River Gauging Stations Research Articles

Temporal Variability of Hydroclimatic Extremes: A Case Study of Vhembe, uMgungundlovu, and Lejweleputswa District Municipalities in South Africa

The current study investigated hydroclimatic extremes in Vhembe, Lejweleputswa, and uMgungundlovu District Municipalities based on streamflow data from 21 river gauge stations distributed across the study site for the period spanning 1985–2023. Statistical metrics such as the annual mean and maximum streamflow, as well as trends in annual, maximum, seasonal, and high/low flow, were used to evaluate the historical features of streamflow in each of the three district municipalities. Moreover, the Standardized Streamflow Index (SSI) time series computed from streamflow observations at 3- and 6-month accumulation periods were used to assess hydroclimatic extremes, including drought episodes, proportion of wet/dry years and trends in SSI, drought duration (DD), and drought severity (DS). The results indicate that the three district municipalities have experienced localized and varying degrees of streamflow levels and drought conditions. The uMgungundlovu District Municipality in particular has experienced a significant decline in annual and seasonal streamflow as well as an increase in drought conditions during the 38-year period of analysis. This is supported by the negative trends observed in most of the assessed metrics (e.g., annual, maximum, seasonal, low/high flow, and SSI), whereas DD and DS showed positive trends in all the stations, suggesting an increase in prolonged duration and severity of drought. The Lejweleputswa District Municipality depicted positive trends in most of the assessed metrics, suggesting that streamflow increased, whereas drought decreased in the region over the 38-year period of study. Moreover, the Vhembe District Municipality experienced both negative and positive trends, suggesting localized variations in dry and wet conditions. The results presented in this study contribute towards drought monitoring and management efforts in support of policy- and decision-making that aim to uplift water resources management and planning at local and district municipality levels.

Extent of gross underestimation of precipitation in India

Abstract. The underestimation of precipitation (UoP) in the hilly and mountainous parts of South Asia is estimated by some studies to be as large as the observed precipitation (P). However, UoP has been analyzed to only a limited extent across India. To help bridge this gap, watershed-scale UoP was analyzed using various P datasets within a water imbalance analysis. Among these P datasets, the often-used Indian Meteorological Department (IMD) dataset is of primary interest. The gross UoP was identified by analyzing the extent of the imbalance in the annual water budget of watersheds corresponding to 242 river gauging stations for which quality-controlled data on catchment boundaries and streamflow are available. The water year (WY)-based volume of observed annual P was compared against the observed annual streamflow (R) and the satellite-based actual evapotranspiration (ET). Across many watersheds of both Northern and Peninsular India, spurious water imbalance scenarios (P≤R or P≪R+ET) were realized. It is shown that the management of water, such as groundwater extraction, reservoir storage and water diversion, is generally minimal compared to the annual P in such watersheds. It is also shown that annual changes in terrestrial water storage are minimal compared to the annual P in such watersheds. Assuming that data on R (and, to a lesser extent, ET) are reliable, it is concluded that UoP is very likely the cause of this imbalance. Inter-watershed groundwater flow (IGF) is assumed to be negligible. While the effect of IGF on R is unknown, examples are provided which show that IGF is unlikely to be the cause of the observed imbalance in certain watersheds. All 12 of the P datasets analyzed here suffer from UoP, but the extent of the UoP varies by dataset and region. The reanalysis-based datasets ERA5-Land and IMDAA are less affected by UoP than the IMD dataset. Based on the 30-year period of WY 1985–2014, P for the whole of India could be as much as 19 % (ERA5-Land) to 37 % (IMDAA) higher than that from the IMD, with substantial variability within years and river basins. The actual magnitude of UoP is speculated to be even greater. Moreover, trends seen in the IMD's P are not always present in ERA5-Land and IMDAA. Studies using IMD should exercise caution since UoP could lead to the misrepresentation of water budgets and long-term trends. Limitations of this study are discussed.

