In July 2021, extreme precipitation caused devastating flooding in Germany, Belgium and the Netherlands, particularly in the Geul River catchment. Such precipitation extremes had not been previously recorded and were not expected to occur in summer. This contributed to poor flood forecasting and, hence, extensive damage. Climate change was mentioned as a potential explanation for these unprecedented events. However, before such a statement can be made, we need a better understanding of the drivers of floods in the Geul and their long-Term variability, which are poorly understood and have not been recently examined. In this paper, we use an event-based approach to identify the dominant flood drivers in the Geul. We also employ (1) a multi-Temporal trend analysis to investigate their temporal variability and (2) a novel methodology to detect the dominant direction of any trend. Results suggest that extreme 24 h precipitation alone is typically insufficient to cause floods. The joint probability of extreme and prolonged rainfall combined with wet initial conditions (compound event) determines the chances of flooding. Flood-producing precipitation shows a consistent increase in the winter half-year, a period in which more than 70 % of extremely high flows have historically occurred. While no consistent trend patterns are evident in the majority of precipitation and extreme flow trends in the summer half-year, an increasing direction is visible in the recent past.

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In July 2021, extreme precipitation caused devastating flooding in Germany, Belgium and the Netherlands, particularly in the Geul River catchment. Such precipitation extremes had not been previously recorded and were not expected to occur in summer. This contributed to poor flood forecasting and, hence, extensive damage. Climate change was mentioned as a potential explanation for these unprecedented events. However, before such a statement can be made, we need a better understanding of the drivers of floods in the Geul and their long-term variability, which are poorly understood and have not been recently examined. In this paper, we use an event-based approach to identify the dominant flood drivers in the Geul. We also employ (1) a multi-temporal trend analysis to investigate their temporal variability and (2) a novel methodology to detect the dominant direction of any trend. Results suggest that extreme 24 h precipitation alone is typically insufficient to cause floods. The joint probability of extreme and prolonged rainfall combined with wet initial conditions (compound event) determines the chances of flooding. Flood-producing precipitation shows a consistent increase in the winter half-year, a period in which more than 70 % of extremely high flows have historically occurred. While no consistent trend patterns are evident in the majority of precipitation and extreme flow trends in the summer half-year, an increasing direction is visible in the recent past.@en

Water resources assessments are essential for effective planning in water-scarce regions such as Jordan. Such assessments require sufficient data in space and time. The WaPOR-based Water Accounting Plus (WA +) framework is relevant as it integrates remote sensing data and the Pixel-Based Soil Water Balance model to simulate a basin’s water balance. However, since it relies on remote sensing, this framework only tracks water consumption in irrigated agriculture and does not consider non-irrigation water use and its return flow. This paper modifies the WaPOR-based WA + framework to include non-irrigation manmade consumption and its return flows. The modified framework provides a more comprehensive water budget for the Amman-Zarqa (AZ) basin, presented in a modified WA + resource base sheet for 2018 through 2021. The results show that water availability in the AZ basin is highly responsive to precipitation changes. Average precipitation was approximately 926 Mm³/year between 2018 and 2020, corresponding to an average available water of 485 Mm³/year. However, a reduction in average precipitation by 28% in 2021 corresponded to a reduction in available water to 243 Mm³/year. Nevertheless, substantial groundwater outflows to neighbouring basins may indicate that available water is being overestimated. Manmade consumption increased by 18% from 2018 to 2021, and the total demand exceeded the available supply by 150%. This underscores the pressing need to investigate supply augmentation and conservation methods. Future studies could focus on improving the representation of groundwater dynamics in the modified framework by improving groundwater dynamics in PixSWAB and testing the modified framework with other remote sensing datasets.

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This research explores the limitations and opportunities of Water Accounting Plus (WA+) for addressing water management issues in the MENA, focusing on Jordan. A comprehensive literature review and interview-based analysis were conducted to identify prevalent water management issues and evaluate information used in decision-making and strategy appraisals. The findings suggest that WA+ can enhance the spatio-temporal coverage of water resource assessments, refine estimates of irrigation water consumption, and facilitate demand management. Quantifying recharge and surface runoff requires integrating WA+ with hydrological models. Addressing climate change’s impact on future water resources requires integrating climate change projections with WA+.

