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Introduction: Thermal grids are key to decarbonizing heating and cooling. However, their development is a complex socio-technical process. This study aims to (1) understand the thermal grid ecosystem’s development under changing political, economic and cultural frame conditions, (2) elicit actors’ perception of options for future development, and (3) identify concrete problem situations for the future development of thermal grids and describe how to address them. Methods: We draw on 18 semi-structured interviews with decision-makers in utilities, municipal administrations, planning and engineering companies and intermediary organizations. Results: With increased strategic importance of thermal grids, the ecosystem has been in a phase of rapid growth and professionalization in the last 10–15 years. This is reflected in a rapid increase in the number of grids, but also in capability and capacity building and greater quality standards. This transformation has been supported by a resource pool (e.g., knowledge base, capacities and capabilities, networks or decision-support tools), built partly through the value network’s activities and partly through purposeful orchestration by associations, authorities, utilities and other actors. While policy developments, energy price fluctuations and changes in customer perceptions spurred demand, thermal grid development is limited by supply-side constraints, especially a skill shortage. Four interrelated problem situations were identified: (1) Municipalities lack the resources to manage the implementation of their energy strategies. (2) The modernization of thermal grids and integration of low-carbon heat sources entails high transaction costs. (3) The implementation of thermal grids is subject to delays and quality risks, with consequences for the timely achievement of decarbonization goals. (4) Customers face uncertainty on available options, as well as on the systemic impacts of their decisions. Discussion: Based on current practices, suggestions by the interviewees, as well as a comparison with the international literature, we identify four development areas to address these problem situations: (1) Empower and support municipalities. (2) Manage the scarcity of qualified workforce. (3) Leverage digital technology for the planning, realization and operation of thermal grids. (4) Pilot technological and organizational innovations together. This complements the set of previously suggested measures to support the development of thermal grids, which focused strongly on technical and regulatory aspects. [ABSTRACT FROM AUTHOR]

We present FORHYCS (FORests and HYdrology under Climate Change in Switzerland), a distributed ecohydrological model to assess the impact of climate change on water resources and forest dynamics. FORHYCS is based on the coupling of the hydrological model PREVAH and the forest landscape model TreeMig. In a coupled simulation, both original models are executed simultaneously and exchange information through shared variables. The simulated canopy structure is summarized by the leaf area index (LAI), which affects local water balance calculations. On the other hand, an annual drought index is obtained from daily simulated potential and actual transpiration. This drought index affects tree growth and mortality, as well as a species-specific tree height limitation. The effective rooting depth is simulated as a function of climate, soil, and simulated above-ground vegetation structure. Other interface variables include stomatal resistance and leaf phenology. Case study simulations with the model were performed in the Navizence catchment in the Swiss Central Alps, with a sharp elevational gradient and climatic conditions ranging from dry inner-alpine to high alpine. In a first experiment, the model was run for 500 years with different configurations. The results were compared against observations of vegetation properties from national forest inventories, remotely sensed LAI, and high-resolution canopy height maps from stereo aerial images. Two new metrics are proposed for a quantitative comparison of observed and simulated canopy structure. In a second experiment, the model was run for 130 years under climate change scenarios using both idealized temperature and precipitation change and meteorological forcing from downscaled GCM-RCM model chains. The first experiment showed that model configuration greatly influences simulated vegetation structure. In particular, simulations where height limitation was dependent on environmental stress showed a much better fit to canopy height observations. Spatial patterns of simulated LAI were more realistic than for uncoupled simulations of the forest landscape model, although some model deficiencies are still evident. Under idealized climate change scenarios, the effect of the coupling varied regionally, with the greatest effects on simulated streamflow (up to 60 mm yr -1 difference with respect to a simulation with static vegetation parameters) seen at the valley bottom and in regions currently above the treeline. This case study shows the importance of coupling hydrology and vegetation dynamics to simulate the impact of climate change on ecosystems. Nevertheless, it also highlights some challenges of ecohydrological modeling, such as the need to realistically simulate the plant response to increased CO2 concentrations and process uncertainty regarding future land cover changes. [ABSTRACT FROM AUTHOR]

