Non-stationary generalized additive modelbased modelling of extreme precipitation indices in East Nusa Tenggara, Indonesia

East Nusa Tenggara (denoted as NTT) is being listed on the first priority of province in Indonesia which is significantly affected by drought. The effective monitoring and measurement of extreme precipitation events are crucial for evaluating future changes and impacts of extreme precipitation. Non-stationarity is often found in hydrological time series data, The Generalized Additive Model for Location, Scale and Shape (GAMLSS) method provides a flexible modelling framework which is more suitable for modelling extreme climate change. This study uses 2 indices of extreme precipitation, the RX5DAY (Maximum 5 Day Precipitation Total) and the CDD (Maximum Number of Consecutive Dry Days), this study also analyzes the effect of SOI and NINO3.4 SST as large scale climate indices. Simulation study towards GAMLSS models is carried out and the best model is chosen to model the extreme precipitation indices. The consistency of the GAMLSS method remains high in both small and large samples based on the simulation study. It also can be concluded that at most stations in NTT, the extreme precipitation events can be better explained by non-stationary models using climate indices as explanatory variables compared to stationary and non-stationary models with time as explanatory variables.