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EPANET hydraulic solver is enhanced with Rigid Water Column Global Gradient Algorithm i.e. unsteady incompressible flow analysis algorithm of WDNs. Valve representation which produces resistance according to valve opening is defined on EPANET. In order to provide convergence globally Convergence Tracking Control Method is added to EPANET. The developments and additions are performed on three WDNs varying complexity.
Lew Rossman; Sam Hatchett; Xuxi; Brad Eck; Mario Castro Gama; Tom Taxon; Mehmet Melih Koşucu; Albay Enes; Mehmet Cüneyd Demirel. MehmetMelihKosucu/epanet-dev: EPANET-RWCGGA. 2021, 1 .
AMA StyleLew Rossman, Sam Hatchett, Xuxi, Brad Eck, Mario Castro Gama, Tom Taxon, Mehmet Melih Koşucu, Albay Enes, Mehmet Cüneyd Demirel. MehmetMelihKosucu/epanet-dev: EPANET-RWCGGA. . 2021; ():1.
Chicago/Turabian StyleLew Rossman; Sam Hatchett; Xuxi; Brad Eck; Mario Castro Gama; Tom Taxon; Mehmet Melih Koşucu; Albay Enes; Mehmet Cüneyd Demirel. 2021. "MehmetMelihKosucu/epanet-dev: EPANET-RWCGGA." , no. : 1.
EPANET hydraulic solver is enhanced with Rigid Water Column Global Gradient Algorithm i.e. unsteady incompressible flow analysis algorithm of WDNs. Valve representation which produces resistance according to valve opening is defined on EPANET. In order to provide convergence globally Convergence Tracking Control Method is added to EPANET. The developments and additions are performed on three WDNs varying complexity.
Lew Rossman; Sam Hatchett; Xuxi; Brad Eck; Mario Castro Gama; Tom Taxon; Mehmet Melih Koşucu; Albay Enes; Mehmet Cüneyd Demirel. MehmetMelihKosucu/epanet-dev: EPANET-RWCGGA. 2021, 1 .
AMA StyleLew Rossman, Sam Hatchett, Xuxi, Brad Eck, Mario Castro Gama, Tom Taxon, Mehmet Melih Koşucu, Albay Enes, Mehmet Cüneyd Demirel. MehmetMelihKosucu/epanet-dev: EPANET-RWCGGA. . 2021; ():1.
Chicago/Turabian StyleLew Rossman; Sam Hatchett; Xuxi; Brad Eck; Mario Castro Gama; Tom Taxon; Mehmet Melih Koşucu; Albay Enes; Mehmet Cüneyd Demirel. 2021. "MehmetMelihKosucu/epanet-dev: EPANET-RWCGGA." , no. : 1.
Effective management of water resources entails the understanding of spatiotemporal changes in hydrologic fluxes with variation in land use, especially with a growing trend of urbanization, agricultural lands and non-stationarity of climate. This study explores the use of satellite-based Land Use Land Cover (LULC) data while simultaneously correcting potential evapotranspiration (PET) input with Leaf Area Index (LAI) to increase the performance of a physically distributed hydrologic model. The mesoscale hydrologic model (mHM) was selected for this purpose due to its unique features. Since LAI input informs the model about vegetation dynamics, we incorporated the LAI based PET correction option together with multi-year LULC data. The Globcover land cover data was selected for the single land cover cases, and hybrid of CORINE (coordination of information on the environment) and MODIS (Moderate Resolution Imaging Spectroradiometer) land cover datasets were chosen for the cases with multiple land cover datasets. These two datasets complement each other since MODIS has no separate forest class but more frequent (yearly) observations than CORINE. Calibration period spans from 1990 to 2006 and corresponding NSE (Nash-Sutcliffe Efficiency) values varies between 0.23 and 0.42, while the validation period spans from 2007 to 2010 and corresponding NSE values are between 0.13 and 0.39. The results revealed that the best performance is obtained when multiple land cover datasets are provided to the model and LAI data is used to correct PET, instead of default aspect-based PET correction in mHM. This study suggests that to minimize errors due to parameter uncertainties in physically distributed hydrologic models, adequate information can be supplied to the model with care taken to avoid over-parameterizing the model.
Ibrahim Busari; Mehmet Demirel; Alice Newton. Effect of Using Multi-Year Land Use Land Cover and Monthly LAI Inputs on the Calibration of a Distributed Hydrologic Model. Water 2021, 13, 1538 .
AMA StyleIbrahim Busari, Mehmet Demirel, Alice Newton. Effect of Using Multi-Year Land Use Land Cover and Monthly LAI Inputs on the Calibration of a Distributed Hydrologic Model. Water. 2021; 13 (11):1538.
Chicago/Turabian StyleIbrahim Busari; Mehmet Demirel; Alice Newton. 2021. "Effect of Using Multi-Year Land Use Land Cover and Monthly LAI Inputs on the Calibration of a Distributed Hydrologic Model." Water 13, no. 11: 1538.
