Water and nutrient simulations using the HYPE model for Sweden vs. the Baltic Sea basin – influence of input-data quality and scale

Arheimer, Berit; Dahné, Joel; Donnelly, Chantal; Lindström, Göran; Strömqvist, Johan

doi:10.2166/nh.2012.010

Cited by 72 publications

(69 citation statements)

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“…The Swedish application (S-HYPE) has also been evaluated against independent internal model variables such as snow pack, lake water level and groundwater fluctuation (Arheimer et al 2011a, b), which also makes that model application more trustworthy. Arheimer et al (2012) thus compared model results for Sweden using both the S-HYPE and the Balt-HYPE and, in short, that study showed that especially water discharge was much better simulated using the S-HYPE, with most relative errors are \10 % for S-HYPE and \25 % for Balt-HYPE. Both the applications normally reproduced mean concentration for N within 25 % of the observed mean values, while P showed a larger scatter.…”

Section: Uncertainties In the Results From The Model Experimentsmentioning

confidence: 99%

“…S2, Electronic supplementary material). A more comprehensive overview of model performance can be found in Arheimer et al (2012), including several goodness-of-fit statistics and a sensitivity study. In general, the Balt-HYPE model overestimates nutrient discharge on an annual basis, especially in the southern part of the catchment.…”

Section: Balt-hype Model Estimation Of Nutrient Loadmentioning

confidence: 99%

“…Balt-HYPE (Donnelly Electronic supplementary material The online version of this article (doi:10.1007/s13280-012-0323-0) contains supplementary material, which is available to authorized users. Arheimer et al 2012). This model simulates water and nutrient concentrations from land to the sea on a daily basis, including major sources and sinks along the flow path.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Climate Change Impact on Riverine Nutrient Load and Land-Based Remedial Measures of the Baltic Sea Action Plan

Arheimer

Dahné

Donnelly

2012

AMBIO

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To reduce eutrophication of the Baltic Sea, all nine surrounding countries have agreed upon reduction targets in the HELCOM Baltic Sea Action Plan (BSAP). Yet, monitoring sites and model concepts for decision support are few. To provide one more tool for analysis of water and nutrient fluxes in the Baltic Sea basin, the HYPE model has been applied to the region (called Balt-HYPE). It was used here for experimenting with land-based remedial measures and future climate projections to quantify the impacts of these on water and nutrient loads to the sea. The results suggest that there is a possibility to reach the BSAP nutrient reduction targets by 2100, and that climate change may both aggravate and help in some aspects. Uncertainties in the model results are large, mainly due to the spread of the climate model projections, but also due to the hydrological model.

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Section: Uncertainties In the Results From The Model Experimentsmentioning

confidence: 99%

Section: Balt-hype Model Estimation Of Nutrient Loadmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Climate Change Impact on Riverine Nutrient Load and Land-Based Remedial Measures of the Baltic Sea Action Plan

Arheimer

Dahné

Donnelly

2012

AMBIO

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“…Uhlenbrook and Sieber, 2005;Vaché and McDonnell, 2006;Iorgulescu et al, 2007;McGuire et al, 2007;Page et al, 2007;Fenicia et al, 2010;Lindström et al, 2010;Lyon et al, 2010b;Birkel et al, 2011a,c;Arheimer et al, 2012;Capell et al, 2012b;Bertuzzo et al, 2013). For example, Dunn et al (2007Dunn et al ( , 2010) used a conceptual model in the context of virtual experiments (cf.…”

Section: Hrachowitz Et Al: What Can Flux Tracking Teach Us About mentioning

confidence: 99%

What can flux tracking teach us about water age distribution patterns and their temporal dynamics?

Hrachowitz

Savenije

Bogaard

et al. 2013

Hydrol. Earth Syst. Sci.