Do protected areas enhance surface water quality across the Brazilian Amazon?

Protected Areas (PAs) are pivotal instruments in natural resource conservation and maintaining or enhancing ecosystem services, including hydrological functions. In the Amazon, the impact of PAs on the quality of river waters remains largely overlooked. Therefore, this study aims to evaluate whether Environmental Protected Areas (EPAs) and Indigenous Lands (ILs) efficiently protect the quality of surface waters in the Brazilian Amazon. Water quality variables from river gauge stations distributed across the lowlands Amazon are analyzed according to baseline river hydrogeochemistry classification, both inside and outside PAs. This study found that whitewater and clearwater rivers coursing within EPAs and ILs show lower turbidity and electrical conductivity compared to those outside PAs, likely due to the buffering effect of dense forest cover within protected landscapes. Moreover, data indicate that protected areas enhance the water quality from upstream unprotected landscapes in the Brazilian Amazon. These results highlight the need to further invest on effective mechanisms of water resource conservation across lowland tropical regions, and particularly in the Earth’s largest watershed.

Global hydrological models continue to overestimate river discharge

Global hydrological models (GHMs) are widely used to assess the impact of climate change on streamflow, floods, and hydrological droughts. For the ‘model evaluation and impact attribution’ part of the current round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a), modelling teams generated historical simulations based on observed climate and direct human forcings with updated model versions. Here we provide a comprehensive evaluation of daily and maximum annual discharge based on ISIMIP3a simulations from nine GHMs by comparing the simulations to observational data from 644 river gauge stations. We also assess low flows and the effects of different river routing schemes. We find that models can reproduce variability in daily and maximum annual discharge, but tend to overestimate both quantities, as well as low flows. Models perform better at stations in wetter areas and at lower elevations. Discharge routed with the river routing model CaMa-Flood can improve the performance of some models, but for others, variability is overestimated, leading to reduced model performance. This study indicates that areas for future model development include improving the simulation of processes in arid regions and cold dynamics at high elevations. We further suggest that studies attributing observed changes in discharge to historical climate change using the current model ensemble will be most meaningful in humid areas, at low elevations, and in places with a regular seasonal discharge as these are the regions where the underlying dynamics seem to be best represented.

The dual role of meltwater in buffering river runoff in the Yarlung Zangbo Basin, Tibetan Plateau

Study regionThe Yarlung Zangbo Basin (YZB) on the Tibetan Plateau, the world's highest river basin, features a significant cryosphere with glaciers and seasonal snow cover crucial to its hydrology. The study focuses on the region between the Nuxia and Dexing river gauging stations, where glaciers cover 15.4 % of the area. Study focusThe research quantifies the contributions of snow melt (SM) runoff, glacier melt (GM) runoff, rainfall runoff, and baseflow to the total runoff in the YZB. The Spatial Processes in Hydrology (SPHY) model, enhanced with a cryosphere module, was utilized, calibrated with runoff data from the Nuxia station and evapotranspiration data from 2003 to 2014. New hydrological insightsThe study found rainfall runoff to be the primary contributor to annual runoff (66.3 %), followed by snow melt runoff (19.7 %), glacier melt runoff (6.2 %), and baseflow (7.8 %). Snow melt runoff is dominant in early spring, while baseflow prevails in winter. Glacier melt runoff contributes directly to river flow (90.1 %) and replenishes groundwater (9.9 %), which then drains as baseflow. In glacier-rich areas, percolated glacier meltwater significantly recharges groundwater, underscoring its vital role in sustaining river flow in the YZB. This research enhances the understanding of hydrological processes in large alpine river basins and highlights the crucial role of glacier and snow melt in maintaining the Tibetan Plateau's water resources.

