The applicability of the FLEX-Topo and FIESTA models in simulating the impacts of land cover change on the streamflow dynamics in the tropical montane cloud forest of the Mestelá catchment, Alta Verapaz, Guatemala was investigated. Understanding these impacts is crucial for effective water resource management in regions vulnerable to deforestation, where cloud forests play a critical role in regulating hydrological processes, and in areas prone to flooding and droughts.

The FIESTA model was integrated to generate spatially distributed meteorological inputs, tailored to unique spatial characteristics of the tropical montane cloud forest region. These inputs informed the FLEX-Topo model, which was adapted to conceptualize the dominant hydrological processes in the study area for simulating streamflow in the Mestelá catchment. The model was calibrated to represent distinct land use classes within the catchment using multi-criteria calibration with different objective functions and constraints to account for parameter uncertainty. Scenario-based simulations, including deforestation, reforestation and the conversion of agricultural land to pine plantations, were conducted to quantify their effects on streamflow dynamics, water balance components and extreme hydrological
events.

The integration of the FIESTA model provided spatially distributed inputs, however further evaluation is needed to assess the accuracy of this distribution. Its spatial variability enabled the inclusion of fog interception into the water balance, representing a key hydrological process in cloud forests. Calibration of the FLEX-Topo model was achieved by optimizing parameters for distinct land use classes and using dynamic land use fractions. Scenario analysis revealed that deforestation potentially increased peak flows by 18.5% (±1.4%), while restoring forest cover reduced extreme flows by 39.5% (±1.9%), highlighting the role of reforestation in flood mitigation. Replacing agriculture with pine trees on steep slopes also reduced extreme flows, while additionally addressing landslide risks.

The combined application of the FLEX-Topo and FIESTA models offers valuable insights into hydrological responses to land use changes, particularly in cloud forest regions, highlighting their potential for informing policy decisions related to land conservation and water management in tropical montane cloud forests.

The natural flows of rivers have been heavily modified in the process of development and economic growth across the basins of the world. This hydrological change affects river ecosystems and their provision of diverse services to society, threatening biodiversity and sustainable basin development. Reservoir construction is one major human activity causing such modification to the river landscape that has largely affected delivery of ecosystem services. This study is motivated to understand how varying patterns of dam development in a basin affect the river flow regime, differentially impacting the provision of river ecosystem services. Focusing on the Upper Cauvery basin in India, the study has developed an integrated modeling analysis examining the ecological and economic impact of varying levels of dam development....@en

Sustainable and resilient agriculture is essential for global food security, especially in the Majority World, where agriculture is vital not only for food security but also for income security, employing a large portion of the population. Given agriculture's dependence on water and its vulnerability to weather extremes, which are further exacerbated by climate change, climate adaptation has become increasingly important. With climate change primarily affecting agriculture through the intensification of the water cycle, agricultural water management (AWM) interventions hold significant potential....@en

This research studies groundwater sustainability in the Ramganga river basin of northern India. This region experiences a trifecta of hydrological stressors from groundwater over-extraction, frequent flooding during wet seasons, and agricultural droughts during dry seasons. There is a growing body of interventions known as Managed Aquifer Recharge (MAR) which attempts to co-manage these three concerns. One such example is a technology known as Underground Transfer of Floods for Irrigation (UTFI) proposed by the International Water Management Institute (IWMI.) The most common mode of UTFI is recharge ponds and IWMI has technically proven its validity along with extensive piloting work in rural regions of the upper Ramganga basin. When it comes to groundwater planning, what is missing is a holistic approach that encompasses rural and urban (R & U) to study their collective demand for groundwater and plan for implementation of recharge structures; thereby ensuring better groundwater sustainability. Considering this, this thesis analyzes opportunities and barriers for UTFI’s scale-up in growing rural-urban regions of the Ramganga basin by unpacking rural-urban linkages. It proposes a holistic R+U approach for land-use planning to incorporate recharge infrastructures and in so doing, identify rural & urban implementation zones like existing ponds and parks for mixed interventions. This work adopts mixed-methods of qualitative and quantitative to conduct desk research and fieldwork, backed by relevant academic theories. The thesis culminates in land-use planning recommendations for the rural and urban to cohesively take steps towards groundwater sustainability and hydrological disaster resilience within a chosen study region. These recommendations are useful for planners and policy makers in the field, along with specific spatial, community, institutional and planning strategy aimed for IWMI’s use.

