Suchergebnisse

Scenario-based impactassessment of global and regional change on the semi-natural flow regime -- Contents -- List of Figures -- List of Tables -- Chapter 1 Introduction -- 1.1 Background -- 1.2 Objective -- 1.3 Scope -- Chapter 2 Study Area -- 2.1 Physiography -- 2.2 Population and economy -- Chapter 3 Materials and Methods -- 3.1 SWAT model -- 3.1.1 Model features -- 3.1.2 Model set-up of the Narew River Basin -- 3.1.3 Auto-calibration tools -- 3.1.4 Calibration and validation approach -- 3.1.5 Calibration parameters -- 3.2 Global change scenarios -- 3.2.1 The emissions scenario and General Circulation Models -- 3.2.2 Downscaling of GCM output -- 3.2.3 Parameter adjustments due to elevated CO2 -- 3.2.4 Projected changes in temperature for the 2050s -- 3.2.5 Projected changes in precipitation for the 2050s -- 3.3 Regional change scenarios -- 3.3.1 Background: SCENES project -- 3.3.2 Driving forces of regional scenarios -- 3.3.3 Qualitative changes in driving forces -- 3.3.4 Translation key -- 3.3.5 Computation of trends and modified SWAT parameters -- 3.4 Environmental flow requirements -- 3.4.1 Selection of biota -- 3.4.2 Building blocks of the environmental flow regime -- 3.4.3 Bankfull flow estimation -- 3.5 Development of indicators -- 3.5.1 State indicators: reliability and resilience -- 3.5.2 Environmental flow impact indicators -- 3.5.3 Experimental design -- Chapter 4 Results and Discussion -- 4.1 SWAT calibration and validation -- 4.1.1 Uncertainty analysis with SUFI-2 -- 4.1.2 Calibration and validation with PSO -- 4.1.3 Spatial extent of the analysis -- 4.1.4 Validation of indicators -- 4.1.5 Discussion -- 4.2 Impact on catchment water balance -- 4.2.1 Annual and seasonal results -- 4.2.2 Scenario decomposition -- 4.3 Environmental Flow Impact Indicators -- 4.3.1 Hands-off flow -- 4.3.2 Spawning and nursery habitats for pike.

The objective of this study was to apply a previously developed large-scale and high-resolution SWAT model of the Vistula and the Odra basins, calibrated with the focus of natural flow simulation, in order to assess the impact of three different dam reservoirs on streamflow using the Indicators of Hydrologic Alteration (IHA). A tailored spatial calibration approach was designed, in which calibration was focused on a large set of relatively small non-nested sub-catchments with semi-natural flow regime. These were classified into calibration clusters based on the flow statistics similarity. After performing calibration and validation that gave overall positive results, the calibrated parameter values were transferred to the remaining part of the basins using an approach based on hydrological similarity of donor and target catchments. The calibrated model was applied in three case studies with the purpose of assessing the effect of dam reservoirs (Włocławek, Siemianówka and Czorsztyn Reservoirs) on streamflow alteration. Both the assessment based on gauged streamflow (Before-After design) and the one based on simulated natural streamflow showed large alterations in selected flow statistics related to magnitude, duration, high and low flow pulses and rate of change. Some benefits of using a large-scale and high-resolution hydrological model for the assessment of streamflow alteration include: (1) providing an alternative or complementary approach to the classical Before-After designs, (2) isolating the climate variability effect from the dam (or any other source of alteration) effect, (3) providing a practical tool that can be applied at a range of spatial scales over large area such as a country, in a uniform way. Thus, presented approach can be applied for designing more natural flow regimes, which is crucial for river and floodplain ecosystem restoration in the context of the European Union's policy on environmental flows.

Abstract
Background
Uncertain results of the status assessment performed as required by the Water Framework Directive can be responsible for misclassification of a water body's status and may lead either to risk due to undertaking unnecessary remediation actions or risk of penalties for refraining from any action and not reaching environmental goals. Based on Bayesian decision theory, optimal decision tables are shown for two examples of water quality indicators, for a river catchment in central Poland. To overcome the problem of scarcity of publicly available monitoring data, the existing SWAT model for the studied catchment was used to generate nutrient concentration time series for the baseline conditions and under different scenarios. The status classes assessed based on annual mean concentrations of daily values for total phosphorus and total nitrogen were adopted as the 'true' status classes of the water bodies based on each indicator. SWAT simulation results enabled calculation of probability distributions of concentrations for the stochastic states of the water body, both for the period before and after the performance of corrective actions.

Results
Bayesian decision tables consisted of alternative management decisions including modernization of the existing wastewater treatment plants in the case of phosphorous and also of fallowing agricultural areas in the case of nitrogen. An example of a penalty calculation procedure is presented in the event that the subject of the case before the EU Court of Justice would be failure to achieve the environmental objectives by all water bodies belonging to the selected catchment.

Conclusions
Detailed discussion of this analysis indicates the potential benefits in terms of minimization of costs/losses that the proposed methodology may bring to the protection of surface waters.
The presented method of risk analysis for making decisions on remedial actions when uncertainty exists about the water status assessment, can be considered as a prototype of a general methodology prepared for implementation in water protection. Unfortunately paying fines instead of taking remediation measures might be optimal for uncertain status of water bodies.

AbstractThis study aimed at evaluating the scale and costs of an environmentally and economically optimal set of Best Management Practices (BMPs) for agricultural pollution abatement in Lithuania in order to reach water protection goals in both inland and marine waters by distributing BMPs optimally in space, while taking climate change impacts into consideration. The assessment of BMPs impact involved the use of the SWAT model by applying two climate change representative concentration pathways (RCP4.5 and RCP8.5) and two time horizons (mid-century and end-century), as well as five BMPs (arable land conversion to grasslands, reduced fertilization, no-till farming, catch-crops, and stubble fields throughout winter). The optimization of the set of BMPs employed a genetic algorithm. The results suggest that the need for BMPs application will increase from 52% of agricultural areas in the historical period up to 65% by the end of century in the RCP8.5 scenario. This means less arable land could actually be used for crop production in the future if water protection targets are met. The high costs for reaching water targets would rise even more, i.e. by 173% for RCP4.5, and by 220% for the RCP8.5 scenario, reaching approximately 200 million euros/year. In such a context, the BMP optimization approach is essential for significant reduction of the costs. Winter cover crops and reduced fertilization show the best effectiveness and cost balance, and will therefore be essential in pursuing water protection targets.

AbstractInput data collection, quality assurance and preparation are central but time_consuming steps in environmental modeling. Errors due to manual processing of model input data can result in an incorrect representation of an environmental system and may consequently lead to implausible model simulations. Correct input data preparation and thorough quality check at an early stage of the model setup procedure are essential to build confidence in model simulation results. Typically, in environmental model applications, many steps in the input data preparation phase have to be repeated with the inflow of new, additional or corrected data. In this study, we selected the widely used SWAT + ecohydrological model as an illustrative example to investigate challenges related to input data preparation. To assist in these tasks, we developed an R package named SWATprepR, which provides functions for typical and repeating SWAT + model input data preparation tasks. The package supports the preparation of weather input files, atmospheric deposition, soil parameters, crop rotations, and observed (control or calibration) data, to name a few, presently with focus on European applications. The SWATprepR functions are integrated in R script workflows and can help SWAT + modelers to avoid repetitive tasks, secure reproducibility and transparently document the data processing steps. Application of the package is illustrated with a test case of a SWAT + model for a small catchment in central Poland.