This paper investigates the spatiotemporal variability in hydrometeorological time-series to evaluate the current and future scenarios of water resources availability from upper Indus basin (UIB). Mann–Kendall and Sen’s slope estimator tests were used to analyze the variability in the temperature, precipitation, and streamflow time-series data at 27 meteorological stations and 34 hydrological stations for the period of 1963 to 2014. The time-series data of entire study period were divided into two equal subseries of 26 years each (1963–1988 and 1989–2014) to assess the overlapping aspect of climate change acceleration over UIB. The results showed a warming pattern at low altitude stations, while a cooling tendency was detected at high-altitude stations. An increase in streamflow was detected during winter and spring seasons at all hydrological stations, whereas the streamflow in summer and autumn seasons exhibited decreasing trends. The annual precipitation showed a significant decreasing trend at ten stations, while a significant increasing trend was observed at Kohat station during second subseries of the study period. The most significant winter drying trends were observed at Gupis, Chitral, Garidopatta, and Naran stations of magnitude of 47%, 13%, 25%, and 18%, respectively, during the second subseries. The annual runoff exhibited significant deceasing trends over Jhelum subbasin at Azad Pattan, Chinari, Domel Kohala, Muzaffarabad, and Palote, while within Indus basin at Chahan, Gurriala, Khairabad, Karora, and Kalam in the second time-series. It is believed that the results of this study will be helpful for the decision-makers to develop strategies for planning and development of future water resources projects.
Tibetan Plateau comprises three major mountainous areas of Asia, that is, Hindukush, Karakoram, and Himalaya (HKH), also known as the “third pole” or “roof of the world” because of the massive volumes of recurrent snow and glacial ice storage in its high-altitude basins [
Numerous climate change studies have been carried out over the UIB. For instance, significant decreasing temperature trends were detected during the monsoon season and warming during the premonsoon season [
Archer [
Glacial melt from the Karakoram region dominated the flows into the main Indus river system [
This study investigates the spatiotemporal variability in the hydrometeorological time-series data and hydrological impacts over UIB by using Mann–Kendall and Sen’s slope estimator tests. Moreover, the impacts of local topographical setting and altitude variations on runoff contributions from glacier melt, snow melt, and monsoon precipitation were also evaluated.
The Indus river basin is one of the world’s largest transboundary river basins with a total drainage area of about 1.08 × 106 km2 [
The upper Indus basin confined in Pakistan boundary showing rivers, elevation, streamflow gauges (Table
The eastern and central part of the Karakoram is covered by the Shyok and Shigar basins, respectively. About 24% of the area of the Shyok river basin is covered with snow [
The Astore basin is located in the western Himalayan and Hunza basin is in the western Karakoram ranges. The glacial coverage within these basins is less than snow coverage as compared to the Shyok and Shigar basins. The glaciers and permanent ice cover within Hunza basin is 28% and 14%, which exhibits almost 21% and 3% of the total UIB glacial coverage within Hunza and Astore basins, respectively [
The Gilgit subbasin ranges between 35.8 and 37 E and 72.5 to 74.4 N comprehends eastern part of Hindukush range and drains towards southeast to join Indus river. The discharge of Gilgit river is measured at Gilgit hydrometric station and at the confluence of Hunza and Gilgit river, which is called Alam bridge. The drainage area of this basin incorporates 12000 km2 with an elevation range from 1481 to 7134 m m.a.s.l. Four climatic stations are installed in this area, that is, Gilgit, Gupis, Yasin, and Ushkore, by the Pakistan Meteorological Department (PMD) and WAPDA. In this study, data at two stations (Gilgit and Gupis) were used from 1960 to 2014. Hasson et al. [
The Mangla basin is located on the southern slope of the Himalayas with elevation ranging from 300 m to 6282 m m.a.s.l. and has basin area of around 33425 km2 at Mangla dam. This dam serves hydropower generation and regulates the flow from Mangla reservoir. About 55% of the area lies in Indian held Kashmir and 45% lies in Pakistan including Azad Kashmir. There are five subcatchments, that is, Jhelum, Poonch, Kanshi, Neelum/Kishanganga, and Kunhar which drain water to Mangla reservoir.
Kabul river, in the eastern Afghanistan and northwestern Pakistan, is 700 km long, of which 560 km lies in Afghanistan. It originates in the Sanglakh ranges located 72 km west of Kabul city. It flows east through Kabul and Jalalabad, north of the Khyber Pass into Pakistan. The river has four major tributaries: the Lowgar, the Panjshēr, the Konar (Kunar), and the Alīngār. Most of area of this catchment lies in Afghanistan. Due to unavailability of data from Afghanistan, the study area was confined to the catchment falling within Pakistan boundary. The Kabul river, a major western flank tributary, joins with Indus near Attock.
