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Production of a large ensemble of climate simulations suitable for impact assessments is an attempt to enhance our knowledge about the associated uncertainties in future projections. However, the actual quantification of the change in the climate and its impact relies on the ensemble of models selected, particularly given the wide availability of climatic simulations from various initiatives, i.e. CMIP5, CORDEX.
Here, we hypothesize that historical streamflow observations contain valuable information to investigate practices for the selection of climate model ensembles. We apply eight selection methods (based on democracy, diversity of GCM, diversity of RCM, maximum information minimum redundancy, best performing hindcasted climate depiction, best performing hydrological model, simple climate model averaging and reliable ensemble average) to subset an ensemble available from 16 combinations of Euro-CORDEX GCM-RCM by comparing observed to simulated streamflow shift of the Danube from a reference period (1960–1989) to an evaluation period (1990–2014). Simulations are carried out with the well-performing Upper Danube COSERO hydrological model, spanning a calibration and evaluation period of more than 100 years. Comparison against no selection shows that an informed selection of ensemble members improves the quantification of climate change impacts where methods that maintain the diversity and information content of the full ensemble are favourable. In addition, the method followed allows the assessment which individual climate models perform best, where only three of 16 models were able to correctly reproduce the direction of streamflow change in each season.
Prior to carrying out climate impact assessments, we propose splitting the long-term historical data and using it to test climate model performance, sub-selection methods, and their agreement in reproducing the indicator of interest, which further provide the expectable benchmark of near- and far-future impact assessments. This test can further be applied in multi-basin experiments to obtain a better understanding of uncertainty propagation and uncertainty reduction in hydrological impact studies.
Jens Kiesel; Philipp Stanzel; Harald Kling; Nicola Fohrer; Sonja C. Jähnig; Ilias Pechlivanidis. The more is not the merrier – an informed selection of climate model ensembles can enhance the quantification of hydrological change. 2021, 1 .
AMA StyleJens Kiesel, Philipp Stanzel, Harald Kling, Nicola Fohrer, Sonja C. Jähnig, Ilias Pechlivanidis. The more is not the merrier – an informed selection of climate model ensembles can enhance the quantification of hydrological change. . 2021; ():1.
Chicago/Turabian StyleJens Kiesel; Philipp Stanzel; Harald Kling; Nicola Fohrer; Sonja C. Jähnig; Ilias Pechlivanidis. 2021. "The more is not the merrier – an informed selection of climate model ensembles can enhance the quantification of hydrological change." , no. : 1.
A recent global meta‐analysis reported a decrease in terrestrial but increase in freshwater insect abundance and biomass (van Klink et al., Science 368, p. 417). The authors suggested that water quality has been improving, thereby challenging recent reports documenting drastic global declines in freshwater biodiversity. We raise two major concerns with the meta‐analysis and suggest that these account for the discrepancy with the declines reported elsewhere. First, total abundance and biomass alone are poor indicators of the status of freshwater insect assemblages, and the observed differences may well have been driven by the replacement of sensitive species with tolerant ones. Second, many of the datasets poorly represent global trends and reflect responses to local conditions or nonrandom site selection. We conclude that the results of the meta‐analysis should not be considered indicative of an overall improvement in the condition of freshwater ecosystems. This article is categorized under: Water and Life > Conservation, Management, and Awareness
Sonja C. Jähnig; Viktor Baranov; Florian Altermatt; Peter Cranston; Martin Friedrichs‐Manthey; Juergen Geist; Fengzhi He; Jani Heino; Daniel Hering; Franz Hölker; Jonas Jourdan; Gregor Kalinkat; Jens Kiesel; Florian Leese; Alain Maasri; Michael T. Monaghan; Ralf B. Schäfer; Klement Tockner; Jonathan D. Tonkin; Sami Domisch. Revisiting global trends in freshwater insect biodiversity. WIREs Water 2020, 8, 1 .
AMA StyleSonja C. Jähnig, Viktor Baranov, Florian Altermatt, Peter Cranston, Martin Friedrichs‐Manthey, Juergen Geist, Fengzhi He, Jani Heino, Daniel Hering, Franz Hölker, Jonas Jourdan, Gregor Kalinkat, Jens Kiesel, Florian Leese, Alain Maasri, Michael T. Monaghan, Ralf B. Schäfer, Klement Tockner, Jonathan D. Tonkin, Sami Domisch. Revisiting global trends in freshwater insect biodiversity. WIREs Water. 2020; 8 (2):1.
