Observed groundwater temperature response to recent climate change

Climate change is known to have a considerable influence on many components of the hydrological cycle. Yet, the implications for groundwater temperature, as an important driver for groundwater quality, thermal use and storage, are not yet comprehensively understood. Furthermore, few studies have examined the implications of climate-change-induced groundwater temperature rise for groundwater-dependent ecosystems. Here, we examine the coupling of atmospheric and groundwater warming by employing stochastic and deterministic models. Firstly, several decades of temperature time series are statistically analyzed with regard to climate regime shifts (CRSs) in the long-term mean. The observed increases in shallow groundwater temperatures can be associated with preceding positive shifts in regional surface air temperatures, which are in turn linked to global air temperature changes. The temperature data are also analyzed with an analytical solution to the conduction-advection heat transfer equation to investigate how subsurface heat transfer processes control the propagation of the surface temperature signals into the subsurface. In three of the four monitoring wells, the predicted groundwater temperature increases driven by the regime shifts at the surface boundary condition generally concur with the observed groundwater temperature trends. Due to complex interactions at the ground surface and the heat capacity of the unsaturated zone, the thermal signals from distinct changes in air temperature are damped and delayed in the subsurface, causing a more gradual increase in groundwater temperatures. These signals can have a significant impact on large-scale groundwater temperatures in shallow and economically important aquifers. These findings demonstrate that shallow groundwater temperatures have responded rapidly to recent climate change and thus provide insight into the vulnerability of aquifers and groundwater-dependent ecosystems to future climate change

Observed groundwater temperature response to recent climate change

Climate change is known to have a considerable influence on many components of the hydrological cycle. Yet, the implications for groundwater temperature, as an important driver for groundwater quality, thermal use and storage, are not yet comprehensively understood. Furthermore, few studies have examined the implications of climate-change-induced groundwater temperature rise for groundwater-dependent ecosystems. Here, we examine the coupling of atmospheric and groundwater warming by employing stochastic and deterministic models. Firstly, several decades of temperature time series are statistically analyzed with regard to climate regime shifts (CRSs) in the long-term mean. The observed increases in shallow groundwater temperatures can be associated with preceding positive shifts in regional surface air temperatures, which are in turn linked to global air temperature changes. The temperature data are also analyzed with an analytical solution to the conduction–advection heat transfer equation to investigate how subsurface heat transfer processes control the propagation of the surface temperature signals into the subsurface. In three of the four monitoring wells, the predicted groundwater temperature increases driven by the regime shifts at the surface boundary condition generally concur with the observed groundwater temperature trends. Due to complex interactions at the ground surface and the heat capacity of the unsaturated zone, the thermal signals from distinct changes in air temperature are damped and delayed in the subsurface, causing a more gradual increase in groundwater temperatures. These signals can have a significant impact on large-scale groundwater temperatures in shallow and economically important aquifers. These findings demonstrate that shallow groundwater temperatures have responded rapidly to recent climate change and thus provide insight into the vulnerability of aquifers and groundwater-dependent ecosystems to future climate change

Groundwater fauna in an urban area: natural or affected?

In Germany, 70 % of the drinking water demand is met by groundwater, for which the quality is the product of multiple physical–chemical and biological processes. As healthy groundwater ecosystems help to provide clean drinking water, it is necessary to assess their ecological conditions. This is particularly true for densely populated urban areas, where faunistic groundwater investigations are still scarce. The aim of this study is, therefore, to provide a first assessment of the groundwater fauna in an urban area. Thus, we examine the ecological status of an anthropogenically influenced aquifer by analysing fauna in 39 groundwater monitoring wells in the city of Karlsruhe (Germany). For classification, we apply the groundwater ecosystem status index (GESI), in which a threshold of more than 70 % of crustaceans and less than 20 % of oligochaetes serves as an indication for very good and good ecological conditions. Our study reveals that only 35 % of the wells in the residential, commercial and industrial areas and 50 % of wells in the forested area fulfil these criteria. However, the study did not find clear spatial patterns with respect to land use and other anthropogenic impacts, in particular with respect to groundwater temperature. Nevertheless, there are noticeable differences in the spatial distribution of species in combination with abiotic groundwater characteristics in groundwater of the different areas of the city, which indicate that a more comprehensive assessment is required to evaluate the groundwater ecological status in more detail. In particular, more indicators, such as groundwater temperature, indicator species, delineation of site-specific characteristics and natural reference conditions should be considered

Exergy analysis of a hybrid ground-source heat pump system

In contrast to energy analysis, the analysis of exergy allows the evaluation of the quality of different energy flows and enables a comprehensive assessment of inefficiencies within a system and its individual components by accounting for exergy consumption. While exergy analysis methods have been applied to a variety of conventional and renewable energy supply systems, there is still a lack of knowledge regarding the exergy flows and exergy efficiency of hybrid ground-source heat pump systems with a supplementary boiler. In this study, we develop a thermodynamic model for each subsystem in a hybrid heating and cooling system of an existing building by applying the concept of cool and warm exergy. A comparison of the exergy consumption of the hybrid system in heating and cooling reveals that there are significant differences regarding the components that attribute most to the overall exergy consumption in the system. Due to these differences the true exergy performance of the system in heating mode (~30%) is twice as high as for cooling mode (~15%), while the natural exergy performance is considerably better in cooling mode (~26% to ~3%). Potential measures to enhance the exergy performance based on changes in the operational settings of the system and the improvement of the building envelope were found to have a more significant effect on heating performance than on cooling performance. In general, measures that affect the amount of thermal energy delivered by the system appear to be more effective than changes to the operational settings of energy supply systems