In this study, hydrological time-series data of 34 stream gauges and meteorological data of 27 stations for the period of 1963 to 2014 were collected from WAPDA and PMD. The information regarding the location of each stream gauge station, area of subbasins, and mean annual streamflow is presented in Table
List of stream gauges used in the present study and their characteristics (period 1: 1963–1988; period 2: 1989–2014).
Sr. no. | Station | Latitude (dd) | Longitude (dd) | Area (Km2) | Mean annual streamflow (m3/s) | |
---|---|---|---|---|---|---|
1963–1988 | 1989–2014 | |||||
1 | Naran | 34.9 | 73.7 | 1036 | 47.7 | 45.6 |
2 | Garhi Habibullah | 34.4 | 73.4 | 2355 | 100 | 105.5 |
3 | Muzaffarabad | 34.4 | 73.5 | 7275 | 342.3 | 321.9 |
4 | Chinari | 34.2 | 73.8 | 13598 | 298.7 | 289 |
5 | Domel | 34.4 | 73.5 | 14504 | 327.3 | 322.3 |
6 | Kohala | 34.1 | 73.5 | 24890 | 776 | 780.5 |
7 | Azad Pattan | 33.7 | 73.6 | 26485 | 1150.7 | 1241.8 |
8 | Kotli | 33.5 | 73.9 | 3238 | 123.9 | 127.3 |
9 | Palote | 33.2 | 73.4 | 1111 | 6 | 5.3 |
10 | Kharmong | 35.2 | 75.9 | 67858 | 462.7 | 465 |
11 | Yogo | 35.2 | 76.1 | 33670 | 341.2 | 368.8 |
12 | Shigar | 35.4 | 75.7 | 6610 | 194.6 | 220.5 |
13 | Kachura | 35.5 | 75.4 | 112665 | 962 | 1159.6 |
14 | Gilgit | 35.9 | 74.3 | 12095 | 277.2 | 333.7 |
15 | Dainyor Br. | 35.9 | 74.4 | 13157 | 365.4 | 295 |
16 | Alam Br. | 35.8 | 74.6 | 26159 | 661.8 | 619.3 |
17 | Bunji | 35.7 | 74.6 | 142709 | 1706 | 1875.3 |
18 | Doyain | 35.5 | 74.7 | 4040 | 118.3 | 149.2 |
19 | Shatial Br. | 35.5 | 73.6 | 150220 | 1938.9 | 2110.6 |
20 | Karora | 34.9 | 72.8 | 635 | 20.4 | 17.5 |
21 | Besham Qila | 34.9 | 72.9 | 162393 | 2350.2 | 2436.8 |
22 | Daggar | 34.5 | 72.5 | 598 | 5.4 | 5.9 |
23 | Phulra | 34.3 | 73.1 | 1057 | 18.6 | 20.5 |
24 | Kalam | 35.5 | 72.6 | 2020 | 85.7 | 86.2 |
25 | Chakdara | 34.6 | 72 | 5776 | 169.1 | 207.1 |
26 | Chitral | 35.9 | 71.8 | 11396 | 264.4 | 285.4 |
27 | Nowshera | 34 | 72 | 88578 | 849 | 824.2 |
28 | Gurriala | 33.7 | 72.3 | 3056 | 26.9 | 24.8 |
29 | Khairabad | 33.9 | 72.2 | 252525 | 3222.7 | 2834.4 |
30 | Thal | 33.4 | 71.5 | 5543 | 27.7 | 22.6 |
31 | Chirah | 33.7 | 73.3 | 326 | 5.7 | 4 |
32 | Chahan | 33.4 | 72.9 | 241 | 1.7 | 1.3 |
33 | Dhok Pathan | 33.1 | 72.3 | 6475 | 44 | 38.4 |
34 | Massan | 33 | 71.7 | 286000 | 3527.2 | 3809.5 |
List of climatic stations in upper Indus basin (period 1: 1963–1988; period 2: 1989–2014).