Chicago/Turabian StyleSonja C. Jähnig; Viktor Baranov; Florian Altermatt; Peter Cranston; Martin Friedrichs‐Manthey; Juergen Geist; Fengzhi He; Jani Heino; Daniel Hering; Franz Hölker; Jonas Jourdan; Gregor Kalinkat; Jens Kiesel; Florian Leese; Alain Maasri; Michael T. Monaghan; Ralf B. Schäfer; Klement Tockner; Jonathan D. Tonkin; Sami Domisch. 2020. "Revisiting global trends in freshwater insect biodiversity." WIREs Water 8, no. 2: 1.
The assessment of climate change and its impact relies on the ensemble of models available and/or sub-selected. However, an assessment of the validity of simulated climate change impacts is not straightforward because historical data is commonly used for bias-adjustment, to select ensemble members or to define a baseline against which impacts are compared—and, naturally, there are no observations to evaluate future projections. We hypothesize that historical streamflow observations contain valuable information to investigate practices for the selection of model ensembles. The Danube River at Vienna is used as a case study, with EURO-CORDEX climate simulations driving the COSERO hydrological model. For each selection method, we compare observed to simulated streamflow shift from the reference period (1960–1989) to the evaluation period (1990–2014). Comparison against no selection shows that an informed selection of ensemble members improves the quantification of climate change impacts. However, the selection method matters, with model selection based on hindcasted climate or streamflow alone is misleading, while methods that maintain the diversity and information content of the full ensemble are favorable. Prior to carrying out climate impact assessments, we propose splitting the long-term historical data and using it to test climate model performance, sub-selection methods, and their agreement in reproducing the indicator of interest, which further provide the expectable benchmark of near- and far-future impact assessments. This test is well-suited to be applied in multi-basin experiments to obtain better understanding of uncertainty propagation and more universal recommendations regarding uncertainty reduction in hydrological impact studies.
Jens Kiesel; Philipp Stanzel; Harald Kling; Nicola Fohrer; Sonja C. Jähnig; Ilias Pechlivanidis. Streamflow-based evaluation of climate model sub-selection methods. Climatic Change 2020, 1 -19.
AMA StyleJens Kiesel, Philipp Stanzel, Harald Kling, Nicola Fohrer, Sonja C. Jähnig, Ilias Pechlivanidis. Streamflow-based evaluation of climate model sub-selection methods. Climatic Change. 2020; ():1-19.
Chicago/Turabian StyleJens Kiesel; Philipp Stanzel; Harald Kling; Nicola Fohrer; Sonja C. Jähnig; Ilias Pechlivanidis. 2020. "Streamflow-based evaluation of climate model sub-selection methods." Climatic Change , no. : 1-19.
The Universal Soil Loss Equation (USLE) is the most commonly used model to assess soil erosion by water. The model equation quantifies long-term average annual soil loss as a product of the rainfall erosivity R, soil erodibility K, slope length and steepness LS, soil cover C, and support measures P. A large variety of methods exist to derive these model inputs from readily available data. However, the estimated values of a respective model input can strongly differ when employing different methods and can eventually introduce large uncertainties in the estimated soil loss. The potential to evaluate soil loss estimates at a large scale is very limited due to scarce in-field observations and their comparability to long-term soil estimates. In this work we addressed (i) the uncertainties in the soil loss estimates that can potentially be introduced by different representations of the USLE input factors and (ii) challenges that can arise in the evaluation of uncertain soil loss estimates with observed data. In a systematic analysis we developed different representations of USLE inputs for the study domain of Kenya and Uganda. All combinations of the generated USLE inputs resulted in 972 USLE model setups. We assessed the resulting distributions in soil loss, both spatially distributed and on the administrative level for Kenya and Uganda. In a sensitivity analysis we analyzed the contributions of the USLE model inputs to the ranges in soil loss and analyzed their spatial patterns. We compared the calculated USLE ensemble soil estimates to available in-field data and other study results and addressed possibilities and limitations of the USLE model evaluation. The USLE model ensemble resulted in wide ranges of estimated soil loss, exceeding the mean soil loss by over an order of magnitude, particularly in hilly topographies. The study implies that a soil loss assessment with the USLE is highly uncertain and strongly depends on the realizations of the model input factors. The employed sensitivity analysis enabled us to identify spatial patterns in the importance of the USLE input factors. The C and K factors showed large-scale patterns of importance in the densely vegetated part of Uganda and the dry north of Kenya, respectively, while LS was relevant in small-scale heterogeneous patterns. Major challenges for the evaluation of the estimated soil losses with in-field data were due to spatial and temporal limitations of the observation data but also due to measured soil losses describing processes that are different to the ones that are represented by the USLE.
