2014
DOI: 10.1111/jawr.12268
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Seasonal and Regional Patterns in Performance for a Baltic Sea Drainage Basin Hydrologic Model

Abstract: This study evaluates the ability of the Catchment SIMulation (CSIM) hydrologic model to describe seasonal and regional variations in river discharge over the entire Baltic Sea drainage basin (BSDB) based on 31 years of monthly simulation from 1970 through 2000. To date, the model has been successfully applied to simulate annual fluxes of water from the catchments draining into the Baltic Sea. Here, we consider spatiotemporal bias in the distribution of monthly modeling errors across the BSDB since it could pot… Show more

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Cited by 8 publications
(5 citation statements)
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References 73 publications
(100 reference statements)
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“…Considering several estimation techniques of various complexity can be useful to identify and isolate strengths and weaknesses across various models or approaches (e.g., Lyon et al. ). Also, since a key challenge in securing and restoring freshwater ecosystem sustainability and floodplain function is synthesizing the knowledge and experience gained from individual case studies into a scientific framework that supports and guides the development of environmental flow standards at the regional scale (Poff et al.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Considering several estimation techniques of various complexity can be useful to identify and isolate strengths and weaknesses across various models or approaches (e.g., Lyon et al. ). Also, since a key challenge in securing and restoring freshwater ecosystem sustainability and floodplain function is synthesizing the knowledge and experience gained from individual case studies into a scientific framework that supports and guides the development of environmental flow standards at the regional scale (Poff et al.…”
Section: Discussionmentioning
confidence: 99%
“…This could be useful in a region like the PK watershed where the dominant hydrologic processes (i.e., Walter et al 2000;Qiu 2010a) could have influence on the validity of modeling assumptions for more process-based approaches (e.g., Easton et al 2008). Considering several estimation techniques of various complexity can be useful to identify and isolate strengths and weaknesses across various models or approaches (e.g., Lyon et al 2015). Also, since a key challenge in securing and restoring freshwater ecosystem sustainability and floodplain function is synthesizing the knowledge and experience gained from individual case studies into a scientific framework that supports and guides the development of environmental flow standards at the regional scale (Poff et al 1997;Arthington et al 2006;Gonz alez et al 2017).…”
Section: Is Such a Pragmatic Approach Good Enough?mentioning
confidence: 99%
“…The improved temporal resolution may help to reduce the uncertainties in monthly and yearly estimates of water quality/or nutrient loads in the coastal zone. Uncertainties in nutrient load estimates in hydrological models are usually determined by the combined uncertainties in discharge and nutrient loading estimates [65][66][67]. The majority of rivers in Sweden are well monitored for discharge, but large uncertainty may arise because of the monthly (or even less frequent) in situ monitoring of nutrients [67,68] and other water quality parameters.…”
Section: Potential Advantages and Challenges For Monitoring Water Quamentioning
confidence: 99%
“…Even with data available for some testing, it may be difficult to check all possible misleading aspects of crossregional parameter application, broad-scale calibration or use of site-specifically untested relationships and constant parameter values across space and time. For example, Lyon et al (2015) showed how calibration and parameterization at one spatiotemporal scale (regional and annually) in the BSDB can lead to fundamental processes misrepresentation in hydrology at another (catchment and monthly) with implications for nutrient transport. There is much debate about the net value of increased parameterization brought about by implementing spatiotemporally variable parameters (e.g., Vaze et al, 2010;Romano et al, 2011;Luo et al, 2012) since the gain is to some degree counteracted by a loss of parsimony, which can complicate identification of causal relationships (Schaefli et al, 2011;Archibald et al, 2014).…”
Section: Introductionmentioning
confidence: 99%