The International Conference on Opportunistic Sensing of Precipitation (OpenSense) took place on 25–26 June 2025 at the Deutscher Wetterdienst (DWD) headquarters in Offenbach, Germany. Organized as the final meeting of the Cooperation in Science and Technology (COST) Action Opportunistic Precipitation Sensing Network (CA20136), the meeting brought together over one hundred participants from across Europe and beyond. The attendees represented a broad spectrum of the meteorological community, including researchers in hydrology, radar meteorology, atmospheric science, computer science, and representatives of several national meteorological and hydrological services (NMHSs). The overarching research topic of the conference was about the advances of opportunistic sensing (OS) for precipitation monitoring. OS uses signals or devices not originally designed for professional, high-quality meteorological purposes—such as commercial microwave links (CMLs), personal weather stations (PWSs), television-satellite microwave links (SMLs), and citizen-science contributions—as rainfall sensors. OS can either complement conventional observations from gauges, radars, and satellites or give observational data in regions with sparse rainfall data. The COST Action OpenSense, launched in 2021, has provided a framework for this community. It aimed to establish a coordinated scientific network, to harmonize processing methods, and prepare the ground for operational uptake of OS methods. Working groups within OpenSense focused on data management, methodological homogenization, data merging, and applications (https://opensenseaction.eu/). The conference was the kickoff of a meeting series on the scientific progress and institutional prospects of OS rainfall estimation. The second edition is already in preparation at the Royal Netherlands Meteorological Institute (KNMI) in De Bilt, the Netherlands, on 23 and 24 June 2026. This first edition of the conference served as the final meeting of OpenSense and also consisted of internal management committee and working group meetings preceding the two conference days. The conference program was structured into thematic sessions described in the following sections and consisted of oral and poster sessions as well as keynote lectures and a panel discussion. This report synthesizes the main themes, cross-cutting insights, and emerging challenges as discussed across the sessions.

AbstractStudy RegionThe study region is the Magdalena River basin in Colombia. The basin was divided into three distinct regions (Andean, Caribbean, and Pacific) and analyzed across different elevations.Study FocusThe study proposes a Spatiotemporal Non-Linear Dynamics Assessment (SNLDA) framework to compare ERA5-Land reanalysis data with in-situ rain gauge observations. It specifically examines the constraints imposed by nonlinear dynamical processes and their associated space-time complexities on the representation of precipitation, particularly in a tropical region. The SNLDA framework incorporates three main components: (i) standard performance metrics (e.g., correlations, RMSE, and dry spell duration), (ii) rainfall spatiotemporal objects (characterizing precipitation events through attributes such as volumes and start-end centroids), and (iii) non-linear dynamics complexity (reconstructing dynamical behavior from time series and evaluating attractors properties, including the Hurst and Lyapunov exponents). These elements were analyzed both individually and in combination. Daily ERA5-Land information (0.1°x0.1°) and in-situ rain gauge data comprising 558 stations from 1980 to 2020 were used, enriched by an Inverse Distance Weighting (IDW) interpolation (0.1°x0.1°) to facilitate comparison across spatial scales.New Hydrological Insights for the RegionOverall, ERA5-Land overestimates precipitation, producing shorter, more frequent events while poorly representing extreme wet and dry spells.Andean region: ERA5-Land overestimates rainfall, with largest errors at low elevations, driven by unresolved spatiotemporal object volumes displacements and nonlinear processes.Caribbean region: ERA5-Land shows the highest errors in nonlinear dynamics and extremes, despite lower annual bias and RMSE.Pacific region: ERA5-Land strongly overestimates precipitation volumes and RMSE, while nonlinear errors remain low; these biases are mainly driven by spatiotemporal objects displacement.

