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Article

Spatiotemporal Runoff Analysis and Associated Influencing Factors in Chitral Basin, Pakistan

by
Fatima Nawaz
1,*,
Tao Wang
1 and
Azfar Hussain
2
1
Key Lab of 3D Information Acquisition and Application, College of Resources, Environment and Tourism, Capital Normal University, Beijing 100048, China
2
International Research Center on Karst under the auspices of UNESCO, Institute of Karst Geology, Chinese Academy of Geological Sciences, Guilin 541004, China
*
Author to whom correspondence should be addressed.
Water 2023, 15(12), 2175; https://doi.org/10.3390/w15122175
Submission received: 3 April 2023 / Revised: 26 May 2023 / Accepted: 29 May 2023 / Published: 9 June 2023
(This article belongs to the Section Hydrology)
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Abstract

:
Global warming has accelerated climate and weather changes, impacting the regional water cycle. This study assesses the temporal trends of seasonal and annual runoff in the Chitral River Basin (CRB) and its responses to regional climatic factors (i.e., temperature, precipitation, and Normalized Difference Vegetation Index (NDVI)) and oceanic indices at large scales (i.e., El Nino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and Pacific Decadal Oscillation (PDO)). The non-parametric Mann–Kendall (MK) test, the Sequential Mann–Kendall test (SQMK) and Sen Slope (SS) is used to evaluate trends and magnitude. In contrast, wavelet analysis is used to assess the coherence. In general, precipitation increases in winter, summer and autumn, whereas it decreases in spring. The temperature increased significantly in winter and spring, while a significant increase in seasonal and annual runoff was evident. Annual NDVI increased, whereas the Normalized Difference Water Index (NDWI) and the Normalized Difference Snow Index (NDSI) decreased. Generally, runoff has significant inter-annual coherences with regional environmental factors, and a significant coherence with NDVI. Monthly runoff has a positive coherence with temperature and NDVI, whereas it has a negative correlation with precipitation, NDWI, and NDSI. In general, ENSO, IOD and PDO show a positive correlation with runoff. The MWC findings indicate that annual runoff prevailed interannual signals with local environmental factors and with the Pacific Ocean, whereas interannual and interdecadal coherences are obvious with the Atlantic Ocean. The results have significant implications for decision-makers seeking to enhance water resource planning, disaster prevention, and mitigation, especially in global warming and the intensification of human activities that influence hydroclimatic changes at high altitudes.