Estimation of Catchment Parameters of Snyder's Synthetic Method for Water Resources Planning and Flood Management in Sri Lanka

Snyder's synthetic unit hydrograph method is widely recognized for synthesizing unit hydrographs. This paper illustrates a study demonstrating how to estimate catchment parameters using Snyder's synthetic method for hydro-meteorological stations maintained by the Irrigation Department and its application to Sri Lanka. Updated Snyder's Unit Hydrograph parameters, including coefficient accounting for flood wave and storage condition (Cp) and coefficient representing variations of catchment slop and storage (Ct) for 15 river gauge stations spread across the island, have been presented in this paper. The Cp values vary from 0.22 to 0.7, and the Ct values vary from 0.48 to 5.76. Updating the Snyder parameters is a long-term requirement, and these values can be used to update the previous values estimated in 1980.

Performance analysis of the lisflood hydrological model in a flood event in the madeira river basin

The largest recorded flood of the Madeira River (largest tributary of the Amazon River) occurred in 2014, remaining above the flood level for 90 days, reaching the maximum level on March 30, at 19.69 m. Among the various tools capable of assisting in monitoring and forecasting floods are hydrological models, such as LisFlood, a distributed hydrological model of the rainfall-runoff type. Thus, the objective of this research was to evaluate the performance of the LisFlood model for maximum flows in the Madeira River basin. There were a total of 7 river gauge stations calibrated and subsequently validated, distributed across 5 sub-basins. The calibration process was carried out using a multi-objective method, applying the NSGA II as an optimization algorithm, with the model's performance being evaluated by the NSE and KGE metrics. The calibration and validation results demonstrated, in general, that the LisFlood model performed well between the simulated and observed flow values for the Madeira River basin. The average of the 07 stations analyzed was 0.81 for KGE and 0.69 for NSE, for the calibration process. In validation, the average metrics were 0.78 in KGE and 0.67 in NSE. In relation to the historic flood event that occurred in 2014, it can be seen that the model followed the maximum flow peak observed at the Porto Velho station, with a difference in the simulated flow of 17% lower than the observed flow, for the year 2014, demonstrating good efficiency of LisFlood in simulating maximum flow. Thus, the study demonstrated that the application of the LisFlood model in large basins is effective in simulating maximum flows, satisfactorily simulating extreme flood peaks.

Anthropogenic climate change has influenced global river flow seasonality.

Riverine ecosystems have adapted to natural discharge variations across seasons. However, evidence suggesting that climate change has already impacted magnitudes of river flow seasonality is limited to local studies, mainly focusing on changes of mean or extreme flows. This study introduces the use of apportionment entropy as a robust measure to assess flow-volume nonuniformity across seasons, enabling a global analysis. We found that ~21% of long-term river gauging stations exhibit significant alterations in seasonal flow distributions, but two-thirds of these are unrelated to trends in annual mean discharge. By combining a data-driven runoff reconstruction with state-of-the-art hydrological simulations, we identified a discernible weakening of river flow seasonality in northern high latitudes (above 50°N), a phenomenon directly linked to anthropogenic climate forcing.

Assessing Extreme Drought Events and Their Temporal Impact: Before and after the Operation of a Hydropower Plant

The probabilistic analysis of streamflow and drought event durations plays a crucial role in the efficient and sustainable management of existing water resources in the region. This approach involves the collection of historical hydrological data from river gauging stations, the use of statistical and probabilistic models, and the assessment of hydrological projections at different return periods to provide valuable information for society to understand the potential impacts of extreme events. The analysis is carried out on the Sinú River in Colombia, with consideration given to both the presence and absence of the Hydropower Plant Urrá I. The results reveal that, under natural conditions, a higher number of return periods correspond to less extreme drought flows and longer temporal durations. However, when the hydropower plant is operational, the occurrence and duration of drought are influenced by the regulations implemented during energy generation. The results of this analysis can guide water resource management policies, considering the operation of the hydroelectric plant, thereby enabling decisions that enhance the resilience and sustainability of the river’s hydrological conditions and communities that depend on it.