The relationship between initial soil moisture and floods is well studied in sloping areas, but not in lowland catchments, where the saturated zone, unsaturated zone and surface water are strongly coupled. The aim of this study was to determine the importance of initial groundwater depth (representing soil wetness) on flood peaks in lowland catchments and to examine if and how this affects the magnitude and timing of floods in the future. We used the rainfall-runoff model WALRUS to investigate the relation between initial groundwater depth (48 h before the peak), effective rainfall sum (over the 48 h before the peak) and the resulting peak discharge and peak volume in 12 lowland catchments, for 109 years of forcing in the current climate and four climate scenarios for both 2050 and 2085. We found that this relation is strong in these catchments, with a stronger dependence on initial groundwater depth in flatter catchments. When climate changes, less precipitation and more evapotranspiration are projected in summer, resulting in deeper groundwater in summer and autumn, reducing flood frequency and magnitude. More rain in autumn, winter and spring will lead to more severe floods in winter and spring only. Averaged over all catchments, scenarios and seasons, the effective rainfall sum is projected to increase with 1.5 % in 2050 and 5.6 % in 2085, while the initial groundwater depth increases with 0.7 % in 2050 and 0.3 % in 2085. This combination leads to more frequent and severe floods, with 1 % more floods and 3 % larger peak volumes in 2050 and 9 % more floods and 21 % larger peak volumes in 2085. Without the mitigating effect of the deeper initial groundwater tables, the higher rainfall sums would have led to more frequent and more severe floods in the future.

Recent developments of higher-resolution and lower-latency reanalysis data allow mapping reference evapotranspiration (ET_o) over large areas in a near real-time manner. This study evaluates the ERA5, AgERA5 and GEOS5 reanalysis datasets for meteorological input in Africa and Southwest Asia by comparing between data products and with 174 in situ sites. The inter-comparison reveals non-stationary differences between datasets and highlights temporal inconsistencies in the GEOS5 data. When evaluated against in situ measurements, GEOS5 demonstrates lower accuracy compared with ERA5 and AgERA5. Additionally, while all datasets accurately estimate air temperature and pressure, they overestimate windspeed and solar radiation, and underestimate vapour pressure. The propagation of uncertainty estimates of ERA5 through the FAO56 ET_o equation shows particularly high uncertainty in the tropics. This study emphasizes the importance of applying multiple uncertainty assessment methods for better-informed use of reanalysis data, especially in data-scarce regions.

Cities in northwestern Europe face increasingly extreme summer heat under climate change, intensifying the need for effective neighbourhood-scale heat mitigation strategies. Using hectometric (100 m) idealized Weather Research and Forecasting (WRF) simulations during three extreme heat events, this study examines how urban blue space configuration, atmospheric forcing, and physical mechanisms regulate air temperature and thermal comfort (wet-bulb globe temperature index) across coastal and inland cities. We assess how surface energy fluxes interact with horizontal advection to propagate cooling beyond waterbodies, while evaluating whether WRF-Lake produces physically realistic outputs for small, shallow urban blue spaces. Our simulations show near-surface horizontal advection as the dominant cooling mechanism, mixing cooler air from blue spaces with warmer urban air. Around midday, this provides approximately 50 W⋅ m−2 cooling potential, amplified by evaporative cooling enhanced by urban-generated turbulence. Daily mean temperature reductions ranged from −0.1◦C to −0.4◦C, with peak morning effectiveness reaching −1.0◦C in coastal areas. Wind speed emerged as the primary control: moderate winds (4.7–5.8 m⋅ s−1) propagated cooling citywide, extending up to three times the city diameter downwind, whereas light winds (1.2 m⋅ s−1) limited cooling locally. Randomly distributed waterbodies created more homogeneous cooling than canal configurations. Thermal comfort analysis revealed a critical temperature–humidity trade-off. Factor analysis (R2 = 0.93) showed air temperature cooling (50.3%) is counteracted by increased relative humidity (42.3%).We identified limitations of WRF-Lake for shallow urban blue spaces. Default roughness lengths underestimate turbulence and fluxes, likely underestimating cooling and causing unrealistic water temperature increases. This underscores the need for improved parametrizations and targeted observations to advance urban hydrometeorological modelling.