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Healthy urban greenery is a fundamental asset to mitigate climate change phenomena such as extreme heat and air pollution. However, urban trees are often affected by abiotic and biotic stressors that hamper their functionality, and whenever not timely managed, even their survival. While the current greenery inspection techniques can help in taking effective measures, they often require a high amount of human labor, making frequent assessments infeasible at city-wide scales. In this article, we present GreenScan, a ground-based sensing system designed to provide health assessments of urban trees at high spatio-temporal resolutions, with low costs. The system uses thermal and multispectral imaging sensors fused using a custom computer vision model to estimate two tree health indexes. The evaluation of the system was performed through data collection experiments in Cambridge, USA. Overall, this work illustrates a novel approach for autonomous mobile ground-based tree health monitoring on city-wide scales at high temporal resolutions with low costs.

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This project (poster) explores and maps transdisciplinary skills in the TU Delft curricula and challenge based education. Courses to address these skills have been identified by means of keyword search in the course descriptions. Interviews are used to explore the transdisciplinary approaches addressing reasons, values, learning activities, assessment and professionalisation. The exploration was initiated by a multidisciplinary group of educators from different TU Delft faculties. The initiators noticed that transdisciplinary skills are regularly part of a hidden curriculum, delicate to define or grasp, bear different names, are rarely made explicit or maybe even are considered a taboo. As such, the transdisciplinary skills remain unspeakable.

The exploration is made within the Technical University of Delft. It is to be expected that lessons learned will not be exclusive to this context and can be applied in other settings that aim for societal impact of science and education as well.@en

Monitoring safe water access in developing countries relies primarily on household health survey and census data. These surveys are often incomplete: they tend to focus on the primary water source only, are spatially coarse, and usually happen every 5-10 years, during which significant changes can happen in urbanisation and infrastructure provision, especially in sub Saharan Africa. In this work, we present a data-driven approach that utilises and compliments survey based data of water access, to provide context-specific and disaggregated monitoring. The level of access to improved water and sanitation has been shown to vary with geographical inequalities related to the availability of water resources and terrain, population density and socio-economic determinants such as income and education. We use such data and successfully predict the level of water access in areas for which data is lacking, providing spatially explicit and community level monitoring possibilities for mapping geographical inequalities in access. This is showcased by applying three machine learning models that use such geographical data to predict the number of presences of water access points of eight different access types across Uganda, with a 1km by 1km grid resolution. Two Multi-Layer-Perceptron (MLP) models and a Maximum Entropy (MaxEnt) model are developed and compared, where the former are shown to consistently outperform the latter. The best performing Neural Network model achieved a True Positive Rate of 0.89 and a False Positive Rate of 0.24, compared to 0.85 and 0.46 respectively for the MaxEnt model. The models improve on previous work on water point modeling through the use of neural networks, in addition to introducing the True Positive - and False Positive Rate as better evaluation metrics to also assess the MaxEnt model. We also present a scaling method to move from predicting only the relative probability of water point presences, to predicting the absolute number of presences. To challenge both the model results and the more standard health surveys, a new household level survey is carried out in Bushenyi, a mid-sized town in the South-West of Uganda, asking specifically about the multitude of water sources. On average Bushenyi households reported to use 1.9 water sources. The survey further showed that the actual presence of a source, does not always imply that it is used. Therefore it is no option to rely solely on models for water access monitoring. For this, household surveys remain necessary but should be extended with questions on the multiple sources that are used by households.@en

Groundwater use has significantly increased in the rapidly urbanising city of Kumasi, Ghana. But there is a lack of understanding of whether the groundwater system can sustain the growing demand in the future amidst climate change and rapid urbanisation. Using remote sensing datasets and a water balance approach, this study estimated the groundwater recharge and assessed how urbanisation has affected its groundwater sustainability. Sustainability is investigated by comparing multi-annual groundwater withdrawals to long-term average annual replenishment. Results show that while groundwater recharge has decreased by 80% from 1986 to 2020, mainly due to substantial (63%) loss of permeable land, groundwater consumption has seen a six-fold increase. Groundwater consumption in 2020 exceeded the long-term average groundwater recharge by 2.2Mm³, suggesting that the current groundwater use trends are unsustainable for future ground-water availability. Under a ‘business-as-usual’ scenario, a four-fold increase in groundwater consumption is predicted by 2050 while climate change and land-cover changes may reduce groundwater recharge by 10% and 55% respectively. Practical measures such as promoting artificial groundwater replenishment approaches, adopting low-impact development and instituting demand management measures must be implemented in the Metropolis. This should be informed by further studies to ascertain the exact condition of the groundwater.

