Analyzing Temperature, Precipitation, and River Discharge Trends in Afghanistan’s Main River Basins Using Innovative Trend Analysis, Mann–Kendall, and Sen’s Slope Methods

Abstract

Afghanistan, a nation already challenged by geopolitical and environmental pressure, faces severe climate change impacts, evident through rising temperatures, decreasing precipitation, and reduced river discharge. These changes profoundly affect the country’s water resources, agriculture, ecosystems, and well-being. This study analyzes trends in mean annual temperature, precipitation, and river discharge across all five of Afghanistan’s river basins from 1980 to 2022, utilizing an innovative trend analysis (ITA), the Mann–Kendall (MK) test, and Sen’s slope (SS) estimator. Climate data were derived from the CRU TS.v4 and TerraClimate gridded datasets, while river discharge data were obtained from GloFAS-ERA5 datasets. The results reveal significant climate shifts, including a notable 1.5 °C rise in mean annual temperature, significantly higher than the global average of 1.3 °C, a 1.2 mm decrease in mean annual precipitation, and a −128 m³/s reduction in river discharge across all basins since 1980. Climate change impacts were particularly severe in the western part of the country. These findings underscore the strain on Afghanistan’s vulnerable water resources, with critical implications for agriculture and water management, highlighting the urgent need for adaptive strategies to mitigate climate-induced risks.

1. Introduction

The most significant impact of climate change is the increase in global average temperature. By 2023, global temperatures had risen by approximately 1.3 °C above pre-industrial levels [1]. This temperature rise has profoundly affected the water cycle, altering precipitation patterns, impacting freshwater availability, and intensifying droughts and floods in various regions worldwide [2]. Afghanistan, bordered by South, Central, and West Asia, is in a region that is particularly vulnerable to climate change [3]. According to the IPCC’s 2022 report, rising temperatures heighten the probability of heatwaves across Asia, droughts in the arid and semi-arid zones of West, Central, and South Asia, floods in monsoon regions, and accelerated glacial melt in the Hindu Kush Himalayan (HKH) region [4]. The HKH region is often called “Asia’s water tower” due to supplying ten major rivers and housing the world’s third-largest frozen water reserves, after the Arctic and Antarctic [5]. The HKH watersheds support up to 1.3 billion people and provide critical resources like food, energy, and ecosystem services.

Within Asia’s water tower, Afghanistan shares transboundary rivers with Iran, Pakistan, Tajikistan, Turkmenistan, and Uzbekistan [6]. Based on geological structures and hydrological systems, Afghanistan’s surface water is organized into five major river basins: Kabul, Helmand, Harirud-Murghab, Northern, and Amu-Darya [7]. Except for the Northern Basin, each is transboundary, supporting agricultural irrigation, hydropower, and ecosystems critical to Afghanistan and neighboring countries [6,8]. The water in these basins primarily originates from snow, glaciers, and permafrost in the Hindu Kush and Pamir Mountains, which release water throughout the year to downstream areas [9]. However, climate change is severely impacting these water sources. Since 1970, rising temperatures, declining precipitation, and reduced river flows have been recorded [10,11,12], intensifying ecosystem degradation and environmental challenges [13]. Higher temperatures accelerate glacier melt and evaporation rates, altering regional weather patterns and hydrological systems [14,15]. Reduced and erratic precipitation aggravates land cover changes, decreasing the extent of agricultural lands, rangelands, and wetlands and escalating the risk of flash floods and droughts [16,17,18]. With water resources and agriculture providing 80% of livelihoods in Afghanistan and 25% of its GDP in 2014 [19], climate change intensifies Afghanistan’s environmental and socioeconomic vulnerabilities [10,20].

In addition to natural hazards, Afghanistan has faced over four decades of conflict, from the Soviet invasion in 1979 to the present [21], which has severely devastated the country’s infrastructure and institutional capacities [22,23]. The resulting insecurity, poverty, poor governance, and widespread migration have placed additional strain on resources [24,25,26]. The conflict has also hampered hydrometeorological data collection since the 1980s [27], creating data gaps that challenge effective water and climate adaptation planning. Satellite remote sensing has become an invaluable tool for addressing these gaps, enabling the analysis of atmospheric, lithospheric, and hydrospheric changes [28,29].

