River Discharge Measurements Research Articles

Runoff characteristics of headwater catchments in a young volcanic region

Because volcanic rocks have high permeability, rainfall quickly infiltrates into the ground. Perennial rivers often do not exist in volcanic areas. However, available studies elucidating hydrologic characteristics and the mechanisms of stream formation in young volcanic areas are limited in number, extent, and coverage prohibiting a comprehensive understanding of runoff formation. By measuring river discharge for two years, we examined the characteristics of surface runoff in four adjacent headwater catchments within a young volcanic area of Mt.Fuji. The runoff coefficient R is small, rainfall thresholds for discharge are large, and the lag time Tlag and duration D of rainfall-runoff events are short. These characteristics can be explained by the groundwater flow as the dominant runoff mechanism with high hydraulic conductivity. Due to differences in topography and the depth of groundwater, some differences existed amongst the four adjacent catchments. In addition, a comparison of runoff characteristics between the young volcanic headwater catchments (VHCs) and non-volcanic headwater catchments (NVHCs) reported in other studies established that young VHCs have a smaller R, a shorter Tlag and D, and a larger runoff threshold for storm runoff than NVHCs. These differences are attributed to differences in the hydraulic conductivity of the underlying geology. These findings should help improve hydrological models and river structures for flood prevention.

Synthetic River Flow Videos for Evaluating Image‐Based Velocimetry Methods

AbstractVarious image‐based velocimetry methods have been developed and increasingly used for surface velocity and discharge measurements in rivers. Their evaluation is challenging as in situ comparisons are limited and affected by uncertainties, and synthetic flow images used to test laboratory velocimetry methods are not representative of outdoor applications. A novel method is proposed to generate synthetic river flow videos with known surface velocities. The method is based on open‐source computer graphics tools: Blender and Mantaflow. The synthetic videos represent realistic situations where the scene configuration, for example, riverbed and riparian areas, water aspect, camera location and orientation or lighting can be easily modified. Two cases studies emphasize the strengths and the limitations of the proposed method. The synthetic datasets generated will allow the improvement of different image‐based velocimetry methods, notably through collaborative evaluations or intercomparisons.

A Data Assimilation Approach to the Modeling of 3D Hydrodynamic Flow Velocity in River Reaches

The measurement of river discharge is essential for sustainable water resource management. The velocity–area approach is the most common method for calculating river discharge. Although several velocity measurement methods exist, they often have varying degrees of technical issues attributed to their operational complexity, time effectiveness, accuracy, and environmental impact. To address these issues, we propose a three-dimensional (3D) hydrodynamic model coupled with data assimilation (DA) for velocity measurement with improved accuracy and efficiency. We then apply this model to the Lanxi River reach in Zhejiang Province, China. The experimental results confirm that the obtained assimilated velocities using our proposed algorithm are much closer to the observed velocities than the simulated velocities. Our results show that when using the proposed method, the RMSE is decreased by 78%, and the SKILL and DASS values are 0.96 and 0.92, respectively. These confirm that the DA scheme of the flow velocity measurement is effective and capable of significantly improving the accuracy of the velocity with lower computational complexity.

Sentinel-2 high-resolution data for river discharge monitoring

River monitoring is an open issue due to many intrinsic problems of the ground monitoring network. Over the last few decades, the role of satellite sensors in river discharge estimation is significantly increased thanks to the strong growth in technologies and applications. Focusing on daily river discharge measurements, a non-linear regression model has been used to link the near-infrared (NIR) reflectance ratio between a dry and a wet pixel around the section of a river to the ground measurements of river discharge. The use of medium-resolution satellite data, such as those from MODIS sensors, enables to monitor high and low flows in medium-sized catchments (<100,000 km2), thanks to satellite frequent revisit time and wide spatial coverage. However, such sensors are not suitable to provide information for medium-narrow rivers (< 250 m wide), nor to study river features and patterns that are averaged within a single pixel. Here, we investigated the use of Sentinel-2 NIR reflectances to support the hypothesis that a higher spatial resolution, i.e. 10 m, is able to better identify the wet pixels, more related to the river dynamics, with obvious advantages for river discharge estimation compared to the medium resolution sensors (e.g., MODIS at 250 m). Moreover, it also allows both a finer distinction between vegetation, soil and water and the characterization of water turbidity in the river area. A new formulation enriched by the sediment component is proposed together with a first step toward an uncalibrated procedure to select the wet pixels. Google Earth Engine (GEE) platform has been employed for the data analysis, allowing to avoid the download of big amounts of data, fostering the reproducibility of the analysis in different locations. The accuracy of the river discharges derived from Sentinel-2 reflectances is evaluated against the in-situ observations from selected gauging stations along two Italian rivers, Po and Tiber. The results confirm the good performances obtained with high-resolution images over the Po River, with average Nash-Sutcliffe efficiency ranging between 0.39 and 0.56 for the different configurations adopted. Relatively worse results were obtained over the Tiber River where the Nash-Sutcliffe efficiency ranged between 0.2 and 0.61, due to an issue on the registration of Sentinel-2 images.

