Assessing Basin’s Dynamic Hydrological Characteristics Using Statistical Analysis on Rainfall – River Discharge Observation Data - Steven Reinaldo Rusli; Theo Senjaya (Journal of the Civil Engineering Forum, 2025)
Hydrological studies often rely on physical-based modelling approaches to simulate water cycles. However, such an approach requires extensive basin physical data inputs, including features, attributes, and properties that are quantifiable, which often are lacking in data-scarce areas. Therefore, this study explores an alternative viewpoint by using simple statistical analysis to assess the dynamic basin’s hydrological characteristics. We collate and divide the rainfall and discharge observation data in the Upper Citarum River basin into three periods: period 1 (2000–2005), period 2 (2000–2010), and period 3 (2000–2015). After defining baseflow separation, we quantify the basin’s baseflow using simple statistical analysis. The 5-years average of the baseflow fluctuations (33.15 m3/s, 12.88 m3/s, and 27.59 m3/s during each period) is in agreement with previous studies’ physical-based results. The subsequent frequency analysis indicates a trend of increasing rainfall, although it is not followed by the trend in the river discharge variable. Due to the stochastic nature of extreme events occurrence and available data length, we evaluate the dynamic basin’s runoff generation using quasi-synthetic rainfall instead of conventional design storm, to equalize the stimuli (rainfall) in evaluating the target system (basin’s hydrological characteristics). Under identical sets of forcing input, the quasi-synthetic river discharge consistently increases in each period, in both the median (15.39% and 25.34%) and extreme (21.86% and 29.46%) values. The results reveal the basin’s evolving hydrological responses, which is mostly influenced by anthropogenic factors. This simple statistical approach enables the evaluation of basin characteristics’ dynamics in data-limited areas, bypassing extensive data collection and random event occurrences, while still providing consistent results.