River Level Research Articles

Regression-based machine learning approaches for estimating discharge from water levels in microtidal rivers

Regression-based machine learning approaches for estimating discharge from water levels in microtidal rivers

A Study of the Rainwater Pumping Station Operation Plan to Reduce River Water Level

Urban flood damage is caused by various and complex effects such as inland flood and external water flooding, so research on this is necessary. This study presented a rainwater pumping station operation technique that can suppress the rise in water levels in discharge rivers based on rainfall and pump operation scenarios in order to reduce inland water flooding and simultaneously consider external water levels. The research method used genetic algorithm (GA) optimization techniques to calculate the optimal operating level for each pump, and based on this, an operating scenario was created to minimize pump operation. In addition, after constructing a rainfall scenario based on the probability rainfall in Seoul, an optimal pump operation technique that can reduce the external water level while minimizing the inundation of domestic water was proposed. The results of the study showed that as the rainfall intensity increases, the flooding increases and the peak depth of the runoff increases. In addition, as the pumping scenario changes from 1 to 11, the number of pumping operations decreases, which reduces the drainage volume of the pumps, which increases the flooding.

Design and Construction Of A River Water Level Monitoring System Based On IOT (Internet Of Things)

The prolonged rainy season due to extreme weather changes that occur in various regions in Indonesia means that people must be more alert to flood disasters. Many losses occurred due to flood disasters. Therefore, an effort is needed to minimize these losses, namely by creating a river level monitoring system based on IoT (Internet of Things). This river water level detection system uses an ultrasonic sensor (water level sensor), NodeMCU ESP8266 to read data and send it via the telegram application and a buzzer which functions to make a sound when the sensor detects water and the potential for flooding. The system will work automatically when water touches the sensor and sends flood detection status information via the telegram application. This system can be implemented in flood-prone areas. With this monitoring system, river water levels can be monitored and the risk of flooding can be reduced.

Seismic Response Characteristics of a Utility Tunnel Crossing a River Considering Hydrodynamic Pressure Effects

As a long lifeline system of buried structures, the utility tunnel (UT) is vulnerable to earthquake invasion. For utility tunnels with inverted siphon arrangements crossing rivers, the seismic response is more complex due to the basin effect of acceleration in the topography and the influence of fluctuating hydrodynamic pressure, but there is currently a gap in targeted seismic response analyses and references. Based on a UT project in Haikou, this paper studied seismic responses of a cast-in-place UT considering the coupled model of water–soil–tunnel structure on ABAQUS software. Herein, the dynamic fluctuation of hydrodynamic pressure is simulated using an acoustic–solid interaction model. A viscoelastic artificial boundary was used to simulate the soil boundary effect, and seismic loads were equivalent to nodal forces. Considering seismic invading direction and varying water elevation, this paper investigates the dynamic response characteristics and damage mechanisms of river-crossing utility tunnels. This study shows that the basin effect causes the soil acceleration around the UT to show variability in different sections, and the amplification factor of the peak acceleration at the central location is almost doubled. The damage and dynamic water pressure of the UT are intensified under bidirectional seismic excitation, and the damage location is concentrated at the junction of the horizontal section and the vertical section. Bending moments and axial forces are the main mechanical behaviors along the axial direction. Changes in river levels have a certain positive effect on the UT peak MISES, DAMAGEC, and SDEG, and it exhibits a certain degree of energy dissipation and seismic damping effect. For the aseismic design of cross-river cast-in-place utility tunnels, bidirectional seismic calculations should be performed, and the influence of river hydrodynamic pressure should not be neglected.

Using satellite tracking to assess the use of protected areas and alternative roosts by Whooper and Bewick's Swans

