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Global change, i.e. climate and land use changes, severely impact natural ecosystems at different scales. Poikilothermic animals as butterflies, amphibians and reptiles have proven to be useful indicators for global change impacts as their phenology, spatial distribution, individual fitness and survival strongly depend on external environmental factors. In this aspect, phenological changes in terms of advanced flight or breeding periods, immigrations of foreign species, range shifts concomitant with temperature increases and even local population declines have been observed in both species groups. However, to date much attention has been paid to global change impacts on the species or population level and analyses concerning entire ecosystems are scarce. Applying a novel statistical modelling algorithm we assessed future changes in the extent and composition of terrestrial ecoregions as classified by the World Wide Fund for Nature (WWF). They are defined as coarse-scale conservation units containing exceptional assemblages of species and ecological dynamics. Our results demonstrate dramatic geographical changes in the extent and location of these ecoregions across all continents and even imply a repriorisation of conservation efforts to cope with future climate change impacts on biodiversity. On the local scale, climate change impacts become unequivocal. Comparing historical to contemporary butterfly assemblages on vineyard fallows of the Trier Region, a significant decline in butterfly richness, but also a severe depletion in trait diversity was observed. Comparisons of community temperature indices reveal a striking shift in community composition leading to a replacement of sedentary and monophagous habitat specialists by ubiquitous species. Similar changes have been observed in nature reserves in the Saar-Mosel-area. Monitoring data reveal strong losses of species diversity and remarkable shifts of community compositions at the expense of habitat specialists. Besides climatic variability, these findings are largely attributed to changes in habitat structures, mostly due to eutrophication and monotonisation. Management activities are unlikely to counterbalance these effects, thus severely questioning current conservation strategies. Most dramatic global change impacts are suspected on closely associated species and disruptions of biotic interactions are often hold responsible for species declines. A strong host-parasite association has developed in Myrmica ants and Maculinea butterflies, the later crucially depending on specific host ants for their larval survival. Applying environmental niche models we determined considerable niche dynamics in the observed parasite-host relation with a pronounced niche plasticity in the butterfly species adapting to previous evasive niche shifts in their host ants. Moreover, the new emergence of species continuously expanding their northernmost range borders concomitant with global warming like the Short-tailed blue (Cupido argiades) is attributed to climate change. However, species distribution models predict a severe habitat loss and shifts of potentially suitable habitats of this species towards north-eastern Europe and higher altitudes under several IPCC scenarios making the presence of this species in the Trier region a contemporary phenomenon. Species distribution models have emerged as powerful tools to predict species distributions over spatial and temporal scales. However, not only the presence of a species, but also its abundance have significant implications for species conservation. The ability to deduce spatial abundance patterns from environmental suitability might more efficiently guide field surveys or monitoring programs over large geographical areas saving time and money. Although the application of species distribution models to deduce vertebrate abundances is well recognized, our results indicate that this method is not an adequate approach to predict invertebrate abundances. Structural and ecological factors as well as climatic patterns acting at the microscale are key drivers of invertebrate occurrence and abundances limiting conclusions drawn from modeling approaches. Population declines should be interpreted with care as in butterflies and amphibians various reasons are debated. Both species groups are acknowledged to be highly susceptible to land use changes and variations in landscape structure. Moreover, climate and land use are not independently operating factors. The combined impact of both is demonstrated in our study linking climate-driven changes in amphibian phenologies to temporal advanced applications of pesticides and fertilizers. Both environmental factors already represent severe threats to amphibians when standing alone, but linking their combined impacts may result in an potentiated risk for amphibian populations. As all amphibians and numerous butterfly species are legally protected under the Federal Nature Conservation Act, intensifications of agricultural land use in large parts of Germany as well as new agrarian practices (including genetically manipulated plants accompanied by new herbicide technologies) might severely challenge regional conservation activities in the future.