Impact-based flood forecasting in the Greater Horn of Africa

Abstract. Every year Africa is hit by extreme floods which, combined with high levels of vulnerability and increasing population exposure, often result in humanitarian crises and population displacement. Impact-based forecasting and early warning for natural hazards is recognized as a step forward in disaster risk reduction, thanks to its focus on people, livelihoods, and assets at risk. Yet, the majority of the African population is not covered by any sort of early warning system. This article describes the setup and the methodological approach of Flood-PROOFS East Africa, an impact-based riverine flood forecasting and early warning system for the Greater Horn of Africa (GHA), with a forecast range of 5 d. The system is based on a modeling cascade relying on distributed hydrological simulations forced by ensemble weather forecasts, link to inundation maps for specific return period, and application of a risk assessment framework to estimate population and assets exposed to upcoming floods. The system is operational and supports the African Union Commission and the Disaster Operation Center of the Intergovernmental Authority on Development (IGAD) in the daily monitoring and early warning from hydro-meteorological disasters in eastern Africa. Results show a first evaluation of the hydrological reanalysis at 78 river gauging stations and a semi-quantitative assessment of the impact forecasts for the catastrophic floods in Sudan and in the Nile River basin in summer 2020. More extensive quantitative evaluation of the system performance is envisaged to provide its users with information on the model reliability in forecasting extreme events and their impacts.

Multiple-model based prediction of weekly discharge of the Brahmaputra-Jamuna by assimilating antecedent hydrological regime

In hydrology, accurate predictions and monitoring of river discharge are critical for river engineering, flood mitigation, water resource management and agricultural purposes. The Brahmaputra-Jamuna in Bangladesh, one of the highest discharge rivers in South Asia, is fundamental to the region’s socio-economic structure and a major driver of flooding. Hence, predicting discharge in this river is crucial for managing water resources, protecting infrastructures and minimizing flood risks. In this study, four machine learning models, namely, Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Regression Tree (GBRT), eXtreme Gradient Boosting (XGB) and their ensemble model were employed for weekly river discharge (Qt) prediction at Bahadurabad Transit. Weekly observed discharge and ERA5 reanalysis rainfall data with lag times from 1976 to 2022 were used for model calibration and validation. Various graphical and statistical evaluation metrics were employed to assess the model’s performance. The findings indicate Rt, Rt-1, Qt-1 is the most effective inputs in predicting discharge (r = 0.92). Individual and ensemble models have a very good performance (R2, NSE: 0.85 to 0.92), and the ensemble model outperforms RF by 4.55%, SVM by 8.24%, GBRT by 3.37% and XGB by 2.22%. For peak discharge simulation, the ensemble model shows the best performance (R2 = 0.94, NSE = 0.94, RMSE = 4013.11 m3/s and MAE = 2843.60 m3/s). The reliability analysis verified the ensemble’s superiority. The models in this study were efficient, adaptable and applicable for river discharge prediction in the Brahmaputra-Jamuna and other river gauge stations.

WRF-Hydro for Streamflow Simulation in the MATOPIBA Region within the Tocantins/Araguaia River Basin—Brazil: Implications for Water Resource Management

The effective management of water resources in regions with a high potential for water resources, such as the Tocantins/Araguaia Basin in Brazil, is crucial in the face of current climate change and urban and agricultural expansion. In this context, this study evaluates the WRF-Hydro hydrological model to simulate the flow of the Manuel Alves Pequeno, Vermelho, and Manuel Alves Grande rivers in the MATOPIBA region (encompassing areas from the states of Maranhão, Tocantins, Piauí, and Bahia), an agricultural frontier and the most key area in terms of grain production in Brazil. The aim is to analyze the hydrological parameters of soil infiltration, surface retention depth, land surface roughness, and Manning’s channel roughness. The simulations are conducted at a spatial resolution of 3 km with a channel network of 100 m, covering a period of heavy rainfall from 13 March to 1 June 2018. For model validation, observational data from three river gauge stations of the National Water and Sanitation Agency are used, with assessments based on the Nash-Sutcliffe efficiency index, standard deviation of observations, root mean square error, percentage bias, and correlation coefficient, resulting in values of 0.69, 0.56, 4.99, and 0.83, respectively. In particular, the adjustment of the infiltration factor and surface roughness parameter has a greater contribution to improving the statistical results than the adjustment of the other two hydrological parameters. Additionally, the quality of discharge simulation at each river gauge station is correlated with the temporal distribution of simulated precipitation compared to observed data in the drainage network. Highlighting WRF-Hydro’s potential as a fine-scale model easily coupled with numerical weather prediction, this study significantly advances regional river dynamics evaluation, crucial for strategic water resource management.

