much shorter duration (flashier) at the streamgages 

Qargha River above Qargha Reservior and Paghman River at 

Pul-i-Sokhta than at the other streamgages.

The annual peak discharges for each of the 12 

streamgages are listed in table 8-2. The recurrence intervals 

for the peak discharge for the period of record are 10 years 

for streamgage at Shatul River at Gulbahar; 10–25 years for 

streamgages Qargha River above Qargha Reservoir, Qargha 

River below Qargha Reservoir, Paghman River at Pul-iSokhta, Logar River at Sang-i-Naweshta, Kabul River at 

Tang-i-Gharu, Panjsher River at Gulbahar, and Panjsher River 

at Shukhi; 25 years for streamgages Chakari River at Bandi-Amir Ghazi, Kabul River at Tang-i-Saidan, and Ghorband 

River at Pul-i-Ashawa; and 50 years for streamgage Salang 

River at Bagh-i-Lala. The instantaneous peak discharge of 

192 m3/s was on April 28, 1967, for streamgage Kabul River 

at Tang-i-Gharu, and the instantaneous peak discharge of 

750 m3/s was on June 12, 1967, for the streamgage Panjsher 

River at Shukhi.

The annual low discharge in any given year is sensitive 

to natural-channel processes, such as evapotranspiration and 

groundwater gains and losses, and human-induced hydrologic 

modifications, such as the operation of many small waterstorage reservoirs; the effects of surface-water withdrawal 

for agricultural, municipal, and industrial use; and the effects 

of return flow to the river. The probabilities of occurrence 

of annual low discharges are presented in table 8-3 for the 

12 streamgages. Probability of occurrence is an estimate of 

the likelihood that a particular discharge in a stream will be 

equaled or exceeded in 1 year or, in the case of low flows, the 

likelihood that the discharge will not be equaled or exceeded 

during the year. The probability of occurrence of low flow is 

called the nonexceedance probability. The table of probability 

of occurrence of annual low discharges for each streamgage 

lists the minimum mean discharge for 1, 3, 7, 14, 30, and 

60 consecutive-day periods for selected nonexceedance 

probabilities and recurrence intervals. Values for the minimum 

mean discharges are computed from the annual low discharge 

values of the corresponding periods using the log-Pearson 

Type III distribution. Probabilities of annual low discharges 

are computed using values for the climatic year (April 1 

through March 31).
Appendix 8. Surface-Water Analysis 119

Table 8-1. Probability of occurrence of annual high discharge for the period of record at streamflow-gaging stations in the Kabul study 

area, Afghanistan.

[m3/s, cubic meters per second]

Exceedance

probability

Recurrence

interval

(years)

Maximum 

instantaneous 

(m3/s)

Maximum mean discharge (m3/s)

3-day period 7-day period 15-day period 30-day period

Kabul River at Tangi Saidan

0.99 1.01 7.4 5.40 4.08 3.44 2.73

0.95 1.05 12.5 8.40 6.93 6.04 4.94

0.90 1.11 16.3 10.60 9.08 8.01 6.63

0.80 1.25 21.9 14.10 12.40 11.10 9.29

0.50 2 36.3 24.30 22.00 19.70 16.70

0.20 5 56.2 41.70 37.10 32.80 28.00

0.10 10 68.7 55.20 48.00 41.90 35.70

0.04 25 83.4 74.40 62.30 53.50 45.30

0.02 50 93.6 90.20 73.30 62.00 52.30

0.01 100 103.0 107.00 84.40 70.40 59.10

Qargha River above Qargha Reservoir

0.99 1.01 1.7 1.51 1.31 1.03 0.90

0.95 1.05 2.3 1.94 1.65 1.33 1.13

0.90 1.11 2.7 2.20 1.87 1.52 1.27

0.80 1.25 3.2 2.54 2.17 1.79 1.48

0.50 2 4.2 3.32 2.88 2.47 2.00

0.20 5 5.3 4.26 3.83 3.43 2.73

0.10 10 5.9 4.81 4.44 4.08 3.23

0.04 25 6.6 5.45 5.20 4.93 3.89

0.02 50 7.0 5.89 5.76 5.57 4.39

0.01 100 7.3 6.30 6.32 6.23 4.90

Qargha River below Qargha Reservoir

0.99 1.01 0.4 0.35 0.32 0.29 0.25

0.95 1.05 0.4 0.37 0.34 0.32 0.28

0.90 1.11 0.4 0.38 0.36 0.34 0.30

0.80 1.25 0.5 0.42 0.40 0.37 0.33

0.50 2 0.7 0.54 0.53 0.49 0.43

0.20 5 1.3 0.87 0.82 0.72 0.62

0.10 10 2.1 1.22 1.11 0.92 0.79

0.04 25 4.0 1.87 1.62 1.26 1.05

0.02 50 6.5 2.59 2.14 1.58 1.28

0.01 100 10.6 3.56 2.82 1.97 1.56
120 Conceptual Model of Water Resources in the Kabul Basin, Afghanistan