Die in einem Einzugsgebiet herrschende räumliche Inhomogenität wird im Wasserhaushaltsmodell LARSIM (Large Area Runoff Simulation Modell) in den einzelnen Modellkomponenten unterschiedlich stark berücksichtigt. Insbesondere die räumliche Verteilung der Abflussprozesse wurde bisher nicht berücksichtigt, weil keine flächenhaft verfügbare Information über eben diese Verteilung vorlag. Für das Einzugsgebiet der Nahe liegt nun seit dem Jahr 2007 eine Bodenhydrologische Karte vor, die flächenhaft den bei ausreichenden Niederschlägen zu erwartenden Abflussprozess ausweist. In der vorliegenden Dissertation wird die Nutzung dieser Prozessinformation bei der Parametrisierung des Bodenmoduls von LARSIM beschrieben: Für drei Prozessgruppen " gesättigter Oberflächenabfluss, Abfluss im Boden, Tiefenversickerung " werden mittels zweier neuer Parameter P_Bilanz und P_Dämpfung inhomogene Parametersätze aus empirisch ermittelten Kennfeldern gewählt, um die Prozessinformation bei der Abflussbildung im Modell zu berücksichtigen. Für die Abbildung der Prozessintensitäten in den Gebietsspeichern werden zwei unterschiedliche Ansätze vorgestellt, die sich in ihrer Komplexität unterscheiden. In der ersten Variante werden fünf Oberflächenabflussspeicher für unterschiedlich schnell reagierende Prozessgruppen eingeführt, in der zweiten Variante wird der erste Ansatz mit dem ursprünglichen Schwellenwert zur Aufteilung in schnelle und langsame Oberflächenabflusskomponenten kombiniert. Es wird gezeigt, dass die Parametrisierung mit den beiden neuen Parametern P_Bilanz und P_Dämpfung einfacher, effektiver und effizienter ist, da beide Parameter minimale Interaktionen aufweisen und in ihrer Wirkungsweise leicht verständlich sind, was auf die ursprünglichen Bodenparameter nicht zutrifft. Es wird ein Arbeitsfluss vorgestellt, in dem die neuen Parameter in Kombination mit Signature Measures und unterschiedlichen Darstellungen der Abflussdauerlinie gemeinsam genutzt werden können, um in wenigen Arbeitsschritten eine Anpassung des Modells in neuen Einzugsgebieten vorzunehmen. Die Methode wurde durch Anwendung in drei Gebieten validiert. In den drei Gebieten konnte in wenigen Kalibrierungsschritten die Simulationsgüte der ursprünglichen Version erreicht und " je nach Zielsetzung " übertroffen werden. Hinsichtlich der Gütemaße zeigte sich bei der Variante, in der die Gebietsspeicher nicht modifiziert wurden, aber kein eindeutiges Bild, ob die ursprüngliche Parametrisierung oder die neue grundsätzlich überlegen ist. Neben der Auswertung der Validierungszeiträume wurden dabei auch die simulierten Ganglinien in geschachtelten Gebieten betrachtet. Die Version, in der die Gebietsspeicher modifiziert wurden, zeigt hingegen vor allem im Validierungszeitraum tendenziell bessere Simulationsergebnisse. Hinsichtlich der Abbildung der Abflussprozesse ist das neue Verfahren dem alten deutlich überlegen: Es resultiert in plausiblen Anteilen von Abflusskomponenten, deren Verteilung und Abhängigkeit von Speicherkapazitäten, Landnutzungen und Eingangsdaten systematisch ausgewertet wurden. Es zeigte sich, dass vor allem die Speicherkapazität des Bodens einen signifikanten Einfluss hat, der aber im hydrologischen Sinn richtig und hinsichtlich der Modellannahmen plausibel ist. Es wird deutlich gemacht, dass die Einschränkungen, die sich ergeben haben, aufgrund der Modellannahmen zustande kommen, und dass ohne die Änderung dieser Annahmen keine bessere Abbildung möglich ist. Für die Zukunft werden Möglichkeiten aufgezeigt, wie die Annahmen modifiziert werden können, um eine bessere Abbildung zu erzielen, indem der bereits bestehende Infiltrationsansatz in die Methode integriert wird.