Christoph Schürz; Bano Mehdi; Jens Kiesel; Karsten Schulz; Mathew Herrnegger. A systematic assessment of uncertainties in large-scale soil loss estimation from different representations of USLE input factors – a case study for Kenya and Uganda. Hydrology and Earth System Sciences 2020, 24, 4463 -4489.
AMA StyleChristoph Schürz, Bano Mehdi, Jens Kiesel, Karsten Schulz, Mathew Herrnegger. A systematic assessment of uncertainties in large-scale soil loss estimation from different representations of USLE input factors – a case study for Kenya and Uganda. Hydrology and Earth System Sciences. 2020; 24 (9):4463-4489.
Chicago/Turabian StyleChristoph Schürz; Bano Mehdi; Jens Kiesel; Karsten Schulz; Mathew Herrnegger. 2020. "A systematic assessment of uncertainties in large-scale soil loss estimation from different representations of USLE input factors – a case study for Kenya and Uganda." Hydrology and Earth System Sciences 24, no. 9: 4463-4489.
The Universal Soil Loss Equation (USLE) is a standard model to assess soil erosion by water. The model equation quantifies long-term average annual soil loss as a product of the rainfall erosivity R, soil erodibility K, slope length and slope steepness LS, the soil cover C and support measures P. Several methods exist to derive each of the model inputs from readily available data. The estimated values of a model input, however, can strongly differ depending on the method that was applied. The multiplication of the input factors with the USLE eventually results in large uncertainties for the soil loss estimates. A comparison of the estimated soil loss to observation data can potentially reduce the uncertainties. Yet, for large scale soil loss estimations, in-field observations are rare and their comparability to long-term soil estimates is limited. This work puts a focus on uncertainty and sensitivity analysis in large scale soil loss estimation employing the USLE with different realizations of the USLE input factors.
In a systematic analysis we developed different representations of the USLE inputs for the study domain of Kenya and Uganda with a spatial resolution of 90 m. All combinations of the generated USLE inputs resulted in 756 USLE model setups. We assessed the resulting distributions in soil loss, both spatially distributed and on district level for Kenya and Uganda. In a sensitivity analysis we analyzed the contributions of the USLE model inputs to the ranges in soil loss and analyzed their spatial patterns. We compared the calculated USLE ensemble soil estimates to available in-field data and other study results and addressed possibilities and limitations of the USLE model evaluation.
The USLE model ensemble resulted in wide ranges of estimated soil loss, exceeding the mean soil loss by over an order of magnitude particularly in hilly topographies. The study implies that a soil loss assessment with the USLE is highly uncertain and strongly depends on the realizations of the model input factors. The employed sensitivity analysis enabled us to identify spatial patterns in the importance of the USLE input factors. The C and K factors showed large scale patterns of importance in the densely vegetated part of Uganda and the dry north of Kenya, respectively. The LS factor estimates were mostly relevant in small scale heterogeneous patterns. Major challenges for the evaluation of the estimated soil losses with in-field data were due to spatial and temporal limitations of the observation data, but also due to measured soil losses describing processes that are different to the ones that are represented by the USLE.
Reference: Schürz, C., Mehdi, B., Kiesel, J., Schulz, K., and Herrnegger, M.: A systematic assessment of uncertainties in large scale soil loss estimation from different representations of USLE input factors – A case study for Kenya and Uganda, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-602, in review, 2019.
Christoph Schürz; Bano Mehdi; Jens Kiesel; Karsten Schulz; Mathew Herrnegger. A systematic assessment of uncertainties in large scale soil loss estimation from different representations of USLE input factors – A case study for Kenya and Uganda. 2020, 1 .
AMA StyleChristoph Schürz, Bano Mehdi, Jens Kiesel, Karsten Schulz, Mathew Herrnegger. A systematic assessment of uncertainties in large scale soil loss estimation from different representations of USLE input factors – A case study for Kenya and Uganda. . 2020; ():1.
Chicago/Turabian StyleChristoph Schürz; Bano Mehdi; Jens Kiesel; Karsten Schulz; Mathew Herrnegger. 2020. "A systematic assessment of uncertainties in large scale soil loss estimation from different representations of USLE input factors – A case study for Kenya and Uganda." , no. : 1.