This study explores the use of satellite-based LULC (Land Use / Land Cover) data while simultaneously correcting potential evapotranspiration (PET) input with Leaf Area Index (LAI) to increase the performance of a physically distributed hydrologic model. The mesoscale hydrologic model (mHM) was selected for this purpose due to its unique features. Since LAI input informs the model about vegetation dynamics, we incorporated the LAI based PET correction option together with multi-year LULC data. The Globcover land cover data was selected for the single land cover cases, and hybrid of CORINE (coordination of information on the environment) and MODIS (Moderate Resolution Imaging Spectroradiometer) land cover datasets were chosen for the cases with multiple land cover datasets. These two datasets complement each other since MODIS has no separate forest class but more frequent (yearly) observations than CORINE. Calibration period spans from 1990 to 2006 and corresponding NSE (Nash-Sutcliffe Efficiency) values varies between 0.23 and 0.42, while the validation period spans from 2007 to 2010 and corresponding NSE values are between 0.13 and 0.39. The results revealed that the best performance is obtained when multiple land cover datasets are provided to the model and LAI data is used to correct PET, instead of default aspect-based PET correction in mHM. This study suggests that to minimize errors due to parameter uncertainties in physically distributed hydrologic models, adequate information can be supplied to the model with care taken to avoid over-parameterizing the model.
Ibrahim Olayode Busari; Mehmet Cüneyd Demirel; Alice Newton. Effect of Using Multi-Year LULC and Monthly LAI Inputs on the Calibration of a Distributed Hydrologic Model. 2021, 1 .
AMA StyleIbrahim Olayode Busari, Mehmet Cüneyd Demirel, Alice Newton. Effect of Using Multi-Year LULC and Monthly LAI Inputs on the Calibration of a Distributed Hydrologic Model. . 2021; ():1.
Chicago/Turabian StyleIbrahim Olayode Busari; Mehmet Cüneyd Demirel; Alice Newton. 2021. "Effect of Using Multi-Year LULC and Monthly LAI Inputs on the Calibration of a Distributed Hydrologic Model." , no. : 1.
In general, calibration of a hydrologic model is essential to better simulate the basin processes and behaviour by fitting the model simulated fluxes to observed fluxes. A major challenge in the calibration process is to choose the appropriate length of the observed data series and spatio-temporal resolution of the model schematization. We present a multi-case calibration approach for determining three pillars of an optimum hydrological model configuration: calibration data length, spin-up period and spatial resolution of the hydrological model. The approach is evaluated for the Moselle River basin using calibration and validation results from the spatially distributed meso-scale Hydrological Model (mHM) for 105 different cases representing the combinations of three calibration data lengths, seven spin-up periods and five spatial model resolutions. A metaheuristic global optimization method, i.e. Dynamically Dimensioned Search (DDS) algorithm, and a well-known hydrological performance metric, i.e. Nash Sutcliffe Efficiency (NSE), are utilized for each of the 105 calibration cases. The results show that a 10-year calibration data length, 2-year spin-up period and a 4 km model resolution are appropriate for the Moselle basin to reduce the computational burden. Analyzing the combined effects further allowed us to understand the interactions of these three usually overlooked pillars in hydrological modeling.
Ömer EkmekcioğluiD; Mehmet Cüneyd DemireliD; Martijn J. BooijiD. Effect of three pillars on hydrological model calibration: data length, spin-up period and spatial model resolution. 2021, 1 .
AMA StyleÖmer EkmekcioğluiD, Mehmet Cüneyd DemireliD, Martijn J. BooijiD. Effect of three pillars on hydrological model calibration: data length, spin-up period and spatial model resolution. . 2021; ():1.
Chicago/Turabian StyleÖmer EkmekcioğluiD; Mehmet Cüneyd DemireliD; Martijn J. BooijiD. 2021. "Effect of three pillars on hydrological model calibration: data length, spin-up period and spatial model resolution." , no. : 1.
In general, calibration of a hydrologic model is essential to better simulate the basin processes and behaviour by fitting the model simulated fluxes to observed fluxes. A major challenge in the calibration process is to choose the appropriate length of the observed data series and spatio-temporal resolution of the model schematization. We present a multi-case calibration approach for determining three pillars of an optimum hydrological model configuration: calibration data length, spin-up period and spatial resolution of the hydrological model. The approach is evaluated for the Moselle River basin using calibration and validation results from the spatially distributed meso-scale Hydrological Model (mHM) for 105 different cases representing the combinations of three calibration data lengths, seven spin-up periods and five spatial model resolutions. A metaheuristic global optimization method, i.e. Dynamically Dimensioned Search (DDS) algorithm, and a well-known hydrological performance metric, i.e. Nash Sutcliffe Efficiency (NSE), are utilized for each of the 105 calibration cases. The results show that a 10-year calibration data length, 2-year spin-up period and a 4 km model resolution are appropriate for the Moselle basin to reduce the computational burden. Analyzing the combined effects further allowed us to understand the interactions of these three usually overlooked pillars in hydrological modeling.
Ömer EkmekcioğluiD; Mehmet Cüneyd DemireliD; Martijn J. BooijiD. Effect of three pillars on hydrological model calibration: data length, spin-up period and spatial model resolution. 2021, 1 .
AMA StyleÖmer EkmekcioğluiD, Mehmet Cüneyd DemireliD, Martijn J. BooijiD. Effect of three pillars on hydrological model calibration: data length, spin-up period and spatial model resolution. . 2021; ():1.