India, a predominantly rural country, relies on agriculture, with smallholder farmers owning a small portion of cultivable land. Maharashtra, a major cotton-growing region, faces water scarcity and drought events, leading to low crop yields and farmer indebtedness, followed by high suicide rates. Micro irrigation, such as drip and sprinkler systems, can improve water use productivity and mitigate the impacts of climate change on farmers’ incomes and thus livelihoods.

Psychosocial factors play a crucial role in farmers’ decisions to adopt irrigation technologies. Incorporating both contextual and psychosocial factors provides deeper insights into the adoption processes of various irrigation systems. The RANAS model has been recognized for behavior change strategies in developing countries and was applied in this research. It integrates psychosocial factors and is used to examine the impact of psychosocial factors on behavioral outcomes.

Regression analysis was performed on survey data in order to identify the factors that are assumed to have the ability to explain irrigation and micro irrigation adoption behavior. Four models were created, each focusing on a specific irrigation system: overall uptake, furrow irrigation, sprinkler irrigation, and drip irrigation. This approach allowed for a deeper understanding of the influential factors for different irrigation systems and avoided the generalization of the results. In an attempt to broaden the understanding that can be obtained from regression results, qualitative, open-ended field interviews were conducted with farmers and key informants. The field insights revealed how the local context dynamics influence the factors that have a predictive capability on the adoption of micro irrigation systems.

Farmers’ adoption of drip irrigation systems is influenced primarily by their confidence in financial capabilities and technical skills. However, the adoption of sprinkler irrigation systems is more complex. In addition to financial and technical considerations, farmers also consider factors such as easy access to water sources, concerns about future water availability, and descriptive norms, i.e. the actions other farmers take. This indicates that the barrier to adopting drip systems is mainly financial, while the adoption of sprinkler systems also involves to a certain extent normative influence and water-related concerns.

The RANAS method is effective in identifying predictive variables for micro irrigation adoption by breaking down the complex problem into manageable components. However, it has limitations. It overlooks the dynamic nature of the adoption process and fails to consider the significance of factors at different stages of behavior change. It also underestimates the role of economic and institutional constraints, which can influence farmers’ investment capacities and perceptions. Additionally, the methods reliance on the design of survey questions may introduce bias and affect the reliability of the interpreted empirical data.

Field insights highlight the significance of context-specific factors and the integration of economic capacities in farmers' adoption decisions. Economic stability is crucial for implementing micro irrigation systems, and financially vulnerable farmers may be risk-averse toward new technology. Community support and reduced risk perception can facilitate adoption. Examining profit margins and market prices provides a better understanding of adoption than just income. Considering practical aspects such as crop suitability and awareness of climate change and market dynamics further explain adoption choices. Trust-building is essential to enhance farmers' willingness to adopt irrigation systems.

This project is a consulting project for Community Cloud Forest Conservation (CCFC) on how to obtain and communicate to relevant stakeholders an understanding of the impact of land use change and climate change on the hydrological balance of the cloud forest ecosystem in the Sierra Yalijux. The outcomes of the project will be used by CCFC and partners in four areas: Rural water committee capacity building with municipal and village leadership groups, environmental education with the ministry of education, reforestation, and conservation carbon/water credit prioritization with the national forestry institute, and to create thesis topics for bachelors level students with Universidad Rafael Landívar and Universidad de San Carlos. In order to achieve this goal, we divided our efforts in four areas: First, a description of the situation and a review of literature to identify gaps in scientific and practical understanding of local cloud forest hydrology (Chapter 2). Second, an analysis of the situation at a regional scale using publicly available historical data such as remote sensing data and data from the national meteorological authority (Chapter 3). Third, identifying important hydrological processes in the Cloud Forest micro-climate (Chapter 4) and prototyping and testing measurement setups (Chapter 5). Fourth, making suggestions on how to apply the results to the intended impact areas that CCFC has (Chapter 6). Our recommendations to CCFC for capacity building with water committees are based on a literature re view, we found that the presence of Cloud Forest is expected to increase base flow in springs due to its ability to capture additional hydrological inputs in the dry season, increase moisture recycling after heavy rain events, and store water in the soil. We recommend working with water committees to outline the recharge zones of their springs, run some simple calculations on water availability based on precipitation, and develop manage ment plans for the area. Our recommendations for further research are based on the research approaches we describe at the regional scale and the prototyping of field methodologies that we tested. A more permanent setup for data collection is being developed jointly with the Universidad de San Carlos at CCFC’s nature preserve.

