Keep Cool in a Changing Climate: An Integrated Modelling Procedure to Identify Cost-Effective, Spatially Differentiated Land-use and Land-cover Measures to Mitigate Rising Temperature in Rural Landscapes

Keep Cool in a Changing Climate: An Integrated Modelling Procedure to Identify Cost-Effective, Spatially Differentiated Land-use and Land-cover Measures to Mitigate Rising Temperature in Rural Landscapes | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Keep Cool in a Changing Climate: An Integrated Modelling Procedure to Identify Cost-Effective, Spatially Differentiated Land-use and Land-cover Measures to Mitigate Rising Temperature in Rural Landscapes Lutz Philip Hecker, Frank Wätzold, Astrid Sturm, Beate Zimmermann, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3904821/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 May, 2025 Read the published version in Climatic Change → Version 1 posted 4 You are reading this latest preprint version Abstract Rising temperatures may negatively impact rural landscapes in temperate climates due to reduced yields in agriculture and forestry, an increased risk of biodiversity loss, changes in the local climate and a decrease in recreational value. One promising way to mitigate increasing land surface temperatures (LST) in rural landscapes is to implement land-use and land-cover measures as adaptation measures that retain precipitation in soils, water bodies, and groundwater to allow vegetation to evaporate more water to reduce LST in summer. We develop an integrated modelling procedure to identify cost-effective spatially differentiated adaptation measures in agriculture and forestry to mitigate LST increases. With cost-effective we mean that the LST mitigation in a landscape is maximised for given costs. The procedure combines the results of a model that predicts the spatially differentiated effects of adaptation measures on LST with the results of an economic model that estimates the respective spatially differentiated costs in an optimisation algorithm. We demonstrate how the procedure works by applying it to the Elbe-Elster-county in Germany. One result is that the introduction of agroforestry systems on cropland, reforestation and the conversion of tree plantations into ecological forest are most cost-effective in mitigating LST increases for costs of 20 Mio. €. We also compare results from our integrated modelling procedure with a (purely natural science) approach that selects those adaptation measures first which perform best in terms of LST mitigation and find that our approach leads to a better heat mitigating effect by a factor of 3.5 – 4.8. climate change adaptation integrated assessment heat mitigation water stress climate water cycle landscape cooling nature-based solutions Figures Figure 1 Figure 2 1. Introduction Climate change adaptation measures with the aim to mitigate increasing temperatures typically focus on overheated settlements to reduce negative impacts of high temperatures on the population as a whole and, in particular, on vulnerable groups (e.g. Hunt and Watkiss 2011, Grafakos et al. 2019, Birkmann et al. 2021, Otto et al. 2021). However, in temperate climate zones increasing temperatures may also negatively impact rural landscapes. Negative consequences may manifest in increasing fluctuations and reduced yields in agriculture (Challinor et al. 2014, Mahato 2014, Jägermeyr et al. 2021) and forestry (Fuhrer et al. 2006, Seidl et al. 2017, Venäläinen et al. 2022). Higher temperatures increase soil moisture stress and accelerate microbial processes in soils, which trigger the leaching of nutrients and the degeneration of soils (Gelybó et al. 2018, Jansson & Hofmockel, 2020). As a consequence, plants have less water and nutrients at their disposal for growth. Increasing temperatures also lead to range shifts of species leading to their endangerment (Bellard et al. 2012, Dasgupta 2021, Gerling et al. 2022) especially as fragmented landscapes prevent species to migrate to new ranges (Vos et al. 2008). In particular, wet habitats such as wet meadows, fens or water bodies are under considerable pressure, losing area or disappearing altogether (Fluet-Chouinard et al. 2023). At the landscape level, there is a systemic risk that an increase of the land-ocean temperature contrast, which is substantially influenced by vegetation and, hence, the disturbance of a natural vegetation cover could cause the climate system (in Central Europe) to pass a tipping point and move from a wet to a dry state (Makarieva et al., 2022). Finally, rural landscapes often serve as recreational areas and their recreational value may decline if they become too hot in summer (Pröbstl-Haider et al. 2021). A promising way to reduce the land surface temperature (LST) is to improve the capabilities of rural landscapes to retain the existing precipitation in the landscape by storing it in soils, water bodies and groundwater to make it available to vegetation for evaporation (Procházka et al. 2019). One key option to improve these processes are changes in land-use and land-cover (henceforth referred to as climate change adaptation measures or simply adaptation measures). Examples of such measures include the enrichment of humus on agricultural land, the transformation of cropland to grassland, and the conversion from deciduous monocultures to near-natural mixed deciduous forests (Hildmann et al. 2022). Since the implementation of such adaptation measures is (often) costly and financial resources are scarce it is important to implement measures in a cost-effective way. Cost-effectiveness here means that measures are selected in such a way that the LST in a region is reduced most for given costs. The purpose of this paper is to develop an integrated modelling procedure to identify cost-effective climate change adaptation measures in agriculture and forestry to mitigate the temperature increase in rural landscapes. The modelling procedure combines results from a model that predicts the impact of adaptation measures on LST and results from economic cost assessments that estimates the respective costs of these measures to mitigate LST increases. To identify the cost-effective allocation of adaptation measures the integrated modelling framework uses an optimisation algorithm. The model is spatially differentiated, i.e. for each agricultural and forest plot, it quantifies the impact on the LST of potentially suitable climate change adaptation measures, estimates associated costs and puts LST mitigation effects and costs of the measures into relation. As a result, it specifies where to carry out which measures in the landscape to maximise the mitigation effect on the LST at the landscape level for given budgets. In order to show the improved performance on mitigating the LST that is realized by applying our cost-effectiveness approach we contrast its results with a (purely natural science) approach that considers only the impact of climate change adaptation measures in terms of LST mitigation. We demonstrate how the integrated modelling procedure works by applying it to the case study area of Elbe-Elster county in the federal state of Brandenburg, Germany, but would like to emphasize that it is transferable to other rural landscapes in temperate climate zones. Elbe-Elster county is a suitable study region as the county is part of the highly climate-sensitive Lusatian region. The sensitivity is a result of the combination of low annual precipitation and sandy soils with little water retention capacity (Kühn et al. 2015, Gerwin et al. 2023). Weather data for the region has shown an increase of air temperatures over the last 30 years (Grünewald 2001, Pohle 2014, DWD 2020), and climate projections predict further temperature increases (Pfeifer et al. 2021). Alongside, rainfall patterns see a shift of precipitation from summer to winter (Zomer et al. 2022). This pattern shift highlights the importance of increasing the water retention capacity to counter the drying out of the landscape and, as a consequence, its overheating during the vegetation period. Our work is at the intersection of three research areas. The first area is the economic literature on heat mitigation in urban areas. Often cost-benefit-analyses are applied to assess and investigate the economic feasibility of measures like the design of green urban water drainage systems (Johnson and Geisendorf, 2019), urban green infrastructure such as green walls (Koch et al., 2020), pervious pavement systems (Wang et al., 2022), tree canopy cover (Bosch et al., 2021), and ecosystem-based approaches (Markanday et al., 2019) to mitigate adverse heat effects of climate change. In contrast, studies that investigate the cost-effectiveness of temperature mitigation measures are relatively scant. Notable exemptions are Zhang and Ayyub (2020) and Di Giuseppe et al. (2021) who respectively assess the cost-effectiveness of deploying cool and green roofs and dry mist systems as a water-based mitigation strategy against urban overheating. The second research area is the analysis of the selection and spatial allocation of cost-effective land-use measures to obtain ecosystem service and biodiversity goals. A seminal paper is by Polasky et al. (2008) who model the biological and economic costs and benefits of land-use patterns to derive spatially explicit, cost-effective land-use practises for species conservation. Examples of the application of this general approach are Tainio et al. (2016) who investigate the cost-effective conservation of grassland butterflies under a changing climate while Gerling et al. (2022) investigate the impact of climate change on the cost-effective allocation and choice of measures to conserve a grassland indicator species. The third area is the natural science literature on the impact of land-use and land-cover on the LST. The interaction of land-use respectively land-cover and LST has been the subject of research for many years (Liu and Weng, 2008, Luyssaert et al., 2014, Jamei et al., 2022). However, other parameters also affect LST, such as elevation (Phan et al., 2018) or tree cover density (Greene and Kedron, 2018). Similar to economic research, studies on temperature mitigation effects of greening measures focus mainly on heat islands in urban areas (Rahman et al., 2020, Gao et al., 2020, Shafiee et al., 2020). However, some studies also exist in the forest sector (e.g. Schwaab et al., 2020, Meeussen et al. (2021) and in the agricultural sector (Brunel-Saldias et al., 2018, Fischer et al., 2019). We combine those three research strings as follows. We take up the challenge of cost-effective heat mitigation investigated in the context of urban heat islands but address it in the context of a rural landscape and the corresponding climate change adaptation measures. The development of our integrated modelling procedure is inspired by the analysis of cost-effective land-use measures to obtain ecosystem service and biodiversity goals and follows in principle the typical structure of integrated models developed in that research field (Polasky et al. 2008). However, instead of biodiversity or ecosystem services typically investigated such as pollination or clean water, we focus on the mitigation impact of adaptation measures on the LST which we estimate using a natural science modelling approach. 