Atmospheric models are strongly dependent on the turbulent exchange of momentum, sensible heat and moisture (latent heat) at the surface. Oceans cover about 70% of the Earth’s surface and understanding the processes that control air-sea exchange is of great importance in order to predict weather and climate. In the atmosphere, for instance, hurricane development, cyclone intensity and track depend on these processes. Ocean waves constitute an obvious example of air-sea interaction and can cause the air-flow over sea to depend on surface conditions in uniquely different ways compared to boundary layers over land. When waves are generated by wind they are called wind sea or growing sea, and when they leave their generation area or propagate faster than the generating wind they are called swell. The air-sea exchange is mediated by turbulent eddies occurring on many different scales. Field measurements and high-resolution turbulence resolving numerical simulations have here been used to study these processes. The standard method to measure turbulent fluxes is the eddy covariance method. A spatial separation is often used between instruments when measuring scalar flux; this causes an error which was investigated for the first time over sea. The error is typically smaller over ocean than over land, possibly indicating changes in turbulence structure over sea. Established and extended analysis methods to determine the dominant scales of momentum transfer was used to interpret how reduced drag and sometimes net upward momentum flux can persist in the boundary layer indirectly affected by swell. A changed turbulence structure with increased turbulence length scales and more effective mixing was found for swell. A study, using a coupled wave-atmosphere regional climate model, gave a first indication on what impact wave mixing have on atmosphere and wave parameters. Near surface wind speed and wind gradients was affected especially for shallow boundary layers, which typically increased in height from the introduced wave-mixing. A large impact may be expected in regions of the world with predominant swell. The impact of swell waves on air-sea exchange and mixing should be taken into account to develop more reliable coupled Earth system models.

Indian summer monsoon (ISM) is an important component of the tropical climate system, known for its regular seasonality and abundance of rainfall over the country. The droughts and floods associated with the year-to-year variation of the average seasonal rainfall have devastating effect on people, agriculture and economy of this region. The demand for prediction of seasonal monsoon rainfall, therefore, is overwhelming. A number of attempts to predict the seasonal mean monsoon have been made over a century, but neither dynamical nor empirical models provide skillful forecasts of the extremes of the monsoon such as the unprecedented drought of 2002. This study investigates the problems and prospects of extended range monsoon prediction. An evaluation of the potential predictability of the ISM with the aid of an ensemble of Atmospheric General Circulation Model (AGCM) simulations indicates that the interannual variability (IAV) of ISM is contributed equally by the slow boundary forcing (‘externally’ forced variability) and the inherent climate noise (‘internal’ variability) in the atmosphere. Success in predicting the ISM would depend on our ability to extract the predictable signal from a background of noise of comparable amplitude. This would be possible only if the ‘external’ variability is separable from the ‘internal’ variability. A serious effort has been made to understand and isolate the sea surface temperature (SST) forced component of ISM variability that is not strongly influenced by the ‘internal’ variability. In addition, we have investigated to unravel the mechanism of generation of ‘internal’ IAV so that the method of isolating it from forced variability may be found. Since the primary forcing mechanism of the monsoon is the large-scale meridional gradient of deep tropospheric heat sources, large-scale changes in tropospheric temperature (TT) due to the boundary forcing can induce interannual variations of the timing and duration of the monsoon season. The concept of interannually varying monsoon season is introduced here, with the onset and withdrawal of monsoon definitions based on the reversal of meridional gradient of TT between north and south. This large scale definition of the monsoon season is representative of the planetary scale influence of the El Ni˜no Southern Oscillation (ENSO) on monsoon through the modification of TT and the cross equatorial pressure gradient over the ISM region. A sig- nificant relationship between ENSO and monsoon, that has remained steady over the decades, is discovered by which an El Ni˜no (La Ni˜na) delays (advances) the onset, advances (delays) the withdrawal and suppresses (enhances) the strength of the monsoon. The integral effect of the meridional gradient of TT from the onset to withdrawal proves to be a useful index of seasonal monsoon which isolates the boundary forced signal from the influence of internal variations that has remained steady even in the recent decades. However, consistent with the estimates of potential predictability, the boundary forced variability isolated with the above definitions explains only about 50% of the total interannual variability of ISM. Detailed diagnostics of the onset and withdrawal processes are performed to understand how the ENSO forcing modifies the onset and withdrawal, and thus the seasonal mean monsoon. It is found that during an El Ni˜no, the onset is delayed due to the enhanced adiabatic subsidence that inhibits vertical mixing of sensible heating from the warm landmass during pre-monsoon months, and the withdrawal is advanced due to the horizontal advective cooling. This link between ENSO and monsoon is realized through the advective processes associated with the stationary waves in the upper troposphere set up by the tropical ENSO heating. The remaining 50% of the monsoon IAV is governed by internal processes. To unravel the mechanism of the generation of internal IAV, we perform another set of AGCM simulations, forced with climatological monthly mean SSTs, to extract the pure internal IAV. We find that the spatial structure of the intraseasonal oscillations (ISOs) in these simulations has significant projection on the spatial structure of the seasonal mean and a common spatial mode governs both intraseasonal and interannual variability. Statistical average of ISO anomalies over the season (seasonal ISO bias) strengthens or weakens the seasonal mean. It is shown that interannual anomalies of seasonal mean are closely related to the seasonal mean of intraseasonal anomalies and explain about 50% of the IAV of the seasonal mean. The seasonal mean ISO bias arises partly due to the broadband nature of the ISO spectrum, allowing the intraseasonal time series to be aperiodic over the season and partly due to a non-linear process where the amplitude of ISO activity is proportional to the seasonal bias of ISO anomalies. The later relationship is a manifestation of the binomial character of the rainfall time series. The remaining part of IAV may arise due to the complex land-surface processes, scale interactions, etc. We also find that the ISOs over the ISM region are not significantly modulated by the Pacific and Indian Ocean SST variations. Thus, even with a perfect prediction of SST, only about 50% of the observed IAV of ISM could be predicted with the best model in forced mode. Even so, prediction of all India rainfall (AIR) representing the average conditions of the whole country and the season may not always serve the purposes of monsoon forecasting. One reason is the large inhomogeneities in the rainfall distribution during a normal seasonal monsoon. Agriculture and hydrological sector could benefit more if provided with regional scale forecasts of active/break spells 2-3 weeks ahead. Therefore, we advocate an alternative strategy to the seasonal prediction. Here, we present a method to estimate the potential predictability of active and break conditions from daily rainfall and circulation from observations for the recent 24 years. We discover that transitions from break to active conditions are much more chaotic than those from active to break, a fundamental property of the monsoon ISOs. The potential predictability limit of monsoon breaks (∼20 days) is significantly higher than that of the active conditions (∼10 days). An empirical real- time forecasting strategy to predict the sub-seasonal variations of monsoon up to 4 pentads (20 days) in advance is developed. The method is physically based, with the consideration that the large-scale spatial patterns and slow evolution of monsoon intraseasonal variations possess some similarity in their evolutions from one event to the other. This analog method is applied on NOAA outgoing longwave radiation (OLR) pentad mean data which is available on a near real time basis. The elimination of high frequency variability and the use of spatial and temporal analogs produces high and useful skill of predictions over the central and northern Indian region for a lead-time of 4-5 pentads. An important feature of this method is that, unlike other empirical methods to forecast monsoon ISOs, this uses minimal time filtering to avoid any possible end-point effects, and hence it has immense potential for real-time applications.