The research question addressed in this study is ”To what extent does the presence of a cloud forest canopy impact the Mestelá River catchment and how will this affect the various involved stakeholders?”. The study aims to investigate the importance of cloud forests in the Mestelá River catchment, Alta Verapaz, Guatemala, related to water security and the social impact of cloud forest conservation and management. The research methods used in this study were a combination of quantitative and qualitative methods.

Cloud forests play a vital role in regulating water flow in catchments. The Mestelá River catchment, where the NGO Community Cloud Forest Conservation (CCFC) is situated, is the focus of this research. The project’s primary aim was to establish a long-term canopy setup, ensuring future data collection. The project’s scope encompasses a range of methodologies, including the installation of a long-term measurement station in the canopy, computation of the Mestelá River discharge, the development of a rating curve, and the utilisation of a FLEX-Topo model to simulate the hydrological cycle in the catchment. Additionally, a stakeholder management analysis was conducted to understand the complex impact of cloud forests (conservation) on various stakeholders.

The study did not explicitly formulate any hypotheses, but the findings provide evidence for the impact of cloud forest canopies on river catchments and discharge. The study also has limitations, including the small sample size and the lack of long-term data. However, the study provides valuable insights into the importance of cloud forest ecosystems for water security and the social impact of cloud forest conservation and management. The stakeholder analysis reveals that for CCFC two methods of advocacy can be used. Whilst the CCFC is effective in bottom-up engagement with the community, in addition, a strip for small children was constructed. For top-down advocacy, using the FLEX-Topo
model for visualising water security in combination with cloud forest protection holds promise.

The implications of this work are substantial for cloud forest conservation and associated ecosystems. The findings offer valuable insights for developing effective conservation strategies that consider the canopy’s impact on the catchment and its stakeholders. It is important to note that the FLEX-Topo model is currently conceptual and requires further refinement and detail for the Mestelá River catchment. Nevertheless, this study contributes significantly to the understanding of cloud forest ecosystems and offers practical and theoretical applications for future research and conservation efforts.

This research is part of the project "Water Efficiency in Sustainable Cotton-based Production Systems” between Solidaridad Asia and TU Delft. The project aims to increase the livelihood of smallholder farmers in the Maharashtra, India through. A socio-hydrological (SH) model is used extensively in this research as an evaluation tool. However, the baseline research indicates that the lack of stress mechanics in the SH model used in the intervention might cause inaccuracies in yield estimation. Furthermore, it has never been validated at a farmer level before. This research aims to implement the stress mechanics in the SH model, evaluate the overall performance of the model in terms of predicting crop yield, identify potential sources of errors, and give recommendations for future studies. The research uses iterative top-down approach due to the large study area and the varied nature of the 308 farmers surveyed. The research implements the water and temperature stress mechanics based on Food and Agriculture Organization's (FAO) AquaCrop framework. For the performance evaluation process, this research uses four model scores namely Nash-Sutcliffe (NS), log of NS, Mean Absolute Error (MAE), and the coefficient of determination. Furthermore, the sources of uncertainties are divided into two categories namely lack of knowledge (i.e. generated by parameter, input, observation, and structural errors) and variability (i.e. generated by climate variations). The lack of knowledge uncertainty is investigated using Monte Carlo Sampling calibration and the Generalized Likelihood Uncertainty Estimation (GLUE) concept is used to obtain the uncertainty intervals of the model. Going further, the errors are divided into residual and structural error. The latter is explained and quantified through a structural error model using a combination of qualitative analysis, Principal Component Analysis, multiple linear regressions, and projection of the data into kernel space. Then residuals between the model yield + structural error vs. the observed yield is thought to be explained by the residual errors. Lastly, the effects of climate variations to the stability of the model are evaluated.
After the initial calibration, the model scores are NS: -0.343 to -0.996, log of NS: -0.655 to -1.91, MAE: 447.1 to 553.2 kg/ha, and r-squared: 0.003 to 0.008. Because of the poor performance of the model, the uncertainty intervals from GLUE are not enough to capture the total errors of the model. However, after adjustment using the structural error model, the model scores become NS: 0.83, log of NS: 0.56, MAE: 149 kg/ha, and r-squared: 0.859. The adjusted yield calculation has a residual error as Gaussian distribution with standard deviation of 150 kg/ha. The qualitative analysis identified several factors that contribute to the errors viz. farmers' capital, irrigation behavior, and crop production process such as canopy cover growth. Lastly, there is no major instability found through the bootstrap analysis. The physical model is not performing well, especially when it is calculating yield for individual farmers over a large study area. However, the structural error model can adjust the yield prediction so that it is close to the observed yields. This indicates the poor performance is likely to be caused by the prevalence of structural errors in the model instead of the uncertainties regarding parameters, input, or observation values. Therefore, it is recommended for future research to address this first. This can be done by further study and incorporation of more crop production processes, soil water simulation, and exploratory interviews to identify patterns and more factors that can influence the errors.