Accurate rainfall observations with high spatial and temporal resolutions are key for hydrological applications, in particular for reliable flood forecasts. However, rain gauge networks operated by regional or national environmental agencies are often sparse, and weather radars tend to underestimate rainfall. As a complementary source of information, rain gauges from personal weather stations (PWSs), which have a network density 100 times higher than dedicated rain gauge networks in the Netherlands, can be used. However, PWSs are prone to additional sources of error compared to dedicated gauges, because they are generally not installed and maintained according to international guidelines. A systematic long-term analysis involving PWS rainfall observations across different seasons, accumulation intervals, and rainfall intensity classes has been missing so far. Here, we quantitatively compare rainfall estimates obtained from PWSs with rainfall recorded by automatic weather stations (AWSs) from the Royal Netherlands Meteorological Institute (KNMI) over the 2018–2023 period, including a sample of 1760 individual rainfall events in the Netherlands. This sample consists of the 10 highest rainfall accumulations per season and accumulation intervals (1, 3, 6, and 24 h) over a 6-year period. It was found that the average of a cluster of PWSs severely underestimates rainfall (around 36 % and 19 % for 1 h and 24 h intervals, respectively). By adjusting the data with areal reduction factors to account for the spatial variability of rainfall extremes and applying a bias correction factor of 1.22 to compensate for instrumental bias, the average relative bias decreases to −5 % for 1 h intervals or almost zero for intervals of 3 h and longer. The highest correlations (0.85 and 0.86) and lowest coefficients of variation (0.14 and 0.18) were found for 24 h intervals during winter and autumn, respectively. We show that most PWSs are able to capture high rainfall intensities up to around 30 mm h−1, indicating that these can be utilized for applications that require rainfall data with a spatial resolution of the order of kilometres, such as for flood forecasting in small, fast-responding catchments. PWSs did not observe the most intense rainfall events, which were associated with return periods exceeding 10 or 50 years (above approximately 30 mm h−1) and occurred in spring and summer. However, the spatial distribution of rainfall likely played a large role in the observed differences rather than instrumental limitations. This emphasizes the importance of having a dense rain gauge network. In addition, the variation in undercatch is likely partly due to the disproportional underestimation of tipping bucket rain gauges with increasing intensities. Outliers during winter were likely caused by solid precipitation and can potentially be removed using a temperature module from the PWS. We recommend additional research on dynamic calibration of the tipping volumes to improve this further.

Urban areas, characterized by dense populations and many socio-economic activities, increasingly suffer from floods, droughts, and heat stress due to land use and climate change. Traditionally, the urban thermal environment and water resources management have been studied separately, using urban land surface models (ULSMs) and urban hydrological models (UHMs). However, as our understanding deepens and the urgency to address future climate disasters grows, it becomes clear that hydrological disasters—such as floods, droughts, severe urban thermal environments, and more frequent heat waves—are actually not isolated events but compound events. This underscores the close interaction between the water cycle and the energy balance. Consequently, the existing separation between ULSMs and UHMs creates significant obstacles to better understanding urban hydrological and meteorological processes, which is crucial for addressing the high risks posed by climate change. Defining the future direction of process-based models for hydro-meteorological predictions and assessments is essential for better managing climate disasters and evaluating response measures in densely populated urban areas. Our review focuses on three critical aspects of urban hydro-meteorological simulation: similarities, differences, and gaps among different models; existing gaps in physical process implementations; and efforts, challenges, and potential for model coupling and integration. We find that ULSMs inadequately represent water surfaces and hydraulic systems, while UHMs lack explicit surface energy balance solutions and detailed building representations. Coupled models show potential for simulating urban hydro-meteorological environments, but face challenges at regional and neighborhood scales. Our review highlights the need for interdisciplinary communication between the urban climatology and urban water management communities to enhance urban hydro-meteorological simulation models.