Hydrological mass balance of boreal watersheds in the Canadian Shield

This study aims at evaluating the hydrological balance of large watersheds of the Canadian Shield in the James Bay area in Northwestern Quebec, Canada. The focus is set on six rivers of the Canadian Shield altogether draining more than 185,000 km2 of the Boreal Shield, Taiga Shield and Hudson Plains ecozones of Canada. River discharge measurements, geochemical data (δ2H, δ18O and electrical conductivity [EC] of water), remote sensing, and GIS models are used jointly to calculate water balances. The approach allows for partitioning the influence of rainwater, snowmelt, surface runoff, evaporation, transpiration, and groundwater discharge to the hydrological balances of watersheds. On an annual basis, the results suggest that runoff from rainwater (30–61 % of total precipitation) and snowmelt (18–40 % of total precipitation) are the main contributions to river discharge, while the contribution of groundwater discharge to rivers represents < 12 % of the total precipitation. Over the study area, this contribution represents 2–5 km3 of water. The stable isotope mass balances allow for estimating watershed-scale evaporation over inflow ratios ranging between 2 and 10 % and suggest that transpiration has an isotopic composition close to summer rainwater. The hydrological balances further suggest that the total pool of water stored in the active portion of watersheds represents 10–20 % of the total annual precipitation, while the exports of groundwater beyond the limits of surface watersheds are negligible. The seasonal trends in the hydrological balances of monitored watersheds were further documented to provide insights into the sensitivity of watersheds as they face climate change. The observations are used to propose recommendations for monitoring of rivers in the Canadian Shield and to identify future research needs.

Amazonian terrestrial water balance inferred from satellite-observed water vapor isotopes

Atmospheric humidity and soil moisture in the Amazon forest are tightly coupled to the region’s water balance, or the difference between two moisture fluxes, evapotranspiration minus precipitation (ET-P). However, large and poorly characterized uncertainties in both fluxes, and in their difference, make it challenging to evaluate spatiotemporal variations of water balance and its dependence on ET or P. Here, we show that satellite observations of the HDO/H2O ratio of water vapor are sensitive to spatiotemporal variations of ET-P over the Amazon. When calibrated by basin-scale and mass-balance estimates of ET-P derived from terrestrial water storage and river discharge measurements, the isotopic data demonstrate that rainfall controls wet Amazon water balance variability, but ET becomes important in regulating water balance and its variability in the dry Amazon. Changes in the drivers of ET, such as above ground biomass, could therefore have a larger impact on soil moisture and humidity in the dry (southern and eastern) Amazon relative to the wet Amazon.

High-resolution satellite images combined with hydrological modeling derive river discharge for headwaters: A step toward discharge estimation in ungauged basins

