The rainfall-runoff model is essential to derive a relationship between Rainfall and Runoff, in which the hydrological response of the catchment can be derived. A real case study is chosen to simulate the rainfall-runoff modeling, in which small urban catchments are selected and are located in the center part of Vizianagaram Town. The present case study aims to develop an event-based rainfall-runoff model for upstream and downstream catchments of peddacheruvu catchment (PC). In this study, Cartosat-10mDEM, Hourly rainfall data are taken from (weather station), i.e., 1st July to 30th September, the Maximum and Minimum Infiltration rates, Evaporation data, soil data, Groundwater parameters, and dry weather flow patterns are used as in input for model simulation to know the wet weather flow and dry weather flow quantity contributing from the catchment. The model simulation is carried out by using the stormwater management model i.e., PC-SWMM version 5.7.1868. The model simulation is performed at two outlet points in the catchment. The upstream and downstream catchments are selected for computing the total runoff hydrographs. The model calibration is done for nine selected streamflow events from 1st July to 31st August 2019, and the remaining three streamflow events are chosen from 1st to 30th September 2019 are set for model validation. The model performance was checked by computing nine goodness of fit measures. The results of this study suggest that simulated runoff values have satisfactory results with the observed streamflow. In recent years, understanding the hydrological modeling and process has become more important in water resource management, especially in analyzing extreme hydrological events like floods or droughts. The availability of metrological and hydrological data is often scarce in a semi-urban catchment. Some of the significant issues are associated with obtaining reliable long-term hydrological data in the semi-urban region. This study investigates the performance of event-based modeling for data-scarce semi-urban catchments using PC-SWMM in computing the total runoff hydrograph. A real case study, i.e., Peddacheruvu (PC) Upstream and Downstream catchment, were selected, and model performance was examined using 12 streamflow events from 1 July 2019 to 31 September 2019. The model performances are evaluated using five goodness of fit measures like root mean square error (RMSE), Nash Sutcliffe efficiency (NSE), coefficient of determination (R2), RSR, and Kling Gupta efficiency (KGE). The model performance is acceptable throughout model calibration (1 July to 31 August 2019) as the NSE and R2 varies between 0.75 to 0.77 and 0.76 to 0.78, respectively. Similarly, the model validation performances (1 September to 31 September 2019) revealed best fitted with the observed hydrograph for NSE and R2 were 0.62 to 0.64 and 0.62 to 0.85. KGE for model calibration and validation model varies between 0.65 to 0.75 and 0.62 to 0.75.

Due to the advancement in satellite and remote sensing technologies, a number of satellite precipitation products (SPPs) are easily accessible online at free of cost. These precipitation products have a huge potential for hydro-meteorological applications in data-scare catchments. However, the use of such products is still limited owing to their lack of accuracy in capturing the ground truth. To improve the accuracy of these products, we have developed a quantile based Bayesian model averaging (QBMA) approach to merge the satellite precipitation products. QBMA is a probabilistic approach to assign optimal weights to the SPPs depending on their relative performances. The QBMA approach is compared with simple model averaging and one outlier removed. TRMM, PERSIANN-CDR, CMORPH products were experimented for QBMA merging during the monsoon season over India’s coastal Vamsadhara river basin. QBMA optimal weights were trained using 2001 to 2013 daily monsoon rainfall data and validated for 2014 to 2018. Results indicated that QBMA approach with bias corrected precipitation inputs outperformed the other merging methods. On monthly evaluation, it is observed that all the products perform better during July and September than that in June and August. The QBMA approaches do not have any significant improvement over the SMA approach in terms of POD. However, the bias-corrected QBMA products have lower FAR. The developed QBMA approach with bias-corrected inputs outperforms the IMERG product in terms of RMSE.

Due to urbanization, there is an increase in the impervious surface and in peak runoff volumes. In the past, there is significant damage caused by urban floods. Urban floods are becoming more frequent disasters in recent decades due to rapid changes in Land Use and Land Cover. In this study, we compare the impact of land-use changes on peak runoff and timing for the last three decades using the PCSWMM model for the semi-urban clusters in South India. Further, we analyze the effect of future development on peak runoff. It was observed that there is a considerable increase in flood peak (82.49%) when compared to the last decade. We proposed and evaluated alternate methods under future land use (2040) using LID wherein different scenarios were generated and tested. To evaluate the changes in hydrological characteristics, three scenarios at a regional scale were explored. The first scenario is the present situation. The second one is the traditional urban development scenario and the third one is urban development with LID controls. The model simulation results confirmed the effectiveness in reducing the surface runoff depth, peak flow rate, total flood volume, and earlier peak times using LIDs.