It has been determined that the plastic waste load in the Petanu river (Bali, Indonesia) can be as high as 2015.5 kg/day in the beginning of the rainy season. To restore the ecosystem and protect human livelihood this load should be reduced drastically. The enormous pollution rate is largely due to the massive amounts of mismanaged (plastic) waste at household level. This is the part of the waste that is either burned or dumped by the households, rather than collected or brought to a recycling facility. Extensive mismanagement of waste is an indication of an inadequate functioning waste management system. In Bali, and this watershed specifically, the existing system is decentralised and its waste management strategies rely heavily upon public participation. As a result, in some areas households have limited options regarding waste handling due to a lack of a collection system and other waste services. At the same time, households need to pay for those services. In order to improve the current waste management, it is therefore important to understand the trade-off made by the households between the environmental impact of plastic waste and cost of the waste management system.

In this report, this trade-off is studied by creating a real-world, coupled economic-environmentalmodel, of the Petanu’s watershed in the Gianyar regency. The model consists of two parts: 1) a production possibility frontier (PPF) and 2) a utility curve. The PPF is an arc curve that visualises the relation between the plastic waste load originating from households and the average impact on the monthly purchasing power of a household. The purchasing power is impacted since users of the waste management system need to pay for the services. The utility curve, on its turn, visualises a households trade-off between plastic waste load and impact on monthly purchasing power. Coupling these curves gives insights in how well the current waste management system satisfies the preference of the households regarding cost versus pollution rate. If the current waste management matches perfectly with these preferences, the point of tangency of both curves would be the location on the curve representing the current plasticwaste load and impact on the purchasing power. If the point of tangency has a lower plastic waste load and hence higher impact on purchasing power, households are willing to invest more in their waste management to improve the environmental quality of the Petanu river.

To gain insight in the trade-offs people make regarding four disposal methods (Self-Service, Pick-Up, Burn and Dump), cost and time, a stated choice experiment was set up. This is done by means of a questionnaire in which respondents were asked to make choices between hypothetical choice situations. The choice sets consist of different combinations of the disposal methods with varying attribute levels for cost and time. The survey has been conducted under 300 respondents from six different villages, located in the watershed of the Petanu river. With the help of data analysing program Biogeme, an open source Python package, the stated choice data has been transformed into the utility curve. Besides a general utility curve for the whole population living in the watershed of the Petanu river, also utility curves have been established for certain segments of the population, based on age, educational level and currently used disposal method. Hence, the questionnaire contained also questions about socio-demographic characteristics, the currently used disposal method and the corresponding cost. The latter two are not only used for the segmented utility curve but also for the creation of the PPF. The PPF is built-up by defining five different scenarios, i.e. the current situation, three scenarios with an improved waste management system and a scenario without a waste management system. The scenarios with an improved system have an increasedwaste collection rate or additional locationswhere recyclables can be handed in, in exchange for money. For all scenarios, the corresponding average cost per household and the plastic waste load in the Petanu river have been calculated. The quadratic best fit to these data points gives the PPF.