We present FORHYCS (FORests and HYdrology under Climate Change in Switzerland), a distributed ecohydrological model to assess the impact of climate change on water resources and forest dynamics. FORHYCS is based on the coupling of the hydrological model PREVAH and the forest landscape model TreeMig. In a coupled simulation, both original models are executed simultaneously and exchange information through shared variables. The simulated canopy structure is summarized by the leaf area index (LAI), which affects local water balance calculations. On the other hand, an annual drought index is obtained from daily simulated potential and actual transpiration. This drought index affects tree growth and mortality, as well as a species-specific tree height limitation. The effective rooting depth is simulated as a function of climate, soil and simulated above-ground vegetation structure. Other interface variables include stomatal resistance and leaf phenology. Case study simulations with the model were performed in the Navizence catchment in the Central Swiss Alps, with a sharp elevational gradient and climatic conditions ranging from dry inneralpine to high alpine. In a first experiment, the model was run for 500 years with different configurations. The results were compared against observations of vegetation properties from national forest inventories, remotely sensed LAI and high-resolution canopy height maps from stereo aerial images. Two new metrics are proposed for a quantitative comparison of observed and simulated canopy structure. In a second experiment, the model was run for 130 years under idealized climate change scenarios: daily temperature was increased by up to 6 K, and precipitation altered by 10 %, with a gradual change over 35 years. The first experiment showed that model configuration greatly influences simulated vegetation structure. In particular, simulations where height limitation was dependent on environmental stress showed a much better fit to canopy height observations. Spatial patterns of simulated LAI were more realistic than for uncoupled simulations of the forest landscape model, although some model deficiencies are still evident. Under idealized climate change scenarios, the effect of the coupling varied regionally, with the greatest effects on simulated streamflow (up to 40 mm y-1 difference with respect to a simulation with static vegetation parameters) seen at the valley bottom and in regions currently above the treeline. This case study shows the importance of coupling hydrology and vegetation dynamics to simulate the impact of climate change on ecosystems. Nevertheless, it also highlights some challenges of ecohydrological modelling, such as the need to realistically simulate plant response to increased CO2 concentrations, and process uncertainty regarding future land cover changes. [ABSTRACT FROM AUTHOR]

Conifers growing at high elevations need to optimize their stomatal conductance (gs) for maximizing photosynthetic yield while minimizing water loss under less favourable thermal conditions. Yet the ability of high‐elevation conifers to adjust their gs sensitivity to environmental drivers remains largely unexplored. We used 4 years of sap flow measurements to elucidate intraspecific and interspecific variability of gs in Larix decidua Mill. and Picea abies (L.) Karst along an elevational gradient and contrasting soil moisture conditions. Site‐ and species‐specific gs response to main environmental drivers were examined, including vapour pressure deficit, air temperature, solar irradiance, and soil water potential. Our results indicate that maximum gs of L. decidua is >2 times higher, shows a more plastic response to temperature, and down‐regulates gs stronger during atmospheric drought compared to P. abies. These differences allow L. decidua to exert more efficient water use, adjust to site‐specific thermal conditions, and reduce water loss during drought episodes. The stronger plasticity of gs sensitivity to temperature and higher conductance of L. decidua compared to P. abies provide new insights into species‐specific water use strategies, which affect species' performance and should be considered when predicting terrestrial water dynamics under future climatic change. Regulation of stomatal conductance to changing environmental conditions is crucial for optimizing tree water use. We found contrasting water use strategies, where the pioneer Larix decidua is more plastic in adjusting stomatal conductance to temperature‐limited conditions compared to late‐successional Picea abies. [ABSTRACT FROM AUTHOR]

Climatic water availability is a major determinant of forest structure and composition, while drought events may severely impact forest dynamics. In recent decades, an increasing number of severe drought events has been reported in forests around the world. In the future, climate models project increasingly dry conditions in many temperate regions. Various tools have been applied to better understand the effects of drought on forests, such as dendrochronological analyses, climatic trend analyses and dynamic models. With these approaches, water availability is often summarized as a single scalar, termed a drought or aridity index. As droughts are complex phenomena, such indices are always associated with a loss of information. Many different such indices exist, and have been developed for various purposes. This review asks whether some of these indices are more suitable than others to quantify water availability in temperate forests. In a first step, the rationale and theoretical background of different drought indices are spelled out and compared among each other. Then, evaluations and intercomparisons of drought indices from the literature are reviewed. The implementation of drought indices in dynamic forest models is also discussed. Finally, two current research questions are identified: the role of dry air for physiological drought, and the suitability of various drought indices under climate change. It appears from this review that indices accounting for evaporative demand generally perform better than indices based on precipitation alone. When comparing sites with different edaphic conditions, indices accounting for soil moisture storage are more suitable. Nevertheless, results from intercomparisons show considerable divergence, and it is not possible to clearly favor one index. Furthermore, a differential response of tree species to different drought indices is often observed, although no clear pattern emerges from this comparison. More intercomparisons of indices, across climates and species, might provide valuable knowledge. Another key finding is that the properties of indices also depend on choices regarding, e.g., the calculation of evaporative demand, or the underlying water balance model. Reporting such methodological details could greatly increase the value of future evaluations of drought indices. [ABSTRACT FROM AUTHOR]