The reliance on ground-based measurements has been a long-standing barrier toward deriving river discharge for ungauged headwaters, which are quite sensitive to climate change due to relatively high contributions of snow and glacier meltwater to river discharge. In addition, information pertaining to discharge is scarce and inconsistent across the globe due to economic, geographic, and political reasons. Therefore, understanding spatiotemporal dynamics of river discharge in ungauged headwaters is extremely challenging, particularly in high-mountain regions such as the Tibetan Plateau (TP). This study presents a methodology of estimating daily continuous discharge using multisource remote sensing and a hydrological model that can simulate cryospheric processes including snow and glacier accumulation and melt. The hydrological model is forced and calibrated purely by satellite-observed information, particularly by river widths derived from high-spatial-resolution images without using in situ river discharge measurements. Poorly gauged or ungauged headwaters with river widths on the order of 100 m, including the Lhasa (the tributary of the upper Brahmaputra River), Salween, Mekong, and Yangtze rivers originating on the TP were chosen to test the methodology. At-a-section river widths were derived from high-resolution satellite images (i.e., IKONOS, QuickBird, RapidEye-1/2/4/5, GeoEye-1, and WorldView-2) and Landsat archives. As exemplified by the Nash–Sutcliffe efficiency coefficient values (NSE) (up to 0.8 and higher than 0.5 overall), simulated daily continuous discharge was reliable in the four testbeds with different river bathymetry, narrow river widths, and complex terrain. Uncertainties associated with the accuracy, number, and variability of calibration references (i.e., remotely sensed widths) and optimization algorithms were fully discussed. This study highlights the potential of deriving daily continuous discharge without a priori information on river flow, paving the way for discharge estimation in ungauged headwaters globally and providing important implications for satellite-based discharge estimation in narrow rivers across high-mountain regions.

Stage estimation of Himalayan rivers using passive microwave radiometer and altimeter

ABSTRACT Hydrological variables such as surface water extent and soil moisture are continuously being monitored from satellites with good accuracy. However, orbital measurement of river stage and discharge remains a major challenge even today. Although altimeters provide valuable information on water level variations with a precision of few centimetres, they suffer from poor revisit time and limited gauging sites. Microwave radiometers that provide brightness temperature (BT) can be used to fill the gaps between altimeter passes with a revisit time of 1–2 days, although with limited accuracy. This paper presents an approach for estimation of river stage based on passive microwave radiometer using BT of river/measurement target (M) and upstream calibration target (C). We have used altimeter data from Jason-2/3 and BT data from Advanced Microwave Scanning Radiometer (AMSR2) at 36.5 GHz (descending orbit and horizontal polarization) for the period January 2013 to August 2020 to estimate river stage at 12 virtual gauging sites along Indus, Ganga, and Brahmaputra rivers. A new approach for selection of calibration targets is presented in this study which results in higher correlation between altimeter observed river stage and AMSR2 derived C/M ratio when compared to existing methods. Coefficient of determination (R 2) for C/M ratio with water level for the period 2013–2017 ranged from 0.45 to 0.89. Model functions were generated between C/M ratio and altimeter observed water level for all the gauging sites. Estimated water levels were validated against altimeter and ground observed values for the period January 2018–August 2020 with RMSE ranging from 0.35 to 1.45 m, which is 7–15% of total stage variability.

A Method for Analysis of Spatial Uncertainty in Image Based Surface Velocimetry

The use of image based velocimetry methods for field-scale measurements of river surface flow and river discharge have become increasingly widespread in recent years, as these methods have several advantages over more traditional methods. In particular, image based methods are able to measure over large spatial areas at the surface of the flow at high spatial and temporal resolution without requiring physical contact with the water. However, there is a lack of tools to understand the spatial uncertainty in these methods and, in particular, the sensitivity of the uncertainty to parameters under the implementer's control. We present a tool specifically developed to assess spatial uncertainty in remotely sensed, obliquely captured, quantitative images, used in surface velocimetry techniques, and selected results from some of our measurements as an illustration of the tool's capabilities. The developed software is freely available via the public repository GitHub. Uncertainty exists in the coordinate transformation between pixel array coordinates (2D) and physical coordinates (3D) because of the uncertainty related to each of the inputs to the calculation of this transformation, and additionally since the transformation itself is generally calculated in a least squares sense from an over determined system of equations. In order to estimate the uncertainty of the transformation, we perform a Monte Carlo simulation, in which we perturb the inputs to the algorithm used to find the coordinate transformation, and observe the effect on the results of transformations between pixel- and physical- coordinates. This perturbation is performed independently a large number of times over a range of the input parameter space, creating a set of inputs to the coordinate transformation calculation, which are used to calculate a coordinate transformation, and predict the physical coordinates of each pixel in the image. We analyze the variance of the physical position corresponding to each pixel location across the set of transformations, and quantify the sensitivity of the transformation to changes in each of the inputs across the field of view. We also investigate the impact on uncertainty of ground control point (GCP) location and number, and quantify spatial change in uncertainty, which is the key parameter for calculating uncertainty in velocity measurements, in addition to positions. This tool may be used to plan field deployments, allowing the user to optimize the number and distribution of GCPs, the accuracy with which their position must be determined, and the camera placement required to achieve a target level of spatial uncertainty. It can also be used to estimate the uncertainty in image-based velocimetry measurements, including how this uncertainty varies over space within the field of view.