Exceedance

probability

Recurrence

interval

(years)

Maximum instantaneous (m3

/s)

Maximum mean discharge (m3/s)

3-day period 7-day period 15-day period 30-day period

Paghman River at Pul-i-Sokhta

0.99 1.01 1.8 0.89 0.63 0.45 0.39

0.95 1.05 3.4 1.70 1.25 0.96 0.79

0.90 1.11 4.7 2.37 1.79 1.41 1.14

0.80 1.25 7.2 3.50 2.71 2.20 1.73

0.50 2 16.0 7.13 5.81 4.78 3.65

0.20 5 35.6 13.9 11.9 9.57 7.18

0.10 10 54.3 19.4 17.0 13.3 9.95

0.04 25 85.0 27.2 24.5 18.5 13.8

0.02 50 114.0 33.7 30.8 22.7 16.9

0.01 100 148.0 40.6 37.6 27.0 20.1

Logar River at Sang-i-Naweshta

0.99 1.01 16.1 13.0 13.3 12.3 11.2

0.95 1.05 22.0 18.0 17.3 15.5 13.7

0.90 1.11 26.0 21.4 20.0 17.8 15.5

0.80 1.25 31.7 26.3 24.0 21.0 18.0

0.50 2 46.0 39.1 34.8 29.9 25.0

0.20 5 66.4 48.0 42.4 36.4 30.0

0.10 10 80.3 58.2 51.4 44.1 36.1

0.04 25 98.0 71.6 63.8 54.7 44.4

0.02 50 111.0 89.4 80.8 69.7 56.4

0.01 100 125.0 103.0 94.5 82.1 66.2

Chakari River at Band-i-Amir Ghazi

0.99 1.01 0.3 0.27 0.27 0.25 0.24

0.95 1.05 0.4 0.39 0.39 0.36 0.34

0.90 1.11 0.5 0.47 0.47 0.44 0.42

0.80 1.25 0.7 0.59 0.59 0.55 0.52

0.50 2 1 0.9 0.89 0.85 0.78

0.20 5 1.4 1.34 1.32 1.27 1.14

0.10 10 1.7 1.64 1.62 1.55 1.38

0.04 25 2 2.02 1.99 1.92 1.67

0.02 50 2.2 2.31 2.27 2.19 1.89

0.01 100 2.4 2.59 2.55 2.46 2.1

Table 8-1. Probability of occurrence of annual high discharge for the period of record at streamflow-gaging stations in the Kabul study 

area, Afghanistan.—Continued

[m3/s, cubic meters per second]
Appendix 8. Surface-Water Analysis 121

Exceedance

probability

Recurrence

interval

(years)

Maximum instantaneous (m3

/s)

Maximum mean discharge (m3/s)

3-day period 7-day period 15-day period 30-day period

Kabul River at Tangi-Gharu

0.99 1.01 27.4 16.2 15.0 12.6 11.6

0.95 1.05 41.5 26.4 23.9 20.4 18.2

0.90 1.11 51.0 33.8 30.4 26.1 22.9

0.80 1.25 64.8 45.1 40.3 35.0 30.1

0.50 2 98.3 75.4 67.9 59.6 50.1

0.20 5 142.0 121.0 111.0 97.9 81.6

0.10 10 169.0 152.0 142.0 125.0 104.0

0.04 25 201.0 192.0 184.0 161.0 135.0

0.02 50 223.0 221.0 215.0 189.0 158.0

0.01 100 244.0 250.0 247.0 217.0 183.0

Panjsher River at Gulbahar

0.99 1.01 209 179 166 146 125

0.95 1.05 255 209 193 172 152

0.90 1.11 282 227 210 188 168

0.80 1.25 318 250 232 209 189

0.50 2 394 300 281 257 234

0.20 5 483 360 343 317 286

0.10 10 534 395 381 353 316

0.04 25 592 436 427 398 349

0.02 50 631 465 460 429 372

0.01 100 668 492 492 460 393

Shatul River at Gulbahar

0.99 1.01 16.1 9.47 8.86 8.20 7.06

0.95 1.05 19.2 11.90 11.20 10.30 9.10

0.90 1.11 21.2 13.40 12.60 11.70 10.40

0.80 1.25 23.7 15.40 14.50 13.40 12.10

0.50 2 29.5 19.80 18.60 17.50 15.90

0.20 5 36.7 25.00 23.60 22.40 20.60

0.10 10 41.1 28.00 26.60 25.40 23.40

0.04 25 46.4 31.50 30.00 28.90 26.70

0.02 50 50.2 33.90 32.40 31.30 28.90

0.01 100 53.9 36.10 34.60 33.70 31.00

Table 8-1. Probability of occurrence of annual high discharge for the period of record at streamflow-gaging stations in the Kabul study 

