Ercan Kahya

U.S. streamflow patterns in relation to the El Niño/Southern Oscillation

The relationship between the El Nino/Southern Oscillation (ENSO) and unimpaired streamflow over the contiguous United States is studied. The extreme phases of the Southern Oscillation have been linked to fairly persistent classes of atmospheric anomalies over the low and middle latitudes at regional and global scales. Of particular interest in this investigation is the identification of regions of land that appear to have strong and consistent ENSO-related streamflow signals. The first harmonic extracted from a 24-month ENSO composite at each station is assumed to be the ENSO-related signal appearing in streamflow anomalies. These regions were identified by the similarity in phase of the harmonic vectors. The vectorial display of these harmonics over a map of the United States provides the areal extents of ENSO influence on streamflow. Coherent and significant streamflow responses to hypothesized ENSO forcing are found in four regions of the United States: the Gulf of Mexico, the Northeast, the North Central, and the Pacific Northwest. Once an ENSO event sets in, a long-range forecasting utility may be available for these regions. The results of this analysis, which are consistent with previous studies on precipitation and temperature, demonstrate the mid-latitude hydrologic response to the tropical ENSO phenomena.

The relationships between U.S. streamflow and La Niña Events

In an earlier study of the teleconnection between streamflow and the warm (El nino) phase of the El Nino/Southern Oscillation (ENSO) cycle, we found a strong relationship evident in four regions of the United States: the Gulf of Mexico, the Northeast, the North Central, and the Pacific Northwest. In this present study we have examined the same four regions for a relationship between streamflow and the cold (La nina) phase of the Southern Oscillation (SO). Invariably, we found evidence of strong and consistent streamflow responses to La nina events within the study regions. In each of the four regions, the strongest La nina signal occurred at the same time of year as had the El nino signal in their respective years. The sign of the seasonal streamflow anomaly associated with the La nina events is the opposite of that associated with the El nino events. This documents the existence of the biennial tendency related to the SO in the streamflow anomaly, which is expected, since La nina/El nino are opposite phases of the ENSO cycle. Finally, the relationships between streamflow and La nino/El nino were found to be statistically significant, based on the hypergeometric distribution. The results of this study demonstrate coherent, consistent, and significant midlatitude streamflow responses to the tropical SO phenomenon. This confirms the results of previous climatological studies that have examined the extratropical teleconnections from a hydrological and meteorological perspective.

Mapping of groundwater potential zones in the Musi basin using remote sensing data and GIS

The objective of this study is to explore the groundwater availability for agriculture in the Musi basin. Remote sensing data and geographic information system were used to locate potential zones for groundwater in the Musi basin. Various maps (i.e., base, hydrogeomorphological, geological, structural, drainage, slope, land use/land cover and groundwater prospect zones) were prepared using the remote sensing data along with the existing maps. The groundwater availability of the basin is qualitatively classified into different classes (i.e., very good, good, moderate, poor and nil) based on its hydrogeomorphological conditions. The land use/land cover map was prepared for the Kharif season using a digital classification technique with the limited ground truth for mapping irrigated areas in the Musi basin. The alluvial plain in filled valley, flood plain and deeply buried pediplain were successfully delineated and shown as the prospective zones of groundwater.

The Influences of Type 1 El Niño and La Niña Events on Streamflows in the Pacific Southwest of the United States

Abstract Streamflows in the Pacific Southwest of the United States in relation to the tropical Type 1 El Nino-Southern Oscillation (T1ENSO) and La Nina events are examined using composite and harmonic analyses for each event during a 24-month evolution period. The hydroclimatic signals associated with either extreme phase of the Southern Oscillation (SO) are explored based on data from 50 streamflow stations in California, Arizona, New Mexico, Colorado, and Utah. A significant level for the results is assessed by the use of a hypergeometric distribution. Highly significant, coherent signals are demonstrated to exist for both events, with opposite sign and almost identical timing. Pacific Southwest streamflow responses to the T1ENSO thermal forcing are characterized by a wet December-July season in the subsequent year of the event. Similarly, a dry February-July season is detected as a period at which the La Nina-streamflow relationship is strong and spatially coherent. An index time series is plotted to d...

Flow forecast by SWAT model and ANN in Pracana basin, Portugal

This study provides a unique opportunity to analyze the issue of flow forecast based on the soil and water assessment tool (SWAT) and artificial neural network (ANN) models. In last two decades, the ANNs have been extensively applied to various water resources system problems. In this study, the ANNs were applied to the daily flow of the Pracana basin in Portugal. The comparison of ANN models and a process-based model SWAT was established based on their prediction accuracy. The ANN model was found to be more successful than the SWAT in relation to better forecast of peak flow. Nevertheless the SWAT model results revealed a better value of mean squared error. The results of this study, in general, showed that ANNs can be powerful tools in daily flow forecasts.

