Recently, there is an upsurge in flood emergencies in Nigeria, in which their frequencies and impacts are expected to exacerbate in the future due to land-use/land cover (LULC) and climate change stressors. The separate and combined forces of these stressors on the Gongola river basin is feebly understood and the probable future impacts are not clear. Accordingly, this study uses a process-based watershed modelling approach - the Hydrological Simulation Program FORTRAN (HSPF) (i) to understand the basin's current and future hydrological fluxes and (ii) to quantify the effectiveness of five management options as adaptation measures for the impacts of the stressors. The ensemble means of the three models derived from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are employed for generating future climate scenarios, considering three distinct radiative forcing peculiar to the study area. Also, the historical and future LULC (developed from the hybrid of Cellular Automata and Markov Chain model) are used to produce the LULC scenarios for the basin. The effective calibration, uncertainty and sensitivity analyses are used for optimising the parameters of the model and the validated result implies a plausible model with efficiency of up to 75 %. Consequently, the results of individual impacts of the stressors yield amplification of the peak flows, with more profound impacts from climate stressor than the LULC. Therefore, the climate impact may trigger a marked peak discharge that is 48 % higher as compared to the historical peak flows which are equivalent to 10,000-year flood event. Whilst the combine impacts may further amplify this value by 27 % depending on the scenario. The proposed management interventions such as planned reforestation and reservoir at Dindima should attenuate the disastrous peak discharges by almost 36 %. Furthermore, the land management option should promote the carbon-sequestering project of the Paris agreement ratified by Nigeria. While the reservoir would serve secondary functions of energy production; employment opportunities, aside other social aspects. These measures are therefore expected to mitigate feasibly the negative impacts anticipated from the stressors and the approach can be employed in other river basins in Africa confronted with similar challenges.
The study investigates the latent pollution sources and most significant parameters that cause spatial variation and develops the best input for water quality modelling using principal component analysis (PCA) and artificial neural network (ANN). The dataset, 22 water quality parameters were obtained from Department of Environment Malaysia (DOE). The PCA generated six significant principal component scores (PCs) which explained 65.40 % of the total variance. Parameters for water quality variation are mainlyrelated to mineral components, anthropogenic activities, and natural processes. However, in ANN three input combination models (ANN A, B, and C) were developed to identify the best model that can predict water quality index (WQI) with very high precision. ANN A model appears to have the best prediction capacity with a coefficient of determination (R2) = 0.9999 and root mean square error (RMSE) = 0.0537. These results proved that the PCA and ANN methods can be applied as tools for decision-making and problem-solving for better managing of river quality.
Recent increase in awareness of the extent of microplastic contamination in marine and freshwater systems has heightened concerns over the ecological and human health risks of this ubiquitous material. Assessing risks posed by microplastic in freshwater systems requires sampling to establish contamination levels, but standard sampling protocols have yet to be established. An important question is whether sampling and assessment should focus on microplastic concentrations in the water or the amount deposited on the bed. On three dates, five replicated water and bed sediment samples were collected from each of the eight sites along the upper reach of the Semenyih River, Malaysia. Microplastics were found in all 160 samples, with mean concentrations of 3.12 ± 2.49 particles/L in river water and 6027.39 ± 16,585.87 particles/m2 deposited on the surface of riverbed sediments. Fibres were the dominant type of microplastic in all samples, but fragments made up a greater proportion of the material on the bed than in the water. Within-site variability in microplastic abundance was high for both water and bed sediments, and very often greater than between-site variability. Patterns suggest that microplastic accumulation on the bed is spatially variable, and single samples are therefore inadequate for assessing bed contamination levels at a site. Sites with the highest mean concentrations in samples of water were not those with the highest concentrations on the bed, indicating that monitoring based only on water samples may not provide a good picture of either relative or absolute bed contamination levels, nor the risks posed to benthic organisms.
