In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et al., 2011b, a). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonises them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth/en/, last access: 28 October 2021). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000 active users and over 1000 scientific publications referencing the data sets provided by the network. As of July 2021, the ISMN now contains the data of 71 networks and 2842 stations located all over the globe, with a time period spanning from 1952 to the present. The number of networks and stations covered by the ISMN is still growing, and approximately 70 % of the data sets contained in the database continue to be updated on a regular or irregular basis. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures. A comprehensive review of the existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage and to shape priorities for the next decade of operations of this unique community-based data repository.

Multipurpose small reservoirs are important for livelihoods in rural semi-arid regions. To manage and plan these reservoirs and to assess their hydrological impact at a river basin scale, it is important to monitor their water storage dynamics. This paper introduces a Bayesian approach for monitoring small reservoirs with radar satellite images. The newly developed growing Bayesian classifier has a high degree of automation, can readily be extended with auxiliary information and reduces the confusion error to the land-water boundary pixels. A case study has been performed in the Upper East Region of Ghana, based on Radarsat-2 data from November 2012 until April 2013. Results show that the growing Bayesian classifier can deal with the spatial and temporal variability in synthetic aperture radar (SAR) backscatter intensities from small reservoirs. Due to its ability to incorporate auxiliary information, the algorithm is able to delineate open water from SAR imagery with a low land-water contrast in the case of wind-induced Bragg scattering or limited vegetation on the land surrounding a small reservoir.

An unsteady fully three-dimensional model of Lake Binaba (a shallow small reservoir) in semi-arid Upper East Region of Ghana has been developed to simulate its temperature dynamics. The model developed is built on the Reynolds Averaged Navier–Stokes (RANS) equations, utilizing the Boussinesq approach. As the results of the model are significantly affected by the physical conditions on the boundaries, allocating appropriate boundary conditions, particularly over a water surface, is essential in simulating the lake’s thermal structure. The thermal effects of incoming short-wave radiation implemented as a heat source term in the temperature equation, while the heat fluxes at the free water surface, which depend on wind speed, air temperature, and atmospheric stability conditions are considered as temperature boundary condition. The model equations were solved using OpenFOAM CFD toolbox. As the flow is completely turbulent, which is affected by the complex boundary conditions, a new heat transfer solver and turbulence model were developed to investigate the spatial and temporal distribution of temperature in small and shallow inland water bodies using improved time-dependent boundary conditions. The computed temperature values were compared with four days of observed field data. Simulated and observed temperature profiles show reasonable agreement where the root mean square error (RMSE) over the simulation period ranges from 0.11 to 0.44 °C in temporal temperature profiles with an average value of 0.33 °C. Results indicate that the model is able to simulate the flow variables and the temperature distribution in small inland water bodies with complex bathymetry.

In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et al., 2011a, b). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonizes them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000 active users and over 1000 scientific publications referencing the data sets provided by the network. As of December 2020, the ISMN now contains data of 65 networks and 2678 stations located all over the globe, with a time period spanning from 1952 to present.The number of networks and stations covered by the ISMN is still growing and many of the data sets contained in the database continue to be updated. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade,including a description of network and data set updates and quality control procedures. A comprehensive review of existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage, and to shape priorities for the next decade of operations of this unique community-based data repository.

River flow observations are notoriously difficult to sustain. A site’s setup,operation and maintenance requires expensive equipment, repetitive field work, and physical contact of instruments and people with water. These issues compounded with the fact that rivers may change their course and behaviour in time, and sites are mostly bound to river crossings such as bridges, make equipment susceptible to theft and vandalism. Over the last decade, several contributors in science have pioneered the use of computer vision methods such as Particle Image Velocimetry, Particle Tracking Velocimetry, and Dense Optical Flow to measure stream velocities, and interpret river flow from short movie snapshots. This has resulted in research oriented software such as FUDAA-LSPIV and a limited set of proprietary software aimed for operational use.In this contribution we will share and demonstrate the first version of OpenRiverCam, a new fully open-source, user-friendly, low cost and sustainable web-software stack with API to establish and maintain river rating curves (relationships  between geometry and river discharge) in small to medium sized streams based on the aforementioned computer vision methods. The software is co-designed with practitioners from The Netherlands (Waterboard Limburg and KNMI) and Tanzania (Wami - Ruvu Basin Authority and TMA) with the principle that organizations should be able to establish and maintain operational flow monitoring sites and networks at low costs. We demonstrate it through operational feeds from two first sites (Geul River, Limburg - The Netherlands and Chuo Kikuu - Dar es Salaam, Tanzania). The software stack will allow a practitioner in hydrology to monitor discharge and maintain a rating curve at low cost with simple yet robust equipment. The required set-up contains a permanent camera providing a view of the river surface and a permanent staff gauge for water level readings. Occasionally a bathymetric survey of the river’s cross section is required that can be performed with standard surveying equipment. The open source software stack is available at no costs and contains a separate python library for processing in case a researcher wishes to use the stack. The software operates with a web-client that connects to a locally or globally deployable server stack (laptop,  desktop, local server or cloud) with database, front-end server and workers, so that scalability is warranted. Other than existing software, OpenRiverCam offers: adding and maintenance of sites and cameras; automated retrieval and processing of movies and rating curve analysis, all in a fully open-source code base. The software can therefore be operated with local people, local devices and open software at any scale leading to job creation and locally sustainable services for National Meteorological and Hydrological Services (NMHS) and their service providers. We plan to extend the software with operational water level measurements and possibly other relevant environmental parameters such as sediment deposit segmentation.

Rivers have been highlighted to play a key role in the transport of plastics into the ocean and especially, urban water systems act as a major source of plastic leakage into the marine environment.  However, observed data on plastic pollution in these urban water systems are scarce.  Our study focuses on the Odaw river basin which is the main drainage systems in Accra, Ghana, and is heavily polluted with macroplastics.  This is due to the high anthropogenic activities which have increased the indiscriminate dumping of waste into the water system.  These macroplastics in the water system accumulates over long periods, and a part of it is emitted into the ocean during high discharges.  Due to limited data on the quantification of macroplastics in the Odaw river, the seasonality and hotspot accumulation zones of these macroplastics in the river are unknown.  Such information is crucial for policymakers to prioritize future plastic debris monitoring and mitigation strategies.  We aimed to quantify the macroplastic emissions from the Odaw river into the ocean.  Using visual plastic counting from four bridges together with a hydrodynamic model, estimations for total yearly plastic fluxes through the Odaw river were made.  This model used rainfall data from 2016-2021 at the input nodes to simulate the discharges in the water system.  We estimated a total plastic flux from the Odaw river into the ocean between 2.6 x 101 and 1.7 x 103 tons per year.  Low plastic fluxes were observed at the bridges during dry periods, but a 10 fold increase in plastic fluxes was observed during and after a rainfall event.  Except for two sampling days, negative plastic fluxes were observed at the two bridges closest to the ocean due to the effect of tides.  These observations made the estimation of the total plastic emissions into the ocean challenging because of the bidirectional flow dynamics of the macroplastics at these locations.  The findings of this study provide baseline data for macroplastic transport through the Odaw river into the ocean.  Future research could focus on investigating the accumulation zones of macroplastics at the bridges closest to the river mouths due to the role of tidal dynamics on the river plastic transport and export into the ocean.