On 29 October 2024, torrential rainfall locally exceeding 300 mm within less than 24 h, caused devastating floods in the province of Valencia in Spain. In this study we quantify and describe the spatial and temporal structure of the rainfall event on this day using rainfall observations from approximately 225 personal weather stations (PWSs), low-cost commercial devices primarily operated by citizens. The network density of PWSs is ∼7 times higher compared to the dedicated rain gauge network operated by the Spanish Meteorological Agency (AEMET) in the province of Valencia, allowing a more detailed analysis of the spatial and temporal rainfall dynamics. In addition, PWS observations are available in near real-time to the public with a temporal resolution of 5 min, whereas the data from AEMET are not available in real time for the public and at a lower publicly available temporal resolution (1 h). Daily rainfall sums recorded by the PWSs showed a high correlation (r=0.94) and low bias (underestimation of 4 %) compared to rainfall reported by AEMET. In the upstream parts of the Magro catchment (1661 km2), a first burst of extreme rainfall, reaching up to 180 mm of rainfall in a few hours, started in the morning, leading to the generation of a first flood wave in the upstream parts of the catchment. While the resulting flood wave was propagating downstream through the channel network, a second rainfall peak occurred, which moved downstream along with the flood wave. This spatial and temporal coincidence has likely exacerbated the devastating power of this event. Based on the PWS data, it could have been anticipated that the extreme rainfall already occurring early in the morning would likely result in flooding in the Magro catchment. Areal rainfall maps based on interpolating PWS data indicated catchment average rainfall exceeding 150 mm d−1 across an area of more than 2500 km2. However, the total accumulated rainfall remains uncertain due to interrupted measurements likely caused by power outage and inherent uncertainty associated with interpolating point measurements. For the Rambla de Poyo catchment, the resulting average discharge was around 900 m3 s−1. The estimated return period of the catchment-average rainfall and resulting discharge from this event exhibits large uncertainties, with on average exceeding 10 000 and 900 years, respectively. This study shows the potential of PWSs for real-time rainfall monitoring and potentially flood early warning systems, by complementing dedicated rain gauge networks in order to reduce the uncertainty from areal rainfall estimates and to localize potential flooding more accurately.

As the spatial coverage of evaporation observations is limited, we propose a novel, opportunistic method to estimate evaporation in which we consider commercial microwave links (CMLs), such as used in cellular telecommunication networks, in combination with scintillometry. Scintillometers are dedicated instruments to measure path-integrated latent and sensible heat fluxes, transmitting electromagnetic radiation that is diffracted by turbulent eddies between transmitter and receiver, causing the so-called scintillation effect. CMLs are line-of-sight devices that transmit electromagnetic radiation at similar frequencies as microwave scintillometers. However, CMLs and their sampling strategies are designed to ensure a continuously functioning wireless communication network rather than to capture the scintillation effect. Here, we estimate 30 min latent heat fluxes and daily evaporation using a former CML. To do so, we use data of a 38 GHz Nokia CML (formerly part of a telecom network) installed over an 856 m path at the Ruisdael Observatory near Cabauw, the Netherlands. We compare our results with estimates from an optical and microwave scintillometer setup, as well as an EC system. To obtain the flux estimates using the CML, we apply the two-wavelength method, in combination with the optical scintillometer, as well as a standalone energy-balance method (EBM), requiring net radiation estimates. For comparison, we also consider the free-convection limit of Monin-Obukhov similarity theory (MOST), instead of the complete scaling. An advantage of this approach is that it does not require horizontal wind speed measurements, which are more difficult to obtain in complex environments. For the net radiation estimates, we use in-situ measured radiation and data products provided by the Satellite Application Facility on Land Surface Analysis (LSA SAF) of EUMETSAT. Considering both turbulent heat fluxes, the two-wavelength method outperforms the EBM. The standalone EBM shows a reasonable performance, but also a large dependence on the quality of the net radiation estimates. When aggregating our 30 min latent heat fluxes to daily evaporation estimates, the relative performance of the methods remains comparable to that at 30 min intervals. These daily evaporation estimates could also be useful for catchment hydrological applications. Application of the free-convection scaling instead of the complete MOST scaling results in a comparable performance for all methods.