By combining the PPF with the general utility curve it was found that an average household in the watershed of the Petanu is willing to pay more for the waste management system than they are currently doing in order to decrease the plastic waste load in the Petanu river. It was even found that households want to achieve very low plastic waste load values and are willing to invest 47,400 IDR per month to reduce their contribution to plastic waste load by 100 grams per day. An exact equilibrium point is however not found as the PPF entails to many uncertainties at very low plastic waste load values. Nevertheless, the model gives a good indication and it is therefore recommended to increase the current collection rate of household waste. Furthermore, it is recommended to invest in TPS3R facilities in which waste is sorted and send to recycling facilities. Moreover, the combination of the PPF with the segmented utility curves gave some very interesting
insides. First of all, the younger generation (· 31 years old) has a higher willingness to pay for the reduction of plastic waste load, than the older generation. This is likely the result of the awareness programs on waste management at schools. Hence, it is recommended to expand this educational program. Secondly, educational level is positively correlated with willingness to pay. Remarkably, income level and gender did not have a significant relation with willingness to pay. Lastly, time is a significant determinant for the choice of disposal method, which corresponds to a certain plastic waste load. Therefore, it is recommended that waste management at household level should be as time-efficient as possible. This means the collection should be as much as possible be done at the doorstep of the houses and recycling bins should be located close-by.

All in all, it has been demonstrated that the households in the watershed of the Petanu river are very willing to pay for the waste management services, however, current waste management options are too limited. It is now the task of the governmental institutions and community leaders to enable collection of waste for every household in every village.

The Climate Risk Informed Decision Analysis (CRIDA) framework incorporates the uncertainties of climate change that impact project planning, socioeconomic justification, and engineering design into a step-wise and collaborative planning process to guide a technical analyst to low-regret risk- and cost-effective solutions; Research has been carried out to demonstrate and improve, through additional guidelines, the usability of CRIDA, in a pilot for the Limari basin in Chile. The added guidelines (1) offer the analyst numerically based justifications for analytical decisions to ensure a more structured application of CRIDA and (2) improves on co-design aspects by incorporating stakeholder risk perceptions and opinions explicitly in the process. The Limari Basin has experienced an increase in drought frequency and severity over the last decades. A strategic approach for adaptation is recommended through CRIDA based on an evaluation of the future risk to climate change and the confidence in this analysis and a subsequent systematic assessments of adaptation options. The resulting strategy requires the increase of water supply robustness by adding new water sources that can be implemented in combination with flexible measures for managing demand (i.e. implementing agricultural meshes and improving irrigation efficiency) in parallel or in series to create adaptation pathways. The study demonstrated the functionality of CRIDA. While the added guidelines required more processing time, subjectivity in the method is reduced thus also reducing possible bias introduced by the analyst. In addition, overall acceptability of the proposed strategies is improved by incorporating stakeholder risk perceptions and opinions explicitly in the process.

All ecosystem services of river are governed by flow regime. The different components of flow regime like topography, geology, and climatic variation contribute to different ecological processes. These ecosystems provide a wide range of direct and indirect benefits to a human being. The ecosystem services such as hydropower, irrigation, drinking water supply, fisheries, breeding ground for aquatic wild life, sanctuary reserves form the lifeline of many stakeholders. All the water management decisions are based on certain hydrological informations. However, the hydrological information these decisions are based on are not precise and is a simplified version of the actual system. Anthropogenic effects of human intervention on natural flow regime significantly disturbs the natural characteristics of the hydrological flow. Hydrological alterations of river flow regime while benefitting human development in many ways have damaged the delivery of ecosystem services. Growth in human population, decline in resources, ever changing climatic conditions, led to an increase in demand of the resources.The situation is further complicated by multitude of water users and stakeholders with conflicting interests this requires the accurate assessment of the river flow regime taking into account the anthropogenic effects of human intervention in the riverine ecosystem. Reservoir construction is one of the major human intervention to the riverine ecosystem. Construction of reservoirs are usually associated with substantial impacts on river hydrology and surrounding ecosystem. As a result, dams have been constructed to meet the needs of irrigation and energy. These constructions affect the connectivity of the river and eventually changes the entire ecosystem. Topography based hydrological models simulates the response of the catchment due to the different hydrological response units(HRUs). These HRUs are natural and doesn’t take into account anthropogenic factors into account. Therefore, the present study aims to understand the effects of the integration of reservoirs into a Flex-Topo model to assess the model performance.