General Circulation Models (GCMs) are tools designed to simulate time series of climate variables globally, accounting for effects of greenhouse gases in the atmosphere. They attempt to represent the physical processes in the atmosphere, ocean, cryosphere and land surface. They are currently the most credible tools available for simulating the response of the global climate system to increasing greenhouse gas concentrations, and to provide estimates of climate variables (e.g. air temperature, precipitation, wind speed, pressure etc.) on a global scale. GCMs demonstrate a significant skill at the continental and hemispheric spatial scales and incorporate a large proportion of the complexity of the global system; they are, however, inherently unable to represent local subgrid-scale features and dynamics. The spatial scale on which a GCM can operate (e.g., 3.75° longitude x 3.75° latitude for Coupled Global Climate Model, CGCM2) is very coarse compared to that of a hydrologic process (e.g., precipitation in a region, streamflow in a river etc.) of interest in the climate change impact assessment studies. Moreover, accuracy of GCMs, in general, decreases from climate related variables, such as wind, temperature, humidity and air pressure to hydrologic variables such as precipitation, evapotranspiration, runoff and soil moisture, which are also simulated by GCMs. These limitations of the GCMs restrict the direct use of their output in hydrology. This thesis deals with developing statistical downscaling models to assess climate change impacts and methodologies to address GCM and scenario uncertainties in assessing climate change impacts on hydrology. Downscaling, in the context of hydrology, is a method to project the hydrologic variables (e.g., rainfall and streamflow) at a smaller scale based on large scale climatological variables (e.g., mean sea level pressure) simulated by a GCM. A statistical downscaling model is first developed in the thesis to predict the rainfall over Orissa meteorological subdivision from GCM output of large scale Mean Sea Level Pressure (MSLP). Gridded monthly MSLP data for the period 1948 to 2002, are obtained from the National Center for Environmental Prediction/ National Center for Atmospheric Research (NCEP/NCAR) reanalysis project for a region spanning 150 N -250 N in latitude and 800 E -900 E in longitude that encapsulates the study region. The downscaling model comprises of Principal Component Analysis (PCA), Fuzzy Clustering and Linear Regression. PCA is carried out to reduce the dimensionality of the larger scale MSLP and also to convert the correlated variables to uncorrelated variables. Fuzzy clustering is performed to derive the membership of the principal components in each of the clusters and the memberships obtained are used in regression to statistically relate MSLP and rainfall. The statistical relationship thus obtained is used to predict the rainfall from GCM output. The rainfall predicted with the GCM developed by CCSR/NIES with B2 scenario presents a decreasing trend for non-monsoon period, for the case study. Climate change impact assessment models developed based on downscaled GCM output are subjected to a range of uncertainties due to both ‘incomplete knowledge’ and ‘unknowable future scenario’ (New and Hulme, 2000). ‘Incomplete knowledge’ mainly arises from inadequate information and understanding about the underlying geophysical process of global change, leading to limitations in the accuracy of GCMs. This is also termed as GCM uncertainty. Uncertainty due to ‘unknowable future scenario’ is associated with the unpredictability in the forecast of socio-economic and human behavior resulting in future Green House Gas (GHG) emission scenarios, and can also be termed as scenario uncertainty. Downscaled outputs of a single GCM with a single climate change scenario represent a single trajectory among a number of realizations derived using various GCMs and scenarios. Such a single trajectory alone can not represent a future hydrologic scenario, and will not be useful in assessing hydrologic impacts due to climate change. Nonparametric methods are developed in the thesis to model GCM and scenario uncertainty for prediction of drought scenario with Orissa meteorological subdivision as a case study. Using the downscaling technique described in the previous paragraph, future rainfall scenarios are obtained for all available GCMs and scenarios. After correcting for bias, equiprobability transformation is used to convert the precipitation into Standardized Precipitation Index-12 (SPI-12), an annual drought indicator, based on which a drought may be classified as a severe drought, mild drought etc. Disagreements are observed between different predictions of SPI-12, resulting from different GCMs and scenarios. Assuming SPI-12 to be a random variable at every time step, nonparametric methods based on kernel density estimation and orthonormal series are used to determine the nonparametric probability density function (pdf) of SPI-12. Probabilities for different categories of drought are computed from the estimated pdf. It is observed that there is an increasing trend in the probability of extreme drought and a decreasing trend in the probability of near normal conditions, in the Orissa meteorological subdivision. The single valued Cumulative Distribution Functions (CDFs) obtained from nonparametric methods suffer from limitations due to the following: (a) simulations for all scenarios are not available for all the GCMs, thus leading to a possibility that incorporation of these missing climate experiments may result in a different CDF, (b) the method may simply overfit to a multimodal distribution from a relatively small sample of GCMs with a limited number of scenarios, and (c) the set of all scenarios may not fully compose the universal sample space, and thus, the precise single valued probability distribution may not be representative enough for applications. To overcome these limitations, an interval regression is performed to fit an imprecise normal distribution to the SPI-12 to provide a band of CDFs instead of a single valued CDF. Such a band of CDFs represents the incomplete nature of knowledge, thus reflecting the extent of what is ignored in the climate change impact assessment. From imprecise CDFs, the imprecise probabilities of different categories of drought are computed. These results also show an increasing trend of the bounds of the probability of extreme drought and decreasing trend of the bounds of the probability of near normal conditions, in the Orissa meteorological subdivision. Water resources planning requires the information about future streamflow scenarios in a river basin to combat hydrologic extremes resulting from climate change. It is therefore necessary to downscale GCM projections for streamflow prediction at river basin scales. A statistical downscaling model based on PCA, fuzzy clustering and Relevance Vector Machine (RVM) is developed to predict the monsoon streamflow of Mahanadi river at Hirakud reservoir, from GCM projections of large scale climatological data. Surface air temperature at 2m, Mean Sea Level Pressure (MSLP), geopotential height at a pressure level of 500 hecto Pascal (hPa) and surface specific humidity are considered as the predictors for modeling Mahanadi streamflow in monsoon season. PCA is used to reduce the dimensionality of the predictor dataset and also to convert the correlated variables to uncorrelated variables. Fuzzy clustering is carried out to derive the membership of the principal components in each of the clusters and the memberships thus obtained are used in RVM regression model. RVM involves fewer number of relevant vectors and the chance of overfitting is less than that of Support Vector Machine (SVM). Different kernel functions are used for comparison purpose and it is concluded that heavy tailed Radial Basis Function (RBF) performs best for streamflow prediction with GCM output for the case considered. The GCM CCSR/NIES with B2 scenario projects a decreasing trend in future monsoon streamflow of Mahanadi which is likely to be due to high surface warming. A possibilistic approach is developed next, for modeling GCM and scenario uncertainty in projection of monsoon streamflow of Mahanadi river. Three GCMs, Center for Climate System Research/ National Institute for Environmental Studies (CCSR/NIES), Hadley Climate Model 3 (HadCM3) and Coupled Global Climate Model 2 (CGCM2) with two scenarios A2 and B2 are used for the purpose. Possibilities are assigned to GCMs and scenarios based on their system performance measure in predicting the streamflow during years 1991-2005, when signals of climate forcing are visible. The possibilities are used as weights for deriving the possibilistic mean CDF for the three standard time slices, 2020s, 2050s and 2080s. It is observed that the value of streamflow at which the possibilistic mean CDF reaches the value of 1 reduces with time, which shows reduction in probability of occurrence of extreme high flow events in future and therefore there is likely to be a decreasing trend in the monthly peak flow. One possible reason for such a decreasing trend may be the significant increase in temperature due to climate warming. Simultaneous occurrence of reduction in Mahandai streamflow and increase in extreme drought in Orissa meteorological subdivision is likely to pose a challenge for water resources engineers in meeting water demands in future.

