## 7 Scope, Limitation, and Future Recommendations  
This study comprehensively analyzes the Tiga Dam in Kano State, Nigeria, focusing on its geotechnical stability and downstream soil behavior. The research employs advanced finite element software (Plaxis 2D version 2024) to assess the dam's stability under various reservoir conditions, including high reservoir level, rapid drawdown, slow drawdown, and low reservoir level. Additionally, it adapts the Revised Universal Soil Loss Equation (RUSLE) to estimate soil credibility factors in the downstream area, offering an innovative cross-disciplinary approach. This study also examines the temporal variation in downstream soil expansion rates from 2010 to 2020, providing insights into seasonal and long-term trends. Furthermore, it uses linear regression modeling with noise to predict soil expansion rates accurately. Although the soil expansion rate dataset spans from 2010 to 2020 with monthly measurements, providing excellent temporal coverage, it lacks spatial granularity. While rainfall patterns from 2010 to 2020 are analyzed, other climate variables like temperature and humidity, which significantly affect soil behavior, are not directly measured or incorporated into the models. The linear regression model's high performance in predicting soil expansion rates suggests predominantly linear relationships. However, while effective, this simplicity might overlook complex, nonlinear soil-water-temperature interactions that become significant under extreme conditions not captured in the dataset. While the linear regression model with noise performed exceptionally well, it is worth exploring advanced machine learning techniques like Gaussian Process Regression or Deep Neural Networks. These could capture subtle, nonlinear effects that become dominant under extreme conditions, enhancing the model's robustness. We could understand how their properties (cohesion, plasticity, etc.) change over time by conducting long-term laboratory tests on soil samples under varying temperature and moisture conditions. These data can validate and refine the observed trends in soil expansion rates. We could install an array of sensors (piezometers, inclinometers, fiber optic strain gauges) in the dam and downstream area and link these to the predictive models for real-time stability and erosion risk assessment, creating an early warning system. Furthermore, we could organize workshops that bring together experts from geotechnical engineering, soil science, hydrology, and climate science and use the RUSLE application as a case study to foster more cross-disciplinary approaches in dam safety. Using the validated models to simulate extreme scenarios that might arise from climate change, such as prolonged droughts followed by intense monsoons could help in designing more resilient dam operational strategies. Given the dam'scritical role and potential risks, engaging with local communities to understand how soil expansion and erosion affect their livelihoods could guide the prioritization of intervention areas and build public trust in the dam's management. Recommendation for Mitigation Measures  
The identified vulnerabilities in the Tiga Dam, particularly during drawdown scenarios and in areas with higher soil erodibility, necessitate a comprehensive suite of mitigation measures. To address the critical stability issues during drawdown (FOS of 1.006 for rapid drawdown and 1.002 for slow drawdown), the implementation of a carefully controlled drawdown rate is paramount. Based on our analysis, a maximum drawdown rate of 0.1 m/day is suggested to allow for better pore pressure dissipation, thereby reducing the risk of slope failure. Complementing this operational measure, installing additional horizontal drains in the dam's downstream face is recommended to improve stability during drawdown conditions. These drains will help reduce pore water pressures and increase the effective stress in the soil, contributing to an enhanced overall factor of safety.