Abstract:The Federal State of Saxony (Germany) transposed the EU Water Framework Directive into state law, identifying 617 surface water bodies (rivers and streams) for implementation of the water framework directive (WFD). Their ecological status was classified by biological quality elements (macrophytes and phytobenthos, benthic invertebrates and fish, and in large rivers, phytoplankton) and specific synthetic and non-synthetic pollutants. Hydromorphological and physico-chemical quality elements were used to identify significant anthropogenic pressures, which surface water bodies are susceptible to, and to assess the effect of these pressures on the status of surface water bodies. In 2009, the data for classification of the ecological status and the main pressures and impacts on water bodies were published in the river basin management plans (RBMP) of the Elbe and Oder rivers. To that date, only 23 (4%) streams achieved an ecological status of "good", while the rest failed to achieve the environmental objective. The two main reasons for the failure were significant alterations to the stream morphology (81% of all streams) and nutrient enrichment (62%) caused by point (industrial and municipal waste OPEN ACCESSWater 2012, 4 888 water treatment plants) and non-point (surface run-off from arable fields, discharges from urban drainages and decentralized waste water treatment plants) sources. It was anticipated that a further 55 streams would achieve the environmental objective by 2015, but the remaining 539 need extended deadlines.

Office of Environmental Restoration and W a s t e EXECUTIVE SUMMARYThis report Summari zes, for the 12-month period Jan-through December 1994, the available dynamic hydrologic data collected on the White Oak Creek (WOC) watershed as well as information collected on surface flow systems in the surrounding vicinity that may affect the quality or quantity of surface water in the watershed. The collection of hydrologic data is one component of numerous, ongoing Oak RidgeNational Laboratory (ORNL) environmental studies and monitoring programs and is intended to 1.characterize the quantity and quality of water in the surface flow system, 2.assist with the planning and assessment of remedial action activities, 3.provide long-term availability of data and quality assurance of these data, and 4.support long-term measures of contaminant fluxes at a spatial scale to provide a comprehensive picture of watershed performance that is commensurate with future remedial actions.Characterization of the hydrology of the WOC watershed provides a better understanding of the processes that influence contaminant transport in the watershed. Identification of spatial and temporal trends in hydrologic parameters and mechanisms that affect the movement of contaminants supports the development of interim Corrective measures and remedial restoration alternatives. In addition, on-going hydrologic monitoring supports long-term assessment of the effectiveness of remedial actions in limiting the transport of contaminants across W a s t e Area Grouping (WAG) boundaries and ultimately to the offsite environment, For these reasons, it is of paramount importance to the Environmental Restoration (ER) Program to collect and report hydrologic data, an activity that contributes to the Surface Water Program of the ER Program.This report provides and describes sources of hydrologic data for ER activities that use monitoring data to quantify and assess the impact from releases of contaminants from ORNL WAGS. The majority of the data summarized in this report is available from the Oak Ridge Environmental Information System (OREIS). Surface-water data available within the WOC flow system include discharge and runoff and surfacewater quality. Climatological data available for the Oak Ridge area include precipitation, temperature, relative humidity, wind speed and wind direction, pan evaporation, and solar radiation. Anomalies in the data and problems with monitoring and accuracy are discussed. Appendices contain daily precipitation measurements, daily discharge at surface-water monitoring stations, descriptions of sutf~wakrmonitoring stations located in the vicinity of the WOC watershed, and rating table updates fbr hydraulic control structures that have been recalibrated since the 1993 report.xvii

Plant phenology is well known to be affected by meteorology. Observed changes in the occurrence of phenological phases are commonly considered some of the most obvious effects of climate change. However, current climate models lack a representation of vegetation suitable for studying future changes in phenology itself. This study presents a statistical-dynamical modeling approach for Bavaria in southern Germany, using over 13,000 paired samples of phenological and meteorological data for analyses and climate change scenarios provided by a state-of-the-art regional climate model (RCM). Anomalies of several meteorological variables were used as predictors and phenological anomalies of the flowering date of the test plant Forsythia suspensa as predictand. Several cross-validated prediction models using various numbers and differently constructed predictors were developed, compared, and evaluated via bootstrapping. As our approach needs a small set of meteorological observations per phenological station, it allows for reliable parameter estimation and an easy transfer to other regions. The most robust and successful model comprises predictors based on mean temperature, precipitation, wind velocity, and snow depth. Its average coefficient of determination and root mean square error (RMSE) per station are 60% and ± 8.6 days, respectively. However, the prediction error strongly differs among stations. When transferred to other indicator plants, this method achieves a comparable level of predictive accuracy. Its application to two climate change scenarios reveals distinct changes for various plants and regions. The flowering date is simulated to occur between 5 and 25 days earlier at the end of the twenty-first century compared to the phenology of the reference period (1961-1990).

