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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 be, Water Resources

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 betwe

Accurate precipitation observations are crucial for hydrological forecasts, notably over rapidly responding urban areas. This study evaluated the accuracy of three gridded spaceborne rainfall products (Integrated Multi-satellitE Retrievals for GPM (IMERG), Meteosat Second Generation Visible (MSG-VIS), and MSG-Infrared (MSG-IR)) and the non-governmental Trans-African Hydro-Meteorological Observatory (TAHMO) gauges across the Odaw catchment (Accra, Ghana) from January 2020-July 2022. IMERG is hardly able to capture the strong spatial variability of rainfall required for flood forecasting, but agrees in annual sums with TAHMO and MSG-IR. MSG-IR has difficulties during the wet season. MSG-VIS, only available during daylight, shows limited accuracy and gives high estimates while other products do not detect rain. TAHMO gauges effectively record high-intensity events and their strong spatial variability, although some (daily) accumulations are doubtful and data gaps exist due to technical issues. These findings assist hydrological modelers in selecting appropriate datasets at suitable spatiotemporal resolutions for their research.

Catchment-scale plastic pollution assessments provide insights in its sources, sinks, and pathways. We present an approach to quantify macroplastic transport and density across the Odaw catchment, Ghana. We divided the catchment into the non-urban riverine, urban riverine, and urban tidal zones. Macroplastic transport and density on riverbanks and land were monitored at ten locations in December 2021. The urban riverine zone had the highest transport, and the urban tidal zone had the highest riverbank and land macroplastic density. Water sachets, soft fragments, and foam fragments were the most abundant items. Our approach aims to be transferable to other catchments globally., Water Resources

Accurate precipitation observations are crucial for hydrological forecasts, notably over rapidly responding urban areas. This study evaluated the accuracy of three gridded spaceborne rainfall products (Integrated Multi-satellitE Retrievals for GPM (IMERG), Meteosat Second Generation Visible (MSG-VIS), and MSG-Infrared (MSG-IR)) and the non-governmental Trans-African Hydro-Meteorological Observatory (TAHMO) gauges across the Odaw catchment (Accra, Ghana) from January 2020-July 2022. IMERG is hardly able to capture the strong spatial variability of rainfall required for flood forecasting, but agrees in annual sums with TAHMO and MSG-IR. MSG-IR has difficulties during the wet season. MSG-VIS, only available during daylight, shows limited accuracy and gives high estimates while other products do not detect rain. TAHMO gauges effectively record high-intensity events and their strong spatial variability, although some (daily) accumulations are doubtful and data gaps exist due to technical issues. These findings assist hydrological modelers in selecting appropriate datasets at suitable spatiotemporal resolutions for their research., Water Resources

We measured floating plastic in the Rhine (Netherlands to Switzerland), Odaw (Ghana) and Mekong (Cambodia) rivers using visual counting from bridges. We also measured riverbank and terrestrial plastic density in the Odaw river through physical sampling and classification. We measured river plastic pollution in three rivers across relevant gradients, including geography, hydrology, and plastic pollution levels. In the Rhine basin (Switzerland, France, Germany, the Netherlands) we showed that urban areas and tributaries may act as entry points of plastic pollution. For the Odaw basin (Ghana) we compared floating, terrestrial and riverbank plastics, identifying source and accumulation zones. Finally, in the Mekong river (Cambodia) we quantified the contribution of the city of Phnom Penh to plastic pollution through quantifying the plastic mass balance in the Mekong-Tonle Sap-Bassac region.

This database is the supplementary material for the scientific paper in preparation: Catchment scale assessment of macroplastic pollution in the Odaw river, Ghana. The database contains the field data of monitored and sampled litter (macroplastics and non plastics) in the river, on land, and at the riverbank at ten locations within the study area over the sampling period ( between 11-23 December, 2021). Since the counted macroplastic items were categorised into the aggregated macroplastic polymer types, the file also contains the categorisation of the detailed items (River Ospar list ) into the macroplastic polymer types.

This paper shares an early-career perspective on potential themes for the upcoming International Association of Hydrological Sciences (IAHS) Scientific Decade (SD). This opinion paper synthesizes six discussion sessions in western Europe identifying three themes that all offer a different perspective on the hydrological threats the world faces and could serve to direct the broader hydrological community: “Tipping points and thresholds in hydrology,” “Intensification of the water cycle,” and “Water services under pressure.” Additionally, four trends were distinguished concerning the way in which hydrological research is conducted: big data, bridging science and practice, open science, and inter- and multidisciplinarity. These themes and trends will provide valuable input for future discussions on the theme for the next IAHS SD. We encourage other early-career scientists to voice their opinion by organizing their own discussion sessions and commenting on this paper to make this initiative grow from a regional initiative to a global movement.

This paper shares an early-career perspective on potential themes for the upcoming International Association of Hydrological Sciences (IAHS) scientific decade (SD). This opinion paper synthesizes six discussion sessions in western Europe identifying three themes that all offer a different perspective on the hydrological threats the world faces and could serve to direct the broader hydrological community: “Tipping points and thresholds in hydrology”, “Intensification of the water cycle”, and “Water services under pressure”. Additionally, four trends were distinguished concerning the way in which hydrological research is conducted: big data, bridging science and practice, open science, and inter- and multidisciplinarity. These themes and trends will provide valuable input for future discussions on the theme for the next IAHS SD. We encourage other Early-Career Scientists to voice their opinion by organizing their own discussion sessions and commenting on this paper to make this initiative grow from a regional initiative to a global movement.