area, Afghanistan.—Continued

[m3/s, cubic meters per second]
122 Conceptual Model of Water Resources in the Kabul Basin, Afghanistan

Exceedance

probability

Recurrence

interval

(years)

Maximum instantaneous (m3

/s)

Maximum mean discharge (m3/s)

3-day period 7-day period 15-day period 30-day period

Ghorband River at Pul-i-Ashawa

0.99 1.01 55.0 48.0 44.4 41.5 38.3

0.95 1.05 67.0 57.1 53.0 49.6 45.7

0.90 1.11 74.2 62.6 58.3 54.6 50.3

0.80 1.25 83.7 70.0 65.5 61.2 56.4

0.50 2 104.0 86.8 81.8 76.3 70.3

0.20 5 129.0 107.0 102.0 94.9 87.7

0.10 10 143.0 120.0 115.0 106.0 98.4

0.04 25 159.0 135.0 131.0 120.0 111.0

0.02 50 170.0 146.0 142.0 130.0 120.0

0.01 100 180.0 157.0 152.0 139.0 130.0

Salang River at Bagh-i-Lala

0.99 1.01 18.9 11.9 10.7 10.1 9.79

0.95 1.05 26.8 17.7 16.7 16.0 15.1

0.90 1.11 32.0 21.6 20.6 19.7 18.5

0.80 1.25 39.4 27.1 26.0 24.7 22.9

0.50 2 57.2 40.1 37.7 34.8 31.8

0.20 5 80.6 56.2 50.3 44.4 40.2

0.10 10 95.2 65.8 56.7 48.6 44.0

0.04 25 113.0 76.7 63.1 52.4 47.3

0.02 50 125.0 84.1 66.9 54.3 49.1

0.01 100 137.0 90.9 70.1 55.8 50.4

 Panjsher River at Shukhi

0.99 1.01 318 248 227 200 178

0.95 1.05 375 296 273 242 215

0.90 1.11 408 325 300 267 238

0.80 1.25 453 362 336 300 268

0.50 2 548 440 412 374 338

0.20 5 659 528 499 462 424

0.10 10 724 578 549 514 477

0.04 25 798 633 605 575 541

0.02 50 849 671 644 618 586

0.01 100 897 705 679 658 630

Table 8-1. Probability of occurrence of annual high discharge for the period of record at streamgages in the Kabul study area, 

Afghanistan.—Continued

[m3/s, cubic meters per second]
Appendix 8. Surface-Water Analysis 123

Table 8-2. Annual peak discharge for streamgages in the Kabul study area for the period of record.

[m3/s, cubic meters per second]

Water

year

Date

Peak

discharge

(m3/s)

Water

year

Date

Peak

discharge

(m3/s)