Rooting zone water storage capacity Sr is a crucial parameter for modeling hydrology, ecosystem gas exchange and vegetation dynamics. Despite its importance, this parameter is still poorly constrained and subject to high uncertainty. We tested the analytical, optimality-based model of effective rooting depth proposed by Guswa (2008, 2010) with regard to its applicability for parameterizing Sr in temperate forests. The model assumes that plants dimension their rooting systems to maximize net carbon gain. Results from this model were compared against values obtained by calibrating a local water balance model against latent heat flux and soil moisture observations from 15 eddy covariance sites. Then, the effect of optimality-based Sr estimates on the performance of local water balance predictions was assessed during model validation. The agreement between calibrated and optimality-based Sr varied greatly across climates and forest types. At a majority of cold and temperate sites, the Sr estimates were similar for both methods, and the water balance model performed equally well when parameterized with calibrated and with optimality-based Sr. At spruce-dominated sites, optimality-based Sr were much larger than calibrated values. However, this did not affect the performance of the water balance model. On the other hand, at the Mediterranean sites considered in this study, optimality-based Sr were consistently much smaller than calibrated values. The same was the case at pine-dominated sites on sandy soils. Accordingly, performance of the water balance model was much worse at these sites when optimality-based Sr were used. This rooting depth parameterization might be used in dynamic (eco)hydrological models under cold and temperate conditions, either to estimate Sr without calibration or as a model component. This could greatly increase the reliability of transient climate-impact assessment studies. On the other hand, the results from this study do not warrant the application of this model to Mediterranean climates or on very coarse soils. While the cause of these mismatches cannot be determined with certainty, it is possible that trees under these conditions follow rooting strategies that differ from the carbon budget optimization assumed by the model. [ABSTRACT FROM AUTHOR]

Rooting zone water storage capacity Sr is a crucial parameter in models of hydrology, ecosystem gas exchange and vegetation dynamics. Despite its importance, this parameter is still poorly constrained and subject to high uncertainty. We tested the analytical, optimality-based model of effective rooting depth proposed by Guswa (2010) with regard to its applicability for parameterizing Sr in temperate forests. The model assumes that plants dimension their rooting systems in order to maximize net carbon gain. Results from this model were compared against values obtained by calibrating a local water balance model against latent heat flux and soil moisture observations from 15 eddy covariance sites. To increase the applicability of the rooting depth model, we provide a numerical approximation of its underlying probabilistic soil moisture model. The calibration and validation of the local water balance model show that the concept of a single rooting zone storage capacity was appropriate at most temperate and cold sites, but not at Mediterranean sites and for very coarse soils. At a majority of sites, the estimates of Sr are generally in good agreement. However, mismatches were found in stands dominated by Norway spruce, especially at high elevations. These mismatches were attributed to the fact that the model does not consider rooting depth limitations due to oxygen stress and low soil temperature. Also, it is not clear whether the rooting behavior of pines on coarse soils is captured properly. Nevertheless, the overall good agreement suggests that this model may be useful for generating estimates of rooting zone storage capacity for both hydrological and ecological applications. Another potential use is the dynamic parameterization of the rooting zone in process-based models, which greatly increases the reliability of transient climate-impact assessment studies. [ABSTRACT FROM AUTHOR]

