The estimation of the flow coefficient is a vital hydrological procedure that holds considerable importance in flood prediction, water resource management, and flood mitigation. The precise estimation of the flow coefficient is imperative in mitigating flood-related damages, administering flood alert mechanisms, and regulating water discharge. It is hard to accurately determine the flow coefficient without a good understanding of the river basin's hydrology, climate, topography, and soil characteristics. A range of methodologies have been documented in the most recent body of literature for flow coefficient modeling. The majority of these methods, however, depend on opaque techniques that lack generalizability. Therefore, this research employed three distinct methodologies-specifically, the Adaptive Neural Fuzzy Inference System (ANFIS), the Simple Membership Function, and the Fuzzy Rules Generation Technique (SMRGT) are all examples of fuzzy inference systems, and Artificial Neural Network (ANN), to achieve its objectives. The Aksu River Basin in Antalya, Turkey, was chosen as the study area. The models underwent multiple permutations of precipitation (P), temperature (T), relative humidity (Rh), wind speed (Ws), land use (LU), and soil properties (Sp) data that were tailored to the particular study region. The study analyzed the results using various performance metrics of the model such as mean absolute error (MAE), Nash-Sutcliffe efficiency coefficient (NSE), root mean square error (RMSE), and correlation coefficient (R2). The results indicate that the SMRGT method resulted in a remarkable degree of accuracy in forecasting the flow coefficient, as demonstrated with the minimal RMSE and MAE values and high correlation coefficient values. The study's findings suggest that the SMRGT method was applied effectively in hydrological analysis to estimate the flow coefficient, contributing to more accurate flood prediction, water resource management, and flood mitigation strategies.

In recent decades, the Himalayas have seen increasing extreme precipitation events. Climate change has impacted the occurrence and distribution of extreme precipitation events across the Himalayas. Patterns of both western disturbances and the Indian summer monsoon are undergoing significant changes in nature due to climate change. However, the magnitude and intensity of flood in a stream are not always linearly dependent on the amount of precipitation. Other factors, such as topography, land use, soil characteristics, and antecedent hydrological conditions, play a pivotal role in modulating the response of a watershed to an extreme precipitation event. On July 07-11, 2023, several districts of Himachal Pradesh faced devastating floods resulting in loss of life, infrastructure, and environmental damage with significant economic consequences. Developing a resilient solution for managing such events and reducing damage requires an in-depth understanding of multiple causative factors of such extreme events. In this paper, we analyzed the meteorological and hydrological factors that caused the flooding situation in Himachal Pradesh during July 2023. Hydrometeorological data from several observation stations were analyzed along with reanalysis data from ERA5, SMAP-L4, and FLDAS-NOAH to understand the causative factors that lead to peak floods. The compounding of extremely heavy rainfall with near-saturation antecedent moisture content and snowmelt was found to be the leading factor in inflating and sustaining the flood peak.