Changes In Streamflow Research Articles

Comprehensive assessment of cascading dams-induced hydrological alterations in the lancang-mekong river using machine learning technique

The development of cascading hydropower dams in river basins has significantly altered natural flow regimes in recent decades. This study investigates hydrological alterations caused by cascading hydropower dams in the Lancang-Mekong River Basin (LMRB) by integrating the Indicators of Hydrologic Alteration (IHA) method with non-regulated flow predicted using the Random Forest (RF) machine learning (ML) technique. The analysis focuses on four hydrological stations: Chiang Saen, Mukdahan, Pakse, and Stung Treng across pre-impact (1961–1991), transition (1992–2008), and post-impact (2009–2021) periods. RF models predict streamflow altered primarily by human activities, particularly hydropower development using spatially averaged daily precipitation, cumulative precipitation, and temperature data. This approach isolates human-induced impacts, unlike previous studies that combined climate change and human activities. Results indicate that post-impact alterations were most pronounced at Chiang Saen (77.3%) and decreased downstream. Alterations increased from the transition to post-impact periods by 31.5%, 22.2%, 26.5%, and 17.6% at Chiang Saen, Mukdahan, Pakse, and Stung Treng, respectively. The median monthly flow, annual extreme conditions, and rate and frequency of flow change groups in the IHA were highly altered compared to natural conditions in the post-impact period. Human activities contributed over 50% of streamflow changes (2017–2021). This approach provides a more precise assessment of dam-induced hydrological alterations, aiding hydropower management by isolating human impacts and offering high-resolution predictions.

Quantification of streamflow response to climate change and human activities within upstream mountainous areas of the Daqing River Basin, Northern China

The Daqinghe River Basin is located in the North China Plain. In recent years, however, climate warming, drying, and intense human activities have led to declining ecosystem functions and shrinking wetlands in the region. Understanding streamflow changes in the upstream mountainous areas of the Daqinghe River Basin in this changing environment and identifying the driving factors can provide a scientific basis for water resources management and optimization in these areas. This study focuses on the Beihedian River watershed, the Xidayang Reservoir watershed, and the Wangkuai Reservoir watershed in the upstream mountainous areas of the Daqinghe River. It is based on hydro-meteorological data collected between 1963 and 2019. The methods used in the study include the linear tendency estimation method, the non-parametric Mann-Kendall trend test, the elasticity coefficient method, and hydrological simulation methods. The results of this study suggest that the streamflow, precipitation, and potential evapotranspiration (PET) in the three watersheds showed an overall decreasing trend. The minimum precipitation decrease rate ranged from −1.09 to −0.55 mm/a, and the minimum streamflow decreasing rate at the Beihedian Hydrological Station was −1.32 mm/a, with a minimum range of 0–176.03 mm. Change-point analysis revealed that the streamflow in the Beihedian River and Xidayang Reservoir watersheds experienced a significant change point around 1999, with a significant level of α=0.05. As for the Wangkuai Reservoir watershed, a significant change point was observed around 1980, which is likely attributable to land system reforms and protective forest projects. The attribution analysis which combined both climate change and human activities using the elasticity coefficient method and hydrological simulation methods indicated that climate change contributed an average of 32.93%, 34.50%, and 35.12% to the reduction in streamflow in the three watersheds, respectively. Human activities accounted for an average contribution of 67.07%, 65.50%, and 64.88%, respectively. Water conservancy projects, afforestation, and other human activities were identified as the primary factors contributing to streamflow decreases.

Direct vegetation response to recent CO2 rise shows limited effect on global streamflow

Global streamflow, crucial for ecology, agriculture, and human activities, can be influenced by elevated atmospheric CO2 (eCO2) though direct regulation of vegetation physiology and structure, which can either decrease or increase streamflow. Despite a 21.8% rise in CO2 over 40 years, its impact on streamflow is not obvious and remains highly debated. Using a full differential approach at the catchment scale and an optimum finger approach globally, both constrained by observed streamflow, here, we find that vegetation responses to eCO2 in 1981–2020 has limited impact on streamflow via direct regulation. The median eCO2 contribution approaches zero across 1116 unimpacted catchments, and global streamflow changes cannot be solely attributed to eCO2. These results offer key insights into the intricate dynamics of CO2 and other factors shaping streamflow changes over the past four decades. Such understanding is vital for attributing current streamflow changes under eCO2 conditions.