For cotton cultivation, large amounts of water are necessary. One of the countries facing water challenges related to cotton cultivation is India. India experiences a monsoon weather pattern causing large amount of rain from June to October and no rain during the other months. One of the challenges India’s small holder cot- ton farmers face is the lack of water after the monsoon. A solution to this problem could be the construction of (small) reservoir structures and renovation of currently present structures. One of the main questions arising here is ‘how can a hydrological model estimate new reservoir locations for small holder cotton farmers and what is the potential of these new structures and current structures in Maharashtra India with Ghatanji block and Hinganghat block as case study areas?’. Estimating potential reservoir locations as well as determining which current reservoir structures have the potential to be renewed could increase the water availability for small holder cotton farmers in Maharashtra, India.
Four focus areas are chosen, these areas are Yavatmal, Nagpur, Wardha and Amravati. Within these fo- cus areas, Ghatanji block and Hinganghat block are used as case study areas for developing and validating different models used for analysing current and suitable potential locations.Remote data based tools used to locate, validate and analyse the potential of current and potential suitable structures are QSWAT (hydrological model), Google Earth Engine (land cover analysis), Python coding, Zoom.Earth (locating current structures), socio-hydrological model (yield and benefit analysis).
Out of the initial 692 potential locations, 315 locations in Ghatanji block and 349 locations in Hinganghat block are classified as suitable potential reservoir locations. A location was marked suitable if a minimum size (200m3) reservoir on this position has the ability to fill up completely at least once a year for an average monsoon season. Additionally, the structure was positioned in a location with more than 30% crop coverage and less than 25% urban coverage. Out of a total of 2212 current structures, 840 locations in Hinganghat and 970 locations in Ghatanji have a high potential of collecting water if renovated and in use. This high potential is a result of small distance to the nearest stream and suitable land cover on the position of the reservoir. Existing structures were classified as suitable if they adhere the same prerequisites as stated above regarding the coverage and for the additional requirement that a structure should be located less than 200 meters from the nearest stream point.
The results of this investigation show that a hydrological model can estimate new potential reservoir locations and that the use of a hydrological model is beneficial for locating these potential locations when com- pared to current or recently constructed locations. The benefit of using a hydrological model is that it can identify the surface runoff paths of water during and after a precipitation event. Furthermore, can remote data based tools help in identifying the suitability of both potential as well as current structures.

This report represents the baseline study of the Project "Cotton Water", a collaboration between Solidaridad Asia, TU Delft and other participating instututions to improve the livelihood of cotton farmers in the Vidarbha and Marathwada regions of Maharashtra, India. This baseline study was divided in three main steps: a desktop study, where high resolution maps of precipitation, potential evaporation, soil type and landuse were used in conjunction with a smallholder socio-hydrological model to identify 'hotspots' where farmers' capital falls below poverty lines; a field survey, in which farmers were extensively questioned on their financial situation and farming practices as well as their perception of water scarcity and irrigation schemes; and a final synthesis where interventions are analysed with the smallholder socio-hydrological model and a psycho-social analysis of farmer behaviour is delivered alongside a mapping of current water productivity of cotton in the study area. The main results found are that the proposed water harvesting and recharge interventions increase and stabilize yields, and the overall effect on capital are moderate. Other factors that do not impact water availability including fertilizer and labour were found to have notable impacts and should be well understood to accurately improve farmers' situations. Financial aspects including cotton sale prices and loan interest rates had strong impacts on farmers' capital development as well, particularly with high interest rates punishing some farmers. An analysis of good- and poor-performing farmers demonstrated that irrigation in general and micro-irrigation did improve probability of good farmer performance as did increased yields. Older men also showed higher rates of profit, demonstrating that the impact of experience may increase profit margins, even if it necessarily doesn't increase likelihood to adopt interventions. What was found to increase probability of adopting irrigation and irrigation technology was low promotion exposure. It is hypothesized that increased promotion may influence many farmers negatively, fostering an attitude of despair rather than informing them of opportunity. The psycho-social evaluation also found that solutions that are reasonably expensive but not too costly have higher chances of being adopted.
Four main recommendations were made to help improve farmer welfare with respect to the scope specified. It was recommended to: limit promotion and to be more selective and positive with the message; focus on localized water storage interventions to increase farmers' access to water; regulate cotton prices through government intervention or contracts with clothing companies to decrease vulnerability to price changes; and improve access to loans from the government and reduce the role of money lenders who often are the ones charging the greatest interest rates.