Freshwater ecosystems have higher proportions of extinct and threatened species than terrestrial and marine ecosystems, with populations of vertebrates declined by 83% between 1970 and 2018. The pressing question is: what are the main drivers for this decline? Here we investigate the reasons for the loss of freshwater biodiversity using globally available gridded datasets at 0.5° spatial resolution on precipitation and temperature, land cover and land use, water use and dams as well as daily hydrological streamflow simulations from the ISIMIP initiative.
Across the past 50 years, we constructed annual change maps of the environmental variables along the global river networks and calculated time-variant indicators of hydrologic alteration (IHA) to depict hydrological change. We then calculated normalized indicators (e.g. proportion of threatened species) describing the current freshwater biodiversity status through species data aggregation of the International Union for Conservation of Nature's Red List of Threatened Species (IUCN Red List) categories.
By applying classification and regression trees (CART), we highlight the importance of environmental- and hydrological change on the freshwater biodiversity status based on IUCN Red List assessments on each grid cell globally. Our results reveal a large-scale spatial classification of the environmental variables and their potential impact on the ongoing freshwater biodiversity crisis.
Jens Kiesel; Tinh Vu; Karan Kakouei; Domisch Sami; Fengzhi He; Björn Guse; Nicola Fohrer; Sonja Jähnig. Disentangling the impact of global change on freshwater biodiversity decline. 2020, 1 .
AMA StyleJens Kiesel, Tinh Vu, Karan Kakouei, Domisch Sami, Fengzhi He, Björn Guse, Nicola Fohrer, Sonja Jähnig. Disentangling the impact of global change on freshwater biodiversity decline. . 2020; ():1.
Chicago/Turabian StyleJens Kiesel; Tinh Vu; Karan Kakouei; Domisch Sami; Fengzhi He; Björn Guse; Nicola Fohrer; Sonja Jähnig. 2020. "Disentangling the impact of global change on freshwater biodiversity decline." , no. : 1.
Climate change has the potential to alter the flow regimes of rivers and consequently affect the taxonomic and functional diversity of freshwater organisms. We modeled future flow regimes for the 2050 and 2090 time horizons and tested how flow regimes impact the abundance of 150 macroinvertebrate species and their functional trait compositions in one lowland river catchment (Treene) and one mountainous river catchment (Kinzig) in Europe. We used all 16 global circulation models (GCMs) and regional climate models (RCMs) of the CORDEX dataset under the RCP 8.5 scenario to calculate future river flows. The high variability in relative change of flow among the 16 climate models cascaded into the ecological models and resulted in substantially different predicted abundance values for single species. This variability also cascades into any subsequent analysis of taxonomic or functional freshwater biodiversity. Our results showed that flow alteration effects are different depending on the catchment and the underlying species pool. Documenting such uncertainties provides a basis for the further assessment of potential climate-change impacts on freshwater taxa distributions.
Karan Kakouei; Sami Domisch; Jens Kiesel; Jochem Kail; Sonja C. Jähnig. Climate model variability leads to uncertain predictions of the future abundance of stream macroinvertebrates. Scientific Reports 2020, 10, 1 -12.
AMA StyleKaran Kakouei, Sami Domisch, Jens Kiesel, Jochem Kail, Sonja C. Jähnig. Climate model variability leads to uncertain predictions of the future abundance of stream macroinvertebrates. Scientific Reports. 2020; 10 (1):1-12.
Chicago/Turabian StyleKaran Kakouei; Sami Domisch; Jens Kiesel; Jochem Kail; Sonja C. Jähnig. 2020. "Climate model variability leads to uncertain predictions of the future abundance of stream macroinvertebrates." Scientific Reports 10, no. 1: 1-12.
Riverine species have adapted to their environment, particularly to the hydrological regime. Hydrological models and the knowledge of species preferences are used to predict the impact of hydrological changes on species. Inevitably, hydrological model performance impacts how species are simulated. From the example of macroinvertebrates in a lowland‐ and a mountainous catchment, we investigate the impact of hydrological model performance and the choice of the objective function based on a set of 36 performance metrics for predicting species occurrences. Besides species abundance, we use the simulated community structure for an ecological assessment as applied for the Water Framework Directive. We investigate when a hydrological model is sufficiently calibrated to depict species abundance. For this, we postulate that performance is not sufficient when ecological assessments based on the simulated hydrology are significantly different (ANOVA, p < 0.05) from the ecological assessments based on observations. The investigated range of hydrological model performance leads to considerable variability in species abundance in the two catchments. In the mountainous catchment, links between objective functions and the ecological assessment reveal a stronger dependency of the species on the discharge regime. In the lowland catchment, multiple stressors seem to mask the dependence of the species on discharge. The most suitable objective functions to calibrate the model for species assessments are the ones that incorporate hydrological indicators used for the species prediction.