Kabul River at Tangi Saidan

1962 April 20, 1962 25.7 1972 April 28, 1972 58.2

1963 May 4, 1963 30.7 1973 April 11, 1973 47.0

1964 April 12, 1964 34.3 1974 April 7, 1974 18.5

1965 April 23, 1965 37.4 1975 April 4, 1975 32.0

1966 April 27, 1966 21.8 1976 April 24, 1976 65.6

1967 April 27, 1967 87.2 1977 May 27, 1977 28.4

1968 April 30, 1968 57.8 1978 July 6, 1978 57.8

1969 April 15, 1969 32.2 1979 August 7, 1979 35.5

1970 April 26, 1970 11.5 1980 April 19, 1980 60.8

1971 March 26, 1971 10.8

Qargha River above Qargha Reservoir

1963 May 12, 1963 3.34 1974 March 21, 1974 5.60

1964 May 8, 1964 4.31 1975 April 4, 1975 3.50

1965 April 21, 1965 3.30 1976 April 22, 1976 4.00

1966 April 24, 1966 6.15 1977 April 15, 1977 3.55

1970 May 1, 1970 4.80 1978 July 6, 1978 5.00

1971 April 16, 1971 2.50 1979 May 19, 1979 4.10

1972 April 7, 1972 5.25 1980 April 5, 1980 6.11

1973 April 2, 1973 2.08

Qargha River below Qargha Reservoir

1965 May 11, 1965 1.27 1973 July 18, 1973 0.70

1966 August 22, 1966 0.44 1974 October 1973 0.60

1967 June 18, 1967 0.58 1975 August 21, 1975 0.55

1968 May 2, 1968 1.20 1976 May 18, 1976 0.65

1969 June 1, 1969 0.93 1977 October 18, 1976 0.45

1970 June 20, 1970 0.63 1978 July 3, 1978 3.06

1971 June 7, 1971 0.50 1979 July 11, 1979 0.56

1972 May 14, 1972 5.90 1980 October 1, 1979 0.48

Paghman River at Pul-i-Sokhta

1963 May 12, 1963 17.80 1972 May 6, 1972 19.50

1964 April 10, 1964 15.50 1973 August 2, 1973 17.10

1965 May 7, 1965 16.00 1974 April 7, 1974 10.50

1966 April 27, 1966 5.56 1975 August 13, 1975 80.00

1967 April 27, 1967 34.80 1976 April 27, 1976 13.10

1968 April 30, 1968 48.80 1977 April 10, 1977 7.40

1969 April 19, 1969 4.65 1978 August 17, 1978 70.50

1970 July 7, 1970 4.00 1979 March 29, 1979 39.20

1971 April 12, 1971 3.42 1980 April 11, 1980 23.70
124 Conceptual Model of Water Resources in the Kabul Basin, Afghanistan

Water

year

Date

Peak

discharge

(m3/s)

Water

year

Date

Peak

discharge

(m3/s)

Logar River at Sang-i-Naweshta

1962 March 31, 1962 36.60 1972 April 28, 1972 58.60

1963 May 13, 1963 52.90 1973 April 13, 1973 62.00

1964 April 13, 1964 65.30 1974 April 8, 1974 41.20

1965 April 25, 1965 85.00 1975 April 5, 1975 40.00

1966 April 14, 1966 27.90 1976 April 27, 1976 57.60

1967 April 28, 1967 95.00 1977 May 28, 1977 28.10

1968 April 9, 1968 36.00 1978 July 9, 1978 88.00

1969 April 20, 1969 34.00 1979 April 14, 1979 45.30

1970 November 12, 1969 25.30 1980 April 19, 1980 53.50

1971 March 26, 1971 19.70

Chakari River at Band-i-Amir Ghazi

1965 July 5, 1965 1.46 1973 April 21, 1973 0.55

1966 May 1, 1966 1.00 1974 June 8, 1974 1.06

1967 May 23, 1967 1.26 1975 October 23, 1974 0.39

1968 December 9, 1967 1.67 1976 May 7, 1976 0.82

1969 March 13, 1969 0.55 1977 August 9, 1977 0.82

1970 July 17, 1970 0.96 1978 June 9, 1978 1.19

1971 May 1, 1971 0.55 1979 September 13, 1979 1.43

1972 June 18, 1972 1.36 1980 April 17, 1980 1.92

Kabul River at Tangi-Gharu

1960 May 2, 1960 175.0 1971 March 26, 1971 43.6

1961 April 18, 1961 98.0 1972 April 28, 1972 107.0

1962 November 15, 1961 39.5 1973 April 11, 1973 107.0

1963 May 19, 1963 165.0 1974 April 7, 1974 48.2

1964 April 10, 1964 158.0 1975 April 4, 1975 68.8

1965 April 25, 1965 156.0 1976 April 25, 1976 128.0

1966 April 26, 1966 79.7 1977 April 18, 1977 47.4

1967 April 28, 1967 192.0 1978 July 7, 1978 109.0

1968 April 30, 1968 115.0 1979 April 14, 1979 69.5

1969 April 19, 1969 85.5 1980 April 19, 1980 107.0

1970 July 7, 1970 101.0

Panjsher River at Gulbahar

1960 July 10, 1960 584 1971 June 9, 1971 321

1961 June 5, 1961 515 1972 June 25, 1972 385

1962 June 10, 1962 488 1973 June 13, 1973 473

1963 June 20, 1963 520 1974 June 16, 1974 292

1964 July 4, 1964 569 1975 June 17, 1975 335

1965 July 15, 1965 300 1976 June 4, 1976 327

1966 June 19, 1966 335 1977 June 23, 1977 338

1967 June 12, 1967 358 1978 June 5, 1978 418

Table 8-2. Annual peak discharge for streamgages in the Kabul study area for the period of record.—Continued

[ m3/s, cubic meters per second]
Appendix 8. Surface-Water Analysis 125

Water

year

Date

Peak

discharge

(m3/s)