2. Case Study 2.1. Description of Elbe-Elster county The Elbe-Elster-County is located in the south of the federal state of Brandenburg in Eastern Germany. It inhabits 100,902 people (Amt für Statistik Berlin-Brandenburg 2023) on an area of 1,899 km 2 with an annual GDP of 2,648 billion € (Euros) in 2020 (VGRDL 2022). About 1,900 employees worked in 2019 in forestry and agriculture contributing 80 million Euros (3%) to the local GDP (VGRDL 2022). 51% of the county are managed as agricultural land, and 36% are covered by forest (Amt für Statistik Berlin-Brandenburg 2022). With a relatively low average land productivity[1] (MLUK 2023) cultivation conditions for agriculture are less favourable compared to the rest of Germany (Kaufmann-Boll et al. 2020, Pfeiffer et al. 2021). Grain farming dominates in the county, mainly with rye and silage corn for energy and fodder production for dairy livestock. About 23% of the agricultural land is grassland (Amt für Statistik Berlin-Brandenburg 2019). Opencast lignite mining played a crucial role to the local economy in past decades. As a result, the landscape transformed deeply with negative impacts on the water retention capacity of the local landscape (Gerwin et al. 2023). 2.2. Water scarcity and climate change in the Elbe-Elster county Annual precipitation amounts to 556,8 mm/a (1971 – 2000, Pfeifer et al. 2021) (for comparison: Germany 922.8 mm/a) and shows a slight, statistically non-significant increase between the periods 1951-1980 and 1986-2015 (Pfeifer et al. 2021). However, there is also an increase of the mean annual temperature by an average of 0.9 K to 9.1 °C (similar to the German average of 9.3 °C) (Pfeifer et al. 2021). The overall result is a decrease in water availability in the region, particularly in the vegetation period (Zimmermann and Hildmann, 2021). According to the classification of Zomer et al. (2022), the region is now considered dry sub-humid. Climate projections (Pfeifer et al. 2021) predict further rising temperatures and an extension of the vegetation period in the Elbe-Elster county. This may increase water demand of plants. Regarding annual amount, frequency and seasonal distribution of precipitation, climate projections indicate a more frequent occurrence of heavy precipitation events and a general shift of rainfall from summer into winter with no substantial changes in annual precipitation amounts. Overall, the impact of climate change is expected to reduce the water availability to plants during the vegetation period. 3. Description of the integrated modelling procedure 3.1. Overview of the procedure To identify cost-effective climate change adaptation measures to mitigate the land surface temperature increase in rural landscapes we developed an integrated modelling procedure. Figure 1 describes the structure of the procedure and how its different components are interrelated. In the following sections 3.2-3.5 we describe how we applied the procedure to the case study region. In a first step (Figure 1, box 1), climate change adaptation measures, which are in principle suitable to mitigate LST increases in a specific region, need to be identified. In a second step (Figure 1, box 2), information at the level of agricultural and forest plots in the study region is gathered regarding the existing land use respectively land cover and land quality information (e.g. soil quality, land productivity, nutrition level).[2] From the overall set of adaptation measures for each plot those are selected which are in principle suitable for this plot (e.g. agricultural measures are dropped for forests plots). This information feeds into an estimation of the impact of adaptation measures on LST (Figure 1, box 3) in a spatially differentiated manner – i.e. for the individual agricultural and forest plots. For the estimation, a statistical model is developed to identify the influence of environmental variables such as tree cover density and soil water retention capacity on the LST, and to predict the effect of an individual measure on a plot. In parallel (Figure 1, box 4), the aggregated and discounted costs of implementing the selected adaptation measures on individual agricultural and forest plots for a period of 30 years are estimated. Finally (Figure 1, box 5), the cost data and the data on the impact of adaptation measures on LST are integrated in an optimisation procedure to identify cost-effective portfolios of measures and their spatial allocation. For that, the effect/cost ratios of all measures for all agricultural and forest plots are ranked according to their effect-cost-relation. On this ranking, an approximation algorithm is applied to identify where in the landscape which measures should be implemented for any given 30-year budget to maximise the reduction in the LST. 3.2. Identification of suitable measures for LST increase mitigation and localisation to plots Table 1 lists six agricultural and two forestry adaptation measures we singled out for our analysis based on a comprehensive review of relevant scientific literature and suggestions from administrative decision makers and other stakeholders in the study region. For a detailed description of all investigated measures and how they influence surface temperatures see Hildmann et al. (2022). Table 1: List of agricultural and forest adaptation measures to mitigate LST increase in the Elbe-Elster county Measures Measure description Agroforestry systems (alley cropping) Combined cultivation of conventional arable crops and rows of perennial woody plants Landscape structure elements Planting hedgerows and field copses along ditches and trails Conversion of cropland into permanent grassland Establishment of evergreen, continuous grass cover Organic fertilization Application of organic matter (compost, manure, straw) to arable land Cultivating deep-rooted crops Cultivation of deep-rooted arable crops for better water supply Afforestation of marginal cropland Afforestation of marginal arable sites with deciduous trees Reforestation Reforestation of degraded forest with site-adapted tree species Ecological forest conversion Conversion from coniferous tree plantations to mixed or deciduous forest From this overall set of adaptation measures those are selected for each plot, which are in principle suitable for it. This localisation of measures is based on specific GIS algorithms that also determine certain plot-specific attributes such as the relative proportion of a plot that can be influenced by a measure. For example, as the measure ‘landscape structure elements’ is located exclusively along paths and ditches, the proportion of these elements varies among plots. 3.3. Assessment of climate change adaptation measures on LST The evaluation of the impact of climate change adaptation measures was based on their potential effect to mitigate LST increases. We only summarise our approach here and refer the interested reader to Zimmermann et al. (2024, under revision)[3] for details. In a first step, we developed a statistical model that quantifies the relationship between different environmental variables (called predictors hereafter) and the LST as the dependent variable. We used thermal images from the Landsat 8 satellite to derive LST during the main vegetation period (May to September) of the years 2013 to 2020 in the study region. In total, 39 satellite images were available that satisfied the quality requirements such as the large absence of clouds. The predictors were identified based on knowledge in the literature of factors that influence the LST in rural landscapes (e.g. Bertoldi et al. 2010, Song et al. 2014, Alavipanah et al. 2015, Du et al. 2016, Kim et al. 2016, Das et al. 2020) and own previous work (Hildmann et al. 2019). They included, for example, land-use/land-cover class, tree cover density, imperviousness density and landscape structure metrics. The predictor values were derived from available geospatial data in the study region on land cover, land use, soil, topography and climate. To fit the model, we applied a Bayesian hierarchical modelling framework using the statistical software R (R Core Team, 2022) and the library brms (Bürkner, 2021). The common geometry for both the predictors and the LST was the raster data format with the LST map resolution of 30 m x 30 m. In the second step, the model was used to predict the LST for predictor values, that can be expected before and after implementation of a climate change adaptation measure for each individual plot. For example, the measure ‘conversion of cropland into permanent grassland’ is accompanied by a change of the predictor land-use/land-cover class (from cropland before implementation to grassland after implementation). The effect on the LST was then calculated as the difference in predicted LST before and after a (hypothetical) measure implementation. In order to transfer the values in the raster cells to the agricultural and forest plots, respectively, we calculated mean values. For measures that do not cover the entire plot, area-weighted mean values were calculated instead of arithmetic mean values, whereby the weights were determined according to the plot-specific attributes (see chapter 3.2). Due to the combination of the 39 satellite images at different times, a scaled value is used for the LST instead of the value in K. The scaling was necessary because the absolute LST values depend on the weather conditions at the time of the satellite overflight and are therefore not directly comparable. The min-max feature scaling (min-max normalization) was used as the scaling method, in which the data is rescaled to the range [0, 1]. 3.4. Cost estimation of climate change adaptation measures We followed the approach of identifying costs that occur when a measure to mitigate LST increase is implemented instead of a business-as-usual scenario (Sturm et al., 2018). With a business-as-usual scenario, we mean the existing land use on a plot in the study region which we assume to maximise the land users’ profits. For the cost assessment of the adaptation measure, we distinguished between investment costs, management costs, and opportunity costs of land-use. Investment costs occur for some measures that require a one-time investment at the beginning of a measure (e.g. the implementation of ‘agroforestry systems (alley cropping)’ requires the plantation of tree seedlings on parts of the cropland). Management costs are ongoing costs of maintaining an implemented measure (e.g. the periodically recurring need of tree care in the case of ‘alley cropping on cropland’), and opportunity costs are the difference between the profit from the existing land use and the profit from an adaptation measure. For example, the introduction of ‘agroforestry systems’ reduces the area for commercial crops as trees are now planted on parts of the cropland. This results in a decreased commercial crop yield and profit per hectare, which is not fully compensated by the profit generated from the sales of agroforestry products. We considered spatially differentiated costs for (individual) agricultural and forest plots. Costs depend on factors such as soil quality and other landscape elements (e.g. slope gradient) (Kaufmann-Boll et al. 2020). We calculated the aggregated costs over a period of 30 years, and applied a discount rate which is common in economics to take into account that future costs and revenues are generally given less weight than present ones (see any textbook such as Boardman et al. (2018) for details). A more detailed description of our cost calculation for all adaptation measures listed in Table 1 provides Appendix A.1 and Hildmann et al. (2022). 