269

443

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Abstract. The complex interactions of runoff generation processes underlying the hydrological response of streams remain not entirely understood at the catchment scale. Extensive research has demonstrated the utility of tracers for both inferring flow path distributions and constraining model parameterizations. While useful, the common use of linearity assumptions, i.e. time invariance and complete mixing, in these studies provides only partial understanding of actual process dynamics. Here we use long-term (< 20 yr) precipitation, flow and tracer (chloride) data of three contrasting upland catchments in the Scottish Highlands to inform integrated conceptual models investigating different mixing assumptions. Using the models as diagnostic tools in a functional comparison, water and tracer fluxes were then tracked with the objective of exploring the differences between different water age distributions, such as flux and resident water age distributions, and characterizing the contrasting water age pattern of the dominant hydrological processes in the three study catchments to establish an improved understanding of the wetness-dependent temporal dynamics of these distributions.The results highlight the potential importance of partial mixing processes which can be dependent on the hydrological functioning of a catchment. Further, tracking tracer fluxes showed that the various components of a model can be characterized by fundamentally different water age distributions which may be highly sensitive to catchment wetness history, available storage, mixing mechanisms, flow path connectivity and the relative importance of the different hydrological processes involved. Flux tracking also revealed that, although negligible for simulating the runoff response, the omission of processes such as interception evaporation can result in considerably biased water age distributions. Finally, the modeling indicated that water age distributions in the three study catchments do have long, power-law tails, which are generated by the interplay of flow path connectivity, the relative importance of different flow paths as well as by the mixing mechanisms involved. In general this study highlights the potential of customized integrated conceptual models, based on multiple mixing assumptions, to infer system internal transport dynamics and their sensitivity to catchment wetness states.

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“…The HYPE (Hydrological Predictions for the Environment) model is a process-based hydrological model developed for high-resolution multi-basin applications, which has been applied on various spatial scales (from tens to millions of square kilometers) and hydroclimatological conditions (Lindström et al, 2010;Strömqvist et al, 2012;Arheimer et al, 2012;Andersson et al, 2015;Gelfan et al, 2017). The model is based on a semi-distributed approach where the hydrological system is represented by a network of sub-basins, which are further divided into classes that can be selected to represent combinations of soil type and land cover or elevation zones.…”

Section: Hype Modelmentioning

confidence: 99%

Near-real-time adjusted reanalysis forcing data for hydrology

Berg

Donnelly

Gustafsson

2018

Hydrol. Earth Syst. Sci.

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Abstract. Extending climatological forcing data to current and real-time forcing is a necessary task for hydrological forecasting. While such data are often readily available nationally, it is harder to find fit-for-purpose global data sets that span long climatological periods through to near-real time. Hydrological simulations are generally sensitive to bias in the meteorological forcing data, especially relative to the data used for the calibration of the model. The lack of highquality daily resolution data on a global scale has previously been solved by adjusting reanalysis data with global gridded observations. However, existing data sets of this type have been produced for a fixed past time period determined by the main global observational data sets. Long delays between updates of these data sets leaves a data gap between the present day and the end of the data set. Further, hydrological forecasts require initializations of the current state of the snow, soil and lake (and sometimes river) storage. This is normally conceived by forcing the model with observed meteorological conditions for an extended spin-up period, typically at a daily time step, to calculate the initial state. Here, we present and evaluate a method named HydroGFD (Hydrological Global Forcing Data) to combine different data sets in order to produce near-real-time updated hydrological forcing data of temperature and precipitation that are compatible with the products covering the climatological period. HydroGFD resembles the already established WFDEI (WATCH Forcing Data-ERA-Interim) method (Weedon et al., 2014) closely but uses updated climatological observations, and for the near-real time it uses interim products that apply similar methods. This allows HydroGFD to produce updated forcing data including the previous calendar month around the 10th of each month. We present the HydroGFD method and therewith produced data sets, which are evaluated against global data sets, as well as with hydrological simulations with the HYPE (Hydrological Predictions for the Environment) model over Europe and the Arctic regions. We show that HydroGFD performs similarly to WFDEI and that the updated period significantly reduces the bias of the reanalysis data. For real-time updates until the current day, extending HydroGFD with operational meteorological forecasts, a large drift is present in the hydrological simulations due to the bias of the meteorological forecasting model.

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Water and nutrient simulations using the HYPE model for Sweden vs. the Baltic Sea basin – influence of input-data quality and scale

Cited by 72 publications

References 0 publications

Climate Change Impact on Riverine Nutrient Load and Land-Based Remedial Measures of the Baltic Sea Action Plan

Climate Change Impact on Riverine Nutrient Load and Land-Based Remedial Measures of the Baltic Sea Action Plan

What can flux tracking teach us about water age distribution patterns and their temporal dynamics?

Near-real-time adjusted reanalysis forcing data for hydrology

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