Water

year

Date

Peak

discharge

(m3/s)

Panjsher River at Gulbahar—Continued

1968 July 8, 1968 412 1979 June 23, 1979 477

1969 June 20, 1969 424 1980 June 9, 1980 220

1970 June 3, 1970 350

Shatul River at Gulbahar

1967 June 11, 1967 41.8 1974 June 4, 1974 21.6

1968 June 25, 1968 41.8 1975 June 17, 1975 30.9

1969 June 16, 1969 28.2 1976 July 6, 1976 24.5

1970 May 21, 1970 27.6 1977 June 22, 1977 20.7

1971 May 19, 1971 20.6 1978 June 5, 1978 27.0

1972 June 12, 1972 40.6 1979 June 13, 1979 35.0

1973 June 11, 1973 35.1

Ghorband River at Pul-i-Ashawa

1960 April 18, 1960 146.0 1970 May 21, 1970 73.2

1961 June 5, 1961 139.0 1971 May 20, 1971 69.7

1962 June 11, 1962 84.4 1972 June 13, 1972 123.0

1963 May 15, 1963 96.2 1973 June 4, 1973 111.0

1964 April 11, 1964 93.0 1974 June 1, 1974 63.2

1965 June 13, 1965 118.0 1975 May 16, 1975 121.0

1966 June 18, 1966 108.0 1976 June 4, 1976 130.0

1967 April 27, 1967 161.0 1977 May 27, 1977 86.4

1968 June 10, 1968 131.0 1978 April 17, 1978 85.6

1969 June 17, 1969 97.5 1979 June 22, 1979 96.8

Salang River at Bagh-i-Lala

1962 April 25, 1962 43.8 1972 May 13, 1972 80.8

1963 May 14, 1963 92.8 1973 June 3, 1973 56.7

1965 June 13, 1965 50.0 1974 May 1, 1974 45.5

1966 April 25, 1966 60.6 1975 May 15, 1975 60.0

1967 April 27, 1967 124.0 1976 April 23, 1976 53.1

1968 April 29, 1968 76.1 1977 May 27, 1977 27.9

1969 April 14, 1969 91.0 1978 April 17, 1978 41.6

1970 April 15, 1970 58.5 1979 April 30, 1979 48.9

1971 May 19, 1971 22.4

Panjsher River at Shukhi

1967 June 12, 1967 750 1974 June 18, 1974 380

1968 June 25, 1968 680 1975 June 17, 1975 451

1969 June 20, 1969 626 1976 June 4, 1976 482

1970 June 3, 1970 509 1977 June 23, 1977 392

1971 May 28, 1971 522 1978 July 7, 1978 590

1972 June 28, 1972 719 1979 June 22, 1979 558

1973 June 13, 1973 700 1980 June 9, 1980 452

Table 8-2. Annual peak discharge for streamgages in the Kabul study area for the period of record.—Continued

[ m3/s, cubic meters per second]
126 Conceptual Model of Water Resources in the Kabul Basin, Afghanistan

Table 8-3. Probability of occurrence of annual low discharge for the period of record at streamgages in the Kabul study area.

[m3/s, cubic meters per second]

Nonexceedance

probability

Recurrence

interval

(years)

Minimum mean discharge (m3/s)