3.5. Optimisation module and cost-effectiveness analysis Based on data input on the spatially differentiated costs of adaptation measures and their effect on the LST we applied an optimisation algorithm to identify at which agricultural or forest plot to implement which measure to maximise the countywide LST increase mitigation effect for given budgets.[4] This optimisation problem corresponds to a higher dimensional knapsack problem which cannot be solved in polynomial time requiring an approximation algorithm (see Skiena 2020 for details). The approximation algorithm selects for a given budget the most cost-effective measure-plot combinations until the budget is spent. This corresponds to a heuristic greedy approach (see Sturm et al. 2018 for an example of how this approach can be applied to identify optimal land-use measures) which performs the following steps: 1) Sorting all possible measure-plot combinations by their ratio of LST impact to cost (benefit/cost-ratio) in a list. 2) Selecting at each time step of the algorithm the most cost-effective measure-plot combination. 3) Checking if the (additional) costs arising from this selection are within the overall available budget and if no measure is already allocated to the plot. 4) If both checks are positive (no plot allocated yet and additional costs do not exceed the budget), the measure is allocated to this plot and the measure-plot combination removed from the measure-plot list. The remaining budget is reduced by the costs corresponding to the implemented measure. 6) If either the plot is already occupied or the cost is too high for the remaining budget, the measure-plot combination is deleted from the list. 7) Repeat the procedure until the budget is exhausted or there is no measure-plot combination left. This greedy approach does not guarantee that the optimal solution is found, but it does provide a good approximation of the optimum. 4. Results 4.1. Cost-effectiveness optimisation We find that average LST increase mitigation effects and average costs vary substantially across climate change adaptation measures. In terms of the impact on LST, ‘afforestation of marginal lands’ has the largest effect, while ‘cultivation of deep-rooted crops’ has virtually no impact. In terms of cost, ‘afforestation of marginal lands’ is the most expensive adaptation measure and the introduction of ‘agroforestry systems’ is the least expensive. Comparing the average benefit/cost ratio of different adaptation measures provides a general assessment of the cost-effectiveness of the measures. On average, implementing ‘agroforestry systems’ on relatively low quality plots renders the highest benefit/cost ratio which can be explained by agroforestry systems having a medium LST effect while being comparatively cheap. By contrast, ‘cultivating deep-rooted crops’ has the lowest ratio. This is because the rotation from profit maximizing crops to deep-root cultivation has hardly any impact on LST while relatively high forgone profits make the change rather costly. The standard deviations of LST increase mitigation effects and costs of the adaptation measures also substantially vary across measures and are quite high for some measures, which shows the importance of their spatial differentiation. In particular, forest related adaptation measures have comparatively large standard deviations for LST increase mitigation effects and costs. With respect to LST, this large spread is explained by spatial variations in the predictor values among plots and differences in the proportion of plot area influenced by a measure (Zimmermann et al. 2024). For example, the predictor ‘tree cover density’ before implementation of the measure ‘ecological forest conversion’ varies between 0% (highly disturbed or recently cleared forest stands) and 100%. Regarding costs, one reason is that fencing which is required to avoid damage caused by game animals represents a substantial cost item for forest climate change adaptation measures. However, the amount of fencing costs varies greatly depending on the size and shape of the plot the forest measures are implemented on. Table 2: Average scaled LST effects, average costs and average benefit/cost ratios of measures for all plots in study region** *LST effects are derived from scaled values. **Values in table derived from combing results of calculations in box 3 and box 4 in figure 1. Table 3 shows the result for the cost-effective allocation of climate change adaptation measures across agricultural and forestry plots in the study region for a small budget (20 million €), a medium budget (40 million €) and large budget (80 million €) over the period of 30 years. The small, medium and large budgets were selected to allow the implementation of measures on a relatively small (47,911 hectares), medium (83,844 hectares), and large (126,859 hectares) share of the agricultural and forest area in the study region which amounts to 164,565 hectares. Table 3: Results of cost-effective LST increase mitigation for a small, medium and large budget * Forest areas in the study region are categorized into four nutrition levels: A = poor, Z = quite poor, M = moderately nutritious, Z+ = between Z and M. For a small budget (20 million €), the cost-effective measure to mitigate LST increase is mainly the introduction of ‘agroforestry systems’ on cropland. This measure is implemented on almost all cropland plots with low productivity (soil quality index < 25) and on almost half of the cropland plots with middle to high productivity (soil quality index ≥ 25). This result is reflected in the comparatively high benefit/cost ratio of 0.93 and 0.63 respectively (Table 2) and can be explained by small opportunity costs for this measure, particularly on land with low productivity, while the LST increase mitigation effect is medium. The low opportunity costs result from the fact that only a small part of the cropland is planted with trees, while cropland continues to be cultivated on the remaining area. Unlike ‘landscape structure elements’, agroforestry strips additionally provide forest products (e.g. timber) that can be sold. Opportunity costs are somewhat higher for cropland with medium to high productivity, as more fertile land results in higher foregone crop yields. Furthermore, two forest measures are assigned to a few plots with specific nutrient levels (Table 3). This is an effect of the difference in benefit/cost ratios in relation to these levels (Table 2). ‘Reforestation’ takes place on a few forest plots with middle to high nutrition levels (M levels), and ‘ecological forest conversion’ also on a few forest plots with a low nutrition level (A level) and with low to medium nutrition level (Z level). Implementing these land-use measures is relatively inexpensive while having a significant LST increase mitigation effect. The results for the middle budget (40 million €) and large budget (80 million €) are largely in line with the findings for the small budget. The measure of ‘agroforestry systems’ is again implemented on almost the entire cropland area with low productivity and, additionally, on almost all cropland area with middle to high productivity. This share increase reflects the comparatively high benefit/cost ratio of ‘agroforestry systems’ for a large share of plots. In line with this, ‘reforestation’ takes place on bigger shares of forest plots with middle to high nutrition M-levels, and ‘conversion to ecological forest’ on bigger shares of forest plots with low nutrition A-levels and low to medium nutrition Z-levels. In addition, ‘landscape structure elements’ are implemented on about half of the grassland for a large budget. This is interesting, as the average benefit/cost ratio for ‘afforestation of marginal land’ and ‘conversion of cropland into grassland’ is slightly higher (0.21 and 0.22) than for ‘landscape structure elements’ (0.19) and one would expect the selection of some plots for those land-use measures. An explanation for the selection of ‘landscape structure elements’ is that on some grassland plots landscape structure elements have a particularly large LST increase mitigation effect. This happens when these elements cover a comparatively large part of the plot, e.g. due to a small plot size in combination with a high proportion of ditches or trails (as noted before, the localisation of measure ‘landscape structure elements’ was limited to these features). This results in high benefit/cost ratios of this measure on these grassland plots. Moreover, as only one measure can be implemented per plot and ‘agroforestry systems’ are already implemented on almost all cropland plots there is little room left for the implementation of other measures on this land-use type. 4.2. Efficiency advantage of cost-effectiveness approach over purely LST focused approach In order to demonstrate the advantage of the cost-effectiveness approach in comparison to a (purely natural science) approach that selects adaptation measures with a higher effect on LST first, we repeated the optimisations for the three considered budgets applying such an approach. The structure of the optimisation problem corresponds to the cost-effectiveness approach and we applied the same heuristic greedy approach with slight modifications resulting in the following modified steps: 1) Sorting all possible measure-plot combinations based on their LST mitigation effect in a list, and 2) Selecting at each time step of the algorithm the measure-plot combination with the highest LST impact. Steps 3-7 are identical to the cost-effective approximation algorithm (chapter 3.5) Compared to the cost-effectiveness approach, the selected adaptation measures differ substantially. Throughout all budgets, the optimisation based on LST-impacts mainly selects ‘afforestation on marginal land’. For medium and large budgets, the ‘conversion of cropland into grassland’ is also frequently chosen (see for details Table A.2 in the appendix). This selection was to be expected as these measures render the highest and second highest LST increase mitigation effects (Table 2). In contrast, the cost-effectiveness optimisation selects mainly the measures ‘agroforestry systems’, ‘reforestation’ and ‘ecological forest conversion’ as they have better benefit/cost ratios (Table 2). Optimisation based solely on LST-impacts also selects much less area than the cost-effectiveness approach. For the small, medium and large budgets it only allocates adaptation measures on 2,762 hectares, 5,918 hectares, and 13,137 hectares respectively versus 47,911 hectares, 83,844 hectares, 126,859 hectares under cost-effectiveness optimisation (Figure 2 illustrates the differences in scope and spatial allocation of the different measures by LST optimisation and cost-effectiveness optimisation for different budgets). Again, this result is not surprising as high cost measures are selected which implies that the budget is exhausted with less measures than under the cost-effectiveness approach. Depending on the available budget, the average LST increase mitigation effect under optimisation based solely on LST-impacts ranges between 0.0833 and 0.1317 in the comparatively small area in which climate change adaptation measures with high LST increase mitigation effects are implemented. In the context of cost-effectiveness optimisation, the average LST increase mitigation effect ranges for different budgets between 0.0297 and 0.0358. However, this effect occurs on the large area on which cost-effective climate change adaptation measures are applied (Table 4). To compare the effectiveness of both optimisation approaches, we relate the LST increase mitigation effects of both optimisations to the same reference area, i.e. the entire study area. When the average LST increase mitigation effect in the entire study area is considered (last column in Table 4), the cost-effectiveness optimisation outperforms the optimisation based solely on LST-impacts by 3.5 to 4.8 times depending on the available budget. Table 4: Comparison of results of cost-effectiveness optimisation and optimisation based solely on LST-impacts *LST increase mitigation effects are derived from scaled values. 5. Discussion and conclusion We developed an integrated modelling framework to identify cost-effective climate change adaptation measures in agriculture and forestry that mitigate warming effects in rural areas. We applied the modelling framework to the case study area of the Elbe-Elster county in Brandenburg, Germany, a highly climate-change sensitive region. Implementing ‘agroforestry systems on cropland’ is the most commonly identified cost-effective measure, regardless of whether small, medium, or large areas in the study region are to be transformed. This is due to a combination of medium cooling effects and low costs for its implementation, which makes it more cost-effective in comparison to other measures. In addition, ‘afforestation on forest land’ with medium to high nutrient content and ‘ecological forest conversion’ on forest land with low to medium nutrient content are promising measures. In general, the measures are mainly implemented on low productivity cropland because opportunity costs are relatively low, while the cooling effect is substantial. To our knowledge, we are the first to develop an integrated modelling procedure to identify cost-effective climate change adaptation measures to mitigate LST increase in rural landscapes, while some work on cost-effective heat mitigation exists in an urban context (Zhang and Ayyub 2020, Di Giuseppe et al. 2021). The modelling procedure is transferable to other rural landscapes and can be used to identify cost-effective adaptation measure to mitigate LST increases. Our approach is subject to limitations that may be addressed in further work. Our cost estimates do not include indirect costs due to limited data availability. For example, introducing ‘agroforestry systems’ increases shadowing on neighbouring fields, and less solar radiation may impact plant growth both negatively and positively which we ignored. Moreover, our cost assessment is a snapshot at a moment in time. However, prices do change. Both aspects introduce some uncertainty in our cost assessments. However, we believe this uncertainty to be minor and not to change our main insights. The statistical modelling approach for predicting the effects of measures on LST is of course also subject to uncertainties, which span the entire process from data acquisition through model development and prediction (Zimmermann et al. 2024). Future research may consider how to deal with these uncertainties. We gave several reasons for the need to mitigate the increase of LST. For some of these reasons, it might be beneficial to spatially target the climate change adaptation measures. For example, recreational activities are often carried out along bicycle lanes and walking paths, and it might be desirable to target cooling measures along these lanes and paths. Similarly, a specific species threatened by climate change may require a certain habitat type, which exists only in parts of the landscape. Again, this calls for the selection of specific areas to target adaptation measures. Further research may integrate such spatial targeting in our modelling procedure. The considered adaptation measures also impact ecosystem services other than regulating the climate. For example, landscape structure elements such as hedgerows may provide important habitats for birds and ecological forest conversion generally improves the resilience of forests against climate change and thus supports the continuous provision of ecosystem services from forests including timber. Future research may investigate possible synergies and trade-offs of adaptation measures between the goal of mitigating the rise of LST and the provision of other ecosystem services and how to identify cost-effective measures that consider such synergies and trade-offs. Declarations Ethics approval and consent to participate Not applicable Consent for publication All authors gave consent for publication Competing Interests No competing interests. Author contributions Lutz Hecker carried out the cost calculations and led the article writing with substantial support from Frank Wätzold. Frank Wätzold and Christian Hildmann developed the idea of the integrated modelling procedure. Beate Zimmermann, Sarah Kruber and Christian Hildmann identified the climate change adaptation measures and assessed their impact on the LST. Astrid Sturm developed the optimisation algorithm and carried out the cost-effectiveness optimisation. 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Detailed description of cost calculation\u003c/p\u003e\n\u003cp\u003esee attachment\u003c/p\u003e","description":"","filename":"A1Descriptionofcostcalculation.docx","url":"https://assets-eu.researchsquare.com/files/rs-3904821/v1/4ec2db0e502cec0b7228df28.docx"},{"id":51499088,"identity":"b115d913-96a5-489c-b27f-d16389c3ddaf","added_by":"auto","created_at":"2024-02-22 16:38:38","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":47414,"visible":true,"origin":"","legend":"\u003cp\u003eA.2. Appendix tables\u003c/p\u003e\n\u003cp\u003esee attachment\u003c/p\u003e","description":"","filename":"A2Appendixtables.docx","url":"https://assets-eu.researchsquare.com/files/rs-3904821/v1/9ffafd87681e8cd4ad5a624a.docx"},{"id":51499091,"identity":"74bc0b68-bd46-4b34-a9ff-2a026a9d24ea","added_by":"auto","created_at":"2024-02-22 16:38:38","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":2630880,"visible":true,"origin":"","legend":"\u003cp\u003eA.3. Appendix submitted version of paper cited in 3.3\u003c/p\u003e\n\u003cp\u003esee attachment\u003c/p\u003e","description":"","filename":"AppendixA3citedpaper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3904821/v1/aaa3706e5b16d2c073d9a447.pdf"}],"financialInterests":"","formattedTitle":"Keep Cool in a Changing Climate: An Integrated Modelling Procedure to Identify Cost-Effective, Spatially Differentiated Land-use and Land-cover Measures to Mitigate Rising Temperature in Rural Landscapes","fulltext":[{"header":"1.\tIntroduction","content":"\u003cp\u003eClimate change adaptation measures with the aim to mitigate increasing temperatures typically focus on overheated settlements to reduce negative impacts of high temperatures on the population as a whole and, in particular, on vulnerable groups (e.g.\u0026nbsp;Hunt and Watkiss 2011,\u0026nbsp;Grafakos\u0026nbsp;et al. 2019,\u0026nbsp;Birkmann\u0026nbsp;et al. 2021, Otto et al. 2021). However, in temperate climate zones increasing temperatures may also negatively impact rural landscapes. Negative consequences may manifest in increasing fluctuations and reduced yields in agriculture (Challinor et al. 2014, Mahato 2014, J\u0026auml;germeyr et al. 2021) and forestry (Fuhrer\u0026nbsp;et al. 2006, Seidl et al. 2017, Ven\u0026auml;l\u0026auml;inen et al. 2022). Higher temperatures increase soil moisture stress and accelerate microbial processes in soils, which trigger the leaching of nutrients and the degeneration of soils (Gelyb\u0026oacute; et al. 2018, Jansson \u0026amp; Hofmockel, 2020). As a consequence, plants have less water and nutrients at their disposal for growth. Increasing temperatures also lead to range shifts of species leading to their endangerment (Bellard et al. 2012, Dasgupta 2021, Gerling et al. 2022) especially as fragmented landscapes prevent species to migrate to new ranges (Vos et al. 2008). In particular, wet habitats such as wet meadows, fens or water bodies are under considerable pressure, losing area or disappearing altogether (Fluet-Chouinard et al. 2023). At the landscape level, there is a systemic risk that an increase of the land-ocean temperature contrast, which is substantially influenced by vegetation and, hence, the disturbance of a natural vegetation cover could cause the climate system (in Central Europe) to pass a tipping point and move from a wet to a dry state (Makarieva et al., 2022). Finally, rural landscapes often serve as recreational areas and their recreational value may decline if they become too hot in summer\u0026nbsp;(Pr\u0026ouml;bstl-Haider et al. 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA promising way to reduce the land surface temperature (LST) is to improve the capabilities of rural landscapes to retain the existing precipitation in the landscape by storing it in soils, water bodies and groundwater to make it available to vegetation for evaporation (Proch\u0026aacute;zka et al. 2019). One key option to improve these processes are changes in land-use and land-cover (henceforth referred to as climate change adaptation measures or simply adaptation measures). Examples of such measures include the enrichment of humus on agricultural land, the transformation of cropland to grassland, and the conversion from deciduous monocultures to near-natural mixed deciduous forests (Hildmann et al. 2022). Since the implementation of such adaptation measures is (often) costly and financial resources are scarce it is important to implement measures in a cost-effective way. Cost-effectiveness here means that measures are selected in such a way that the LST in a region is reduced most for given costs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe purpose of this paper is to develop an integrated modelling procedure\u0026nbsp;to\u0026nbsp;identify cost-effective climate change adaptation measures in agriculture and forestry to mitigate the temperature increase in rural landscapes.