Simulating River/Lake–Groundwater Exchanges in Arid River Basins: An Improvement Constrained by Lake Surface Area Dynamics and Evapotranspiration

Surface water–groundwater interactions in arid zones are characterized by water exchange processes in a complex system comprising intermittent streams/terminal lakes, shallow aquifers, riparian zone evapotranspiration, and groundwater withdrawal. Notable challenges arise when simulating such hydrological systems; for example, field observations are scarce, and hydrogeological parameters exhibit considerable spatial heterogeneity. To reduce the simulation uncertainties, in addition to groundwater head and river discharge measurements, we adopted remote sensing-based evapotranspiration data and lake area dynamics as known conditions to calibrate the model. We chose the Ejina Basin, located in the lower reaches of the Heihe River Basin in arid northwest China, as the study area to validate our modelling approach. The hydrological system of this basin is characterized by intensive, spatiotemporally variable surface water–groundwater interactions. The areas of the terminal lakes into which all river runoff flows vary significantly depending on the ratio between river runoff and lake evaporation. Simulation results with a monthly time step from 2000 to 2017 indicate that river leakage accounted for approximately 61% of the total river runoff. Our study shows that for areas where surface water and groundwater observations are sparse, combining remote sensing product data of surface water areas and evapotranspiration can effectively reduce the uncertainty in coupled surface water and groundwater simulations.

Geophysical investigations in the Radovna River Spring area (Julian Alps, NW Slovenia)

vodeAbstractThe Radovna River Valley is located in the north-western part of Slovenia in the Julian Alps, where there is an extensive intergranular aquifer whose depth to pre-Quaternary bedrock is unknown. Therefore, to obtain information about the depth of the valley and the geometry of the aquifer two geophysical methods were used in our study; ground penetrating radar (GPR) and seismic reflection method. The low-frequency GPR method has shown to be useful for determining the depth of the groundwater and the predominant groundwater recharge. Also, the high-resolution seismic method provided an insight about the morphology of the pre-Quaternary basement with the deepest point at 141 meters below surface. Measurements of hydrogeological parameters such as groundwater level and river discharge measurements were carried out in the study area. Both data analyses showed that groundwater level and river discharge are highly fluctuating and rapidly changing, indicating a well-permeable aquifer, implying that such an aquifer is extremely sensitive and vulnerable to extreme climate events. Both the geophysical methods and the hydrogeological information have provided important information about the morphology of the valley and the alluvial aquifer, as well as increasing the knowledge about the Radovna springs system, which will contribute very important information for future hydrogeological studies.

Validation of a High-Resolution Numerical Weather Prediction Land Surface Scheme Using Catchment Water Balances

AbstractAn adequate representation of the interaction between the land surface and the atmosphere is critical for both numerical weather prediction and climate models. The surface energy and mass balances are tightly coupled to the terrestrial water cycle, mainly through the state of soil moisture. An inadequate representation of the terrestrial water cycle will deteriorate the state of the land surface model and introduce biases to the atmospheric model. The validation of land surface models is challenging, as there are very few observations and the soil is highly heterogeneous. In this paper, a validation framework for land surface schemes based on catchment mass balances is presented. The main focus of our development lies in the application to kilometer-resolution numerical weather prediction and climate models, although the approach is scalable in both space and time. The methodology combines information from multiple observation-based datasets. Observational uncertainties are estimated by using independent sets of observations. It is shown that the combination of observation-based datasets and river discharge measurements close the water balance fairly well for the chosen catchments. As a showcase application, the framework is then applied to compare and validate four different versions of TERRA ML, the land surface scheme of the COSMO numerical weather prediction and climate model over five mesoscale catchments in Switzerland ranging from 105 to 1713 km2. Despite large observational uncertainties, validation results clearly suggest that errors in terrestrial storage changes are closely linked to errors in runoff generation and emphasize the crucial role of infiltration processes.

