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River flow analyses for flood projection in the Kabul River Basin

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Flooding is one of the critical natural disasters in Afghanistan, causing huge social and economic losses on an annual basis. Due to lack of historical data and long gaps in the recorded data, flood predictions are usually associated with large uncertainties. The available hydrological data are collected before and after the Afghan civil war period. This long gap and climate change effects split the dataset and faces a challenge of using either dataset alone for predicting flood characteristics. In this study, first, the two datasets are compared to find river flow variation in terms of peak and frequency. Next, the river flow variation effects on flood peaks for each return period are analyzed to determine the flood projection. The results show that flood peaks have raised while the mean discharge in the basin is reduced during the second period. The frequency analyses show a change in high and low flow days in the recent period. In addition, the flood recurrence results show that the utilization of single period data for return period flood predictions yield huge variation, while the analyses using the combined dataset show a reasonable estimation of flood characteristics. Furthermore, the comparison ofcalculated flood peaks based on the first period and combined dataset show that flood peaks have an upward trend.
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Central Asian Journal of Water Research (2020) 6(1): 1-17
© The Author(s) 2020.
ISSN: 2522-9060
Published by Kazakh-German University, Almaty / Kazakhstan
River flow analyses for flood projection in the Kabul River Basin
Mohammad Assem Mayar
1*
, Hamidullah Asady
2
, Jonathan Nelson
3
1
Water Resources and Environmental Engineering, Kabul Polytechnic University
2
Organization for Skill Development and Social Services (OSDSS)
3
Geomorphology and Sediment Transport Laboratory, USGS
*Corresponding author
Email: assem.mayar@hotmail.com
Received: 16 June 2019; Received in revised form: 23 November 2019; Accepted: 22 January 2020; Published
online: 03 March 2020.
doi: 10.29258/CAJWR/2020-R1.v6-1/1-17.eng
Abstract
Flooding is one of the critical natural disasters in Afghanistan, causing huge social and economic losses on an
annual basis. Due to lack of historical data and long gaps in the recorded data, flood predictions are usually
associated with large uncertainties. The available hydrological data are collected before and after the Afghan
civil war period. This long gap and climate change effects split the dataset and faces a challenge of using either
dataset alone for predicting flood characteristics. In this study, first, the two datasets are compared to find river
flow variation in terms of peak and frequency. Next, the river flow variation effects on flood peaks for each
return period are analyzed to determine the flood projection. The results show that flood peaks have raised while
the mean discharge in the basin is reduced during the second period. The frequency analyses show a change in
high and low flow days in the recent period. In addition, the flood recurrence results show that the utilization of
single period data for return period flood predictions yield huge variation, while the analyses using the
combined dataset show a reasonable estimation of flood characteristics. Furthermore, the comparison of
calculated flood peaks based on the first period and combined dataset show that flood peaks have an upward
trend.
Keywords: River flow, variation, flood, projection, Kabul River Basin
Paper type: Research
paper
1. Introduction
Every year, several large and medium scale floods occur in Afghanistan. According to
Afghanistan Spatial Data Center (ASDC), 7.5 million people (22.3 % of the country
population) and one million buildings are at flood risk. The Kabul River Basin (KRB), located
in the central-east part of Afghanistan (Figure 1), is one of the most vulnerable region from
flood disasters. This basin covers thirteen administrative provinces and is divided into seven
watersheds. KRB is a densely populated basin in Afghanistan with 35 % of the Afghan
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Central Asian Journal of Water Research (2020) 6(1): 1- 17
population and 11 % of the areal extent of the country (Favre and Kamal 2004). 41 % of
KRB’s population live in the cities while 59 % live in rural areas near the rivers and cultivated
land (NSIA 2018; World Bank 2010). The basin has a mountainous topography with higher
altitudes in the north-east and lower altitudes in the south-western parts.
Figure 1. Kabul river basin map. The numbered stations’ details are given in Table I.
The main sources of surface water in the Kabul River are glaciers and snow in the Hindu Kush
mountains that are part of Himalayas (Vick 2014). According to Haritashya et al. (2009),
glaciers in the Wakhan valley of Pamir Afghanistan considerably retreated and thinned.