Sr. no. | Station | Elevation (m) | Max. temp (°C) | Min. temp (°C) | Precipitation (mm) | |||
---|---|---|---|---|---|---|---|---|
1963–1988 | 1989–2014 | 1963–1988 | 1989–2014 | 1963–1988 | 1989–2014 | |||
1 | Astore | 2168 | 15.4 | 15.8 | 4 | 4.1 | 39 | 42 |
2 | Bagh | 1067 | 25.4 | 19.9 | 4 | 4.6 | 13 | 13 |
3 | Balakot | 995.5 | 15.4 | 15.9 | 14.5 | 14.2 | 50 | 50 |
4 | Bunji | 1372 | 24 | 23.7 | 14 | 14.3 | 15 | 17 |
5 | Cherat | 1372 | 21.9 | 21.1 | 8.9 | 8.3 | 33 | 38 |
6 | Chilas | 1250 | 26.6 | 26.2 | 8.3 | 7.8 | 130 | 123 |
7 | Chitral | 1497.8 | 22.8 | 23.8 | 11.1 | 11.4 | 44 | 49 |
8 | Dir | 1375 | 22.5 | 23.3 | 7.9 | 7.4 | 11 | 11 |
9 | Drosh | 1463.9 | 23.8 | 24.3 | 7.1 | 6.1 | 7 | 14 |
10 | Garidopatta | 813.5 | 15.4 | 15.9 | 9.3 | 10.2 | 117 | 116 |
11 | Gilgit | 1460 | 23.5 | 24.3 | 17.1 | 17 | 45 | 45 |
12 | Gujar Khan | 457 | 28.1 | 29 | 9.4 | 6.9 | 65 | 67 |
13 | Gupis | 2156 | 18.7 | 18.9 | 15.8 | 16.4 | 36 | 37 |
14 | Kakul | 1308 | 22.7 | 23.3 | 9.3 | 10.2 | 117 | 116 |
15 | Kohat | 1440 | 28.9 | 30.3 | 17.1 | 17 | 45 | 45 |
16 | Kotli | 610 | 28.4 | 28.4 | 16.6 | 14.9 | 1272 | 1183 |
17 | Mangla | 282 | 30.4 | 30.9 | 17.6 | 17 | 35 | 39 |
18 | Murree | 2206 | 16.3 | 18 | 8.9 | 8.4 | 1765 | 1734 |
19 | Muzaffarabad | 702 | 15.4 | 15.9 | 17.6 | 17 | 35 | 39 |
20 | Naran | 2363 | 14.1 | 10.5 | 8.7 | 9.6 | 42 | 44 |
21 | Palandri | 1402 | 15.4 | 15.9 | 17.6 | 17 | 35 | 39 |
22 | Parachinar | 1725 | 21.1 | 21.3 | 9.4 | 6.9 | 65 | 67 |
23 | Peshawar | 320 | 29.3 | 29.7 | 15.8 | 16.4 | 36 | 37 |
24 | Rawalakot | 1677 | 20 | 21.1 | 8.7 | 9.6 | 44 | 46 |
25 | Risalpur | 575 | 29.5 | 29.9 | 14.6 | 14.2 | 55 | 54 |
26 | Saidu Sharif | 961 | 25.6 | 26.3 | 12.3 | 11.9 | 90 | 90 |
27 | Skardu | 2317 | 18 | 19.2 | 5.1 | 4.7 | 17 | 20 |
The hydrometeorological time-series data of entire study period (1963–2014) were divided into two equal subseries, that is, 1963 to 1988 and 1989 to 2014, to analyze the aspects of acceleration of climate change. Mean monthly, seasonal, and annual values of Tmax,
To detect climate impacts and possible climate change acceleration over the past 52 years, Student’s
The application of Student’s
If the variances for the two periods are different, then the
The nonparametric Mann–Whitney
We have
The relative change (%) in the annual and seasonal temperature, precipitation, and streamflow was assessed by using the following equation:
For detection of trends, we (i) prewhitened time-series to eliminate effect of serial correlation of observations, (ii) applied Mann–Kendall trend analysis to identify if trends are significant, and (iii) assessed the trend slope line by means of Sen’s estimator. Analysis is common, and reference is made to applications in [
A nonparametric rank-based Mann–Kendall (MK) trend analysis test was used to evaluate the variations in the hydrometeorological time-series data over UIB [
A positive value of
Sen’s nonparametric method [
Sen’s estimator is the median,
Student’s
Relative change (%) in annual and seasonal temperature and precipitation in 2nd period (1989–2014) with respect to 1st period (1963–1988) (bold, underline, and
Sr. no. | Climatic stations | Maximum temperature | Minimum temperature | Precipitation | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Annual | Winter | Spring | Summer | Autumn | Annual | Winter | Spring | Summer | Autumn | Annual | Winter | Spring | Summer | Autumn | ||
1 | Astore | 3 |
|
4 | −2 |
|
1 | −6 |
|
− |
1 | 4 |
|
−12 |
|
7 |