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The explosion of citizen science (CS) in data collecting, including hydrology and water resources management, results from information and communication technology development. This approach is still a new topic in Vietnam, while CS development stages are not popular in the previous works. This paper demonstrates how a CS project can be developed, a pilot area at Lien Mac 2 sluice, Noi Bridge, and Dong Bong 1 pumping station on the Nhue River, Vietnam. There are seven main stages to implement a CS project, in which monitosring observation choose is a crucial factor to attract participants and obtain long- recorded observations. Social network and smartphone are a tool to boost CS development and the young generation is interested in this approach. Citizen-based water level monitoring was conducted by images that will be uploaded on social networks. The water levels obtained from the CS on the Nhue River are as good as the conventional approach. The step-by-step CS approach can be applied in other aspects of water resources management such as land use, water quality monitoring to promote the CS development.@en

Rivers and wetlands in Myanmar provide essential services to people in terms of transportation, agriculture, fisheries and a myriad of other ecosystem services, all of which are dependent on a healthy ecosystem. Irrigation channels are also an important part of the infrastructure for daily water use in Myanmar. The objective of this research is to describe the aquatic ecosystem of irrigation channels using aquatic macroinvertebrate communities. The research focused on the taxonomic composition of the aquatic macroinvertebrates of the Zawgyi River and the associated irrigation channels in central Myanmar, east of the city of Mandalay. Significant differences between the river and channels, and among individual channels, were shown using an analysis of similarity: Bray-Curtis similarity, a multivariate equivalent of the univariate statistical method of analysis of variance: ANOSIM and an analysis of similarity percentages: SIMPER by Plymouth Routines in Multivariate Ecological Research: PRIMER v6 software. The initial findings suggest that there is a clear separation between macroinvertebrate communities at the morpho-species level of identification between river and irrigation channels, while there is less separation between functional feeding groups (FFG) between them. The lower taxonomic level of discrimination at the family level using a water quality index showed no significant difference between river and channels. The preliminary field results indicate that a recently modified biomonitoring index method could be applied in Myanmar to assess the ecological water quality of the modified river, as well as human-made channels.

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Anthropogenic pressures such as river infrastructure, agriculture and power generation are rapidly increasing in Southeast Asia, aimed at providing food security within the region. However, this will lead to unintended river health consequences, and, currently, most Southeast Asian countries have no country-specific tools for monitoring river health. In Myanmar, one of Southeast Asia's poorest and most rapidly developing countries, no country-specific tools exist, and there is an urgent need to provide tools that can inform better management and trade-off decision making. This research evaluated three rapid macroinvertebrate bioassessment methods under Myanmar conditions. The objective of the research was to assess the applicability of existing internationally accepted indexing methods for use in Myanmar. Through taxa identification in the laboratory and statistical analysis, it was concluded that the method with the best fit for Myanmar taxa is The Asia Foundation index method, although differences were small. This Asia Foundation method is comparable to the AustralianWaterwatch method but includes a family present in our samples that is not included in the Waterwatch method. We then modified this method to include Myanmar taxa not recorded in The Asia Foundation method. The modified index method could be further developed into a Myanmar specific tool for widespread use potentially in combination with the also tested miniSASS, a much easier order-based method better suitable for non-professionals. We recommend additional testing using sites on other rivers across the country to establish a professional indexing method for Myanmar.

