Uncertainty contributions to low-flow projections in Austria

The main objective of the paper is to understand the contributions to the uncertainty in low-flow projections resulting from hydrological model uncertainty and climate projection uncertainty. Model uncertainty is quantified by different parameterisations of a conceptual semi-distributed hydrologic model (TUWmodel) using 11 objective functions in three different decades (1976–1986, 1987–1997, 1998–2008), which allows for disentangling the effect of the objective function-related uncertainty and temporal stability of model parameters. Climate projection uncertainty is quantified by four future climate scenarios (ECHAM5-A1B, A2, B1 and HADCM3-A1B) using a delta change approach. The approach is tested for 262 basins in Austria. The results indicate that the seasonality of the low-flow regime is an important factor affecting the performance of model calibration in the reference period and the uncertainty of Q95 low-flow projections in the future period. In Austria, the range of simulated Q95 in the reference period is larger in basins with a summer low-flow regime than in basins with a winter low-flow regime. The accuracy of simulated Q95 may result in a range of up to 60 % depending on the decade used for calibration. The low-flow projections of Q95 show an increase of low flows in the Alps, typically in the range of 10–30 % and a decrease in the south-eastern part of Austria mostly in the range −5 to −20 % for the climate change projected for the future period 2021–2050, relative the reference period 1978–2007. The change in seasonality varies between scenarios, but there is a tendency for earlier low flows in the northern Alps and later low flows in eastern Austria. The total uncertainty of Q95 projections is the largest in basins with a winter low-flow regime and, in some basins the range of Q95 projections exceeds 60 %. In basins with summer low flows, the total uncertainty is mostly less than 20 %. The ANOVA assessment of the relative contribution of the three main variance components (i.e. climate scenario, decade used for model calibration and calibration variant representing different objective function) to the low-flow projection uncertainty shows that in basins with summer low flows climate scenarios contribute more than 75 % to the total projection uncertainty. In basins with a winter low-flow regime, the median contribution of climate scenario, decade and objective function is 29, 13 and 13 %, respectively. The implications of the uncertainties identified in this paper for water resource management are discussed

Integrated impact modelling of climate change and adaptation policies on land use and water resources in Austria"

Climate change is a major driver of land use and ecosystems. Changes in climatic conditions will affect the quality and quantity of water resources. Autonomous adaptation by farmers can influence the compliance with the good ecological and chemical status according to the EU Water Framework Directive. We present results from an integrated impact modelling framework (IIMF) to analyze policy options for planned adaptation in agricultural land use and sustainable management of land and water resources until 2040. The IIMF consists of the bio-physical process model EPIC, the regional land use optimization model PASMA[grid], the quantitative precipitation/runoff TUW model, and the surface water emission model MONERIS. Stakeholder driven scenarios facilitate multi-actor knowledge transfer. Climate change scenarios are combined with socio-economic and policy pathways. The latter include water protection measures on fertilization management, soil and crop rotation management. The results show that the selected climate change and policy scenarios impact average agricultural gross margins by ±2%. However, regional impacts are more severe particularly under assumptions of decreasing precipitation patterns. The water protection policies can alleviate pressures compared to the business as usual scenario but do not lead to sufficient conditions in all watersheds. To conclude, the IIMF is able to capture the interfaces between water quality and land use and to cover multiple policy and climate scenarios. However, despite efforts to increase the robustness of data and model interfaces, uncertainties need to be tackled in subsequent studies

Kapitel 8. Landnutzung und Klimawandel im Kontext der Nachhaltigen Entwicklungsziele

Dieses Kapitel präsentiert und bewertet den aktuellen Stand des Wissens zum Konnex Landnutzung und Klimawandel in Österreich aus dem systemischen Blickwinkel der UN Agenda 2030 für eine Nachhaltige Entwicklung. Dabei wird dem Thema entsprechend auf die Verflechtungen zwischen den lokalen, nationalen und internationalen Ebenen eingegangen. Die Menschheit befindet sich in kritischen, vielfältigen und vernetzten Krisen. Integrative und globale Lösungsansätze, wie sie in der Agenda 2030 festgeschrieben sind, haben für diese multiplen Krisen ein hohes Lösungspotenzial