Number of consecutive days

1 3 7 14 30 60

Kabul River at Tangi Saidan

0.05 20 0.000 0.009 0.017 0.045 0.073 0.089

0.10 10 0.008 0.017 0.029 0.060 0.093 0.115

0.20 5 0.023 0.033 0.052 0.084 0.124 0.153

0.50 2 0.089 0.104 0.135 0.159 0.208 0.254

Qargha River above Qargha Reservoir

0.05 20 0.023 0.023 0.023 0.023 0.023 0.025

0.10 10 0.024 0.025 0.025 0.025 0.026 0.029

0.20 5 0.027 0.028 0.029 0.029 0.031 0.036

0.50 2 0.034 0.035 0.037 0.040 0.044 0.053

Paghman River at Pul-i-Sokhta

0.05 20 0.000 0.000 0.000 0.000 0.000 0.000

0.10 10 0.000 0.000 0.000 0.000 0.000 0.004

0.20 5 0.000 0.003 0.006 0.006 0.007 0.008

0.50 2 0.012 0.013 0.015 0.017 0.021 0.026

Logar River at Sang-i-Naweshta

0.05 20 0.000 0.000 0.000 0.004 0.009 0.020

0.10 10 0.000 0.005 0.008 0.008 0.016 0.034

0.20 5 0.012 0.014 0.018 0.019 0.033 0.063

0.50 2 0.068 0.069 0.077 0.096 0.124 0.205

Chakari River at Band-i-Amir Ghazi

0.05 20 0.000 0.000 0.000 0.008 0.009 0.01

0.10 10 0.007 0.007 0.007 0.009 0.01 0.012

0.20 5 0.009 0.009 0.009 0.011 0.013 0.015

0.50 2 0.014 0.014 0.014 0.015 0.02 0.025

Kabul River at Tangi-Gharu

0.05 20 0.000 0.000 0.013 0.018 0.021 0.045

0.10 10 0.011 0.012 0.022 0.030 0.037 0.069

0.20 5 0.028 0.031 0.043 0.057 0.069 0.115

0.50 2 0.112 0.125 0.141 0.181 0.217 0.316
Appendix 8. Surface-Water Analysis 127

Nonexceedance

probability

Recurrence

interval

(years)

Minimum mean discharge (m3/s)

Number of consecutive days

1 3 7 14 30 60

Panjsher River at Gulbahar

0.05 20 6.39 6.47 6.58 6.72 6.85 7.20

0.10 10 6.88 7.00 7.14 7.28 7.44 7.78

0.20 5 7.53 7.69 7.85 8.01 8.19 8.53

0.50 2 8.91 9.14 9.33 9.51 9.75 10.10

Shatul River at Gulbahar

0.05 20 0.006 0.006 0.007 0.100 0.011 0.016

0.10 10 0.007 0.007 0.009 0.012 0.014 0.020

0.20 5 0.009 0.010 0.012 0.015 0.019 0.027

0.50 2 0.018 0.019 0.023 0.030 0.038 0.053

Ghorband River at Pul-i-Ashawa

0.05 20 3.27 3.34 3.43 3.57 3.76 4.12

0.10 10 3.80 3.89 3.99 4.15 4.39 4.80

0.20 5 4.54 4.65 4.76 4.95 5.23 5.69

0.50 2 6.26 6.44 6.58 6.78 7.10 7.62

Salang River at Bagh-i-Lala

0.05 20 1.28 1.54 1.58 1.63 1.73 1.86

0.10 10 1.41 1.66 1.70 1.75 1.86 1.99

0.20 5 1.59 1.81 1.87 1.92 2.03 2.17

0.50 2 2.00 2.17 2.25 2.30 2.41 2.55

Panjsher River at Shukhi

0.05 20 20.7 20.9 21.2 21.5 21.8 23.0

0.10 10 21.6 21.8 22.0 22.4 22.8 24.0

0.20 5 22.7 22.8 23.1 23.4 23.9 25.2

0.50 2 24.8 24.9 25.1 25.4 26.2 27.5

Table 8-3. Probability of occurrence of annual low discharge for the period of record at streamgages in the Kabul study area.—

Continued

[m3/s, cubic meters per second]
128 Conceptual Model of Water Resources in the Kabul Basin, Afghanistan

This page intentionally left blank.
Appendix 9. Groundwater Levels 129

Appendix 9. Groundwater Levels

Contents

Groundwater levels ...................................................................................................................................130

Logar subbasin..................................................................................................................................130

Central Kabul subbasin....................................................................................................................130

Deh Sabz subbasin ...........................................................................................................................130

Paghman and upper Kabul subbasin.............................................................................................131

Shomali subbasin..............................................................................................................................131

References cited...............................................................................................................................131
130 Conceptual Model of Water Resources in the Kabul Basin, Afghanistan

Appendix 9. Groundwater Levels

Groundwater-level conditions in the Logar, Central 

Kabul, Deh Sabz, Paghman and Upper Kabul, and Shomali 

subbasins are discussed below with references to historical 

conditions where possible. Groundwater-level hydrographs 

for the subbasins of the Kabul Basin are provided by Akbari 

and others (2007). Monthly groundwater data do not exist for 

the Panjsher River subbasin; however, conditions are likely to 

be similar to the northernmost parts of the Shomali subbasin 

where the aquifer is influenced by Panjsher River losses.

Logar Subbasin

The depth to water on the western bank of the Logar 

River (Logar aquifer) is reported to be between 30 to 

80 centimeters (cm) in the lower flat-lying area and between 

1.5 and 2 meters (m) in the higher regions (elevations) during 

the period of investigation (2003–2005). Böckh (1971) 

reported that the water levels on the eastern bank of the Logar 

River ranged from around 5 to 10 m below land surface 

and that the depth to water fluctuated between 1 and 2 m 

in response to pumping for irrigation. The large difference 

between the depth to water on the west and east banks of the 

Logar River were attributed to differences in land-surface 

elevation. The Kabul aquifer generally has depths to water that 

range from 2 to 12 m and water-level fluctuations up to 5 m. 