The use of triple collocation approach to merge satellite- and model-based terrestrial water storage for flood potential analysis

Terrestrial water storage anomaly (TWSA) retrieved from the Gravity Recovery and Climate Experiment (GRACE) mission can be used as a good indicator of flood potential, but it is limited to regional analysis due to the coarse resolution (∼300 km). This study proposed an optimal least square merging scheme to spatially downscale GRACE-based TWSA via integrating it with model-based TWSA estimates on to a finer resolution (0.125°) with associated uncertainty estimates. TWSA products estimated from GRACE, an advanced Land Surface Model (LSM), and reanalysis data were used in the merging process. Error uncertainties in each input data sets and the merged TWSA product were calculated based on the triple collocation approach. The merged TWSA was subsequently used to compute the flood potential index and results were compared against measured river discharge and historical flood records. Results showed that the merged TWSA provides a better representation of the spatial variability of TWSA at a sub-regional scale relative to GRACE-based TWSA along with improved uncertainties. Merged TWSA also showed improved capability in capturing flood events that occurred at sub-regional to local scales relative to GRACE-based TWSA when compared to historical flood records.

PENGARUH PERTAMBANGAN PASIR TERHADAP EROSI DAN SEDIMENTASI SUNGAI (STUDI KASUS DI DESA TANJUNG ALAM KECAMATAN SEI DADAP KABUPATEN ASAHAN)

Sand mining in Tanjung Alam Village, Sei Dadap District, Asahan Regency, which is carried out with a suction machine is thought to have a negative impact on the environment. This study aims to examine the effect of sand mining on erosion and sedimentation in that river. The analysis steps begins with measuring the river profile, measuring flow velocity, measuring river discharge, measuring sediment concentration, measuring rainfall, measuring slope and calculating the estimated rate of erosion. To calculate the estimated erosion rate in this study using the USLE method and to calculate the sediment load using SNI 3414: 2008 with the depth-integrating technique. Based on the calculation results, it is known that sand mining activity causes narrowing of the river profile, accelerates river flow, increases river discharge so that it increases the rate of erosion, and sedimentation. The highest sediment load value is at point 5 which is located downstream of mining 1 and 2 with a value of 2922.48 tons/day. While the highest estimated erosion rate is at point 5 with a value of 125.9 tons/ha/year which is included in the heavy category. Measurements at each sampling point prove that sand mining activities have a high impact on increasing the rate of erosion and sedimentation in rivers.

Using Remotely Sensed Sea Surface Salinity and Colored Detrital Matter to Characterize Freshened Surface Layers in the Kara and Laptev Seas during the Ice-Free Season

The overall volume of freshwater entering the Arctic Ocean has been growing as glaciers melt and river runoff increases. Since 1980, a 20% increase in river runoff has been observed in the Arctic system. As the discharges of the Ob, Yenisei, and Lena rivers are an important source of freshwater in the Kara and Laptev Seas, an increase in river discharge might have a significant impact on the upper ocean circulation. The fresh river water mixes with ocean water and forms a large freshened surface layer (FSL), which carries high loads of dissolved organic matter and suspended matter into the Arctic Ocean. Optically active material (e.g., phytoplankton and detrital matter) are spread out into plumes, which are evident in satellite data. Russian river signatures in the Kara and Laptev Seas are also evident in recent SMOS Sea Surface Salinity (SSS) Arctic products. In this study, we compare the new Arctic+ SSS products, produced at the Barcelona Expert Center, with the Ocean Color absorption coefficient of colored detrital matter (CDM) in the Kara and Laptev Seas for the period 2011–2019. The SSS and CDM are found to be strongly negatively correlated in the regions of freshwater influence, with regression coefficients between −0.72 and −0.91 in the studied period. Exploiting this linear correlation, we estimate the SSS back to 1998 using two techniques: one assuming that the relationship between the CDM and SSS varies regionally in the river-influenced areas, and another assuming that it does not. We use the 22-year time-series of reconstructed SSS to estimate the interannual variability of the extension of the FSL in the Kara and Laptev Seas as well as their freshwater content. For the Kara and Laptev Seas, we use 32 and 28 psu as reference salinities, and 26 and 24 psu isohalines as FSL boundaries, respectively. The average FSL extension in the Kara Sea is 2089–2611 km2, with a typical freshwater content of 11.84–14.02 km3. The Laptev Sea has a slightly higher mean FSL extension of 2320–2686 km2 and a freshwater content of 10.15–12.44 km3. The yearly mean freshwater content and extension of the FSL, computed from SMOS SSS and Optical data, is (as expected) found to co-vary with in situ measurements of river discharge from the Arctic Great Rivers Observatory database, demonstrating the potential of SMOS SSS to better monitor the river discharge changes in Eurasia and to understand the Arctic freshwater system during the ice-free season.