Recent studies utilizing gridded and modeled datasets have started exploring climate change impacts in Afghanistan. For instance, Aich et al. (2017) found a 1.8 °C increase in mean temperature since 1950. Research by the Stockholm Environment Institute indicates a mean annual temperature increase of 0.6 °C (or 0.13 °C per decade) since 1960, alongside a decrease in precipitation by 0.5 mm per month (2 mm per decade) [10]. Climate models project further warming, ranging from 1.7 to 2.3 °C by 2050 to 2.7 to 6.4 °C by 2090, based on different emission scenarios [10]. Additional studies address climate change impacts on specific river basins, droughts, floods, agriculture, and livelihoods [20,30,31,32,33,34,35,36,37,38]. These studies span national, basin, and provincial levels, with mixed studies of climatic and other natural hazards published in papers and national, international, and UN agency reports.

Nonetheless, there is a substantial gap in comprehensive climate research. This study addresses this by analyzing time series data on annual temperature, precipitation, and river discharge, using gridded datasets with MK, SS, and ITA statistical methods. Importantly, it includes recent data to calculate the mean annual river discharge using the GloFAS-ERA5 dataset [39]. Given Afghanistan’s data limitations, river discharge estimations have remained unclear, yet they are critical for water resource management, agriculture, hydropower, and climate adaptation strategies.

2. Materials and Methods

2.1. Physiography and Climate

Afghanistan is landlocked, with a 652,230 km² area in the subtropical zone between South and Central Asia [40]. It is dominated by mountains with varying elevations, from the Hindu Kush and the Pamir Mountains’ peak at 7492 m in Naushaq to 230 m in the Helmand and Sistan Basin [41] (Figure 1A). This elevation difference generates significant atmospheric energy, influencing the hydrological cycle and resulting in diverse climate conditions, soil types, and vegetation; these variations in elevation impact agricultural practices and livelihoods. Afghanistan has an arid and semi-arid continental climate characterized by temperature and precipitation regimes attributed to deserts, steppes, and highlands [42].

According to the Köppen–Geiger climate classification, Afghanistan’s climate encompasses six zones, primarily determined by elevation, temperature, and precipitation [43]. These zones are illustrated in Figure 1B:

• Arid Hot Desert (BWh): Predominately in the southwest, parts of the Amu Darya Basin, and the south;
• Arid Steppe Cold (BSk): Covering a broad central area;
• Temperate and Cold Summer Regions (Csa, Csb): Found in mid-elevation areas;
• Cold, Dry, Hot Summer (Dsa) and Cold, Dry, Warm Summer (Dsb): These are located at high elevations;
• Tundra (ET): Present in the highest elevations, lacking a true summer season.

These climatic variations correlate with elevation changes. Precipitation generally increases with altitude; the high mountains of the northeast, such as the Hindu Kush and Pamir, receive up to 1100–1400 mm annually. In contrast, the southwestern deserts, such as the Helmand and Sistan Basin, receive less than 50 mm of rainfall [9]. Most precipitation in the higher mountains falls as snow, primarily between January and March, significantly contributing to the country’s water resources.

2.1.1. Temperature

The temperature in Afghanistan varies with elevation, with colder temperatures in the higher regions and warmer temperatures in lower desert areas. Temperatures can drop to −10 °C in winter and rise to 34 °C in summer [42], while summer temperatures in desert regions like the Helmand Basin can exceed 45 °C, creating challenges for agriculture and other human activities. Despite these conditions, wheat remains the primary crop, making up 80% of the country’s crop production, alongside various fruits and other crops [45].

2.1.2. Hydrology

Afghanistan’s hydrology is shaped by its diverse geological structures and river systems. As shown in Figure 2, the country is divided into five major river basins: Kabul, Helmand, Harirud-Murghab, Northern, and Amu Darya River Basins [9]. Four are transboundary, with waters flowing into neighboring countries like Pakistan, Iran, Turkmenistan, Uzbekistan, and Tajikistan. The headwaters of all these river systems originate in the Hindu Kush Mountains, where snowmelt contributes significantly to water flow, especially during the spring and summer [46]. The snowpack and glaciers regulate water availability as natural storage, ensuring continuous river flow and aquifer recharge. However, Afghanistan faces challenges with water storage infrastructure, making the country vulnerable to seasonal water shortages and destructive flash floods, particularly during the rainy season.

The following is a summary of each river basin:

• Kabul River Basin: This basin covers 71,266 km² and drains into the Indus River, contributing to flows that reach the Arabian Sea;
• Amu Darya River Basin: One of Central Asia’s longest rivers, covering 95,946 km² in Afghanistan, essential for agriculture in the region and historically connected to the Aral Sea;
• Helmand River Basin: This basin is the largest in Afghanistan, covering 327,662 km² (51% of the country). The Helmand River flows about 1290 km before reaching the Hamun Wetlands on the Afghanistan–Iran border;
• Harirud-Murghab River Basin: Spanning 78,060 km², it flows through Herat and forms part of the Afghanistan–Iran border before entering Turkmenistan;
• Northern Basin: The smallest of the five, covering 70,995 km², drains entirely within Afghanistan and does not contribute to transboundary flows.