High-resolution projections of the future climate are required to assess climate change realistically at a regional scale. This is in particular important for climate change impact studies since global projections are much too coarse to represent local conditions adequately. A major concern is thereby the change of extreme values in a warming climate due to their severe impact on the natural environment, socio-economical systems and the human health. Regional climate models (RCMs) are, however, able to reproduce much of those local features. Current horizontal resolutions are about 18-25km, which is still too coarse to directly resolve small-scale processes such as deep-convection. For this reason, projections of a possible future climate were simulated in this study with the regional climate model COSMO-CLM at horizontal resolutions of 4.5km and 1.3km for the region of Saarland-Lorraine-Luxemburg and Rhineland-Palatinate for the first time. At a horizontal scale of about 1km deep-convection is treated explicitly, which is expected to improve particularly the simulation of convective summer precipitation and a better resolved orography is expected to improve near surface fields such as 2m temperature. These simulations were performed as 10-year long time-slice experiments for the present climate (1991"2000), the near future (2041"2050) and the end of the century (2091"2100). The climate change signals of the annual and seasonal means and the change of extremes are analysed with respect to precipitation and 2m temperature and a possible added value due to the increased resolution is investigated. To assess changes in extremes, extreme indices have been applied and 10- and 20-year return levels were estimated by "peak-over-threshold" models. Since it is generally known that model output of RCMs should not directly be used for climate change impact studies, the precipitation and temperature fields were bias-corrected with several quantile-matching methods. Among them is a new developed parametric method which includes an extension for extreme values and is hence expected to improve the correction. In addition, the impact of the bias-correction on the climate change signals and on the extreme value statistics was investigated. The results reveal a significant warming of the annual mean by about +1.7 -°C until 2041"2050 and +3.7 -°C until 2091"2100, but considerably stronger signals of up to +5 -°C in summer in the Rhine Valley. Furthermore, the daily variability increases by about +0.8 -°C in summer but decreases by about -0.8 -°C in winter. Consequently, hot extremes increase moderately until the mid of the century but strongly thereafter, in particular in the Rhine Valley. Cold extremes warm continuously in the complete domain in the next 100 years but strongest in mountainous areas. The change signals with regard to annual precipitation are of the order -±10% but not significant. Significant, however, are a predicted increase of +32% of the seasonal precipitation in autumn until 2041"2050 and a decrease of -28% in summer until 2091-2100. No significant changes were found for days with intensities > 20 mm/day, but the results indicate that extremes with return periods ≤2 years increase as well as the frequency and duration of dry periods. The bias-corrections amplified positive signals but dampened negative signals and considerably reduced the power of detection. Moreover, absolute values and frequencies of extremes were altered by the correction but change signals remained approximately constant. The new method outperformed other parametric methods, in particular with regard to extreme value correction and related extreme indices and return levels. Although the bias correction removed systematic errors, it should be treated as an additional layer of uncertainty in climate change studies. Finally, the increased resolution of 1.3km improved predominantly the representation of temperature fields and extremes in terms of spatial heterogeneity. The benefits for summer precipitation were not as clear due to a severe dry-bias in summer, but it could be shown that in principle the onset and intensity of convection improves. This work demonstrates that climate change will have severe impacts in this investigation area and that in particular extremes may change considerably. An increased resolution provides thereby an added value to the results. These findings encourage further investigations, for other variables as for example near-surface wind, which will be more feasible with growing computing resources. These analyses should, however, be repeated with longer time series, different RCMs and anthropogenic scenarios to determine the robustness and uncertainty of these results more extensively.
