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.

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.

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.

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.

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.

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.

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.

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.

Nowcasting leverages real-time atmospheric conditions to forecast weather over short periods. State-of-the-art models, including PySTEPS, encounter difficulties in accurately forecasting extreme weather events because of their unpredictable distribution patterns. In this study, we design a physics-informed neural network to perform precipitation nowcasting using the precipitation and meteorological data from the Royal Netherlands Meteorological Institute (KNMI). This model draws inspiration from the novel Physics-Informed Discriminator GAN (PID-GAN) formulation, directly integrating physics-based supervision within the adversarial learning framework. The proposed model adopts a GAN structure, featuring a Vector Quantization Generative Adversarial Network (VQ-GAN) and a Transformer as the generator, with a temporal discriminator serving as the discriminator. Our findings demonstrate that the PID-GAN model outperforms numerical and SOTA deep generative models in terms of precipitation nowcasting downstream metrics.

Hydrometeorological processes are often assumed to be key drivers of plastic transport. However, the predominant focus on these factors overlooks the impact of anthropogenic factors, such as mismanaged plastic waste (MPW) on plastic transport variability. Here, we investigate the roles of both anthropogenic and hydrometeorological factors on plastic pollution in the Odaw catchment, Ghana. Data on macroplastic transport and density were collected at ten locations between December 2021 and December 2022. We tested for differences between the wet and dry seasons and applied a multiple regression analysis to examine the separate and combined impact of hydrometeorological variables (rainfall, discharge, and windspeed) on macroplastic transport. Additionally, we analyzed the spatial correlation in macroplastic transport/density with MPW and population density. Data collection involved visual counting of floating macroplastics at 10 river locations and counting litter at 9 riverbanks and land locations. Rainfall data was sourced from TAHMO (Trans-African Hydrometeorological Observatory), discharge was measured during field campaigns, and windspeed data sourced from a global climate data provider. We used globally modelled MPW estimates to represent anthropogenic factors. Contrary to previous studies, we found no seasonal differences in macroplastic pollution and only weak correlations were observed between the hydrometeorological variables and macroplastic transport. However, a strong correlation was observed between MPW and macroplastic pollution. We hypothesize that, the influence of hydrometeorological factors on macroplastic transport depend on the relative impact of anthropogenic factors. Our research highlights the limited role of hydrometeorology, showing the significant role of mismanaged plastic waste to field monitored macroplastic pollution variability in the catchment. This insight is essential for future research as it highlights the importance of holistically investigating both anthropogenic and hydrometeorological factors in explaining plastic transport and retention dynamics. This insight is essential for developing interventions that effectively address plastic pollution in catchments.

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.

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.

Plastic is an emerging pollutant, and the quantities in rivers and oceans are expected to increase. Rivers are assumed to transport land-based plastic into the ocean, and the fluvial and marine transport processes have been relatively well studied to date. However, the processes controlling the transport in tidal rivers and estuaries, the interface between fluvial and marine systems, remain largely unresolved. For this reason, current estimates of riverine plastic pollution and export into the ocean remain highly uncertain. Hydrodynamics in tidal rivers and estuaries are influenced by tides and freshwater discharge. As a consequence, flow velocity direction and magnitude can change diurnally. In turn, this impacts the transport dynamics of solutes and pollutants, including plastics. Plastic transport dynamics in tidal rivers and estuaries remain understudied, yet the available observations suggest that plastics can be retained here for long time periods, especially during periods of low net discharge. Additional factors such as riparian vegetation and riverbank characteristics, in combination with bi-directional flows and varying water levels, can lead to an even higher likelihood of long-term retention. Here, we provide a first observation-based estimate of net plastic transport on a daily timescale in tidal rivers. For this purpose, we developed a simple Eulerian approach using sub-hourly observations of plastic transport and discharge during full tidal cycles. We applied our method to the highly polluted Saigon River, Vietnam, throughout six full tidal cycles in May 2022. We show that the net plastic transport is about 20%-33% of the total plastic transport. We found that plastic transport and river discharge are positively and significantly correlated (Pearson's R2Combining double low line0.76). The net transport of plastic is higher than the net discharge (20%-33% and 16%, respectively), suggesting that plastic transport is governed by factors other than water flow. Such factors include wind, varying plastic concentrations in the water, and entrapment of plastics downstream of the measurement site. The plastic net transport rates alternate between positive (seaward) net transport and negative (landward) net transport as a result of the diurnal inequality in the tidal cycles. We found that soft and neutrally buoyant items had considerably lower net transport rates than rigid and highly buoyant items (10%-16% vs. 30%-38%), suggesting that transport dynamics strongly depend on item characteristics. Our results demonstrate the crucial role of tidal dynamics and bi-directional flows in plastic transport dynamics. With this paper we emphasize the importance of understanding fundamental transport dynamics in tidal rivers and estuaries to ultimately reduce the uncertainties of plastic emission estimates into the ocean.