The total average surface water volume from 2007 to 2016 was estimated at 49 billion cubic meters (BCM), with the Kabul and Amu Darya River Basins contributing the most significant shares (17 and 19 BCM, respectively) [47]. Despite having abundant surface water, Afghanistan’s ability to manage and store water remains underdeveloped, leading to frequent floods and water management challenges [17].

2.2. Data

2.2.1. Temperature

The mean annual time series temperature data from 1980 to 2022 was obtained from version 4 of the Climate Research Unit (CRU TS.v4) Gridded Time Series dataset [48]. This dataset offers a high-resolution (0.5° latitude and longitude grid) monthly global climate record, excluding Antarctica, from 1901 to 2022. It includes time series data for ten observed and synthetic climate variables [49]. The data are publicly accessible from the CRU website in NetCDF format and suitable for GIS analysis in [50].

2.2.2. Precipitation

Mean annual time series precipitation data for the study period (1980–2022) was sourced from a TerraClimate dataset. TerraClimate provides high-spatial-resolution (1/24°, approximately 4 km) monthly climate and water balance data for global terrestrial surfaces, covering 1958–2015 [51]. It integrates WorldClim spatial climatology with time-varying data from the coarser-resolution CRU TS4.0, producing a monthly dataset for precipitation, temperature extremes, wind speed, vapor pressure, and solar radiation. The dataset for GIS analysis was downloaded from the TerraClimate website [52].

Both the CRU TS.v4 and TerraClimate datasets undergo rigorous validation and quality control through multiple techniques, including interpolation and correlation with station network data. Figure 3 displays a randomly selected station evenly distributed across each river basin and throughout the country. The mean annual temperature and precipitation data from 1980 to 2022 were extracted for each station.

2.2.3. River Discharge

GloFAS-ERA5 is a modeled dataset that provides a gridded river discharge time series. Developed by the Global Flood Awareness System (GloFAS), the dataset is generated by driving the LISFLOOD hydrological model with ERA5 reanalysis meteorological data, interpolated to a 0.1° grid at daily intervals [39]. The dataset spans from 1979 to 2023 and covers all land areas except Antarctica, offering data at a 0.05° resolution. GloFAS-ERA5 has been evaluated against global river discharge observation networks, and data are available through the Copernicus Climate Data Store [53]. Due to the uniform grid interpolation, the river discharge estimates are generalized across grid points, with minimal discharge observed outside river channels.

Table 1. Summary of the meteorological and gauging stations, including elevation and period of records.

River Gauging Stations
Station Name	Lat	Long	Elevation (m)	Record Period	N. of Year
Zarang	30.7352	61.7735	420	1980–2022	42
Farah	31.4790	61.4775	475	1980–2022	42
Adraskan	31.6280	61.2758	450	1980–2022	42
Hari Rod	34.8219	61.0772	710	1980–2022	42
Murghab	35.7235	63.2250	502	1980–2022	42
Shirin Tagab	37.1271	65.2847	260	1980–2022	42
Kunduz	37.0225	68.2253	360	1980–2022	42
Kokcha	37.1233	69.4296	500	1980–2022	42
Kabul	34.2762	71.1259	393	1980–2022	42

outlines the specifications of the river gauging stations, as illustrated in Figure 4.

River gauging stations where the river flow crosses international borders were selected, utilizing data from the GloFAS-ERA5 dataset.

Afghanistan’s Köppen–Geiger climate classification map was adapted from version V2 of the global Köppen–Geiger climate classification [43]. The elevation map was generated using the GTOPO30 global raster digital elevation model (DEM), sourced from the U.S. Geological Survey [44]. The base maps used in this study, derived from Esri, NASA, the USGS, and the FAO, provide foundational geographic data and aid in the spatial visualization of climate, elevation, and hydrological features (Figure 1 and Figure 2).