This study aims to estimate the cotton yield at the field and regional level via the APSIM/OZCOT crop model, using an optimization-based recalibration approach based on the state variable of the cotton canopy - the leaf area index (LAI), derived from atmospherically corrected Landsat-8 OLI remote sensing images in 2014. First, a local sensitivity and global analysis approach was employed to test the sensitivity of cultivar, soil and agronomic parameters to the dynamics of the LAI. After sensitivity analyses, a series of sensitive parameters were obtained. Then, the APSIM/OZCOT crop model was calibrated by observations over a two-year span (2006-2007) at the Aksu station, combined with these sensitive cultivar parameters and the current understanding of cotton cultivar parameters. Third, the relationship between the observed in-situ LAI and synchronous perpendicular vegetation indices derived from six Landsat-8 OLI images covering the entire growth stage was modelled to generate LAI maps in time and space. Finally, the Particle Swarm Optimization (PSO) and general-purpose optimization approach (based on Nelder-Mead algorithm) were used to recalibrate four sensitive agronomic parameters (row spacing, sowing density per row, irrigation amount and total fertilization) according to the minimization of the root-mean-square deviation (RMSE) between the simulated LAI from the APSIM/OZCOT model and retrieved LAI from Landsat-8 OLI remote sensing images. After the recalibration, the best simulated results compared with observed cotton yield were obtained. The results showed that: (1) FRUDD, FLAI and DDISQ were the major cultivar parameters suitable for calibrating the cotton cultivar. (2) After the calibration, the simulated LAI performed well with an RMSE and mean absolute error (MAE) of 0.45 and 0.33, respectively, in 2006 and 0.46 and 0.41, respectively, in 2007. The coefficient of determination between the observed and simulated LAI was 0.83 and 0.97, respectively, in 2006 and 2007. The Pearson- correlation coefficient was 0.913 and 0.988 in 2006 and 2007, respectively, with a significant positive correlation between the simulated and observed LAI. The difference between the observed and simulated yield was 776.72 kg/ha and 259.98 kg/ha in 2006 and 2007, respectively. (3) Cotton cultivation in 2014 was obtained using three Landsat-8 OLI images - DOY136 (May), DOY 168 (June) and DOY 200 (July) - based on the phenological differences in cotton and other vegetation types. (4) The yield estimation after the assimilation closely approximated the field-observed values, and the coefficient of determination was as high as 0.82, after recalibration of the APSIM/OZCOT model for ten cotton fields. The difference between the observed and assimilated yields for the ten fields ranged from 18.2 to 939.7 kg/ha. The RMSE and MAE between the assimilated and observed yield was 417.5 and 303.1 kg/ha, respectively. These findings provide scientific evidence for the feasibility of coupled remote sensing and APSIM/OZCOT model at the field level. (5) Upscaling from field level to regional level, the assimilation algorithm and scheme are both especially important. Although the PSO method is very efficient, the computational efficiency is also the shortcoming of the assimilation strategy on a regional scale. Comparisons between the PSO and general-purpose optimization method (based on the Nelder-Mead algorithm) were implemented from the RSME, LAI curve and computational time. The general-purpose optimization method (based on the Nelder-Mead algorithm) was used for the regional assimilation between remote sensing and the APSIM/OZCOT model. Meanwhile, the basic unit for regional assimilation was also determined as cotton field rather than pixel. Moreover, the crop growth simulation was also divided into two phases (vegetative growth and reproductive growth) for regional assimilation. (6) The regional assimilation at the vegetative growth stage between the remote sensing derived and APSIM/OZCOT model-simulated LAI was implemented by adjusting two parameters: row spacing and sowing density per row. The results showed that the sowing density of cotton was higher in the southern part than in the northern part of the study area. The spatial pattern of cotton density was also consistent with the reclamation from 2001 to 2013. Cotton fields after early reclamation were mainly located in the southern part while the recent reclamation was located in the northern part. Poor soil quality, lack of irrigation facilities and woodland belts of cotton fields in the northern part caused the low density of cotton. Regarding the row spacing, the northern part was larger than the southern part due to the variation of two agronomic modes from military and private companies. (7) The irrigation and fertilization amount were both used as key parameters to be adjusted for regional assimilation during the reproductive growth period. The result showed that the irrigation per time ranged from 58.14 to 89.99 mm in the study area. The spatial distribution of the irrigation amount is higher in the northern part while lower in southern study area. The application of urea fertilization ranged from 500.35 to 1598.59 kg/ha in the study area. The spatial distribution of fertilization was lower in the northern part and higher in the southern part. More fertilization applied in the southern study area aims to increase the boll weight and number for pursuing higher yields of cotton. The frequency of the RSME during the second assimilation was mainly located in the range of 0.4-0.6 m2/m2. The estimated cotton yield ranged from 1489 to 8895 kg/ha. The spatial distribution of the estimated yield is also higher in the southern part than the northern study area.