Protected areas are one of the major tools used in the conservation of biodiversity, but animals are unlikely always to remain within these human‐made boundaries. Understanding when and why species choose to leave protected areas can help us to improve the effectiveness of these management tools. Here, we investigate the use of protected and non‐protected areas by two migratory species undergoing rapid wintering population changes in northwest Europe: Whooper Swans Cygnus cygnus and Bewick's Swans Cygnus columbianus bewickii. Global positioning system tags were fitted to 15 Whooper Swans in winter 2008/09 and to 18 Bewick's Swans from winter 2013/14 to 2014/15 at the Ouse Washes Special Protection Area (an internationally important roost for wintering waterbirds) and on adjacent fields in southeast England. Here, swans feed on farmland during the day but return to designated reserves to roost at night, where they receive protection from predators and disturbance within managed roost habitats. When swans roost elsewhere at alternative sites, they may face more adverse conditions, and so understanding the extent and causes of the use of alternative roosts is important for swan conservation efforts. The alternative roosting proportion, defined as the proportion of nights spent outside protected reserves, was 0.237 for Bewick's Swans and challenging to quantify accurately for Whooper Swans. A generalized additive mixed model to model repeated measurements on individuals showed that the proportion of time that Bewick's Swans spent at alternative roosts correlated positively with river level and negatively with temperature. Competition and foraging flight distances are thought to drive these relationships, as swans seek access both to roost space and to nearby feeding habitats. Our findings improve our understanding of the environmental conditions under which migratory waterbirds may choose to roost outside protected areas.

Climate-driven peatlands development and vegetation dynamics in Northeastern China since the mid-Holocene: New evidence from Huanan peatlands

Studying peatland evolution and vegetation patterns in response to climate change provides valuable insights into future ecosystem trends. This study focuses on the Huanan peatland located in Changbai Mountain regions in Northeast China, utilizing phytolith analysis and stable carbon isotope composition (δ13C) to reconstruct late Holocene vegetation dynamics. Additionally, grain size, total organic carbon (TOC), and total nitrogen (TN) analyses were conducted to understand peatland development and paleoclimate since the late Holocene. The research identifies three distinct climate periods since 5500 cal yr BP: an initial warm, humid phase (5500–4000 cal yr BP) characterized by high river levels that formed lacustrine sediments; a subsequent transition to cold, dry conditions (4000–1500 cal yr BP) that initiated peat formation; and continued dry, cold conditions (1500 cal yr BP to the present) with sustained peat growth. Phytolith data reveal a dominant forest vegetation type since 2000 cal yr BP, further divided into three periods: 2000–1300 cal yr BP, marked by diverse and dense vegetation; 1300–750 cal yr BP, with declining plant cover; and 750 cal yr BP to the present, characterized by an increase in woody plants but a reduction in local grass cover.

Utilizing LSTM-GRU for IOT-Based Water Level Prediction Using Multi-Variable Rainfall Time Series Data

This research describes experiments using LSTM, GRU models, and a combination of both to predict floods in Semarang based on time series data. The results show that the LSTM model is superior in capturing long-term dependencies, while GRU is better in processing short-term patterns. By combining the strengths of both models, this hybrid approach achieves better accuracy and robustness in flood prediction. The LSTM-GRU hybrid model outperforms the individual models, providing a more reliable prediction framework. This performance improvement is due to the complementary strengths of LSTM and GRU in handling various aspects of time series data. These findings emphasize the potential of advanced neural network models in addressing complex environmental challenges, paving the way for more effective flood management strategies in Semarang. The performance graph of the LSTM, GRU, and LSTM-GRU models in various scenarios shows significant differences in the performance of predicting river water levels based on rainfall input. The MAPE, MSE, RMSE, and MAD metrics are presented for training and validation data in six scenarios. Overall, the GRU model and the LSTM-GRU combination provide good performance when using more complete input variables, namely, downstream and upstream rainfall, compared to only using downstream rainfall.

Long-term AI prediction of ammonium levels in rivers using transformer and ensemble models

This study provides a cutting-edge machine learning approach to forecast ammonium (NH4+) levels in River Lee London. Ammonium concentrations were predicted over several time intervals using a complete dataset that includes temperature, turbidity, chlorophyll, dissolved oxygen, conductivity, and pH. Our technique captures the intricate connections between environmental conditions and ammonium concentrations using developed algorithms, including Temporal Fusion Transformer (TFT), Random Forest (RF) and Extreme Gradient Boosting (XGBoost) levels versus the important factors, considerably improving prediction accuracy. The novel aspect of this study is the utilisation of the TFT model for multi-horizon forecasting, which offers high accuracy and interpretability in hydrological predictions by combining convolutional components with an attention mechanism. The study also demonstrates the effectiveness of the TFT model in capturing short-term fluctuations while retaining accuracy over long time periods, which is a major difficulty in environmental modelling. The models used, have exceptional forecasting skills, predicting 150, 200, 365, 730, and 1095 days based on daily average and 12, 24 and 30 months based on monthly average. This dual-scale model combines flexibility and resilience, making it an effective tool for forecasting both short- and long-term environmental changes. The RF model excelled in long-term forecasts, sustaining high R-squared (R²) (0.97) values and low root mean square error (RMSE) (0.18), and the second best one was the XGBoost with optimiser with R2 of (0.92) and RMSE of (0.25) with forecasting 1095 days. The results also found that whilst the TFT captured the fluctuations in the short-term, it struggled with the longer-term predictions due to data granularity. The XGBoost model did remarkably well in monthly forecasts up to 12 months, maintaining low RSME. The findings also highlight the necessity of proactive water management techniques to reduce the risk of potential ecological effects, including hypoxia and oxygen depletion. The findings support resource managers in addressing prospective ammonium toxicity concerns such as oxygen depletion and ecological stress.