2.3. Analysis Methods

2.3.1. Innovative Trend Analysis (ITA)

As proposed by Şen (2012), the ITA method assesses trends in time series data by dividing the dataset into two halves and analyzing them in a Cartesian coordinate system. The first half of the time series is plotted on the horizontal axis (x-axis), while the second half is plotted on the vertical axis (y-axis). Both halves are arranged in ascending order, and the resulting plot is evaluated with a 45° diagonal line, known as the 1:1 straight line [54]. When points in the scatter plot appear above the 1:1 line, this indicates a positive (upward) trend in the data, whereas points below the line indicate a negative (downward) trend. If points lie directly on the 1:1 line, this suggests no significant trend in the data. The ITA method thus offers a straightforward way to visually and quantitatively assess whether a time series exhibits an increasing, decreasing, or stable trend over time. For this study, time series data from 1980 to 2022 were selected to evaluate the impacts of climate change over the past four decades. Specifically, data on temperature, precipitation, and river discharge were analyzed. The year 1980 was excluded to ensure an even number of data points, and the analysis was based on two subseries: the first half (1981–2001) on the x-axis and the second half (2002–2022) on the y-axis.

The ITA method also incorporates the magnitude of the trends by calculating the absolute differences between a point’s x and y values. This distance from the 1:1 line provides insight into the strength of increasing or decreasing trends. The overall trend of the time series can be quantified by averaging these differences, with the trend indicator (S) defined as the difference between the arithmetic averages of the two subseries divided by the total number of data points (n). This trend indicator is expressed using the following equation [55]:

$$S={\displaystyle \frac{2\left(\overline{y}-\overline{x}\right)}{n}}$$

(1)

where $\overline{x}$ and $\overline{y}$ represent the arithmetic averages of the first and second halves of the time series, respectively. The ITA method allows for trends to be evaluated within a 5% relative error threshold compared to deterministic trend models, making it a reliable tool for trend detection [56].

2.3.2. Mann–Kendall (MK) Trend Analysis

The Mann–Kendall (MK) [57,58] is a non-parametric statistical test used to identify trends in time series data, particularly in environmental and climate studies. It assesses whether there is a statistically significant trend (either upward or downward) over time without assuming any specific distribution of the data. This test is widely used for analyzing trends in hydrological, meteorological, and water quality data. The tests have been widely applied in various studies [59,60,61].

MK trend analysis is determined by calculating the following parameters. It compares all pairs of observations in the dataset, assesses the direction of the trend (increase or decrease), and then tests its statistical significance.

$$\begin{gathered} \mathrm{The} \mathrm{S-statistic} \mathrm{sums} \mathrm{the} \mathrm{signs} \mathrm{of} \mathrm{the} \mathrm{differences} \mathrm{between} \mathrm{pairs} \mathrm{of} \mathrm{observations} \\ \mathrm{computed} \mathrm{using} \mathrm{the} \mathrm{following} \mathrm{equation}:\mathrm{S}={\sum }_{\mathrm{i}=1}^{\mathrm{n}-1}{\sum }_{\mathrm{j}=\mathrm{i}+1}^{\mathrm{n}}\mathrm{s}\mathrm{g}\mathrm{n}\left({\mathrm{X}}_{\mathrm{j}}-{\mathrm{X}}_{\mathrm{i}}\right) \end{gathered}$$

(2)

n is the total number of data points, X_j and X_i are the rank of data values of time series i and j (j > i), respectively, and the sgn(X_j − X_i) series is the sign function defined by Equation (2):

$$sgn=\left(X_j-X_i\right)=\left\{\begin{array}{c}+1, if X_j-X_i>0\\ 0, if X_j-X_i=0\\ -1, if X_j-X_i<0\end{array}\right\}$$

(3)

The variance of the S-statistic in Equation (2) is given by Equation (4):

$$Var\left(S\right)={\displaystyle \frac{n\left(n-1\right)\left(2n+5\right)-\sum _{j=1}^m{t}_j\left(t_j-1\right)\left({2t}_j+5\right)}{18}}$$

(4)

where n is the number of data points, m is the number of tied groups, and t_j is the tied rank, each with t_j tied points. The tied group is the data with the same value. In this study, n is 42.

A standardized test statistic (Z) is derived from S, which is used to determine the significance level.

$$z=\left\{\begin{array}{c}{\displaystyle \frac{S-1}{\sqrt{Var\left(S\right)}}} if S>0\\ 0 if S=0\\ {\displaystyle \frac{S+1}{\sqrt{Var\left(S\right)}}} if S<0\end{array}\right.$$

(5)

A positive value of Z indicates an upward trend in the time series data, and a negative value of Z indicates a downward trend.

Kendall’s Tau: Kendall’s Tau (τ) is a nonparametric statistic used to measure the strength and direction of association between two ordinal-level variables based on their rank correlation. It was introduced by Maurice Kendall in 1938 [62]. Kendall’s Tau measures the ordinal association between two variables based on the ranks. The range is −1 (perfect negative correlation) to +1 (perfect positive correlation), with 0 indicating no association. A pair is concordant if the ranks of both elements are in the same order (both increase or decrease together) and discordant if they are in the opposite order.