Ensemble learning model identifies adaptation classification and turning points of river microbial communities in response to heatwaves.

Heatwaves are a global issue that threaten microbial populations and deteriorate ecosystems. However, how river microbial communities respond to heatwaves and whether and how high temperatures exceed microbial adaptation remain unclear. In this study, we proposed four types of pulse temperature-induced microbial responses and predicted the possibility of microbial adaptation to high temperature in global rivers using ensemble machine learning models. Our findings suggest that microbial communities in parts of South American (e.g., Brazil and Chile) and Southeast Asian (e.g., Vietnam) countries are likely to change due to heatwave disturbance from 25 to 37°C for consecutive days. Furthermore, the microbial communities in approximately 48.4% of the global river gauge stations are prone to fast stress inadaptation, with approximately 76.9% of these stations expected to exceed microbial adaptation after heatwave disturbances. If emissions of particulate matter with sizes not more than 2.5 μm (PM2.5, an indicator of human activities) increase by twofold, the number of global rivers associated with the fast stress adaptation type will decrease by ~13.7% after heatwave disturbances. Understanding microbial responses is crucially important for effective ecosystem management, especially for fragile and sensitive rivers facing heatwave events.

Seasonal decomposition of different types of rivers in Slovakia: Implications for water management and agriculture

This study conducts an analysis of the seasonal decomposition of various river types in Slovakia in order to enhance the understanding of hydrological time series analysis and its potential implications for water management and agriculture. A comprehensive statistical methodology including decomposition of time series and correlation analysis was applied to the data collected from multiple river gauging stations across Slovakia. The results of the study indicate distinct seasonal variations in the hydrological characteristics of different river types, with a clear differentiation between lowland and mountain rivers. These findings have crucial implications for water management and agriculture as they suggest that a deeper understanding of seasonal patterns in river flow can lead to more efficient and effective utilization of water resources. Overall, this study highlights the significance of hydrological time series analysis in gaining a better comprehension of water resources and their management in Slovakia.

A novel workflow for streamflow prediction in the presence of missing gauge observations

AbstractStreamflow predictions are vital for detecting flood and drought events. Such predictions are even more critical to Sub-Saharan African regions that are vulnerable to the increasing frequency and intensity of such events. These regions are sparsely gaged, with few available gaging stations that are often plagued with missing data due to various causes, such as harsh environmental conditions and constrained operational resources. This work presents a novel workflow for predicting streamflow in the presence of missing gage observations. We leverage bias correction of the Group on Earth Observations Global Water and Sustainability Initiative ECMWF streamflow service (GESS) forecasts for missing data imputation and predict future streamflow using the state-of-the-art temporal fusion transformers (TFTs) at 10 river gaging stations in the Benin Republic. We show by simulating missingness in a testing period that GESS forecasts have a significant bias that results in poor imputation performance over the 10 Beninese stations. Our findings suggest that overall bias correction by Elastic Net and Gaussian Process regression achieves superior performance relative to traditional imputation by established methods. We also show that the TFT yields high predictive skill and further provides explanations for predictions through the weights of its attention mechanism. The findings of this work provide a basis for integrating Global streamflow prediction model data and the state-of-the-art machine learning models into operational early-warning decision-making systems in resource-constrained countries vulnerable to drought and flooding due to extreme weather events.

Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States

Abstract. Accurate river streamflow forecasts are a vital tool in the fields of water security, flood preparation and agriculture, as well as in industry more generally. Traditional physics-based models used to produce streamflow forecasts have become increasingly sophisticated, with forecasts improving accordingly. However, the development of such models is often bound by two soft limits: empiricism – many physical relationships are represented empirical formulae; and data sparsity – long time series of observational data are often required for the calibration of these models. Artificial neural networks have previously been shown to be highly effective at simulating non-linear systems where knowledge of the underlying physical relationships is incomplete. However, they also suffer from issues related to data sparsity. Recently, hybrid forecasting systems, which combine the traditional physics-based approach with statistical forecasting techniques, have been investigated for use in hydrological applications. In this study, we test the efficacy of a type of neural network, the long short-term memory (LSTM), at predicting streamflow at 10 river gauge stations across various climatic regions of the western United States. The LSTM is trained on the catchment-mean meteorological and hydrological variables from the ERA5 and Global Flood Awareness System (GloFAS)–ERA5 reanalyses as well as historical streamflow observations. The performance of these hybrid forecasts is evaluated and compared with the performance of both raw and bias-corrected output from the Copernicus Emergency Management Service (CEMS) physics-based GloFAS. Two periods are considered, a testing phase (June 2019 to June 2020), during which the models were fed with ERA5 data to investigate how well they simulated streamflow at the 10 stations, and an operational phase (September 2020 to October 2021), during which the models were fed forecast variables from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS), to investigate how well they could predict streamflow at lead times of up to 10 d. Implications and potential improvements to this work are discussed. In summary, this is the first time an LSTM has been used in a hybrid system to create a medium-range streamflow forecast, and in beating established physics-based models, shows promise for the future of neural networks in hydrological forecasting.

THE IMPACT OF MEGA INFRASTRUCTURE ON THE HYDROLOGY OF RUIRU AND KAMITI RIVER CATCHMENTS

Ruiru, a town within the metropolitan area of Nairobi Kenya, has experienced rapid urbanization which has generated a lot of challenges and problems – economically, socially, and environmentally The Purpose of this research was to study the impact of mega infrastructure on the hydrology of Ruiru and Kamiti rivers. The catchment area was defined by River gauging station (RGS) 3BB12 on River Kamiti and delineated using the RGS as the catchment outlet and was based on SRTM void filled Digital Elevation Model (DEM). Data sets utilized in the study were landsat images, google earth images, river flow data obtained from from water resources authorities (WRA), river abstraction records, water production data and rainfall data. Various GIS methods employed to analyze the data included normalized difference built up index (NDBI), hydrological characterization using the threshold method, change detection, and GIS software (ILWIS and Grass GIS). The supervised classification built up index for the study area increased from 2.8% to 51.8% for the period under study. The mean flows over this period decreased from 2.2m3/s to 0.53m3/s. This indicated that there was a definite effect of change in land use to the river flow regime. However, to conclude that mega projects had a separate effect to ‘normal’ urban development would not have been correct. This is because the flow duration curves for the different periods (1958-1987, 1988-2000, 2005-2010, 2011-2016) showed a similar pattern with no abnormal changes seen in the previous 10-15 years. It is therefore recommended that developmental control be enforced. This will assist in mitigating against the negative effects of land use conversion. The present development control authorities need to be strengthened to enforce development control. The zoning guidelines and planning regulations form the basis for developmental control and should be enforced.