Linear genetic programming application for successive-station monthly streamflow prediction

In recent decades, artificial intelligence (AI) techniques have been pronounced as a branch of computer science to model wide range of hydrological phenomena. A number of researches have been still comparing these techniques in order to find more effective approaches in terms of accuracy and applicability. In this study, we examined the ability of linear genetic programming (LGP) technique to model successive-station monthly streamflow process, as an applied alternative for streamflow prediction. A comparative efficiency study between LGP and three different artificial neural network algorithms, namely feed forward back propagation (FFBP), generalized regression neural networks (GRNN), and radial basis function (RBF), has also been presented in this study. For this aim, firstly, we put forward six different successive-station monthly streamflow prediction scenarios subjected to training by LGP and FFBP using the field data recorded at two gauging stations on Coruh River, Turkey. Based on Nash-Sutcliffe and root mean squared error measures, we then compared the efficiency of these techniques and selected the best prediction scenario. Eventually, GRNN and RBF algorithms were utilized to restructure the selected scenario and to compare with corresponding FFBP and LGP. Our results indicated the promising role of LGP for successive-station monthly streamflow prediction providing more accurate results than those of all the ANN algorithms. We found an explicit LGP-based expression evolved by only the basic arithmetic functions as the best prediction model for the river, which uses the records of the both target and upstream stations. We compared FFBP, GRNN, RBF neural networks and LGP for successive-station monthly streamflow prediction.Both ANNs and LGP models are more reliable in low and medium flow prediction.LGP is more capable of capturing extreme values than ANNs.LGP is superior to ANN in terms of overall accuracy and practical applicability.In contrast with implicit ANNs, LGP provided explicit equation for streamflow prediction.

Critical Drought Analysis: Case Study of Göksu River (Turkey) and North Atlantic Oscillation Influences

Droughts are complex events which may impair social, economic, agricultural, and other activities of a society. The present hydroclimatological study comprises three stages. First, a Markov chain model based on annual flows at a gauging station located on the Goksu River (Turkey) is utilized. Second, a critical drought analysis is conducted. Third, the influences of the North Atlantic Oscillation (NAO) on the probability distribution functions (PDFs) of critical droughts have been documented. Drought duration is used as a key parameter in the Markov chain model. As the model degree should be defined prior to applying a Markov chain model to a series of observations, a model degree of first order has been selected according to the results of the Akaike information criteria and Bayesian information criteria. The exact PDFs of the critical drought duration in a finite sample that follows the first-order Markov chain have been determined by the enumeration technique. The critical drought duration is the possi...

Successive-station monthly streamflow prediction using neuro-wavelet technique

This study investigates the effect of discrete wavelet transform data pre-processing method on neural network-based successive-station monthly streamflow prediction models. For this aim, using data from two successive gauging stations on Çoruh River, Turkey, we initially developed eight different single-step-ahead neural monthly streamflow prediction models. Typical three-layer feed-forward (FFNN) topology, trained with Levenberg-Marquardt (LM) algorithm, has been employed to develop the best structure of each model. Then, the input time series of each model were decomposed into subseries at different resolution modes using Daubechies (db4) wavelet function. At the next step, eight hybrid neuro-wavelet (NW) models were generated using the subseries of each model. Ultimately, root mean square error and Nash-Sutcliffe efficiency measures have been used to compare the performance of both FFNN and NW models. The results indicated that the successive-station prediction strategy using a pair of upstream-downstream records tends to decrease the lagged prediction effect of single-station runoff-runoff models. Higher performances of NW models compared to those of FFNN in all combinations demonstrated that the db4 wavelet transform function is a powerful tool to capture the non-stationary feature of the successive-station streamflow process. The comparative performance analysis among different combinations showed that the highest improvement for FFNN occurs when simultaneous lag-time is considered for both stations.

Successive-station monthly streamflow prediction using different artificial neural network algorithms

In this study, applicability of successive-station prediction models, as a practical alternative to streamflow prediction in poor rain gauge catchments, has been investigated using monthly streamflow records of two successive stations on Çoruh River, Turkey. For this goal, at the first stage, based on eight different successive-station prediction scenarios, feed-forward back-propagation (FFBP) neural network algorithm has been applied as a brute search tool to find out the best scenario for the river. Then, two other artificial neural network (ANN) techniques, namely generalized regression neural network (GRNN) and radial basis function (RBF) algorithms, were used to generate two new ANN models for the selected scenario. Ultimately, a comparative performance study between the different algorithms has been performed using Nash–Sutcliffe efficiency, squared correlation coefficient, and root-mean-square error measures. The results indicated a promising role of successive-station methodology in monthly streamflow prediction. Performance analysis showed that only 1-month-lagged record of both stations was satisfactory to achieve accurate models with high-efficiency value. It is also found that the RBF network resulted in higher performance than FFBP and GRNN in our study domain.

The Impacts of NAO on the Hydrology of the Eastern Mediterranean

The impacts of the NAO on the hydrology of eastern Mediterranean countries, such as Turkey, Iran, Kuwait, Oman, and Israel, are documented here from a general perspective. Results for the eastern Mediterranean countries differ from one location to another in terms of consistent NAO signal. Patterns of precipitation, streamflow, and lake levels in Turkey are discussed to show the NAO impacts. A special attention is devoted to the NAO influences on the formation of streamflow homogeneous region and on the probability distribution functions of critical droughts. The results of all these analyses clearly showed that the NAO signals are quite identifiable in various hydrologic variables in Turkey. For example, the NAO during winter was found to influence precipitation and streamflow patterns. In contrast temperature patterns appeared to be less sensitive to the NAO. The results of wavelet analysis showed that the Tuz, Sapanca, and Uluabat lakes reflect strong NAO influences. In southwest Iran, the October–December season is influenced mostly by the NAO in both dry and wet spells. Positive significant correlation values were found between the NAO and the total rainfall in the centre and southern Israel and with some of the rainfall categories. High correlations between the winter mode of the NAO and temperature and sea level pressure in Israel were also noted. In conclusion, current evidences have shown that the NAO has detectable influences on the hydrology of eastern Mediterranean countries with different magnitudes.