Chicago/Turabian StyleÖmer EkmekcioğluiD; Mehmet Cüneyd DemireliD; Martijn J. BooijiD. 2021. "Effect of three pillars on hydrological model calibration: data length, spin-up period and spatial model resolution." , no. : 1.
The mapping of vegetation and Land Cover (LC) is important for research and for public policy planning but, in Brazil, although diverse maps exist there are few studies comparing them. The semiarid region of the Caatinga, in northeastern Brazil is an area long neglected by scientific research and its vegetation is diverse and relatively rich despite years of human occupation and very little preservation effort. In this study we make a comparison between the main maps made for the Caatinga from four different sources: IBGE (Brazilian Institute of Geography and Statistics), TCN (Third National Communication), ProBio (Project for Conservation and Sustainable Use of Biological Biodiversity) and MapBiomas. We also test these maps against well-known Land Cover maps from ESA and NASA: ESA’s GlobCover and Climate Change Initiative (CCI) Land Cover, and NASA’s MODIS MCD12Q1. This was done on a sample area where many of the Caatinga’s vegetation physiognomies can be found, using well-established Difference metrics and the new SPAtial EFficiency (SPAEF) algorithm as they present complementary viewpoints to test the correspondence of mapped classes as well as that of their spatial patterns. Our results show considerable disagreement between the maps tested and their class semantics, with IBGE’s and ProBio’s being the most similar among all national maps and MapBiomas’ the most closely related to global LC maps. The nature of the observed disagreement between these maps shows they diverge not only in the application of their classification systems, but also in their mapped spatial pattern, signaling the need for a better classification system and a better map of vegetation and land cover for the region.
E. Bontempo; M. C. Demirel; C. Corsini; F. Martins; D. Valeriano. CLASSIFICATION SYSTEM DRIVES DISAGREEMENT AMONG BRAZILIAN VEGETATION MAPS AT A SAMPLE AREA OF THE SEMIARID CAATINGA. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2020, XLII-3/W12, 201 -206.
AMA StyleE. Bontempo, M. C. Demirel, C. Corsini, F. Martins, D. Valeriano. CLASSIFICATION SYSTEM DRIVES DISAGREEMENT AMONG BRAZILIAN VEGETATION MAPS AT A SAMPLE AREA OF THE SEMIARID CAATINGA. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2020; XLII-3/W12 ():201-206.
Chicago/Turabian StyleE. Bontempo; M. C. Demirel; C. Corsini; F. Martins; D. Valeriano. 2020. "CLASSIFICATION SYSTEM DRIVES DISAGREEMENT AMONG BRAZILIAN VEGETATION MAPS AT A SAMPLE AREA OF THE SEMIARID CAATINGA." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W12, no. : 201-206.
Missing rainfall data are a major limitation for distributed hydrological modeling and climate studies. Practitioners need reliable approaches that can be employed on a daily basis, often with too limited data in space to feed complex predictive models. In this study we compare different automatic approaches for missing data imputation, including geostatistical interpolation and pattern-based estimation algorithms. We introduce two pattern-based approaches based on the analysis of historical data patterns: (i) an iterative version of K-nearest neighbor (IKNN) and (ii) a new algorithm called vector sampling (VS) that combines concepts of multiple-point statistics and resampling. Both algorithms can draw estimations from variably incomplete data patterns, allowing the target dataset to be at the same time the training dataset. Tested on five case studies from Denmark, Australia, and Switzerland, the algorithms show a different performance that seems to be related to the terrain type: on flat terrains with spatially homogeneous rain events, geostatistical interpolation tends to minimize the average error, while in mountainous regions with nonstationary rainfall statistics, data mining can recover better the rainfall patterns. The VS algorithm, requiring minimal parameterization, turns out to be a convenient option for routine application on complex and poorly gauged terrains.
Fabio Oriani; Simon Stisen; Mehmet Cüneyd Demirel; Gregoire Mariethoz. Missing Data Imputation for Multisite Rainfall Networks: A Comparison between Geostatistical Interpolation and Pattern-Based Estimation on Different Terrain Types. Journal of Hydrometeorology 2020, 21, 2325 -2341.
AMA StyleFabio Oriani, Simon Stisen, Mehmet Cüneyd Demirel, Gregoire Mariethoz. Missing Data Imputation for Multisite Rainfall Networks: A Comparison between Geostatistical Interpolation and Pattern-Based Estimation on Different Terrain Types. Journal of Hydrometeorology. 2020; 21 (10):2325-2341.
Chicago/Turabian StyleFabio Oriani; Simon Stisen; Mehmet Cüneyd Demirel; Gregoire Mariethoz. 2020. "Missing Data Imputation for Multisite Rainfall Networks: A Comparison between Geostatistical Interpolation and Pattern-Based Estimation on Different Terrain Types." Journal of Hydrometeorology 21, no. 10: 2325-2341.