Similarly, Sarikaya et al. (2012) analyzed eastern Hindu Kush (higher altitudes of the KRB)
glaciers between 1976 and 2007. Their results showed that 76% of the sampled glaciers
retreated. In addition, the land use and land cover change analyses of Najmuddin et al. (2018)
in the KRB from 2001 to 2010 revealed that cropland, grassland, water-bodies and
urbanization areas increased, while forest, unused, and snow/ice areas decreased. Sadid et al.
(2017) also reported an increase of suspended sediment concentrations by comparing 1965–
1968 and 2012–2015 periods of measurements on the Maidan River partially due to land
cover changes in the KRB. All of these factors might lead to a variation of discharge, flood
peaks, and flood frequency in the study area. Besides, the international flood databases
(CRED/EM-DAT data) and literature such as Alfieri et al. (2015) also reported the change in
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Central Asian Journal of Water Research (2020) 6(1): 1- 17
flood frequency, but the flood peaks and their effect on future floods predictions are not
considered seriously or reported.
Floods management in Afghanistan have a short history. The systematic record of the river
flow in the country first started in 1946 from the Helmand river basin (Westfall and Latkovich
1966). Subsequently, the network of hydrologic data collection was extended countrywide and
recorded data until the 1980s. Following this period of data collection, the Afghan civil war
destroyed all infrastructures including the hydrological data network, which caused a long gap
in the water cycle dataset. In 2003, the hydrologic data collection in Afghanistan was restarted
by the financial support of the international donors. The stations' record start and end dates
have significant differences. The starting dates in the first period of the hydrological dataset
were determined by the expansion of the hydrological network, but the finishing dates of the
early part of the record are associated with security and war problems. In the second period,
the start and ending dates of stations were influenced both by financial and security
limitations.
The available hydrologic historical data in the KRB is related to pre- (1950 – 1980) and post-
(2003 – 2018) Afghan civil war periods. The station records contain a gap of about three
decades. During and subsequent to the period of missing data, intense global warming and
climate change, urbanization, deforestation, and land cover changes effects on the river flow
are not negligible in the region.
The long gap and environmental changes split the discharge time-series dataset into two parts.
Due to the short record durations of each record periods, estimations of flood return periods
are only feasible with the help of analytic methods. However, these methods also require a
considerable duration of the records. Therefore, for longer return period predictions, using
either a single period data is likely to result in huge variations and uncertainties; while using a
combined full dataset will average over the effects of environmental changes that occurred
after the first recording period. These challenges make the basin an ideal test case for
determining flood projection using river flow analyses from the two discontinuous periods.
Therefore, this paper first compares the river flow of both periods and later focuses on the
flood problem and tries to identify the best practical method for long term flood estimation
using the available discontinuous data.
2. Material and methods
2.1. Data
For flood projection analyses, instantaneous flood data does not exist. This means that the
daily average discharge data must be used and the annual maximum daily value is considered
as the flood situation. The water year in Afghanistan starts from the first of October. Thus, the
annual statistical parameters of the flow are calculated for each water year in the period
between October first and September 30th. There are 36 stations in the KRB with periods of
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Central Asian Journal of Water Research (2020) 6(1): 1- 17
record ranging from 3.7 to 21.0 years in the earlier (1950–1980) period of data collection, and
from 3.8 to 15.5 years in the later second period (2003–2018). In addition to the issue of
different starting and ending record dates, the locations of some stations in both periods have
changed for unknown reasons. Thus, the number of stations having recorded data at the same
location during both periods reduces to 20 stations with record durations of (3.7-21.0) and
(5.9-15.5) years in the first and second periods respectively. The total record duration of both
period is from 9.7 to 34.4 years. A list of these stations with location coordinates and
recording duration details is provided in Table I. The gaging stations are shown on the basin
map in Figure 1.
2.2.Methods
First, the data quality of both datasets was checked. Both periods’ data were compared and
those stations with illogical differences were identified as having errors. Further, spatial
consistency of the stations recorded values was evaluated. The stations which do not match
upstream and downstream stations were also removed from the calculation. In addition, the
recorded values were evaluated for gage reading uncertainty. The existing data of some
stations contained peak values from different times that were the same. For flood return period
analyses, one maximum value from several equal discharge values in a year was selected.
