Comparison Performance of the Multi-Regional Climate Model (RCM) in Simulating Rainfall and Air Temperature in Batanghari Watershed

Many scientists assume that RCM output is directly used as input for climate change impact models, while it consists of systematic errors. Consequently, RCM still requires bias correction to be used as an input model. The purpose of this study was to analyze the RCM performance before and after bias correction, its best performance from several models, as well as to clarify the importance of bias correction before it is used to analyze climate change. As a result of this, the method used for bias correction was Distribution Mapping method (for rainfall) and Average Ratio-method (for air temperature). While the Generalized Extrem Valuedistribution (GEV) was used to analysis extreme rainfall. To determine the performance of the model before and after bias correction, statistical analysis was used namelyR2, NSE, and RMSE. Furthermore, ranking for every single model and Taylor Diagram was used to determine the best model. The results showed that the RCMs performance improved with bias correction. However, CSIRO-Mk3-6-0, CCSM4, GFDL-ESM2M, and MPI-ESM-MR models can be ignored as ensemble models, because they demonstrated poor performance in simulating rainfall. From this study, it was suggested that the best model in simulating daily and monthly rainfall was ACCESS1-0, while MIROC-ESM-CHEM (daily air temperature) and ACCESS1-0 (monthly air temperature) were best models used in simulating air temperature. Key words: RCM, bias correction, performance, rainfall, air temperatur

The impact of land-use and climate change on water and sediment yields in Batanghari Watershed, Sumatra, Indonesia

The Batanghari River flows from the province of West Sumatra into the West Coast of Jambi, with the main river extending up to 870 km. Also, the Batanghari watershed Land use changes have shown a decreasing forest cover and an increasing agricultural area. Therefore, this study aims to calculate the impact of land use and climate change on water and sediment yield using Soil and Water Assessment Tools (SWAT) hydrological modeling. Land-use change analysis was performed with projections in 2040 while, near future and future climate projections under Representative Concentration Pathway (R.C.P.) 4.5 and 8.5 were used for global climate change scenarios. The results show a changing pattern of growing agricultural area and decreasing forest area in 1990, 1997, 2005, 2015, and 2040. SWAT hydrological model used for the simulation was calibrated automatically with SWAT-CUP and the results were validated on good criteria. The sensitivity analysis results showed that effective hydraulic conductivity in main channel alluvium (CH_K2) and Base flow alfa factor (Alfa_BF) formed the most sensitive parameters for discharge. Furthermore, the model simulation showed an increase in surface runoff and a decrease in lateral flow and base flow due to land-use changes, which increased sediment loading over time. The impact of climate change on water and sediment yield increased the average flow discharge ratio, resulting in more frequent droughts and floods events

Reconstructing Disrupted Water Level Records in A Tide Dominated Region Using Data Mining Technique

Abstract. A continuous time-series of certain hydrographical data, such as water levels, is required for various purposes such as time series analysis to study system behaviour and to perform predictions. However, due to some technical failure or natural obstacles, disruptions of measurements may occur. Data gap filling technique is then required to obtain a reliable reconstructed continuous time-series. Linear regression is an example of the simplest technique in data gap filling for parameters that can be linearized. Most of hydrographical data, however, are highly non-linear. Therefore a more advanced techniques are required to complete the missing data. This paper discusses the application of data mining technique in obtaining a continuous water level data using the M5 model tree. The main idea of the M5 model tree machine-learning technique is that the algorithm splits the parameter space into subspaces and then builds a linear regression model for each subspaces. Therefore, the resulting model can be regarded as a modular model. This technique was applied to reconstruct a disrupted water level record of the Mahakam Delta, East Kalimantan, Indonesia. A datasets obtained during a measurement campaign in 2008-2009 were split into the training and validation sets. The model was trained using the three-hourly water level data from the Delta Apex and Tenggarong measurement stations. Water level records show the semi-diurnal character of tides in the region, and that the tides are still dominant in the upstream area at the Tenggarong station located about 40 km from the Delta Apex. Four previous time-step data from the Tenggarong station were included as input to the model to cover the time lag of tide propagation between the two stations. Nash"“Sutcliffe coefficient of Efficiency were used to evaluate the model. Nine model rules (using smoothed linear models) were obtained from the training of the M5 model tree, which are executed sequentially until suitable conditions are matched. Validation shows that M5 model tree can satisfactorily be applied as an alternative tool for water level data gap filling in the tide dominated region. Keyword: data mining, hydrographical data, water levels, time-serie