Urban water bodies offer nature-based heat mitigation solutions, yet the mechanistic contributions of turbulent exchange versus advective transport in redistributing canal-induced cooling remain unquantified. We employed PALM4U large-eddy simulation with realistic urban geometry to investigate canal cooling mechanisms in Amsterdam, Netherlands, a dense historic center whose semi-circular canal ring enables systematic study of varying wind-canal orientations within a single setting. Model evaluation against eddy covariance and urban weather station observations demonstrated strong performance for sensible heat flux, net radiation, and near-surface air temperature. Through controlled experiments, we decomposed cooling into turbulent and advective components, revealing that effectiveness depends fundamentally on wind-canal orientation. When wind flows perpendicular to canals, mechanically-driven canyon vortices redistribute cool air from the water surface to surrounding areas, with advection accounting for 60 to 80% of the total cooling effect over adjacent pavement and above the canopy, extending cooling asymmetrically to 2 to 3 building heights vertically and into adjacent canyons. When wind flows parallel to canals, the absence of a cross-canyon vortex limits redistribution, leaving cooling concentrated near the water surface and driven predominantly by local sensible heat flux reduction. Wind-canal orientation does not markedly alter overall cooling intensity but governs how cooling is spatially redistributed within the canopy, producing pronounced thermal heterogeneity, with temperature differences up to 1.5 °C within individual street canyons. While these magnitudes reflect a single summer day and Amsterdam's specific morphology, the mechanism points to a transferable principle: orienting blue infrastructure relative to prevailing summer winds can steer cooling toward pedestrian spaces.

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.

Rainfall estimates from commercial microwave links (CMLs) have become valuable complements to conventional rainfall sensors, especially in the Global South, where ground-based radar and raingauge networks are sparse. Spaceborne instruments aboard low Earth orbiting satellites also offer valuable precipitation data, especially over inaccessible areas. This study is the first to compare CML rainfall estimates with those derived from the radiometer on the Global Precipitation Measurement (GPM) core satellite across countries with different climates (Netherlands, Sri Lanka, Nigeria) using data from the GPM radar as reference. The weaknesses of CML and radiometers are distinct: radiometers tend to underestimate high-intensity events with a relative bias varying between 10 and 50% depending on the country and the threshold used to differentiate between dry conditions and rainfall, while CML estimates often miss low-intensity rainfall, with a probability of detection (POD) varying between 0.1 (Nigeria) and 0.5 (the Netherlands).

Disdrometers are specialized sensors designed to measure key properties of falling hydrometeors. Their observations are essential for characterizing precipitation particle size distributions (PSDs) and support a wide range of applications, including precipitation microphysics research, the development and evaluation of remote-sensing precipitation retrievals, and the modelling of microwave signal propagation through the atmosphere for telecommunication systems. However, the broader use of disdrometer data is hindered by limited access to existing datasets, heterogeneous raw data formats, and the lack of standardized, reproducible processing workflows. This article presents the DISDRODB infrastructure and the associated open-source Python package disdrodb, a community framework for standardized sharing, processing, and analysis of disdrometer data. DISDRODB combines a centralized metadata archive with a decentralized data-sharing model, allowing institutions to retain control of raw data while making their datasets globally discoverable and straightforward for users to access and download through a common interface. The disdrodb software converts heterogeneous raw measurements into analysis-ready products through a modular three-level pipeline: L0 for standardized ingestion and formatting into netCDF4, L1 for temporal resampling, quality control, and hydrometeor/precipitation-type classification, and L2 for derivation of PSD integral parameters, parametric PSD model fitting, and simulation of polarimetric radar variables at multiple frequencies. The framework provides a transparent, configurable, and reproducible open-source workflow for disdrometer data processing, with scalable execution from local environments to distributed computing systems. It also supports automatic generation of summary diagnostics for scientific analysis and is built on a modular, flexible architecture designed for community-driven extensions. DISDRODB lowers barriers to both data access and analysis by enabling straightforward discovery and download of disdrometer datasets alongside reproducible processing workflows, thereby supporting large-sample studies of PSD variability, improved disdrometer intercomparison, and broader use of disdrometer observations in atmospheric science and remote sensing.

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.

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.

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.

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.

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.

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.

The Goddard Profiling algorithm (GPROF) converts radiometer observations from Global Precipitation Measurement (GPM) constellation satellites into precipitation estimates. Typically, high-quality ground-based estimates serve as reference to evaluate GPROF's performance. To provide a fair comparison, the ground-based estimates are often spatially aligned to GPROF. However, GPROF combines observations from various sensors and channels, each associated with a distinct footprint. Consequently, uncertainties related to the representativeness of the sampled areas are introduced in addition to the uncertainty when converting brightness temperatures into precipitation intensities. The exact contribution of resampling precipitation estimates, required to spatially and temporally align different resolutions when combining or comparing precipitation observations, to the overall uncertainty remains unknown. Here, we analyze the current performance of GPROF over the Netherlands during a 4-year period (2017-2020) while investigating the uncertainty related to sampling. The latter is done by simulating the reference precipitation as satellite footprints that vary in size, geometry, and applied weighting technique. Only GPROF estimates based on observations from the conical-scanning radiometers of the GPM constellation are used. The reference estimates are gauge-adjusted radar precipitation estimates from two ground-based weather radars from the Royal Netherlands Meteorological Institute (KNMI). Echo top heights (ETHs) retrieved from the same radars are used to classify the precipitation as shallow, medium, or deep. Spatial averaging methods (Gaussian weighting vs. arithmetic mean) minimally affect the magnitude of the precipitation estimates. Footprint size has a higher impact but cannot explain all discrepancies between the ground- and satellite-based estimates. Additionally, the discrepancies between GPROF and the reference are largest for low ETHs, while the relative bias between the different footprint sizes and implemented weighting methods increase with increasing ETHs. Lastly, our results do not show a clear difference between coastal and land simulations. We conclude that the uncertainty introduced by merging different channels and sensors cannot fully explain the discrepancies between satellite- and ground-based precipitation estimates. Hence, uncertainties related to the retrieval algorithm and environmental conditions are found to be more prominent than resampling uncertainties, in particular for shallow and light precipitation.