2 | Bagh |
|
|
|
|
|
|
7 |
|
|
|
2 |
|
8 | −9 | −3 |
3 | Balakot |
|
|
|
|
|
|
|
|
|
|
−6 | −1 |
|
−6 | 8 |
4 | Bunji | −1 |
|
1 | − |
−1 |
|
|
|
|
|
|
|
−17 |
|
|
5 | Cherat | − |
|
0 |
|
− |
|
0 |
|
|
|
−13 | −3 |
|
−10 | −13 |
6 | Chilas | −1 | 0 |
|
− |
−1 | 2 |
|
2 |
|
1 |
|
|
5 |
|
|
7 | Chitral |
|
|
|
0 |
|
|
− |
− |
|
|
|
|
|
|
|
8 | Dir |
|
|
|
|
|
− |
|
|
− |
− |
−3 |
|
|
|
|
9 | Drosh |
|
|
|
0 | 1 |
|
|
|
|
|
−2 | 14 | −12 | 9 |
|
10 | Garidopatta |
|
|
|
|
|
|
−2 |
|
|
|
|
13 |
|
−14 |
|
11 | Gilgit |
|
|
|
−1 |
|
− |
|
|
− |
− |
16 |
|
−2 |
|
|
12 | Gujar Khan | 3 |
|
2 |
|
|
|
|
|
|
|
−3 | 3 |
|
−4 | 6 |
13 | Gupis | 1 |
|
|
− |
|
|
|
|
|
− |
|
|
|
|
|
14 | Kakul |
|
|
3 | 1 |
|
|
|
− |
|
− |
|
13 | 1 |
|
8 |
15 | Kohat |
|
|
|
|
|
|
−2 | −1 |
|
|
|
|
−2 |
|
18 |
16 | Kotli | 0 | 1 | 2 | −1 | −2 |
|
−3 |
|
|
|
−7 | 4 | −8 | −7 | −16 |
17 | Mangla |
|
3 |
|
|
−36 |
|
− |
− |
|
|
−5 | −8 |
|
1 | 4513 |
18 | Murree |
|
|
|
|
|
−5 |
|
|
|
|
−2 | 6 | −9 | 0 | −5 |
19 | Muzaffarabad |
|
|
|
1 |
|
|
|
2 | −1 | −1 | 7 |
|
3 | 6 | 2 |
20 | Naran |
|
|
|
− |
−4 |
|
|
|
|
|
|
|
|
|
|
21 | Palandri |
|
|
4 | −1 |
|
|
7 |
|
|
|
|
1 |
|
|
−10 |
22 | Parachinar |
|
|
|
|
|
|
|
|
|
|
−3 | 8 | −8 | −5 | 2 |
23 | Peshawar |
|
|
2 | 1 |
|
|
|
|
0 |
|
|
|
2 | 1 |
|
24 | Rawalakot |
|
|
|
|
|
|
|
|
|
|
−7 | 7 | −10 |
|
−16 |
25 | Risalpur |
|
|
|
|
|
−1 |
|
|
|
|
|
|
|
|
|
26 | Saidu Sharif |
|
|
|
|
|
− |
|
|
− |
− |
|
|
|
|
|
27 | Skardu |
|
|
|
1 |
|
− |
−11 | −2 | − |
− |
|
|
12 | 30 | 20 |
Percent number of stations with positive (upward) and negative (downward) trends in annual and seasonal time-series for different periods and number of stations with significant trends by Mann–Kendall test at
The summary of the trend analyses and the spatial variation in annual, winter, spring (premonsoon), summer (monsoon), and autumn (postmonsoon) maximum and minimum temperature are presented in Figures
Spatial distribution of trends detected by Mann–Kendall test and estimated by Sen’s method in seasonal maximum temperature showing change in °C·decade−1 (upward and downward arrows show positive and negative trends, respectively; blue arrow shows significant trend at
Spatial distribution of trends detected by Mann–Kendall test and estimated by Sen’s method in annual maximum temperature showing change in °C·decade−1 (upward and downward arrows show positive and negative trends, respectively; blue arrow shows significant trend at
Spatial distribution of trends detected by Mann–Kendall test and estimated by Sen’s method in seasonal minimum temperature showing change in °C decade−1 (upward and downward arrows show positive and negative trends, respectively; bold (blue) arrow shows significant trend at
Spatial distribution of trends detected by Mann–Kendall test and estimated by Sen’s method in annual minimum temperature showing change in °C·decade−1 (upward and downward arrows show positive and negative trends, respectively; blue arrow shows significant trend at
Significant differences were observed at Naran and Gupis stations for all seasons, but a different pattern was revealed during winter and summer. It was observed that percent change values that are statistically significant are relatively large at few stations with values in the range of +25% to −25%. Highest increase in percent changes of precipitation was detected at Gupis and Naran stations during all seasons but these changes became negative and quite lower at low-altitude stations.
