At gauged watersheds, the time of concentration can be estimated using rainfall-runoff data; however, at ungauged watersheds, empirical methods are used instead. Large errors in the application of empirical methods may cause inaccurate modeling of floods and unreliable structure design. In this paper, methods for calculating the time of concentration (Tc) were compared to identify the best equation for estimating Tc in ungauged watersheds of an arid region. The graphical method, based on measured data, was compared to 15 empirical methods to determine which empirical method returned the best results. The graphical method was applied to 33 rainfall-runoff events in four rural sub-watersheds located in the central parts of Hormozgan province, Iran. A ranking-based procedure was used to select the best performing empirical methods. To minimize bias and improve accuracy, the best performing empirical methods were modified by adjusting their formulas. According to the study, three empirical methods: (1) Williams, (2) Pilgrim and Mac Dermott, and (3) Arizona DOT, performed the best in the study areas. The results also showed that the modified Williams and Arizona DOT’s formulas were able to estimate the time of concentration in ungauged watersheds with an error lower than 1%.

The evaluation of climate change impact on hydrological cycle includes uncertainty. This study aimed to evaluate the uncertainty of climate change impact on the Zayandeh-Rud Reservoir inflow during the future 2020–2049 period. The outputs of 22 GCM models have been used under the three emission scenarios including RCP2.6, RCP4.5, and RCP8.5. The Bayesian model averaging (BMA) was used as the uncertainty analysis for weighting the outputs of 22 GCM models (precipitation and temperature) based on their ability to simulate baseline 1980–2005 period. Then the time series of equivalent precipitation and temperature were introduced to the hydrological model (i.e. IHACRES) and the output of runoff was estimated under different climate change scenarios. Results showed that different GCM models have different abilities in estimating climatic variables and the application of uncertainty analysis in climate change studies is necessary. The results showed that the winter stream flow will rise by 16 to 55% under different climate change scenarios. However, a reduction of (20–50)% is expected during spring months which complicated the regional water resource management during the following warm seasons. On the annual scale, the Zayandeh-Rud Reservoir’s Inflow will decrease (1–25)% under different emission scenarios which make the region more vulnerable to climate change than before. Therefore, adaptation strategies should be identified and some changes in Zayandeh-Rud Reservoir’s rule curve must be applied by water resource managers in the future.

Understanding spatial variation in ocean waves is critical for erosion planning and infrastructure projects. The study is aimed at (1) performing a cluster analysis to categorize the wave statistics over space and (2) determining the important drivers affecting spatial variations of wave statistics in a low energy, fetch limited environment. In this study, 29 wave gauges were deployed in Back Bay Biloxi, Mississippi. Raw pressure and processed wave height were clustered using two algorithms: Euclidian and Dynamic Time Warping. The Euclidean algorithm was applied to raw and processed data. However, due to the computationally expensive nature of Dynamic Time Warping, this algorithm could not be used on raw pressure data and was only applied to processed wave data. Therefore, three combinations of distance algorithms and data were compared: (1) Euclidean algorithm on raw pressure data, (2) Euclidean algorithm on processed wave height data, and (3) Dynamic Time Warping algorithm on processed wave height data. The results showed similar clustering for Euclidean and Dynamic Time Warping on processed data, with most gauges falling into one cluster. Results from this study reveal that the dendrogram trees of the Euclidean and Dynamic Time Warping algorithms on processed data are similar, where most of the wave gauges fall in one cluster. Conversely, the Euclidian algorithm on the raw pressure data distributed the wave gauges more evenly between the clusters. Additionally, the Euclidean algorithm on the raw pressure data showed that water depth significantly affects wave clustering.