Dual-polarization weather radars have improved the accuracy of precipitation estimates. However, challenges persist in evaluating hydrometeor classification (HMC) algorithms, thereby impacting the accuracy of precipitation estimates. This study proposes to use full Doppler spectra in both polarizations from a Ka- and W-band Doppler-polarimetric profiler with a 45° elevation angle to provide insights into hydrometeor characteristics. A novel methodology was developed to link the observed spectra with the output of an HMC scheme. We applied the wradlib HMC scheme using C-band weather radar data from the Netherlands for six cases (2021–2022). The HMC scheme output is used to calculate mixing ratios that are combined with the corresponding scattering properties using the Atmospheric Radiative Transfer Simulator microwave single scattering properties database (frozen hydrometeors) and T-matrix calculations (liquid hydrometeors) to simulate Doppler spectra of polarimetric variables that would be measured by the profiler. Comparing these simulations with actual profiler measurements enables a quality assessment. The method works in stratiform cases, but convective cases reveal the influence of turbulence and wind variability. Uncertainty arises from the selection of specific parameterizations for the particle size distribution and the relationship between hydrometeor size and terminal fall velocity as well as from the derived mixing ratios. Additionally, the 45° angle complicates separating horizontal wind from hydrometeor fall velocities, although the Mie notch in the dual-wavelength ratio can be effectively used to remove the radial wind component. Our results underline limitations that must be addressed but also show that integrating spectral and dual-frequency observations could yield valuable insights into hydrometeor characteristics.

Scintillometers are used to estimate path-integrated evaporation and sensible heat fluxes. Commercial microwave links (CMLs), such as are used in cellular telecommunication networks, are similar line-of-sight instruments that also measure signal intensity of microwave signals, just like microwave scintillometers do. However, CMLs are not designed to capture scintillation fluctuations. Here, we investigate if and under what conditions CMLs can be used to obtain the structure parameter of the refractive index, Cnn, which would be a first step in computing turbulent heat fluxes with CMLs using scintillation theory. We use data from three collocated microwave links installed over an 856 m path at the Ruisdael Observatory near Cabauw, the Netherlands. Two of these links are 38 GHz CMLs formerly employed in telecom networks in the Netherlands, a Nokia Flexihopper and an Ericsson MiniLink. We compare Cnn estimates obtained from the received signal intensity of these links, sampled at 20 Hz, with those obtained from measurements of a 160 GHz microwave scintillometer (RPG-MWSC) sampled at 1 kHz and with those of an eddy-covariance system. After comparison of the unprocessed Cnn, we rejected the Ericsson MiniLink because its 0.5 dB power quantization (i.e. the discretization of the signal intensity) was found to be too coarse to be applied as a scintillometer. Based on the power spectra of the Nokia Flexihopper and the microwave scintillometer, we propose two methods to correct for the white noise present in the signal of the Nokia Flexihopper: (1) we apply a high-pass filter and subtract a low quantile of the resulting variances of the Nokia Flexihopper and (2) we correct for the noise by comparing with a microwave scintillometer (MWS) and select the parts of the power spectra where the Nokia Flexihopper behaves in correspondence with scintillation theory, also considering different crosswind conditions, and correct for the underrepresented part of the scintillation spectrum based on theoretical scintillation spectra. The comparison and noise determination with the microwave scintillometer provide the best-possible Cnn estimates for the Nokia Flexihopper, although this method is not feasible in operational settings for CMLs. Both of our proposed methods show an improvement in Cnn estimates in comparison to uncorrected estimates, albeit with larger uncertainty than when using the reference instruments. Our study illustrates the potential for using CMLs as scintillometers but also outlines some major drawbacks, most of which are related to unfavourable design choices made for CMLs. If these were overcome, given their global coverage, there is potential for CMLs to perform large-scale evaporation monitoring.