In this thesis, we investigated modes of intraseasonal variability (ISV) observed in the Indian monsoon rainfall and how these modes modulate rainfall over India. We identified a decreasing trend in the intensity of low-frequency intraseasonal mode with increasing strength in synoptic variability over India. We also made an attempt to understand the reason for these observed trends using numerical simulations. In the first part of the thesis, satellite rainfall estimates are used to understand the spatiotem-poral structures of convection in the intraseasonal timescale and their intensity during boreal sum-mer over south Asia. Two dominant modes of variability with periodicities of 10–20-days (high-frequency) and 20–60-days (low-frequency) are found, with the latter strongly modulated by sea surface temperature. The 20–60-day mode shows northward propagation from the equatorial In-dian Ocean linked with eastward propagating modes of convective systems over the tropics. The 10–20-day mode shows a complex space-time structure with a northwestward propagating anoma-lous pattern emanating from the Indonesian coast. This pattern is found to be interacting with a structure emerging from higher latitudes propagating southeastwards. This could be related to ver-tical shear of zonal wind over northern India. The two modes exhibit variability in their intensity on the interannual time scale and contribute a significant amount to the daily rainfall variability in a season. The intensities of the 20–60-day and 10–20-day modes show significantly strong inverse and direct relationship, respectively, with the all-India June–September rainfall. This study also establishes that the probability of occurrence of substantial rainfall over central India increases significantly if the two intraseasonal modes simultaneously exhibit positive anomalies over the region. There also exists a phase-locking between the two modes. In the second part of the thesis, we investigated the changing nature of these intraseasonal modes over Indian region, and their association with extreme rainfall events using ground based observed rainfall. We found that the relative strength of the northward propagating 20–60-day mode has a significant decreasing trend during the past six decades, possibly attributed to the weakening of large-scale circulation in the region during monsoon. This reduction is compensated by a gain in synoptic-scale (3–9 days) variability. The decrease in the low-frequency ISV is associated with a significant decreasing trend in the percentage of extreme events during the active phase of the monsoon. However, this decrease is balanced by a significant increasing trend in the percentage of extreme events in break phase. We also find a significant rise in occurrence of extremes during early- and late-monsoon months, mainly over the eastern coastal regions of India. We do not observe any significant trend in the high-frequency ISV. In the last part of the thesis, we used numerical simulations to understand the observed changes in the ISV features. Using the atmospheric component of a global climate model (GCM), we have performed two experiments: control experiment (CE) and heating experiment (HE). The CE is the default simulation for 10 years. In HE, we prescribed heating in the atmosphere in such a way that it mimics the conditions for extreme rainfall events as observed over central India during June– September. Heating is prescribed primarily during the break phase of the 20–60-day mode. This basically increases the number of extremes, majority of which are in break phase. The design of the experiment reflects the observed current scenario of increased extreme events during breaks. We found that the increased extreme events in the HE decreased the intensity of the 20–60-day mode over the Indian region. This reduction is associated with a reduction of rainfall in active phase and increase in the length of break phase. A reduction in the seasonal mean over India is also observed. The reduction of active phase rainfall is linked with an increased stability of the atmosphere over central India. Lastly, we propose a possible mechanism for the reduction of rainfall in active phase. We found that there is a significant reduction in the strength of the vertical easterly shear over the northern Indian region during break–active transition phase. This basically weakens the conditions for the growth of Rossby wave instability, thereby elongating break phase and reducing the rainfall intensity in the following active phase. This study highlights the redistribution of rainfall intensity among periodic (low-frequency) and non-periodic (extreme) modes in a changing climate scenario, which is further tested in a modeling study. The results presented in this thesis will provide a pathway to understand, using observations and numerical model simulations, the ISV and its relative contribution to the Indian summer monsoon. It can also be used for model evaluation.

Ce travail documente la variabilité spatiale et temporelle du rayonnement solaire à la surface sur le sud-ouest de l'océan Indien (SOOI) et l'île de La Réunion à l'aide de deux modèles régionaux de climat (MRC) : les modèles RegCM et WRF. La première partie de ce travail est dédiée à l'analyse de la variabilité temporelle du rayonnement solaire à l'aide du modèle RegCM sur le SOOI avec une résolution spatiale modérée (50km). S'agissant du premier travail sur la modélisation régionale du climat pour l'étude du rayonnement solaire dans le SOOI, une première série de tests pour illustrer les performances du modèle et sa sensibilité au choix des paramétrisations physiques (transfert radiatif, convection), à la taille du modèle, et à la résolution spatiale, est effectuée. Le schéma radiatif par défaut, le schéma CCM, et le schéma convectif mixte : Grell sur les terres et Emanuel sur les océans, donnent les résultats les plus satisfaisants pour la région, comparés aux autres options disponibles. La variabilité climatique interannuelle, intrasaisonnière et jour-à-jour est ensuite examinée sur la base des indices climatiques. Dans un premier temps, plusieurs paramètres (vent horizontal, température, humidité relative) issus des réanalyses ERA-Interim et utilisés comme paramètres d'entrée pour le modèle RegCM, sont analysés en lien avec ceux correspondant fournis en sortie du modèle, pour vérifier l'aptitude du modèle à maintenir les signaux ENSO (El-Nino Southern Oscillation), IOD (Indian Ocean Dipole), MJO (Madden-Julian Oscillation) et les Talwegs Tropicaux-Tempérés (TTT). Dans un second temps, le rayonnement solaire à la surface simulé par le modèle RegCM est mis en lien avec ces différents modes de variabilité. La seconde partie du travail est consacrée à l'analyse de la variabilité spatiale du rayonnement solaire à la surface à La Réunion à l'aide du modèle WRF à très haute résolution spatiale (750m) pour différentes échelles de temps : interannuelle, intrasaisonnière, jour-à-jour. Une classification est appliquée sur les sorties de rayonnement produites par WRF, et le lien avec la circulation atmosphérique de grande échelle est analysé dans chacune des classes. Les résultats de la modélisation sont validés à l'aide des données d'observations du réseau Météo France et des produits satellite CM SAF. Les résultats indiquent que les MRC ont la capacité de représenter la variabilité temporelle et spatiale du rayonnement solaire à La Réunion. / This work documents the temporal and spatial variability of surface solar radiation (SSR) over the southwest Indian Ocean (SWIO) and Reunion Island using two complementary Regional Climate Models (RCMs): RegCM4 and WRF. The first part of the work is dedicated to the analysis of the temporal variability of SSR based on RegCM4 over the SWIO at a moderate spatial resolution (50km). Because RegCM4 is the first RCM that focuses on the solar radiation research over the SWIO region, a first series of test experiments with this model to illustrate the model performance and its sensitivity to the choice of the physical parameterizations (radiation, convection), the domain size, and the spatial resolution, are performed. The default CCM radiative and the mixed convective scheme: Grell scheme over land and Emanuel scheme over ocean, give better performance over the SWIO compared to the other available options. The interannual, intraseasonal and synoptic climate variability is then examined through the climate indices and several ERA-Interim parameters (U, V, T and RH) are firstly analyzed along with the corresponding RegCM4 output data to check whether the RegCM4 model forced by ERA-Interim reanalyses is able to maintain the El-Nino Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), the Madden-Julian Oscillation (MJO) and the Tropical Temperate Trough (TTT) signals. Secondly, simulated SSR in association with the different modes of variability is examined. In the second part, SSR spatial variability over Reunion Island is analyzed based on WRF simulations at very fine resolution (750m) for seasonal, intraseasonal, and daily time scales. Clustering classification is applied to WRF simulated SSR over Reunion and the effect from the atmospheric circulation is checked together. Météo France observations and CM SAF are used to validate the results of the model. The results indicate that regional climate models have the ability to present the temporal and spatial variability of SSR over Reunion.