After quality control, 17 stations were finalized for the analyses. The stations removed from
the analysis are highlighted in Table I.
Table I. Recording details of the stations with both period data in the Kabul River Basin. The
stations are ordered according to maximum average discharge. Few stations have short
missing data in the recording period which is marked by * in the duration column.
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Central Asian Journal of Water Research (2020) 6(1): 1- 17
Station
number
Station name Station coordinates 1950 - 1980 period Duration
Mean
Discharge
Maximum
Discharge
Minimum
Discharge
2003 - 2018 period Duration
Latitude
Longitude Start End Years m
3
s
-1
m
3
s
-1
m
3
s
-1
Start End Years
1 DAKAH 34.23071
71.03855 21-02-68
22-07-80 12.43 640.963 2970 63.4 01-04-07
30-09-18 11.50
2 KONAR RIVER AT NAWABAD 34.81969
71.12032 01-04-76
30-09-79 3.74 491.682 2000 80.6 21-03-07
30-09-18 11.54
3 KONAR RIVER NEAR ASMAR 34.91501
71.20172 23-02-60
30-09-71 11.61 378.294 1472 24.2 01-10-11
30-09-18 7.00
4 NAGHLU 34.63726
69.71704 01-10-59
30-09-80 21.01 112.205 880 10.5 11-08-08
30-09-18 10.14
5 PANJSHER RIVER AT SHUKHI 34.93617
69.48439 01-10-66
30-09-80 14.01 92.804 608 20.4 21-03-03
30-09-18 15.54
6 LAGHMAN RIVER AT PUL-I-
QARGHAI 34.54698
70.24249 01-10-60
30-09-79 19.01 59.029 421 0.90 21-03-07
30-09-18 11.54
7 PECH RIVER AT CHAGHASARAI
34.90927
71.12884 23-02-60
28-02-79 19.02 58.566 505 2.34 21-03-07
30-09-18 11.54
8 PANJSHER RIVER AT
GULBAHAR 35.15933
69.28868 01-10-59
30-09-80 21.01 54.488 461 6.43 01-10-07
30-09-18 11.01
9 PANJSHER RIVER AT OMARZ 35.37583
69.64085 01-10-62
30-09-80 17.67* 33.41 235 3.44 14-05-09
30-09-18 8.39*
10 GHORBAND RIVER AT PUL-I-
ASHAWA 35.08880
69.14189 01-10-59
04-02-80 20.52 22.86 134 1.50 07-05-08
30-09-18 10.41
11 TANGI-I-GHARU 34.56988
69.40217 01-10-59
30-09-80 21.01 15.399 192 0.00 26-05-05
30-09-18 13.36
12 SALANG RIVER AT BAGH-I-
LALA 35.15176
69.22051 01-10-61
29-02-80 17.73* 10.125 95.2 1.10 01-01-09
30-09-18 9.75
13 LOGAR RIVER AT SANG-I-
NAWESHTA 34.41819
69.19113 01-10-61
30-09-80 19.01 9.632 93.8 0.00 23-07-05
30-09-18 13.20
14 TANGI SAIDAN 34.40898
69.10441 01-10-61
30-09-80 19.01 4.057 87.2 0.00 21-03-07
30-09-18 11.54
15 SURKHRUD RIVER NEAR
SULTANPUR 34.41567
70.29584 08-03-68
31-03-80 11.78* 3.000 77.0 0.00 01-10-09
30-09-18 9.00
16 HAZARNAW RIVER AT SABAY 34.15458
70.44006 26-12-75
30-09-79 3.76 2.384 36.0 0.10 01-11-06
30-09-12 5.92
17 QARGHA RIVER ABOVE
QARGHA RESERVOIR 34.34000
69.01000 16-04-63
30-09-80 14.01* 0.333 5.50 0.00 01-04-07
30-09-18 11.51
18 KONAR RIVER AT PUL-I-KAMA 34.46871
70.55703 28-12-66
30-09-79 12.76 482.21 2350 45.0 09-07-07
30-09-18 11.24
19 MATUN RIVER AT MATUN 33.23000
69.53000 23-12-62
20-05-79 16.42 0.801 16.0 0.01 01-01-15
30-09-18 3.75
20 BELOW QARGHA RESERVOIR 34.33000
69.02000 01-10-64
30-09-80 16.01 0.216 4.15 0.02 23-05-05
30-09-18 13.36
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Central Asian Journal of Water Research (2020) 6(1): 1- 17
To find the difference of river flows over both periods, first, statistical mean, maximum and
minimum of the selected stations were compared. As the periods of both records are not
equal, the calculation interval for the longer period was set equal to the shorter period
duration of the same station. Then the calculation interval was moved one year forward on
longer period and the required statistics were recalculated. The process was continued until
reaching the end of the longer period. Subsequently, the result of each calculation interval was
compared with the shorter period data and the differences were calculated by using Equation
1. Then, the difference of mean, maximum and minimum discharges between the short period
and each calculating interval of the longer period were averaged and 90 % confidence range
for differences of each item were calculated. The summary of these analyses was used to
explore trends in the mean, maximum and minimum flows.