Radon (222Rn), an inert gas, is considered a silent killer due to its carcinogenic characteristics. Dhaka city is situated on the banks of the Buriganga River, which is regarded as the lifeline of Dhaka city because it serves as a significant source of the city's water supply for domestic and industrial purposes. Thirty water samples (10 tap water from Dhaka city and 20 surface samples from the Buriganga River) were collected and analyzed using a RAD H2O accessory for 222Rn concentration. The average 222Rn concentration in tap and river water was 1.54 ± 0.38 Bq/L and 0.68 ± 0.29 Bq/L, respectively. All the values were found below the maximum contamination limit (MCL) of 11.1 Bq/L set by the USEPA, the WHO-recommended safe limit of 100 Bq/L, and the UNSCEAR suggested range of 4-40 Bq/L. The mean values of the total annual effective doses due to inhalation and ingestion were calculated to be 9.77 μSv/y and 4.29 μSv/y for tap water and river water, respectively. Although all these values were well below the permissible limit of 100 μSv/y proposed by WHO, they cannot be neglected because of the hazardous nature of 222Rn, especially considering their entry to the human body via inhalation and ingestion pathways. The obtained data may serve as a reference for future 222Rn-related works.
Plant litter is the major source of energy and nutrients in stream ecosystems and its decomposition is vital for ecosystem nutrient cycling and functioning. Invertebrates are key contributors to instream litter decomposition, yet quantification of their effects and drivers at the global scale remains lacking. Here, we systematically synthesized data comprising 2707 observations from 141 studies of stream litter decomposition to assess the contribution and drivers of invertebrates to the decomposition process across the globe. We found that (1) the presence of invertebrates enhanced instream litter decomposition globally by an average of 74%; (2) initial litter quality and stream water physicochemical properties were equal drivers of invertebrate effects on litter decomposition, while invertebrate effects on litter decomposition were not affected by climatic region, mesh size of coarse-mesh bags or mycorrhizal association of plants providing leaf litter; and (3) the contribution of invertebrates to litter decomposition was greatest during the early stages of litter mass loss (0-20%). Our results, besides quantitatively synthesizing the global pattern of invertebrate contribution to instream litter decomposition, highlight the most significant effects of invertebrates on litter decomposition at early rather than middle or late decomposition stages, providing support for the inclusion of invertebrates in global dynamic models of litter decomposition in streams to explore mechanisms and impacts of terrestrial, aquatic, and atmospheric carbon fluxes.
Rivers are the main sources of freshwater supply for the world population. However, many economic activities contribute to river water pollution. River water quality can be monitored using various parameters, such as the pH level, dissolved oxygen, total suspended solids, and the chemical properties. Analyzing the trend and pattern of these parameters enables the prediction of the water quality so that proactive measures can be made by relevant authorities to prevent water pollution and predict the effectiveness of water restoration measures. Machine learning regression algorithms can be applied for this purpose. Here, eight machine learning regression techniques, including decision tree regression, linear regression, ridge, Lasso, support vector regression, random forest regression, extra tree regression, and the artificial neural network, are applied for the purpose of water quality index prediction. Historical data from Indian rivers are adopted for this study. The data refer to six water parameters. Twelve other features are then derived from the original six parameters. The performances of the models using different algorithms and sets of features are compared. The derived water quality rating scale features are identified to contribute toward the development of better regression models, while the linear regression and ridge offer the best performance. The best mean square error achieved is 0 and the correlation coefficient is 1.
Mediterranean climate river systems are among the most threatened ecosystems worldwide, due to a long history of anthropogenic impacts and alien invasive species introductions. Many of such rivers naturally exhibit a non-perennial flow regime, with distinct seasonal, inter-annual and spatial heterogeneity. The present study seeks to detect diatom community patterns and to understand the processes that cause these structures in an Austral Mediterranean river system among different months and river sections. In general, most environmental variables showed an increasing trend downstream for both months, with the exception of pH, dissolved oxygen, PO₄3- and substrate embeddedness, which decreased downstream. A total of 110 diatom species between the two study months (October - 106 taxa; January - 78 taxa) were identified, dominated by 30 species with at least >2% abundance. Diatom community structure differed significantly across river zones, while no significant differences were observed between the study months. A boosted regression trees model showed that B (43.3%), Cu (20.8%), Fe (3.4%) and water depth (3.2%) were the most significant variables structuring diatoms. Diatom species communities reflected environmental variables (i.e., sediment and water chemistry) in this Mediterranean climate river system, as sediment metals such as B, Cu and Fe were found to be important in structuring diatom communities. Biotic influences from fish communities had little effect on diversity, but shifted diatom community structure. Therefore, the current study highlights how river systems have complex interactions that play an important role in determining diatom species composition.