• We review the use of Sequential Montel Carlo (SMC) algorithms for model calibration. • We present a new SMC algorithm and benchmark it against a state-of-the-art MCMC. • The advantage of SMC algorithms over MCMC algorithms is that they can be parallelized. • With appropriate settings to avoid conversion problems, our algorithm can outperform MCMCs for model runtimes >20 ms on multi-core systems. • We provide our algorithm as an r package. Bayesian inference has become an important framework for calibrating complex ecological and environmental models. Markov-Chain Monte Carlo (MCMC) algorithms are the methodological backbone of this framework, but they are not easily parallelizable and can thus not make optimal use of modern computer architectures. A possible solution is the use of Sequential Monte Carlo (SMC) algorithms. Currently, SMCs are used mainly for Bayesian state updating, for example in weather forecasting, and are thought to be less efficient for parameter calibration than MCMCs. Unlike MCMCs, however, SMCs are easily parallelizable. Thus, SMCs may become an interesting alternative when modelers have access to parallel computing environments. The purpose of this paper is to provide an introduction to SMC algorithms for Bayesian model calibration, and to explore the trade-off between efficiency and parallelizability for MCMC and SMC algorithms. To that end, we discuss different SMC variants, and benchmark them against a state-of-the-art MCMC algorithm by calibrating three ecological models of increasing complexity. Our results show that, with appropriately chosen settings, SMCs can be faster than state-of-the-art MCMC algorithms when a sufficiently large number of parallel cores are available and when the model runtime is large compared to communication overhead for parallelization (on our hardware, a model runtime of 20 ms was enough to favor SMC algorithms). Efficient SMC settings were characterized by a balanced mix of SMC filtering and MCMC mutation steps, suggesting that mixing MCMC and SMC principles may be ideal for creating efficient and parallelizable calibration algorithms. The algorithms used in this study are provided within the BayesianTools R package for Bayesian inference with complex ecological models. [ABSTRACT FROM AUTHOR]

Creating new business models is crucial for the implementation of clean technologies for industrial decarbonization. With incomplete knowledge of market processes and uncertain conditions, assessing the prospects of a technology-based business model is challenging. This study combines business model innovation, system dynamics, and exploratory model analysis to identify new business opportunities in a context of sociotechnical transition and assess their prospects through simulation experiments. This combination of methods is applied to the case of a potential business model for Distribution System Operators aiming at ensuring the stability of the electrical grid by centralizing the management of flexible loads in industrial companies. A system dynamics model was set up to simulate the diffusion of flexible electrification technologies. Through scenario definition and sensitivity analysis, the influence of internal and external factors on diffusion was assessed. Results highlight the central role of energy costs and customer perception. The chosen combination of methods allowed the formulation of concrete recommendations for coordinated action, explicitly accounting for the various sources of uncertainty. We suggest testing this approach in further business model innovation contexts. [ABSTRACT FROM AUTHOR]

We developed an uneven-aged forest economic decision-making framework that combines: (i) a size-structured matrix model, based on growth and mortality predictions of a dynamic process-based forest landscape model, (ii) an optimal control model that determines the dynamics of control and state variables, which in turn are defined by tree harvesting and forest stock, respectively, and (iii) a water yield function that depends on changes in the leaf area index (LAI), the latter being affected by forest management. This framework was used to simulate the effects of economic-driven harvesting decisions on water yields on a catchment of South-Western Swiss Alps when both timber and water benefits are considered. Water benefits are estimated as environmental prices considering current water demands for drinking, irrigation and hydropower production. We simulated optimal harvesting decisions given the initial forest structure at each 200 m × 200 m grid cells, a set of restrictions to harvesting, and specific species survival, recruitment and growth probabilities, all of which are affected by the stand's LAI. We applied this model using different harvesting restriction levels over a period of 20 to 40-years, and accounting for single and joint timber and water benefits. The results suggested that at the environmental prices estimated at the catchment area, water benefits have a slight influence on harvesting decisions, but when water is accounted for, harvesting decisions would include more tree species and different diameter classes, which, in principle, is expected to favor more diverse forest structures. [ABSTRACT FROM AUTHOR]

Projections of landscape dynamics are uncertain, partly due to uncertainties in model formulations. However, quantitative comparative analyses of forest landscape models are lacking. We conducted a systematic comparison of all forest landscape models currently applied in temperate European forests (LandClim, TreeMig, LANDIS-II, iLand). We examined the uncertainty of model projections under several future climate, disturbance, and dispersal scenarios, and quantified uncertainties by variance partitioning. While projections under past climate conditions were in good agreement with observations, uncertainty under future climate conditions was high, with between-model biomass differences of up to 200 t ha−1. Disturbances strongly influenced landscape dynamics and contributed substantially to uncertainty in model projections (~25–40% of observed variance). Overall, model differences were the main source of uncertainty, explaining at least 50% of observed variance. We advocate a more rigorous and systematic model evaluation and calibration, and a broader use of ensemble projections to quantify uncertainties in future landscape dynamics. • The first systematic comparison of forest landscape models is presented. • Variance of model projections under several future scenarios is substantial. • Model differences explain most of the simulated variance. [ABSTRACT FROM AUTHOR]