Rare precipitation extremes are projected to increase relatively more than common extremes under future climate change. However, it is unconfirmed whether these projected differences in relative change are observable in historical precipitation data. Therefore, this study investigated whether rare precipitation extremes have changed differently from more common ones during the period 1951 to 2010, in Western Europe. The differences in change between rare and common extremes (Drare-common) and its correlation with temperature is analysed, using daily temperature and precipitation data. The Drare-common values are calculated based on the relative change of a specific return level estimated by three methods: the Full series, the Generalized Extreme Value (GEV) and the Metastatistical Extreme Value (MEV) distribution. A correlation with daily temperature is obtained by analysing first all day temperatures and compare these with the pattern in the Drare-common values. Secondly, the temperatures at the days of the annual extremes is analysed.
The results indicate that, on average for Western Europe, historical precipitation extremes have increased in magnitude by 3.0 up to 5.4%, depending on the method. Regional variations are present in Western Europe, showing negative relative changes for the most southern regions. A Drare-common for Western Europe equal to 1.0 and 2.3%pt is obtained, for respectively the MEV and GEV method, indicating that the rarest precipitation extremes have increased more in magnitude compared to the common extremes. However, a comparison of extremes based on the time series length using the Full series indicated no difference in relative changes for return levels of 0.1 and 10 years. A statistically significant correlation between the Drare-common and temperature is obtained between the results of the GEV and Tmax and MEV method and Tmin. This correlations suggests observing a relative high Drare-common value in a region in which the change in maximum temperature is low and the change in the minimum temperature is high, depending on the method used. Analysing the temperatures during annual extremes showed an increase in both the minimum and maximum temperature on the days of the annual extremes, despite a shift from warmer to colder months in which annual extremes occur. When dividing the annual extremes into common and rare, it was concluded that a higher increase in temperature is observed during common annual extremes compared to the rare annual extremes.
It is recommended to analyse this phenomenon in more recent years. To understand the behaviour of precipitation extremes in greater depth, other driving factors causing precipitation to become extreme could be studied. Examples of these driving factors include atmospheric water vapor and the temperature in regions where precipitation is generated.

Vegetation strongly influences evaporation from land by transporting water from the subsurface to the atmosphere through root water uptake. The amount and timing of this water flux depends on the aboveground (e.g., the amount of leaves) and belowground (e.g., the root extent) characteristics of the vegetation. Although these vegetation characteristics vary strongly both in space and time, there is lack of adequate representation of this vegetation variability in large scale hydrological and land surface models. This causes deficiencies in representing the associated variability in modeled water and energy states and fluxes, which introduces uncertainties in future predictions of the hydrological cycle, including hydrological extremes such as droughts and floods. To address this issue, this research aims to develop more realistic model representations of spatial and temporal vegetation variability, and explore their potential for improving modeled water fluxes in large scale hydrological and land surface models.

Chapter 2 focuses on model representations of spatial and temporal variability of aboveground vegetation characteristics based on satellite remote sensing data. Interannual variability of land cover and leaf area index (LAI) from latest global remote sensing datasets are integrated into the land surfacemodel Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land (HTESSEL). Furthermore, datasets of LAI and the fraction of green vegetation cover are used to develop and integrate a spatially and temporally varying model parameterization of the effective vegetation cover. The effects of these three implementations on simulated hydrology are evaluated using offline (land-only) model simulations. The results show that the enhanced variability of aboveground vegetation characteristics considerably improves the simulated variability of evaporation and near-surface soil moisture. These improvements are connected to a framework that describes how the implemented vegetation variability influences internal model interactions between vegetation, soil moisture, and evaporation.

Chapter 3 evaluates how climate-controlled root zone parameters influence water flux simulations with the land surfacemodel HTESSEL. To this aim catchment scale root zone storage capacity Sr (mm), defined as the maximum volume of subsurfacemoisture that can be accessed by the vegetation roots, is estimated using the memory method. In this method Sr is derived from soil water deficits, reflecting the ability of vegetation to adapt to the local climate conditions by sizing their roots in such a way to guarantee continuous access to water, keeping memory of past water deficit conditions. Climatecontrolled Sr is estimated with the memory method for 15 catchments in Australia to adequately represent the spatial variability of the vegetation roots. These estimates are integrated into HTESSEL, replacing the static root representation based on soil types and uniform soil depth. The results of offline model simulations show that climatecontrolled Sr representation significantly improves the timing of modeled discharge in the study regions. This suggests that a climate-controlled representation of the model Sr has potential for improving water flux simulations by land surface models in a global context.