This study reports the concentrations of trace metals in core sediments profile from the coastal and four rivers estuary in the Kuching Division of Sarawak, Malaysia, and the controlling mechanisms influencing their availability in sediments of the studied area. The bonding of trace metals with non-mobile fractions was confirmed with the sequential extraction. Inductively coupled plasma-optical emission spectroscopy (ICP-OES) was used to measure the concentrations of the trace metals. Granulometric analyses were performed using normalized sieve apertures to determine the textural characteristics of the sediments. Enrichment factor was used to evaluate the level of metal enrichment. Heavy metals concentrations in sediment samples varied in the range: Pb (8.9-188.9 mg/kg d.w.), Zn (19.4-431.8 mg/kg d.w.), Cd (0.014-0.061 mg/kg d.w.), Ni (6.6-33.4 mg/kg d.w.), Mn (2.4-16.8 mg/kg d.w.), Cu (9.4-133.3 mg/kg d.w.), Ba (1.3-9.9 mg/kg d.w.), As (0.4-7.9 mg/kg d.w.), Co (0.9-5.1 mg/kg d.w.), Cr (1.4-7.8 mg/kg d.w.), Mg (68.8-499.3 mg/kg d.w.), Ca (11.3-64.9 mg/kg d.w.), Al (24.7-141.7 mg/kg d.w.), Na (8.8-29.4 mg/kg d.w.), and Fe (12,011-35,124.6 mg/kg d.w.). The estimated results of the enrichment factor suggested enrichments of Pb, Zn, and Cu in all the core sediment samples and depths at all sites. The other trace metals showed no enrichments in almost all the sampled stations. Continuous accumulation of Pb, Zn, and Cu metals over a period can be detrimental to living organisms and the ecology. The results obtained from the statistical analyses suggested that the deposition of trace metals in the studied sites is due to anthropogenic inputs from the adjacent land-based sources.
Environmental exposure to active pharmaceutical ingredients (APIs) can have negative effects on the health of ecosystems and humans. While numerous studies have monitored APIs in rivers, these employ different analytical methods, measure different APIs, and have ignored many of the countries of the world. This makes it difficult to quantify the scale of the problem from a global perspective. Furthermore, comparison of the existing data, generated for different studies/regions/continents, is challenging due to the vast differences between the analytical methodologies employed. Here, we present a global-scale study of API pollution in 258 of the world's rivers, representing the environmental influence of 471.4 million people across 137 geographic regions. Samples were obtained from 1,052 locations in 104 countries (representing all continents and 36 countries not previously studied for API contamination) and analyzed for 61 APIs. Highest cumulative API concentrations were observed in sub-Saharan Africa, south Asia, and South America. The most contaminated sites were in low- to middle-income countries and were associated with areas with poor wastewater and waste management infrastructure and pharmaceutical manufacturing. The most frequently detected APIs were carbamazepine, metformin, and caffeine (a compound also arising from lifestyle use), which were detected at over half of the sites monitored. Concentrations of at least one API at 25.7% of the sampling sites were greater than concentrations considered safe for aquatic organisms, or which are of concern in terms of selection for antimicrobial resistance. Therefore, pharmaceutical pollution poses a global threat to environmental and human health, as well as to delivery of the United Nations Sustainable Development Goals.
The study sought to investigate the chronology of events and timing of embryogenesis, as well as breeding performances of three strains of Heterobranchus longifilis from Nigeria. Fish samples were collected from Benue River in Makurdi, Niger River in Onitsha, and Rima River in Sokoto for this study. Induced spawning of the strains was carried out so that egg development could be tracked from fertilization to hatching using a simple microscope. The microphotographs obtained showed that the embryogenesis of the strains followed a similar pattern to those of other members of the family Clariidae, however with changes occurring in the specific timing of the sequences of events (i.e. interstrain and interspecies differences). When the different strains were compared, the study noted similarities (P > 0.05) in the overall breeding performance (except for fertilization rate), survival at different stages of development, timing of embryogenesis, and larvae characteristics. The outcomes of this study, therefore, provide baseline information on what genetic improvement of the species through strain crossing can be attempted in future studies.
Island biogeography is one of the most powerful subdisciplines of ecology: its mathematical predictions that island size and distance to mainland determine diversity have withstood the test of time. A key question is whether these predictions follow at a population-genomic level. Using rigorous ancient-DNA protocols, we retrieved approximately 1,000 genomic markers from approximately 100 historic specimens of two Southeast Asian songbird complexes from across the Sunda Shelf archipelago collected 1893-1957. We show that the genetic affinities of populations on small shelf islands defy the predictions of geographic distance and appear governed by Earth-historic factors including the position of terrestrial barriers (paleo-rivers) and persistence of corridors (Quaternary land bridges). Our analyses suggest that classic island-biogeographic predictors may not hold well for population-genomic dynamics on the thousands of shelf islands across the globe, which are exposed to dynamic changes in land distribution during Quaternary climate change.