Climate models are the tool of choice for scientists researching climate change. Like all models they suffer from errors, particularly systematic and location-specific representation errors. One way to reduce these errors is model output statistics (MOS) where the model output is fitted to observational data with machine learning. In this work, we assess the use of convolutional Deep Learning climate MOS approaches and present the ConvMOS architecture which is specifically designed based on the observation that there are systematic and location-specific errors in the precipitation estimates of climate models. We apply ConvMOS models to the simulated precipitation of the regional climate model REMO, showing that a combination of per-location model parameters for reducing location-specific errors and global model parameters for reducing systematic errors is indeed beneficial for MOS performance. We find that ConvMOS models can reduce errors considerably and perform significantly better than three commonly used MOS approaches and plain ResNet and U-Net models in most cases. Our results show that non-linear MOS models underestimate the number of extreme precipitation events, which we alleviate by training models specialized towards extreme precipitation events with the imbalanced regression method DenseLoss. While we consider climate MOS, we argue that aspects of ConvMOS may also be beneficial in other domains with geospatial data, such as air pollution modeling or weather forecasts.

Some of the most obvious consequences of anthropogenic climate change are observed changes in the dates of the occurrence of phenological events. Most prominently, observations from the Northern Hemisphere's extratropics indicate an earlier occurrence of spring events. Recent climate models include land surface schemes that provide representation of the vegetation. However, they are limited in simulating the plants' response to climate change. In this study we present results of a dynamical-statistical modeling approach for phenology in southeastern Germany, combining climate change simulations provided by a high resolution, state-of-the-art regional climate model (RCM) with three different types of regression methods: ordinary least squares (OLS), least absolute deviation (LAD) and random forest (RFO). We focus on changes in the day of the year (DOY) of Forsythia suspensa flowering, the earliest phenophase of the growing season in Bavaria. Based on roughly 2600 observations, collected at 94 phenological and 26 meteorological stations between 1952 and 2013, we compare the regressions via a bootstrap, using once 13 and once 4 meteorological variables as predictors. Altogether, we find the regressions with less variables to be more robust, while the regression estimates are nearly identical. Explained variance and RMSE (root mean square error) are 54.8 % and 8.8 days for RFO and 51.2 % and 9.1 days for the other regressions. These trained and cross validated statistical models are used to estimate the effects of future climate change on the DOY by applying them to the RCM simulations. For OLS or LAD, under a low (high) greenhouse gas emission scenario, we find a mean advance of the DOY of 8 (15) days by the end of the 21th century compared to the base period from 1961 to 1990. The spatial pattern of the change resembles the topography, with the strongest trends in the DOY over mountainous regions as a consequence of a simultaneous rise in temperatures and reduction in snow depth. RFO is restricted to the range of the observations and hence the response to the simulated climate is damped, resulting in an advance of DOY of only 5 (8) days and a reduction in variance. There is no apparent spatial pattern identifiable. Altogether, we find OLS and LAD to be more suitable for dynamical-statistical modeling of phenology than RFO. Zusammenfassung: Zu den augenfälligsten Folgen des anthropogenen Klimawandels gehören beobachtete Veränderungen im zeitlichen Auftreten von phänologischen Ereignissen. Am markantesten deuten Beobachtungen aus den Außertropen der Nordhalbkugel auf den früheren Eintritt von Frühlingsereignissen hin. Aktuelle Klimamodelle verfügen über Landoberflächenschemata zur Abbildung der Vegetationsdynamik, allerdings sind sie nur eingeschränkt dazu in der Lage die Reaktion von Pflanzen auf Klimaänderungen zu simulieren. In dieser Studie präsentieren wir Ergebnisse eines dynamisch-statistischen Modellierungsansatzes für Phänologie in Bayern. Dafür kombinieren wir hochaufgelöste Klimawandelsimulationen eines ak...