Based on the difference between the number of concordant (C) and discordant (D) pairs of observations, Kendall’s Tau is computed as follows [63]:

$$C={\displaystyle \frac{C-D}{\frac{1}{2}n\left(n-1\right)}}$$

(6)

n is the total number of observations.

p-value: The p-value (probability value) is a key concept in statistical hypothesis testing used to assess the strength of evidence against the null hypothesis [64]. In hypothesis testing, two main hypotheses are formulated as follows:

• Null hypothesis (H₀): This assumes no significant trend or effect in the data;
• Alternative hypothesis (H₁): This suggests a significant trend (increasing or decreasing) exists.

A commonly accepted threshold for significance is a p-value of 0.05. Suppose the p-value is less than or equal to 0.05. In that case, this indicates that the observed data are unlikely under the null hypothesis, leading to its rejection in favor of the alternative hypothesis, thus indicating a significant trend. On the other hand, if the p-value is greater than 0.05, it suggests that no significant trend is present.

2.3.3. Sen’s Slope (SS) Analysis

Sen’s slope is a non-parametric method for estimating the slope or trend of a dataset over time. It is particularly useful when analyzing time series data. Kumar Sen developed it in 1968 [65]. It is a robust trend estimation method that is especially advantageous for data that do not assume normality or contain outliers.

The median slope of Sen’s slope estimator is computed as follows:

$$S=Median\left({\displaystyle \frac{X_j-X_k}{j-k}}\right)j>k$$

(7)

S is the median slope between data points X_j and X_k when k (j > k).

This study calculated ITA, MK, SS, Kendall’s Tau, and p-value using MS Excel.

2.4. Data Process and Analysis

NetCDF raster datasets of CRU TS.v4, TerraClimate, and GloFAS-ERA5 for Afghanistan were downloaded from their respective sources and imported into the ArcGIS Pro environment. Using the Subset Multidimensional Raster tool in ArcGIS Pro (2024), the raster datasets were converted to Cloud Raster Format (CRF) and then transformed into grids through the Raster to Point function. The mean annual temperature and precipitation values were extracted from 1980 to 2022 for all stations (points) using the Zonal Statistics function. Figure 3 illustrates randomly selected stations spaced equally across each river basin and throughout the country. Average annual temperature and precipitation values from 1980 to 2022 were then computed for each river basin and averaged for the entire country. River discharge data were similarly extracted for points aligned with gauging stations, again using the Zonal Statistics function (Figure 4). Figure 4 displays the 0.05° grid for the GloFAS-ERA5 river discharge dataset. TerraClimate precipitation data were processed similarly. Finally, Excel tables were generated to compile the time series data for each station across all river basins and the country. The ITA, MK, and SS statistical tests were then conducted in MS Excel to analyze trends, using a 0.05 significance level, ensuring a 95% confidence interval. Figure 5 presents a workflow diagram of the data processing and trend analysis methodology.

3. Results

The mean annual temperature, precipitation, and river discharge time series data from 1980 to 2022 were analyzed for trends using three methods: the Mann–Kendall (MK) test, Sen’s slope (SS) estimator, and an innovative trend analysis (ITA). The trends were consistent across the country’s major river basins, though the magnitude of change varied depending on differences in climate and elevation. Here are the details of the trend analysis results for the three variables and testing methods.

3.1. Temperature Trends

The mean annual temperature trend across Afghanistan and its five major river basins, as analyzed by the ITA, MK, and SS methods, indicates a consistent upward trend. As summarized in

Table 2. MK, SS, and ITA test results for mean annual temperature from 1980 to 2022.

River Basin	Trend	p-Value	Sen’s Slope	τ	S	Var(S)	Z	ITA Slope	Increase in Temperature 1980–2022 (°C)
Average	(+)	0.000	0.034	0.495	447	7275	5.229	0.036	1.46
Kabul	(+)	0.000	0.023	0.359	322	6735	3.912	0.025	0.99
Helmand	(+)	0.000	0.041	0.541	687	7588	7.875	0.045	1.76
Harirud	(+)	0.000	0.040	0.508	576	7440	6.666	0.037	1.72
Northern	(+)	0.000	0.028	0.406	668	7104	7.914	0.028	1.20
Amu Darya	(+)	0.000	0.022	0.344	747	6708	9.109	0.026	0.95

, the data confirm this positive trend with statistical significance, showing a steady increase in the mean annual temperature over the study period from 1980 to 2022.