For identifying the variation in discharge frequency, a constant ten intervals for each station
of the first period (1950–1980) data were set from zero to the maximum discharge. Next, the
frequency of daily discharge for each interval (discharge bin) was calculated. Then, the
frequency of second period daily discharges was calculated based on the same discharge bins
(intervals) as the first period, to compare the flow occurrence in every interval. The frequency
of discharge in the second period (2003–2018) which exceeds the tenth interval due to rise in
flow peak, were collected into the eleventh interval. Due to the difference in the duration of
the two periods, first the frequency values were normalized over the recorded durations and
then the difference of the second period relative to the first period was calculated by using
Equation 1.

%
=

− 


100%(1)
where
%
is the relative changes in percent between the first and second periods.

and

represent the first and second period items (mean, maximum, minimum discharges and
normalized frequency values), respectively.
According to Equation 1, whenever the flow in a given bin has not occurred in the reference
(first) interval, the frequency of occurrence value for this interval is equal to zero and the
result of the relative change is undefined. In that case, the frequency of discharge in the
second period is given with the percentage of its record duration in the brackets. This clarifies
that flow occurred in this interval during the second period and the occurrence time is shown
by percentage of the first period duration where the reference discharge interval value is zero.
An interval with both periods having no occurrence of flow is kept blank, while zero percent
(0 %) is used for intervals in which the discharge has occurred, but has not changed.
For evaluating the effects of flow variation on future flood peaks, the HEC-SSP (Brunner and
Fleming 2010) software was used for the flood recurrence analyses. HEC-SSP is a statistical
software developed by Hydrologic Engineering Centre (HEC) of the U.S. Army Corps of
Engineers (USACE) that computes flood frequency analysis according to U.S. Federal agency
guidelines reported in Bulletin 17B (Interagency Advisory Committee on Water Data 1982)
and Bulletin 17C (England et al. 2015). Bulletin 17B uses the historical weighting procedure
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Central Asian Journal of Water Research (2020) 6(1): 1- 17
and Conditional Probability Adjustment (CPA) methods, while, Bulletin 17C uses Expected
Moments Algorithm (EMA) methods for estimating the moment of Log-Pearson Type III
frequency distribution (Bartles et al. 2016). Besides, both options have minor differences in
low flood, confidence intervals, low outliers and plotting position calculating methods. In the
analyses for this paper, the 17B methods option of the software was used because the Bulletin
17C (EMA methods) does not process data series which includes gap. Furthermore, as the
regional skew value for stations in Afghanistan is unknown, the individual stations’ skew
values were used in the model. The remaining settings of the software were set to the default
values. The analysis bundle contained three cases for flood peak recurrence estimations. The
first and second cases corresponded to each single period individually and the third case was
for a combination of all dataset including the gap in the time series. Estimated results were
compared with first period results to evaluate effects of using each case on 10, 20, 50, 100,
200, and 500 year returning floods. As the result of stations with small discharge is critical to
change in relative percentage, thus the final summary was achieved by combining the results
of station with dominant discharge (stations 1–10).
