@article{FohlmeisterPlessenDudashvilietal.2017,
  author    = {Fohlmeister, Jens Bernd and Plessen, Birgit and Dudashvili, Alexey Sergeevich and Tjallingii, Rik and Wolff, Christian Michael and Gafurov, Abror and Cheng, Hai},
  title     = {Winter precipitation changes during the Medieval Climate Anomaly and the Little Ice Age in arid Central Asia},
  series = {Quaternary science reviews : the international multidisciplinary research and review journal},
  volume    = {178},
  journal   = {Quaternary science reviews : the international multidisciplinary research and review journal},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {0277-3791},
  doi       = {10.1016/j.quascirev.2017.10.026},
  pages     = {24 -- 36},
  year      = {2017},
  abstract  = {The strength of the North Atlantic Oscillation (NAO) is considered to be the main driver of climate changes over the European and western Asian continents throughout the last millennium. For example, the predominantly warm Medieval Climate Anomaly (MCA) and the following cold period of the Little Ice Age (LIA) over Europe have been associated with long-lasting phases with a positive and negative NAO index. Its climatic imprint is especially pronounced in European winter seasons. However, little is known about the influence of NAO with respect to its eastern extent over the Eurasian continent. Here we present speleothem records (PC, 8180 and Sr/Ca) from the southern rim of Fergana Basin (Central Asia) revealing annually resolved past climate variations during the last millennium. The age control of the stalagmite relies on radiocarbon dating as large amounts of detrital material inhibit accurate 230Th dating. Present-day calcification of the stalagmite is most effective during spring when the cave atmosphere and elevated water supply by snow melting and high amount of spring precipitation provide optimal conditions. Seasonal precipitation variations cause changes of the stable isotope and Sr/ Ca compositions. The simultaneous changes in these geochemical proxies, however, give also evidence for fractionation processes in the cave. By disentangling both processes, we demonstrate that the amount of winter precipitation during the MCA was generally higher than during the LIA, which is in line with climatic changes linked to the NAO index but opposite to the higher mountain records of Central Asia. Several events of strongly reduced winter precipitation are observed during the LIA in Central Asia. These dry winter events can be related to phases of a strong negative NAO index and all results reveal that winter precipitation over the central Eurasian continent is tightly linked to atmospheric NAO modes by the westerly wind systems. (C) 2017 Elsevier Ltd. All rights reserved.},
  language  = {en}
}
@article{GusePfannerstillGafurovetal.2017,
  author    = {Guse, Bj{\"o}rn and Pfannerstill, Matthias and Gafurov, Abror and Kiesel, Jens and Lehr, Christian and Fohrer, Nicola},
  title     = {Identifying the connective strength between model parameters and performance criteria},
  series = {Hydrology and earth system sciences : HESS},
  volume    = {21},
  journal   = {Hydrology and earth system sciences : HESS},
  publisher = {Copernicus},
  address   = {G{\"o}ttingen},
  issn      = {1027-5606},
  doi       = {10.5194/hess-21-5663-2017},
  pages     = {5663 -- 5679},
  year      = {2017},
  abstract  = {In hydrological models, parameters are used to represent the time-invariant characteristics of catchments and to capture different aspects of hydrological response. Hence, model parameters need to be identified based on their role in controlling the hydrological behaviour. For the identification of meaningful parameter values, multiple and complementary performance criteria are used that compare modelled and measured discharge time series. The reliability of the identification of hydrologically meaningful model parameter values depends on how distinctly a model parameter can be assigned to one of the performance criteria.\& para;\& para;To investigate this, we introduce the new concept of connective strength between model parameters and performance criteria. The connective strength assesses the intensity in the interrelationship between model parameters and performance criteria in a bijective way. In our analysis of connective strength, model simulations are carried out based on a latin hypercube sampling. Ten performance criteria including Nash-Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE) and its three components (alpha, beta and r) as well as RSR (the ratio of the root mean square error to the standard deviation) for different segments of the flow duration curve (FDC) are calculated.\& para;\& para;With a joint analysis of two regression tree (RT) approaches, we derive how a model parameter is connected to different performance criteria. At first, RTs are constructed using each performance criterion as the target variable to detect the most relevant model parameters for each performance criterion. Secondly, RTs are constructed using each parameter as the target variable to detect which performance criteria are impacted by changes in the values of one distinct model parameter. Based on this, appropriate performance criteria are identified for each model parameter.\& para;\& para;In this study, a high bijective connective strength between model parameters and performance criteria is found for low- and mid-flow conditions. Moreover, the RT analyses emphasise the benefit of an individual analysis of the three components of KGE and of the FDC segments. Furthermore, the RT analyses highlight under which conditions these performance criteria provide insights into precise parameter identification. Our results show that separate performance criteria are required to identify dominant parameters on low- and mid-flow conditions, whilst the number of required performance criteria for high flows increases with increasing process complexity in the catchment. Overall, the analysis of the connective strength between model parameters and performance criteria using RTs contribute to a more realistic handling of parameters and performance criteria in hydrological modelling.},
  language  = {en}
}
@article{HeVorogushynUngerShayestehetal.2018,
  author    = {He, Zhihua and Vorogushyn, Sergiy and Unger-Shayesteh, Katy and Gafurov, Abror and Kalashnikova, Olga and Omorova, Elvira and Merz, Bruno},
  title     = {The Value of Hydrograph Partitioning Curves for Calibrating Hydrological Models in Glacierized Basins},
  series = {Water resources research},
  volume    = {54},
  journal   = {Water resources research},
  number    = {3},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {0043-1397},
  doi       = {10.1002/2017WR021966},
  pages     = {2336 -- 2361},
  year      = {2018},