Screen level temperature is measured at 2 meters above the ground. It is one of the most used atmospheric characteristics in various applications in meteorology and other fields related to weather prediction. Essential is not only the knowledge of its current state, but also its prediction. It is forecasted by numerical weather prediction (NWP) models from the atmospheric current state. Its long-term characteristics can be obtained from the integration of climate models. This text discusses fundamental parametriza- tions, mostly related to temperature forecast, used in the NWP model ALADIN and the regional climate model RegCM. Physical processes which influence temperature are studied using ALADIN in several cases which include the presence of low cloudiness, gravity waves and inappropriate thermic coefficient. A detailed description of the most relevant parametrization schemes is given and the results are studied in a form of indi- vidual feedback loops. Most dominant processes are also found. However, the level of 2 meters above the ground is not the model level, so temperature at 2 meters is obtained by interpolation from the surface temperature and the lowest model level temperature. Using RegCM, two differently complex interpolation schemes are compared to each other. 1

The impact of climate change is increasingly important to the design of urban water infrastructure like stormwater systems, sewage systems and drinking water systems. Growing evidence indicates that the water sector will not only be affected by climate change, but it will reflect and deliver many of its impacts through floods, droughts, or extreme rainfall events. Water resources will change in both quantity and quality, and the infrastructure of stormwater and wastewater facilities may face greater risk of damage caused by storms, floods and droughts. The effect of the climate change will put more difficulties on operations to disrupted services and increased cost of the water and wastewater services. Governments, urban planners, and water managers should therefore re-examine development processes for municipal water and wastewater services and are adapt strategies to incorporate climate change into infrastructure design, capital investment projects, service provision planning, and operation and maintenance. According to the Intergovernmental Panel on Climate Change, the global mean temperature has increased by 0,7 °C during the last 100 years and, as a consequence, the hydrological cycle has intensified with, for example, more acute rainfall events. As urban drainage systems have been developed over a long period of time and design criteria are based upon climatic characteristics, these changes will affect the systems and the city accordingly. The overall objective of this thesis is to increase the knowledge about the climate change impacts on the stormwater system in Al Hillah city/Iraq. In more detail, the objective is to investigate how climate change could affect urban drainage systems specifically stormwater infrastructure, and also to suggest an adaptation plan for these changes using adaptation plans examples from international case studies. Three stochastic weather generators have been investigated in order to understand the climate and climate change in Al Hillah. The stochastic weather generators have been used in different kind of researches and studies; for example in hydrology, floods management, urban water design and analysis, and environmental protection. To make such studies efficient, it is important to have long data records (typically daily data) so the weather generator can generate synthetic daily weather data based on a sound statistical background. Some weather generators can produce the climate change scenarios for different kind of global climate models. They can be used also to produce synthetic data for a site that does not have enough data by using interpolation methods. To ensure that the weather generator is fitting the climate of the region properly, it should be tested against observed data, whether the synthetic data are sufficiently similar. At the same time, the accuracy of the weather generator is different from region to region and depends on the respective climate properties. Testing three weather generators GEM6, ClimGen and LARS-WG at eight climate stations in the region of Babylon governorate/Iraq, where Al Hillah is located, is one of the purposes of the first part of this study. LARS-WG uses a semi-parametric distribution (developed distribution), whereas GEM6 and ClimGen use a parametric distribution (less complicated distribution). Different statistical tests have been selected to compare observed and synthetic weather data for the same kind, for instance, the precipitation and temperature distribution (wet and dry season). The result shows that LARS-WG represents the observed data for Babylon region in a better way than ClimGen, whereas GEM6 seems to misfit the observed data. The synthetic data will be used for a first simulation of urban run-off during the wet season and the consequences of climate change for the design and re-design of the urban drainage system in Al Hillah. The stochastic weather generator LARS is then used to generate ensembles of future weather data using five Global Climate Models (GCMs) that best captured the full range of uncertainty. These Global Climate Models are used to construct future climate scenarios of temperature and precipitation over the region of Babylon Governorate in Iraq. The results show an increase in monthly temperatures and a decrease in the total amount of rain, yet the extreme rain events will be more intense in a shorter time. Changes in the amount, timing, and intensity of rain events can affect the amount of stormwater runoff that needs to be controlled. The climate change calculated projections may make existing stormwater-related flooding worse. Different districts in Al Hillah city may face more frequent stormwater floods than before due to the climate change projections. All the results that have been taken from the Global Climate Models are in a daily resolution format and in order to run the Storm Water Management Model it is important to have all data in a minimum of one hour resolution. In order to fulfill this condition a disaggregation model has been used. Some hourly precipitation data were required to calibrate the temporal disaggregation model; however none of the climate stations and rain gauges in the area of interest have hourly resolution data, so the hourly data from Baghdad airport station have been used for that calibration. The changes in the flood return periods have been seen in the projected climate change results, and a return period will only remain valid over time if environmental conditions do not change. This means that return periods used for planning purposes may need to be updated more often than previously, because values calculated based on the past 30 years of data may become unrepresentative within a relatively short time span. While return periods provide useful guidance for planning the effects of flooding and related impacts, they need to be used with care, and allowances have to be made for extremes that may occur more often than may be expected. In the study area with separated stormwater systems, the Storm Water Management Model simulation shows that the number of surface floods as well as of the floods increases in the future time periods 2050s and 2080s. Future precipitation will also increase both the flooding frequency and the duration of floods; therefore the need to handle future situations in urban drainage systems and to have a well-planned strategy to cope with future conditions is evident. The overall impacts on urban drainage systems due to the increase of intensive precipitation events need to be adapted. For that reason, recommendations for climate change adaptation in the city of Al Hillah have been suggested. This has been accomplished by merging information from the review of five study cases, selected based on the amount and quality of information available. The cities reviewed are Seattle (USA), Odense (Denmark), Tehran (Iran), and Khulna (Bangladesh). / Die Auswirkungen des Klimawandels auf die Gestaltung der städtischen Wasserinfrastruktur wie Regenwasser, Kanalisation und Trinkwassersysteme werden immer wichtiger. Eine wachsende Anzahl von Belegen zeigt, dass der Wassersektor nicht nur durch den Klimawandel beeinflusst werden wird, aber er wird zu reflektieren und liefern viele seiner