3. Results and discussion
Data quality analyses identified errors in the Matun River at Matun and Below Qargha
Reservoir stations. In addition, the Pul-e-Kama station on the Kunar River does not have
spatial consistency. In view of these issues, these three stations were removed from the
calculations. The record of several equal maximum values suggests that readings of gaging
stations had uncertainty. This might originate from the conversion of flow depth or stage to
discharge. This is explicit in many stations, especially in Sabay and Pul-e-Ashwa stations. It is
assumed that the reading uncertainty did not significantly influence peak discharge values for
the flood analyses so the data is accepted for the analyses presented here.
Statistical analyses result in Figure 2 indicates a small reduction of the mean discharge at the
stations with larger discharges (Stations 1–10 average: -4.6 %). This trend is not clear for the
rivers with lower discharges, which have large differences in variation between the early and
later period data, as shown at stations like Sultanpur, Above Qargha Reservoir and Tangi-i-
Gharu stations. The reason for this large variation in mean discharge is that these stations
have smaller catchments and discharge values. Thus, a slight variation or uncertainty in gauge
reading, results in a higher relative percentage value mathematically. In additions, the
reduction in mean discharge originates to the occurrence of several droughts in the recent
period. Omar (2018) identified droughts in the 2007–2009, 2011–2013, and 2016–2018
periods.
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Central Asian Journal of Water Research (2020) 6(1): 1- 17
Figure 2. Variation of the mean discharge with 90 % confidence range between the recent
and first periods, along with the station’s average discharge. The numbered stations’ details
are given in Table I.
On the other hand, the analyses show (Figure 3) that peak discharge levels have mostly
increased especially at the stations with larger discharges (Stations 1–10 average: 17.5 %).
Rivers with smaller discharges show inconclusive results, as seen for the Sabay (Station 16),
Sang-i-Naweshta (Station 13), Tangi Saidan (Station 14) and Tangi Gharu (Station 11)
stations. The Chaghsarai (Station 7) has a smaller catchment, while the Asmar (Station 3)
located near to Chaghsarai on the Kunar River has a larger catchment. Hence changes in the
mean and maximum discharge values of these two stations are different. The stations with
smaller discharge are located on tributaries and in the lower altitude and south-western areas,
which receive less heavy precipitations. Thus, peak discharge values have also declined.
Furthermore, WFP et al. (2016) reported that spring heavy precipitation events have increased
10–25 % in the mountainous areas of Hindu Kush and eastern part of the KRB. Thus, stations
close to mountainous areas have higher increment percentage in the maximum discharge,
while the stations located in the southern parts represent a decline or minor change in the
maximum discharge.
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Central Asian Journal of Water Research (2020) 6(1): 1- 17
Figure 3. Variation of the maximum discharge with 90 % confidence range between the
recent and first periods, along with the station’s maximum discharge. The numbered stations’
details are given in Table I.
Some rivers of the KRB are completely dry during the summer season, so the minimum
discharge is zero and it is not possible to make a significant projection about minimum flows.
However, the minimum discharge variation of most stations has a decline (Figure 4).
According to the river network in Figure 1, the Kunar River is a large tributary of the Kabul
River, thus flow changes in the Asmar (Station 3) leads to changes in the Dakah (Station 1).
Therefore, the change of minimum discharge in these two stations is positive. The Pul-i-
Ashwa (Station 10) and Sabay (Station 16) have very small mean and minimum discharges so
a very slight change result to higher relative percentage compared to early period data.
Furthermore, the flow in northwest parts of the basin is controlled by a dam reservoir just
before Naghlu (Station 4); so the minimum discharge released from the reservoir is essentially
constant. Upstream station peaks are significantly reduced by this reservoir, however, the flow
peak still raised at this station in the second period. The reduction of mean discharge at this
station also suggests the change of the water balance in the upstream part of the basin.
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Central Asian Journal of Water Research (2020) 6(1): 1- 17
Figure 4. Variation of the minimum discharges with 90 % confidence range between the
recent and first periods, along with the station’s minimum discharge. The numbered stations
details are given in Table I.
