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Wahno Subdistrict in Abepura District, Jayapura City, faces drainage problems that disrupt the area’s water management function. The varied topography, population growth, and the community’s habit of disposing waste into drainage channels have reduced the system’s capacity and caused frequent waterlogging during rainfall. This study aims to analyze the existing condition of the drainage channels in Wahno and to evaluate their effectiveness in conveying stormwater flow. The research methods include field surveys, channel dimension measurements, capacity calculations, and analysis of the effective volume compared to actual conditions affected by waste and sedimentation. The data were analyzed using quantitative and descriptive approaches to assess the performance level of the drainage system. The results show that most drainage channels in Wahno are not functioning optimally, with an average effectiveness of only 45–60% due to blockages caused by waste and sedimentation. The main factors reducing drainage effectiveness are decreased storage capacity, substandard design, and lack of regular maintenance. It is concluded that improvements to channel capacity, better waste management, and routine maintenance programs are necessary to enhance the drainage system’s effectiveness.

Currently, the world is facing Global Warning Phenomena, El Nino and La Nina, each of these natural phenomena makes above-average rainfall at a time and place and an area, especially impacting large cities that are passed through by water due to high rainfall. With inadequate city channels to accommodate rainwater flow, it results in, among other things, high floods, landslides due to the soil no longer being able to accommodate high rainfall, high Rob floods due to seawater overflowing into coastal areas of the city. Based on the disaster data due to high rainfall, Research, Handling and Mitigation Planning is needed with the aim of obtaining data for drainage design. The study is the observation of rainfall for 2 years, 5 years, 10 years, and 20 years, so that by calculating the intensity of rain using the Gumbel and Pearson III Log distribution methods, accurate data is obtained to determine the drainage dimensions that must be made in a city and the dimensions of retaining walls along the coast to overcome storm disasters in coastal areas or big cities.

Rainfall potential analysis plays a critical role in the management of air resources, mitigation of hydrometeorological disasters, and agricultural activity planning. Accurate estimation of rainfall patterns is essential to ensure effective decision-making in irrigation systems, water resource management, and disaster risk reduction strategies. This study aims to model the probability of rainfall occurrence using a statistical approach based on historical data obtained from the Bureau of Meteorology. The data spans a multi-year period and captures seasonal and regional variability in rainfall events. To characterize rainfall patterns, various probability distributions are tested, including the exponential distribution and the Weibull distribution, which are commonly applied in hydrological studies. Furthermore, the Markov chain method is employed to assess the likelihood of rainfall occurrence on a given day based on the conditions of the preceding day, thereby capturing temporal dependencies. Parameter estimation is conducted using Maximum Likelihood Estimation (MLE), a robust statistical method that enhances the precision of the model. The suitability of each probability distribution in representing the observed rainfall data is evaluated through goodness-of-fit tests such as the Kolmogorov-Smirnov test. The findings reveal that certain distributions align more closely with the local rainfall characteristics, demonstrating the importance of regional analysis in climate modeling. The combination of probabilistic modeling, Markov analysis, and rigorous statistical testing provides a reliable framework for forecasting rainfall. These results are expected to serve as a scientific basis for stakeholders in agriculture, environmental planning, and disaster preparedness, offering insights that support sustainable water resource utilization and risk management.