Auswirkungen durch Überschwemmungen, Dürren oder extreme Niederschlagsereignisse. Die Wasserressourcen werden sich in Quantität und Qualität verändern, und die Infrastruktur von Regen-und Abwasseranlagen kann einer größeren Gefahr von Schäden durch Stürme, Überschwemmungen und Dürren ausgesetzt sein. Die Auswirkungen des Klimawandels werden zu mehr Schwierigkeiten im Betrieb gestörter Dienstleistungen und zu erhöhten Kosten für Wasser-und Abwasserdienstleistungen führen. Regierungen, Stadtplaner, und Wasser-Manager sollten daher die Entwicklungsprozesse für kommunale Wasser-und Abwasserdienstleistungen erneut überprüfen und Strategien anpassen, um den Klimawandel in Infrastruktur-Design, Investitionsprojekte, Planung von Leistungserbringung, sowie Betrieb und Wartung einzuarbeiten. Nach Angaben des Intergovernmental Panel on Climate Change hat die globale Mitteltemperatur in den letzten 100 Jahren um 0,7 °C zugenommen, und in der Folge hat sich der hydrologische Zyklus intensiviert mit, zum Beispiel, stärkeren Niederschlagsereignisse. Da die städtischen Entwässerungssysteme über einen langen Zeitraum entwickelt wurden und Design-Kriterien auf klimatischen Eigenschaften beruhen, werden diese Veränderungen die Systeme und die Stadt entsprechend beeinflussen. Das übergeordnete Ziel dieser Arbeit ist es, das Wissen über die Auswirkungen des Klimawandels auf das Regenwasser-System in der Stadt Hilla / Irak zu bereichern. Im Detail ist das Ziel, zu untersuchen, wie der Klimawandel die Siedlungsentwässerung und insbesondere die Regenwasser-Infrastruktur betreffen könnte. Desweiteren soll ein Anpassungsplan für diese Änderungen auf der Grundlage von beispielhaften Anpassungsplänen aus internationalen Fallstudienvorgeschlagen werden. Drei stochastische Wettergeneratoren wurden untersucht, um das Klima und den Klimawandel in Hilla zu verstehen. Stochastische Wettergeneratoren wurden in verschiedenen Untersuchungen und Studien zum Beispiel in der Hydrologie sowie im Hochwasser-Management, Siedlungswasser-Design- und Analyse, und Umweltschutz eingesetzt. Damit solche Studien effizient sind, ist es wichtig, lange Datensätze (in der Regel Tageswerte) haben, so dass der Wettergenerator synthetische tägliche Wetterdaten erzeugen kann, dieauf einem soliden statistischen Hintergrund basieren. Einige Wettergeneratoren können Klimaszenarien für verschiedene Arten von globalen Klimamodellen erzeugen. Sie können unter Verwendung von Interpolationsverfahren auch synthetische Daten für einen Standort generieren, für den nicht genügend Daten vorliegen. Um sicherzustellen, dass der Wettergenerator dem Klima der Region optimal entspricht, sollte gegen die beobachteten Daten geprüft werden, ob die synthetischen Daten ausreichend ähnlich sind. Gleichzeitig unterscheidet sich die Genauigkeit des Wettergenerator von Region zu Region und abhängig von den jeweiligen Klimaeigenschaften. Der Zweck des ersten Teils dieser Studie ist es daher, drei Wettergeneratoren, namentlich GEM6, ClimGen und LARS-WG, an acht Klimastationen in der Region des Gouvernements Babylon / Irak zu testen. LARS-WG verwendet eine semi-parametrische Verteilung (entwickelte Verteilung), wohingegen GEM6 und ClimGen eine parametrische Verteilung (weniger komplizierte Verteilung) verwenden. Verschiedene statistische Tests wurden ausgewählt, um die beobachteten und synthetischen Wetterdaten für identische Parameter zu vergleichen, zum Beispiel die Niederschlags- und Temperaturverteilung (Nass-und Trockenzeit). Das Ergebnis zeigt, dass LARS-WG die beobachteten Daten für die Region Babylon akkurater abzeichnet, als ClimGen, wobei GEM6 die beobachteten Daten zu verfehlen scheint. Die synthetischen Daten werden für eine erste Simulation des städtischen Run-offs in der Regenzeit sowie der Folgen des Klimawandels für das Design und Re-Design des städtischen Entwässerungssystems in Hilla verwendet. Der stochastische Wettergenerator LARS wird dann verwendet, um Gruppen zukünftiger Wetterdaten unter Verwendung von fünf globalen Klimamodellen (GCM), die das gesamte Spektrum der Unsicherheit am besten abdecken, zu generieren. Diese globalen Klimamodelle werden verwendet, um zukünftige Klimaszenarien der Temperatur und des Niederschlags für die Region Babylon zu konstruieren. Die Ergebnisse zeigen, eine Steigerung der monatlichen Temperaturen und eine Abnahme der Gesamtmenge der Regen, wobei es jedoch extremere Regenereignissen mit höherer Intensivität in kürzerer Zeit geben wird. Veränderungen der Höhe, des Zeitpunkt und der Intensität der Regenereignisse können die Menge des Abflusses von Regenwasser, die kontrolliert werden muss, beeinflussen. Die Klimawandel-Prognosen können bestehende regenwasserbedingte Überschwemmungen verschlimmern. Verschiedene Bezirke in Hilla können stärker von Regenfluten betroffen werden als bisher aufgrund der Prognosen. Alle Ergebnisse, die von den globalen Klimamodellen übernommen wurden, sind in täglicher Auflösung und um das Regenwasser-Management-Modell anzuwenden, ist es wichtig, dass alle Daten in einer Mindestauflösung von einer Stunde vorliegen. Zur Erfüllung dieser Bedingung wurde ein eine Aufschlüsselungs-Modell verwendet. Einige Stunden-Niederschlagsdaten waren erforderlich, um das zeitliche Aufschlüsselungs-Modell zu kalibrieren. Da weder die Klimastationen noch die Regen-Messgeräte im Interessenbereich über stundenauflösende Daten verfügt, wurden die Stundendaten von Flughäfen in Bagdad verwendet. Die Veränderungen in den Hochwasserrückkehrperioden sind in den projizierten Ergebnissen des Klimawandels ersichtlich, und eine Rückkehrperiode wird nur dann über Zeit gültig bleiben, wenn sich die Umweltbedingungen nicht ändern. Dies bedeutet, dass Wiederkehrperioden, die für Planungszwecke verwendet werden, öfter als bisher aktualisiert werden müssen, da die auf Grundlage von Daten der letzten 30 Jahre berechneten Werte innerhalb einer relativ kurzen Zeitspanneunrepräsentativ werden können. Während Wiederkehrperioden bieten nützliche Hinweise für die Planung die Effekte von Überschwemmungen und die damit verbundenen Auswirkungen, müssen aber mit Vorsicht verwendet werden, und Extreme, die öfter eintreten könnten als erwartet, sollten berücksichtigt werden. Im Studienbereich mit getrennten Regenwassersystemen zeigt die Simulation des Regenwasser-Management-Modells, dass sich die Anzahl der Oberflächenhochwasser sowie der Überschwemmungen im Zeitraum 2050e-2080 erhöhen wird. Zukünftige Niederschläge werdensowohl die Hochwasser-Frequenz als auch die Dauer von Überschwemmungen erhöhen. Daher ist die Notwendigkeit offensichtlich, zukünftige Situationen in städtischen Entwässerungssystemen zu berücksichtigen und eine gut geplante Strategie zu haben, um zukünftige Bedingungen zu bewältigen. Die gesamten Auswirkungen auf die Siedlungsentwässerungssyteme aufgrund der Zunahme von intensiven Niederschlagsereignissen müssen angepasst werden. Aus diesem Grund wurden Empfehlungen für die Anpassung an den Klimawandel in der Stadt Hilla vorgeschlagen. Diese wurden durch die Zusammenführung von Informationen aus der Prüfung von fünf Fallstudien, ausgewählt aufgrund der Menge und Qualität der verfügbaren Informationen, erarbeitet,. Die bewerteten Städte sind Seattle (USA), Odense (Dänemark), Teheran (Iran), und Khulna (Bangladesch).