Moreover, the result of normalized frequency comparison analyses in Table II shows that high
and low flow frequencies are increased (highlighted in Table II) in the second period in
comparison to the first period. This is the result of significant increase in heavy precipitation
reported by WFP et al. (2016) and increased snowmelt due to the rise of average temperature.
The stations with reduced frequencies in the higher discharge interval are due to reduction of
annual peak discharges discussed earlier and increment of consumption due to population
growth. The increment of low flow frequencies suggests a change in flow regime and
originate from droughts, rapid snowmelt and changes in precipitation pattern. The highlighted
cells in Table II show the increment of river flow frequency in that interval.
Table II. Normalized frequency variation of the discharge intervals for the KRB stations
between the first and second recording periods. The highlighted cells show the increment of
river flow frequency in that interval. The values represent relative percentage of normalized
flow frequencies. The flow range from low to high discharge is set in the first to eleventh
interval.
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Central Asian Journal of Water Research (2020) 6(1): 1- 17
Station
number
Station name Mean
Discharge
Frequency intervals
(Q
min
) (Q
max
)
m
3
s
-1
1 2 3 4 5 6 7 8 9 10 11
1 DAKAH 640.963 -0.8% 7.1% 9.9% 21.1% -9.1% -2.9% -57.8% -68.3%
-60.5% 131.3%
2 KONAR RIVER AT NAWABAD 491.682 -29.4% 93.2% 8.0% 12.6% -8.4% -55.6% 66.9% 37.6% -84.8% -89.9% [1*329%]
3 KONAR RIVER NEAR ASMAR 378.294 -20.0% 56.0% -1.2% -15.4% 3.9% -28.5% -23.4% -50.5%
63.0% 551.4% [29*60%]
4 NAGHLU 112.205 6.6% 31.5% -22.0% -42.3% -54.4% -86.4% -91.5% -91.0%
-22.3% 55.4% [2*48%]
5 PANJSHER RIVER AT GULBAHAR 92.804 4.63% -5.78% 5.61% 48.01% -20.45%
-78.26%
-83.92%
-96.18%
-90.91%
-100%
6 LAGHMAN RIVER AT PUL-I-QARGHAI 59.029 -4.5% 17.0% 34.1% 39.7% -13.6% -5.2% -33.6% -73.9%
-74.4% -75.3% [5*61%]
7 PECH RIVER AT CHAGHASARAI 58.566 -0.9% 52.6% -13.3% 15.0% -68.8% -73.2% -95.7% -100.0%
-100%
8 PANJSHER RIVER AT SHUKHI 54.488 -5.63% 28.31% 7.29% -1.27% 35.47% 12.18% -25.26%
-22.08%
-41.30%
-62.88%
[12*111%]
9 PANJSHER RIVER AT OMARZ 33.41 -7.14% 3.30% 2.44% 11.12% 56.22% 115.47%
-8.81% -27.01%
-54.85%
-9.71% [18*47%]
10 GHORBAND RIVER AT PUL-I-ASHAWA 22.86 9% -25% 24% -10% -21% -10% 10% -38% -100% -72% [1*51%]
11 TANGI-I-GHARU 15.399 28.18% -63.73%
-18.29%
-42.50%
-85.70%
-30.59%
-26.96%
-92.51%
151.71%
-100%
12 SALANG RIVER AT BAGH-I-LALA 10.125 -2.4% 18.7% -0.9% -12.1% -39.4% 107.8% 294.0% 991.0%
-9.1% -100.0%
[1*54%]
13 LOGAR RIVER AT SANG-I-NAWESHTA 9.632 26.84% -20.57%
-67.56%
0.71% -2.99% -0.66% -54.51%
-60.72%
-100% -100%
14 TANGI SAIDAN 4.057 1% 0% -9% -46.1% 5.9% -17.6% -52.9%
229.5% -100%
15 SURKHRUD RIVER NEAR SULTANPUR 3.000 -8.5% 162.3% -29.5% -24.7% 141.0% 344.9% 161.7% -56.4%
[1*76%]
30.8% [6*76%]
16 HAZARNAW RIVER AT SABAY 2.384 6.8% 15.6% -89.4% -100% -100% -100% [1*157%]
-100%
17 QARGHA RIVER ABOVE QARGHA
RESERVOIR 0.333 3% -37% -1% 2% -18% -23% -13% -29% 192% 168% [43*82%]
AVERAGE OF STATIONS (1 – 10) -4.8% 25.8% 5.4% 7.8% -10.0% -21.2% -34.3% -53.0%
-51.8% 22.8%
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Central Asian Journal of Water Research (2020) 6(1): 1- 17
Similarly, the flood recurrence results show that predictions using either of the single datasets
are not reliable. These predictions show large variations, with overestimated and
underestimated results especially for stations with shorter record durations. The comparison
of the second period with reference to first period represent larger values for 10, 20, 50, 100,