Chapter 4 presents the influence of irrigation on the estimation of Sr with the memory method. The memory method Sr is derived from the seasonal patterns of root zone water input and output. Besides precipitation as input, irrigation supplies additional water to the root zone in irrigated agricultural fields. However, the influence of irrigation on the memory method Sr estimates has not been assessed previously. In this study two methods based on different globally available irrigation datasets are developed to account for irrigation in the memory method for estimating Sr. The Sr estimates fromthese two methods are compared to a case without considering irrigation for a large sample of catchments globally. The results show, for the first time, that irrigation considerably reduces Sr in regions with extensive irrigation, highlighting the relevance of irrigation for adequately estimating ecosystem scale Sr.

Chapter 5 investigates the influence of climate, landscape, and vegetation variables on Sr globally. So far, there is limited insight on the controls of global-scale root development and their spatial variation. A random forest model is used to predict Sr as estimated with the memory method based on 21 variables for a large sample of catchments globally. The results indicate that hydro-climatic variables are the dominant, but spatially varying, driver of ecosystemscale Sr, while landscape and vegetation play aminor role. Based on the importance of the various drivers, a reduced parsimoniousmodel using four variables is used to predict Sr on a global scale. These predictions largely resemble other global estimates of root characteristics based on more complex methods and datasets. This indicates that the here developed parsimonious model to estimate global scale Sr based on four simple globally available variables adequately represents the spatial variability of Sr globally. Together with the results from Chapter 2, it can be concluded that integration of these estimates into large scale hydrological and land surface models has potential to improve model water fluxes.

The findings of this dissertation directly contribute to the large scale hydrological and climate model communities by providing methods to adequately represent spatial and temporal vegetation variability. The results demonstrate the potential of these methods to improve modeled water fluxes by large scale hydrological and land surface models, with major implications for the accuracy of hydrological and climate predictions. This dissertation lays the foundation for future research aimed at further improving the realism of model vegetation variability. @en

This study evaluates the performance of the CMIP6 models in simulating monsoon rainfall and moisture tracking in the Yangtze River basin. The findings reveal varying degrees of accuracy across different regions of the basin during the monsoon period. Downstream and midstream regions demonstrate higher accuracy, whereas upstream areas exhibit lower precision, along with an overall trend of overestimation. The evaluation encompasses the timing of monsoon months, as well as the peak month, while analyzing the simulation’s accuracy for rainfall. It also entails an overarching examination through a Taylor Diagram and Taylor skill scores, which spotlight models with superior and inferior performance. EC-Earth3 exhibits commendable performance, whereas models like IITM-ESM showcase poorer results. Furthermore, moisture tracking assessments, utilizing the WAM2layers model, identify limitations within the CMIP6 model in terms of replicating water vapor sources and pathways, especially in proximity to geographical features such as the Himalayas and the coastline. In addition to the basin itself, the CMIP6 model simulates central Asia as the main source of evaporation, rather than the Indian Ocean, according to the results of
ERA5. However, no obvious pattern differences are shown between the different CMIP6 GCMs. Persistent challenges stem from data availability and numerical inconsistencies, necessitating enhancements in both the CMIP6 models and the WAM2layers code.

