A Scenario-Based Approach for Assessing the Hydrological Impacts of Land Use and Climate Change in the Marboreh Watershed, Iran

Abstract

In separate analyses of the impacts of land use change and climate change, a scenario-based approach using remote sensing and hydro-climatological data was developed to assess changes in hydrological indices. The data comprised three Landsat TM images (1988, 1998, 2008) and meteorological and hydrological data for the Aligudarz and Doroud stations in the Marboreh watershed, Iran. The QUAC module and supervised classification maximum likelihood (ML) algorithm in ENVI 5.1 were used for remote sensing, the SWAT model for hydrological modelling and the Mann-Kendall and t test methods for statistical analysis. To create scenarios, the study period was divided into three decades (1983-1992, 1993-2002, 2003-2012) with clearly different land use/land cover (LULC). After hydrological modelling, 10 hydrological indices related to high and low flow indices (HDI and LDI) were analysed for seven scenarios developed by combining pre-defined climate periods and LULC maps. The major changes in land use were degradation of natural rangeland (− 18.49%) and increasing raid-fed farm area (+ 16.70%) and residential area (+ 0.80%). The Mann-Kendall test results showed a statistically significant (p < 0.05) decreasing trend in rainfall and flow during 1983-2012. In the scenarios evaluated, hydrological index trends were more sensitive to climate change than to LULC changes in the study area. Low flow indices were more affected than high flow indices in both land use and climate change scenarios. The results show little impact of land use change and indicate that climate change is the main driver of hydrological variations in the catchment. This is useful information in outlining future strategies for sustainable water resources management and policy decision-making in the Marboreh watershed.

Unsustainability Syndrome—From Meteorological to Agricultural Drought in Arid and Semi-Arid Regions

“…The expanding agriculture sector, river regulation, and increasing frequency of meteorological droughts have led to a reduction in available surface water in recent years [29], with major lakes in the province drying up [29,45] Thus, farmers are increasingly dependent on groundwater resources. As a result, a significant decrease in groundwater levels has been recorded [40,46], and river water levels are critically low [41,47].…”

“…The other two types of drought arise when use of water resources exceeds renewal of surface and groundwater, or when water demand outstrips supply.Many drought indices have been developed to date, such as the Palmer drought severity index [26], decile index [27], standardized precipitation index (SPI) [28], streamflow drought index (SDI) [15], and standard contribution of rainfall to runoff (SCRI) [29]. Of these, SPI and SDI have been widely used in different climate zones to monitor and assess meteorological drought [30][31][32][33][34][35][36] and hydrological drought [37][38][39][40].Although there has been considerable progress in understanding the impacts of drought in terms of engineering, economics, social science, and geography, the complex links between human and natural processes need more attention. In arid and semi-arid regions with high dependency on agriculture, these links are more complicated, as they affect the livelihoods of local people.…”

“…Many drought indices have been developed to date, such as the Palmer drought severity index [26], decile index [27], standardized precipitation index (SPI) [28], streamflow drought index (SDI) [15], and standard contribution of rainfall to runoff (SCRI) [29]. Of these, SPI and SDI have been widely used in different climate zones to monitor and assess meteorological drought [30][31][32][33][34][35][36] and hydrological drought [37][38][39][40].…”

Climate-Smart Agro-Hydrological Model for a Large Scale Rice Irrigation Scheme in Malaysia

“…This is due to efforts to meet industrial and urbanization demands. Climate change is reported to be the main driver of hydrological variations in catchments [1]. The competition for water has increased in recent years to a stage of physical scarcity [2].…”

Urban Flood Hazard Modeling Using Self-Organizing Map Neural Network

“…Darabi et al (2019) [21] used two state-of-the-art machine learning models (GARP and QUEST) for flood risk mapping in Sari city, Iran. Weakly labeled support vector machine (WELLSVM) has been used to assess urban flood susceptibility in Beijing, China [22][23][24]. One of the challenges in urban flood studies is that the number of predictors is limited, and there is no hydrometric station to record runoff and discharge [25,26].…”