1. Introduction

Global climate changes and urbanization [1,2,3] have an impact on the hydrological cycle and runoff patterns [4], which has led to major issues in managing and protecting water resources [5,6,7]. Climate and land-use changes primarily control watershed hydro-climatic behavior [8,9]. The territory that provides approximately one-fifth of the global supply of fresh water in the Himalayan region is home to about 1.7 billion people [7,10]. The glacier-fed perennial river systems rely on the water stored in the Himalayan glaciers. However, global warming has challenged regional food and water security due to the accelerated melting of the glaciers, resulting in frequent flash and riverine floods [11,12]. During the summer, the intensity of extreme floods has increased in recent decades through the irregular variation patterns of monsoons. In 2010, almost 45 million people were exposed to flooding risks, accounting for almost 65% of the global flood-exposed population [13]. The 2017 flood in South Asia affected 41 million people in Bangladesh, India, Nepal, and Pakistan [14]. Recent studies projected an increase in the magnitude of the climatic mean and extremes in the Hindukush-Karakoram and Himalayan (HKH) region [15,16]. A study [17] revealed that the climatic trends vary from place to place in the HKH mountains, as climate, land use change, and population dynamics are the three primary causes of environmental change in the HKH [18]. The increase in temperature consistently causes an increase in river flows, as runoff in this region is primarily influenced by meltwater from snow and ice [19]. Therefore, it is relevant to assess the regional runoff trends concerning various regional and large-scale climatic indicators to understand the prevailing association of runoff with the influencing factors for the management of water resources at a watershed scale and for the prevention of various natural disasters.
A trend assessment on runoff provides basic information about changes in the flow regimes concerning controlling factors, such as precipitation and temperature [20]. Changes in temperature and precipitation characteristics directly impact river flows [21]. In contrast, increased precipitation and temperature amplify the runoff trend, especially in regions dominated by glacier and snowmelt regimes [22]. Between 1981 and 2015, the maximum temperature (Tmax), minimum temperature (Tmin), and precipitation increased by 0.61 °C/decade, 0.23 °C/decade, and 39.2 mm/decade, respectively, whereas runoff increased to 32.42 m3/s per decade, according to the Sens slope estimates in the Chitral River Basin (CRB) [23]. According to Baig et al. [24], temperature decreases annually, with an average of 0.30 °C if precipitation changes by one unit in the CRB. Overall, the relationship between temperature and precipitation is negative, which suggests that an increase in one variable causes a lowering of the other. In addition, Yaseen, et al. [25] reported a significant increase and decrease in seasonal and annual Tmax and Tmin, respectively, during the seasonal and annual increase in precipitation, except in the spring. Moreover, Ahmad, et al. [26] found increasing trends in annual precipitation and runoff, and such an increase in precipitation and snow cover area could be the reason for the increased flows in the CRB. Moreover, the mixed and evergreen forest, shrubland, savannas, and barren land decreased during 2001–2018, whereas an increase in snow cover from 8.79% to 10.71% is observed.
Furthermore, Hussain, et al. [27] observed significantly increasing winter, spring, summer and annual runoff trends at 0.28, 0.93, 2.58, and 0.88 m3/s, respectively, except in autumn, for the CRB. Ahmad et al. [28] observed increasing trends in CRB runoff during 1964–2006, with higher variations observed from June to September. According to Khalid, et al. [29], mean monthly Tmax significantly influences runoff. The peak runoff occurs during May and September from the snow/glacial melt, and the discharge is usually less than the mean in the spring period. According to Shakir and Ehsan [30], the CRB’s leading river flow sources are glaciers, followed by groundwater, snow and rain. Moreover, Yaseen et al. [25] found significantly increasing CRB seasonal and annual runoff trends. The annual stream flows in the Kabul basin have decreased and increased in the CRB [31]. In addition, a decrease in the mean annual and summer flow in the CRB was witnessed [32]. The snow cover area and flows in the CRB show an increasing trend, showing that runoff in the basin largely depends on snowmelt and temperature seasonality [23]. Moreover, an increase in winter and spring at all gauging stations has been observed, while summer and autumn exhibited decreasing trends in the Upper Indus Basin (UIB) [25]. Deforestation is one of the major issues in this region, which might effect the regional water and hydrological cycles [33]. An increase in winter and spring seasons, with a decrease in summer in the UIB, has also been observed [34].
The research on runoff variations and their correlations with local environmental parameters and broad oceanic indices interact with the runoff in various ways. To this end, (1) most of the previous studies performed in the CRB only highlighted the overall general trends in the runoff; (2) most studies discussed above used the basic climatic factors (i.e., temperature, precipitation and humidity); (3) they often used traditional correlation methods; (4) a comprehensive and systematic study using most climatic parameters and oceanic indices was missing with regard to the CRB, and (5) most studies used the basic correlation coefficient method without further analysis and employing advanced methods. To the best of our knowledge, no prior research has calculated the single, integrated and coupled influence of various meteorological, environmental, and oceanic indices across the CRB.
Therefore, to fill the gaps of previous studies, this research employed classical methods to explore the temporal variabilities in runoff and to determine how the regional climatic factors (i.e., temperature and precipitation) and oceanic factors, such as the Atlantic Multidecadal Oscillation (AMO), Arctic Oscillation (AO), North Atlantic Oscillation (NAO), El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and Pacific Decadal Oscillation (PDO), influence the regional runoff in the CRB. The detailed objectives are to: (1) assess the trends in seasonal and annual runoff from CRB; (2) assess the temporal trends and abrupt changes in runoff; (3) assess the implications on runoff from regional climatic factors (i.e., temperature and precipitation); and (3) assess the implications on runoff from Oceanic indices (AMO, AO, NAO, ENSO, IOD, PDO).