This research evaluated the performance of Land Surface Models (LSMs) in simulating droughts, examining Land-Hist offline simulations from the Land, Surface, Snow and Soil Moisture Intercomparison Project (LS3MIP). It is well known that LSMs possess uncertainties and biases due to oversimplifications or the absence of certain physical processes (e.g., groundwater interactions and lateral connectivity). Therefore, the objective of this research was to identify the strengths and weaknesses of various LSMs and how this relates to the performances in simulating soil moisture droughts. To address this objective, eight LSMs were evaluated: CESM2, CMCC-ESM2, E3SM-1-1, EC-Earth3-Veg, HadGEM3-GC31-LL, IPSL-CM6A-LR, MIROC6, and UKESM1-0-LL. Two reference evaporation data sets (DOLCE V3 and an ensemble of FLUXCOM-RS, BESS and PML) and a reference soil moisture data set (SoMo.ml) were utilized for the evaluation. After a global analysis on the LSM evaporation characteristics, six climate diverse study areas were selected for further investigation. A long-term analysis was performed by examining the water balance and implementing the LSMs into the Budyko Framework. Subsequently, soil moisture deficits were calculated for the driest periods in time, and the resulting accumulated deficits were compared with the reference evaporation data. The timing and progression of the deficits were evaluated utilizing the reference soil moisture data. Finally, the sensitivity of the model was evaluated by examining the response of evaporation anomalies to precipitation anomalies and comparing this with the reference evaporation data. The results showed that there was a large spread in output and performance among the LSMs across all parts of the evaluation. The greatest contrasts among the LSMs were found in the dry to wet transition zones within the tropics. In this latitudinal range, the worst performing LSMs overestimated the accumulation of soil moisture deficits and the severity of droughts, while the opposite was found for the extratropical regions. Additionally, the models showed, in general, to be overly sensitive to precipitation anomalies. When ranking the implemented model bases in the LSMs based on their performance during droughts, the findings showed that the Community Land Model (implemented in CMCC-ESM2, E3SM-1-1 and CESM2) was predominantly the best performing, followed by ORCHIDEE (IPSL-CM6A-LR) and HTESSEL (EC-Earth3-Veg). MATSIRO (MIROC6) and JULES (HadGEM3-GC31-LL and UKESM1-0-LL) were the least performing model bases. From a hydrological perspective, the findings of this research could be linked to some known limitations of LSMs. Oversimplified soil and vegetation dynamics could contribute to the LSMs being overly sensitive to precipitation anomalies while the contrasts between the tropical and extratropical regions could be attributed to the representation of the soil moisture-evaporation coupling, which plays a greater role in the tropical study areas. Ultimately, this research could contribute to LS3MIP and the Land Surface Modeling community, as the results highlight the strengths and weaknesses of the LSMs in simulating soil moisture droughts. From there, this research could contribute to improving LSMs, understanding drought mechanisms, and addressing climate change impacts, especially in drought-prone regions.

Extreme precipitation can be characterized by the tail behavior of precipitation probability distributions.The tail contains the most extreme precipitation events and tells something about both the magnitude and frequency of these events. Here the heaviness amplification factor is used to represent the tail behavior. This research determines the most important hydro-climatological controls of the heaviness amplification factor. The study area is the entire world, where Antarctica and the oceans are not taken into account. A pre-defined set of 17 controls divided into four groups is used. The four groups are: general climate condition, geography, land cover and climate variability. The relation between the heaviness and the controls is determined with multiple linear regression, where the standardized controls are used. Since multiple linear regression is applied, multicollinearity might be a problem. This term refers to the occasion where independent variables are correlated to another independent variable or a linear combination of variables. This could lead to erroneous and unreliable results. To deal with this problem, three elimination methods are applied. This first method consists of a correlation analysis, where two controls are highly correlated if their correlation coefficient is higher than 0.9. The control with the lowest regression coefficient is eliminated. This is followed by another regression analysis with all the remaining controls, where controls with a low regression coefficient are also eliminated. The second method applies an iterative regression procedure, where in each iteration the control with the lowest coefficient is eliminated until three controls remain. The last method calculates the variance inflation factor, which is a measure for multicollinearity. It removes the controls with the highest variance inflation factor if higher than 10, until none of the controls has a score above this limit. The described analysis is done for the entire world, but also for 44 IPCC climate reference regions. The conclusion of the analysis is that the most important controls for the heaviness belong to the general climate condition and the climate variability. In total, 13 of the 17 controls occur in a top three for at least one region in one or more of the elimination methods. The most important control is precipitation variability which is in 57% of the regions in the top three based on the results of methods 1, 2 and 3. Each region gives a different top three or at least a different order of controls. The heaviness is calculated based on the outcomes of the multiple linear regression. The R-squared ranges from 0.2 to 0.8, with an average value around 0.3. This suggests that the controls explain about 30% of the variance. The average error for the world between the data and the calculation of the heaviness is 0.11. When the heaviness of a single region is calculated, the absolute error between the data and the calculation of the heaviness reduces from 0.12 to 0.10 when using regional coefficients. This result leads to the conclusion that regional processes do play a role in the behavior of extreme precipitation. This makes it less appropriate to define a global set of most important controls. None of the three elimination methods is able to solve the problem of multicollinearity completely. On average, 35% of the controls have a opposite sign between the correlation and regression coefficient.