Water levels in 1965 were from 2 to 5 m below land surface. 

The depth to water in the Paghman aquifer is very shallow 

near the center of the basin and increases towards the valley 

sides. Depth-to-water measurements made in 1962–1963 

indicate a water level of 19 m below land surface near the 

confluence of the Cheltan and Paghman streams.

The Logar subbasin includes urban population centers 

and rural, agricultural land. Nine monitoring wells in the 

Logar subbasin ranged in total depth from 25 to 79.1 m. The 

surficial geology at the wells varied from Quaternary loess 

to Quaternary conglomerate and sandstone. Depth-to-water 

measurements under static conditions ranged from a minimum 

of 1.5 m (1,783.4 m above sea level) adjacent to the Logar 

River during May 2005 to a maximum of 10.2 m (1,781.1 m 

above sea level) during the fall of 2004 and 2006 at a well 

not in the vicinity of any streams or rivers. Seasonal waterlevel fluctuations from fall 2005 through spring 2006 ranged 

from about 1.3 to 4 m in the Logar subbasin. Water-level 

increases of up to 3.9 m from October 2004 until June 2006 

were measured in a well along the Logar River. Many of the 

wells along the Logar River are municipal water-supply wells 

that are frequently pumped. Taking this into account, there 

does appear to be a strong seasonal recharge component that 

can be correlated to times of peak flow during winter and 

spring in the Logar River. Depth to water in one well rose 

from 9.3 m (1,783.3 m above sea level) in early January 2005 

to 5 m (1,787.5 m above sea level) in May 2005. This change 

in water level corresponded to near-normal winter and spring 

precipitation and corresponding flow in the Logar River.

Central Kabul Subbasin

The Central Kabul subbasin includes the primary 

population center of Afghanistan (city of Kabul) in the 

western part of the subbasin and more rural lands in the 

eastern part of the subbasin. Twenty-four monitoring wells in 

the Central Kabul subbasin ranged in total depth from 6.6 to 

160 m. One well was located on an outcrop of gneiss; at all 

other well sites, the surface geology consisted of Quaternary 

loess, fan alluvium and colluvium, or conglomerate and 

sandstone. Depth-to-water measurements under static 

conditions ranged from a minimum of about 2.5 m (1,790 m 

above sea level) during June 2005 to a maximum of about 

23 m (1,778 m above sea level) during January and February 

2006. Monitoring wells that have water levels not affected 

by pumping have seasonal fluctuations from 0.5 to 3 m. 

The water level in the supply well on the grounds of the 

Afghanistan Geological Survey building had a seasonal 

fluctuation of about 2 m. In general, it appears the wells 

near the Kabul River had increasing water levels during the 

monitoring period, and wells that are distant from the river 

had decreases in water level. The water-level summary plot 

for Well 208 shows a recovery of approximately 9 m from a 

dynamic water-level measurement in March 2005 to a static 

water-level measurement in early April 2005. The water-level 

measurement in this well in late April 2005 recorded a drop of 

about 8 m because of pumping. Larger water-level fluctuations 

caused by pumping were recorded in the Khair Khana wells; 

pumping-induced drawdown was up to 25 m.

Deh Sabz Subbasin

The Deh Sabz subbasin is a sparsely populated mixed 

agricultural and industrial area. Nine monitoring wells in the 

Deh Sabz subbasin ranged in depth from 7.2 to 150 m. The 

surficial geology at the wells varied from Quaternary loess 

to Quaternary conglomerate and sandstone. There are no 

perennial rivers or streams in this subbasin, although some 

ephemeral streams and some perennial springs discharge from 

the base of the mountains on the east side of this subbasin. 

Depth-to-water measurements under static conditions ranged 

from a minimum of 4 m (1,963.6 m above sea level) measured 

in early March 2007 to a maximum of 40 m (1,812.6 m above 

sea level) in August 2006. Seasonal water-level fluctuations 

ranged from 0.5 to 9 m. Seasonal water-level fluctuations are 

thought to be attributed to precipitation that falls locally on 

the valley floor or mountains to the east. Two wells in the Deh 

Sabz subbasin showed generally declining water levels during 

the monitoring period ranging from 0.9 m from April 2005 to 

September 2006 in one well and 1.0 m from August 2005 to 

May 2006 in another well.

