Trend analysis and change point determination for hydro-meteorological and groundwater data of Kizilirmak basin

Çıtakoğlu, Hatice; Minarecioğlu, Necmiye

doi:10.1007/s00704-021-03696-9

Cited by 32 publications

(9 citation statements)

References 82 publications

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“…On the other hand, because the GRACE mission considers anthropogenic forces (such as groundwater withdrawal) (Cui et al, 2021), it can be stated that the detected droughts over the KB may be mainly ascribed to anthropogenic parameters. A great part of the KB is located in central Anatolia, where water resources are crippled by the side effects of the climate crisis in an arid region (Citakoglu & Minarecioglu, 2021). Since agriculture dominates the economic activities of the residents, there is a widespread extraction of groundwater, as the only water source in the region (Bozdağ, 2016).…”

Section: Discussionmentioning

confidence: 99%

“…Since agriculture dominates the economic activities of the residents, there is a widespread extraction of groundwater, as the only water source in the region (Bozdağ, 2016). This situation is particularly aggravated during meteorological drought periods, resulting in the detriment of its hydrologic balance and accelerating the decline of water resources (Citakoglu & Minarecioglu, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Since agriculture dominates the economic activities of the residents, there is a widespread extraction of groundwater, as the only water source in the region(Bozda g, 2016). This situation is particularly aggravated during meteorological drought periods, resulting in the detriment of its hydrologic balance and accelerating the decline of water resources(Citakoglu & Minarecioglu, 2021).4.3 | UncertaintiesVariegated sources of uncertainty are engaged in the analysis of this study. To do away with the uncertainties associated with the GRACE F I G U R E 9 Illustration of the areal distribution of drought based on Water Storage Deficit Index (WSDI) from 2003 to 2020.…”

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confidence: 99%

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An appraisal of the local‐scale spatio‐temporal variations of drought based on the integrated GRACE/GRACE‐FO observations and fine‐resolution FLDAS model

Khorrami,

Ali,

Gündüz

2023

Hydrological Processes

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The gravity recovery and climate experiment (GRACE) observations have so far been utilized to detect and trace the variations of hydrological extremes worldwide. However, applying the coarse resolution GRACE estimates for local‐scale analysis remains a big challenge. In this study, a new version of the fine resolution (1 km) Famine early warning systems network Land Data Assimilation System (FLDAS) model data was integrated into a machine learning model along with the GRACE data to evaluate the subbasin‐scale variations of water storage, and drought. With a correlation of and a root mean square error (RMSE) of of its results, the downscaling model turned out to be very successful in modelling the finer resolution variations of TWSA. The water storage deficit (WSD) and Water Storage Deficit Index (WSDI) were used to determine the episodes and severity of drought events. Accordingly, two severe droughts (January 2008 to March 2009 and September 2019 to December 2020) were discerned in the Kizilirmak Basin (KB) located in Central Türkiye. The characterization of droughts was evaluated based on WSDI, scPDSI, and model‐based drought indices of the soil moisture storage percentile (SMSP) and groundwater storage percentile (GWSP). The results indicated discrepancies in the drought classes based on different indices. However, the WSDI turned out to be more correlated with GWSP, suggesting its high ability to monitor groundwater droughts as well.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

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An appraisal of the local‐scale spatio‐temporal variations of drought based on the integrated GRACE/GRACE‐FO observations and fine‐resolution FLDAS model

Khorrami,

Ali,

Gündüz

2023

Hydrological Processes

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“…For a time-series data points Y = {x 1, x 2, x 3, x 4, x 5….. x n } with n > 10. The Mann-Kendall test statistic, S is calculated as (Haktanir and Citakoglu 2014 ; Tefaruk and Hatice 2015 ; Citakoglu and Minarecioglu 2021 ) …”

Section: Methodsmentioning

confidence: 99%

Pre- and post-dam river water temperature alteration prediction using advanced machine learning models

Vishwakarma

Ali

Bhat

et al. 2022

Environ Sci Pollut Res

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Dams significantly impact river hydrology by changing the timing, size, and frequency of low and high flows, resulting in a hydrologic regime that differs significantly from the natural flow regime before the impoundment. For precise planning and judicious use of available water resources for agricultural operations and aquatic habitats, it is critical to assess the dam water’s temperature accurately. The building of dams, particularly several dams in rivers, can significantly impact downstream water. In this study, we predict the daily water temperature of the Yangtze River at Cuntan. Thus, this work reveals the potential of machine learning models, namely, M5 Pruned (M5P), Random Forest (RF), Random Subspace (RSS), and Reduced Error Pruning Tree (REPTree). The best and effective input variables combinations were determined based on the correlation coefficient. The outputs of the various machine learning algorithm models were compared with recorded daily water temperature data using goodness-of-fit criteria and graphical analysis to arrive at a final comparison. Based on a number of criteria, numerical comparison between the models revealed that M5P model performed superior ( R 2 = 0.9920, 0.9708; PCC = 0.9960, 0.9853; MAE = 0.2387, 0.4285; RMSE = 0.3449, 0.4285; RAE = 6.2573, 11.5439; RRSE = 8.0288, 13.8282) in pre-impact and post-impact spam, respectively. These findings suggest that a huge wave of dam construction in the previous century altered the hydrologic regimes of large and minor rivers. This study will be helpful for the ecologists and river experts in planning new reservoirs to maintain the flows and minimize the water temperature concerning spillway operation. Finally, our findings revealed that these algorithms could reliably estimate water temperature using a day lag time input in water level. They are cost-effective techniques for forecasting purposes.

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“…On the other hand, many trend analysis studies are carried out with the MK test and ITA for the whole of Turkey or its different regions using different climatic values, especially precipitation and temperature. (Kadioğlu, 1997;Dogan et al, 2014;Ay, 2019;Ceribasi and Iyad Ceyhunlu, 2020;Citakoglu and Minarecioglu, 2021). However, a few studies focused on the Southeastern Anatolia Region; for example, Partal and Kahya (2006) carried out a trend analysis of monthly and annual precipitations in Turkey.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of meteorological time series trends in Southeastern Anatolia, Turkey

et al. 2023

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In this study, trend analyses of six climatic variables (mean, minimum, and maximum temperature, relative humidity, wind speed, and precipitation) for 1966-2020 are conducted for the Southeastern Anatolia Region, which is the main focus of the integrated development project in Turkey (Turkish acronym GAP). The trends for seasonal and annual periods are determined using the Mann-Kendall (MK) test, and Sen's slope method and regression analyses are used to find the trends' slopes. Moreover, Innovative Trend Analysis (ITA) is also used to find the time series changes for low, medium, and high values. As a result of the analyses, the mean, minimum, and maximum temperatures in the GAP region show increasing trends according to both methods. Significant trends are obtained at a limited number of stations for the precipitation, relative humidity, and wind speed with the MK test, while consistent decreasing trends are found at most stations with the ITA method.

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Trend analysis and change point determination for hydro-meteorological and groundwater data of Kizilirmak basin

Cited by 32 publications

References 82 publications

An appraisal of the local‐scale spatio‐temporal variations of drought based on the integrated GRACE/GRACE‐FO observations and fine‐resolution FLDAS model

An appraisal of the local‐scale spatio‐temporal variations of drought based on the integrated GRACE/GRACE‐FO observations and fine‐resolution FLDAS model

Pre- and post-dam river water temperature alteration prediction using advanced machine learning models

Evaluation of meteorological time series trends in Southeastern Anatolia, Turkey

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