Second-order Gaussian kernels have been utilized to develop three algorithms that could automatically extract ridge lines for hydrodynamic modelling. Isotropic second-order Gaussian kernels produce inaccurate lines at crossings and junctions. To avoid the malfunctioning of Second-order Gaussian kernels, one default and two alternative algorithms were developed. The first, default algorithm is based on isotropic kernels and non-maximum suppression. For the first alternative algorithm, isotropic and anisotropic kernels have been applied for the filter process. The third algorithm uses skeletonization instead of non-maximum suppression. A verification was applied to analyzed the performance of the algorithms. The Matthews correlation coefficient (MCC) of the default algorithm and the alternative algorithm that included anisotropic kernels was found to be 0.17. For the algorithm based on skeletonization a value of 0.08 was obtained. Hence it has been concluded that the algorithms that utilized non maximum suppression instead could more accurately detect ridge lines than the model based on skeletonization. However, the latter generated lines that contained less discontinuities. Furthermore this algorithm turned out to be computationally less demanding in comparison to the other two algorithms.

Since the mid-19th century global temperatures have increased significantly, so does the intensity of extreme rainfall. Previous studies have suggested that by the end of the century extreme rainfall intensity would increase by 10.5% and 28.2% for the low and high emission scenarios, and the centennial extreme rainfall would increase by 13.5% and 38.3%. For this model-based result, our aim is to verify it with observed historical data and find out the growth rate of extreme rainfalls. In this study, the MEV method was used to analyze the changes in the intensity of daily extreme rainfall in the 1-year, 10-year, and 100-year return periods at 29 stations and linearly fit them to determine the trends, and finally found that: the extreme rainfall showed an increasing trend in 70% of the stations in all data years, and the average growth rates in the 1-year, 10-year, and 100-year return period are 9.73%, 8.06% and 12.00%, re- spectively. From 1950 to the present, 56% of the stations have an increasing trend of extreme rainfall and the growth rate is 6% and 4% for 1-year and 10-year events, 2.4% for the 100-year events. For the growth rate per decade, the 1-year and 10-year events grow faster after 1950 than data from 1850, and the growth rate of the 100-year events becomes smaller.

This work provides a water balance-based approach to improve understanding of drought trends by calculating root storage deficits in the United States. To do this, data is ultimately compiled from 1125 watersheds for the years 2001 to 2016. The root storage deficit is then determined using iteratively optimized transpiration values minus the water available to plants and compared to the latent heat of the FLUXCOM energy fluxes data. The stimulus for this work is a better understanding of desiccation processes. Answers to this are given only indirectly. For example, no concrete consequences can be derived from the calculated root storage deficits. Further-more, it turns out that the water cycle-based approach provides clearly interpretable results only for catchments with sufficient water supply. Thus, increasingly arid catchments do not satisfy the general conditions required for the calculations. Catchments in which the root storage deficit exceeds one meter are declared as ever-growing deficit. In these catchments an increased dry-ing process is to be assumed. In all other catchments, temporally congruent developments are shown for both applied methods. Due to the increased dependence on incoming precipitation, calculations performed with the CHIRPS dataset were repeated with the CRUNCEP dataset. This turns out to be generally smaller but confirms the drying processes suggested by the CHIRPS dataset. In contrast, the extents of drying suggested for both methods differ by up to 972 mm for the CHIRPS precipitation dataset, with holistically calculated root storage deficit maxima differing by an average of 178mm. This work is nonetheless useful because otherwise only grid dataset-based and thus statistically adjusted approaches are common in climatological science. Thus, the water cycle-based approach provides a concrete application of the raster values to point measurements.