Jens Kiesel; Karan Kakouei; Björn Guse; Nicola Fohrer; Sonja C. Jähnig. When is a hydrological model sufficiently calibrated to depict flow preferences of riverine species? Ecohydrology 2020, 13, 1 .
AMA StyleJens Kiesel, Karan Kakouei, Björn Guse, Nicola Fohrer, Sonja C. Jähnig. When is a hydrological model sufficiently calibrated to depict flow preferences of riverine species? Ecohydrology. 2020; 13 (3):1.
Chicago/Turabian StyleJens Kiesel; Karan Kakouei; Björn Guse; Nicola Fohrer; Sonja C. Jähnig. 2020. "When is a hydrological model sufficiently calibrated to depict flow preferences of riverine species?" Ecohydrology 13, no. 3: 1.
Christoph Schürz; Bano Mehdi; Jens Kiesel; Karsten Schulz; Mathew Herrnegger. Supplementary material to "A systematic assessment of uncertainties in large scale soil loss estimation from different representations of USLE input factors – A case study for Kenya and Uganda". 2019, 1 .
AMA StyleChristoph Schürz, Bano Mehdi, Jens Kiesel, Karsten Schulz, Mathew Herrnegger. Supplementary material to "A systematic assessment of uncertainties in large scale soil loss estimation from different representations of USLE input factors – A case study for Kenya and Uganda". . 2019; ():1.
Chicago/Turabian StyleChristoph Schürz; Bano Mehdi; Jens Kiesel; Karsten Schulz; Mathew Herrnegger. 2019. "Supplementary material to "A systematic assessment of uncertainties in large scale soil loss estimation from different representations of USLE input factors – A case study for Kenya and Uganda"." , no. : 1.
The Universal Soil Loss Equation (USLE) is the most commonly used model to assess soil erosion by water. The model equation quantifies long-term average annual soil loss as a product of the rainfall erosivity R, soil erodibility K, slope length and steepness LS, soil cover C and support measures P. A large variety of methods exist to derive these model inputs from readily available data. However, the estimated values of a respective model input can strongly differ when employing different methods and can eventually introduce large uncertainties in the estimated soil loss. The potential to evaluate soil loss estimates at a large scale are very limited, due to scarce in-field observations and their comparability to long-term soil estimates. In this work we addressed (i) the uncertainties in the soil loss estimates that can potentially be introduced by different representations of the USLE input factors and (ii) challanges that can arise in the evaluation of uncertain soil loss estimates with observed data. In a systematic analysis we developed different representations of USLE inputs for the study domain of Kenya and Uganda. All combinations of the generated USLE inputs resulted in 756 USLE model setups. We assessed the resulting distributions in soil loss, both spatially distributed and on district level for Kenya and Uganda. In a sensitivity analysis we analyzed the contributions of the USLE model inputs to the ranges in soil loss and analyzed their spatial patterns. We compared the calculated USLE ensemble soil estimates to available in-field data and other study results and addressed possibilities and limitations of the USLE model evaluation. The USLE model ensemble resulted in wide ranges of estimated soil loss, exceeding the mean soil loss by over an order of magnitude particularly in hilly topographies. The study implies that a soil loss assessment with the USLE is highly uncertain and strongly depends on the realizations of the model input factors. The employed sensitivity analysis enabled us to identify spatial patterns in the importance of the USLE input factors. The C and K factors showed large scale patterns of importance in the densely vegetated part of Uganda and the dry north of Kenya, respectively, while LS was relevant in small scale heterogeneous patterns. Major challenges for the evaluation of the estimated soil losses with in-field data were due to spatial and temporal limitations of the observation data, but also due to measured soil losses describing processes that are different to the ones that are represented by the USLE.
Christoph Schürz; Bano Mehdi; Jens Kiesel; Karsten Schulz; Mathew Herrnegger. A systematic assessment of uncertainties in large scale soil loss estimation from different representations of USLE input factors – A case study for Kenya and Uganda. 2019, 2019, 1 -35.