Commercial microwave links (CMLs) from cellular telecommunication networks can provide a valuable 'opportunistic' source of high-resolution space-time rainfall information, complementing traditional in-situ measurement devices (rain gauges, disdrometers) and remote sensors (weather radars, satellites). Their greatest potential lies in areas with low gauge densities and lack of weather radars, often in developing countries with a subtropical or tropical climate and generally large spatial rainfall variability. Here, the open-source R package RAINLINK is employed to retrieve CML rainfall maps covering the majority of Sri Lanka for a 3.5 month period based on CML data from on average 1140 link paths. These are compared locally to hourly and daily rain gauge data, as well as to rainfall maps from the dual-frequency precipitation radar on board the global precipitation measurement core observatory satellite. The potential of CMLs for real-time tropical rainfall monitoring is demonstrated., Water Resources

Applications like drought monitoring and forecasting can profit from the global and near-real-time availability of satellite-based precipitation estimates once their related uncertainties and challenges are identified and treated. To this end, this study evaluates the IMERG V06B Late Run precipitation product from the Global Precipitation Measurement mission (GPM), a multisatellite product that combines space-based radar, passive microwave (PMW), and infrared (IR) data into gridded precipitation estimates. The evaluation is performed on the spatiotemporal resolution of IMERG (0.1° × 0.1°, 30 min) over the Netherlands over a 5-yr period. A gauge-adjusted radar precipitation product from the Royal NetherlandsMeteorological Institute (KNMI) is used as reference, against which IMERG shows a large positive bias. To find the origin of this systematic overestimation, the data are divided into seasons, rainfall intensity ranges, echo top height (ETH) ranges, and categories based on the relative contributions of IR, morphing, and PMW data to the IMERG estimates. Furthermore, the specific radiometer is identified for each PMW-based estimate. IMERG’s detection performance improves with higher ETH and rainfall intensity, but the associated error and relative bias increase as well. Severe overestimation occurs during low-intensity rainfall events and is especially linked to PMW observations. All individual PMW instruments show the same pattern: overestimation of low-intensity events and underestimation of high-intensity events. IMERG misses a large fraction of shallow rainfall events, which is amplified when IR data are included. Space-based retrieval of shallow and low-intensity precipitation events should improve before IMERG can become accurate over the middle and high latitudes., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Water Resources

Commercial microwave links (CMLs) from cellular telecommunication networks can provide a valuable "opportunistic" source of high-resolution space-time rainfall information, complementing traditional in-situ measurement devices (rain gauges, disdrometers) and remote sensors (weather radars, satellites). Their greatest potential lies in areas with low gauge densities and lack of weather radars, often in developing countries with a subtropical or tropical climate and generally large spatial rainfall variability. Here, the open-source R package RAINLINK is employed to retrieve CML rainfall maps covering the majority of Sri Lanka for a 3.5-month period based on CML data from on average 1140 link paths. These are compared locally to hourly and daily rain gauge data, as well as to rainfall maps from the Dual-frequency Precipitation Radar on board the Global Precipitation Measurement Core Observatory satellite. The potential of CMLs for real-time tropical rainfall monitoring is demonstrated.

The correct estimation of Sensible Heat Flux (H) and Latent Heat Flux (LE) (i.e., turbulent fluxes) is vital in the understanding of exchange of energy and mass among hydrosphere, atmosphere, and biosphere in an ecosystem. One of the most popular methods to measure these fluxes is the Eddy Covariance (EC) technique; however, there are a number of setbacks to its application, especially in remote and topographically complex terrain such as the higher altitudes of the Andes. Efforts have been made by the scientific community to parameterise these fluxes based on other more commonly measured variables. One of the most widespread methods is the so-called bulk method, which relates average temperature, humidity, and wind vertical profiles to the turbulent fluxes. Another approach to estimate LE is the Penman-Monteith (PM) equation which uses meteorological measurements at a single level. The objective of this study was to validate these methods for the first time in the Tropical Andes in Southern Ecuador (in the páramo ecosystem at 3780 m a.s.l.) using EC and meteorological measurements. It was determined that the bulk method was the best to estimate H, although some adjustments had to be made to the typical assumptions used to estimate surface meteorological values. On the other hand, the PM equation yielded the best LE estimations. For both fluxes, the error in the estimations was within the uncertainty range of the EC measurements. It can be concluded that it is possible to accurately estimate H and LE using the methods described in this paper in this ecosystem when no direct measurements are available.

The correct estimation of Sensible Heat Flux (H) and Latent Heat Flux (LE) (i.e., turbulent fluxes) is vital in the understanding of exchange of energy and mass among hydrosphere, atmosphere, and biosphere in an ecosystem. One of the most popular methods to measure these fluxes is the Eddy Covariance (EC) technique; however, there are a number of setbacks to its application, especially in remote and topographically complex terrain such as the higher altitudes of the Andes. Efforts have been made by the scientific community to parameterise these fluxes based on other more commonly measured variables. One of the most widespread methods is the so-called bulk method, which relates average temperature, humidity, and wind vertical profiles to the turbulent fluxes. Another approach to estimate LE is the Penman-Monteith (PM) equation which uses meteorological measurements at a single level. The objective of this study was to validate these methods for the first time in the Tropical Andes in Southern Ecuador (in the paramo ecosystem at 3780 m a.s.l.) using EC and meteorological measurements. It was determined that the bulk method was the best to estimate H, although some adjustments had to be made to the typical assumptions used to estimate surface meteorological values. On the other hand, the PM equation yielded the best LE estimations. For both fluxes, the error in the estimations was within the uncertainty range of the EC measurements. It can be concluded that it is possible to accurately estimate H and LE using the methods described in this paper in this ecosystem when no direct measurements are available.