1. Introduction
Land is an essential natural resource, both for humanity, and the maintenance of all terrestrial ecosystems [
1]. Increases in population density and human activities have caused increased demand for water, arable land, wood, grazing areas, and other types of resources [
2,
3]. Water is an indispensable resource and a key element in human livelihood and socioeconomic development [
4]. As water consumption increases, water availability may become a critical factor for human activities. This makes it necessary to manage water resources for their sustainable use [
3].
Precipitation is the main source of water in the world. It is unevenly distributed spatially and temporally and strongly influenced by climate and land use [
5]. Precipitation and runoff are the main hydrologic components in water resources assessment [
6].
Runoff is the result of interactions among climate, terrain, and land use in a water basin. Climate change may change the spatial and temporal distribution of precipitation, affecting the amount and spatial configuration of runoff. Changes in land use may alter the flow processes, which also affects runoff. Although climate change plays a key role in runoff changes, the impact of human activities cannot be ignored [
7]. The impact of land cover and climate change on hydrology are global issues affecting the hydrological processes of river basins [
8,
9].
The impact of vegetation on water runoff is complex. The influence of vegetation, its growth, regeneration, and succession depend on soil properties and micro-topography of the terrain, among other things [
10]. The common understanding is that increasing the vegetation cover decreases surface runoff and erosion, but the relationships are not always straightforward since the effect of tree cover depends on tree species and the vertical structure of the canopy [
11]. Forests have a limited capability to retain precipitation, even when the forest cover is high. This inadequacy is most apparent during extreme rainfall events [
12,
13,
14]. The results of some studies have indicated that rainfall intensity was the most important factor that influenced runoff [
15], and forests and other vegetation were not always capable of retaining much of the rainfall. Differences in natural and geographical conditions or the type and structure of vegetation have an impact on water interception, surface runoff, groundwater, and evaporation, which affect the spatio-temporal patterns of water cycles [
10].
Land-use changes have direct effects on the hydrological processes of watersheds, which depend on land-cover characteristics [
16,
17,
18,
19]. Changes in land use in areas characterized by large and fast runoff (e.g., areas with much rain but little vegetation) have immediate impacts, while changes in areas with little rain and much vegetation have smaller and delayed impacts [
20].
Studies on the relationship between changes in vegetation cover and water runoff dynamics provide information for the regulation of water resources and effective mitigation of the damage caused by land-use changes [
10,
16]. Watershed management is based on controlling the hydrological processes, mainly runoff [
5,
21]. Analyzing the long-term dynamics of runoff has practical significance since the new knowledge enhances the management and sustainable use of water resources [
10].
Many hydrologic models such as HYMOD, LRHM, and TANK exist for calculating runoff [
22,
23,
24,
25]. In Iran, the “curve number method” of the Soil Conservation Service (SCS) of the United States Department of Agriculture (SCS-CN method) is the most frequently used method [
26,
27]. The runoff curve number (CN) is the key factor of the SCS-CN method and depends on land use and land cover (LULC), soil type, and soil moisture [
16,
21,
28]. Often, an area-weighted average curve number for the entire watershed is used to estimate the runoff of a watershed. This type of analysis ignores the details of the spatial variation in the watershed. A GIS tool called ArcCN-Runoff facilitates detailed and spatially explicit runoff estimation, reducing processing times and improving the efficiency of the analyses [
29].
Water runoff and erosion depend on several factors, among which land use has been most studied [
30]. Large-scale land-use mapping based on fieldwork is expensive and time-consuming and field mapping of past years’ land uses is impossible. The use of remote sensing data may alleviate these problems because old imageries are available at a low cost. Lacking or inaccurate ground truth information is sometimes a partial hindrance to the effective use of old imageries [
10].
GIS is an effective and flexible tool for analyzing and visualizing the effects of LULC [
31,
32]. Remote sensing provides a synoptic view and wall-to-wall data on watershed basins. The GIS environment facilitates the integration of different data sources and performs complicated spatial analyses to support decision making related to hydrological processes [
33,
34,
35].
One of the recent interests in hydrologic modeling is the assessment of the effects of land-use changes on water resources. As watersheds become more influenced by human activities, they also become more active hydrologically with changing runoff components, streamflows, and flood volumes [
36].