Exploring the power of data-driven models for groundwater system conceptualization: a case study of the Grazer Feld Aquifer, Austria

AbstractDeveloping a reliable conceptual model is crucial for analyzing groundwater systems. An essential part of the aquifer conceptualization is the identification of the hydrological stresses that control the hydraulic head fluctuations. By effectively capturing and understanding these stresses, the propagation of potential errors and uncertainties through subsequent modeling steps can be minimized. This study aims to test data-driven models as screening models for conceptualizing a groundwater system. The case study is applied to the Grazer Feld Aquifer in southeast Austria. Time series models are applied to: (1) identify the stresses likely influencing the observed head fluctuations and their spatial variability; (2) identify locations where a lack of understanding of head fluctuations exists; and (3) discuss the limitations and opportunities associated with data-driven models to support system conceptualization. Time series models were created for 144 monitoring wells where sufficient head observations were available during the calibration period (2005–2015). A total of 576 models were developed, incorporating the combinations of stresses: recharge, river level, and a step trend. Following the model selection process, each model was categorized based on its performance and divided into four groups. At 88 sites, recharge and river level variations were identified as the primary controlling stresses influencing head fluctuations. The inclusion of the step trend was found to be necessary at five sites to accurately simulate heads due to dam construction. The application of data-driven models in this study enhanced the identification of key aquifer stresses, facilitating a more informed understanding of the groundwater system.

Modeling Climate and Tectonic Controls on Bias in Measured River Incision Rates

AbstractRates of land surface processes provide insights into climatic and tectonic influences on topography. Bedrock incision rates are estimated by dating perched landforms such as strath terraces, assuming a constant bedrock incision rate from terrace abandonment to the next terrace level or present river level. These estimates express biases from the stochastic nature of sediment and water discharge in controlling river incision as well as from using a mobile channel elevation as a reference frame, leading to different incision rates when calculated over different timeframes. We introduce a 1‐D model incorporating fluvial mechanics, tectonics, sediment, and climate variability to predict these biases and assess their sensitivity to climate and tectonics. Findings suggest biases intensify under highly variable climates and slow rock uplift, with climate periodicity being a primary control for our modeled scenarios. Our model provides a mechanism to improve river incision measurement uncertainty, impacting paleoclimate and tectonic geomorphology reconstructions.

Flow velocity determines detritus availability and microbial food web patterns in a river confluence

Detritus is the vital support for the microbial food web, which would further affect river ecological conditions. Determining the effects of detritus availability on microbial food webs in rivers is critical for protecting river ecological functions. However, the detritus availability was difficult to estimate directly, since the detritus transformation processes (i.e. detritus availability) and flow-induced transport processes are interdependent in rivers. Therefore, this study quantified the detritus transformation processes in a natural river and further identified the impacts of detritus availability on microbial food web patterns. Results revealed that the flow velocity was the main physical driver determining the detritus availability. The decreased velocity would promote detritus availability. Moreover, the increased detritus availability significantly promoted the diversity of bacteria, protozoan and metazoan (p < 0.05). The responses of low trophic levels to detritus availability were significantly greater than those of higher trophic levels, emphasizing the bottom-up cascading effect of detritus availability on microbial food web composition (p < 0.05). From microbial food web perspectives, the detritus availability was amplified with flow velocity decreased, promoting trophic transfer efficiency between different trophic levels. Results and findings revealed the ecological effect of detritus transformation processes on multi-trophic levels in rivers and provided advantageous information for river management.