2. Study Area

Pakistan is primarily a dryland region, with approximately 80% of the land being arid or semi-arid [35]. The CRB is a sub-catchment of the Kabul River Basin (KRB), which feeds the Indus Basin in Pakistan. It lies between 36°15′ N and 37°8′ N latitude and 72°22′ E and 74°6′ E longitude; the total population of the region is 0.9 million [36]. The CRB drainage area is approximately 11,400 km2 in the Chitral valley. The CRB maximum flow is recorded in July (Figure 1b). The CRB is flood-prone in the summer season, with regular floods that are induced by the local monsoonal precipitation and flash floods, with devastating local and remote scale downstream impacts. The Hindukush range is part of the complicated terrain influenced by different primary circulation systems, such as the sub-Mediterranean system in the winter, known as the westerlies; the monsoon trough in the summer; and the Tibetan anticyclones [37]. For the CRB, the maximum runoff is observed in monsoons, with peak flow in July and August (Figure 1b), while runoff closely follows the temperature seasonality (Figure 1c). The highest precipitation is observed during January–May, while the mean temperature peaks in the summer season (Figure 1c).

3. Data and Methods

3.1. Data

For trend estimation, long-term monthly climate and runoff data from 1964 to 2012 was acquired from the Pakistan Meteorological Department (PMD) and the Water and Power Development Authority (WAPDA). The streamflow data were first checked for missing values, and a small number of missing observations (<2%) were found and filled with linear interpolation techniques and gap-filling approaches that were adopted for similar purposes with regard to hydrological datasets [38]. The data was checked for homogeneity [39,40,41]. The seasons are classified as winter (December, January, and February), spring (March, April, May), summer (June, July, August), and autumn (September, October and November) [27].
The Climate Research Unit (CRU), precipitation and temperature records are incorporated to improve the data quality [42]. The CRU-Time Series (CRU-TS, version 3.21) consists of monthly precipitation, temperature, cloud cover, and related variables [42,43]. The dataset is produced from more than 4000 weather stations that are gridded at a spatial resolution of 0.5 degrees. The CRU precipitation and temperature datasets are widely used for global and regional scale analysis of drought, validation, and trend assessment studies [43]. The dataset was obtained from http://www.cru.uea.ac.uk/data (accessed on 1 March 2022); readers that are interested in further details about CRU-TS datasets are referred to [42,43]. The land-use/cover change (LUCC) greatly influences regional environmental factors [44,45]. Here, the Landsat monthly Normalized Difference Vegetation Index (NDVI) was acquired to assess the long-term vegetation trends and their influence on regional runoff.

3.2. Methods

The Mann-Kendall (MK), sequential Mann-Kendall (SQMK), and Sen Slope are employed to access the trends and magnitudes. Furthermore, a correlation and wavelet analysis was applied to assess the magnitude of possible associations of environmental factors and oceanic indices as potential drivers of the runoff changes.

3.2.1. Trends and Magnitude Estimation

The nonparametric Mann–Kendall (MK) test developed by [46,47] is employed in this study to evaluate the significant trends in environmental variables (i.e., temperature, precipitation, potential evapotranspiration, NDVI, NDWI and NDSI). This approach is traditionally used in trend-based hydro-climatic studies because it resists missing values and outliers [27]. The null hypothesis in this test suggests the presence of no trends in the time series, whereas the alternative hypothesis suggests an increasing or decreasing trend [27,40,48,49]. A positive value of z in MK indicates an increasing trend in the runoff, whereas a negative value suggests a decreasing trend. The trend is considered statistically significant if its statistical Z value surpasses the confidence interval of ±1.96 at the 95% confidence level [48]. The Sen’s slope estimator (SSE) [50] is used to assess the magnitude of trends in environmental variables (i.e., temperature, precipitation) [27,40,51,52]. The positive/negative values suggest the increase/decrease in the respective variable with the same unit as the input.
The SQMK test determines the annual time-variant mutation of the MK test [53]. The trend is presented by a two-time series constructed from the respective input data in forward (UF) and backward direction (UB) [40]. UF is a standardized variable with zero mean and unit standard deviation in this test. The nature of UF is the same as the Z values that start from the first year on the input data and extend to the last year, respectively. The value of UB is computed backward, starting from the endpoint to the first point of the time series [49]. The points/years where the forward and backward test statistics cross each other indicate the possible turning year of the trend. The trend turning point is considered significant at a 95% corresponding threshold level.