Su dağıtım sistemlerindeki su kayıplarının azaltılması su, enerji, arıtma ve zaman tasarrufu açısından oldukça gereklidir. Su kayıplarının azaltılması, ancak hidrolik model yardımıyla basınç yönetimi yapılarak efektif olarak gerçekleştirilebilir. Sabit basınç çıkışlı basınç düşürücü vanalarla bu işlem yapılmaya çalışıldığında ya kritik noktanın minimum basınç değerinin altına düşmesi, ya da basınç yönetiminden istenen verimin alınamaması muhtemeldir. Bu sebeple özellikle su tüketim paterninin büyük salınımlar gösterdiği büyük şehirlerde gerçek zamanlı kontrol yöntemiyle basınç yönetimi, kayıp su miktarını azaltmak açısından oldukça faydalıdır. Bu çalışmada iki önemli faaliyet yürütülmüştür. Bunlardan ilki, hipotetik bir şebekenin hidrolik modelinde Gerçek Zamanlı basınç yönetiminin farklı su kaybı senaryoları altında gerçekleştirilerek bir hassasiyet analizi yapılmasıdır. İkincisi ise gerçek bir şebekenin hidrolik modelinde Gerçek Zamanlı basınç yönetimi uygulanarak bu yöntemin su kayıplarını azaltmada başarılı olduğunun ortaya konmasıdır.
Mehmet Melih Koşucu; Ömer Sari; Mehmet Cüneyd Demirel; Samet Kiran; Abdurrahman Yilmaz; Abdulbaki Aybakan; Enes Albay; Veysel Şadan Özgür Kirca. Gerçek Zamanlı Basınç Yönetimiyle Su Dağıtım Şebekesinde Su Kaybının Azaltılması. Teknik Dergi 2020, 32, 1 .
AMA StyleMehmet Melih Koşucu, Ömer Sari, Mehmet Cüneyd Demirel, Samet Kiran, Abdurrahman Yilmaz, Abdulbaki Aybakan, Enes Albay, Veysel Şadan Özgür Kirca. Gerçek Zamanlı Basınç Yönetimiyle Su Dağıtım Şebekesinde Su Kaybının Azaltılması. Teknik Dergi. 2020; 32 (1):1.
Chicago/Turabian StyleMehmet Melih Koşucu; Ömer Sari; Mehmet Cüneyd Demirel; Samet Kiran; Abdurrahman Yilmaz; Abdulbaki Aybakan; Enes Albay; Veysel Şadan Özgür Kirca. 2020. "Gerçek Zamanlı Basınç Yönetimiyle Su Dağıtım Şebekesinde Su Kaybının Azaltılması." Teknik Dergi 32, no. 1: 1.
In the era of big data, missing data imputation remains a delicate topic for both the analysis of natural processes and to provide input data for physical models. We propose here a comparative study for missing data imputation on daily rainfall, a variable that can exhibit a complex structure composed of a dry/wet pattern and anisotropic sharp variations.
The seven algorithms considered can be grouped in two families: geostatistical interpolation techniques based on inverse-distance weighting and Kriging, widely used in gap-filling [1], and data-driven techniques based on the analysis of historical data patterns. This latter family of algorithms has been already applied to rainfall generation [2, 3], but it is not originally suitable to historical datasets presenting many data gaps. This happens because they usually operate in a rigid framework where, when a rainfall value is estimated for a station, the others are considered as predictor variables and require to be informed. To overcome this limitation, we propose here i) an adaptation of k-nearest neighbor (KNN) and ii) a new algorithm called Vector Sampling (VS), that combines concepts of multiple-point statistics and resampling. These data-driven algorithms can draw estimations from largely and variably incomplete data patterns, allowing the target dataset to be at the same time the training dataset.
Tested on different case studies from Denmark, Australia, and Switzerland, the algorithms show a different performance that seems to be related to the terrain type: on flat terrains with spatially uniform rain events, geostatistical interpolation tends to minimize the error, while, in mountainous regions with non-stationary rainfall statistics, data mining can recover better the complex rainfall patterns. The VS algorithm, being faster than KNN and requiring minimal parametrization, turns out to be a convenient option for routine application if a representative historical dataset is available. VS is open-source and freely available at https://bitbucket.org/orianif/vs/src/master/.
REFERENCES:
[1] Di Piazza, A., F. Lo Conti, L. V. Noto, F. Viola, and G. La Loggia, 2011: Comparative analysis of different techniques for spatial interpolation of rainfall data to create a serially complete monthly time series of precipitation for Sicily, Italy. International Journal of Applied Earth Observation and Geoinformation, 13 (3), 396–408, https://doi.org/10.1016/j.jag.2011.01.005
[2] Oriani, F., J. Straubhaar, P. Renard, and G. Mariethoz, 2014: Simulation of rainfall time series from different climatic regions using the direct sampling technique. Hydrology and Earth System Sciences, 18 (8), 3015–3031, https://doi.org/10.5194/hess-18-3015-2014
[3] Apipattanavis, S., G. Podesta, B. Rajagopalan, and R. W. Katz, 2007: A semiparametric multivariate and multisite weather generator. Water Resources Research, 43 (11), W11 401, https://doi.org/10.1029/2006WR005714
Fabio Oriani; Simon Stisen; Mehmet C. Demirel; Gregoire Mariethoz. Missing data imputation for multisite rainfall networks: a comparison between geostatistical interpolation and data-mining estimation on different terrain types. 2020, 1 .