Miri city has a dynamic coastal environment, mainly influenced by intensive sedimentation from the Baram River and excessive trace metal loading by the Miri River, which are significant environmental concerns. As the mobility, bioavailability, and toxicity of the trace metals in the sediments are largely controlled by their particulate speciation, the modified BCR sequential extraction protocol was applied to determine the particulate speciation of trace metals in the coastal sediments of Miri, to unravel the seasonal geochemical processes responsible for known observations, and to identify possible sources of these trace metals. The granulometric analysis results showed that littoral currents aided by the monsoonal winds have influenced the grain size distribution of the sediments, enabling us to divide the study area into north-east and south-west segments where the geochemical composition are distinct. The Cu (>84%) and Zn (82%) concentrations are predominantly associated with the exchangeable fraction, which is readily bioavailable. Pb and Cd are dominant in non-residual fractions and other metals viz., Fe, Mn, Co, Ni, and Cr are dominant in the residual fraction. Using Pearson's correlation and factor analysis, the major mechanisms controlling the chemistry of the sediments are identified as association of Cu and Zn with fine fraction sediments, sulphide oxidation in the SW segment of the study area, atmospheric fallout of Pb and Cd in the river basins, precipitation of dissolved Fe and Mn supplied from the rivers and remobilization of Mn from the coastal sediments. Based on various pollution indices, it is inferred that the coastal sediments of NW Borneo are contaminated with Cu and Zn, and are largely bioavailable, which can be a threat to the local aquatic organisms, coral reefs, and coastal mangroves.
Southern River Terrapin, Batagur affinis, is a freshwater turtle listed as critically endangered on the IUCN Red List since 2000. Many studies suggest that faecal DNA metabarcoding can shield light on the host-associated microbial communities that play important roles in host health. Thus, this study aimed to characterise and compare the faecal bacterial community between captive and wild B. affinis using metabarcoding approaches. A total of seven faeces samples were collected from captive (N = 5) and wild (N = 2) adult B. affinis aseptically, crossing the East and West coast of peninsular Malaysia. The DNA was extracted from the faeces samples, and the 16S rRNA gene (V3-V4 region) was amplified using polymerase chain reaction (PCR). The amplicon was further analysed using SILVA and DADA2 pipelines. In total, 297 bacterial communities taxonomic profile (phylum to genus) were determined. Three phyla were found in high abundance in all faeces samples, namely Firmicutes (38.69%), Bacteroidetes (24.52%), and Fusobacteria (6.95%). Proteobacteria were detected in all faeces samples (39.63%), except the wild sample, KBW3. Under genus level, Cetobacteriumwas found as the most abundant genus (67.79%), followed by Bacteroides (24.56%) and Parabacteroides (21.78%). The uncultured genus had the highest abundance (88.51%) even though not detected in the BK31 and KBW2 samples. The potential probiotic genera (75.00%) were discovered to be more dominant in B. affinis faeces samples. Results demonstrated that the captive B. affinis faeces samples have a greater bacterial variety and richness than wild B. affinis faeces samples. This study has established a starting point for future investigation of the gut microbiota of B. affinis.
Due to urbanization, solid waste pollution is an increasing concern for rivers, possibly threatening human health, ecological integrity, and ecosystem services. Riverine management in urban landscapes requires best management practices since the river is a vital component in urban ecological civilization, and it is very imperative to synchronize the connection between urban development and river protection. Thus, the implementation of proper and innovative measures is vital to control garbage pollution in the rivers. A robot that cleans the waste autonomously can be a good solution to manage river pollution efficiently. Identifying and obtaining precise positions of garbage are the most crucial parts of the visual system for a cleaning robot. Computer vision has paved a way for computers to understand and interpret the surrounding objects. The development of an accurate computer vision system is a vital step toward a robotic platform since this is the front-end observation system before consequent manipulation and grasping systems. The scope of this work is to acquire visual information about floating garbage on the river, which is vital in building a robotic platform for river cleaning robots. In this paper, an automated detection system based on the improved You Only Look Once (YOLO) model is developed to detect floating garbage under various conditions, such as fluctuating illumination, complex background, and occlusion. The proposed object detection model has been shown to promote rapid convergence which improves the training time duration. In addition, the proposed object detection model has been shown to improve detection accuracy by strengthening the non-linear feature extraction process. The results showed that the proposed model achieved a mean average precision (mAP) value of 89%. Hence, the proposed model is considered feasible for identifying five classes of garbage, such as plastic bottles, aluminum cans, plastic bags, styrofoam, and plastic containers.