Soil erosion is the most common type of land degradation, and it has become a global environmental issue that reduces soil productivity and water quality. LULC change related with climatic and geomorphologic states of the area affects land degradation. This study aims at estimating the annual average soil erosion for the study area, Nagavali River Basin, and also focuses to analyze the impact of land use land cover change on the annual average soil erosion rates of the Nagavali river basin. In this study, the soil properties, elevation, and topography of the study are considered to be constant. This study was done by using the Revised Universal Soil Loss Equation (RUSLE) model. This equation includes factors like rainfall, soil, land cover, cultivation practices, and slope, for the estimation of soil erosion. Each layer of the factors affecting soil erosion was prepared and integrated using GIS techniques. By using the RUSLE model, the present study identified that the soil erosion rates for the years 1990 and 2020 ranged from 0 - 2364.46 t/ha/yr and 0 - 7857.21 t/ha/yr respectively for the Nagavali river basin. The LULC change analysis for the years 1990 and 2020 revealed that the erosion rate increased from 2364.46 t/ha/yr to 7857.21 t/ha/yr in the Nagavali river basin. The results also depicted that the area under very severe erosion class increased drastically from 1990 to 2020 while the area under very slight erosion class decreased from 1990 to 2020. This shows that LULC change has a significant impact on increase of soil erosion rates in Nagavali River Basin.

Estimation of design flood is a crucial task in water resources engineering. Regional Flood Frequency Analysis is one of the widely used approaches for estimating design flood in ungauged basin. In the present research, we develop an eXtreme Gradient Boost based ML model for RFFA. The proposed approach relies on developing a regression model between flood quantiles and the commonly available catchment descriptors. In this study, the CAMELs data for 671 catchments from USA was used to study the efficiency of the approach. Further, the results were compared with the traditional methods such Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN). XGB is a decision-tree-based ensemble machine learning algorithm that uses gradient boosting as a framework. The results revealed that the XGB based approach resulted in estimates with highest accuracy when using all the available catchment descriptors (i.e., mean annual rainfall(MAR), drainage area, fraction forest, mean annual potential evapotranspiration (MAPET), mean annual temperature, rainfall intensity, slope, fraction snow, soil porosity, and soil conductivity) both during training and validation. Four distinct models consisting of three to ten descriptors were examined for 2-, 5-, 10-, 25-, 50-, and 100-year return periods, all of the models exhibit smaller mean absolute error values and root mean square error values with percentage bias ranging from -10 to +10. A model with three predictor variables has comparable performance to other models. Drainage area, rainfall intensity, MAR, and fraction snow are the most efficient predictor variables, while MAPET, Slope, Temperature, Fraction Forest, Soil Porosity, and Soil Conductivity have low significance in predicting design flood for an ungauged catchment. The XGB modeling approach that has been proposed can be applied to different places throughout the world.

Wastewater is a valuable resource that requires effective treatment management solutions on a global scale. Wastewater has become a major source of irrigation in peri-urban areas in developing countries. Natural treatment systems (NTS) have recently gained appeal in peri-urban environments due to their lower cost and maintenance requirements. The operating efficiency of natural systems was tested in this study using case studies from Vizianagaram, Andhra Pradesh, India. The efficacy of nutrient (Phosphorous and Nitrogen content) and organic matter removal was investigated in four NTS with varying operation maintenance and loading rates. According to the study, Natural treatment systems have a lot of potential for treating peri-urban wastewater. Waste-stabilization systems outperform systems based on duckweed and hyacinth plants, according to the findings. Regularly maintained and operated systems have an organic and nutrient removal efficiency of over 80%, outperforming others. According to the findings, decentralized, well-maintained Waste Stabilization Ponds (WSP) provide a feasible, self-sustaining, and environmentally beneficial wastewater treatment option for rural irrigation water.

With the availability of satellite-based precipitation products, it is pertinent to develop methods to use these data products for the purpose of design of hydraulic structures. The satellite precipitation products play a vital role in ungauged locations or when information is required a catchment scale. Prior to such applications, the accuracy and uncertainty associated with the products have to be investigated. This study compares Intensity Duration Frequency (IDF) curves using the recent precipitation product Global Precipitation Measurement (GPM-IMERG V6) with ground-based gauge data over the southeastern part of India and quantifies the uncertainty associated. Further, for comparison, a bias-corrected dataset is used in the study to understand the implication of bias correction of the satellite product in the IDF generation. The spatial correlation between the satellite IDF and the gauge-based IDF improves significantly after bias correction and the value is as high as 0.75 for 2- 10 year return period. The bias between the satellite IDF and gauge IDF is low in the north part of the study region and is high in the southeastern part, prone to extreme rainfall. Further, a significant percentage of the satellite-based IDFs (with and without bias correction) lie inside the confidence interval of the gauge-based data.

In recent decades, the application of Digital Elevation Models (DEMs) has been widely used in various aspects such as land management and flood planning since it reflects the actual topographic characteristic on the Earth’s surface. However, obtaining a high-quality DEM is often quite challenging because it is time-consuming, costly, and often confidential. This study presents an innovative approach to derive an improved vertical accuracy of CARTOSAT 10m DEM by blending it with publicly available SRTM (Shuttle Radar Topography Mission) DEM using machine learning methods such as Genetic Programming (GP) and Artificial Neural Networks (ANN). SRTM-1 DEM and CARTOSAT DEM in India are applied to GP and ANN to generate improved vertical accuracy high-quality DEM. The results revealed that the proposed approach improves the vertical accuracy by considering the reference as Ground control Points (GCPs) elevation from Differential Global Positioning System (DGPS) survey data. A significant improvement of 47 and 35% generated DEMs in RMSE compared to the SRTM-1 and CARTOSAT, respectively.