Comparing Climate Impact Assessments for Rural Adaptation Planning in Germany and the Netherlands

The consensus nowadays is that there is a need to adapt to increasingly occurring climate impacts by means of adaptation plans. However, only a minority of European cities has an approved climate adaptation plan by now. To support stakeholder dialogue and decision-making processes in climate adaptation planning, a detailed spatial information and evidence base in terms of a climate impact assessment is needed. This article aims to compare the climate impact assessment done in the context of two regional climate change adaptation planning processes in a Dutch and a German region. To do so, a comparison of guidelines and handbooks, methodological approaches, available data, and resulting maps and products is conducted. Similarities and differences between the two approaches with a particular focus on the input and output of such analysis are identified and both processes are assessed using a set of previously defined quality criteria. Both studies apply a similar conceptualisation of climate impacts and focus strongly on issues concerning their visualisation and communication. At the same time, the methods of how climate impacts are calculated and mapped are quite different. The discussion and conclusion section highlights the need to systematically consider climatic and socio-economic changes when carrying out a climate impact assessment, to focus on a strong visualisation of results for different stakeholder groups, and to link the results to planning processes and especially funding opportunities.

Time-Dependent Climate Impact of Utilizing Residual Biomass for Biofuels—The Combined Influence of Modelling Choices and Climate Impact Metrics

Understanding the influence of method choices on results in life-cycle assessments is essential to draw informed conclusions. As the climate impact of bioenergy remains a debated topic, the focus of this study is how the chosen temporal framing influences a comparison of the climate impact of utilizing residual biomass for biofuel production to that of leaving the biomass to decay. In order to compare the biofuel scenario to its corresponding reference scenario where biomass is left to decay, a variety of analytical approaches were used: using time-aggregated and time-dependent life-cycle inventories and climate-impact assessment methods, assuming biogenic carbon to be climate neutral or not, using metrics for cumulative or instantaneous climate impact, and with different time horizons. Two cases of residual biofuel feedstocks were assessed: logging residues from Norway spruce forest, and straw from wheat cultivation. Consideration of the studied method choices appears to be especially relevant for forest residual biomass, as illustrated by the ranges of parity times for logging residues (25 to 95 years), and the results which vary with the chosen climate-impact metric, time-horizon, and approach for including biogenic carbon. Illustrating the time-dependence of results can, in general, provide a better understanding of the climate impact of utilizing residual biomass for biofuels.

Exploring the Effect of Occurrence-Bias-Adjustment Assumptions on Hydrological Impact Modeling

Bias adjustment of climate model simulations is a common step in the climate impact assessment modeling chain. For precipitation intensity, multiple bias-adjusting methods have been developed, but less so for precipitation occurrence. Intensity-bias-adjusting methods such as ‘Quantile Delta Mapping’ can adjust too many wet days, but not too many dry days. Some occurrence-bias-adjusting methods have been developed to resolve this by the addition of the ability to adjust too dry simulations. Earlier research has shown this to be important when adjusting on a continental scale, when both types of biases can occur. However, the newer occurrence-bias-adjusting methods have their weakness: they might retain a bias in the number of dry days when adjusting data in a region that only has too many wet days. Yet, if this bias is small enough, it is more practical and economical to apply the newer methods when data in the larger region are adjusted. In this study, we consider two recently introduced occurrence-bias-adjusting methods, Singularity Stochastic Removal and Triangular Distribution Adjustment, and compare them in a region with only wet-day biases. This bias adjustment is performed for precipitation intensity and precipitation occurrence, while the evaluation is performed on precipitation intensity, precipitation occurrence and discharge, which combines the former two variables. Despite theoretical weaknesses, we show that both Singularity Stochastic Removal and Triangular Distribution Adjustment perform well. Thus, the methods can be applied for both too wet and too dry simulations, although Triangular Distribution Adjustment may be preferred as it was designed with a broad application in mind.

How evaluation of hydrological models influences results of climate impact assessment—an editorial

This paper introduces the Special Issue (SI) “How evaluation of hydrological models influences results of climate impact assessment.” The main objectives were as follows: (a) to test a comprehensive model calibration/validation procedure, consisting of five steps, for regional-scale hydrological models; (b) to evaluate performance of global-scale hydrological models; and (c) to reveal whether the calibration/validation methods and the model evaluation results influence climate impacts in terms of the magnitude of the change signal and the uncertainty range. Here, we shortly describe the river basins and large regions used as case studies; the hydrological models, data, and climate scenarios used in the studies; and the applied approaches for model evaluation and for analysis of projections for the future. After that, we summarize the main findings. The following general conclusions could be drawn. After successful comprehensive calibration and validation, the regional-scale models are more robust and their projections for the future differ from those of the model versions after the conventional calibration and validation. Therefore, climate impacts based on the former models are more trustworthy than those simulated by the latter models. Regarding the global-scale models, using only models with satisfactory or good performance on historical data and weighting them based on model evaluation results is a more reliable approach for impact assessment compared to the ensemble mean approach that is commonly used. The former method provides impact results with higher credibility and reduced spreads in comparison to the latter approach. The studies for this SI were performed in the framework of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP).