Radar rainfall nowcasting has mostly been applied to relatively large (often rural) domains (e.g., river basins), although rainfall nowcasting in small urban areas is expected to be more challenging. Here, we selected 80 events with high rainfall intensities (at least one 1-km ² grid cell experiences precipitation >15 mm h ²¹ for 1-h events or 30 mm day ²¹ for 24-h events) in five urban areas (Maastricht, Eindhoven, The Hague, Amsterdam, and Groningen) in the Netherlands. We evaluated the performance of 9060 probabilistic nowcasts with 20 ensemble members by applying the short-term ensemble prediction system (STEPS) from Pysteps to every 10-min issue time for the selected events. We found that nowcast errors increased with decreasing (urban) areas especially when below 100 km ². In addition, at 30-min lead time, the underestimation of nowcasts was 38% larger and the discrimination ability was 11% lower for 1-h events than for 24-h events. A set of gridded correction factors for the Netherlands, CARROTS (Climatology-based Adjustments for Radar Rainfall in an Operational Setting) could adjust the bias in real-time QPE and nowcasts by 70%. Yet, nowcasts were still found to underestimate rainfall more than 50% above 40-min lead time relative to the reference, which indicates that this error originates from the nowcasting model itself. Also, CARROTS did not adjust the rainfall spatial distribution in urban areas much. In summary, radar-based nowcasting for urban areas (between 67 and 213 km ²) in the Netherlands exhibits a short skillful lead time of about 20 min, which can only be used for last-minute warning and preparation.

Machine learning (ML) models offer advantages over process-based models for real-time reservoir operation modelling, yet the impact of input variable selection (IVS) and data pre-processing on model performance remains underexplored. This study investigates various input variables for simulating daily reservoir outflow, using the Sirikit reservoir in Thailand as a case study. The datasets include daily Sirikit storage and inflow, outflow of Bhumibol (neighbouring reservoir), downstream discharge, and temporal factors (month and day of the week). Time series decomposition and correlation analyses were used to assess data relationships. We tested seven ML models: multiple linear regression, support vector machine, K-nearest neighbour, classification and regression tree, random forest, multi-layer perceptron, and recurrent neural network (RNN). The optimal input set comprised the previous day’s storage, inflow from 2 days before to 2 days after, and month. With these inputs, all ML models simulated outflow adequately (KGEtraining = 0.42–1.0 and KGEtesting = 0.46–0.56), with RNN showing the most potential for improvement. Input scaling significantly enhanced model performance, reducing RMSEtraining by 44 m3 s-1 and RMSEtesting by 14 m3 s-1. This study’s novelty lies in its comprehensive insights of IVS and data scaling, highlighting their critical roles in enhancing ML model application for operational reservoir simulations.

The number of rainfall observations from personal weather stations (PWSs) has increased significantly over the past years; however, there are persistent questions about data quality. In this paper, we reflect on three quality control algorithms (PWSQC, PWS-pyQC, and GSDR-QC) designed for the quality control (QC) of rainfall data. Technical and operational guidelines are provided to help interested users in finding the most appropriate QC to apply for their use case. All three algorithms can be accessed within the OpenSense sandbox where users can run the code. The results show that all three algorithms improve PWS data quality when cross-referenced against a rain radar data product. The considered algorithms have different strengths and weaknesses depending on the PWS and official data availability, making it inadvisable to recommend one over another without carefully considering the specific setting. The authors highlight a need for further objective quantitative benchmarking of QC algorithms. This requires freely available test datasets representing a range of environments, gauge densities, and weather patterns.

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.

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.