Surface Flow Velocities From Space: Particle Image Velocimetry of Satellite Video of a Large, Sediment-Laden River

Conventional, field-based streamflow monitoring in remote, inaccessible locations such as Alaska poses logistical challenges. Safety concerns, financial considerations, and a desire to expand water-observing networks make remote sensing an appealing alternative means of collecting hydrologic data. In an ongoing effort to develop non-contact methods for measuring river discharge, we evaluated the potential to estimate surface flow velocities from satellite video of a large, sediment-laden river in Alaska via particle image velocimetry (PIV). In this setting, naturally occurring sediment boil vortices produced distinct water surface features that could be tracked from frame to frame as they were advected by the flow, obviating the need to introduce artificial tracer particles. In this study, we refined an end-to-end workflow that involved stabilization and geo-referencing, image preprocessing, PIV analysis with an ensemble correlation algorithm, and post-processing of PIV output to filter outliers and scale and geo-reference velocity vectors. Applying these procedures to image sequences extracted from satellite video allowed us to produce high resolution surface velocity fields; field measurements of depth-averaged flow velocity were used to assess accuracy. Our results confirmed the importance of preprocessing images to enhance contrast and indicated that lower frame rates (e.g., 0.25 Hz) lead to more reliable velocity estimates because longer capture intervals allow more time for water surface features to translate several pixels between frames, given the relatively coarse spatial resolution of the satellite data. Although agreement between PIV-derived velocity estimates and field measurements was weak (R2 = 0.39) on a point-by-point basis, correspondence improved when the PIV output was aggregated to the cross-sectional scale. For example, the correspondence between cross-sectional maximum velocities inferred via remote sensing and measured in the field was much stronger (R2 = 0.76), suggesting that satellite video could play a role in measuring river discharge. Examining correlation matrices produced as an intermediate output of the PIV algorithm yielded insight on the interactions between image frame rate and sensor spatial resolution, which must be considered in tandem. Although further research and technological development are needed, measuring surface flow velocities from satellite video could become a viable tool for streamflow monitoring in certain fluvial environments.

Estimation of river flow using CubeSats remote sensing

River flow characterizes the integrated response from watersheds, so it is essential to quantify to understand the changing water cycle and underpin the sustainable management of freshwaters. However, river gauging stations are in decline with ground-based observation networks shrinking. This study proposes a novel approach of estimating river flows using the Planet CubeSats constellation with the possibility to monitor on a daily basis at the sub-catchment scale through remote sensing. The methodology relates the river discharge to the water area that is extracted from the satellite image analysis. As a testbed, a series of Surface Reflectance PlanetScope images and observed streamflow data in Araguaia River (Brazil) were selected to develop and validate the methodology. The study involved the following steps: (1) survey of measurements of water level and river discharge using in-situ data from gauge-based Conventional Station (CS) and measurements of altimetry using remote data from JASON-2 Virtual Station (JVS); (2) survey of Planet CubeSat images for dates in step 1 and without cloud cover; (3) image preparation including clipping based on different buffer areas and calculation of the Normalized Difference Vegetation Index (NDVI) per image; (4) water bodies areas calculation inside buffers in the Planet CubeSat images; and (5) correlation analysis of CubeSat water bodies areas with JVS and CS data. Significant correlations between the water bodies areas with JVS (R2 = 88.83%) and CS (R2 = 96.49%) were found, indicating that CubeSat images can be used as a CubeSat Virtual Station (CVS) to estimate the river flow. This newly proposed methodology using CubeSats allows for more accurate results than the JVS-based method used by the Brazilian National Water Agency (ANA) at present. Moreover, CVS requires small areas of remote sensing data to estimate with high accuracy the river flow and the height variation of the water in different timeframes. This method can be used to monitor sub-basin scale discharge and to improve water management, particularly in developing countries where the presence of conventional stations is often very limited.