How evaluation of global hydrological models can help to improve credibility of river discharge projections under climate change

Importance of evaluation of global hydrological models (gHMs) before doing climate impact assessment was underlined in several studies. The main objective of this study is to evaluate the performance of six gHMs in simulating observed discharge for a set of 57 large catchments applying common metrics with thresholds for the monthly and seasonal dynamics and summarize them estimating an aggregated index of model performance for each model in each basin. One model showed a good performance, and other five showed a weak or poor performance in most of the basins. In 15 catchments, evaluation results of all models were poor. The model evaluation was supplemented by climate impact assessment for a subset of 12 representative catchments using (1) usual ensemble mean approach and (2) weighted mean approach based on model performance, and the outcomes were compared. The comparison of impacts in terms of mean monthly and mean annual discharges using two approaches has shown that in four basins, differences were negligible or small, and in eight catchments, differences in mean monthly, mean annual discharge or both were moderate to large. The spreads were notably decreased in most cases when the second method was applied. It can be concluded that for improving credibility of projections, the model evaluation and application of the weighted mean approach could be recommended, especially if the mean monthly (seasonal) impacts are of interest, whereas the ensemble mean approach could be applied for projecting the mean annual changes. The calibration of gHMs could improve their performance and, consequently, the credibility of projections.

Towards a resilient water future via inter-basin water transfer: climate impact assessment and feasibility study

<p>Uneven distribution of water resources in the face of climate change and population growth is imposing increasing threats to communities as well as challenging decision-makers. Inter-basin water transfer (IBT) schemes have been appreciated as one of the common approaches to tackle this issue. This work presents a framework for climate impact assessment and feasibility study for IBTs. The framework investigates negative impacts of IBTs on the donor and receiving bodies. This is done by calculating hydrological drought risk and environmental risks to freshwater habitats under 1200 future climatic scenarios and two different transfer scenarios. 2.2 Km resolution time-series from UK’s Met Office most recent climate projection (UKCP18) is used as the input scenario and a water resources model developed at Newcastle University is implemented to determine allocation and calculate the above risk factors. This work considers transferring raw water from England’s water-rich North East to its water-stressed South East as the case study. This case was chosen because England, with no major IBT scheme, is experiencing challenges from more frequent climate change and increasing demand for water in London. Additionally, organisations such as National Infrastructure Commission (NIC) and Environment Agency (EA) have encouraged England’s water companies to consider IBT as one of the options to improve water supply resilience. In this study, we assess schemes to transfer water using the existing infrastructures of water companies located from North East to South East of England to minimise costs and environmental impacts. Results suggest that, under a wide range of future scenarios, meeting London’s annual water shortage through transfers from the North East during wet season of each year not only increases London’s water supply resilience but also boosts flood resilience in the North East donor basin while still meeting environmental requirements.</p>

Comprehensive global climate impact assessment for crop yields

<p>Climate change impacts on agriculture are subject to large uncertainties from a variety of sources. One of the most important sources of uncertainty is the uncertainty in the realization of climate change itself. In the absence of clear climate mitigation strategies and substantial uncertainties on population growth, economic development, technology and lifestyles, a very broad set of greenhouse gas emission scenarios has been developed to inform climate modeling. Climate models often differ in the spatial patterns of projected changes in particular with respect to changes in precipitation. The Coupled Model Intercomparison Project (CMIP5, CMIP6) provides a broad range of future climate change projections.</p><p>Crop models are often applied at selected sites or with global coverage, as in the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Global crop model applications have been shown to have some skill, but also add additional uncertainty, given that many processes cannot be calibrated properly for the lack of suitable reference data and because management information is largely absent (Müller et al., 2017).</p><p>However, already the computational power required to compute the comprehensive set of climate projections prohibits such applications. Instead, typically, small and largely random selections of climate scenarios are used to project impacts, such as agricultural crop yields. McSweeney and Jones (2016) find that a selection of 5 climate models as often applied, is insufficient to cover the range of projections in all regions.</p><p>Here we present initial results of a comprehensive global climate impact assessment for crop yields that explores the full range of the CMIP6 climate projection archive. For this, we use a set of 9 global gridded crop model emulators (Franke et al., 2019b) that were trained on a very large systematic input sensitivity analysis with up to 1404 global-coverage, 31-year simulation data sets per crop and crop model (Franke et al., 2019a). The training domain includes variations in atmospheric carbon dioxide (CO2) concentrations (4 levels from 360 ppm to 810 ppm), air temperature (7 levels from -1 to +6°C), water supply (8 levels from -50 to +30% and full irrigation), nitrogen fertilization (3 levels from 10 to 200 kgN/ha) and adaptation (2 levels: none and regained growing seasons) and thus represents an unprecedented rich data base for emulator training. The emulators, in form of grid-cell specific regression models with 27 coefficients, are computationally light-weight and can thus be applied to the full CMIP6 data archive.</p><p>We here present first results from this analysis, breaking down the different sources of uncertainty (emission concentration pathways, climate model, crop model). Results will help to interpret crop model simulations in general: the unstructured reduction of the uncertainty space from selecting a small number of climate scenarios by e.g. first availability and/or individual crop models has so far hampered to quantify the uncertainty in crop model projections.</p><p> </p><p>Franke (2019a) Geoscientific Model Development Discuss, 2019:1-30.</p><p>Franke (2019b)  Geoscientific Model Development, submitted</p><p>McSweeney & Jones, (2016) Climate Services, 1:24-29.</p><p>Müller (2017) Geoscientific Model Development, 10:1403-1422.</p><p> </p>