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This paper presents an extended Model Predictive Control scheme called Multi-objective Model Predictive Control (MOMPC) for real-time operation of a multi-reservoir system. The MOMPC approach incorporates the non-dominated sorting genetic algorithm II (NSGA-II), multi-criteria decision making (MCDM) and the receding horizon principle to solve a multi-objective reservoir operation problem in real time. In this study, a water system is simulated using the De Saint Venant equations and the structure flow equations. For solving multi-objective optimization, NSGA-II is used to find the Pareto-optimal solutions for the conflicting objectives and a control decision is made based on multiple criteria. Application is made to an existing reservoir system in the Sittaung river basin in Myanmar, where the optimal operation is required to compromise the three operational objectives. The control objectives are to minimize the storage deviations in the reservoirs, to minimize flood risks at a downstream vulnerable place and to maximize hydropower generation. After finding a set of candidate solutions, a couple of decision rules are used to access the overall performance of the system. In addition, the effect of the different decision-making methods is discussed. The results show that the MOMPC approach is applicable to support the decision-makers in real-time operation of a multi-reservoir system.

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Macroplastic pollution (> 0.5 cm) negatively impacts aquatic life and threatens human livelihood on land, in oceans and river systems. Reliable information on the origin, fate and pathways of plastic in river systems is required to optimize prevention, mitigation and reduction strategies. Yet, accurate and long-term data on plastic transport are still lacking. Current macroplastic monitoring strategies involve labor intensive sampling methods, require investment in infrastructure, and are therefore infrequent. Crowd-based observations of riverine macroplastic pollution may potentially provide frequent cost-effective data collection over a large geographical range. We extended the CrowdWater citizen science app for hydrological observations with a module for observations of plastic in rivers. In this paper, we demonstrate the potential of crowd-based observations of floating macroplastic and macroplastic on riverbanks. We analyzed data from two case studies: (1) floating plastic measured in the Klang (Malaysia), and (2) plastic on riverbanks along the Rhine (the Netherlands). Crowd-based observations of floating plastic in the Klang yield similar estimates of plastic transport (2,000–3,000 items h⁻¹), cross-sectional distribution (3–7 percent point difference) and polymer categories (0–6 percent point difference) as reference observations. It also highlighted the high temporal variation in riverine plastic transport. The riverbank observations provided the first data of macroplastic pollution on the most downstream stretch of the Rhine, revealing peaks close to urban areas and an increasing plastic density toward the river mouth. The mean riverbank density estimates are also similar for the crowd-based and reference methods (573–1,033 items km⁻¹). These results highlight the value of including crowd-based riverine macroplastic observations in future monitoring strategies. Crowd-based observations may provide reliable estimations of plastic transport, density, spatiotemporal variation and composition for a larger number of locations than conventional methods.

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Intensive and incessant disruptions in watercourses such as dams are taking place due to the growing demand for hydroelectric generation, and can result in severe deterioration of ecosystem integrity. This research concentrates on the impact of dams on macroinvertebrate communities downstream of two hydropower dams on tributaries of the upper Ayeyarwady River basin (Myitnge and Chaungmagyi Rivers) in Myanmar. A total of 52 and 49 aquatic invertebrate taxa with a total abundance of 2,743 and 1,356 were collected from the Myitnge and Chuangamgyi Rivers, respectively. We found the natural flow regime had changed in both study rivers after the construction of the dams. Analysis of similarity (ANOSIM) indicated the communities of morphospecies of taxa and the very sensitive insects (Ephemeroptera, Plecoptera, Trichoptera, and Odonata, EPTO) were significantly different between upstream and downstream of both dams. We used the preliminary Myanmar Aquatic Biomonitoring Assessment Index (MABA) detect ecological water quality. The MABA analysis using all taxa at family-level showed that upstream of the dams was rated as good quality whereas downstream of the dams was rated as fair quality. Our research showed that macroinvertebrates communities can be used as a bioindicator to detect the impact of human influences on river health such as dams. We concluded that the novel biomonitoring assessment tool recently developed for Myanmar is a promising monitoring tool as further river development occurs in Myanmar, which could also be linked to citizen science projects.@en