3.2.2. Wavelet Analysis

The wavelet analysis assesses the relationship between the runoff with regional environmental variables and oceanic indices. It provides excellent knowledge about various frequency intervals, making it an ideal tool for studying low-frequency events and the nature of a time series [40]. It is an appropriate tool for evaluating the local fluctuations in the power in a time series. We used the wavelet transform coherence (WTC), a correlation coefficient localized in the time and frequency domain, to evaluate the degree of a linear relationship in time and frequency [54,55]. It can also detect the quasiperiodic component of a system anomaly [56,57]. The WTC is more appropriate due to the local phase-locked behavior [56], which performed better [58,59]. For more details with regard to WTC, the readers are referred to references [56,60,61,62,63]. The Monte Carlo techniques use 1000 simulations and a 95% confidence constraint to quantify the significance level [27,56]. The thick contours in WTC denote 5% significance levels against red noise. Pale regions denote the cone of influence where edge effects might distort the results. Colors indicate the measure of coherence—a pink color implies a high degree of coherence. Arrows show the relative phase relationship (in-phase, arrows point right; anti-phase, arrows point left). Pale regions denote the cone of influence where edge effects might distort the results. Multiple Wavelet Coherence is used to assess the combined influence of annual runoff on regional environmental factors and oceanic indices (Pacific and Atlantic Oceans), whereas the Monte Carlo method computes the WTC and MWC at the 95% confidence level [27].

4. Results

4.1. Seasonal and Annual Trends in Climate and Runoff in the CRB

Figure 2 shows the coefficient of variability (CV) of the mean runoff during 1964−2012 for each decade. The variability increased with a relatively higher CV during 1994–2003 during winter (Figure 2a), whereas a higher CV is observed during the first decade of spring (Figure 2b). For summer, a higher CV was evident between 1994–2003 (Figure 2c), which was similar to winter. Similarly, a higher CV was also evident between 1994–2003 for both winter and summer. It can be deduced that the variability showed an increase after 1984.
Table 1 shows the MK and Sen’s Slope (SS) of precipitation, temperature and runoff changes in the CRB on the annual and seasonal time series. The SS results show an overall temperature increase in annual winter and spring, whereas they show a decrease in summer and autumn. The winter and spring increase is statistically significant at 0.28 and 0.36 °C/decade, respectively, whereas summer observed a significant decrease of −0.25 °C/decade in the CRB. Moreover, the precipitation in winter, summer and autumn has increased at 0.02, 0.54, and 1.06 mm/decade, respectively, whereas it decreased on annual and in spring at −0.38 and −7.27 mm/decade, respectively. The MK test suggests an increasing trend in annual and seasonal runoff in the CRB (Table 1). Runoff increased significantly during annual, winter, spring and autumn, at 0.63, 0.18, 0.59 and 0.59 m3s−1, respectively, whereas a minimal increase of 1.47 m3s−1 was observed in summer. The overall precipitation and temperature trends are diverse; however, winter warming might be a reason for the increasing runoff in the CRB. Furthermore, a positive relationship with temperature was only evident during the annual, winter, and spring with runoff. Warming in the Indian Ocean has increased due to increase in the greenhouse gases [64]. Such activities has triggered the increase in global temperature resulting from global warming and precipitation seasonality that induced changes in regional-scale flow regimes and glacier-fed rivers [65].

4.2. Temporal Variations

Figure 3 shows the temporal variation, abrupt changes, and significant change points. The annual runoff (Figure 3a) has generally increased, with three potential turning points from 1983 to 1993. Runoff showed a significant increasing trend during 1964, and then an abrupt decrease and a moderate increase. During the winter (Figure 3b), runoff showed a deviation with the initially increasing trend during 1970 followed by a decrease, and a significant increase afterward was noted. The spring (Figure 3c) has shown a convex trend, with a decrease until 1990 and a constant increase until 2012. For summers (Figure 3d), the runoff exhibited a normal variation until 1983, with slight deviations. However, after 1983 the magnitude appeared to be increasing, with several turning points from 1980 until the present. An increasing trend after 1980 in the autumn (Figure 3e) was evident, and was more prominent and significant than it was in the rest of the years. From Figure 3, it can be inferred that seasonal runoff has shown a constant increase. In the first two decades, no obvious trend was evident. However, the overall runoff has significantly increased, with few turning points from 1980 onwards.
The long-term linear trend for different seasons and the annual mean runoff in the CRB are shown in Figure 4. In general, it can be concluded that the runoff in the CRB is increasing in all seasons, especially in the winter (Figure 4b) and autumn (Figure 4e). The increase in the runoff can be due to changes in the hydrological and meteorological factors, including precipitation and temperature. The linear trends of regional environmental factors, annual runoff, precipitation, temperature, NDVI, NDWI and NDSI in the CRB are shown in Figure 5. Annual runoff (Figure 5a), temperature (Figure 5c), and NDVI (Figure 5d) from the CRB have increased, whereas annual precipitation (Figure 5b), NDWI (Figure 5e) and NDSI (Figure 5f) have decreased. Due to global warming, a potential change in the circulation intensity, precipitation extremes, and temperature dynamics of the region has been previously reported, and this factor has caused an increase in runoff [27,66].

