Search Results (3)

Rivers are the main source of fresh water in mountainous and downstream areas. It is crucial to investigate the possible threats of climate change and understand their impact on river watersheds. In this research, climate change’s impact on the mountainous watershed of the Jajrood River, upstream of Latyan Dam in Iran, was assessed by using a multivariate recursive quantile-matching nesting bias correction (MRQNBC) and the soil and water assessment tool (SWAT). Also, this study considered ten global circulation models (GCMs) from the coupled model intercomparison project phase VI (CMIP6). With a higher correlation coefficient, the MIROC6 model was selected among other models. For the future period of 2031–2060, the large-scale outputs of the MIROC6 model, corresponding to the observational data were extracted under four common socioeconomic path scenarios (SSPs 1–2.6, 2–4.5, 3–7.0, 5–8.5). The bias was corrected and downscaled by the MRQNBC method. The downscale outputs were given to the hydrological model to predict future flow. The results show that, in the period 2031–2060, the flow will be increased significantly compared to the base period (2005–2019). This increase can be seen in all scenarios. In general, changes in future flow are caused by an increase in precipitation intensity, as a result of an increase in temperature. The findings indicate that, although the results show an increase in the risk of flooding, considering the combined effects of three components, i.e., increased precipitation concentration, temperature, and reduced precipitation, climate change is intensifying the problem of water scarcity. Full article

Nitrate is one of the most complicated forms of nitrogen found in aquatic surface systems, which results in the eutrophication of the water. During the last few decades, due to agriculture and animal husbandry activities, as well as urban development, a significant amount of pollutants have accumulated in the Jajrood river in northern Iran. In this research, we simulated nitrate load in a rural watershed to assess the outlet stream’s qualitative status and evaluate the influence of best management practices (BMPs). To accomplish this, we prepared, processed, and integrated different datasets, including land-use land-cover (LULC) maps, physiographic layers, and hydrological and agricultural datasets. In the modeling section, the Soil and Water Assessment Tool (SWAT) was used to simulate nitrate load over 28 years (1991–2019). Additionally, the multi-objective optimization algorithm (MOPSO) was implemented to reduce the intended objective functions, including the number of best management practices and the nitrate concentration considering different scenarios. The calibration of the basin’s discharge and nitrate indicated that the SWAT model performed well in simulating the catchment’s streamflow (R² = 0.71) and nitrate (R² = 0.69). The recommended BMPs for reducing nutrient discharge from the basin are using vegetated filter strips on river banks and fertilizer reduction in agricultural activities. According to the results from this investigation, the integrated model demonstrates a strong ability to optimally determine the type, size, and location of BMPs in the watershed as long as the reduction criteria change. In a situation of water scarcity, the studies reported here could provide useful information for policymakers and planners to define water conservation policies and strategies. Full article

Quantifying the effect of non-point source pollution from different land use types (e.g., agricultural lands, pastures, orchards, and urban areas) on stream water quality is critical in determining the extent and type of land use. The relationship between surface water quality as the primary source of drinking water and land use patterns in suburban areas with an accelerated pace of industrial development and progressive growth of population has drawn much attention recently. This study aims to determine the type and portion of the land use changes over three-time intervals from 2000 to 2015 in the Jajrood River Catchment (Tehran metropolis, north of Iran). We used satellite images of Landsat TM and ETM for 2005, 2010, and 2015 to analyze land use changes as a spatiotemporal model. According to the image processing and analysis, we classified the land uses of the study area into irrigated farming, orchards, pastures, and residential areas. In addition, we used temporal data from sampling stations to identify the relationship between land use and water quality based on a multivariate regression model. The analysis shows a significant correlation between the type and extent of land use and water quality parameters, including pH, Na⁺, Ca⁺, Mg⁺, Cl⁻, SO₄²⁻, NO₃⁻, and TDS. Pastures and residential areas had the highest impact on water quality parameters among all land use types. Besides, we have used the regression analysis results to determine the maximum permissible areas of each land use type. Consequently, effective management strategies such as land use optimization in catchment scale for this catchment and similar areas will help to consciously protect and manage the quality of drinking water resources. Full article