\u0026nbsp;The modelling procedure combines results from a model that predicts the impact of adaptation measures on LST and results from economic cost assessments that estimates the respective costs of these measures to mitigate LST increases. To identify the cost-effective allocation of adaptation measures the integrated modelling framework uses\u0026nbsp;an optimisation algorithm. The model is spatially differentiated, i.e. for each agricultural and forest plot, it quantifies the impact on the LST of potentially suitable climate change adaptation measures, estimates associated costs and puts LST mitigation effects and costs of the measures into relation. As a result, it specifies where to carry out which measures in the landscape to maximise the mitigation effect on the LST at the landscape level for given budgets. In order to show the improved performance on mitigating the LST that is realized by applying our cost-effectiveness approach we contrast its results with a (purely natural science) approach that considers only the impact of climate change adaptation measures in terms of LST mitigation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe demonstrate how the integrated modelling procedure works by applying it to the case study area of Elbe-Elster county in the federal state of Brandenburg, Germany, but would like to emphasize that it\u0026nbsp;is transferable to other rural landscapes in temperate climate zones.\u0026nbsp;Elbe-Elster county is a suitable study region as the county is part of the highly climate-sensitive\u0026nbsp;Lusatian region. The sensitivity is a result of the combination of low annual precipitation and sandy soils with little water retention capacity (K\u0026uuml;hn et al. 2015, Gerwin et al. 2023). Weather data for the region has shown an increase of air temperatures over the last 30 years (Gr\u0026uuml;newald 2001, Pohle 2014, DWD 2020), and climate projections predict further temperature increases (Pfeifer et al. 2021). Alongside, rainfall patterns see a shift of precipitation from summer to winter (Zomer et al. 2022). This pattern shift highlights the importance of increasing the water retention capacity to\u0026nbsp;counter the\u0026nbsp;drying out of the landscape and, as a consequence, its overheating during the vegetation period.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur work is at the intersection of three research areas. The first area is the economic literature on heat mitigation in urban areas. Often cost-benefit-analyses are applied to assess and investigate the economic feasibility of measures like the design of green urban water drainage systems (Johnson and Geisendorf, 2019), urban green infrastructure such as green walls (Koch et al., 2020), pervious pavement systems (Wang et al., 2022), tree canopy cover (Bosch et al., 2021), and ecosystem-based approaches (Markanday et al., 2019) to mitigate adverse heat effects of climate change. In contrast, studies that investigate the cost-effectiveness of temperature mitigation measures are relatively scant. Notable exemptions are Zhang and Ayyub (2020) and Di Giuseppe et al. (2021) who respectively assess the cost-effectiveness of deploying cool and green roofs and dry mist systems as a water-based mitigation strategy against urban overheating.\u003c/p\u003e\n\u003cp\u003eThe second research area is the analysis of the selection and spatial allocation of cost-effective land-use measures to obtain ecosystem service and biodiversity goals. A seminal paper is by\u0026nbsp;Polasky et al.\u0026nbsp;(2008) who model the biological and economic costs and benefits of land-use patterns to derive spatially explicit, cost-effective land-use practises for species conservation. Examples of the application of this general approach are Tainio et al. (2016) who investigate the cost-effective conservation of grassland butterflies under a changing climate while Gerling et al. (2022) investigate the impact of climate change on the cost-effective allocation and choice of measures to conserve a grassland indicator species.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe third area is the natural science literature on the impact of land-use and land-cover on the LST. The interaction of land-use respectively land-cover and LST has been the subject of research for many years (Liu and Weng, 2008, Luyssaert et al., 2014, Jamei et al., 2022). However, other parameters also affect LST, such as elevation (Phan et al., 2018) or tree cover density (Greene and Kedron, 2018). Similar to economic research, studies on temperature mitigation effects of greening measures focus mainly on heat islands in urban areas (Rahman et al., 2020, Gao et al., 2020, Shafiee et al., 2020). However, some studies also exist in the forest sector (e.g. Schwaab et al., 2020, Meeussen et al. (2021) and in the agricultural sector (Brunel-Saldias et al., 2018, Fischer et al., 2019).\u003c/p\u003e\n\u003cp\u003eWe combine those three research strings as follows. We take up the challenge of cost-effective heat mitigation investigated in the context of urban heat islands but address it in the context of a rural landscape and the corresponding climate change adaptation measures. The development of our integrated modelling procedure is inspired by the analysis of cost-effective land-use measures to obtain ecosystem service and biodiversity goals and follows in principle the typical structure of integrated models developed in that research field (Polasky et al. 2008). However, instead of biodiversity or ecosystem services typically investigated such as pollination or clean water, we focus on the mitigation impact of adaptation measures on the LST which we estimate using a natural science modelling approach.\u0026nbsp;\u003c/p\u003e"},{"header":"2.\tCase Study","content":"\u003cp\u003e2.1. Description of Elbe-Elster county\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Elbe-Elster-County is located in the south of the federal state of Brandenburg in Eastern Germany. It inhabits 100,902 people (Amt f\u0026uuml;r Statistik Berlin-Brandenburg 2023) on an area of 1,899 km\u003csup\u003e2\u003c/sup\u003e with an annual GDP of 2,648 billion \u0026euro; (Euros) in 2020 (VGRDL 2022). About 1,900 employees worked in 2019 in forestry and agriculture contributing 80 million Euros (3%) to the local GDP (VGRDL 2022). 51% of the county are managed as agricultural land, and 36% are covered by forest (Amt f\u0026uuml;r Statistik Berlin-Brandenburg 2022). With a relatively low average land productivity[1] (MLUK 2023) cultivation conditions for agriculture are less favourable compared to the rest of Germany (Kaufmann-Boll et al. 2020, Pfeiffer et al. 2021). Grain farming dominates in the county, mainly with rye and silage corn for energy and fodder production for dairy livestock. About 23% of the agricultural land is grassland (Amt f\u0026uuml;r Statistik Berlin-Brandenburg 2019). Opencast lignite mining played a crucial role to the local economy in past decades. As a result, the landscape transformed deeply with negative impacts on the water retention capacity of the local landscape (Gerwin et al. 2023).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.2. Water scarcity and climate change in the Elbe-Elster county\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnnual precipitation amounts to 556,8 mm/a (1971 \u0026ndash; 2000, Pfeifer et al. 2021) (for comparison: Germany 922.8 mm/a) and shows a slight, statistically non-significant increase between the periods 1951-1980 and 1986-2015 (Pfeifer et al. 2021). However, there is also an increase of the mean annual temperature by an average of 0.9 K to 9.1 \u0026deg;C (similar to the German average of 9.3 \u0026deg;C) (Pfeifer et al. 2021). The overall result is a decrease in water availability in the region, particularly in the vegetation period (Zimmermann and Hildmann, 2021). According to the classification of Zomer et al. (2022), the region is now considered dry sub-humid. Climate projections (Pfeifer et al. 2021) predict further rising temperatures and an extension of the vegetation period in the Elbe-Elster county. This may increase water demand of plants. Regarding annual amount, frequency and seasonal distribution of precipitation, climate projections indicate a more frequent occurrence of heavy precipitation events and a general shift of rainfall from summer into winter with no substantial changes in annual precipitation amounts. Overall, the impact of climate change is expected to reduce the water availability to plants during the vegetation period. \u003c/p\u003e"},{"header":"3.\tDescription of the integrated modelling procedure","content":"\u003cp\u003e3.1. Overview of the procedure\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo identify cost-effective climate change adaptation measures to mitigate the land surface temperature increase in rural landscapes we developed an integrated modelling procedure. Figure 1 describes the structure of the procedure and how its different components are interrelated. In the following sections 3.2-3.5 we describe how we applied the procedure to the case study region.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn a first step (Figure 1, box 1), climate change adaptation measures, which are in principle suitable to mitigate LST increases in a specific region, need to be identified. In a second step (Figure 1, box 2), information at the level of agricultural and forest plots in the study region is gathered regarding the existing land use respectively land cover and land quality information (e.g. soil quality, land productivity, nutrition level).[2] From the overall set of adaptation measures for each plot those are selected which are in principle suitable for this plot (e.g. agricultural measures are dropped for forests plots). This information feeds into an estimation of the impact of adaptation measures on LST (Figure 1, box 3) in a spatially differentiated manner \u0026ndash; i.e. for the individual agricultural and forest plots. For the estimation, a statistical model is developed to identify the influence of environmental variables such as tree cover density and soil water retention capacity on the LST, and to predict the effect of an individual measure on a plot. In parallel (Figure 1, box 4), the aggregated and discounted costs of implementing the selected adaptation measures on individual agricultural and forest plots for a period of 30 years are estimated. Finally (Figure 1, box 5), the cost data and the data on the impact of adaptation measures on LST are integrated in an optimisation procedure to identify cost-effective portfolios of measures and their spatial allocation. For that, the effect/cost ratios of all measures for all agricultural and forest plots are ranked according to their effect-cost-relation. On this ranking, an approximation algorithm is applied to identify where in the landscape which measures should be implemented for any given 30-year budget to maximise the reduction in the LST.