Global Distribution of Human-Associated Fecal Genetic Markers in Reference Samples from Six Continents

Numerous bacterial genetic markers are available for the molecular detection of human sources of fecal pollution in environmental waters. However, widespread application is hindered by a lack of knowledge regarding geographical stability, limiting implementation to a small number of well-characterized regions. This study investigates the geographic distribution of five human-associated genetic markers (HF183/BFDrev, HF183/BacR287, BacHum-UCD, BacH, and Lachno2) in municipal wastewaters (raw and treated) from 29 urban and rural wastewater treatment plants (750-4»400»000 population equivalents) from 13 countries spanning six continents. In addition, genetic markers were tested against 280 human and nonhuman fecal samples from domesticated, agricultural and wild animal sources. Findings revealed that all genetic markers are present in consistently high concentrations in raw (median log10 7.2-8.0 marker equivalents (ME) 100 mL-1) and biologically treated wastewater samples (median log10 4.6-6.0 ME 100 mL-1) regardless of location and population. The false positive rates of the various markers in nonhuman fecal samples ranged from 5% to 47%. Results suggest that several genetic markers have considerable potential for measuring human-associated contamination in polluted environmental waters. This will be helpful in water quality monitoring, pollution modeling and health risk assessment (as demonstrated by QMRAcatch) to guide target-oriented water safety management across the globe.Fil: Mayer, René E.. Vienna University of Technology; Austria. Interuniversity Cooperation Centre for Water and Health; AustriaFil: Reischer, Georg. Vienna University of Technology; AustriaFil: Ixenmaier, Simone K.. Vienna University of Technology; Austria. Interuniversity Cooperation Centre for Water and Health; AustriaFil: Derx, Julia. Vienna University of Technology; AustriaFil: Blaschke, Alfred Paul. Vienna University of Technology; AustriaFil: Ebdon, James E.. University of Brighton; Reino UnidoFil: Linke, Rita. Vienna University of Technology; Austria. Interuniversity Cooperation Centre Water And Health; AustriaFil: Egle, Lukas. Vienna University of Technology; AustriaFil: Ahmed, Warish. Csiro Land And Water; AustraliaFil: Blanch, Anicet R.. Universidad de Barcelona; EspañaFil: Byamukama, Denis. Makerere University; UgandaFil: Savill, Marion. Affordable Water Limited;Fil: Mushi, Douglas. Sokoine University Of Agriculture; TanzaniaFil: Cristobal, Hector Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Investigaciones para la Industria Química. Universidad Nacional de Salta. Facultad de Ingeniería. Instituto de Investigaciones para la Industria Química; ArgentinaFil: Edge, Thomas A.. Canada Centre for Inland Waters. Environment and Climate Change Canada; CanadáFil: Schade, Margit A.. Bavarian Environment Agency; AlemaniaFil: Aslan, Asli. Georgia Southern University; Estados UnidosFil: Brooks, Yolanda M.. Michigan State University; Estados UnidosFil: Sommer, Regina. Interuniversity Cooperation Centre Water And Health; Austria. Medizinische Universitat Wien; AustriaFil: Masago, Yoshifumi. Tohoku University; JapónFil: Sato, Maria I.. Cia. Ambiental do Estado de Sao Paulo. Departamento de Análises Ambientais; BrasilFil: Taylor, Huw D.. University of Brighton; Reino UnidoFil: Rose, Joan B.. Michigan State University; Estados UnidosFil: Wuertz, Stefan. Nanyang Technological University. Singapore Centre for Environmental Life Sciences Engineering and School of Civil and Environmental Engineering; SingapurFil: Shanks, Orin. U.S. Environmental Protection Agency; Estados UnidosFil: Piringer, Harald. Vrvis Research Center; AustriaFil: Mach, Robert L.. Vienna University of Technology; AustriaFil: Savio, Domenico. Karl Landsteiner University of Health Sciences; AustriaFil: Zessner, Matthias. Vienna University of Technology; AustriaFil: Farnleitner, Andreas. Vienna University of Technology; Austria. Interuniversity Cooperation Centre Water And Health; Austria. Karl Landsteiner University of Health Sciences; Austri