Pharmaceuticals in aquatic environment have raised wide attention in recent years due to their potential adverse effects and bioaccumulation in biota. China has been a major producer and consumer of pharmaceuticals, however, the potential human health risk of these chemicals is yet to be determined in China. In this study, we evaluated available exposure data for twenty pharmaceuticals in surface waters from Chinese five major river basins (the Yangtze, Haihe, Pearl, Songliao, and Yellow River Basins), and human health risk assessment was performed. Based on the concentration data and risk data, we conducted research on the source, cause, and control measures of the pharmaceuticals. The twenty pharmaceuticals were found to be ubiquitous in China with median concentrations between 0.09 and 304 ng/L. The estimated daily intake of pharmaceuticals from drinking water and eating fish was calculated. The intake via drinking water was significantly lower than that via eating fish. The risk quotients via water intake and fish consumption ranged from 0 to 17.2, with estrogen and sulfapyridine highest among the twenty pharmaceuticals. High risks of exposure were mainly in North China, including the Haihe and Songliao River Basins. This is the first analysis in Chinese major river basins that has filled the gaps in the research on the human health risks of pharmaceuticals. The results of the study provide basic information of pharmaceutical intake from drinking water and eating fish in China and provide insights into the risk management guidance of pharmaceuticals, and will facilitate the optimization of health advisories and policy making.
The concentration of soluble salts in surface water and rivers such as sodium, sulfate, chloride, magnesium ions, etc., plays an important role in the water salinity. Therefore, accurate determination of the distribution pattern of these ions can improve better management of drinking water resources and human health. The main goal of this research is to establish two novel wavelet-complementary intelligence paradigms so-called wavelet least square support vector machine coupled with improved simulated annealing (W-LSSVM-ISA) and the wavelet extended Kalman filter integrated with artificial neural network (W-EKF- ANN) for accurate forecasting of the monthly), magnesium (Mg+2), and sulfate (SO4-2) indices at Maroon River, in Southwest of Iran. The monthly River flow (Q), electrical conductivity (EC), Mg+2, and SO4-2 data recorded at Tange-Takab station for the period 1980-2016. Some preprocessing procedures consisting of specifying the number of lag times and decomposition of the existing original signals into multi-resolution sub-series using three mother wavelets were performed to develop predictive models. In addition, the best subset regression analysis was designed to separately assess the best selective combinations for Mg+2 and SO4-2. The statistical metrics and authoritative validation approaches showed that both complementary paradigms yielded promising accuracy compared with standalone artificial intelligence (AI) models. Furthermore, the results demonstrated that W-LSSVM-ISA-C1 (correlation coefficient (R) = 0.9521, root mean square error (RMSE) = 0.2637 mg/l, and Kling-Gupta efficiency (KGE) = 0.9361) and W-LSSVM-ISA-C4 (R = 0.9673, RMSE = 0.5534 mg/l and KGE = 0.9437), using Dmey mother that outperformed the W-EKF-ANN for predicting Mg+2 and SO4-2, respectively.
Non-point source (NPS) pollution has become a vital contaminant source affecting the water environment because of its wide distribution, hydrodynamic complexity, and difficulty in prevention and control. In this study, the identification and evaluation of NPS pollution risk based on landscape pattern were carried out in the Hanjiang River basin above Ankang hydrological section, Shaanxi province, China. Landscape distribution information was obtained through land use data, analyzing the contribution of "source-sink" landscape to NPS pollution through the location-weighted landscape contrast index. Using the NPS pollution risk index to identify and evaluate the regional NPS pollution risk considering the slope, cost distance, soil erosion, and precipitation erosion affect migration of pollutants. The results showed that (i) the pollution risk was generally high in the whole watershed, and the sub-watersheds dominated by "source" landscapes account for 74.61% of the whole basin; (ii) the high-risk areas were distributed in the central, eastern, and western regions of the river basin; the extremely high-risk areas accounted for 12.7% of the whole watershed; and the southern and northern regions were dominated by forestland and grassland with little pollution risk; (iii) "source" landscapes were mostly distributed in areas close to the river course, which had a great impact on environment, and the landscape pattern units near the water body needed to be further adjusted to reduce the influence of NPS pollution.