Fresh water resources in coastal areas are under intense pressure from groundwater consuming activities, sea level rise and extreme weather events, which effectively amplify salt water intrusion (SWI) into coastal freshwater aquifers. Literature on SWI describe the effects of SWI, model SWI scenarios and explain possible SWI mitigation strategies. To our knowledge there are, however, no studies that combine socioeconomic data with groundwater simulations to estimate SWI mitigation prevalence within an area. In this study, we demonstrate how a socioeconomic model can be combined with groundwater simulations to explore the possible role of behaviour willingness in finding suitable SWI adaptation/ mitigation strategies. The coastal province of Tra Vinh, located in the populated Vietnamese Mekong Delta, is an area affected by SWI and was used as case study area. Data collected from 313 households spread over nine communes, within the province, was used to construct a Bayesian Belief Network (BBN) model. The model integrates both socioeconomic (e.g. education, age) and psychosocial (behavioural) characteristics, to predict the willingness to change of the population; willingness to change from groundwater to a different source of water. The validated BBN model showed good model performances. The research found amongst others, using three model analysis, how the BBN model could be used on both regional (province) and local (commune) scale. The two different scales can help in defining both target groups and suitable locations for SWI mitigation/ adaptation interventions. Moreover, this study did not focus on finding suitable alternative water sources, that can serve as a substitute for groundwater abstraction. Though, the model analysis can help to more effectively define the possibilities of alternative sources and/ or water-saving interventions.

Flooding is globally one of the most damaging natural hazard. Flood risk will most likely increase in the near future due to increases in flood frequency attributed to climate change and growth in population and wealth in flood prone areas. This growth in wealth and population is increasingly considered as a major driver for the increase in flood losses in the last decades. Floodplains are susceptible to floods, but historically people have always been settling in floodplains. The growth of population in floodplains, which is a substantial cause for increased flood risk, is essential to consider for decision making in floodplain development, as improper development increases flood exposure and aggravates flood risk. The science of socio-hydrology tries to capture the interaction between humans and floods in the floodplain. But, it is necessary to identify these mechanisms on a broader scale. A way of doing this, is to look at the development of floodplain population density over the years, but population data is not available on a long temporal scale. Therefore, Nighttime light data was used to model the gaps in the availability of population data. Nighttime light data data captures the illumination on earth, and is available on a large temporal and spatial scale, and has a high correlation with population data. However, the relationship between Nighttime light data and population data is not straightforward. This study tries to model a population proxy through the use of Nighttime light data data and explains when and why it does or does not work. Validation of the model shows that in some regions the predicted data is relatively precise, but ultimately, due to the lack of data, the accuracy is unknown. This study shows that understanding the behaviour of NTL is valuable, because it has the potential to map Socio-Economic variables in data-scarce areas.

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.

A water crisis is looming for Lebanon. Economic growth, natural population growth and the inflow of refugees from Syria cause an increased water demand in Lebanon. In the meanwhile, the precipitation rates of Lebanon are decreasing. An important water source of Lebanon is the Litani river, which is totally located within the borders of Lebanon. The water of this river is used for agricultural purposes, production of hydropower and supply of domestic water. At the moment, domestic water supply is only a small user of the water of the Litani river. However, water supply from the Litani river to the capital of Lebanon, Beirut is proposed. The Litani already nearly dries up each year and therefore the water allocation of the Litani river has to be optimised. This research aims to optimise the water allocation of Lebanon, in order to protect the country against the looming water crisis. Since the Litani is the most important river of Lebanon, the research focuses only on this basin: the Litani River Basin (LRB).
Optimal water allocation is achieved when the social benefits gained from the deviation of water are at a maximum. The social benefits are described by three principles: economic efficiency, social equity and sustainability. Since the Litani river is an important source for the Lebanese economy, the economic efficiency principle is assumed to be the most important principle in optimising the water allocation.This means that the optimal water allocation is achieved when the profits from the deviation of water are maximised. In order to optimise the water allocation in the LRB, the current water allocation is needed. In this research project, first the current water allocation will be described. Afterwards,
scenarios will be applied to see what the effect is on the current water allocation. The research ends with the implementation of measures in order to see their effect on the water allocation.
The current water allocation is based on hydrological data of the area and the assumption that in the current water allocation everyone tries to maximise the total income of the LRB.