Each method corroborates the warming trend, with some variation in their results. The ITA graphical results (Figure 6) demonstrate that the mean annual and seasonal temperature data consistently lie above the 1:1 straight line, signifying an upward trend. Similarly, the graphical output of Sen’s slope (SS) in Figure 7 aligns with the ITA findings. Across Afghanistan and the five river basins, the calculated parameters, whether using the MK, SS, or ITA methods, point to a uniform conclusion of increasing temperature.

For the MK test, the calculated p-values, as outlined in Table 2, confirm that the detected trends are statistically significant. The magnitude of temperature increase, as determined using Sen’s slope method, shows a steady yearly rise in mean annual temperatures. The similarity between the slope values derived from SS and the ITA is notable, with the ITA showing a slightly higher rate of increase. The mean annual temperature increases for the entire study period (1980–2022) are calculated as 0.034 °C/year using SS and 0.036 °C/year using the ITA, leading to an overall rise of 1.46 °C over 42 years.

A closer look at the temperature changes in the various river basins reveals regional variations. The temperature increased by 0.99 °C and 0.95 °C in the Kabul and Amu Darya River Basins. However, more substantial warming occurred in the Helmand (1.76 °C), Harirud (1.72 °C), and Northern (1.20 °C) River Basins. The more significant temperature increases in these southern and western basins, as highlighted in the last column of Table 2, underscore the considerable warming in these regions compared to the Kabul and Amu Darya basins.

The results demonstrate a marked and statistically significant rise in temperatures across Afghanistan, with variations between the river basins. In particular, the Helmand River Basin has experienced the most notable increase in temperature, reflecting broader global warming trends and more pronounced local effects in certain regions. This rise in temperature has essential implications for Afghanistan’s water resources, agriculture, and environmental stability, given the country’s reliance on these river basins for sustaining livelihoods and ecosystems.

3.2. Precipitation Trends

The ITA analysis of the mean annual precipitation in Afghanistan and its river basins is presented in Figure 8. Most data points were evenly distributed along a 1:1 straight line, indicating no significant trend in the precipitation data, except for the Northern River Basin, which showed a positive trend (Figure 8C). Despite this, the MK test results indicated no statistically significant trend in the overall precipitation data (as summarized in

Table 3. MK, SS, ITA, and regression test results for mean annual precipitation in Afghanistan and major river basins from 1980 to 2022.

River Basin	Trend	p-Value	Sen’s Slope	ITA Slope	Regression Slope	Decrease in Precipitation 1980–2022 (mm)
Average	(0)	0.578	0.000	0.023	−0.028	−1.187
Amu Darya	(0)	0.949	0.000	0.059	−0.030	−1.286
Northern	(+)	0.499	0.050	0.085	0.022	0.929
Harirod	(0)	0.440	0.000	0.023	−0.083	−3.582
Helmand	(−)	0.155	−0.069	−0.047	−0.084	−3.608
Kabul	(0)	0.918	0.000	0.213	−0.041	−1.754

). This suggests that the precipitation data do not exhibit monotonic trends but are non-monotonic fluctuations over time.

A regression analysis was applied to the time series precipitation data to understand these non-monotonic trends better, offering a parametric method to assess trends. This method identified linear trends in the precipitation data across the country and its major river basins, as depicted in Figure 9. The results revealed a predominantly negative trend in precipitation across Afghanistan, except for the Northern River Basin, which demonstrated a positive trend (Figure 9C).

More specifically, the overall negative trend in precipitation across Afghanistan was calculated at −0.028 mm/y, amounting to a total decrease of 1.19 mm over the 42-year study period. The decline in precipitation was more pronounced in specific basins, with the Helmand and Harirod River Basins experiencing a total decrease of 3.6 mm. In contrast, the Amu Darya and Kabul River Basins showed smaller reductions in total precipitation, with decreases of 1.29 mm and 1.74 mm, respectively (Table 3).

One of the most critical consequences of climate change in Afghanistan is the significant reduction in precipitation, leading to a decline in surface water resources. This phenomenon has been well documented in numerous studies and field observations across the country [11,47,66]. Experts, local communities, and farmers have consistently reported a gradual decrease in precipitation, irregular rainfall patterns, and a subsequent reduction in surface water. These factors have contributed to widespread water scarcity, particularly affecting agriculture, which remains a crucial sector for Afghanistan’s economy and livelihoods.

The Northern River Basin is especially vulnerable to these changes, with climate change exacerbating surface water and groundwater depletion. This region, already prone to climate variability, now faces an increased frequency of floods and droughts, further undermining agricultural productivity and diminishing green cover [11,67]. These changes threaten food security and worsen the socio-economic conditions of rural communities, which are heavily dependent on natural resources for their livelihoods.