Worldwide, delta areas are under stress due to climate change. With rising sea
levels and decreasing freshwater availability, surface water salinization due to
groundwater exfiltration is expected to increase in these low-lying areas. To
counteract surface water salinization, freshwater diverted from rivers is used to flush agricultural ditches. In this paper, we demonstrate a Model Predictive Control (MPC) scheme to control salinity and water levels in a water course while minimizing freshwater usage. A state space description of the discretized De Saint Venant and advection-dispersion equations for water and salt transport, respectively, is used as the internal model of the controller. The developed MPC scheme is tested using groundwater exfiltration data from two different representative Dutch polders. The tests demonstrate that water levels and salinity concentrations can successfully be controlled within set limits while minimizing the freshwater used.@en

We present a systematic approach for salinity sensor placement in a polder network, where the objective is to estimate the unmeasured salinity levels in the main polder channels. We formulate this problem as optimization of the estimated salinity levels using root mean square error (RMSE) as the "goodness of fit" measure. Starting from a hydrodynamic and salt transport model of the Lissertocht catchment (a low-lying polder in the Netherlands), we use principal component analysis (PCA) to produce a low-order PCA model of the salinity distribution in the catchment. This model captures most of the relevant salinity dynamics and is capable of reconstructing the spatial and temporal salinity variation of the catchment. Just using three principal components (explaining 93% of the variance of the dataset) for the low-order PCA model, three optimally placed sensors with a greedy algorithm make the placement robust for modeling and measurement errors. The performance of the sensor placement for salinity reconstruction is evaluated against the detailed hydrodynamic and salt transport model and is shown to be close to the global optimum found by an exhaustive search with a RMSE of 82.2 mg/L.@en

Citizen science, as a complement to ground-based and remotely-sensed precipitation measurements, is a promising approach for improving precipitation observations. During the 2018 monsoon (May to September), SmartPhones4Water (S4W) Nepal—a young researcher-led water monitoring network—partnered with 154 citizen scientists to generate 6,656 precipitation measurements in Nepal with low-cost (<1 USD) S4W gauges constructed from repurposed soda bottles, concrete, and rulers. Measurements were recorded with Android-based smartphones using Open Data Kit Collect and included GPS-generated coordinates, observation date and time, photographs, and observer-reported readings. A year-long S4W gauge intercomparison revealed a −2.9% error compared to the standard 203 mm (8-inch) gauge used by the Department of Hydrology and Meteorology (DHM), Nepal. We analyzed three sources of S4W gauge errors: evaporation, concrete soaking, and condensation, which were 0.5 mm day−1 (n = 33), 0.8 mm (n = 99), and 0.3 mm (n = 49), respectively. We recruited citizen scientists by leveraging personal relationships, outreach programs at schools/colleges, social media, and random site visits. We motivated ongoing participation with personal follow-ups via SMS, phone, and site visit; bulk SMS; educational workshops; opportunities to use data; lucky draws; certificates of involvement; and in certain cases, payment. The average citizen scientist took 42 measurements (min = 1, max = 148, stdev = 39). Paid citizen scientists (n = 37) took significantly more measurements per week (i.e., 54) than volunteers (i.e., 39; alpha level = 0.01). By comparing actual values (determined by photographs) with citizen science observations, we identified three categories of observational errors (n = 592; 9% of total measurements): unit (n = 50; 8% of errors; readings in centimeters instead of millimeters); meniscus (n = 346; 58% of errors; readings of capillary rise), and unknown (n = 196; 33% of errors). A cost per observation analysis revealed that measurements could be performed for as little as 0.07 and 0.30 USD for volunteers and paid citizen scientists, respectively. Our results confirm that citizen science precipitation monitoring with low-cost gauges can help fill precipitation data gaps in Nepal and other data scarce regions@en