The water level in a well that was 7.2 m deep increased 

approximately 3 m from August 2006 to March 2007. In a 

150-m deep well, the static water level increased about 3 m 

from March 2005 to March 2007. The drawdown during 
Appendix 9. Groundwater Levels 131

pumping in this well ranged from approximately 20 m to as 

much as 40 m. Recovery after pumping stopped was fairly 

rapid. The water-level elevation in mid-May 2006 under 

dynamic conditions was 1,784.6 m above sea level. The waterlevel elevation measured under static conditions in early June 

2006 was 1,822.6 m above sea level, so the water level had 

increased about 38 m.

Paghman and Upper Kabul Subbasin

The Paghman and Upper Kabul subbasin includes the 

urban population centers of the western city of Kabul, and 

more rural agrarian lands in the western part of the subbasin. 

Twelve monitoring wells in the Paghman and Upper Kabul 

subbasin ranged in depth from 4.9 to 99.7 m. One well 

was located on an outcrop of gneiss; all other wells were 

in Quaternary loess, alluvium, or colluvium. Five of the 

monitoring wells are municipal water-supply wells. Three 

are located approximately 1 km west of the Kabul River near 

the confluence of the Kabul River and Paghman Stream. The 

other two are located much further west of the Kabul River 

and within a kilometer of the Paghman Stream. Depth-towater measurements under static conditions ranged from 

3 m (1,805.6 m above sea level) to 28 m (1,840.3 m above 

sea level). Seasonal water-level fluctuations from fall 2005 

through spring 2006 ranged from about 0.5 to 3 m. The water 

levels in two wells declined from August 2004 until February 

2007. One well declined about 3 m and the other about 8 m. 

These two wells were approximately 0.8 km apart and north of 

the Paghman Stream. Three wells had increases in water levels 

ranging from 3 to 5 m from February 2005 through March 

2007. Only one municipal supply well had static water-level 

measurements spanning a fall-winter-spring precipitation 

cycle. Measurements under static conditions in this well from 

November 2004 until June 2005 showed an increase in the 

water level from 1,802.5 m above sea level to 1,804.6 m above 

sea level, an increase of 2.1 m. The water levels in wells close 

to the Kabul River had larger seasonal water-level increases 

than wells that are not near major surface-water sources.

Shomali Subbasin

The Shomali subbasin is a predominantly rural area that 

contains the largest irrigated agricultural area in the Kabul 

Basin. A number of springs discharge from the mountains 

on the western border of this subbasin. The flow from these 

springs is the source for a number of small perennial streams 

in the western part of the Shomali subbasin. The water in these 

streams is used for irrigation with much of the water being 

diverted to irrigation canals. As a result, these small streams 

eventually dry up and do not contribute flow to the Barik Ab 

River, the largest ephemeral stream in the subbasin. Fifteen 

monitoring wells in the Shomali subbasin ranged in depth 

from 9 to 102 m. The surficial geology at the well locations 

consisted of Quaternary loess or Quaternary fan alluvium and 

colluvium. The range of depth to water under static conditions 

was from 3.4 m below land surface in two wells (1,540 m 

above sea level in October 2005 and 1,877.7 m above sea level 

in May 2006) to 27.8 m below land surface (1,784.9 m above 

sea level) in August 2004. Seasonal water-level fluctuations 

ranged from approximately 0.5 to 8 m. The largest increase 

in the water level was approximately 13 m from 1,786.7 m 

above sea level in March 2005 to 1,799.7 m above sea level 

in May 2006. While the water level in most wells increased 

during the monitoring period, one well appeared to have an 

overall decline in water level from 1,644.4 m above sea level 

in late August 2004 to 1,642.9 m above sea level in October 

2006. This decline may be attributed to pumping of the well 

before the October 2006 measurement. Two wells showed a 

steady increase in water levels during the monitoring period 

with minimal seasonal fluctuations. The seasonal high water 

levels in six wells increased with time and in three wells 

decreased with time. The deepest well in the monitoring 

network in the Shomali subbasin (102-m deep) had about a 

0.5 m decline in water level between the July 2005 maximum 

and the late September 2005 minimum. The water level in 

this well increased during the monitoring period by about 8 m 

from 1,791.7 to 1,799.7 m above sea level. The water-level 

maximums in most wells were prior to mid-July, and in 2006 

were prior to mid-June. Ephemeral streams in the Shomali 

subbasin are potential sources of recharge, especially during 

years when the Paghman Mountains receive near normal 

precipitation.

References Cited

Akbari, M.A., Tahir, M., Litke, D.W., and Chornack, M.P., 

2007, Groundwater levels in the Kabul Basin, Afghanistan, 

2004–07: U.S. Geological Survey Open-File Report 

2007–1294, 46 p.
132 Conceptual Model of Water Resources in the Kabul Basin, Afghanistan

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