3.3. River Discharge Trend

The results of the ITA, SS, and MK analyses are presented in Figure 10 and Figure 11 and

Table 4. MK, SS, and ITA test results for mean annual river discharge at major gauging stations in main river basins.

River Basin	Trend	p-Value	Sen’s Slope	ITA Slope	τ	S	Var(S)	Z	Decrease in River Discharge 1980–2022 (m3/s)
Zarang	(−)	0.004	−6.147	−8.258	−0.249	−225	1203	−6.459	−258.17 m3/s
Farah	(−)	0.000	−1.531	−1.998	−0.333	−301	4963	−4.258	−64.30 m3/s
Adraskan	(−)	0.039	−1.347	−2.902	−0.229	−207	3738	−3.369	−56.57 m3/s
Hari Rod	(−)	0.008	−2.182	−2.918	−0.240	−217	1203	−6.228	−91.64 m3/s
Murghab	(0)	0.109	−1.125	−1.581	−0.169	−153	3282	−2.653	−47.25 m3/s
Tagab	(−)	0.019	−1.38	−2.341	−0.225	−203	2295	−4.217	−57.96 m3/s
Kundoz	(−)	0.013	−6.962	−7.992	−0.262	−237	1203	−6.805	−292.40 m3/s
Kokcha	(−)	0.019	−3.829	−4.524	−0.238	−215	2295	−4.467	−160.82 m3/s
Kabul	(−)	0.015	−2.716	−3.592	−0.227	−205	3282	−3.561	−114.07 m3/s
Average	(−)	0.000	−3.058	−4.012	−0.331	−299	3736	−4.876	−128.06 m3/s

. The results highlight the significant decline in river discharge across Afghanistan’s major river basins from 1980 to 2022. This decline was observed at nine key gauging stations located at the points where river flows exit Afghanistan, as shown in Figure 4.

The results from the ITA test show that the river discharge data for all stations fall below the 1:1 straight line, signaling clear negative downward trends (Figure 10). The slope calculated for the ITA further underscores a pronounced decrease in river discharge across all basins. This consistent negative trend suggests that river flows have steadily diminished over the four-decade period. Figure 11 complements the ITA findings, displaying similar negative trends using the SS method. The magnitude of decline revealed by SS is consistent with the ITA results, reinforcing the reliability of these observations. Except for one location, Murghab (Figure 11E), the p-values for the MK test across all gauging stations were below 0.05, confirming statistically significant negative trends. The consistency of these three methods (ITA, SS, and MK) strengthens the credibility of the findings.

Table 4 summarizes the results from all three statistical methods applied to the gauging stations. The calculated river discharge from Afghanistan since 1980 reveals significant variability between the SS and ITA methods, although both point toward a general decline in water flow. The highest rates of discharge reduction were recorded in the Kunduz (−6.96 m³/s) and Zarang (−6.15 m³/s) rivers, which typically exhibit higher annual discharge rates.

The cumulative reduction in river discharge from all Afghan river basins was calculated to have been −128 m³/s from 1980 to 2022, equivalent to an annual reduction of −3 m³/s. This substantial decrease in water flow has far-reaching implications for Afghanistan, particularly for its water resource management, agriculture, hydropower generation, and ecological health. Moreover, the negative impact is not limited to Afghanistan; downstream riparian countries also face challenges from the reduced water supply, as these rivers form part of transboundary water systems.

4. Discussion

The findings of this trend analysis illuminate significant shifts in Afghanistan’s climate and hydrology over the past four decades, with critical implications for water resources, agriculture, and regional socio-economic stability. By employing the MK test, SS, and an ITA, this study provides a multi-dimensional view of changes in temperature, precipitation, and river discharge across Afghanistan’s major river basins from 1980 to 2022. Each variable has shown distinctive trends, reflecting the broader impacts of climate change, especially within Afghanistan’s unique geographical and climatic context.

• Temperatures Rise and Regional Variations

The increasing trend in temperature across Afghanistan, consistently demonstrated by all three trend analysis methods (the ITA, MK test, and SS), confirms a marked warming pattern. With a mean annual temperature increase of approximately 0.034–0.036 °C or a cumulative rise of 1.46 °C over the study period, this warming trend exceeds the global climate change of 1.3 °C. For instance, the Helmand and Harirud River Basins, located in the south and west of the country, have experienced the most significant warming, with increases of 1.76 °C and 1.72 °C, respectively. These areas are already vulnerable due to arid climates and limited water resources, and intensified warming could further exacerbate water scarcity, desertification, and agricultural stress. The rise in temperature has profound implications for Afghanistan’s agricultural systems, which are heavily dependent on climate-sensitive water availability. Temperature increases can lead to accelerated glacial melt, increased evaporation rates, and heightened water demand for crops, potentially reducing yields and stressing rural communities. The differences in warming rates between regions also suggest that localized adaptation strategies will be crucial for addressing the unique impacts in each river basin. For instance, the more substantial temperature rise in the Helmand and Harirud Basins indicates an urgent need for adaptive water management and drought-resilient crops in these regions.