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.2. Identification of suitable measures for LST increase mitigation and localisation to plots\u003c/p\u003e\n\u003cp\u003eTable 1 lists six agricultural and two forestry adaptation measures we singled out for our analysis based on a comprehensive review of relevant scientific literature and suggestions from administrative decision makers and other stakeholders in the study region. For a detailed description of all investigated measures and how they influence surface temperatures see Hildmann et al. (2022).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1: List of agricultural and forest adaptation measures to mitigate LST increase in the Elbe-Elster county\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.43648208469055%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeasures\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"61.56351791530945%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeasure description\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.43648208469055%\" valign=\"top\"\u003e\n \u003cp\u003eAgroforestry systems (alley cropping)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"61.56351791530945%\" valign=\"top\"\u003e\n \u003cp\u003eCombined cultivation of conventional arable crops\u0026nbsp;and\u0026nbsp;rows of perennial woody plants\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.43648208469055%\" valign=\"top\"\u003e\n \u003cp\u003eLandscape structure elements\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"61.56351791530945%\" valign=\"top\"\u003e\n \u003cp\u003ePlanting hedgerows and field copses along ditches and trails\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.43648208469055%\" valign=\"top\"\u003e\n \u003cp\u003eConversion of cropland into permanent grassland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"61.56351791530945%\" valign=\"top\"\u003e\n \u003cp\u003eEstablishment of evergreen, continuous grass cover\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.43648208469055%\" valign=\"top\"\u003e\n \u003cp\u003eOrganic fertilization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"61.56351791530945%\" valign=\"top\"\u003e\n \u003cp\u003eApplication of organic matter (compost, manure, straw) to arable land\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.43648208469055%\" valign=\"top\"\u003e\n \u003cp\u003eCultivating deep-rooted crops\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"61.56351791530945%\" valign=\"top\"\u003e\n \u003cp\u003eCultivation of deep-rooted arable crops for better water supply\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.43648208469055%\" valign=\"top\"\u003e\n \u003cp\u003eAfforestation of marginal cropland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"61.56351791530945%\" valign=\"top\"\u003e\n \u003cp\u003eAfforestation of marginal arable sites with deciduous trees\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.43648208469055%\" valign=\"top\"\u003e\n \u003cp\u003eReforestation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"61.56351791530945%\" valign=\"top\"\u003e\n \u003cp\u003eReforestation of degraded forest with site-adapted tree species\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.43648208469055%\" valign=\"top\"\u003e\n \u003cp\u003eEcological forest conversion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"61.56351791530945%\" valign=\"top\"\u003e\n \u003cp\u003eConversion from coniferous tree plantations to mixed or deciduous forest\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFrom this overall set of adaptation measures those are selected for each plot, which are in principle suitable for it. This localisation of measures is based on specific GIS algorithms that also determine certain plot-specific attributes such as the relative proportion of a plot that can be influenced by a measure. For example, as the measure \u0026lsquo;landscape structure elements\u0026rsquo; is located exclusively along paths and ditches, the proportion of these elements varies among plots.\u003c/p\u003e\n\u003cp\u003e3.3. Assessment of climate change adaptation measures on LST\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe evaluation of the impact of climate change adaptation measures was based on their potential effect to mitigate LST increases. We only summarise our approach here and refer the interested reader to Zimmermann et al. (2024, under revision)[3] for details. In a first step, we developed a statistical model that quantifies the relationship between different environmental variables (called predictors hereafter) and the LST as the dependent variable. We used thermal images from the Landsat 8 satellite to derive LST during the main vegetation period (May to September) of the years 2013 to 2020 in the study region. In total, 39 satellite images were available that satisfied the quality requirements such as the large absence of clouds.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe predictors were identified based on knowledge in the literature of factors that influence the LST in rural landscapes (e.g. Bertoldi et al. 2010, Song et al. 2014, Alavipanah et al. 2015, Du et al. 2016, Kim et al. 2016, Das et al. 2020) and own previous work (Hildmann et al. 2019). They included, for example, land-use/land-cover class, tree cover density, imperviousness density and landscape structure metrics. The predictor values were derived from available geospatial data in the study region on land cover, land use, soil, topography and climate.\u003c/p\u003e\n\u003cp\u003eTo fit the model, we applied a Bayesian hierarchical modelling framework using the statistical software R (R Core Team, 2022) and the library brms (B\u0026uuml;rkner, 2021). The common geometry for both the predictors and the LST was the raster data format with the LST map resolution of 30 m x 30 m.\u003c/p\u003e\n\u003cp\u003eIn the second step, the model was used to predict the LST for predictor values, that can be expected before and after implementation of a climate change adaptation measure for each individual plot. For example, the measure \u0026lsquo;conversion of cropland into permanent grassland\u0026rsquo; is accompanied by a change of the predictor land-use/land-cover class (from cropland before implementation to grassland after implementation). The effect on the LST was then calculated as the difference in predicted LST before and after a (hypothetical) measure implementation. In order to transfer the values in the raster cells to the agricultural and forest plots, respectively, we calculated mean values. For measures that do not cover the entire plot, area-weighted mean values were calculated instead of arithmetic mean values, whereby the weights were determined according to the plot-specific attributes (see chapter 3.2).\u003c/p\u003e\n\u003cp\u003eDue to the combination of the 39 satellite images at different times, a scaled value is used for the LST instead of the value in K. The scaling was necessary because the absolute LST values depend on the weather conditions at the time of the satellite overflight and are therefore not directly comparable. The min-max feature scaling (min-max normalization) was used as the scaling method, in which the data is rescaled to the range [0, 1].\u003c/p\u003e\n\u003cp\u003e3.4. Cost estimation of climate change adaptation measures\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe followed the approach of identifying costs that occur when a measure to mitigate LST increase is implemented instead of a business-as-usual scenario (Sturm et al., 2018). With a business-as-usual scenario, we mean the existing land use on a plot in the study region which we assume to maximise the land users\u0026rsquo; profits. For the cost assessment of the adaptation measure, we distinguished between investment costs, management costs, and opportunity costs of land-use. Investment costs occur for some measures that require a one-time investment at the beginning of a measure (e.g. the implementation of \u0026lsquo;agroforestry systems (alley cropping)\u0026rsquo; requires the plantation of tree seedlings on parts of the cropland). Management costs are ongoing costs of maintaining an implemented measure (e.g. the periodically recurring need of tree care in the case of \u0026lsquo;alley cropping on cropland\u0026rsquo;), and opportunity costs are the difference between the profit from the existing land use and the profit from an adaptation measure. For example, the introduction of \u0026lsquo;agroforestry systems\u0026rsquo; reduces the area for commercial crops as trees are now planted on parts of the cropland. This results in a decreased commercial crop yield and profit per hectare, which is not fully compensated by the profit generated from the sales of agroforestry products.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe considered spatially differentiated costs for (individual) agricultural and forest plots. Costs depend on factors such as soil quality and other landscape elements (e.g. slope gradient) (Kaufmann-Boll et al. 2020). We calculated the aggregated costs over a period of 30 years, and applied a discount rate which is common in economics to take into account that future costs and revenues are generally given less weight than present ones (see any textbook such as Boardman et al. (2018) for details). A more detailed description of our cost calculation for all adaptation measures listed in Table 1 provides Appendix A.1 and Hildmann et al. (2022).\u003c/p\u003e\n\u003cp\u003e3.5. Optimisation module and cost-effectiveness analysis \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBased on data input on the spatially differentiated costs of adaptation measures and their effect on the LST we applied an optimisation algorithm to identify at which agricultural or forest plot to implement which measure to maximise the countywide LST increase mitigation effect for given budgets.[4] This optimisation problem corresponds to a higher dimensional knapsack problem which cannot be solved in polynomial time requiring an approximation algorithm (see Skiena 2020 for details). The approximation algorithm selects for a given budget the most cost-effective measure-plot combinations until the budget is spent. This corresponds to a heuristic greedy approach (see Sturm et al. 2018 for an example of how this approach can be applied to identify optimal land-use measures) which performs the following steps: 1) Sorting all possible measure-plot combinations by their ratio of LST impact to cost (benefit/cost-ratio) in a list. 2) Selecting at each time step of the algorithm the most cost-effective measure-plot combination. 