Potential for seasonal flood forecasting in West Africa using climate indexes

AbstractFloods are among the most devastating natural disasters and are expected to become more severe with changing climate and population growth. Flood forecasting is one of the key components of flood risk reduction. The potential for seasonal flood forecasting through climate indexes has not been studied for West Africa so far. This work investigates how climate indicators can be used to predict in advance, one to several months ahead of the flood season, above or below normal flood discharge in West Africa. Six global and regional climate indexes were screened for their potential to predict flood discharge of 56 river gauging stations across West Africa. Forecasting models are developed, based on simple and multiple linear regressions between climate indexes and annual maximum discharge, and evaluated using the relative operating characteristics and the relative operating levels scores. The western dipole mode index is the most skillful individual climate index for above normal flood prediction. Combining climate indexes via multiple linear regressions outperforms individual climate indexes for both above and below normal flood prediction. The models show forecasting skills for up to 4 months prior to the flood season. Hence, this study opens promising possibilities for seasonal flood forecasting in West Africa. This may help alert disaster reduction agencies of entering a period of an increased chance of flooding and may trigger adequate mitigation measures.

Ocean Color Remote Sensing of Suspended Sediments along a Continuum from Rivers to River Plumes: Concentration, Transport, Fluxes and Dynamics

This study investigates the capability of high and medium spatial resolution ocean color satellite data to monitor the transport of suspended particulate matter (SPM) along a continuum from river to river mouth to river plume. An existing switching algorithm combining the use of green, red and near-infrared satellite wavebands was improved to retrieve SPM concentrations over the very wide range (from 1 to more than 1000 g.m−3) encountered over such a continuum. The method was applied to time series of OLI, MSI, and MODIS satellite data. Satisfactory validation results were obtained even at the river gauging station. The river liquid discharge is not only related to the SPM concentration at the gauging station and at the river mouth, but also to the turbid plume area and SPM mass estimated within the surface of the plume. The overall results highlight the potential of combined field and ocean color satellite observations to monitor the transport and fluxes of SPM discharged by rivers into the coastal ocean.

Sediment and terrestrial organic carbon budgets for the offshore Ayeyarwady Delta, Myanmar: Establishing a baseline for future change

Large river deltas serve as globally important archives of terrestrial and shallow marine biogeochemical signatures and because of rapid sedimentation have the potential to impact global biogeochemical cycling. The Ayeyarwady Delta in Myanmar ranks as the world's third largest river delta in terms of sediment supply; however, modern increases in regional anthropogenic impacts risk severe alteration to sediment and TerrOC loads within this major system. By investigating modern sediment and terrestrial organic carbon (TerrOC) accumulation within the offshore Ayeyarwady Delta this study estimates baseline sediment and TerrOC budgets for this understudied mega-delta. Using 210Pb geochronology of 27 sediment cores collected from the continental shelf, we estimate that 405 (+52/-47) Mt of sediment, or ~70‐80% of fluvial sediment discharged from the Ayeyarwady and Thanlwin rivers (the main inputs to the delta), accumulates there annually. Sediment not retained on the shelf is likely partitioned between the Ayeyarwady floodplain, shoreline accretion, and minor deep-sea export. Estimates of TerrOC (based on δ13C mixing models) were coupled with modern sediment accumulation rates to determine an annual burial of 1.93 (+1.09/-0.15) Mt C on the shelf, with TerrOC burial fluxes being highest in the foreset beds of the subaqueous delta, coincident with the area of highest sediment accumulation rate. Based on estimates of the Ayeyarwady and Thanlwin rivers' TerrOC delivery, an apparent ~100% of TerrOC input is preserved on the continental shelf. However, an across shelf trend of increasing TerrOC degradation with distance offshore is also observed, indicating that while the shelf has high apparent TerrOC sequestration, carbon remineralization is also occurring prior to deposition within the subaqueous delta. Based on these conflicting outcomes, we suggest that input of TerrOC from additional sources other than the Ayeyarwady and Thanlwin rivers roughly balance the observed carbon remineralization. Main additional sources of TerrOC include the Sittang and several smaller rivers, and the Ayeyarwady delta plain below the river gauging station. As anthropogenic development within the Ayeyarwady and Thanlwin watersheds continues to increase, these sediment and TerrOC budgets provide a baseline from which future changes within the offshore Ayeyarwady Delta can be monitored.