Simulating long-term effect of Hyrcanian forest loss on phosphorus loading at the sub-watershed level

Conversion of forest land to farmland in the Hyrcanian forest of northern Iran increases the nutrient input, especially the phosphorus(P) nutrient, thus impacting the water quality. Modeling the effect of forest loss on surface water quality provides valuable information for forest management. This study predicts the future impacts of forest loss between 2010 and 2040 on P loading in the Tajan River watershed at the sub-watershed level. To understand drivers of the land cover, we used Land Change Modeler(LCM) combining with the Soil Water Assessment Tool(SWAT) model to simulate the impacts of land use change on P loading. We characterized priority management areas for locating comprehensive and cost-effective management practices at the sub-watershed level. Results show that agricultural expansion has led to an intense deforestation. During the future period 2010–2040, forest area is expected to decrease by 34,739 hm~2. And the areas of pasture and agriculture are expected to increase by 7668 and 27,071 hm~2, respectively. In most sub-watersheds, P pollution will be intensified with the increase in deforestation by the year 2040. And the P concentration is expected to increase from 0.08 to 2.30 mg/L in all of sub-watersheds by the year 2040. It should be noted that the phosphorous concentration exceeds the American Public Health Association′s water quality standard of 0.2 mg/L for P in drinking water in both current and future scenarios in the Tajan River watershed. Only 30% of sub-watersheds will comply with the water quality standards by the year 2040. The finding of the present study highlights the importance of conserving forest area to maintain a stable water quality.     

Surface drainage nitrate loading estimate from agriculture fields and its relationship with landscape metrics in Tajan watershed

Pasture, forest, and farmland are the dominant land covers in the Tajan River watershed and this landscape status has a direct connection with nitrate pollution. Understanding the correlations between landscape variables and nitrate pollutant is a priority in order to assess pollutants loading and predicting the impact on surface water quality. The soil and water assessment tool was used to simulate nitrate loads in different land cover types in different years. The landscape pattern was calculated by FRAGSTATS. The contributing share of each land use/land cover shows nitrate pollutant produced by grassland (5.7%) and forest (29%) are less than those produced by agricultural land (64.2%). Agricultural land was identified as the main source of nitrate pollution. Paddy fields and orchards had the most intensive soluble nitrate loss especially in spring and summer. Statistical analysis indicated that nitrate was positively associated with patch density, edge density, patch number, total edge, effective mesh size, largest patch index, and landscape shape index (p ≤ 0.01). We then analyzed how nitrate was related to landscape attributes in six different sites. Also the regression analysis results suggested that landscape metrics could account for more than 94% of the variance of nitrate in the whole catchment. The regression models confirmed the great importance of the agriculture metrics and forest metric in predicting nitrate in watershed. Defining the generation and extent of pollution in this particular watershed which discharges into the Caspian Sea can constitute an important step toward protecting this ecosystem.     

Predicting soil organic carbon density using auxiliary environmental variables in northern Iran

In the present study, artificial neural networks (ANNs) were employed to develop models to predict soil organic carbon density (SOCD) at different depths of soil layers. Selected environmental variables such as vegetation indices, soil particle size distribution, land use type, besides primary and secondary terrain attributes were considered as the input variables. According to the results, the ANN models explained 77% and 72% of the variability in SOCD at soil layer depths of 0–20 cm and 20–40 cm, respectively, at the site studied. Sensitivity analyses showed that the most considerable positive contribution of variables for predicting SOCD included the land use type, normalized difference vegetation index (NDVI) > normalized difference water index (NDWI) > silt > clay > elevation in the 0–20 cm soil layer. On the other hand, for the 20–40 cm soil layer, the land use type following NDVI > NDWI > clay > silt were identified as the most powerful predictive factors. In the Deylaman region, in both soil layers, sand had a considerable negative effect on SOCD and most of the terrain attributes had no significant impact on the SOCD prediction. Therefore, these results provide valuable information for sustainable management and decision-making on a landscape scale for governors and other users.