Wise management of water resources requires data. Nevertheless, the amount of streamflow data being collected globally continues to decline. Generating hydrologic data together with citizen scientists can help fill this growing hydrological data gap. Our aim herein was to (1) perform an initial evaluation of three simple streamflow measurement methods (i.e., float, salt dilution, and Bernoulli run-up), (2) evaluate the same three methods with citizen scientists, and (3) apply the preferred method at more sites with more people. For computing errors, we used midsection measurements from an acoustic Doppler velocimeter as reference flows. First, we (authors) performed 20 evaluation measurements in headwater catchments of the Kathmandu Valley, Nepal. Reference flows ranged from 6.4 to 240 L s−1. Absolute errors averaged 23 %, 15 %, and 37 % with average biases of 8 %, 6 %, and 26 % for float, salt dilution, and Bernoulli methods, respectively. Second, we evaluated the same three methods at 15 sites in two watersheds within the Kathmandu Valley with 10 groups of citizen scientists (three to four members each) and one “expert” group (authors). At each site, each group performed three simple methods; experts also performed SonTek FlowTracker midsection reference measurements (ranging from 4.2 to 896 L s−1). For float, salt dilution, and Bernoulli methods, absolute errors averaged 41 %, 21 %, and 43 % for experts and 63 %, 28 %, and 131 % for citizen scientists, while biases averaged 41 %, 19 %, and 40 % for experts and 52 %, 7 %, and 127 % for citizen scientists, respectively. Based on these results, we selected salt dilution as the preferred method. Finally, we performed larger-scale pilot testing in week-long pre- and post-monsoon Citizen Science Flow campaigns involving 25 and 37 citizen scientists, respectively. Observed flows (n=131 pre-monsoon; n=133 post-monsoon) were distributed among the 10 headwater catchments of the Kathmandu Valley and ranged from 0.4 to 425 L s−1 and from 1.1 to 1804 L s−1 in pre- and post-monsoon, respectively. Future work should further evaluate uncertainties of citizen science salt dilution measurements, the feasibility of their application to larger regions, and the information content of additional streamflow data.@en

Surface water salinization in deltaic areas due to saline groundwater exfiltration is an important issue. Fresh water diverted from the rivers is used for flushing the canals and the ditches in coastal areas to remove the low quality saline surface water mixed with saline groundwater. Worldwide, deltaic areas are under stress due to climate change, sea level increase and decrease in fresh water availability. The current fresh water management strategies in polders to overcome the salinization problem solely depends on uncontrolled freshwater use. However, this operation will not be effective during a scarce freshwater availability scenario and has to be revised for efficient management possibilities. With the advances in real time measurement of salinity and water level measurements, using a Model Predictive Control (MPC) scheme for the operation of a polder system is gaining popularity. MPC is a powerful control tool that can handle multiple objectives, consider the constraints and the uncertainties of the system. However, a MPC scheme requires a simple and reliable internal model that will be used to calculate the optimum control actions. The internal model should be robust, should reflect the system behaviour with enough detail and should not be computationally costly. In this paper, a MPC scheme is proposed using the discretized linearized De Saint Venant (SV) and Advection-Diffusion (AD) equations as the internal model of the controller. The proposed scheme will be able to control salinity and water level at any discretization point by manipulating the flushing and outflow discharges. This is an ongoing research with tests continuing on a realistic test case.@en

Inhabitants and ecosystems in delta areas are becoming increasingly vulnerable to the effects of subsidence, caused by anthropogenic activities. Yangon is a city on the periphery of the Irrawaddy delta in Myanmar where little is known about the true extent of this hazard, while its effects can potentially harm millions of its inhabitants. This research presents the magnitude and extent of the subsidence hazard in Yangon through a Persistent Scatterer Interferometry (PSI) time-series analysis on the Sentinel-1 data archives in the period of December 2015 through April 2017. The PSI analysis revealed four distinct zones of varying sizes where vertical velocity differences over 20 mm/yr were found, locally exceeding 110 mm/yr. The significant subsidence zones are exclusively located in young Alluvium deposits and 95% of velocity differences over 10 mm/yr are found at Quaternary age deposits. The addition of loads, such as buildings, predominantly affect subsidence rates in the first decade after placement. Estimates of groundwater extraction for domestic supply, used by more than 2 million inhabitants, correlate with the township average subsidence rates which shows that most subsidence in Yangon can be explained by groundwater extraction. Current operation of groundwater extraction wells induces an aquifer volume loss of 5.5 million cubic meter per year in the aquifer system of Yangon city. Unless groundwater extractions are mitigated, Yangon will be increasingly vulnerable to infrastructural damages, flooding events, and degrading aquifer quality.@en