4.3. Nexus between Runoff with Regional Environmental Factors and Oceanic Indices

Figure 6 represents the wavelet coherence between monthly runoff and the local factors (i.e., temperature, precipitation, NDVI, NDWI and NDSI). Generally, runoff has significant inter-annual variability modes with the regional environmental factors. The regional environmental factors remained sporadic and significant, ranging from 4 to 16 months (0.2–1.3 years) and 64 months (6.3 years) with NDVI (Figure 6c). The monthly runoff generally has a positive correlation with temperature and NDVI, whereas a negative correlation is observed with precipitation, NDWI and NDSI.
Figure 7 represents the wavelet coherence of the monthly runoff and oceanic indices. The influence of ENSO, IOD, PDO, AMO, AO, and NAO on the monthly runoff in the CRB is synchronous, as revealed by the inter-annual significant modes. The oscillations remained sporadic and significant across the timescale. In general, ENSO, IOD and PDO share a positive coherence with the monthly runoff in the CRB, whereas no prominent coherence was observed with AMO, AO, and NAO. Overall, on a <1.0-year timeline, all indices displayed a robust but sporadic coherence. Generally, runoff correlates more with regional environmental factors than global indices (Figure 6 and Figure 7, Table 2). In regional environmental factors, precipitation shared a higher correlation magnitude, whereas AMO shared a higher correlation with global oceanic indices.
The MWC finding between annual runoff with local and large-scale oceanic indices is presented in Figure 8. Overall, the annual runoff prevailed with interannual signals with local environmental factors and with the Pacific Ocean, whereas interannual and interdecadal correlations are obvious with the Atlantic Ocean. The local environmental factors prevailed in a significant inter-annual variability mode for 4 to 6 years (Figure 8a,b), whereas the inter-decadal variability mode prevailed for more than 12 years (Figure 8c). Table 2 presents the Pearson correlation of runoff with individual environmental and oceanic indices, and it was applied to complement the findings of the wavelet analysis. Here, higher magnitudes of correlation are observed with precipitation (0.21), followed by AMO (0.15) and temperature (0.12), while ENSO, PDO, and NAO share a similar correlation (0.10). To some extent, the higher correlation of the annual runoff with the Atlantic Ocean (Figure 8d) is also noticeable from the findings of the correlation coefficient, as AMO has a higher coherence with respect to the remaining indices.
Table 2 presents the Pearson correlation of runoff with individual environmental and oceanic indices, and it was applied to complement the findings of the wavelet analysis. Here, higher magnitudes of correlation are observed with precipitation (0.21), followed by AMO (0.15) and temperature (0.12), while ENSO, PDO, and NAO share a similar correlation (0.10).