The impact of climate change is increasingly important to the design of urban water infrastructure like stormwater systems, sewage systems and drinking water systems. Growing evidence indicates that the water sector will not only be affected by climate change, but it will reflect and deliver many of its impacts through floods, droughts, or extreme rainfall events. Water resources will change in both quantity and quality, and the infrastructure of stormwater and wastewater facilities may face greater risk of damage caused by storms, floods and droughts. The effect of the climate change will put more difficulties on operations to disrupted services and increased cost of the water and wastewater services. Governments, urban planners, and water managers should therefore re-examine development processes for municipal water and wastewater services and are adapt strategies to incorporate climate change into infrastructure design, capital investment projects, service provision planning, and operation and maintenance. According to the Intergovernmental Panel on Climate Change, the global mean temperature has increased by 0,7 °C during the last 100 years and, as a consequence, the hydrological cycle has intensified with, for example, more acute rainfall events. As urban drainage systems have been developed over a long period of time and design criteria are based upon climatic characteristics, these changes will affect the systems and the city accordingly. The overall objective of this thesis is to increase the knowledge about the climate change impacts on the stormwater system in Al Hillah city/Iraq. In more detail, the objective is to investigate how climate change could affect urban drainage systems specifically stormwater infrastructure, and also to suggest an adaptation plan for these changes using adaptation plans examples from international case studies. Three stochastic weather generators have been investigated in order to understand the climate and climate change in Al Hillah. The stochastic weather generators have been used in different kind of researches and studies; for example in hydrology, floods management, urban water design and analysis, and environmental protection. To make such studies efficient, it is important to have long data records (typically daily data) so the weather generator can generate synthetic daily weather data based on a sound statistical background. Some weather generators can produce the climate change scenarios for different kind of global climate models. They can be used also to produce synthetic data for a site that does not have enough data by using interpolation methods. To ensure that the weather generator is fitting the climate of the region properly, it should be tested against observed data, whether the synthetic data are sufficiently similar. At the same time, the accuracy of the weather generator is different from region to region and depends on the respective climate properties. Testing three weather generators GEM6, ClimGen and LARS-WG at eight climate stations in the region of Babylon governorate/Iraq, where Al Hillah is located, is one of the purposes of the first part of this study. LARS-WG uses a semi-parametric distribution (developed distribution), whereas GEM6 and ClimGen use a parametric distribution (less complicated distribution). Different statistical tests have been selected to compare observed and synthetic weather data for the same kind, for instance, the precipitation and temperature distribution (wet and dry season). The result shows that LARS-WG represents the observed data for Babylon region in a better way than ClimGen, whereas GEM6 seems to misfit the observed data. The synthetic data will be used for a first simulation of urban run-off during the wet season and the consequences of climate change for the design and re-design of the urban drainage system in Al Hillah. The stochastic weather generator LARS is then used to generate ensembles of future weather data using five Global Climate Models (GCMs) that best captured the full range of uncertainty. These Global Climate Models are used to construct future climate scenarios of temperature and precipitation over the region of Babylon Governorate in Iraq. The results show an increase in monthly temperatures and a decrease in the total amount of rain, yet the extreme rain events will be more intense in a shorter time. Changes in the amount, timing, and intensity of rain events can affect the amount of stormwater runoff that needs to be controlled. The climate change calculated projections may make existing stormwater-related flooding worse. Different districts in Al Hillah city may face more frequent stormwater floods than before due to the climate change projections. All the results that have been taken from the Global Climate Models are in a daily resolution format and in order to run the Storm Water Management Model it is important to have all data in a minimum of one hour resolution. In order to fulfill this condition a disaggregation model has been used. Some hourly precipitation data were required to calibrate the temporal disaggregation model; however none of the climate stations and rain gauges in the area of interest have hourly resolution data, so the hourly data from Baghdad airport station have been used for that calibration. The changes in the flood return periods have been seen in the projected climate change results, and a return period will only remain valid over time if environmental conditions do not change. This means that return periods used for planning purposes may need to be updated more often than previously, because values calculated based on the past 30 years of data may become unrepresentative within a relatively short time span. While return periods provide useful guidance for planning the effects of flooding and related impacts, they need to be used with care, and allowances have to be made for extremes that may occur more often than may be expected. In the study area with separated stormwater systems, the Storm Water Management Model simulation shows that the number of surface floods as well as of the floods increases in the future time periods 2050s and 2080s. Future precipitation will also increase both the flooding frequency and the duration of floods; therefore the need to handle future situations in urban drainage systems and to have a well-planned strategy to cope with future conditions is evident. The overall impacts on urban drainage systems due to the increase of intensive precipitation events need to be adapted. For that reason, recommendations for climate change adaptation in the city of Al Hillah have been suggested. This has been accomplished by merging information from the review of five study cases, selected based on the amount and quality of information available. The cities reviewed are Seattle (USA), Odense (Denmark), Tehran (Iran), and Khulna (Bangladesh).:Preface Acknowledgment Abstract Kurzfassung Contents List of Figures List of Tables List of Listing List of Abbreviation Introduction 1.1. Background of The Research 1.2. The Climate Change Challenge 1.3. Urban Water Systems and Climate Change 1.4. Climate Change and Urban Drainage Adaptation Plan 1.5. Objectives of the Research 1.6. Research Problems and Hypothesis 1.7. Dissertation Structure 1.8. Delimitations Climate History and Climate Change Projections in Al Hillah City Chapter One: State of the Art on Climate Change 2.1.1. The Earth’s Climate System 2.1.2. Climate Change 2.1.3. Emission Scenarios 2.1.4. Global Climate Change 2.1.5. Climate Models 2.1.6. Downscaling Chapter Two: Topography and Climate of the Study Area 2.2.1. Location 2.2.2. Topography 2.2.3. Climate Chapter Three: Climate Change - Methodology and Data 2.3.1. Methodology 2.3.1.1. Stochastic Weather Generators 2.3.1.2. Description of Generators Used in the Comparison 2.3.1.3. Statistical Analysis Comparison Test 2.3.2. Data 2.3.2.1. Required data for modelling 2.3.2.2. Historical daily data required for the weather generators 2.3.2.3. Minimum requirements 2.3.2.4. Data Availability Chapter Four: Results Analysis and Evaluation of Climate Change 2.4.1. Weather Generators Comparison Test results 2.4.1.1.The p-value test Temperature Comparison results Precipitation Comparison Results 2.4.2. LARS Weather Generator Future Scenario 2.4.2.1.1. Climate Change Scenarios for the region of Babylon governorate Storm Water System and Urban Flooding in Al Hillah City Chapter one: Urban Water Modelling 3.1.1. General Overview and Background 3.1.1.1. Storm water systems 3.1.2. Urban Runoff Models 3.1.3. An Overview of Runoff Estimation Methods 3.1.3.1. Computer Modelling in Urban Drainage 3.1.3.2.Statistical Rational Method (SRM) 3.1.4. Models Based on Statistical Rational Method 3.1.5. Urban Rainfall-Runoff Methods 3.1.6. Accuracy Level in Urban Catchment Models Chapter Two: Urban Water System in Al Hillah City and Data Requirement for Modelling 3.2.1. History 3.2.2. Current Situation 3.2.2.1. Urban water system Iraq 3.2.2.2. Urban Water description in Babylon governorate 3.2.2.3. Drinking water network 3.2.2.4. Sewerage infrastructure 3.2.3. Required data for modelling Chapter Three: Methodology to Disaggregate Daily Rain Data and Model Storm Water Runoff 3.3.1. Temporal Disaggregation (hourly from daily) 3.3.1.1. Background of Disaggregation 3.3.1.2. Disaggregation techniques 3.3.1.3. DiMoN Disaggregation Tool 3.3.1.4. Input Data 3.3.1.5. Methods Formerly Used 3.3.2. EPA Storm Water Management Model (SWMM) 3.3.2.1. Verification and Calibration 3.3.2.2. Stormwater Management Model PCSWMM 3.3.2.3. Complete support for all USEPA SWMM5 engine capabilities Chapter Four: Urban Flooding Results 3.4.1. Disaggregation of the daily rain data to hourly data 3.4.1.1.The 1 hour events properties 3.4.1.2. Estimating