Plastic pollution is a global environmental challenge that negatively impacts species, ecosystems, and human livelihoods. River basins, with high population densities and poor waste management, are particularly exposed to plastic pollution. Floods amplify the presence of plastic in rivers by mobilizing previously deposited materials and introducing new plastics. Yet, the fate of these mobilized plastics remains unclear, with observations suggesting either downstream export or floodplain deposition. This study assesses flood impact on macroplastic deposition along river floodplains, using data from 14 events─five floods and nine nonflood conditions─across two Dutch rivers. Higher flood return periods increased macroplastic deposition, with the two largest floods depositing two to three times more macroplastic than nonflood conditions. Deposition mechanisms varied by flood type. Obstruction-based deposition dominated during an extreme summer flood, when macroplastics accumulated mainly in inundated vegetation. Low-energy deposition prevailed during a long winter flood, with high plastic concentrations found in wide floodplain sections where flow velocities decreased. Flood severity and plastic entry into the environment are both projected to increase. Therefore, we expect an even more prominent role for floods in the global distribution of plastic pollution.

Study region: Madeira River basin, southwestern Amazonia Study focus: This study investigates spatial and temporal changes in precipitation, evaporation, and streamflow, and their relationship with deforestation in the Madeira River basin, the largest Amazonian sub-basin. We applied Mann-Kendall trend analysis, change-point detection, and correlation analysis across multiple spatial scales, using satellite, reanalysis, and observed data from 1981 to 2015. These methods enabled us to detect long-term trends, identify shifts, and quantify the relationships between forest loss and hydrological changes New hydrological insights for the region: The basin experienced an average deforestation rate of 2810 km² per year from 2001 to 2020, predominantly in the Brazilian portion. Between 1981 and 2016, we observed statistically significant negative trends in precipitation, evaporation, and streamflow, especially in the most deforested areas during the wet season. Correlation analysis (2001–2015) showed a statistically significant and positive relationship between forest area and evaporation in wet months (r = 0.73, p < 0.1) and a negative correlation between forest area and streamflow during the same season (r = –0.6, p < 0.1). These findings highlight the critical role of forests in modulating hydrological processes, supporting the hypothesis that deforestation may reduce evaporation, alter moisture recycling, and slow the water cycle. While our results are robust, we acknowledge that factors such as climate variability and land management practices may also influence hydrological changes and should be considered in future research.

Mismanaged plastic waste (MPW) is a major source of plastic pollution in rivers, particularly in regions with insufficient waste management infrastructure. Despite prior studies on MPW drivers, the entry points of MPW into riverine environments across the lifecycle of specific plastic items have not been assessed. This study addresses this gap by analyzing the lifecycle of the three most polluting plastic items, drinking water sachets, small bottles, and expanded polystyrene (EPS) food packaging in the Odaw catchment, located in Accra, Ghana to identify their critical entry points into the riverine environment. The Odaw is known for its high contribution to environmental plastic pollution due to the high anthropogenic activities, coupled with its inadequate waste management systems. Using a qualitative methodology, interviews and focus group discussions were conducted with 15 stakeholders involved in production, retail, consumption, waste management, and regulation across the plastic lifecycle. Data collected through audio recordings, were transcribed and analysed through inductive content analysis approach in ATLAS.ti. The findings reveal that all MPW entry points occur during post-consumption stages, with four of eight identified practices classified as high-impact. EPS packs was not recovered for recycling, bottles were exported overseas for recycling, and water sachets recovery faced challenges due to the low market prices and limited recycling capacity. This highlights the catchment's limited recycling infrastructure. The study provides localized insights for targeted mitigation strategies and support targeted monitoring efforts. Furthermore, it offers a replicable methodological framework for regions with limited waste operations data, serving as a baseline for data-rich regions.

Rivers are one of the main conduits that deliver plastic from land into the sea, and also act as reservoirs for plastic retention. Yet, our understanding of the extent of river exposure to plastic pollution remains limited. In particular, there has been no comprehensive quantification of the contributions from different river compartments, such as the water surface, water column, riverbank and floodplain to the overall river plastic transport and storage. This study aims to provide an initial quantification of these contributions. We first identified the main relevant transport processes for each river compartment considered. We then estimated the transport and storage terms, by harmonizing available observations on surface, suspended and floodplain plastic. We applied our approach to two river sections in The Netherlands, with a focus on macroplastics (≥2.5 cm). Our analysis revealed that for the studied river sections, suspended plastics account for over 96% of item transport within the river channel, while their relative contribution to mass transport is only 30%–37% (depending on the river section considered). Surface plastics predominantly consisted of heavier items (mean mass: 7.1 g/#), whereas suspended plastics were dominated by lighter fragments (mean mass: 0.1 g/#). Additionally, the majority (98%) of plastic mass was stored within the floodplains, with the river channel accounting for only 2% of the total storage. Our study developed a harmonized approach for quantifying plastic transport and storage across different river compartments, providing a replicable methodology applicable to different regions. Our findings emphasize the importance of systematic monitoring programs across river compartments for comprehensive insights into riverine plastic pollution.