200, and 500-year return flows at stations where maximum discharges are increased, such as
in Pul-i-Qarghai (Station 6) and Naghlu (Station 4). This also shows a decline where peak
flows are reduced, as in Sang-i-Naweshta (Station 13) and Chaghsarai (Station 7). The
predictions based on the third case (combined full dataset including gap), showed smaller
changes and better results compared to larger uncertainties in estimations using either single
dataset. For example, predictions based on all three cases for (A) Naghlu, (B) Pul-i-Qarghai
and (C) Nowabad stations are shown in Figure 5. The results show that using of single dataset
is insufficient and yield unreasonable predictions, while the third case result has a logical
trend and a better estimation. Significant variations based on single dataset were seen in most
of the station analyses. The best results were found where the stations had longer periods of
the record. Hence despite the effects of the environmental changes and long gap, application
of the combined dataset is recommended for flood return period analyses.
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Central Asian Journal of Water Research (2020) 6(1): 1- 17
Figure 5. Flood return period estimations for (A) Naghlu, (B) Pul-i-Qarghai and (C) Nowabad stations for the first, second and third cases
(1950-1980 period data, 2003-2018 period data and combined data including the long gap) respectively.
14
Published by Kazakh-German University, Almaty / Kazakhstan
Finally, for identifying the flood peak projection, the estimated values for each return period
based on the full dataset was compared to the first period results by using Equation 1. The
result in Table III shows that estimated flood peak increased significantly in the stations
where the maximum discharge peaks were raised and vice versa. The average percentage of
the stations with significant discharge (Stations 1–10) shows an increment of 3.3–15.2 % in
the 10–500 year return period floods. Table III also shows the change rate for every station
individually. The highest change is observed in the longer return period, while shorter return
period indicated smaller variation.
Table III. Variation of the estimated flood peaks between first (1950–1980) and combined
(1950–2018) cases including the missing period. The highlighted cells show the increment of
flood peak in relative percentage with reference to the first period.
Station
numbe
r Station name Return period [Years]
500 200 100 50 20 10
1 DAKAH 6.91% 2.76% -0.12% -2.75% -5.79% -7.66%
2 KONAR RIVER AT NAWABAD -25.94%
-19.18%
-13.85%
-8.41% -1.20% 4.04%
3 KONAR RIVER NEAR ASMAR 51.17% 41.67% 34.76% 28.09% 19.60% 13.39%
4 NAGHLU 12.77% 9.13% 6.44% 3.78% 0.35% -2.20%
5 PANJSHER RIVER AT GULBAHAR 27.79% 23.22% 19.80% 16.42% 11.94% 8.56%
6 LAGHMAN RIVER AT PUL-I-QARGHAI 44.34% 36.27% 30.32% 24.50% 16.91% 11.21%
7 PECH RIVER AT CHAGHASARAI 0.58% -1.06% -2.33% -3.59% -5.34% -6.68%
8 PANJSHER RIVER AT SHUKHI -11.03%
-8.65% -6.90% -5.24% -3.35% -2.39%
9 PANJSHER RIVER AT OMARZ 45.88% 39.21% 34.16% 29.12% 22.36% 17.06%
10 GHORBAND RIVER AT PUL-I-ASHAWA -0.11% -0.42% -0.70% -1.08% -1.55% -2.08%
11 TANGI-I-GHARU 13.62% 8.32% 4.35% 0.49% -4.65% -8.51%
12 SALANG RIVER AT BAGH-I-LALA 240.0% 228.61%
218.68%
207.58%
191.28%
175.68%
13 LOGAR RIVER AT SANG-I-NAWESHTA -21.34%
-18.02%
-15.36%
-12.45%
-8.31% -4.84%
14 TANGI SAIDAN -1.98% 0.32% 2.01% 3.75% 5.87% 7.30%
15 SURKHRUD RIVER NEAR SULTANPUR 132.68%
112.99%
97.36% 81.38% 60.21% 44.90%
16 HAZARNAW RIVER AT SABAY -19.52%
-15.71%
-13.55%
-12.48%
-13.01%
-16.05%
17 QARGHA RIVER ABOVE QARGHA RESERVOIR
602.94%