The water of the Litani river is used by three different sectors: agriculture, energy and supply of domestic water. In most of these sectors, water does not directly generate a profit. For example the agricultural sector: yields are increased by the availability of water and these yields will be sold. Formulas of the Food and Agricultural Organisation (FAO) are used in order to determine the actual yields. The actual yield depends on reference evapotranspiration, actual evapotranspiration and crop characteristics. 훼 is added to the equation in order
to model the current situation. 훼 describes the current power of the agricultural sector. This value is based on the current amount of water flowing to this sector and the profits that are generated within this sector. The value of this objective function is limited by the hydrological conditions of the area. The current power of the agricultural sector is 0.445. Therefore, the power of the hydropower sector is 0.555. When water is used for the production of hydropower, it can be used for other purposes afterwards. A part of the water that is used for the production of hydropower is used for the supply of domestic water afterwards, therefore the domestic water sector is not included in the objective function.

In the current water allocation 71.7% of the water is used in the agricultural sector. The other 28.3% is used to produce hydropower. Of this water 23.4% is afterwards supplied as domestic water to the inhabitants of the LRB. The agricultural water use is mainly high in the subbasins with a high marginal
value. If one extra unit of water is flowing to these subbasins, the increase in the profits will be significant. The model is based on the assumption that all individuals try to maximise the total income of the Litani River Basin and therefore the water will be used in the subbasins with the highest marginal
value.

Two scenarios are applied to see the effect on the current water allocation: a water supply to Beirut and a flow to the downstream subbasins. Water supply to Beirut is a proposed solution in order to provide the inhabitants of Beirut with enough domestic water. The water that will be supplied to Beirut, first will be used to produce hydropower. Therefore, the minimum flow to the hydropower stations will be 93 million 푚3/year. In order to reach this amount of water, less water will be used for agriculture. This causes a decrease in the profits generated within the area. Furthermore, the Qaraoun Lake is over-exploited. In the end of the season in which hydropower is produced, the Qaraoun Lake is almost empty. In the first years, 93 million 푚3/year can be delivered to Beirut. However, the Lake has to be over-exploited to reach this amount and therefore the water could not be supplied in the last years.The demand of Beirut is too high to be supplied by the LRB.
The supply of water to the downstream basins will only take place during the dry months. In the current water allocation the water level is high during these months, since the water is not used for the production of hydropower in these months. During the dry period, the demand of the agriculture in the downstream subbasins is high. During these months 10% of the water stored in the Qaraoun Lake will flow downstream. The profit will remain more or less constant, however the flow to the hydropower stations will decrease. The amount flowing from the Qaraoun Lake to the downstream subbasins is small compared to the area of the downstream subbasins and therefore the effect will not be significant.

Measures are proposed to see how the water allocation could be optimised. Replacing water sensitive crops by water insensitive crops is the best way to optimise the water allocation within the LRB. There are two reasons why this measure is the most effective one. The first reason has to do with the yield
response factor. Water insensitive crops have a lower yield response factor and therefore the decrease in yield will be less significant when a water-deficit occurs. Furthermore, the water insensitive seasonal crops are groundnut and tobacco. These crops are profitable crops and this will positively affect the water allocation within the LRB as well. The other principles of optimising the water allocation are social equity and sustainability. It is difficult to give a conclusion about the sustainability, since the exact water levels in the Litani are unknown. However, the system is not sustainable when it has to supply water to Beirut. In order to reachthis demand, the water of the lake has to be over-exploited. A conclusion can be drawn about thesocial equity principle, agricultural water is available within the entire LRB and the agricultural profits generated per hectare are equally divided over the area. Since there is enough water available for the inhabitants of the LRB and agricultural water is available over the entire basin, the water allocation is optimal according to the social equity principle. However, when Beirut is included in this social equity principle, the water allocation will be optimal when water is supplied to Beirut as well.

Forests can influence local and regional weather and climate by various mechanisms. The importance of forests as a key regulator of moisture circulation has been shown by many studies. The Amazon rainforest is the lifeline for the conservation of the biodiversity of the region. However, since the last three decades, the deforestation trend in the region has increased. The local governments have passed legislation against illegal deforestation, specifically, the local governments of Mato Grasso state of Brazil have been the most vocal against the deforestation practices in its central and southern states. With cropping and animal grazing being one of the biggest reasons towards the clearing of the rainforest, often in response to growing global food demand, this research focused on a study to understand how the regional water balance and agricultural (soybean) induced land cover change in the region are interlinked. Soybean cultivation in the region is used as a proxy for the food demand in the water-food nexus.