Postbreeding ecology of wood ducks in the Illinois River Valley

AbstractThe wood duck (Aix sponsa) consistently ranks within the top 5 harvested duck species for both Illinois and the Mississippi Flyway. While substantial research has been done on wood ducks, especially their breeding ecology, few studies have investigated the postbreeding ecology of the species. We captured and marked wood ducks with either a very high frequency (VHF) radio transmitter or a solar‐charged global system of mobile communication (GSM) transmitter during the postbreeding period from August through September 2018–2020. Capture locations were within the La Grange Pool of the Illinois River extending from near Pekin, Illinois to the La Grange Lock and Dam near Meredosia, Illinois, USA. We used conventional radio‐telemetry techniques to track wood ducks to determine cover type use, home range size, daily movement patterns, survival, and migration chronology. Home range size (95% minimum convex polygon) for wood ducks averaged 6,820 ± 572 ha (SE) and we did not find evidence for a difference by age, sex, or transmitter type. Daily movement distance in August (2,031 ± 51 m) was similar to daily movement distance in September (1,922 ± 44 m), but daily movement distances for August and September were less than daily movement distance for October (3,509 ± 53 m) and November (3,347 ± 106 m). Wood ducks primarily used wetlands with woody (45.0%) and emergent vegetation (40.4%), and the most commonly used wetland types by wood ducks were impounded wetlands (53.8%), lakes (17.6%), and ponds (10.7%). Model‐derived survival during the postbreeding period was 0.79 (95% CI = 0.74–0.84). Daily survival was positively related to increased river level and had a mean increase of 4.06 ± 0.67% for every 0.3‐m increase in the Illinois River level at low river levels (1.5–3.0 m) and a mean increase of 1.38 ± 0.32% for every 0.3‐m increase in the Illinois River level at high river levels (4.0–5.5 m). Average departure date of wood ducks leaving the Illinois River Valley was 27 October (range =13 August–15 December), and adult male wood ducks left the study area 11–16 days earlier than the other age and sex cohorts (H2 = 11.6, P = 0.01). Providing additional waterfowl sanctuaries that contain wooded wetlands, especially in years of low river levels, may increase survival for wood ducks during the postbreeding period.

Application of machine learning models in predicting discharge coefficient of side B-type piano key weir

Side weir is a hydraulic structure within a channel which is usually used to discharge excess water, to divert the flow, and to regulate water surface levels in rivers and irrigation and drainage networks. In general, piano key weirs (PKW) have been used as weirs perpendicular to the flow direction in straight channels. However, the use of the PKW as a side weir in the outer arch of the channels is a new approach to enhance the weir's performance. In this study, 289 tests were first performed on the B-type rectangular side piano key weir (RSPKW) at two arc angles of 30 and 120°. Then, Fuzzy Inference System (FIS), Adaptive Neuro-Fuzzy Inference System (ANFIS), ANFIS and Teaching Learning Based Optimization (TLBO), ANFIS and Grasshopper Optimization Algorithm (GOA), Extreme Learning Machine (ELM) and Outlier Robust ELM (ORELM) models were used to predict the weir discharge coefficient. The results showed that two optimization models of TLBO and GOA increased the accuracy of the ANFIS model. The results showed that the ANFIS-GOA model has accuracy of Root Mean Squared Error (RMSE) = 0.0361, Coefficient of determination (R2) = 0.9772, and Kling Gupta coefficient (KGE) = 0.9858. The ANFIS-TLBO, ANFIS, and FIS models were ranked, respectively. Also, the results showed that ELM and ORELM models have accuracy close to ANFIS-GOA and can be a suitable alternative for complex fuzzy models. According to the statistical analysis, it was found that the parameters of the ratio of weir height to flow depth at the upstream edge of weir (P/h1), arc angle (α), and the ratio of height of the foundation to the main channel width (pd/B) had the greatest role in the development of the models, respectively.

Physical-statistical model for forecasting maximum snowmelt-induced stages (the Ob river near the city of Barnaul as a case study)

For the regions with a long-term snow cover, the amount of solid precipitation is one of the driving factors in the formation of floods. The article considers the possibility of using the orographic correction to the velocity of vertical movements of air masses due to the relief in estimating winter precipitation and forecasting maximum snowmelt-induced stages. The significant influence of autumn freezing on the floodplain inundation and, accordingly, maximum water levels of the Ob River near the city of Barnaul is shown.