Protecting forests from agricultural expansion and wildfires while the world population is growing and the climate is warming remains one of the biggest challenges humanity currently faces. While global modelling and regional observation based studies have found significant effects from deforestation on precipitation, leading mostly to drying precipitation trends and shorting rainy seasons, this study represents the first global estimate of first order deforestation effects on precipitation. Using a recently developed precipitationshed database and actual deforestation data, a new measure is developed to quantify potential deforestation impact per grid cell which in turn is related to annual precipitation trends as well as seasonal differences in tropical regions. In seven regions analysed, a majority of subregions suggested a relationship between deforestation impact and a relative drying precipitation trend in the 2001-2018 study period compared to the long term average. While these results provide further evidence of deforestation contributing to a downwind drying precipitation trend across different continents and climate regions, five other regions studied showed no significant relation or suggest relative wetting related to deforestation impact. One of this regions is the South America Tropical (SAT) region, the region most well-known for its widespread and intense Amazonian deforestation. The two regions downwind of the SAT region however are highly impacted by SAT deforestation and experience most relative drying in the areas impacted most impacted by deforestation, suggesting strong teleconnecting effects. In the seasonal analysis, only two out of four tropical regions studied show more subregions linking deforestation impact to relative drying in the first wet month compared to the wettest month. While these results provide new insights into the global influence deforestation can have on moisture availability, more research needs to be done into the indirect and feedback effects related to deforestation. Additionally, a more robust way of including other factors influencing precipitation trends like large scale oscillations could further enhance the understanding of this important issue.

Due to global warming, temperatures are expected to rise and, with it, the increase in moisture holding capacity of the atmosphere. Changes in precipitation extremes with temperature are governed by the Clausius-Clapeyron relationship (CC), which states that precipitation increases 7% per degree of warming. While global models and observations are consistent with CC, research at hourly observations have showed deviations in the CC relationship, showing steeper increase up to 14% per degree (super-CC) and <7% per degree for larger temperatures (sub-CC). Studies have shown that scaling rates can be influenced by several factors, such as the length of observations, rainfall percentiles or the timescale of rainfall, among others. Apart from methodological limitations, research on scaling rates is not readily available in all parts of the world, with scarce studies carried out in the African continent. In this context, there is a need for better understanding the relationship between precipitation and temperature as well as the implications for future extremes. To address these challenges, this research investigated 3 main topics. Firstly, existing methodologies were compared and analysed for 353 rain gauges in Germany. Using as extreme rainfall indicators wet percentiles (80th-99th quantile), 3 methods were tested and statistically compared to estimate scaling rates with dew point temperature (DPT): (i) binning methods, (ii) quantile regression and (iii) change-point models at an hourly timescale. Secondly, the presence of super-CC was evaluated for storm events instead of fixed hourly intervals. For that, data from Germany at 10-min resolution was used. The last step involved the analysis of scaling rates for a dense network of newly available rain stations in 4 countries in sub-Saharan Africa: Ghana,Kenya, Tanzania and Uganda. The results from measuring the scaling of extreme precipitation with DPT shows that two regimes arise for Germany: for DPT below 13ºC- 15ºC the scaling rates are close to the CC rate across all quantiles. For warmer DPT of 15ºC or more, scaling rates above the CC are observed, with increasing slope for the highest quantiles. The study of storm events shows that, for mean intensity and peak rainfall intensity, there is a strong sensitivity to DPT, with higher DPT associated with higher rainfall. The analysis of storm duration indicates that shorter events are more frequent for larger temperatures than for shorter ones. The study in sub-Saharan countries shows consistent negative scaling rates with dew point temperature. In addition, for large surface air temperatures (SAT), there is a decrease in relative humidity, suggesting that there is a general lack of moisture availability at the higher SAT ranges. To better understand the physical processes that influence these results, the use of alternative predictors that describe the atmospheric moisture are proposed. In a future warming world, current literature indicates that the greatest rainfall increases will be more visible for short-duration storms rather than increases in annual mean precipitation. As such, within this project, a novel analysis on scaling rates for storm properties shows the potential of these approach to obtain detailed information on the storm properties and their relationship with temperature, which could be used in storm simulations and projects characterising current or future climate.