Urban areas, characterized by dense populations and many socioeconomic activities, increasingly suffer from floods, droughts and heat stress due to land use and climate change. Traditionally, the urban thermal environment and water resource management have been studied separately, using urban land-surface models (ULSMs) and urban hydrological models (UHMs). However, as our understanding deepens and the urgency to address future climate disasters grows, it becomes clear that hydroclimatological extremes – such as floods, droughts, severe urban thermal environments and more frequent heat waves – are actually not always isolated events but can be compound events. This underscores the close interaction between the water cycle and the energy balance. Consequently, the existing separation between ULSMs and UHMs creates significant obstacles in better understanding urban hydrological and meteorological processes, which is crucial for addressing the high risks posed by climate change. Defining the future direction of process-based models for hydrometeorological predictions and assessments is essential for better managing extreme events and evaluating response measures in densely populated urban areas. Our review focuses on three critical aspects of urban hydrometeorological simulation: similarities, differences and gaps among different models; existing gaps in physical process implementations; and efforts, challenges and potential for model coupling and integration. We find that ULSMs inadequately represent water surfaces and hydraulic systems, while UHMs lack explicit surface energy balance solutions and detailed building representations. Coupled models show the potential for simulating urban hydrometeorological environments but face challenges at regional and neighbourhood scales. Our review highlights the need for interdisciplinary communication between the urban climatology and the urban water management communities to enhance urban hydrometeorological simulation models.

This chapter reviews the state-of-the-art of land surface rainfall estimation using measurements from weather radars, personal weather stations, and commercial microwave links, including comparisons to rain gauge measurements. These studies are related to recently emerging field of opportunistic weather sensing using the existing communications infrastructure.

Spaceborne microwave radiometers represent an important component of the Global Precipitation Measurement (GPM) mission due to their frequent sampling of rain systems. Microwave radiometers measure microwave radiation (brightness temperatures Tb), which can be converted into precipitation estimates with appropriate assumptions. However, detecting shallow precipitation systems using spaceborne radiometers is challenging, especially over land, as their weak signals are hard to differentiate from those associated with dry conditions. This study uses a random forest (RF) model to classify microwave radiometer observations as dry, shallow, or nonshallow over the Netherlands}a regionwith varying surface conditions and frequent occurrence of shallow precipitation. The RF model is trained on five years of data (2016–20) and tested with two independent years (2015 and 2021). The observations are classified using ground-based weather radar echo top heights. Various RF models are assessed, such as using only GPM Microwave Imager (GMI) Tb values as input features or including spatially aligned ERA5 2-m temperature and freezing level reanalysis and/or Dual-Frequency Precipitation Radar (DPR) observations. Independent of the input features, the model performs best in summer and worst in winter. The model classifies observations from high-frequency channels ($85 GHz) with lower Tb values as nonshallow, higher values as dry, and those in between as shallow. Misclassified footprints exhibit radiometric characteristics corresponding to their assigned class. Case studies reveal dry observations misclassified as shallow are associated with lower Tb values, likely resulting from the presence of ice particles in nonprecipitating clouds. Shallow footprints misclassified as dry are likely related to the absence of ice particles. SIGNIFICANCE STATEMENT: Published research concerning rainfall retrieval algorithms from microwave radiometers is often focused on the accuracy of these algorithms. While shallow precipitation over land is often characterized as problematic in these studies, little progress has been made with these systems. In particular, precipitation formed by shallow clouds, where shallow refers to the clouds being close to Earth’s surface, is often missed. This study is focused on detecting shallow precipitation and its physical characteristics to further improve its detection from spaceborne sensors. As such, it contributes to understanding which shallow precipitation scenes are challenging to detect from microwave radiometers, suggesting possible ways for algorithm improvement.