3) Checking if the (additional) costs arising from this selection are within the overall available budget and if no measure is already allocated to the plot. 4) If both checks are positive (no plot allocated yet and additional costs do not exceed the budget), the measure is allocated to this plot and the measure-plot combination removed from the measure-plot list. The remaining budget is reduced by the costs corresponding to the implemented measure. 6) If either the plot is already occupied or the cost is too high for the remaining budget, the measure-plot combination is deleted from the list. 7) Repeat the procedure until the budget is exhausted or there is no measure-plot combination left. This greedy approach does not guarantee that the optimal solution is found, but it does provide a good approximation of the optimum.\u0026nbsp;\u003c/p\u003e"},{"header":"4.\tResults","content":"\u003cp\u003e4.1. Cost-effectiveness optimisation\u003c/p\u003e\n\u003cp\u003eWe find that average LST increase mitigation effects and average costs vary substantially across climate change adaptation measures. In terms of the impact on LST, \u0026lsquo;afforestation of marginal lands\u0026rsquo; has the largest effect, while \u0026lsquo;cultivation of deep-rooted crops\u0026rsquo; has virtually no impact. In terms of cost, \u0026lsquo;afforestation of marginal lands\u0026rsquo; is the most expensive adaptation measure and the introduction of \u0026lsquo;agroforestry systems\u0026rsquo; is the least expensive. Comparing the average benefit/cost ratio of different adaptation measures provides a general assessment of the cost-effectiveness of the measures. On average, implementing \u0026lsquo;agroforestry systems\u0026rsquo; on relatively low quality plots renders the highest benefit/cost ratio which can be explained by agroforestry systems having a medium LST effect while being comparatively cheap. By contrast, \u0026lsquo;cultivating deep-rooted crops\u0026rsquo; has the lowest ratio. This is because the rotation from profit maximizing crops to deep-root cultivation has hardly any impact on LST while relatively high forgone profits make the change rather costly.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe standard deviations of LST increase mitigation effects and costs of the adaptation measures also substantially vary across measures and are quite high for some measures, which shows the importance of their spatial differentiation. In particular, forest related adaptation measures have comparatively large standard deviations for LST increase mitigation effects and costs. With respect to LST, this large spread is explained by spatial variations in the predictor values among plots and differences in the proportion of plot area influenced by a measure (Zimmermann et al. 2024). For example, the predictor \u0026lsquo;tree cover density\u0026rsquo; before implementation of the measure \u0026lsquo;ecological forest conversion\u0026rsquo; varies between 0% (highly disturbed or recently cleared forest stands) and 100%. Regarding costs, one reason is that fencing which is required to avoid damage caused by game animals represents a substantial cost item for forest climate change adaptation measures. However, the amount of fencing costs varies greatly depending on the size and shape of the plot the forest measures are implemented on.\u003c/p\u003e\n\u003cp\u003eTable 2: Average scaled LST effects, average costs and average benefit/cost ratios of measures for all plots in study region**\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" style=\"width: 728px; height: 566.393px;\" width=\"728\" height=\"566.393\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e*LST effects are derived from scaled values.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e**Values in table derived from combing results of calculations in box 3 and box 4 in figure 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3 shows the result for the cost-effective allocation of climate change adaptation measures across agricultural and forestry plots in the study region for a small budget (20 million \u0026euro;), a medium budget (40 million \u0026euro;) and large budget (80 million \u0026euro;) over the period of 30 years. The small, medium and large budgets were selected to allow the implementation of measures on a relatively small (47,911 hectares), medium (83,844 hectares), and large (126,859 hectares) share of the agricultural and forest area in the study region which amounts to 164,565 hectares.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3: Results of cost-effective LST increase mitigation for a small, medium and large budget\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" style=\"width: 871px; height: 368.528px;\" width=\"871\" height=\"368.528\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e* Forest areas in the study region are categorized into four nutrition levels: A = poor, Z = quite poor, M = moderately nutritious, Z+ = between Z and M.\u003c/p\u003e\n\u003cp\u003eFor a small budget (20 million \u0026euro;), the cost-effective measure to mitigate LST increase is mainly the introduction of \u0026lsquo;agroforestry systems\u0026rsquo; on cropland. This measure is implemented on almost all cropland plots with low productivity (soil quality index \u0026lt; 25) and on almost half of the cropland plots with middle to high productivity (soil quality index \u0026ge; 25). This result is reflected in the comparatively high benefit/cost ratio of 0.93 and 0.63 respectively (Table 2) and can be explained by small opportunity costs for this measure, particularly on land with low productivity, while the LST increase mitigation effect is medium. The low opportunity costs result from the fact that only a small part of the cropland is planted with trees, while cropland continues to be cultivated on the remaining area. Unlike \u0026lsquo;landscape structure elements\u0026rsquo;, agroforestry strips additionally provide forest products (e.g. timber) that can be sold. Opportunity costs are somewhat higher for cropland with medium to high productivity, as more fertile land results in higher foregone crop yields. Furthermore, two forest measures are assigned to a few plots with specific nutrient levels (Table 3). This is an effect of the difference in benefit/cost ratios in relation to these levels (Table 2). \u0026lsquo;Reforestation\u0026rsquo; takes place on a few forest plots with middle to high nutrition levels (M levels), and \u0026lsquo;ecological forest conversion\u0026rsquo; also on a few forest plots with a low nutrition level (A level) and with low to medium nutrition level (Z level). Implementing these land-use measures is relatively inexpensive while having a significant LST increase mitigation effect.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results for the middle budget (40 million \u0026euro;) and large budget (80 million \u0026euro;) are largely in line with the findings for the small budget. The measure of \u0026lsquo;agroforestry systems\u0026rsquo; is again implemented on almost the entire cropland area with low productivity and, additionally, on almost all cropland area with middle to high productivity. This share increase reflects the comparatively high benefit/cost ratio of \u0026lsquo;agroforestry systems\u0026rsquo; for a large share of plots. In line with this, \u0026lsquo;reforestation\u0026rsquo; takes place on bigger shares of forest plots with middle to high nutrition M-levels, and \u0026lsquo;conversion to ecological forest\u0026rsquo; on bigger shares of forest plots with low nutrition A-levels and low to medium nutrition Z-levels. In addition, \u0026lsquo;landscape structure elements\u0026rsquo; are implemented on about half of the grassland for a large budget. This is interesting, as the average benefit/cost ratio for \u0026lsquo;afforestation of marginal land\u0026rsquo; and \u0026lsquo;conversion of cropland into grassland\u0026rsquo; is slightly higher (0.21 and 0.22) than for \u0026lsquo;landscape structure elements\u0026rsquo; (0.19) and one would expect the selection of some plots for those land-use measures. An explanation for the selection of \u0026lsquo;landscape structure elements\u0026rsquo; is that on some grassland plots landscape structure elements have a particularly large LST increase mitigation effect. This happens when these elements cover a comparatively large part of the plot, e.g. due to a small plot size in combination with a high proportion of ditches or trails (as noted before, the localisation of measure \u0026lsquo;landscape structure elements\u0026rsquo; was limited to these features). This results in high benefit/cost ratios of this measure on these grassland plots. Moreover, as only one measure can be implemented per plot and \u0026lsquo;agroforestry systems\u0026rsquo; are already implemented on almost all cropland plots there is little room left for the implementation of other measures on this land-use type.\u003c/p\u003e\n\u003cp\u003e4.2. Efficiency advantage of cost-effectiveness approach over purely LST focused approach\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn order to demonstrate the advantage of the cost-effectiveness approach in comparison to a (purely natural science) approach that selects adaptation measures with a higher effect on LST first, we repeated the optimisations for the three considered budgets applying such an approach. The structure of the optimisation problem corresponds to the cost-effectiveness approach and we applied the same heuristic greedy approach with slight modifications resulting in the following modified steps: 1) Sorting all possible measure-plot combinations based on their LST mitigation effect in a list, and 2) Selecting at each time step of the algorithm the measure-plot combination with the highest LST impact. Steps 3-7 are identical to the cost-effective approximation algorithm (chapter 3.5)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompared to the cost-effectiveness approach, the selected adaptation measures differ substantially. Throughout all budgets, the optimisation based on LST-impacts mainly selects \u0026lsquo;afforestation on marginal land\u0026rsquo;. For medium and large budgets, the \u0026lsquo;conversion of cropland into grassland\u0026rsquo; is also frequently chosen (see for details Table A.2 in the appendix). This selection was to be expected as these measures render the highest and second highest LST increase mitigation effects (Table 2). In contrast, the cost-effectiveness optimisation selects mainly the measures \u0026lsquo;agroforestry systems\u0026rsquo;, \u0026lsquo;reforestation\u0026rsquo; and \u0026lsquo;ecological forest conversion\u0026rsquo; as they have better benefit/cost ratios (Table 2). Optimisation based solely on LST-impacts also selects much less area than the cost-effectiveness approach. For the small, medium and large budgets it only allocates adaptation measures on 2,762 hectares, 5,918 hectares, and 13,137 hectares respectively versus 47,911 hectares, 83,844 hectares, 126,859 hectares under cost-effectiveness optimisation (Figure 2 illustrates the differences in scope and spatial allocation of the different measures by LST optimisation and cost-effectiveness optimisation for different budgets). Again, this result is not surprising as high cost measures are selected which implies that the budget is exhausted with less measures than under the cost-effectiveness approach.