AMA StyleChristoph Schürz, Bano Mehdi, Jens Kiesel, Karsten Schulz, Mathew Herrnegger. A systematic assessment of uncertainties in large scale soil loss estimation from different representations of USLE input factors – A case study for Kenya and Uganda. . 2019; 2019 ():1-35.
Chicago/Turabian StyleChristoph Schürz; Bano Mehdi; Jens Kiesel; Karsten Schulz; Mathew Herrnegger. 2019. "A systematic assessment of uncertainties in large scale soil loss estimation from different representations of USLE input factors – A case study for Kenya and Uganda." 2019, no. : 1-35.
The universal soil loss equation (USLE) is widely used to identify areas of erosion risk at regional scales. In Brandenburg, USLE R factors are usually estimated from summer rainfall, based on a relationship from the 1990s. We compared estimated and calculated factors of 22 stations with 10-minutes rainfall data. To obtain more realistic estimations, we regressed the latter to three rainfall indices (total and heavy-rainfall sums). These models were applied to estimate future R factors of 188 climate stations. To assess uncertainties, we derived eight scenarios from 15 climate models and two representative concentration pathways (RCP), and compared the effects of index choice to the choices of climate model, RCP, and bias correction. The existing regression model underestimated the calculated R factors by 40%. Moreover, using heavy-rainfall sums instead of total sums explained the variability of current R factors better, increased their future changes, and reduced the model uncertainty. The impact of index choice on future R factors was similar to the other choices. Despite all uncertainties, the results indicate that average R factors will remain above past values. Instead, the extent of arable land experiencing excessive soil loss might double until the mid-century with RCP 8.5 and unchanged land management.
Andreas Gericke; Jens Kiesel; Detlef Deumlich; Markus Venohr. Recent and Future Changes in Rainfall Erosivity and Implications for the Soil Erosion Risk in Brandenburg, NE Germany. Water 2019, 11, 904 .
AMA StyleAndreas Gericke, Jens Kiesel, Detlef Deumlich, Markus Venohr. Recent and Future Changes in Rainfall Erosivity and Implications for the Soil Erosion Risk in Brandenburg, NE Germany. Water. 2019; 11 (5):904.
Chicago/Turabian StyleAndreas Gericke; Jens Kiesel; Detlef Deumlich; Markus Venohr. 2019. "Recent and Future Changes in Rainfall Erosivity and Implications for the Soil Erosion Risk in Brandenburg, NE Germany." Water 11, no. 5: 904.
Freshwater species are adapted to and depend on various discharge conditions, such as 32 indicators of hydrologic alteration (IHA). Knowing how these indicators will be altered under climate change is essential for predicting species response and to develop mitigation concepts. The simulation of IHA under climate change is subject to considerable uncertainties which should be considered to obtain credible and robust predictions. Therefore, we investigated the major uncertainties inherent in climate change data and processing: general circulation model (GCM) and regional climate model (RCM) choice, representative concentration pathway (RCP) scenario, bias correction (BC) method, all within three mesoscale catchments in the European ecoregions: Central Plains, Central Highlands, and Alpine. Highest uncertainties were caused by the GCM and RCM choice, followed by the type of BC and the RCP. For the prediction, we reduced these uncertainties tailored to the ideal depiction of the IHA in each ecoregion. Together with a significance test, this enabled a robust depiction of the change in IHA for two future time periods. We found diverging changes within the ecoregions, caused by the complex interaction between precipitation, temperature and the governing catchment hydrological processes. The results provide an important basis for further impact research, especially for ecological freshwater studies.
Jens Kiesel; Andreas Gericke; Hendrik Rathjens; Annett Wetzig; Karan Kakouei; Sonja C. Jähnig; Nicola Fohrer. Climate change impacts on ecologically relevant hydrological indicators in three catchments in three European ecoregions. Ecological Engineering 2018, 127, 404 -416.
AMA StyleJens Kiesel, Andreas Gericke, Hendrik Rathjens, Annett Wetzig, Karan Kakouei, Sonja C. Jähnig, Nicola Fohrer. Climate change impacts on ecologically relevant hydrological indicators in three catchments in three European ecoregions. Ecological Engineering. 2018; 127 ():404-416.