A historical Llewellyn Ivor Price fossil fish collection from the Middle to Late Miocene of the Brazilian Amazon

Brazilian Miocene fluvial and lacustrine deposits are mainly found in the southwestern Amazon basin in the state of Acre, where fossiliferous sections from the Middle to Late Miocene of the Solimões Formation (Fm.) emerge in riverbanks during the dry season and low river-water level periods. The Solimões Fm. is notable for its high fossil vertebrate diversity (Myliobatiformes, Siluriformes, Ceratodontiformes, Osteoglossiformes, Characiformes, Acregoliathidae, Squamata, Crocodilia, Testudines, Suliformes, Rodentia, Notoungulata, Litopterna, Astrapotheria, Proboscidea, Didelphimorphia, Pilosa, Sirenia, Cetacea, Primates, Chiroptera, and Artiodactyla) deposited in the paleo-Amazon context. This fossiliferous faunal assemblage reflects the major geological and paleoclimatic Miocene/Pliocene transition events that triggered the greatest extinction of megafauna and other groups of animals in the Paleo-Amazon and, alongside the establishment of the largest Amazon transcontinental river in South America, led to species turnover and the diversity explosion to current levels. Paleontological expeditions carried out between 1950s and 1970s at Solimões Fm. outcrops along the Acre and Upper Juruá rivers allowed the recovery of an extraordinary vertebrate paleontological collection, albeit published in fragmentary contributions. Despite supporting the largest continental fish fauna, comprising ca. 2400 valid extant species, the Amazon hydrographic system harbors a still poorly known fossil ichthyofauna. This study describes a fossil fish assemblage from the Solimões Fm. for the first time based on high accuracy data acquired by improved microCT techniques, computerized systems, and examinations of the best recent and fossil fish collections. This fish assemblage is employed to elucidate ancestral Amazonian fauna paleodiversity and paleoenvironmental context interpretations and explore the elements that led to coeval paleo-ichthyofauna from the northwestern paleo-Amazon region. Miocene species Phractocephalus (†P. acreornatus and †P. nassi) are proposed as fossil guides.

Performance assessment of indicators of a multi-hazards early warning system in an urban mountain region

The urban basins in the mountainous Andean regions are exposed to rapid and poorly planned urbanization processes, causing drastic changes in the landscape, climate, and hydrology. These factors have led to an intensification in the frequency of floods, landslides, falling trees, and droughts, among many other disasters. Therefore, the city of Manizales has implemented an Early Warning System (EWS) to address emergencies and prevent deaths and damage from some of these disasters. However, these hazards can occur simultaneously, further complicating emergency response efforts and requiring the EWS to address multiple hazards; therefore, it has become the Multi-Hazards Early Warning System (MHEWS), which requires indicators and thresholds to generate alerts within sufficient time. The MHEWS implemented in Manizales addresses initially the hazards of floods and landslides, using indicators and thresholds related to recent rainfall and river levels. This article evaluates these proposed indicators by assessing their forecasting capability, effectiveness, and efficiency in increasing warning times, the alarm duration compared to the event duration, and their forecasting ability based on the location of rainfall triggering floods, landslides and other hazards identify. The analyzed indicators focus on hazards and do not consider exposure or vulnerability in communities. The results indicate that a single hazard indicator is not enough, and including multiple indicators based on complementary sources of information improves the performance of MHEWS in addressing emergencies caused by rain.

Rising damp in heritage sites under urban expansion: A comprehensive case study of the Jinsha Earthen Site

Rising damp poses a significant threat to cultural heritage, yet its dynamics within actual heritage sites remain inadequately understood, particularly regarding its evolution and the impact of urbanization. The Jinsha Site, an earthen site from the Shang periods, is primarily composed of silt, with a layer of sand beneath and pebbles at the bottom, making it susceptible to rising damp. This study investigates the Jinsha Site as a case, monitoring soil water content and electrical conductivity, tracking changes in groundwater, precipitation, and river water levels, and investigating the relationship between urbanization and rising damp. The study aims to understand the process, causes, and influencing factors of rising damp and evaluate previous preservation strategies. The findings indicate that groundwater level in the sand layer trigger dampness, whereas the pebble layer exhibits dryness. Urban construction from 2006 to 2013 and 2014–2017 caused fluctuating groundwater levels, leading to repeated transitions between dryness and dampness at the site. Rainfall and river levels have negligible effect; even rainfall exceeding 100 mm (frequency <0.0056) only raises groundwater levels by approximately 1 m and increases water content by less than 2 % for a brief duration, typically less than half a month. The 2022 groundwater pumping strategy effectively maintained levels beneath the pebble layer (506 m), decreasing moisture by 12.71 % and conductivity by 0.27 dS/m. This study reflects on previous protection effortsand offers evidence-based strategies for heritage conservation amidst urban expansion.