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDepending on the available budget, the average LST increase mitigation effect under optimisation based solely on LST-impacts ranges between 0.0833 and 0.1317 in the comparatively small area in which climate change adaptation measures with high LST increase mitigation effects are implemented. In the context of cost-effectiveness optimisation, the average LST increase mitigation effect ranges for different budgets between 0.0297 and 0.0358. However, this effect occurs on the large area on which cost-effective climate change adaptation measures are applied (Table 4). To compare the effectiveness of both optimisation approaches, we relate the LST increase mitigation effects of both optimisations to the same reference area, i.e. the entire study area. When the average LST increase mitigation effect in the entire study area is considered (last column in Table 4), the cost-effectiveness optimisation outperforms the optimisation based solely on LST-impacts by 3.5 to 4.8 times depending on the available budget. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4: Comparison of results of cost-effectiveness optimisation and\u0026nbsp;optimisation based solely on LST-impacts\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" style=\"width: 646px; height: 289.393px;\" width=\"646\" height=\"289.393\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*LST increase mitigation effects are derived from scaled values.\u003c/strong\u003e\u003c/p\u003e"},{"header":"5.\tDiscussion and conclusion ","content":"\u003cp\u003eWe developed an integrated modelling framework to identify cost-effective climate change adaptation measures in agriculture and forestry that mitigate warming effects in rural areas. We applied the modelling framework to the case study area of the Elbe-Elster county in Brandenburg, Germany, a highly climate-change sensitive region. Implementing \u0026lsquo;agroforestry systems on cropland\u0026rsquo; is the most commonly identified cost-effective measure, regardless of whether small, medium, or large areas in the study region are to be transformed. This is due to a combination of medium cooling effects and low costs for its implementation, which makes it more cost-effective in comparison to other measures. In addition, \u0026lsquo;afforestation on forest land\u0026rsquo; with medium to high nutrient content and \u0026lsquo;ecological forest conversion\u0026rsquo; on forest land with low to medium nutrient content are promising measures. In general, the measures are mainly implemented on low productivity cropland because opportunity costs are relatively low, while the cooling effect is substantial. To our knowledge, we are the first to develop an integrated modelling procedure to identify cost-effective climate change adaptation measures to mitigate LST increase in rural landscapes, while some work on cost-effective heat mitigation exists in an urban context (Zhang and Ayyub 2020, Di Giuseppe et al. 2021). The modelling procedure is transferable to other rural landscapes and can be used to identify cost-effective adaptation measure to mitigate LST increases.\u003c/p\u003e\n\u003cp\u003eOur approach is subject to limitations that may be addressed in further work. Our cost estimates do not include indirect costs due to limited data availability. For example, introducing \u0026lsquo;agroforestry systems\u0026rsquo; increases shadowing on neighbouring fields, and less solar radiation may impact plant growth both negatively and positively which we ignored.\u0026nbsp;Moreover, our cost assessment is a snapshot at a moment in time. However, prices do change. Both aspects introduce some uncertainty in our cost assessments. However, we believe this uncertainty to be minor and not to change our main insights. The statistical modelling approach for predicting the effects of measures on LST is of course also subject to uncertainties, which span the entire process from data acquisition through model development and prediction (Zimmermann et al. 2024). Future research may consider how to deal with these uncertainties.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe gave several reasons for the need to mitigate the increase of LST. For some of these reasons, it might be beneficial to spatially target the climate change adaptation measures. For example, recreational activities are often carried out along bicycle lanes and walking paths, and it might be desirable to target cooling measures along these lanes and paths. Similarly, a specific species threatened by climate change may require a certain habitat type, which exists only in parts of the landscape. Again, this calls for the selection of specific areas to target adaptation measures. Further research may integrate such spatial targeting in our modelling procedure.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe considered adaptation measures also impact ecosystem services other than regulating the climate. For example, landscape structure elements such as hedgerows may provide important habitats for birds and ecological forest conversion generally improves the resilience of forests against climate change and thus supports the continuous provision of ecosystem services from forests including timber. Future research may investigate possible synergies and trade-offs of adaptation measures between the goal of mitigating the rise of LST and the provision of other ecosystem services and how to identify cost-effective measures that consider such synergies and trade-offs.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll authors gave consent for publication\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting Interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNo competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthor contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLutz Hecker carried out the cost calculations and led the article writing with substantial support from Frank W\u0026auml;tzold. Frank W\u0026auml;tzold and Christian Hildmann developed the idea of the integrated modelling procedure. Beate Zimmermann, Sarah Kruber and Christian Hildmann identified the climate change adaptation measures and assessed their impact on the LST. Astrid Sturm developed the optimisation algorithm and carried out the cost-effectiveness optimisation.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the\u0026nbsp;Federal Ministry of Education and Research (BMBF) with grant number\u0026nbsp;FKZ 01LR2004A-B.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmt f\u0026uuml;r Statistik Berlin-Brandenburg (2019). 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Version 3 of the Global Aridity Index and Potential Evapotranspiration Database 2022, Scientific Data, 9, 409.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"climatic-change","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clim","sideBox":"Learn more about [Climatic Change](https://www.springer.com/journal/10584)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/clim/default.aspx","title":"Climatic Change","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"climate change adaptation, integrated assessment, heat mitigation, water stress, climate water cycle, landscape cooling, nature-based solutions ","lastPublishedDoi":"10.21203/rs.3.rs-3904821/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3904821/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Rising temperatures may negatively impact rural landscapes in temperate climates due to reduced yields in agriculture and forestry, an increased risk of biodiversity loss, changes in the local climate and a decrease in recreational value. One promising way to mitigate increasing land surface temperatures (LST) in rural landscapes is to implement land-use and land-cover measures as adaptation measures that retain precipitation in soils, water bodies, and groundwater to allow vegetation to evaporate more water to reduce LST in summer. We develop an integrated modelling procedure to identify cost-effective spatially differentiated adaptation measures in agriculture and forestry to mitigate LST increases. With cost-effective we mean that the LST mitigation in a landscape is maximised for given costs. The procedure combines the results of a model that predicts the spatially differentiated effects of adaptation measures on LST with the results of an economic model that estimates the respective spatially differentiated costs in an optimisation algorithm. We demonstrate how the procedure works by applying it to the Elbe-Elster-county in Germany. One result is that the introduction of agroforestry systems on cropland, reforestation and the conversion of tree plantations into ecological forest are most cost-effective in mitigating LST increases for costs of 20 Mio. €. We also compare results from our integrated modelling procedure with a (purely natural science) approach that selects those adaptation measures first which perform best in terms of LST mitigation and find that our approach leads to a better heat mitigating effect by a factor of 3.5 – 4.8.","manuscriptTitle":"Keep Cool in a Changing Climate: An Integrated Modelling Procedure to Identify Cost-Effective, Spatially Differentiated Land-use and Land-cover Measures to Mitigate Rising Temperature in Rural Landscapes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-22 16:38:33","doi":"10.21203/rs.3.rs-3904821/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-02-28T01:44:13+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-19T16:54:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-01-30T02:33:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"Climatic Change","date":"2024-01-28T23:11:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"climatic-change","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clim","sideBox":"Learn more about [Climatic Change](https://www.springer.com/journal/10584)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/clim/default.aspx","title":"Climatic Change","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"752941e3-d6ed-4093-bba6-a8d5027405e6","owner":[],"postedDate":"February 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-12T16:01:17+00:00","versionOfRecord":{"articleIdentity":"rs-3904821","link":"https://doi.org/10.1007/s10584-025-03929-0","journal":{"identity":"climatic-change","isVorOnly":false,"title":"Climatic Change"},"publishedOn":"2025-05-09 15:57:24","publishedOnDateReadable":"May 9th, 2025"},"versionCreatedAt":"2024-02-22 16:38:33","video":"","vorDoi":"10.1007/s10584-025-03929-0","vorDoiUrl":"https://doi.org/10.1007/s10584-025-03929-0","workflowStages":[]},"version":"v1","identity":"rs-3904821","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3904821","identity":"rs-3904821","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}