Bayesian inference of structural error in inverse models of thermal response tests

For the design of ground-source heat pumps (GSHPs), two design parameters, namely the ground thermal conductivity and borehole thermal resistance are estimated by interpreting thermal response test (TRT) data using a physical model. In most cases, the parameters are fitted to the measured data assuming that the chosen model can fully reproduce the actual physical response. However, two significant sources of error make the estimation uncertain: random error from experiments and structural bias error that describes the discrepancy between the model and actual physical phenomena. Generally, these two error sources are not evaluated separately. As a result, the suitability of selected models to correctly infer parameters from TRTs are not well understood. In this study, the Bayesian calibration framework proposed by Kennedy and O'Hagan is employed to estimate the GSHP design parameters and quantify the random and structural errors in the inference. The calibration framework enables us to examine structural errors in the commonly used infinite line source model arising due to the conditions in which the TRT takes place. Two in situ TRT datasets were used: TRT1, influenced by contextual disturbances from the outdoor environment, and TRT2, influenced by a strong groundwater flow caused by heavy rainfall. We show that the Bayesian calibration framework is able to quantify the structural errors in the TRT interpretation and therefore can yield more accurate estimates of design parameters with full quantification of uncertainties

Sensitivity analysis methods for building energy models: Comparing computational costs and extractable information

Though sensitivity analysis has been widely applied in the context of building energy models (BEMs), there are few studies that investigate the performance of different sensitivity analysis methods in relation to dynamic, high-order, non-linear behaviour and the level of uncertainty in building energy models. We scrutinise three distinctive sensitivity analysis methods: (a) the computationally efficient Morris method for parameter screening, (b) linear regression analysis (medium computational costs) and (c) Sobol method (high computational costs). It is revealed that the results from Morris method taking the commonly used measure for parameter influence can be unstable, while using the median value yields robust results for evaluations with small sample sizes. For the dominant parameters the results from all three sensitivity analysis methods are in very good agreement. Regarding the evaluation of parameter ranking or the differentiation of influential and negligible parameters, the computationally costly quantitative methods provide the same information for the model in this study as the computational efficient Morris method using the median value. Exploring different methods to investigate higher-order effects and parameter interactions, reveals that correlation of elementary effects and parameter values in Morris method can also provide basic information about parameter interactions.This study was conducted as part of the ‘Bayesian Building Energy Management (B.bem)’ project funded by the Engineering and Physical Sciences Research Council (EPSRC reference: EP/L024454/1). The financial support by travel grants for Kathrin Menberg and Yeonsook Heo as part of the European Commission Marie Curie BIMautoGEN IRSES grant is gratefully acknowledged

Influence of error terms in Bayesian calibration of energy system models

Calibration represents a crucial step in the modelling process to obtain accurate simulation results and quantify uncertainties. We scrutinize the statistical Kennedy & O’Hagan framework, which quantifies different sources of uncertainty in the calibration process, including both model inputs and errors in the model. In specific, we evaluate the influence of error terms on the posterior predictions of calibrated model inputs. We do so by using a simulation model of a heat pump in cooling mode. While posterior values of many parameters concur with the expectations, some parameters appear not to be inferable. This is particularly true for parameters associated with model discrepancy, for which prior knowledge is typically scarce. We reveal the importance of assessing the identifiability of parameters by exploring the dependency of posteriors on the assigned prior knowledge. Analyses with random datasets show that results are overall consistent, which confirms the applicability and reliability of the framework

Sensitivity analysis methods for building energy models: Comparing computational costs and extractable information

Though sensitivity analysis has been widely applied in the context of building energy models (BEMs), there are few studies that investigate the performance of different sensitivity analysis methods in relation to dynamic, high-order, non-linear behaviour and the level of uncertainty in building energy models. We scrutinise three distinctive sensitivity analysis methods: (a) the computationally efficient Morris method for parameter screening, (b) linear regression analysis (medium computational costs) and (c) Sobol method (high computational costs). It is revealed that the results from Morris method taking the commonly used measure for parameter influence can be unstable, while using the median value yields robust results for evaluations with small sample sizes. For the dominant parameters the results from all three sensitivity analysis methods are in very good agreement. Regarding the evaluation of parameter ranking or the differentiation of influential and negligible parameters, the computationally costly quantitative methods provide the same information for the model in this study as the computational efficient Morris method using the median value. Exploring different methods to investigate higher-order effects and parameter interactions, reveals that correlation of elementary effects and parameter values in Morris method can also provide basic information about parameter interactions

Water-energy nexus in shallow geothermal systems

Ground-source heat pumps (GSHPs) reduce CO2 emissions compared to conventional heating and cooling systems. The thermally altered zone (thermal plume) is a key aspect for land management of GSHPs