Humans have always populated in the vicinity of river systems, where thesupply of water, nourishment and transportation is obtained from the river.However, inundation is a re-occurring problem and impact of floods are ex-pected to increase due to climate change. Accurate flood forecasting andearly warning is critical for disaster risk management. Tackling the problemof forecasting, in data scarce environments, has become increasingly impor-tant due to the changing climate. Remotely sensed river monitoring can bean effective, systematic and time-efficient technique to monitor and forecastextreme floods. Conventional flood forecasting systems require extensivedata inputs and software to model floods. Moreover, most models rely ondischarge data, which is not always available and is less accurate in a over-bank flow situations. There is a need for an alternative method which de-tects riverine inundation, using open-source data and software. This thesisaims to research the use of passive microwave radiometry for the detection,classification and forecasting of inundation.Brightness temperatures are extracted from the passive microwave radiom-etry and are converted in a discharge estimator: the C/M-ratio. Surfacewater has a low emission, thus let the C/M-ratio increase as the surfacewater percentage in the pixel increases. Sharp increases are observed forover-bank flow conditions. The research combines the identification of in-undation with a probability analysis via a quantile regressional fit. Floodforecasts can be obtained from an upstream catchment area. In the mostideal situation with a delay of2,5hours. This allows for probabilistic earlywarning decision making, with a lead time up to14days. (location specific)Strong Spearmans correlation coefficients between the discharge and C/M-ratio are found (>0.883). Allowing the model to forecast floods as gaugeddischarge records do. The model used has a comparable skill to the localGloFas forecast. This research investigated the impact the remote sensedtechnology could have on the flood forecast, response and warning system.An added model to an Early Action Protocol has the ability to lower uncer-tainty within decision making and enlarges the intervention window. Theadvice is to use such a model in combination with other forecasting modelssuch as GloFas.The challenge using this technology is the integration of hydrological com-plexity. The method allows for automated, global-covered creation of gridbased flood forecasts, independent to cloud coverage. Creating low spatialresolution flood forecasts combined with a probability bound in hours aftersatellite detection. The method has a high potential for data scarce flood-prone river basins around the world. The future for this technology lies inthe global daily availability of the data. With satellite sensors improving,spatial resolution is expected to increase. Allowing for even better floodforecasting ability.

Climate models have difficulties in predicting the frequency and intensity of future droughts on regional scales, possibly caused by inadequate representation of land surface hydrological processes. Vegetation is controlling the Earth's water and energy balance by transporting water from the subsurface to the atmosphere, through its roots. The water storage capacity in the vegetation's rootzone is a key parameter in predicting evaporation fluxes in land surface models because its size determines how long into the dry season vegetation is able to evaporate. Ecosystems design the size of their rootzone water storage reservoir to optimally function and to overcome dry periods, based on climatic conditions. Whereas climate is the major driver of root development, the rootzone storage capacity in the HTESSEL land surface scheme is only dependent on soil type and modelled soil depth. Moreover, the model describes root parameters by tables based on scarce observations of individual plants that do not represent ecosystem scales. This research aims to analyse the effect of the climate-based mass balance method for estimating the maximum water storage capacity in the vegetation's rootzone on the representation of water and energy fluxes in the HTESSEL land surface model. Maximum rootzone water storage capacities are estimated for 15 river catchments in Australia based on catchment-scale water balances. These estimates are implemented in the current surface parameterisation of the HTESSEL land surface model and offline simulations are performed. The current model performance and the model performance with adapted rootzone water storage capacities are evaluated regarding simulation of water and energy fluxes. According to this study, the storage capacity in the vegetation’s rootzone represented in HTESSEL is larger than the mass-balance derived estimates. The model strongly overestimates evaporation fluxes and thereby underestimates river discharge, with larger relative simulation errors in the dry season than in the wet season. The climate-based mass balance total rootzone water storage capacities have small effects on the representation of water and energy fluxes by the model, but contribute to a consistent improvement in predicting these fluxes. Nash Sutcliffe Efficiencies of the modelled river flows improve on average from 0.44 in the base model to 0.51 in the model with mass balance rootzone water storage capacities. The results indicate that the inadequate rootzone representation is a source of modelling error. However, it is expected that other hydrological process are also inadequately represented by the model, as the modelling simulation errors remain large when implementing mass balance rootzone water storage capacities. Moreover, it was found that internal vegetation dependent model parameters strongly influence the simulated fluxes and could therefore be another source of model bias. This study shows that investigating uncertainties in the representation of the rootzone in the HTESSEL land surface model is paramount. More research on the representation of hydrological processes in land surface models could lead to significant improvements in climate model predictions.

