Gauging stations of meteorological networks generally record rainfall on a daily basis. However, sub-daily rainfall observations are required for modelling flood control structures, or urban drainage systems. In this respect, determination of temporal distribution of daily rainfall, and estimation of standard duration of rainfall are significant in hydrological studies. Although sub-daily rainfall gauges are present at meteorological networks, especially in the developing countries, their number is very low compared to the gauges that record daily rainfall.
This study aims at developing a method for estimating temporal distribution of maximum daily rainfall, and hence for generating maximum rainfall envelope curves. For this purpose, the standard duration of rainfall was examined. Among various regression methods, it was determined that the temporal distribution of 24-hour rainfall successfully fits the logarithmic model. The logarithmic model’s regression coefficients (named a and b) were then linked to the geographic and meteorological characteristics of the gauging stations. The developed model was applied to 47 stations located at two distinct geographical regions: the Marmara Sea Region and Eastern Black Sea Region, Turkey. Various statistical criteria were used to test the method's accuracy, and the proposed model provided successful results. For instance, the RMSE values of the regression coefficients a and b in Marmara Regions are 0.004 and 0.027. On the other hand, RMSE values are 0.007 and 0.02 for Eastern Black Sea Region.

Beyşehir Lake is the largest freshwater lake in the Mediterranean region of Turkey that is used for drinking and irrigation purposes. The aim of this paper is to examine the potential for data-driven methods to predict long-term lake levels. The surface water level variability was forecast using conventional machine learning models, including autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), and seasonal autoregressive integrated moving average (SARIMA). Based on the monthly water levels of Beyşehir Lake from 1992 to 2016, future water levels were predicted up to 24 months in advance. Water level predictions were obtained using conventional time series stochastic models, including autoregressive moving average, autoregressive integrated moving average, and seasonal autoregressive integrated moving average. Using historical records from the same period, prediction models for precipitation and evaporation were also developed. In order to assess the model’s accuracy, statistical performance metrics were applied. The results indicated that the seasonal autoregressive integrated moving average model outperformed all other models for lake level, precipitation, and evaporation prediction. The obtained results suggested the importance of incorporating the seasonality component for climate predictions in the region. The findings of this study demonstrated that simple stochastic models are effective in predicting the temporal evolution of hydrometeorological variables and fluctuations in lake water levels.