AMA StyleFabio Oriani, Simon Stisen, Mehmet C. Demirel, Gregoire Mariethoz. Missing data imputation for multisite rainfall networks: a comparison between geostatistical interpolation and data-mining estimation on different terrain types. . 2020; ():1.
Chicago/Turabian StyleFabio Oriani; Simon Stisen; Mehmet C. Demirel; Gregoire Mariethoz. 2020. "Missing data imputation for multisite rainfall networks: a comparison between geostatistical interpolation and data-mining estimation on different terrain types." , no. : 1.
We were not aware of an error made in the proofreading phase; therefore, we wish to make the following correction to Figure 1b from this paper
Mehmet Melih Koşucu; Mehmet Cüneyd Demirel; V.S. Ozgur Kirca; Mehmet Özger. Correction: Koşucu, M.M., et al. Hydrodynamic and Hydrographic Modeling of Istanbul Strait. Processes 2019, 7, 710. Processes 2020, 8, 205 .
AMA StyleMehmet Melih Koşucu, Mehmet Cüneyd Demirel, V.S. Ozgur Kirca, Mehmet Özger. Correction: Koşucu, M.M., et al. Hydrodynamic and Hydrographic Modeling of Istanbul Strait. Processes 2019, 7, 710. Processes. 2020; 8 (2):205.
Chicago/Turabian StyleMehmet Melih Koşucu; Mehmet Cüneyd Demirel; V.S. Ozgur Kirca; Mehmet Özger. 2020. "Correction: Koşucu, M.M., et al. Hydrodynamic and Hydrographic Modeling of Istanbul Strait. Processes 2019, 7, 710." Processes 8, no. 2: 205.
The climate modelling community has trialled a large number of metrics for evaluating the temporal performance of general circulation models (GCMs), while very little attention has been given to the assessment of their spatial performance, which is equally important. This study evaluated the performance of 36 Coupled Model Intercomparison Project 5 (CMIP5) GCMs in relation to their skills in simulating mean annual, monsoon, winter, pre-monsoon, and post-monsoon precipitation and maximum and minimum temperature over Pakistan using state-of-the-art spatial metrics, SPAtial EFficiency, fractions skill score, Goodman–Kruskal's lambda, Cramer's V, Mapcurves, and Kling–Gupta efficiency, for the period 1961–2005. The multi-model ensemble (MME) precipitation and maximum and minimum temperature data were generated through the intelligent merging of simulated precipitation and maximum and minimum temperature of selected GCMs employing random forest (RF) regression and simple mean (SM) techniques. The results indicated some differences in the ranks of GCMs for different spatial metrics. The overall ranks indicated NorESM1-M, MIROC5, BCC-CSM1-1, and ACCESS1-3 as the best GCMs in simulating the spatial patterns of mean annual, monsoon, winter, pre-monsoon, and post-monsoon precipitation and maximum and minimum temperature over Pakistan. MME precipitation and maximum and minimum temperature generated based on the best-performing GCMs showed more similarities with observed precipitation and maximum and minimum temperature compared to precipitation and maximum and minimum temperature simulated by individual GCMs. The MMEs developed using RF displayed better performance than the MMEs based on SM. Multiple spatial metrics have been used for the first time for selecting GCMs based on their capability to mimic the spatial patterns of annual and seasonal precipitation and maximum and minimum temperature. The approach proposed in the present study can be extended to any number of GCMs and climate variables and applicable to any region for the suitable selection of an ensemble of GCMs to reduce uncertainties in climate projections.
Kamal Ahmed; Dhanapala A. Sachindra; Shamsuddin Shahid; Mehmet C. Demirel; Eun-Sung Chung. Selection of multi-model ensemble of general circulation models for the simulation of precipitation and maximum and minimum temperature based on spatial assessment metrics. Hydrology and Earth System Sciences 2019, 23, 4803 -4824.
AMA StyleKamal Ahmed, Dhanapala A. Sachindra, Shamsuddin Shahid, Mehmet C. Demirel, Eun-Sung Chung. Selection of multi-model ensemble of general circulation models for the simulation of precipitation and maximum and minimum temperature based on spatial assessment metrics. Hydrology and Earth System Sciences. 2019; 23 (11):4803-4824.
Chicago/Turabian StyleKamal Ahmed; Dhanapala A. Sachindra; Shamsuddin Shahid; Mehmet C. Demirel; Eun-Sung Chung. 2019. "Selection of multi-model ensemble of general circulation models for the simulation of precipitation and maximum and minimum temperature based on spatial assessment metrics." Hydrology and Earth System Sciences 23, no. 11: 4803-4824.
The aim of this study is to model the hydrodynamic processes of the Istanbul Strait with its stratified flow characteristics, and calibrate the most important parameters using local and global search algorithms. For that, two open boundary conditions are defined, which are in the northern and southern parts of the Strait. Observed bathymetric, hydrographic, meteorological, and water-level data are used to set up the Delft3D-FLOW model. First, the sensitivities of the model parameters on the numerical model outputs are assessed using Parameter EStimation Tool (PEST) toolbox. Then, the model is calibrated based on the objective functions, focusing on the flow rates of the upper and lower layers. The salinity and temperature profiles of the strait are only used for model validation. The results show that the calibrated model outputs of the Istanbul Strait are reliable and consistent with the in situ measurements. The sensitivity analysis reveals that the spatial low-pass filter coefficient, horizontal eddy viscosity, Prandtl–Schmidt number, slope in log–log spectrum, and Manning roughness coefficient are most sensitive parameters affecting the flow rate performance of the model. The agreement between observed salinity profiles and simulated model outputs is promising, whereas the match between observed and simulated temperature profiles is weak, showing that the model can be improved, particularly for simulating the mixing layer.