the rain events in each climate change scenario 3.4.1.3. Past, Current and future return periods 3.4.2. Storm Water Management Model PCSWMM Calibration 3.4.3.Return periods and Urban Floods 3.4.3.1.Network simulation 3.4.3.2.Properties with previous flooding problems 3.4.3.3.Storm water system simulation under 1 hour-2, 5 and 10 years return period 3.4.3.4.Storm water system simulation under 1 hour-25 years return period 3.4.3.5.Storm water system simulation under 1 hour-50 years return period 3.4.3.6. Storm water system simulation under 1 hour – 100, 200, 500 and 1000 years return period 3.4.3.7.Total Flooding Adaptation Plan for Al Hillah City Chapter One: International Case Studies 4.1.1. Historical precipitation analysis 4.1.2. Current and projected future climate change, impacts and adaptation plan for each selected city 4.1.2.1. Seattle 4.1.2.2. Odense 4.1.2.3. Tehran 4.1.2.4. Khulna 4.1.2.5. Melbourne 4.1.3. Drainage System of the Studied Cities 4.1.3.1. Drainage System in Seattle 4.1.3.2. Drainage System in Odense 4.1.3.3. Drainage System in Tehran 4.1.3.4. Drainage System in Khulna 4.1.3.5. Drainage System in Melbourne Chapter Two: Adaptation Plan for Al Hillah City 4.2.1. Conclusions from Adaptation Options Analysed 4.2.2. Suggestions for Al Hillah City 4.2.3. Adaptation Actions Overall Conclusion Bibliography / Die Auswirkungen des Klimawandels auf die Gestaltung der städtischen Wasserinfrastruktur wie Regenwasser, Kanalisation und Trinkwassersysteme werden immer wichtiger. Eine wachsende Anzahl von Belegen zeigt, dass der Wassersektor nicht nur durch den Klimawandel beeinflusst werden wird, aber er wird zu reflektieren und liefern viele seiner Auswirkungen durch Überschwemmungen, Dürren oder extreme Niederschlagsereignisse. Die Wasserressourcen werden sich in Quantität und Qualität verändern, und die Infrastruktur von Regen-und Abwasseranlagen kann einer größeren Gefahr von Schäden durch Stürme, Überschwemmungen und Dürren ausgesetzt sein. Die Auswirkungen des Klimawandels werden zu mehr Schwierigkeiten im Betrieb gestörter Dienstleistungen und zu erhöhten Kosten für Wasser-und Abwasserdienstleistungen führen. Regierungen, Stadtplaner, und Wasser-Manager sollten daher die Entwicklungsprozesse für kommunale Wasser-und Abwasserdienstleistungen erneut überprüfen und Strategien anpassen, um den Klimawandel in Infrastruktur-Design, Investitionsprojekte, Planung von Leistungserbringung, sowie Betrieb und Wartung einzuarbeiten. Nach Angaben des Intergovernmental Panel on Climate Change hat die globale Mitteltemperatur in den letzten 100 Jahren um 0,7 °C zugenommen, und in der Folge hat sich der hydrologische Zyklus intensiviert mit, zum Beispiel, stärkeren Niederschlagsereignisse. Da die städtischen Entwässerungssysteme über einen langen Zeitraum entwickelt wurden und Design-Kriterien auf klimatischen Eigenschaften beruhen, werden diese Veränderungen die Systeme und die Stadt entsprechend beeinflussen. Das übergeordnete Ziel dieser Arbeit ist es, das Wissen über die Auswirkungen des Klimawandels auf das Regenwasser-System in der Stadt Hilla / Irak zu bereichern. Im Detail ist das Ziel, zu untersuchen, wie der Klimawandel die Siedlungsentwässerung und insbesondere die Regenwasser-Infrastruktur betreffen könnte. Desweiteren soll ein Anpassungsplan für diese Änderungen auf der Grundlage von beispielhaften Anpassungsplänen aus internationalen Fallstudienvorgeschlagen werden. Drei stochastische Wettergeneratoren wurden untersucht, um das Klima und den Klimawandel in Hilla zu verstehen. Stochastische Wettergeneratoren wurden in verschiedenen Untersuchungen und Studien zum Beispiel in der Hydrologie sowie im Hochwasser-Management, Siedlungswasser-Design- und Analyse, und Umweltschutz eingesetzt. Damit solche Studien effizient sind, ist es wichtig, lange Datensätze (in der Regel Tageswerte) haben, so dass der Wettergenerator synthetische tägliche Wetterdaten erzeugen kann, dieauf einem soliden statistischen Hintergrund basieren. Einige Wettergeneratoren können Klimaszenarien für verschiedene Arten von globalen Klimamodellen erzeugen. Sie können unter Verwendung von Interpolationsverfahren auch synthetische Daten für einen Standort generieren, für den nicht genügend Daten vorliegen. Um sicherzustellen, dass der Wettergenerator dem Klima der Region optimal entspricht, sollte gegen die beobachteten Daten geprüft werden, ob die synthetischen Daten ausreichend ähnlich sind. Gleichzeitig unterscheidet sich die Genauigkeit des Wettergenerator von Region zu Region und abhängig von den jeweiligen Klimaeigenschaften. Der Zweck des ersten Teils dieser Studie ist es daher, drei Wettergeneratoren, namentlich GEM6, ClimGen und LARS-WG, an acht Klimastationen in der Region des Gouvernements Babylon / Irak zu testen. LARS-WG verwendet eine semi-parametrische Verteilung (entwickelte Verteilung), wohingegen GEM6 und ClimGen eine parametrische Verteilung (weniger komplizierte Verteilung) verwenden. Verschiedene statistische Tests wurden ausgewählt, um die beobachteten und synthetischen Wetterdaten für identische Parameter zu vergleichen, zum Beispiel die Niederschlags- und Temperaturverteilung (Nass-und Trockenzeit). Das Ergebnis zeigt, dass LARS-WG die beobachteten Daten für die Region Babylon akkurater abzeichnet, als ClimGen, wobei GEM6 die beobachteten Daten zu verfehlen scheint. Die synthetischen Daten werden für eine erste Simulation des städtischen Run-offs in der Regenzeit sowie der Folgen des Klimawandels für das Design und Re-Design des städtischen Entwässerungssystems in Hilla verwendet. Der stochastische Wettergenerator LARS wird dann verwendet, um Gruppen zukünftiger Wetterdaten unter Verwendung von fünf globalen Klimamodellen (GCM), die das gesamte Spektrum der Unsicherheit am besten abdecken, zu generieren. Diese globalen Klimamodelle werden verwendet, um zukünftige Klimaszenarien der Temperatur und des Niederschlags für die Region Babylon zu konstruieren. Die Ergebnisse zeigen, eine Steigerung der monatlichen Temperaturen und eine Abnahme der Gesamtmenge der Regen, wobei es jedoch extremere Regenereignissen mit höherer Intensivität in kürzerer Zeit geben wird. Veränderungen der Höhe, des Zeitpunkt und der Intensität der Regenereignisse können die Menge des Abflusses von Regenwasser, die kontrolliert werden muss, beeinflussen. Die Klimawandel-Prognosen können bestehende regenwasserbedingte Überschwemmungen verschlimmern. Verschiedene Bezirke in Hilla können stärker von Regenfluten betroffen werden als bisher aufgrund der Prognosen. Alle Ergebnisse, die von den globalen Klimamodellen übernommen wurden, sind in täglicher Auflösung und um das Regenwasser-Management-Modell anzuwenden, ist es wichtig, dass alle Daten in einer Mindestauflösung von einer Stunde vorliegen. Zur Erfüllung dieser Bedingung wurde ein eine Aufschlüsselungs-Modell verwendet. Einige Stunden-Niederschlagsdaten waren erforderlich, um das zeitliche Aufschlüsselungs-Modell zu kalibrieren. Da weder die Klimastationen noch die Regen-Messgeräte im Interessenbereich über stundenauflösende Daten verfügt, wurden die Stundendaten von Flughäfen in Bagdad verwendet. Die Veränderungen in den Hochwasserrückkehrperioden sind in den projizierten Ergebnissen des Klimawandels ersichtlich, und eine Rückkehrperiode wird nur dann über Zeit gültig bleiben, wenn sich die Umweltbedingungen nicht ändern. Dies bedeutet, dass Wiederkehrperioden, die für Planungszwecke verwendet werden, öfter als bisher aktualisiert werden müssen, da die auf Grundlage von Daten der letzten 30 Jahre berechneten Werte innerhalb einer relativ kurzen Zeitspanneunrepräsentativ werden können. Während Wiederkehrperioden bieten nützliche Hinweise für die Planung die Effekte von Überschwemmungen und die damit verbundenen Auswirkungen, müssen aber mit Vorsicht verwendet werden, und Extreme, die öfter eintreten könnten als erwartet, sollten berücksichtigt werden. Im Studienbereich mit getrennten Regenwassersystemen zeigt die Simulation des Regenwasser-Management-Modells, dass sich die Anzahl der Oberflächenhochwasser sowie der Überschwemmungen im Zeitraum 2050e-2080 erhöhen wird. Zukünftige Niederschläge werdensowohl die Hochwasser-Frequenz als auch die Dauer von Überschwemmungen erhöhen. Daher ist die Notwendigkeit offensichtlich, zukünftige Situationen in städtischen Entwässerungssystemen zu berücksichtigen und eine gut geplante Strategie zu haben, um zukünftige Bedingungen zu bewältigen. Die gesamten Auswirkungen auf die Siedlungsentwässerungssyteme aufgrund der Zunahme von intensiven Niederschlagsereignissen