Patterns and Trends in Northern Hemisphere River Ice Phenology from 2000 to 2021

River ice plays a crucial role in hydrology, ecosystems, and socio-economic systems in mid-to-high latitude regions. However, there is a notable gap in the analysis of patterns and trends in river ice phenologies and their influence factors at the river level. In this study, we developed a practical method to identify river ice phenologies by leveraging Landsat observations from 2000 to 2021, and established a comprehensive dataset encompassing 7970 river segments across the entire Northern Hemisphere. Our results revealed that the mean freeze-up date, break-up date, and ice duration for these river segments were October 24, May 4, and 192.1 days, respectively. Notably, 56% of the river segments exhibited a warming trend, with the average freeze-up date delayed by 2.7 days per decade, break-up date advanced by 2.5 days per decade, and ice duration shortened by 5.5 days per decade—nearly five times the documented decrease rate over the past 150 years. Furthermore, high-latitude polar regions and the Qinghai-Tibet Plateau demonstrated the most pronounced decreases in ice duration, exceeding 7.0 days per decade on average. We observed a substantial relative contribution, reaching up to 70%, of surface air temperature (SAT) in middle and low latitude regions characterized by relatively high winter temperatures. However, as winter temperatures decrease, the relative influence of SAT diminishes, while the contributions of precipitation, solar radiation, and residuals increase. Our dataset provided essential baseline for further understanding the fundamental physical processes within river systems, offering crucial insights for river management, hydrological disaster risk control, climate change research, and ecosystem conservation efforts.

LID Implementation in an urban basin: a Brazilian case study

Problems related to urban drainage systems and the disorderly growth of large urban centers have led to the search for alternative drainage techniques. These techniques have been called Low Impact Development (LID), and generally influence the reduction of peak flow and runoff volume. In an urban sub-basin in the city of Recife, PE, the hypothesis of replacing existing roofs with a green roof (GR) was considered in three scenarios: (1) pre-urbanized, (2) current, (3.1) 30% of GR, (3.2) 50% of GR, (3.3) 75% of GR, (3.4) 100% of T.V. For this, simulations were carried out in PCSWMM based on the current urbanization situation. Linimetric readings were taken for calibration, obtaining 0.61 NSE and 0.903 R². Validation was carried out using images at two points within the basin. The reduction in peak flow values ranged from 0.74 to 2.10 m³/s, in addition to the time being delayed from 31 to 90 minutes. As for river level values, the variation was between 4 and 13 cm, while volume reductions recorded values between 67.42 and 190.81 m³. Overall, this proposed methodology can help stormwater managers better evaluate the performance of LID techniques at different hydrological scales, showing the importance of prioritizing urban adaptation and green infrastructure implementation.

Using the Potential Transformation of Dissolved Organic Matter to Understand Carbon Emissions from Inland Rivers.

Understanding the transformation of river dissolved organic matter (DOM) is important for assessing the emissions of greenhouse gases (GHGs) in inland waters. However, the relationships between the variations in DOM components and GHGs remain largely unknown. Here, parallel factor analysis (PARAFAC) was applied to investigate the DOM components in 46 inland rivers in China. We found that the GHG emissions in peri-urban rivers were 1.10-2.15 times greater than those in urban rivers. Microbial and environmental factors (e.g., living cell numbers, microbial activity and pH) explained more than 70% of the total variance in GHG emissions in rivers. DOM variations relationships between different components ware revealed based on compositional data principal component analysis (CoDA-PCA). Microbial-mediated DOM production and degradation were quantified, and the degradation levels in peri-urban rivers were 11.8-25.2% greater than those in urban rivers. Differences in carbon emission potential between urban and peri-urban rivers were related to DOM variances and transformations and were affected by water chemistry (e.g., NH4-N and As). This study clarifies the regulatory effects of DOM composition variations and transformations on GHG emissions, and enhances the understanding of the DOM biogeochemical cycle.