The Modified DISPRIN Model for Transforming Daily Rainfall-Runoff Data Series on a Small Watershed in Archipelagic Region

Abstract

<jats:p>The existence of the translation effect component on the application of the original Dee Investigation Simulation Program for Regulating Network (DISPRIN) model would be counter-productive when applied to rainfall-runoff analysis on small watersheds that have the level of sharp fluctuations that commonly occur in tropical islands. Modifying the original DISPRIN model by ignoring the components proved to mask existing weaknesses. This article tries to compare the performance of the original DISPRIN model and the modified DISPRIN model in the case of the transformation of rainfall data series into discharge data series on a daily period. The calibration process of the parameters of both models uses the evolution differential algorithm (DE). The case study is Lesti watershed at the control point of AWLR Tawangrejeni station (319.14 km2) located in East Java, Indonesia. The test model uses 10-year daily data sets, from January 1, 2007, to December 31, 2016. Data series from 2007 to 2013 as a training data set used for the process of model calibration and model validation, data series from 2014 to 2016 as a test data set for model verification. The results show that the modified DISPRIN model is more effective than the original DISPRIN model in terms of accuracy and iteration time in achieving convergent conditions. The original DISPRIN model was able to respond to fluctuations in a seasonal flow, but was unable to respond to the sharp fluctuations in daily flows. The modified DISPRIN model can fix that vulnerability and can generate an NSE &gt; 0.8 value in the validation and verification phase.</jats:p>

The Effect of Training Data Series Length on the Performance of the Tank Model for Transforming Rainfall into Runoff Data Series

“…In the heuristic method, the objective function is expressed as a fitness function. The definition of the fitness function in the case of optimization of hydrological model parameters has been proposed by many previous researchers, including the minimization of root mean square error or RMSE (Hsu, 2015;Wang et al, 2012;Sulianto et al, 2018;Sulianto et al, 2020), minimization of sum square error (SSE) (Darikandeh et al, 2014;Paik et al, 2005), maximizing Nash-Sutcliffe model efficiency or NSE (Xuesong Zhang et al, 2008;Bao et al, 2010;Tolson and Shoemaker, 2007), minimization of mean square error or MSE (Ngoc et al, 2013), minimization of relative error or RE (Santos et al, 2011;Kuok et al, 2011)…”

“…In the field of hydrological modeling, the DE algorithm has been successfully applied to the optimization of SWAT model parameters (Xuesong Zhang et al, 2008), and the optimization of HBV and GR4J model parameters (Piotrowski et al, 2017). It was also successfully applied in the case of multi-objective optimization of in-situ bioremediation of groundwater (Kumar et al, 2015), optimization of DISPRIN model parameters (Sulianto et al, 2018), and optimization of the Modified DISPRIN model (Sulianto et al, 2020). The analysis in the DE Algorithm contains 4 (four) components, namely, 1) initialization, 2) mutation, 3) crossover, and 4) selection.…”