müssen angepasst werden. Aus diesem Grund wurden Empfehlungen für die Anpassung an den Klimawandel in der Stadt Hilla vorgeschlagen. Diese wurden durch die Zusammenführung von Informationen aus der Prüfung von fünf Fallstudien, ausgewählt aufgrund der Menge und Qualität der verfügbaren Informationen, erarbeitet,. Die bewerteten Städte sind Seattle (USA), Odense (Dänemark), Teheran (Iran), und Khulna (Bangladesch).:Preface Acknowledgment Abstract Kurzfassung Contents List of Figures List of Tables List of Listing List of Abbreviation Introduction 1.1. Background of The Research 1.2. The Climate Change Challenge 1.3. Urban Water Systems and Climate Change 1.4. Climate Change and Urban Drainage Adaptation Plan 1.5. Objectives of the Research 1.6. Research Problems and Hypothesis 1.7. Dissertation Structure 1.8. Delimitations Climate History and Climate Change Projections in Al Hillah City Chapter One: State of the Art on Climate Change 2.1.1. The Earth’s Climate System 2.1.2. Climate Change 2.1.3. Emission Scenarios 2.1.4. Global Climate Change 2.1.5. Climate Models 2.1.6. Downscaling Chapter Two: Topography and Climate of the Study Area 2.2.1. Location 2.2.2. Topography 2.2.3. Climate Chapter Three: Climate Change - Methodology and Data 2.3.1. Methodology 2.3.1.1. Stochastic Weather Generators 2.3.1.2. Description of Generators Used in the Comparison 2.3.1.3. Statistical Analysis Comparison Test 2.3.2. Data 2.3.2.1. Required data for modelling 2.3.2.2. Historical daily data required for the weather generators 2.3.2.3. Minimum requirements 2.3.2.4. Data Availability Chapter Four: Results Analysis and Evaluation of Climate Change 2.4.1. Weather Generators Comparison Test results 2.4.1.1.The p-value test Temperature Comparison results Precipitation Comparison Results 2.4.2. LARS Weather Generator Future Scenario 2.4.2.1.1. Climate Change Scenarios for the region of Babylon governorate Storm Water System and Urban Flooding in Al Hillah City Chapter one: Urban Water Modelling 3.1.1. General Overview and Background 3.1.1.1. Storm water systems 3.1.2. Urban Runoff Models 3.1.3. An Overview of Runoff Estimation Methods 3.1.3.1. Computer Modelling in Urban Drainage 3.1.3.2.Statistical Rational Method (SRM) 3.1.4. Models Based on Statistical Rational Method 3.1.5. Urban Rainfall-Runoff Methods 3.1.6. Accuracy Level in Urban Catchment Models Chapter Two: Urban Water System in Al Hillah City and Data Requirement for Modelling 3.2.1. History 3.2.2. Current Situation 3.2.2.1. Urban water system Iraq 3.2.2.2. Urban Water description in Babylon governorate 3.2.2.3. Drinking water network 3.2.2.4. Sewerage infrastructure 3.2.3. Required data for modelling Chapter Three: Methodology to Disaggregate Daily Rain Data and Model Storm Water Runoff 3.3.1. Temporal Disaggregation (hourly from daily) 3.3.1.1. Background of Disaggregation 3.3.1.2. Disaggregation techniques 3.3.1.3. DiMoN Disaggregation Tool 3.3.1.4. Input Data 3.3.1.5. Methods Formerly Used 3.3.2. EPA Storm Water Management Model (SWMM) 3.3.2.1. Verification and Calibration 3.3.2.2. Stormwater Management Model PCSWMM 3.3.2.3. Complete support for all USEPA SWMM5 engine capabilities Chapter Four: Urban Flooding Results 3.4.1. Disaggregation of the daily rain data to hourly data 3.4.1.1.The 1 hour events properties 3.4.1.2. Estimating the rain events in each climate change scenario 3.4.1.3. Past, Current and future return periods 3.4.2. Storm Water Management Model PCSWMM Calibration 3.4.3.Return periods and Urban Floods 3.4.3.1.Network simulation 3.4.3.2.Properties with previous flooding problems 3.4.3.3.Storm water system simulation under 1 hour-2, 5 and 10 years return period 3.4.3.4.Storm water system simulation under 1 hour-25 years return period 3.4.3.5.Storm water system simulation under 1 hour-50 years return period 3.4.3.6. Storm water system simulation under 1 hour – 100, 200, 500 and 1000 years return period 3.4.3.7.Total Flooding Adaptation Plan for Al Hillah City Chapter One: International Case Studies 4.1.1. Historical precipitation analysis 4.1.2. Current and projected future climate change, impacts and adaptation plan for each selected city 4.1.2.1. Seattle 4.1.2.2. Odense 4.1.2.3. Tehran 4.1.2.4. Khulna 4.1.2.5. Melbourne 4.1.3. Drainage System of the Studied Cities 4.1.3.1. Drainage System in Seattle 4.1.3.2. Drainage System in Odense 4.1.3.3. Drainage System in Tehran 4.1.3.4. Drainage System in Khulna 4.1.3.5. Drainage System in Melbourne Chapter Two: Adaptation Plan for Al Hillah City 4.2.1. Conclusions from Adaptation Options Analysed 4.2.2. Suggestions for Al Hillah City 4.2.3. Adaptation Actions Overall Conclusion Bibliography

In dieser Arbeit wird die Entwicklung eines Gebäudeenergiemodells (BEM) und eines Schemas für die mittlere Strahlungstemperatur ($T_mrt$) vorgestellt, das in das Doppel-Canyon basierte städtische Bestandsschichtsschema (DCEP) integriert ist. Das erweiterte DCEP-BEM Modell zielt darauf ab, eine Verbindung zwischen anthropogener Wärme und dem Stadtklima herzustellen, indem Gebäude in Straßenschluchten einbezogen werden, um die Energieflüsse auf städtischen Oberflächen, die Auswirkungen der anthropogenen Wärme auf die Atmosphäre, die Innenraumlufttemperatur und die Abwärme von Klimaanlagen zu untersuchen. Das DCEP-BEM wird mit dem mesoskaligen Klimamodell COSMO-CLM (COnsortium for Small-scale MOdelling in CLimate Mode, im Folgenden CCLM) gekoppelt und zur Simulation des Winters und Sommers 2018 in Berlin. Die Auswertung der Wintersimulationen zeigt, dass CCLM/DCEP-BEM den mittleren Tagesverlauf der gemessenen turbulenten Wärmeströme gut reproduziert und die simulierte 2-m-Lufttemperatur und den städtischen Wärmeinseleffekt (UHI) verbessert. Im Sommer bildet das CCLM/DCEP-BEM die Innenraumlufttemperatur richtig ab und verbessert die Ergebnisse für die 2-m-Lufttemperatur und die UHI leicht. Außerdem wird das CCLM/DCEP-BEM angewendet, um die Abwärmeemissionen von Klimaanlagen im Sommer zu untersuchen. Die Abwärmeemissionen der Klimaanlagen erhöhen die Lufttemperatur in Oberflächennähe erheblich. Der Anstieg ist in der Nacht und in hochurbanisierten Gebieten stärker ausgeprägt. Es werden zwei Standorte für die AC-Außengeräte betrachtet: entweder an der Wand eines Gebäudes (VerAC) oder auf dem Dach eines Gebäudes (HorAC). Die Auswirkung von HorAC ist im Vergleich zu VerAC insgesamt geringer, was darauf hindeutet, dass HorAC einen kleineren Einfluss auf die oberflächennahe Lufttemperatur und den UHI hat. Ein Schema für $T_mrt$ wird für das CCLM/DCEP-BEM entwickelt und umfassend validiert. Es wird gezeigt, dass dieses Schema eine zuverlässige Darstellung von $T_mrt$ bietet. / This work presents the development of a building energy model (BEM) and a mean radiant temperature ($T_mrt$) scheme integrated in the urban canopy scheme Double Canyon Effect Parametrization (DCEP). The extended DCEP-BEM model aims to establish a link between anthropogenic heat emissions and urban climate by including the interior of buildings in urban street canyons to investigate the energy fluxes on urban surfaces, the effects of anthropogenic heat on the atmosphere, the evolution of indoor air temperature, and waste heat from air conditioning (AC) systems. DCEP-BEM is coupled with the mesoscale climate model COSMO-CLM (COnsortium for Small-scale MOdelling in CLimate Mode, hereafter CCLM) and applied to simulate the winter and summer 2018 of Berlin. The evaluation for winter simulations indicates that CCLM/DCEP-BEM reproduces well the average diurnal characteristics of the measured turbulent heat fluxes and considerably improves the simulated 2-m air temperature and urban heat island (UHI). In summer, CCLM/DCEP-BEM accurately reproduces the indoor air temperature, and slightly improves the performance of the 2-m air temperature and the UHI effect. Furthermore, CCLM/DCEP-BEM is applied to explore the waste heat emissions from AC systems in summer. AC waste heat emissions considerably increase the near-surface sensible heat flux and air temperature. The increase is more pronounced during the night and in highly urbanised areas. Two locations for the AC outdoor units are considered: either on the wall of a building (VerAC) or on the rooftop of a building (HorAC). The effect of HorAC is overall smaller compared to VerAC, indicating that HorAC has a smaller impact on the near-surface air temperature and the UHI effect. A $T_mrt$ scheme is developed for CCLM/DCEP-BEM and extensively evaluated. It is shown that this scheme provides a reliable representation of $T_mrt$.