This study attempts to find out the best-fit probability distribution function to low flows using the up-to-date data of intermittent and non-intermittent rivers in four hydrological basins from different regions in Turkey. Frequency analysis of D = 1-, 7-, 14-, 30-, 90- and 273-day low flows calculated from the daily flow time series of each stream gauge was performed. Weibull (W2), Gamma (G2), Generalized Extreme Value (GEV) and Log-Normal (LN2) are selected among the 2-parameter probability distribution functions together with the Weibull (W3), Gamma (G3) and Log-Normal (LN3) from the 3-parameter probability distribution function family. Selected probability distribution functions are checked for their suitability to fit each D-day low flow sequence. LN3 mostly conforms to low flows by being the best-fit among the selected probability distribution functions in three out of four hydrological basins while W3 fits low flows in one basin. With the use of the best-fit probability distribution function, the low flow-duration-frequency curves are determined, which have the ability to provide the end-users with any D-day low flow discharge of any given return period.
Soil moisture predictability on seasonal to decadal (S2D) continuum timescales over North America is examined from the Community Earth System Modeling (CESM) experiments. The effects of ocean and land initializations are disentangled using two large ensemble datasets—initialized and uninitialized experiments from the CESM. We find that soil moisture has significant predictability on S2D timescales despite limited predictability in precipitation. On sub-seasonal to seasonal timescales, precipitation variability is an order of magnitude greater than soil moisture, suggesting land surface processes, including soil moisture memory, reemergence, land–atmosphere interactions, transform a less predictable precipitation signal into a more predictable soil moisture signal.
The difficulty of monitoring hydrometeorological trends remains relevant because of the importance of climate change. In this study, innovative polygon trend analysis (IPTA), new graphical methods, have recently been presented, Mann-Kendall and innovative trend analysis (ITA) with significance tests are employed at monthly and annual hydrometeorological data for Ankara province in Turkey. Finally, sequential Mann-Kendall (SQ-MK), CUSUM, and standard normal homogeneity test (SNHT) are applied to detect any abrupt changes in annual time series. The results indicate that ITA with significance test, IPTA, and MK capture precipitation trends in 83, 94, and 0.2% of all months (3 stations × 12 months), temperature trends in 86, 75, and 22% of all months, relative humidity trends in 80, 80, and 30% of all months, and evapotranspiration series in 91, 80, and 47% of all months. These findings suggest that the ITA with significance test and IPTA are more sensitive than the MK test, with the precipitation series being the most sensitive and the evapotranspiration series being the least sensitive. SQ-MK, CUSUM, and SNHT tests on station 17664 were successful in detecting a change in annual evapotranspiration in 2005. The annual total evaporation series have been rising since 2005, according to the SQ-MK test.
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