Chicago/Turabian StyleJens Kiesel; Andreas Gericke; Hendrik Rathjens; Annett Wetzig; Karan Kakouei; Sonja C. Jähnig; Nicola Fohrer. 2018. "Climate change impacts on ecologically relevant hydrological indicators in three catchments in three European ecoregions." Ecological Engineering 127, no. : 404-416.
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. 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. 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. 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.
Björn Guse; Matthias Pfannerstill; Abror Gafurov; Jens Kiesel; Christian Lehr; Nicola Fohrer. Identifying the connective strength between model parameters and performance criteria. Hydrology and Earth System Sciences 2017, 21, 5663 -5679.
AMA StyleBjörn Guse, Matthias Pfannerstill, Abror Gafurov, Jens Kiesel, Christian Lehr, Nicola Fohrer. Identifying the connective strength between model parameters and performance criteria. Hydrology and Earth System Sciences. 2017; 21 (11):5663-5679.
Chicago/Turabian StyleBjörn Guse; Matthias Pfannerstill; Abror Gafurov; Jens Kiesel; Christian Lehr; Nicola Fohrer. 2017. "Identifying the connective strength between model parameters and performance criteria." Hydrology and Earth System Sciences 21, no. 11: 5663-5679.
Jens Kiesel. Review of HESSD "Mapping (dis)agreement in hydrologic projections". 2017, 1 .
AMA StyleJens Kiesel. Review of HESSD "Mapping (dis)agreement in hydrologic projections". . 2017; ():1.
Chicago/Turabian StyleJens Kiesel. 2017. "Review of HESSD "Mapping (dis)agreement in hydrologic projections"." , no. : 1.
Jens Kiesel. Review of HESSD "Incorporation of the equilibrium temperature approach in a Soil and Water Assessment Tool hydroclimatological stream temperature model". 2017, 1 .
AMA StyleJens Kiesel. Review of HESSD "Incorporation of the equilibrium temperature approach in a Soil and Water Assessment Tool hydroclimatological stream temperature model". . 2017; ():1.
Chicago/Turabian StyleJens Kiesel. 2017. "Review of HESSD "Incorporation of the equilibrium temperature approach in a Soil and Water Assessment Tool hydroclimatological stream temperature model"." , no. : 1.
In hydrological models, parameters are used to adapt the model to the conditions of the catchments. Hereby, the parameters need to be identified based on their role in controlling the hydrological behaviour in the model. For parameter identification, multiple and complementary performance criteria are used, which have to capture the different aspects of hydrological response of catchments. A reliable parameter identification depends on how distinctly a model parameter can be assigned to one of the performance criteria. We introduce an analysis that reveals the connective strength between model parameters and performance criteria. The connective strength assesses the intensity in the inter-relationship between model parameters and performance criteria. In our analysis of connective strength, model simulations are carried out based on a Latin Hypercube sampling. Ten performance criteria in cluding the NSE, the KGE and its three components (alpha, beta and r) as well as the RSR for different segments of the flow duration curve (FDC) are calculated. With a joint analysis of two regression trees (RT), it is derived how a model parameter is connected to the different performance criteria. At first, RTs are constructed using each performance criteria as target variable to detect the most relevant model parameters for each performance criteria. A second RT approach using each parameter as target variable detects which performance criterion is impacted by changes in parameter values. Based on this, appropriate performance criteria are identified for each model parameter. A high bijective connective strength is calculated for low and mid flow conditions. Moreover, the RT analyses emphasise the benefit of an individual analysis of the three components of the KGE and of the FDC segments. It is emphasised under which conditions these performance criteria provide insights into a precise parameter identification. 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 the process complexity in the catchment. Overall, the analysis of the connective strength using RTs contribute towards a better handling of parameters and performance criteria in hydrological modelling.
Björn Guse; Matthias Pfannerstill; Abror Gafurov; Jens Kiesel; Christian Lehr; Nicola Fohrer. Identifying the connective strength between model parameters and performance criteria. 2017, 2017, 1 -30.
AMA StyleBjörn Guse, Matthias Pfannerstill, Abror Gafurov, Jens Kiesel, Christian Lehr, Nicola Fohrer. Identifying the connective strength between model parameters and performance criteria. . 2017; 2017 ():1-30.
Chicago/Turabian StyleBjörn Guse; Matthias Pfannerstill; Abror Gafurov; Jens Kiesel; Christian Lehr; Nicola Fohrer. 2017. "Identifying the connective strength between model parameters and performance criteria." 2017, no. : 1-30.