G‐RUN ENSEMBLE: A Multi‐Forcing Observation‐Based Global Runoff Reanalysis

AbstractRiver discharge is an Essential Climate Variable (ECV) and is one of the best monitored components of the terrestrial water cycle. Nonetheless, gauging stations are distributed unevenly around the world, leaving many white spaces on global freshwater resources maps. Here, we use a machine learning algorithm and historical weather data to upscale sparse in situ river discharge measurements. We provide a global reanalysis of monthly runoff rates for periods covering decades to the past century at a resolution of 0.5° (about 55 km), and with up to 525 ensemble members based on 21 different atmospheric forcing data sets. This global runoff reconstruction, named Global RUNoff ENSEMBLE (G‐RUN ENSEMBLE), is evaluated using independent observations from large river basins and benchmarked against other publicly available runoff data sets over the period 1981–2010. The accuracy of the data set is evaluated on observed river flow from basins not used for model calibration and is found to compare favorably against state‐of‐the‐art global hydrological model simulations. The G‐RUN ENSEMBLE estimates the global mean runoff volume to range between 3.2 × 104 and 3.8 × 104 km3 yr−1. This publicly available data set (https://doi.org/10.6084/m9.figshare.12794075) has a wide range of applications, including regional water resources assessments, climate change attribution studies, hydro‐climatic process studies as well as the evaluation, calibration and refinement of global hydrological models.

ADHI: the African Database of Hydrometric Indices (1950–2018)

Abstract. The African continent is probably the one with the lowest density of hydrometric stations currently measuring river discharge despite the fact that the number of operating stations was quite important until the 1970s. This new African Database of Hydrometric Indices (ADHI) provides a wide range of hydrometric indices and hydrological signatures computed from different sources of data after a quality control. It includes 1466 stations with at least 10 years of daily discharge data over the period 1950–2018. The average record length is 33 years, and 131 stations have complete records over 50 years. With this new dataset spanning most climatic regions of the African continent, several hydrometric indices have been computed, representing mean flow characteristics and extremes (low flows and floods), and are accessible to the scientific community. The database will be updated on a regular basis to include more hydrometric stations and longer time series of river discharge. The ADHI is available for download at: https://doi.org/10.23708/LXGXQ9 (Tramblay and Rouché, 2020).

CARRYING CAPACITY OF MERCURY POLLUTION TO RIVERS IN THE GOLD MINING AREA OF PANCURENDANG VILLAGE, BANYUMAS

Wastewater from small-scale gold processing is causing mercury contamination in the river. It is important to know the estimation of the carrying capacity of river pollution due to mercury to support the improvement of the river ecosystem. The research objective was to calculate the load capacity of mercury pollution (Hg) in rivers around the gold mining and processing area of Pancurendang Village, Ajibarang District, Banyumas Regency, Central Java. The research begins with river discharge measurements. River discharge is measured by referring to SNI 8066: 2015. Water sampling was carried out at 12 river points with a purposive method that took into account the location (upstream, middle, downstream) and river characteristics, according to SNI 6989-78: 2008. Actual mercury levels in river water were tested at the UGM Integrated Research and Testing Laboratory (LPPT) with Mercury Analyzer. The results of the mercury and discharge levels are used to calculate the actual pollution load. The maximum pollution load is based on the quality standard of mercury in class 2 rivers, namely 0.005 ppm multiplied by river discharge. The load capacity of mercury pollution is obtained from the difference between the maximum pollution load and the actual pollution load. The results of river discharge measurements at the research location are quite varied, ranging from 0.0024-2.925 meters/second. The results of the calculation of the carrying capacity of mercury pollution loads in the river area around the gold mining and processing of Pancurendang Village show a range of 0.0000048-0.005904 kg/day with an average of 0.001325948 kg/day. The capacity of the river in Pancurendang Village has not exceeded its capacity.