<p><span>Adaptation to climate change is an inevitable challenge in many regions. In our study area, which is located in the state of Brandenburg in </span><span>e</span><span>astern Germany, land use is increasingly affected by long-lasting soil moisture deficits in the vegetation period. It is therefore important to implement measures for water retention at the landscape scale that postpone and mitigate the severity of these drought periods. Our objective is to identify cost-effective measures in a manner that maximi</span><span>z</span><span>es expected ecological benefits for </span><span>available </span><span>budgets. For this purpose, we combine a scientific analysis of the </span><span>determinants</span><span> of land surface temperature with site-specific cost calculations. </span></p><p><span>The distribution of land surface temperature serves as a proxy for environmental conditions that favor water retention and, as a consequence, provide a certain cooling effect during hot and dry periods. Landsat thermal images from the vegetation seasons of 2013 to 2020 were rescaled (min-max normalization) and used as the response variable for a Bayesian </span><span>multilevel</span><span> model. Several parameters of the physical environment such as land cover, forest and crop type, soil water holding capacity, canopy cover and degree of soil sealing were used as explanatory variables. In addition, an antecedent moisture index and potential evapotranspiration at time of satellite overpass were incorporated into the model. First results highlight the importance of land </span><span>use</span><span> and canopy cover for land surface temperature </span><span>distribution</span><span>. In general, the analysis enables the identification of overheated landscapes. Moreover, model predictions after </span><span>hypothetical </span><span>implementation of adaptation measures provide a</span><span>n ecological</span><span> benefit assessment based on the cooling capacities. We also </span><span>determine</span><span> the costs of the different measures in a spatially differentiated manner. An integrated modeling procedure combines </span><span>the </span><span>results from the </span><span>ecological and economic assessments</span><span>.</span></p><p><span>In this contribution, we will present the results of the Bayesian modeling and discuss a first example of the cost-effectiveness analysis in an agricultural landscape.</span></p>
<p><span lang="en-US">In the state of Brandenburg in </span><span lang="en-US">e</span><span lang="en-US">astern Germany, land use is increasingly affected by long-lasting soil moisture deficits in the vegetation period. Therefore, it is important to take measures to improve water retention at the landscape level to delay and mitigate the effects of droughts.</span></p> <p><span lang="en-US">As a first step, we developed a catalog of possible measures that can be implemented on agricultural land, in forests, settlements, and nature reserves in our study area, a 1900 km&#178; county in Brandenburg. </span><span lang="en-US">Our objective </span><span lang="en-US">wa</span><span lang="en-US">s </span><span lang="en-US">then </span><span lang="en-US">to </span><span lang="en-US">quantify the</span><span lang="en-US">ir</span><span lang="en-US"> bio-physical efficacy</span><span lang="en-US">. The distribution of land surface temperature </span><span lang="en-US">(LST), </span><span lang="en-US">which we derived from </span><span lang="en-US">Landsat thermal images from the vegetation seasons of 2013 to 2020, </span><span lang="en-US">serve</span><span lang="en-US">d</span><span lang="en-US"> as a proxy for environmental conditions that favor water retention. </span><span lang="en-US">We modeled</span><span lang="en-US"> LST as </span><span lang="en-US">a function of s</span><span lang="en-US">everal parameters of the physical environment such as land cover, forest and crop type</span><span lang="en-US">. In addition, </span><span lang="en-US">we incorporated </span><span lang="en-US">an antecedent moisture index and potential evapotranspiration at time of satellite overpass into the model. With the help of meteorological time series from climate projections, we can </span><span lang="en-US">thus </span><span lang="en-US">check to what extent the model results could change in the future.</span></p> <p><span lang="en-US">In this contribution, we will present the</span><span lang="en-US"> model</span><span lang="en-US">ing</span><span lang="en-US"> framework and </span><span lang="en-US">result</span><span lang="en-US">s. The model predictions provide a ranking of measures in terms of their effectiveness both within and between land use classes</span><span lang="en-US">. In agricultural landscapes, for example, the conversion of cropland to forest and, albeit to a lesser extent, to permanent grassland is much more efficient than organic fertilization, agroforestry, or the cultivation of permanent crops.</span> <span lang="en-US">Finally, we discuss possible approaches to using the results for practical recommendations despite the various uncertainties (</span><span lang="en-US">data and </span><span lang="en-US">model uncertainty, uncertainty of climate projection data).</span></p>
<p>A sound prediction of water and energy fluxes at the soil-atmosphere interface is important for many practical questions regarding e.g. irrigation and salinity management. Precise knowledge of soil hydraulic properties (SHP) is mandatory for such predictions. The SHP can be measured either in the laboratory within a wide moisture range or at the field scale, e.g. by inverse simulation techniques based on <em>in situ</em> matric potential and water content measurements. Depending on the installation depth of the sensors, soil texture, and boundary conditions, field-determined SHP are often limited to a quite narrow range of moisture conditions. Prediction of actual surface fluxes on basis of this limited information is highly uncertain. With well-instrumented large weighable lysimeters, systems are now available that allow to measure very precisely surface (and bottom) water fluxes under natural atmospheric conditions. In particular, they can be used to quantify the difference between potential evaporation, <em>Ep</em>, and observed actual evaporation, <em>Ea</em>. The difference (<em>Ep</em>-<em>Ea</em>) increases during the drying process when the soil hydraulic conductivity becomes limiting for the evaporation process. Thus, our hypothesis was that this information can be used to improve the identification of SHP of soils.</p><p>Accordingly, the aim of this study was to see whether the information on (<em>Ep</em>-<em>Ea</em>), measured during a calibration period and supplemented by water content and matric potential data measured inside of a lysimeter, is sufficient to inversely estimate the SHP. Furthermore, we were interested to see if the prediction of <em>Ea</em> was possible and reliable also for time periods beyond the calibration period.&#160; For a proof-of-concept study, we conducted forward simulations with Hydrus-1D where we generated synthetic data of actual surface fluxes and soil hydraulic internal state variables. The atmospheric boundary was given by natural precipitation and potential evaporation rates in a semi-arid climate. The study showed that it was possible to identify SHP by inverse modeling, and prediction of the cumulative actual evaporation after the calibration period was successful. In a second step, the methodology was applied to data of a real large bare-soil field-lysimeter. Our simulation results showed also here a good match between observed and predicted cumulative evaporation.</p>
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