Mehmet Melih Koşucu; Mehmet Cüneyd Demirel; V.S. Ozgur Kirca; Mehmet Özger. Hydrodynamic and Hydrographic Modeling of Istanbul Strait. Processes 2019, 7, 710 .
AMA StyleMehmet Melih Koşucu, Mehmet Cüneyd Demirel, V.S. Ozgur Kirca, Mehmet Özger. Hydrodynamic and Hydrographic Modeling of Istanbul Strait. Processes. 2019; 7 (10):710.
Chicago/Turabian StyleMehmet Melih Koşucu; Mehmet Cüneyd Demirel; V.S. Ozgur Kirca; Mehmet Özger. 2019. "Hydrodynamic and Hydrographic Modeling of Istanbul Strait." Processes 7, no. 10: 710.
Although the complexity of physically-based models continues to increase, they still need to be calibrated. In recent years, there has been an increasing interest in using new satellite technologies and products with high resolution in model evaluations and decision-making. The aim of this study is to investigate the value of different remote sensing products and groundwater level measurements in the temporal calibration of a well-known hydrologic model i.e., Hydrologiska Bryåns Vattenbalansavdelning (HBV). This has rarely been done for conceptual models, as satellite data are often used in the spatial calibration of the distributed models. Three different soil moisture products from the European Space Agency Climate Change Initiative Soil Measure (ESA CCI SM v04.4), The Advanced Microwave Scanning Radiometer on the Earth Observing System (EOS) Aqua satellite (AMSR-E), soil moisture active passive (SMAP), and total water storage anomalies from Gravity Recovery and Climate Experiment (GRACE) are collected and spatially averaged over the Moselle River Basin in Germany and France. Different combinations of objective functions and search algorithms, all targeting a good fit between observed and simulated streamflow, groundwater and soil moisture, are used to analyze the contribution of each individual source of information.
Mehmet Cüneyd Demirel; Alparslan Özen; Selen Orta; Emir Toker; Hatice Kübra Demir; Ömer Ekmekcioğlu; Hüsamettin Tayşi; Sinan Eruçar; Ahmet Bilal Sağ; Ömer Sarı; Ecem Tuncer; Hayrettin Hancı; Türkan Irem Özcan; Hilal Erdem; Mehmet Melih Koşucu; Eyyup Ensar Başakın; Kamal Ahmed; Awat Anwar; Muhammet Bahattin Avcuoğlu; Ömer Vanlı; Simon Stisen; Martijn J. Booij. Additional Value of Using Satellite-Based Soil Moisture and Two Sources of Groundwater Data for Hydrological Model Calibration. Water 2019, 11, 2083 .
AMA StyleMehmet Cüneyd Demirel, Alparslan Özen, Selen Orta, Emir Toker, Hatice Kübra Demir, Ömer Ekmekcioğlu, Hüsamettin Tayşi, Sinan Eruçar, Ahmet Bilal Sağ, Ömer Sarı, Ecem Tuncer, Hayrettin Hancı, Türkan Irem Özcan, Hilal Erdem, Mehmet Melih Koşucu, Eyyup Ensar Başakın, Kamal Ahmed, Awat Anwar, Muhammet Bahattin Avcuoğlu, Ömer Vanlı, Simon Stisen, Martijn J. Booij. Additional Value of Using Satellite-Based Soil Moisture and Two Sources of Groundwater Data for Hydrological Model Calibration. Water. 2019; 11 (10):2083.
Chicago/Turabian StyleMehmet Cüneyd Demirel; Alparslan Özen; Selen Orta; Emir Toker; Hatice Kübra Demir; Ömer Ekmekcioğlu; Hüsamettin Tayşi; Sinan Eruçar; Ahmet Bilal Sağ; Ömer Sarı; Ecem Tuncer; Hayrettin Hancı; Türkan Irem Özcan; Hilal Erdem; Mehmet Melih Koşucu; Eyyup Ensar Başakın; Kamal Ahmed; Awat Anwar; Muhammet Bahattin Avcuoğlu; Ömer Vanlı; Simon Stisen; Martijn J. Booij. 2019. "Additional Value of Using Satellite-Based Soil Moisture and Two Sources of Groundwater Data for Hydrological Model Calibration." Water 11, no. 10: 2083.