Various natural hazards such as floods, landslides, and bed erosion at riverbanks are increasingly observed in northern Iran, including the Haraz River basin with an area of 6774 square kilometers. Vegetation degradation and land-use changes are believed to be important reasons for the increased hazards. This study analyzed the effect of land-use change on runoff in the Haraz River basin during 15 years, using remote sensing data, GIS methods, and the ArcCN-Runoff tool. The objective was to provide useful information for regulating water resources, to reduce soil erosion and improve the management of the watershed.
4. Discussion
This study used remote sensing to detect changes in land use and the SCS curve number method to calculate the effect of land-use change on water runoff. The runoff generation method is highly complex, nonlinear, dynamic in character, and affected by numerous interconnected physical factors. Similar methods have been used in the past [
29,
44,
45] and the approach has been deemed suitable for analyzing the effect of land use on hydrological processes [
10,
21,
44]. In the current study, the trends of land-use changes and the Kappa statistics calculated for the classified Landsat images are consistent with the results of Mula-aghajanzadeh et al. [
40]. The results indicated good accuracy, suggesting that Landsat imageries have high potential in land-use mapping and hydrological studies [
21,
29,
37]. The use of remote sensing and GIS techniques in combination with hydrological models offers cost-effective analyses as compared with the conventional approaches discussed, for example, for example, Sajikumar and Reyma [
18], Kumar et al. [
21], and Al-Ghobari et al. [
46].
The curve number method is widely used in several countries. Its advantages are simplicity, predictability, stability, a low number of parameters, and its responsiveness to major runoff-producing watershed properties such as soil type, land use, and surface condition [
21,
28,
44]. The disadvantages include marked sensitivity to curve number, varying accuracy for different biomes, and the fixing of the initial abstraction ratio at 0.2 in Equation (2) [
47].
D’Asaro and Grillone [
48] suggested, according to recent studies, that the initial abstraction ratio should be 0.05 rather than 0.2. Woodward et al. [
49], using rain event and runoff data from several hundred plots, also found that a value of about 0.05 gave a better fit to the data and would be more appropriate than 0.2 in runoff calculations. Because of the above suggestions, we calculated the runoff results of our case study area also with the 0.05 initial abstraction ratio. The magnitude of the runoff volume did not change much. The 15-year increase in runoff volume was 14.68 mill. m
3 (6.7%) instead of the 20.05 mill. m
3 (8.98%) obtained with an initial abstraction ratio of 0.2. The runoff depths and volumes of different land-use classes did not change much.
Huang et al. [
50] found that the standard CN method could underestimate large runoff events and overestimate small events. They developed a correction equation based on the relationship between slope and the observed and theoretical CN values. Their improved method predicted runoff depths with an R2 of 0.822. Garen and Moore [
51] mentioned that the use of the curve number method was appropriate for flood hydrograph engineering applications, but more physically based algorithms were needed for nonpoint source water quality modeling.
Our analyses ignored the fact that the CN depends on antecedent moisture conditions (AMC), which may change during the year [
52]. This simplification was done because of insufficient data for generating temporal variation in AMC and CN. However, calculations with different initial abstraction factors suggest that this simplification does not have any major impact on the main conclusions of the study.
The results of this study on runoff depth and runoff volume cannot be easily compared with previous research because of differences in the amount and distribution of precipitation, land uses, topography, and soil types [
17]. In the calculation example of Zhan and Huang [
29], the proportion of surface runoff was 3.8% of precipitation, whereas it was about 9% in our study. In addition, Singh et al. [
53] calculated that, in the Varekhadi River basin (India), the runoff after the heaviest rain events could be as much as 62–74% of precipitation.
Climate change may also affect water availability and surface runoff [
54,
55]. The surface runoff will increase if heavy rain events become more frequent. However, most changes can be traced back to human interferences with natural ecosystems. Il Eum et al. [
56] and Yin et al. [
55] also concluded that land-cover changes played a larger role in the trends of surface runoff than climate change.
In our case study area, land-use changes resulted in a 9% increase in runoff volume during the 15 years. Most of the increase was related to the changes that occurred in rangeland, bare land, and residential land, which was in line with the results of Kamuju [
57]. Most changes were caused by human activities, which increased the demand for natural resources and often represented exploitative and unsustainable use. Although the increase is significant, clearly higher increases have been reported in the literature [
2].