Assessment of Hydrological Response to Climatic Variables over the Hindu Kush Mountains, South Asia
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Climatological and Hydrological Data
2.3. Calculations by Expert Team on Climate Change Detection and Indices (ETCCDI) Climatic Variables Using the RClimDex
2.4. Evapotranspiration Calculation
2.5. Spatial Analysis by Inverse Distance Weighting (IDW)
2.6. Trend Analysis
Mann–Kendall Test
2.7. Partial Least Squares Regression
2.8. Quantification of Streamflow Variation
2.9. Estimated Climatic Variables
3. Results
3.1. Spatial Distribution of Climatic Variables
3.2. Spatial Distribution of Trend Analysis for Climatic Variables
3.3. Pearson Correlation between the Variables
3.4. Partial Least Squares Regression
Dominant Climatic Variables
4. Discussion
5. Conclusions
- The MK test based on “z” values indicated a rise in precipitation during the last 30 years over the UIB, as most variables showed increasing trends. The TNx is the only increasing variable in temperature indices. Projected trends of calculated variables are shown in the figures above.
- Based on the variable importance in projection (VIP), there are four key climatic variables: R99p, meaning extremely wet days; PRCPTOT, denoting yearly total precipitation; Rx5day; and R25mm. These parameters were discovered to considerably influence the yearly streamflow, highlighting the significance of precipitation-related variables in determining streamflow patterns.
- The TXn and Tmax mean, conversely, are the main temperature factors affecting streamflow. In regions with snow accumulation, these elements are the leading causes of glaciers and snowmelt. More specifically, in these snow-covered areas, greater values of TXn and Tmax mean temperatures might hasten the melting process and influence streamflow.
- Most sub-basins are located in low-temperature regions where evapotranspiration (ET) has little effect on changes in streamflow. This is because these colder areas evaporate water at a slower rate. However, due to the increased rate of evaporation in regions with moderate temperatures, ET impacts streamflow variability.
- This study concluded that temperature (T) plays a much lesser effect than precipitation (P) in determining streamflow generation in the UIB. The use of the PLSR model led to discovery. The model was used to measure streamflow changes and found that, in most basins, the yearly streamflow caused by climate declined from 2000 to 2019. Comparing the streamflow to the baseline period of 1990–1999 revealed this drop. Consequently, the results point to a substantial change in streamflow patterns over the decades, caused mainly by variations in precipitation.
- In the period from 2000 to 2009, there was a notable increase in streamflow: Kalam experienced a rise of 3.94%, while Shigar saw a more minor increase of 0.48%. However, the decade from 2010 to 2019 showed a more pronounced increase. Kalam’s streamflow went up by 10.30%, and notably, Shigar’s streamflow surged by 37.37%.
- This knowledge can help with choosing the right climatic variables for catchment hydrological models.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Sr. No. | Station Name | Longitude (°) | Latitude (°) | Elevation (m) |
---|---|---|---|---|
1 | Astore | 74.90 | 35.33 | 2450 |
2 | Bunji | 74.63 | 35.67 | 1400 |
3 | Chilas | 74.10 | 35.42 | 1265 |
4 | Chirtal | 71.83 | 35.85 | 1494 |
5 | Drosh | 71.80 | 35.56 | 1360 |
6 | Gilgit | 74.33 | 35.92 | 1500 |
7 | Gupis | 73.44 | 36.22 | 2713 |
8 | Hunza | 74.65 | 36.31 | 2438 |
9 | Peshawar | 71.56 | 34.02 | 331 |
10 | Cherat | 71.88 | 33.82 | 892 |
11 | Kalam | 72.57 | 35.49 | 2001 |
12 | Saidu Sharif | 72.35 | 34.73 | 970 |
13 | Dir | 71.87 | 35.19 | 420 |
14 | Shigar | 75.69 | 35.49 | 2230 |
15 | Skardu | 75.55 | 35.32 | 2228 |
Sr. No. | Station Name | Longitude (°) | Latitude (°) | Area (km2) |
---|---|---|---|---|
1 | Astore at Doiyan | 74.7 | 35.5 | 4040 |
2 | Skardu at Kachura | 75.4 | 35.5 | 112,665 |
3 | Kalam at Chakdara | 34.6 | 72.0 | 5776 |
4 | Chitral | 71.8 | 35.9 | 11,396 |
5 | Indus at Besham Qilla | 72.9 | 34.9 | 162,393 |
6 | Gilgit at Alam Br. | 74.3 | 35.9 | 26,159 |
7 | Jhansi Post | 71.4 | 33.8 | 1257 |
8 | Shigar | 75.7 | 35.4 | 6610 |
