@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} } @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{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} }