381.54%
264.52%
181.03%
94.44% 51.02%
AVERAGE OF STATION (1 -10) 15.24%
12.30%
10.16%
8.08% 5.39% 3.33%
4. Conclusion
The results of this study revealed that the flow peak is increased (17.5 %) in the basin from
the early to the more recent period. Over the same period, the mean discharge exhibits a
reduction (-4.6 %) due to several droughts in the recent period. In addition, the river flow
15
Central Asian Journal of Water Research (2020) 6(1): 1- 17
frequency results suggest that peak and low flow frequencies have significantly increased.
This indicates the increment of flooding and low flow days in the basin and may challenge the
irrigation during the low and medium flow days. Furthermore, the flood recurrence analyses
show that use of a single dataset for flood return period predictions is not appropriate, while
the combined dataset including the gap duration analyses shown a reasonable result. This
suggests that the environmental change effects are reflected by river flow variations and
influenced subsequent results. Furthermore, the comparison of long-term flood peaks for each
return period showed that flood peak has an upward trend. This originates to the recent
variation of the flow peaks. Finally, the study also helps researchers who perform simulations
using the first period data and calibrate or cross-validate their models using data from the
more recent period, by defining the amount of flow change at each station.
5. Limitations
It is worth mentioning that floods are poorly studied in this region. The study was associated
with a shortage of recorded data and limitations on the available data. Maximum efforts have
been carried out to collect all available data. But, unfortunately due to war, the existing
historical data has short durations in both the pre- and post-war periods. Using all these data,
results obtained are sufficient for the purpose at hand and provide a better insight into the
flood situation in the basin. For a more specific result, more data and detailed analyses are
required.
6. Acknowledgements
This research was financially supported by the United States Agency for International
Development (USAID) with technical support from National Academy of Science (NAS) of
the U.S. under grant number: No. AID-OAA-A-11-00012. The authors thank Dr. Kevin
Vining from USGS and Professor J.F. Shroder from the University of Nebraska for providing
discharge data of 1950–1980 period. Finally, authors appreciate the cooperation of the
Ministry of Energy and Water (MEW) of the Islamic Republic of Afghanistan for sharing
discharge data for the recent (2003–2018) period
.
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Spatial Data Center -ASDC/IMMAP (Web, map, dashboard, infographic)
  • Asdc
  • Afghanistan
ASDC. Afghanistan Spatial Data Center -ASDC/IMMAP (Web, map, dashboard, infographic). Afghanistan. Available online at: http://asdc.immap.org/. (Verified on 16 June 2019).
HEC-SSP Statistical Software Package User's Manual, Version 2.1. ed. US Army Corps of Engineers
  • M Bartles
  • G Brunner
  • M Fleming
  • B Faber
  • J Slaughter
Bartles, M., Brunner, G., Fleming, M., Faber, B. and Slaughter, J., 2016. HEC-SSP Statistical Software Package User's Manual, Version 2.1. ed. US Army Corps of Engineers, Hydrologic Engineering Center, Davis, CA 95616. Available at: https://www.hec.usace.army.mil/software/hec-ssp/documentation.aspx.
HEC-SSP Statistical Software Package
  • G Brunner
  • M Fleming
Brunner, G. and Fleming, M., 2010. HEC-SSP Statistical Software Package. US Army Corps Eng. Inst. Water Resour. Hydrol. Eng. Cent. HEC. Available at: https://www.hec.usace.army.mil/software/hec-ssp/.