• Precipitation Decrease and Spatial Heterogeneity

This study indicates a declining trend in annual precipitation across most of Afghanistan, with a calculated average decrease of 0.028 mm per year, a reduction of 1.19 mm over the study period. This decline is particularly pronounced in the Helmand and Harirud Basins, which experienced reductions of approximately 3.6 mm, compared to smaller decreases in the Amu Darya and Kabul Basins. However, the Northern River Basin presents a contrasting scenario, showing a slightly positive trend in precipitation, highlighting the spatial heterogeneity of Afghanistan’s climate. This reduction in precipitation poses severe challenges for Afghanistan, where water resources are already under significant strain. Agriculture, which relies heavily on surface water and seasonal rainfall, will likely face increased variability in water availability, leading to more frequent droughts and potentially lower crop yields. Furthermore, declining precipitation and erratic rainfall patterns can reduce groundwater recharge rates, impacting drinking water supplies and compounding the already precarious water security in the region.

• River Discharge Decreases and Downstream Impacts

The decreasing trend in river discharge across Afghanistan’s major river basins, revealed by all three statistical methods, suggests a critical reduction in surface water resources, with a cumulative decrease of 128 m³/s since 1980. Notably, the Kunduz and Helmand rivers showed the highest decline rates, recording reductions of 6.96 m³/s and 6.15 m³/s, respectively. The steady decline in river discharge reflects reductions in precipitation and increased temperatures, which accelerate evapotranspiration rates and alter hydrological cycles. Reducing river discharge has cascading impacts on Afghanistan and neighboring countries, as many Afghan rivers are part of transboundary water systems. For Afghanistan, the implications are immediate and far-reaching: diminished river flows compromise agriculture, hydropower generation, and ecological health. In downstream areas, reduced water availability can lead to heightened tensions over water allocation, particularly in regions where water scarcity already fuels political and economic instability. Afghanistan’s downstream riparian countries, including Iran and Pakistan, are highly dependent on these water sources, and further declines in discharge could exacerbate regional water conflicts. For communities that rely on these rivers for irrigation, lower levels of river discharge mean less water for crop cultivation, which could impact food security and economic livelihoods. In this context, river basin management strategies that consider regional climate projections and incorporate water-saving technologies become essential to mitigate these cascading effects.

5. Limitations

Remote sensing and geospatial climate datasets are essential for studying climate change in data-scarce regions like Afghanistan. The global temperature and river discharge data are consistent with observed ground data, but the precipitation showed discrepancies. While some global datasets show an increasing trend in precipitation [47,68], actual ground observations in Afghanistan often show a decrease, likely due to climate change [11]. This discrepancy introduces challenges in validating climate models and datasets.

Although MK and SS analyses show statistically insignificant trends in precipitation, a regression analysis reveals a more nuanced understanding of changes over time. This suggests that single-method approaches may not provide a complete picture and that comprehensive methods are needed to capture variability and trends more accurately. It is worth mentioning that the remote sensing data are limited by their spatial resolution, which can hinder the ability to capture localized climate changes [69,70], especially in Afghanistan’s diverse terrain. These data gaps create challenges in assessing the true extent of climate impacts, particularly on water resources in critical areas like the Northern River Basin. Therefore, integrating remote sensing data with ground-based observations is essential for improving the accuracy of climate models. Improving ground data collection, expanding the spatial resolution of remote sensing data, and using more advanced interpolation methods could help mitigate these limitations and provide a more reliable basis for climate change adaptation in Afghanistan.

6. Conclusions

This comprehensive analysis, employing the MK, SS, and ITA methods, underscores the urgent and substantial impacts of climate change in Afghanistan. The findings reveal persistent upward trends in temperature alongside reductions in precipitation and river discharge. These changes present serious environmental and socioeconomic challenges for the country, impacting public health, agriculture, water resources, and economic stability. This situation underscores the critical need for immediate and collaborative action. Addressing these issues will require adaptive strategies that respond to present conditions while preparing for future climate scenarios, including effective water management practices, climate-resilient agricultural techniques, and cooperative regional initiatives to secure sustainable water resources for Afghanistan and neighboring countries.