5. Discussion

Global climate change has triggered extreme climate events in recent decades, such as droughts, storms, and floods [67]. Floods due to extreme precipitation, accelerated snowmelt, and glacier outbursts incurred by increasing regional temperature and other climatic factors have long-lasting impacts at local and regional scales. For instance, Mehmood, et al. [68] observed a decrease in different flood indicators in the northern part of the CRB, which he attributed to the decrease in annual and monsoonal rainfall and a corresponding positive mass balance of glaciers in the region. However, the flood risk has increased in the southern part of the basin, possibly due to increase in maximum five day precipitation. Thus, such processes must be thoroughly studied at a regional scale, especially in flood-prone regions such as Pakistan, which is already under threat due to changing climate-induced hazards that took place frequently over the last decade [69]. Less information about the degree of climate change is available in such regions due to the limited observations available [70].
Regional environmental factors and oceanic indices directly influence the variations in runoff, whereas global climate change also plays a significant role. The current study has analyzed the trend and regional variations in regional environmental factors and oceanic indices, and their influence on the runoff over CRB, using historical records from 1964 to 2012. Various non-parametric statistical methods, including Mann–Kendall and Sen Slope, are employed to evaluate the variations and trends in the local climate and in the runoff. In contrast, the wavelet analysis and correlation have been performed to evaluate the influence of regional and environmental factors and oceanic indices and their influence on the runoff in CRB. The mean annual runoff from the CRB is 0.63 m3/s, followed by a higher magnitude in the summer season at 1.47 m3/s. The summer runoff is increasing despite a significant decrease in summer temperatures, but can be explained by an increase in precipitation. It is worthy of note that seasonal water supplies are affected by changes in temperature, whereas runoff volumes are affected by changes in precipitation and snow accumulation. The variation in regional climate (temperature and precipitation), especially in snow-dominated watersheds, affects the flow regimes [71]. However, this association is quite the opposite in spring, as temperature and precipitation increase significantly in this season. The spring results suggest that the CRB seasonal flow follows the meteorological cycle of the region [39]. The flow pattern from January-December appeared to closely represent the Gumble and Pearson Type III distribution. The possible reasons for the changes in flow could be changes in precipitation and temperature, as suggested by numerous global studies [39,72].
The potential drivers (precipitation and temperature) behind this increasing trend appeared to be the primary causes for changes in the river flow regime. Due to the warming of the winter and autumn seasons, the enhanced rate of snowmelt can also cause an increase in river flow and thus lead to flooding. Runoff correlates more robustly with regional environmental factors than oceanic indices (Table 2). In general, the findings of this study partly agree with previous studies on CRB. Similar to Hussain, et al. [27], we observed significantly increasing runoff trends in CRB on a seasonal and annual scale, except in autumn. Overall, precipitation increased in the region, except in annual and spring during 1964–2012 in the CRB, as these findings agree with those of [73]. Annual precipitation and mean annual runoff displayed increasing trends, and [26] attributed the increase in precipitation and snow cover to the possible reasons for increased runoff in the CRB. Extensive urbanization and a sharp rise in population in the middle and upper Punjab, particularly in the 1980s, may have contributed to the increase in precipitation [74]. Land degradation is a severe issue in mountainous environments, and in recent years, the issue has gotten worse due to population development in sensitive regions [45]. Such variations may be brought on by mechanical variability that results from the increased surface lumpiness and a sense of heat from the warm metropolitan air. Moreover, [32] also found that the CRB runoff is highly dependent on snowmelt, which is influenced by temperature seasonality. The general discrepancies in results compared to previous studies could have prevailed from different timescales, densities of meteorological stations used to infer changes, trends in precipitation temperature and runoff, data preparation, and technique selection [27].
The effect of ENSO, PDO, and NAO on monthly runoff across the headwaters of the Indus basin are synchronized, according to [27], and there is a connection between the monthly runoff and global oceanic indicators, as shown by the inter-annual significant modes from 1980 and 2000, while inter-annual and inter-decadal oscillations with AO were observed. Moreover, [55] revealed the strongest correlations with AO in the northern parts of Pakistan at interdecadal scales compared to IOD, ENSO, PDO, SOI, NAO, and AMO. The western disturbances migrate from the west to the east and to the lowest latitudes, affecting Pakistan’s hydrological and climatic variability and neighboring areas [75].
Apart from precipitation and temperature, land-use practices and deforestation can also be considered to be reasons affecting the runoff, infiltration magnitude, and river flow. Due to changes in global temperature, extreme events are expected to increase, such as extreme precipitation, floods, and storms [76]. Such events could increase flow magnitude, impacting the runoff and snowmelt rate. The variation in large-scale circulation-induced changes in the hydro-meteorological cycle may be another aspect to consider when studying such trends at a regional scale. The changes in flow could impact the flooding frequency at local and remote scales. With increased river flow, the chances of landslides and riverside scouring can also be triggered, which can be considered as an indirect hazard associated with changes in river flow. The domestic, livestock, and agricultural water supply can be impacted. Thus, a detailed study with more thorough observation and modelling approaches is recommended to document the potential threats and associated hazards. Based on the observed changes in runoff variations, our findings should be taken with caution, and we suggest further studies to carefully assess the relationship between runoff and its various regional influencing mechanisms.