Terrestrial microwave links are increasingly being used to estimate path-averaged precipitation by determining the attenuation caused by rainfall along the link path, mostly with commercial microwave links from cellular telecommunication networks. However, the temporal resolution of these rainfall estimates and the method to derive them are often determined by the temporal sampling strategy that is employed by the mobile network operators. Currently, the links are most often sampled at a temporal resolution of 15 min with a recording of the minimum and maximum values, while more recently, a form of instantaneous sampling with possible intervals up to 1 s has also been set up. For rainfall research purposes, often high temporal resolutions in combination with averaged values are preferred. However, it is uncertain how these various temporal sampling strategies affect the estimated rainfall intensity. Here we aim to understand how temporal sampling strategies affect the measured rainfall intensities using microwave links. To do so, we use data from three collocated microwave links, two 38 GHz and one 26 GHz, sampled at 20 Hz and covering a 2.2 km path over the city of Wageningen, the Netherlands. We aggregate the microwave link power levels to multiple time intervals (1 s to 60 min) and use a mean, instantaneous, and minimum and maximum value to characterize the signal. Based on the aggregated data, we compute rainfall intensities and compare these with 20 Hz rainfall estimates, such that we isolate errors and uncertainties caused by the sampling strategies from instrumental effects, such as different biases between instruments and representativeness errors. In general, our results show that for all sampling strategies, an increase in sampling time interval reduces the performance of the rainfall estimates, which especially holds for the instantaneous sampling strategy. Even the mean sampling strategy, which generally performs best of all strategies, is sensitive to this reduction in temporal resolution and could lead to significant underestimations. This sensitivity of the mean sampling to the temporal resolution seems to be largely affected by the non-linear relation between attenuation and rainfall. The min–max sampling strategy is mostly prone to minor underestimations or large overestimations of the path-averaged rainfall intensities. Moreover, our results, including a comparison with theoretical events, show that the attenuation due to wet antennas not only affects the comparison between the rainfall estimates obtained with a microwave link and another reference instrument but also has a significant influence on the performance of the rainfall retrieval algorithm, especially for devices with relatively long duration of the wet-antenna attenuation combined with the longer sampling time intervals. Overall, this study demonstrates the effect a selected sampling strategy can have on rainfall intensity estimates using (commercial) microwave links.

Rivers represent one of the main conduits for the delivery of plastics to the sea, while also functioning as reservoirs for plastic retention. In tropical regions, rivers are exposed to both high levels of plastic pollution and invasion of water hyacinths. This aquatic plant forms dense patches at the river surface that drift due to winds and currents. Recent work suggests that water hyacinths play a crucial role in influencing plastic transport, by efficiently trapping the majority of surface plastic within their patches. However, a comprehensive understanding of the interaction between water hyacinths and plastics is still lacking. We hypothesize that the properties relevant to plastic transport change due to their trapping in water hyacinth patches. In particular, the length scale, defined as the characteristic size of the transported material, is a key property in understanding how materials move within rivers. Here, we show that water hyacinth patches trap on average 54%–77% of all observed surface plastics at the measurement site (Saigon river, Vietnam). Both temporally and spatially, we found that plastic and water hyacinth presence co-occur. The formation of plastic-plant aggregates carries significant implications for both clean-up and monitoring purposes, as these aggregates can be detected from space and need to be jointly removed. In addition, the length scale of trapped plastics (∼4.0 m) was found to be forty times larger than that of open water plastics (∼0.1 m). The implications of this increased length scale for plastic transport dynamics are yet to be fully understood, calling for further investigation into travel distances and trajectories. The effects of plastic trapping likely extend to other key properties of plastic-plant aggregates, such as effective buoyancy and mass. Given the prevalence of plant invasion and plastic pollution in rivers worldwide, this research offers valuable insights into the complex environmental challenges faced by numerous rivers.