  abstract  = {This study refines the method for calibrating a glacio-hydrological model based on Hydrograph Partitioning Curves (HPCs), and evaluates its value in comparison to multidata set optimization approaches which use glacier mass balance, satellite snow cover images, and discharge. The HPCs are extracted from the observed flow hydrograph using catchment precipitation and temperature gradients. They indicate the periods when the various runoff processes, such as glacier melt or snow melt, dominate the basin hydrograph. The annual cumulative curve of the difference between average daily temperature and melt threshold temperature over the basin, as well as the annual cumulative curve of average daily snowfall on the glacierized areas are used to identify the starting and end dates of snow and glacier ablation periods. Model parameters characterizing different runoff processes are calibrated on different HPCs in a stepwise and iterative way. Results show that the HPC-based method (1) delivers model-internal consistency comparably to the tri-data set calibration method; (2) improves the stability of calibrated parameter values across various calibration periods; and (3) estimates the contributions of runoff components similarly to the tri-data set calibration method. Our findings indicate the potential of the HPC-based approach as an alternative for hydrological model calibration in glacierized basins where other calibration data sets than discharge are often not available or very costly to obtain.},
  language  = {en}
}
@article{HeUngerShayestehVorogushynetal.2019,
  author    = {He, Zhihua and Unger-Shayesteh, Katy and Vorogushyn, Sergiy and Weise, Stephan M. and Kalashnikova, Olga and Gafurov, Abror and Duethmann, Doris and Barandun, Martina and Merz, Bruno},
  title     = {Constraining hydrological model parameters using water isotopic compositions in a glacierized basin, Central Asia},
  series = {Journal of hydrology},
  volume    = {571},
  journal   = {Journal of hydrology},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0022-1694},
  doi       = {10.1016/j.jhydrol.2019.01.048},
  pages     = {332 -- 348},
  year      = {2019},
  abstract  = {Water stable isotope signatures can provide valuable insights into the catchment internal runoff processes. However, the ability of the water isotope data to constrain the internal apportionments of runoff components in hydrological models for glacierized basins is not well understood. This study developed an approach to simultaneously model the water stable isotopic compositions and runoff processes in a glacierized basin in Central Asia. The fractionation and mixing processes of water stable isotopes in and from the various water sources were integrated into a glacio-hydrological model. The model parameters were calibrated on discharge, snow cover and glacier mass balance data, and additionally isotopic composition of streamflow. We investigated the value of water isotopic compositions for the calibration of model parameters, in comparison to calibration methods without using such measurements. Results indicate that: (1) The proposed isotope-hydrological integrated modeling approach was able to reproduce the isotopic composition of streamflow, and improved the model performance in the evaluation period; (2) Involving water isotopic composition for model calibration reduced the model parameter uncertainty, and helped to reduce the uncertainty in the quantification of runoff components; (3) The isotope-hydrological integrated modeling approach quantified the contributions of runoff components comparably to a three-component tracer-based end-member mixing analysis method for summer peak flows, and required less water tracer data. Our findings demonstrate the value of water isotopic compositions to improve the quantification of runoff components using hydrological models in glacierized basins.},
  language  = {en}
}
@misc{GusePfannerstillGafurovetal.2017,
  author    = {Guse, Bj{\"o}rn and Pfannerstill, Matthias and Gafurov, Abror and Kiesel, Jens and Lehr, Christian and Fohrer, Nicola},
  title     = {Identifying the connective strength between model parameters and performance criteria},
  series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {657},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-41914},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-419142},
  pages     = {17},
  year      = {2017},
  abstract  = {In hydrological models, parameters are used to represent the time-invariant characteristics of catchments and to capture different aspects of hydrological response. Hence, model parameters need to be identified based on their role in controlling the hydrological behaviour. For the identification of meaningful parameter values, multiple and complementary performance criteria are used that compare modelled and measured discharge time series. The reliability of the identification of hydrologically meaningful model parameter values depends on how distinctly a model parameter can be assigned to one of the performance criteria.\& para;\& para;To investigate this, we introduce the new concept of connective strength between model parameters and performance criteria. The connective strength assesses the intensity in the interrelationship between model parameters and performance criteria in a bijective way. In our analysis of connective strength, model simulations are carried out based on a latin hypercube sampling. Ten performance criteria including Nash-Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE) and its three components (alpha, beta and r) as well as RSR (the ratio of the root mean square error to the standard deviation) for different segments of the flow duration curve (FDC) are calculated.\& para;\& para;With a joint analysis of two regression tree (RT) approaches, we derive how a model parameter is connected to different performance criteria. At first, RTs are constructed using each performance criterion as the target variable to detect the most relevant model parameters for each performance criterion. Secondly, RTs are constructed using each parameter as the target variable to detect which performance criteria are impacted by changes in the values of one distinct model parameter. Based on this, appropriate performance criteria are identified for each model parameter.\& para;\& para;In this study, a high bijective connective strength between model parameters and performance criteria is found for low- and mid-flow conditions. Moreover, the RT analyses emphasise the benefit of an individual analysis of the three components of KGE and of the FDC segments. Furthermore, the RT analyses highlight under which conditions these performance criteria provide insights into precise parameter identification. Our results show that separate performance criteria are required to identify dominant parameters on low- and mid-flow conditions, whilst the number of required performance criteria for high flows increases with increasing process complexity in the catchment. Overall, the analysis of the connective strength between model parameters and performance criteria using RTs contribute to a more realistic handling of parameters and performance criteria in hydrological modelling.},
  language  = {en}
}