6. Conclusions

This study assessed the CRB’s seasonal and annual runoff trends between 1964 and 2012 and their responses to regional climate factors (temperature, precipitation NDVI, NDSI and NDWI) and oceanic indices (ENSO, IOD, PDO, AMO, AO and NAO). The conclusions drawn from this study include the following:
  • The MK and SS showed increased winter, summer and autumn precipitation, andR a decrease in annual and spring precipitation. In general, the temperature has increased annually, and in winter and spring. Moreover, a significant increasing trend in annual and seasonal runoff in the CRB is evident, except with a non-significant increase in summer.
  • The seasonal runoff has shown a continual increase. In the first two decades, no obvious trend was evident. However, the overall runoff has significantly increased, with few turning points from 1980 onwards. The long-term linear trend for different seasons and the annual mean runoff in the CRB is increasing in all seasons, especially in the winter and autumn.
  • The annual runoff, temperature and NDVI from the CRB have increased, whereas the annual precipitation, NDWI, and NDSI have decreased.
  • The overall PDF analysis shows that the mean runoff has shown positive shifts with sharp skewness, significantly affecting the runoff trends in the CRB.
  • In general, the runoff has significant inter-annual variability modes with the regional environmental factors. All of the regional environmental factors remained sporadic and significant, ranging from 4 to 16 months, while a significant oscillation of 64 months (6.3 years) is evident with NDVI. In general, monthly runoff has a positive relationship with temperature and NDVI, whereas a negative relationship is observed with precipitation, NDWI, and NDSI.
  • The WTC analysis indicates that the oscillations persisted sporadically and significantly across the timescale. In general, ENSO, IOD, and PDO share a positive coherence with the monthly runoff in the CRB, whereas no significant relation was observed with AMO, AO and NAO. Overall, on a <1.0-year timeline, all indices displayed a robust but sporadic relationship.
  • The MWC findings indicate that the annual runoff prevailed with regard to interannual signals with local environmental factors and with the Pacific Ocean, whereas interannual and interdecadal coherences are obvious with the Atlantic Ocean.

Author Contributions

Conceptualization: F.N. and A.H.; Methodology: A.H. and F.N.; Formal analysis and investigation: F.N. and A.H.; Writing–original draft preparation: F.N. and A.H.; Writing—review and editing: F.N. and T.W., Funding: T.W. All authors have read and agreed to the published version of the manuscript.

Funding

The NSFC research grant No. 41671403 partially supported the authors.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Acknowledgments