The results of analysis by applying Mann–Kendall test and Sen’s slope estimator methods in the annual precipitation time-series were summarized for two consecutive 26-year periods, that is, 1963–1988 and 1989–2014. The annual precipitation increased significantly at five stations, while it decreased at four stations during the first period. It was noted that the Gupis station exhibited significant increasing precipitation at the rate of 32% per year with 99% level of confidence. In the 2nd period at two stations the annual precipitation has increased significantly but decreased at ten stations (Table
Spatial distribution of trends detected by Mann–Kendall test and estimated by Sen’s method in seasonal precipitation showing change in % of data period averages (upward and downward arrows show positive and negative trends, respectively; blue arrow shows significant trend at
Spatial distribution of trends detected by Mann–Kendall test and estimated by Sen’s method in annual precipitation showing change in % of data period averages (upward and downward arrows show positive and negative trends, respectively; blue arrow shows significant trend at
The annual runoff in Kurram, Soan, and Indus subbasins decreased by 18%, 13%, and 12%, respectively; however, the runoff variations are found to be statistically significant in Indus subbasin. The winter season showed the largest variations compared to other seasons. Moreover, all subbasins showed positive variations during winter season except for Kurram river subbasin as shown in Table
Relative change (%) in annual and seasonal streamflow during the 2nd period (1989–2014) with respect to the 1st period (1963–1988) (bold, underline, and
Stream gauge | Annual | Winter | Spring | Summer | Autumn |
---|---|---|---|---|---|
Naran | −4 | −6 | −3 | −10 |
|
Garhi Habibullah | 5 | 17 |
|
−5 | 21 |
Muzaffarabad | −6 | 13 | 3 |
|
6 |
Chinari | −3 | 6 | 0 | −7 | −5 |
Domel | −2 | 13 | 2 | −8 | 0 |
Kohala | 1 |
|
6 | −8 | 8 |
Azad Pattan | 8 |
|
12 | 0 | 13 |
Kotli | 3 |
|
6 | −10 | 10 |
Palote | −12 | 27 | −27 | −14 | −17 |
Kharmong | 1 |
|
7 | −7 | 1 |
Yogo |
|
4 |
|
6 |
|
Shigar |
|
2 |
5 |
|
3 |
Kachura |
|
|
|
|
|
Gilgit | 20 |
|
|
16 | 26 |
Dainyor Br. |
|
|
5 |
|
−6 |
Alam Br. |
|
|
|
|
3 |
Bunji |
|
|
|
5 |
|
Doyain |
|
|
|
|
|
Shatial Br. |
|
|
|
|
|
Karora |
|
|
|
|
14 |
Besham Qila | 4 |
|
|
−1 |
|
Daggar | 9 |
|
|
−6 | 2 |
Phulra | 10 |
|
13 | 0 | 5 |
Kalam | 1 |
|
|
−5 | 1 |
Chakdara |
|
|
|
8 |
|
Chitral |
|
|
|
6 |
|
Jhansi post | −23 | −21 |
|
−1 | −30 |
Nowshera | −3 | 8 | 5 | −9 | 1 |
Gurriala | −8 | 24 | 5 | −18 |
−11 |
Khairabad |
|
|
|
|
|
Thal |
|
|
|
−1 |
|
Chirah |
|
−11 | −29 |
|
−16 |
Chahan | −21 | 0 | −18 |
|
6 |
Dhok Pathan | −13 | 18 | −3 |
|
15 |
Massan |
|
|
7 |
2 |
|
The results of annual mean streamflow at 34 stations using MK test of two consecutive 26-year periods are presented in Figures
Spatial distribution of trends detected by Mann–Kendall test and estimated by Sen’s method in seasonal streamflow showing change in % of data period averages (upward and downward arrows show positive and negative trends, respectively; blue arrow shows significant trend at
Spatial distribution of trends detected by Mann–Kendall test and estimated by Sen’s method in annual streamflow showing change in % of data period averages (upward and downward arrows show positive and negative trends, respectively; blue arrow shows significant trend at
UIB is a region that is famous for conflicting signals of climate and contrasting hydrological regime [
Most of the river gauges during winter (DJF)) showed the significant increasing river flows during the first-time series. Mukhopadhyay and Khan [
We observed significant changes in the second period as compared to the first period within UIB, which are consistent and in agreement with the global warming trends reported by Hasson et al. [
The hydrometeorological time-series data used to support the findings of this study are available from the corresponding author upon request.
The authors declare that they have no conflicts of interest.
This study was supported by the National Natural Science Foundation of China (nos. 51509141 and 51809150).