9 | Bunji | 74.6 | 35.7 | 142,709 |
10 | Indus at Massan | 71.7 | 33.0 | 286,000 |
Sr. No. | Variable | Abbreviation | Unit | Description |
---|---|---|---|---|
1 | Very wet days | R95p | mm | Annual total PRCP when RR > 95th percentile |
2 | Extremely wet days | R99p | mm | Annual total PRCP when RR > 99th percentile |
3 | Consecutive wet days | CWD | days | Maximum number of consecutive days with RR ≥ 1 mm |
4 | Consecutive dry days | CDD | days | Maximum number of consecutive days with RR < 1 mm |
5 | Annual total wet days P | PRCPTOT | mm | Annual total PRCP in wet days (RR ≥ 1 mm) |
6 | Number of very heavy P days | R20 | days | Annual count of days when PRCP ≥ 20 mm |
7 | Number of heavy precipitation days | R10 | days | Annual count of days when PRCP ≥ 10 mm |
8 | Simple daily intensity index | SDII | mm/days | Divided by the number of rainy days (defined as PRCP ≥ 1.0 mm) in the year, the annual total precipitation |
9 | Warm spell duration indicator | WSDI | days | Days per year with at least six consecutive days when TX was higher than 90% |
10 | Cold spell duration indicator | CSDI | days | Days each year with at least six straight days with TN below the 10th percentile |
11 | Max 1-day P amount | RX1 day | mm | Monthly maximum 1-day precipitation |
12 | Max 5-day P amount | RX5 day | mm | Monthly maximum consecutive 5-day precipitation |
13 | Max Tmax | TXx | °C | Monthly max value of daily max temperature |
14 | Max Tmin | TNx | °C | Monthly max value of daily min temperature |
15 | Min Tmax | TXn | °C | Monthly min value of daily max temperature |
16 | Min Tmin | TNn | °C | Monthly min value of daily min temperature |
17 | Number of heavy precipitation days | R25mm | days | Annual count of days when P was more significant than 25 mm |
18 | Average of max T | Tmax mean | °C | Average of monthly maximum value of daily maximum temperature |
19 | Average of min T | Tmin mean | °C | Average of monthly minimum value of daily minimum temperature |
20 | Potential evapotranspiration | PET | mm | Yearly evapotranspiration as calculated by the Penman–Montieth equation |
Sr. No. | Station Name | Correlation Percentage | Significance Level |
---|---|---|---|
1 | Astore at Doiyan | 78% | p < 0.05 |
2 | Skardu at Kachura | 76% | p < 0.05 |
3 | Kalam at Chakdara | 77% | p < 0.05 |
4 | Chitral | 78% | p < 0.05 |
5 | Indus at Besham Qilla | 66% | p < 0.05 |
6 | Gilgit at Alam Br. | 81% | p < 0.05 |
7 | Peshawar at Jhansi Post | 54% | p < 0.05 |
8 | Shigar | 51% | p < 0.05 |
9 | Bunji | 80% | p < 0.05 |
10 | Indus at Massan | 52% | p < 0.05 |
Sr. No. | Hydrological Station | Q | ∆Q | ||||
---|---|---|---|---|---|---|---|
P-I | P-II | P-III | P-II | P-III | |||
1 | Astore at Doiyan | 155.68 | 138.1 | 146.22 | ↓ | 11.29% | 6.07% |
2 | Skardu at Kachura | 1225.44 | 1106.91 | 1008.813 | ↓ | 9.03% | 17.60% |
3 | Kalam at Chakdara | 203.51 | 211.54 | 224.5 | ↑ | 3.94% | 10.30% |
4 | Chitral | 292.46 | 259.60 | 280.714 | ↓ | 11.20% | 4.01% |
5 | Indus at Besham Qilla | 2470.85 | 2338.35 | 2422.8 | ↓ | 5.36% | 1.94% |
6 | Gilgit at Alam Br. | 664.96 | 623.83 | 635.5158 | ↓ | 6.10% | 4.40% |
7 | Jhansi Post | 5.96 | 4.74 | 5.234575 | ↓ | 20.40% | 12.30% |
8 | Shigar | 207.18 | 206.75 | 283.59 | ↑ | 0.48% | 37.37% |
9 | Bunji | - | 1842.04 | 1450.4 | ↓ | - | 27.03% |
10 | Indus at Massan | 4144.44 | 3854 | 4071.86 | ↓ | 7.00% | 1.75% |
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Masood, M.U.; Haider, S.; Rashid, M.; Naseer, W.; Pande, C.B.; Đurin, B.; Alshehri, F.; Elkhrachy, I. Assessment of Hydrological Response to Climatic Variables over the Hindu Kush Mountains, South Asia. Water 2023, 15, 3606. https://doi.org/10.3390/w15203606
Masood MU, Haider S, Rashid M, Naseer W, Pande CB, Đurin B, Alshehri F, Elkhrachy I. Assessment of Hydrological Response to Climatic Variables over the Hindu Kush Mountains, South Asia. Water. 2023; 15(20):3606. https://doi.org/10.3390/w15203606
Chicago/Turabian StyleMasood, Muhammad Umer, Saif Haider, Muhammad Rashid, Waqar Naseer, Chaitanya B. Pande, Bojan Đurin, Fahad Alshehri, and Ismail Elkhrachy. 2023. "Assessment of Hydrological Response to Climatic Variables over the Hindu Kush Mountains, South Asia" Water 15, no. 20: 3606. https://doi.org/10.3390/w15203606