The aim of this study is to model hydrodynamic processes of the Istanbul Strait with its stratified flow characteristic and calibrate the most important parameters using local and global search algorithms. For that two open boundary conditions are defined, which are in the North and South part of the Strait. Observed bathymetric, hydrographic, meteorological and water level data are used to set up the Delft3D-FLOW model. First, the sensitivities of model parameters on the numerical model outputs are assessed using PEST toolbox. Then, the model is calibrated based on the objective functions focusing on the flowrates of upper and lower layers. The salinity and temperature profiles of the Strait are only used for model validation. The results show that the calibrated model outputs of Istanbul Strait are reliable and consistent with the in-situ measurements. The sensitivity analysis reveals that the Spatial Low-Pass Filter Coefficient, Horizontal Eddy Viscosity, Prandtl-Schmidt Number, Slope in log-log Spectrum and Manning Roughness Coefficient are most sensitive parameters affecting flowrate performance of the model. The agreement between observed salinity profiles and simulated model outputs is promising whereas the match between observed and simulated temperature profiles is weak showing that the model can be improved particularly for simulating the mixing layer.
Mehmet Melih Koşucu; Mehmet Cüneyd Demirel; V.S. Ozgur Kirca; Mehmet Özger. Hydrodynamic and Hydrographic Modeling of Istanbul Strait. 2019, 1 .
AMA StyleMehmet Melih Koşucu, Mehmet Cüneyd Demirel, V.S. Ozgur Kirca, Mehmet Özger. Hydrodynamic and Hydrographic Modeling of Istanbul Strait. . 2019; ():1.
Chicago/Turabian StyleMehmet Melih Koşucu; Mehmet Cüneyd Demirel; V.S. Ozgur Kirca; Mehmet Özger. 2019. "Hydrodynamic and Hydrographic Modeling of Istanbul Strait." , no. : 1.
Groundwater is regarded as one of the most reliable and vulnerable sources of drinking water in many countries. Declining groundwater levels, due to over-exploitation and climate-change impacts, emphasize the need for sustainable management of this valuable resource. The concept of reliability-resiliency-vulnerability (RRV) has been adopted in this study to assess the spatial changes in the sustainability of aquifers for different periods to identify the main factors affecting groundwater sustainability in Pakistan. This is important for the country, as the substantial decline of groundwater levels in recent years has affected the water security of the growing economy. The satellite-based gridded Gravity Recovery and Climate Experiment (GRACE) groundwater anomaly data for the period 2002–2016 were used for this spatial assessment. The results revealed precipitation as the dominant factor associated with changing groundwater storage in Pakistan. A large decrease in aquifer storage was found over the study period. The groundwater-level decline was found to be greater in the region where agriculture is more intense, resulting in over-exploitation of groundwater for irrigation. The reduction of groundwater storage has led to a decrease in sustainability, especially in recent years (2008–2016) compared with previous periods (2002–2010 and 2005–2013). This study emphasized the need for groundwater resource management strategies such as reduction of groundwater abstraction in drought years, rescheduling the crop calendar to take advantage of rainfall, switching to less water-intensive crops, etc., particularly in groundwater depleting regions.
Kamal Ahmed; Shamsuddin Shahid; Mehmet Cüneyd Demirel; Nadeem Nawaz; Najeebullah Khan. The changing characteristics of groundwater sustainability in Pakistan from 2002 to 2016. Hydrogeology Journal 2019, 27, 2485 -2496.
AMA StyleKamal Ahmed, Shamsuddin Shahid, Mehmet Cüneyd Demirel, Nadeem Nawaz, Najeebullah Khan. The changing characteristics of groundwater sustainability in Pakistan from 2002 to 2016. Hydrogeology Journal. 2019; 27 (7):2485-2496.
Chicago/Turabian StyleKamal Ahmed; Shamsuddin Shahid; Mehmet Cüneyd Demirel; Nadeem Nawaz; Najeebullah Khan. 2019. "The changing characteristics of groundwater sustainability in Pakistan from 2002 to 2016." Hydrogeology Journal 27, no. 7: 2485-2496.
The climate modelling community has trialled a large number metrics to evaluate the temporal performance of the Global Circulation Models (GCMs) for the selection of GCMs, while very little attention has been given to spatial performance of GCMs which is equally important. This study evaluated the performance of 20 Coupled Model Intercomparison Project 5 (CMIP5) GCMs pertaining to their skills in simulating mean annual, monsoon and winter precipitation over Pakistan using state-of-the-art spatial metrics; SPAtial EFficiency, Goodman–Kruskal's lambda, Fractions Skill Score, Cramer’s V, Mapcurves, and Kling-Gupta efficiency for the period 1961–2005. The multi-model ensemble (MME) precipitation was generated through intelligent merging of simulated precipitation of selected GCMs employing Random Forest (RF) regression and Simple Mean (SM). The results indicated some differences in the ranks of GCMs for different metrics. The overall ranks indicated NorESM1-M, CESM1-CAM5, GFDL-CM3 and GFDL-ESM2G as the best GCMs in simulating the spatial patterns of mean annual, monsoon and winter precipitation over Pakistan. MME precipitation generated based on the best performing GCMs showed more similarities with observed precipitation compared to precipitation simulated by individual GCMs. The MME developed using RF displayed better performance than the MME-based on SM. Multiple spatial metrics have been used for the first time for selecting GCMs based on their capability to mimic the spatial patterns of annual and seasonal precipitation. The approach suggested in the present study can be extended to any number of GCMs and climate variables and applicable to any region for the suitable selection of an ensemble of GCMs to reduce uncertainties in climate projections.