Climate change causes temperatures to rise worldwide. Up until now it is unclear what effect this has on the global water cycle. In this study the output of two GFDL model experiments were thoroughly investigated and used in the moisture tracking model WAM-2layers, accounting for model runs in a past case in the end-20th century and future case in the end-21st century, based on RCP8.5. This is done in order to acquire knowledge on what will happen to the global as well as regional water cycle in the future and to find out what processes provide climate change to have an effect on the water cycle. Changes in precipitation and evaporation rates are spatially highly dissimilar, therefore regional differences can be distinguished. Past studies have suggested that a DDWW paradigm - where dry regions dry out further and wet regions get wetter - will take place with climate change. Even though in some regions this might happen, results show that on land this is not necessarily the case - at least, if precipitation rate is the benchmark for a region to get drier or wetter. Processed GFDL data concludes that dry region Western Sahara gets wetter while dry region Middle East gets drier and wet region Indonesia gets wetter while wet region Amazonia gets drier. The DDWW paradigm covers another aspect as well, that there will be a larger spatial variability for variables characterising a region to be wet or dry. However, while everywhere on the globe the temperatures will rise according to the GFDL data, the spread of the yearly mean temperatures over the globe will actually decrease, meaning that mean yearly temperature in the coldest place will lie closer to the mean yearly temperature in the warmest place. This is also the case for mean yearly precipitation and evaporation rates. The mean yearly precipitation and evaporation rate in the driest place will lie closer to the mean yearly precipitation and evaporation rate in the wettest place in the future case. Continents show divergent effects in water cycle due to climate change. The continents with relatively larger sources of terrestrial moisture - North America, Europe and Asia - have an increased water cycle with higher precipitation and evaporation rates in the future. The increases in precipitation rates on these continents will originate from terrestrial evaporation and a higher percentage of evaporation will return as precipitation on land. South America shows a distinct effect, different than all other continents. This continent shows a decreased water cycle with lower precipitation and evaporation rates in the future. Of this precipitation a lower percentage comes from land and the evaporated moisture will return less on land. Africa and Oceania show another pattern. Both these continents will experience more precipitation in the future case, but this moisture will come from oceanic evaporation. Two case study areas are examined in more depth by looking at seasonality effects. A case study in the Amazon forest shows a distinct dry season in the future case, the effect of possible land use change in the Amazon forest. Less evaporation that is an effect of deforestation of the rain forest gives less moisture for clouds to form and precipitate. Moisture evaporates from the oceans on the East of Amazon region but moves over the region before it can precipitate. A case study in Western Africa shows a magnified rain season. The analyses show that during the wet season in the future case there in an increased amount of moisture coming from the oceans West and South of Western Africa and meanwhile there was still moisture coming in from the tropical rain forest East of the area.

This thesis describes the approaches applied to attempt to solve the numerical problems of the regional atmospheric model incorporated in the coupled modelling of regional water balance and anthropogenic land cover change in Amazon basin. For computational efficiency, the previous atmospheric model is evaluated at monthly scale. In order to cope with the numerical instability, nearest neighbouring averaging interpolation is iteratively performed to smooth the solutions as a transitional approach. Therefore a subsequent study is conducted to investigate the origin of the numerical instability and whether there are feasible measures to fix the numerical problem of the modelling. Chapter 1 serves as an introduction, which briefly introduces the background and research question — Whether there is any possible remedy that can solve the numerical instability of the monthly-timestep regional water balance model and obtain convergent solutions? Chapter 2 contains 7 sections, each of which gives statement of the specific problem, the experimental method applied, the corresponding results and related discussions. It has been concluded from the series of experiments that — a. adding diffusion terms makes no sense; b. applying smaller fractions of wind helps alleviate instability but the applied monthly timestep length seems to make the model paradoxic and inherently not convergent; c. instability is not really relevant with the iterative method; d. after correcting a dormant error in previous research, the results gets no better; e. the model may be so oversimplified that cannot reflect the reality; f . wind and monstrous timestep length are the keys to the problem, especially the latter is more problematic. Chapter 3 summarizes the discussions and conclusions from chapter 2 and proposes several recommendations for future research such as trade-offs between model complexity and efficiency, a heuristic way of making wind endogenous and reconsideration of the model architecture.