The authors acknowledge the Pakistan Meteorology Department and Water and Power Development Authority for providing climate and runoff data for this research.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. The study area including the elevation, river, and gauging stations in the CRB (a); mean monthly runoff (b); and mean climatology in the CRB (c).
Figure 1. The study area including the elevation, river, and gauging stations in the CRB (a); mean monthly runoff (b); and mean climatology in the CRB (c).
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Figure 2. Box and whisker plots representing the coefficient of variability of decadal runoff for the (a) winter, (b) spring, (c) summer, and (d) autumn seasons. The boxes indicate the 25th and 75th inter-quantile range; while the central line indicates the median line. The whisker represents the 5th and 95th percentile.
Figure 2. Box and whisker plots representing the coefficient of variability of decadal runoff for the (a) winter, (b) spring, (c) summer, and (d) autumn seasons. The boxes indicate the 25th and 75th inter-quantile range; while the central line indicates the median line. The whisker represents the 5th and 95th percentile.
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Figure 3. Sequential Mann–Kendall test of (a) average annual mean and interannual mean runoff for (b) winter, (c) spring, (d) summer, and (e) autumn seasons calculated at a significance level of 5%.
Figure 3. Sequential Mann–Kendall test of (a) average annual mean and interannual mean runoff for (b) winter, (c) spring, (d) summer, and (e) autumn seasons calculated at a significance level of 5%.
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Figure 4. The linear trend of runoff (a) average annual, (b) winter, (c) spring, (d) summer, and (e) autumn seasons. The blue line indicates the best fit, and the red line indicates the five-year moving means.
Figure 4. The linear trend of runoff (a) average annual, (b) winter, (c) spring, (d) summer, and (e) autumn seasons. The blue line indicates the best fit, and the red line indicates the five-year moving means.
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Figure 5. Annual linear trends in (a) runoff, (b) precipitation, (c) temperature, (d) NDVI, (e) NDWI, and (f) NDSI in CRB. The smooth blue line indicates linear fit.
Figure 5. Annual linear trends in (a) runoff, (b) precipitation, (c) temperature, (d) NDVI, (e) NDWI, and (f) NDSI in CRB. The smooth blue line indicates linear fit.
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Figure 6. Wavelet coherence between monthly runoff in the CRB with (a) precipitation, (b) temperature, (c) NDVI, (d) NDSI, and (e) NDWI. Thick contours in WTC denote 5% significance levels against pink noise, while the pink color implies a high degree of coherence. Arrows show the relative phase relationship (in-phase, the arrows point right; anti-phase, the arrows point left).
Figure 6. Wavelet coherence between monthly runoff in the CRB with (a) precipitation, (b) temperature, (c) NDVI, (d) NDSI, and (e) NDWI. Thick contours in WTC denote 5% significance levels against pink noise, while the pink color implies a high degree of coherence. Arrows show the relative phase relationship (in-phase, the arrows point right; anti-phase, the arrows point left).
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Figure 7. Wavelet coherence between the monthly runoff in CRB with (a) ENSO, (b) IOD, (c) PDO, (d) NAO, (e) AO, and (f) AMO. Thick contours in WTC denote 5% significance levels against pink noise, while the pink color implies a high degree of coherence. Arrows show the relative phase relationship (in-phase, the arrows point right; anti-phase, the arrows point left).
Figure 7. Wavelet coherence between the monthly runoff in CRB with (a) ENSO, (b) IOD, (c) PDO, (d) NAO, (e) AO, and (f) AMO. Thick contours in WTC denote 5% significance levels against pink noise, while the pink color implies a high degree of coherence. Arrows show the relative phase relationship (in-phase, the arrows point right; anti-phase, the arrows point left).
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Figure 8. Multiple Wavelet Coherence (MWC) between annual runoff in CRB with (a) temperature and precipitation, (b) NDVI, NDSI and NDWI, (c) Pacific Ocean (ENSO, IOD and PDO), (d) Atlantic Ocean (AMO, AO and NAO. Thick contours in MWC denote 5% significance levels against red noise, while the red color implies a high degree of coherence.
Figure 8. Multiple Wavelet Coherence (MWC) between annual runoff in CRB with (a) temperature and precipitation, (b) NDVI, NDSI and NDWI, (c) Pacific Ocean (ENSO, IOD and PDO), (d) Atlantic Ocean (AMO, AO and NAO. Thick contours in MWC denote 5% significance levels against red noise, while the red color implies a high degree of coherence.
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Table
Table 1. The MK and SS of precipitation (mm/decade), temperature (°C/decade) and runoff (m3s−1) in the CRB. Bold values indicate significant trends at a 95% confidence level.
Table 1. The MK and SS of precipitation (mm/decade), temperature (°C/decade) and runoff (m3s−1) in the CRB. Bold values indicate significant trends at a 95% confidence level.
TemperaturePrecipitationRunoff
Annual0.08−0.380.63
Winter 0.280.020.18
Spring0.36−7.270.59
Summer−0.250.541.47
Autumn−0.051.060.59
Table
Table 2. Correlation coefficient of annual runoff with regional environmental factors and oceanic indices. The values are significant at a 95% confidence level.
Table 2. Correlation coefficient of annual runoff with regional environmental factors and oceanic indices. The values are significant at a 95% confidence level.
VariableCorrelation Coefficient
Temperature0.12
Precipitation0.21
NDVI0.04
NDSI0.02
NDWI0.02
ENSO0.10
IOD0.04
PDO0.10
NAO0.10
AO0.07
AMO0.15
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Nawaz, F.; Wang, T.; Hussain, A. Spatiotemporal Runoff Analysis and Associated Influencing Factors in Chitral Basin, Pakistan. Water 2023, 15, 2175. https://doi.org/10.3390/w15122175

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Nawaz F, Wang T, Hussain A. Spatiotemporal Runoff Analysis and Associated Influencing Factors in Chitral Basin, Pakistan. Water. 2023; 15(12):2175. https://doi.org/10.3390/w15122175

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Nawaz, Fatima, Tao Wang, and Azfar Hussain. 2023. "Spatiotemporal Runoff Analysis and Associated Influencing Factors in Chitral Basin, Pakistan" Water 15, no. 12: 2175. https://doi.org/10.3390/w15122175

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