Kamal Ahmed; Dhanapala Arachchige Sachindra; Shamsuddin Shahid; Mehmet Cüneyd Demirel; Eun-Sung Chung. Selection of multi-model ensemble of GCMs for the simulation of precipitation based on spatial assessment metrics. 2019, 2019, 1 -35.
AMA StyleKamal Ahmed, Dhanapala Arachchige Sachindra, Shamsuddin Shahid, Mehmet Cüneyd Demirel, Eun-Sung Chung. Selection of multi-model ensemble of GCMs for the simulation of precipitation based on spatial assessment metrics. . 2019; 2019 ():1-35.
Chicago/Turabian StyleKamal Ahmed; Dhanapala Arachchige Sachindra; Shamsuddin Shahid; Mehmet Cüneyd Demirel; Eun-Sung Chung. 2019. "Selection of multi-model ensemble of GCMs for the simulation of precipitation based on spatial assessment metrics." 2019, no. : 1-35.
Hydrologic models are conventionally constrained and evaluated using point measurements of streamflow, which represent an aggregated catchment measure. As a consequence of this single objective focus, model parametrization and model parameter sensitivity typically do not reflect other aspects of catchment behavior. Specifically for distributed models, the spatial pattern aspect is often overlooked. Our paper examines the utility of multiple performance measures in a spatial sensitivity analysis framework to determine the key parameters governing the spatial variability of predicted actual evapotranspiration (AET). The Latin hypercube one-at-a-time (LHS-OAT) sampling strategy with multiple initial parameter sets was applied using the mesoscale hydrologic model (mHM) and a total of 17 model parameters were identified as sensitive. The results indicate different parameter sensitivities for different performance measures focusing on temporal hydrograph dynamics and spatial variability of actual evapotranspiration. While spatial patterns were found to be sensitive to vegetation parameters, streamflow dynamics were sensitive to pedo-transfer function (PTF) parameters. Above all, our results show that behavioral model definitions based only on streamflow metrics in the generalized likelihood uncertainty estimation (GLUE) type methods require reformulation by incorporating spatial patterns into the definition of threshold values to reveal robust hydrologic behavior in the analysis.
Mehmet Cüneyd Demirel; Julian Koch; Gorka Mendiguren; Simon Stisen. Spatial Pattern Oriented Multicriteria Sensitivity Analysis of a Distributed Hydrologic Model. Water 2018, 10, 1188 .
AMA StyleMehmet Cüneyd Demirel, Julian Koch, Gorka Mendiguren, Simon Stisen. Spatial Pattern Oriented Multicriteria Sensitivity Analysis of a Distributed Hydrologic Model. Water. 2018; 10 (9):1188.
Chicago/Turabian StyleMehmet Cüneyd Demirel; Julian Koch; Gorka Mendiguren; Simon Stisen. 2018. "Spatial Pattern Oriented Multicriteria Sensitivity Analysis of a Distributed Hydrologic Model." Water 10, no. 9: 1188.
Hydrologic models are conventionally constrained and evaluated using point measurements of streamflow, which represents an aggregated catchment measure. As a consequence of this single objective focus, model parametrization and model parameter sensitivity are typically not reflecting other aspects of catchment behavior. Specifically for distributed models, the spatial pattern aspect is often overlooked. Our paper examines the utility of multiple performance measures in a spatial sensitivity analysis framework to determine the key parameters governing the spatial variability of predicted actual evapotranspiration (AET). Latin hypercube one-at-a-time (LHS-OAT) sampling strategy with multiple initial parameter sets was applied using the mesoscale hydrologic model (mHM) and a total of 17 model parameters were identified as sensitive. The results indicate different parameter sensitivities for different performance measures focusing on temporal hydrograph dynamics and spatial variability of actual evapotranspiration. While spatial patterns were found to be sensitive to vegetation parameters, streamflow dynamics were sensitive to pedo-transfer function (PTF) parameters. Above all, our results show that behavioral model definition based only on streamflow metrics in the generalized likelihood uncertainty estimation (GLUE) type methods require reformulation by incorporating spatial patterns into the definition of threshold values to reveal robust hydrologic behavior in the analysis.
Mehmet Cüneyd Demirel; Julian Koch; Gorka Mendiguren; Simon Stisen. Spatial Pattern Oriented Multi-Criteria Sensitivity Analysis of a Distributed Hydrologic Model. 2018, 1 .
AMA StyleMehmet Cüneyd Demirel, Julian Koch, Gorka Mendiguren, Simon Stisen. Spatial Pattern Oriented Multi-Criteria Sensitivity Analysis of a Distributed Hydrologic Model. . 2018; ():1.
Chicago/Turabian StyleMehmet Cüneyd Demirel; Julian Koch; Gorka Mendiguren; Simon Stisen. 2018. "Spatial Pattern Oriented Multi-Criteria Sensitivity Analysis of a Distributed Hydrologic Model." , no. : 1.
Mehmet Cüneyd Demirel. Short comment on the literature review, model type and sensitivity analysis. 2018, 1 .
AMA StyleMehmet Cüneyd Demirel. Short comment on the literature review, model type and sensitivity analysis. . 2018; ():1.
Chicago/Turabian StyleMehmet Cüneyd Demirel. 2018. "Short comment on the literature review, model type and sensitivity analysis." , no. : 1.