Evaluation of sustainability of river Krishna under present and future climate scenarios

Pollution source identification and abatement for water quality sections in Huangshui River basin, China

Accurately obtaining the pollution sources and their contribution rates is the basis for refining watershed management. Although many source analysis methods have been proposed, a systematic framework for watershed management is still lacking, including the complete process of pollution source identification to control. We proposed a framework for identification and abatement of pollutants and applied in the Huangshui River Basin. A newer contaminant flux variation method based on a one-dimensional river water quality model was used to calculate the contribution of pollutants. The contributions of various factors to the over-standard parameters of water quality sections at different spatial and temporal scales were calculated. Based on the calculation results, corresponding pollution abatement projects were developed, and the effectiveness of the projects was evaluated through scenario simulation. Our results showed that the large scale livestock and poultry farms and sewage treatment plants were the largest sources of total nitrogen (TP) in Xiaoxia bridge section, with contribution rates of 46.02% and 36.74%, respectively. Additionally, the largest contribution sources of ammonia nitrogen (NH3-N) were sewage treatment plants (36.17%) and industrial sewage (26.33%). Three towns that contributed the most to TP were Lejiawan Town (14.4%), Ganhetan Town (7.3%) and Handong Hui Nationality town (6.6%), while NH3-N mainly from the Lejiawan Town (15.9%), Xinghai Road Sub-district (12.4%) and Mafang Sub-district (9.5%). Further analysis found that point sources in these towns were the main contributor to TP and NH3-N. Accordingly, we developed abatement projects for point sources. Scenario simulation indicated that the TP and NH3-N could be significantly improved by closing down and upgrading relevant sewage treatment plants and building facilities for large scale livestock and poultry farms. The framework adopted in this study can accurately identify pollution sources and evaluate the effectiveness of pollution abatement projects, which is conducive to the refined water environment management.

Prediction and monitoring of LULC shift using cellular automata-artificial neural network in Jumar watershed of Ranchi District, Jharkhand

Jumar watershed of Ranchi district is agrarian in nature. The unplanned and exponentially growing urban sprawl has become one of the probable threats in achieving sustainable development goals (SDG-15). The purpose of this research study is to monitor the urban sprawl in Jumar watershed within three decades i.e. from the year 1990 to 2021. Land use land cover (LULC) change has been monitored using satellite data from LANDSAT (4, 5 and 8). Various indices are calculated like normalised difference vegetation index (NDVI), normalised difference built-up index (NDBI), normalised difference water index (NDWI) and built-up index (BUI) to monitor LULC change in the area. For prediction of urban sprawl, cellular automata and artificial neural network (CA-ANN) with GIS application technique is used. The model is validated by using Kappa coefficient. The prediction results showed increase in built-up area by 8.23 sq. km in the next decade. The built-up and barren land together increase up to 42.85 sq. km by 2030 and 34.61 sq. km in 2021. The NDVI for 3-decade period showed significant decrease in the healthy vegetation and increase in sparse vegetation. The NDBI showed a slight increase in urban area but massive increase in uncultivated and barren land. NDWI showed a decrease in area of the surface water. The LULC studies showed a major shift from healthy vegetation to agriculture and then to barren land. To assess the impact of urbanisation on water quality, water samples are taken seasonally from J1to J11 sampling locations and are analysed as per APHA procedure. The sites are classified as urban, semi urban and rural area as per their location. The water quality index (WQI) varied between 42.14 to 61.42 during pre-monsoon, 62.20 to 68.7995 during monsoon and 43.48 to 60.12 during post-monsoon. The quality of water is found poor in all seasons at all sampling sites. The water is found highly turbid and alkaline throughout the year. Overall, it can be concluded that the water needs to be pre-treated for drinking purposes throughout the year.

Source contribution analysis of nutrient pollution in a P-rich watershed: Implications for integrated water quality management

It is still a great challenge to address nutrient pollution issues caused by various point sources and non-point sources on the watershed scale. Source contribution analysis based on watershed modeling can help watershed managers identify major pollution sources, propose effective management plans and make smart decisions. This study demonstrated a technical procedure for addressing watershed-scale water pollution problems in an agriculture-dominated watershed, using the Dengsha River Watershed (DRW) in Dalian, China as an example. The SWAT model was improved by considering the constraints of soil nutrient concentration, i.e., nitrogen (N) and phosphorus (P), when modeling the nutrient uptake by a typical crop, corn. Then the modified SWAT model was used to quantify the contributions of all known pollution sources to the N and P pollution in the DRW. The results showed that crop production and trans-administrative wastewater discharge were the two dominant sources of nutrient pollution. This study further examined the responses of nutrient loss and crop yield to different fertilizer application schemes. The results showed that N fertilizer was the limiting factor for crop yield and that excessive levels of P were stored in the agricultural soils of the DRW. An N fertilizer application rate of approximately 40% of the current rate was suggested to balance water quality and environmental protection with crop production. The long-term impact of legacy P was investigated with a 100-year future simulation that showed the crop growth could maintain for 12 years even after P fertilization ceased. Our study highlights the need to consider source attribution, fertilizer application and legacy P impacts in agriculture-dominated watersheds. The analysis framework used in this study can provide a scientifically sound procedure for formulating adaptive and sustainable nutrient management strategies in other study areas.