Alternative climate products, such as gauge-based gridded data, ground-based weather radar, satellite precipitation and climate reanalysis products, are being increasingly applied for hydrological modelling. This review aims to summarize the studies that have evaluated alternative climate products within Soil and Water Assessment Tool (SWAT) applications and to propose future research directions, primarily for modelers who wish to study limited gauge, ungauged or transnational river basins. A total of 126 articles have been identified since 2004, the majority of which have been published within the last five years. About 58% of the studies were conducted in Asia, mostly in China and India, while another 14% were reported for United States studies. CFSR and TRMM are the most popular applied products in SWAT modelling, followed by PERSIANN, CMADS, APHRODITE, CHIRPS and NEXRAD. Generally, the performance of climate products is region-dependent; e.g., CFSR typically performs well in the United States and South America, but performs more poorly for Asia, Africa and mountainous basin conditions, as compared to other products. In contrast, the CMADS, TRMM, APRHODITE and NEXRAD have shown the strongest capability for supporting SWAT modelling in these regions. However, most of the evaluated products contain only precipitation input; therefore, merging reliable precipitation with CFSR-temperature is recommended for hydro-climatic modelling. Future research directions include: (1) examination of optimal combinations; e.g. CHIRPS-precipitation and CFSR-temperature, for simulating streamflow in different types of river basins; (2) development of a standardized validation scheme which incorporates the commonly accepted products, statistical approaches and temperature variables; (3) further evaluation of existing climate data products to accurately capture extreme events, pattern and indices as well as WGEN statistics; (4) improvement of climate data in terms of averaging approach, bias correction and additional factors or indices integration; and (5) bias correction of CMIP6 climate projections using the optimal climate data combinations.
Microplastic pollution is widely recognised as a global issue, posing risks to natural ecosystems and human health. The combination of rapid industrial and urban development and relatively limited environmental regulation in many tropical countries may increase the amount of microplastic entering rivers, but basic data on contamination levels are lacking. This is especially the case in tropical South East Asian countries. In this paper, the abundance, composition and spatio-temporal variation of microplastic in the Langat River, Malaysia, were assessed, and the relationship between microplastic concentration and river discharge was investigated. Water samples were collected over a 12-month period from 8 sampling sites on the Langat, extending from forested to heavily urbanised and industrial areas. All 508 water samples collected over this period contained microplastic; mean concentration across all sites and times was 4.39 particles/L but extended up to 90.00 particles/L in some urban tributaries. Most microplastics were secondary in origin, and dominated by fibres. Microplastic counts correlated directly with river discharge, and counts increased and decreased in response to changes in flow. A time-integrated assessment of the microplastic load conveyed by the Langat suggested that the river is typically (50 % of the time) delivering around 5 billion particles per day to the ocean. The positive correlation between the concentration of microplastics and suspended sediments in the Langat suggested that continuously logging turbidity sensors could be used to provide better estimates of microplastic loads and improve assessment of human and ecological health risks.
A major programme of dam building is underway in many of the world's tropical countries. This raises the question of whether existing research is sufficient to fully understand the impacts of dams on tropical river systems. This paper provides a systematic review of what is known about the impacts of dams on river flows, sediment dynamics and geomorphic processes in tropical rivers. The review was conducted using the SCOPUS® and Web of Science® databases, with papers analysed to look for temporal and geographic patterns in published work, assess the approaches used to help understand dam impacts, and assess the nature and magnitude of impacts on the flow regimes and geomorphology ('hydromorphology') of tropical rivers. As part of the review, a meta-analysis was used to compare key impacts across different climate regions. Although research on tropical rivers remains scarce, existing work is sufficient to allow us to draw some very broad, general conclusions about the nature of hydromorphic change: tropical dams have resulted in reductions in flow variability, lower flood peaks, reductions in sediment supply and loads, and complex geomorphic adjustments that include both channel incision and aggradation at different times and downstream distances. At this general level, impacts are consistent with those observed in other climate regions. However, studies are too few and variable in their focus to determine whether some of the more specific aspects of change observed in tropical rivers (e.g. time to reach a new, adjusted state, and downstream recovery distance) differ consistently from those in other regions. The review helps stress the need for research that incorporates before-after comparisons of flow and geomorphic conditions, and for the wider application of tools available now for assessing hydromorphic change. Very few studies have considered hydromorphic processes when designing flow operational policies for tropical dams.