Technische Zusammenfassung

Die Technische Zusammenfassung des APCC-Sonderberichts ″Landnutzung und Klimawandel in Österreich″ umfasst die Kernbotschaften der Kapitel 1–9. In ihr sind die Hauptaussagen zu den sozioökonomischen und klimatischen Treibern der Landnutzungsänderungen, zu den Auswirkungen von Landnutzung und -bewirtschaftung auf den Klimawandel, zu Minderungs- und Anpassungsoptionen im Kontext nachhaltiger Entwicklungsziele sowie zu Synergien, Zielkonflikten und Umsetzungsbarrieren von Klimamaßnahmen enthalten

An evaluation of analytical stream to groundwater exchange models: a comparison of gross exchanges based on different spatial flow distribution assumptions

In this paper, a new method for estimating gross gains and losses between streams and groundwater is developed and evaluated against two existing approaches. These three stream to groundwater exchange (SGE) estimation methods are distinct in their assumptions on the spatial distribution of the inflowing and outflowing fluxes along the stream. The two existing methods assume that the fluxes are independent and in a specific sequence, while the third and newly derived method assumes that both fluxes occur simultaneously and uniformly throughout the stream. The analytic expressions in connection to the underlying assumptions are investigated through numerical stream simulations to evaluate the individual and mutual dynamics of the SGE estimation methods and to understand the causes for the differences in performance. The results show that the three methods produce significantly different results and that the mean absolute normalized error can have up to an order of magnitude difference between the methods. These differences between the SGE methods are entirely due to the assumptions of the SGE spatial dynamics of the methods, and the performance for a particular approach strongly decreases if its assumptions are not fulfilled. The assessment of the three methods through numerical simulations, representing a variety of SGE dynamics, shows that the method introduced, considering simultaneous stream gains and losses, presents overall the highest performance according to the simulations. As the existing methods provide the minimum and maximum realistic values of SGE within a stream reach, all three methods could be used in conjunction for a full range of estimates. These SGE methods can also be used in conjunction with other end-member mixing models to acquire even more hydrologic information as both require the same type of input data

Mass Balance of Selected Pharmaceuticals in an Austrian River Catchment Area: Estimation of the Different Source Contributions

The study refers to the assessment of the annual mass loadings of two pharmaceuticals (PhCs), sulfamethoxazole (SMX) and carbamazepine (CBZ), released in the river of an Austrian catchment area by wastewater treatment plant (WWTP) effluent, combined sewage overflows (CSOs), surface runoff, tile drainage and deep water (referring to the sludge- or manure-amended soil). WWTP effluent and excess sludge loadings, based on PhC national human consumption, literature excretion rates, were modeled by Activity SimpleTreat. CSO loading was modelled by MoRE. PhC load in manure was estimated on the basis of the animal annual production and literature data of concentration. Surface runoff, tile drainage and deep water loadings were modelled integrating a literature “leachate” approach with the expected PhC phenomena of accumulation, degradation and erosion occurring in the soil. The study develops a mass balance of the different pharmaceutical loading contributions to the receiving water body by means of STAN model and highlights the uncertainties associated to the selected values in the estimation. It emerges that manure amount applied on the soil is fundamental in defining the priority contributions among the different sources (WWTP effluent, CSOs, surface runoff, tile drainage and deep water)