Understanding the implications of climate change for Australia’s surface water resources: Challenges and future directions

Climate change is altering Australia’s streamflow and water resources due to a complex interplay of shifts in temperature, rainfall, evaporation, vegetation dynamics, soil moisture, and rainfall-runoff partitioning. Meanwhile increases in human activity and urbanisation continue to alter the landscape, further affecting streamflow and flood volumes. Understanding the nature of these recent changes is critical for evaluating future risks and ensuring planning decisions are sustainable. Positively, our knowledge of changes to Australia’s hydrologic regimes has greatly advanced in the last decade. Here we review our current understanding of historical hydrologic changes, focusing first on the drivers of change and then observed changes in streamflow and flooding, before turning our attention to the role that historical observations can play in anticipating future climate change impacts. We conclude with recommendations to help better understand and plan for an unknown future.Increases in extreme rainfalls have increased the risk of large flood events. However, declines in mean rainfall across large parts of the continent, increases in diversions, and increasing evaporate demand have led to an overall drying. Significant advances in our understanding have been made through analysing trends in the observational record and investigating the associated drivers. While these learnings may not reliably inform the potential effects of future change, they can still inform process understanding ultimately helping accurately model and project future impacts. Projecting climate change impacts on water resources in Australia remains challenging, and progress will require improved instrumentation to observe and understand catchment responses together with methods and management techniques that incorporate this additional uncertainty. We argue it is through process-informed models together with multi-scale observational data that this gap can be breached.

Application of coupled MIKE SHE/MIKE HYDRO on streamflow dynamics under climate change in Lake Tana sub basin, Abay Basin, Ethiopia

AbstractWater availability and quality are fluctuating due to climate change, which has disastrous effects on life. Modeling climate change impact on streamflow in the Lake Tana sub‐basin (LTSB) in selected watersheds was the main goal of the research. This research is unique in that it applies the coupled MIKE SHE/MIKE HYDRO model technique, which has not been applied before to investigate changes in streamflow due to climate change in LTSB. Streamflow in the LTSB was forecasted using the MIKE SHE/MIKE HYDRO model from seven ensembles of GCMs under two emission scenarios (SSP2–4.5 and SSP5–8.5) between 2041 and 2,100. According to the calibration and validation results in the period of 1985–2007, MIKE SHE performs well while simulating streamflow in LTSB. During calibration and validation, the Nash‐Sutcliffe efficiency (NSE) values in all watersheds were greater than 0.8, except for the Ribb watershed calibration, where it was 0.75. The mean monthly changes in temperatures indicate an incremental tendency in the next decades. According to the simulation result, the monthly mean rainfall for SSP2–4.5 and SSP5–8.5 will increase from 0.05 to 61.9% and 2.02 to 46.26% in the 2050s and 8.7 to 44.38% to 6.44 to 66.24% in the 2080s, respectively. Gilgle Abay, Gumara, and Ribb watersheds will lose 56.2, 56.65, 57.01% of their mean monthly streamflow in the 2050s, according to SSP2–4.5. For the Gilgle Abay, Gumara, and Ribb watersheds, streamflow will increase by 6.7% to 21.94%, 6.08% to 23.26%, and 6.51% to 22.88%, accordingly, in the 2050s under SSP5–8.5. Increased rainfall in the watershed could result in floods in the already‐flooded Gumara and Ribb flood plains. The government and community should implement coping mechanisms to lessen the impact of climate change like early warring and planting more trees. Overall, the study showed that it is expected that conditions related to streamflow may change in the future due to climate alteration. By offering a better understanding of current and future climates, the integrated modeling system developed can assist environmentalists, hydrologists, and policymakers in water management and policy intervention.

Sensitivity of surface water and groundwater contributions to streamflow in a tropical glacierized basin under climate change scenarios

Abstract While mountain water faces threats posed by climate change, particularly in snow-dominated and glacierized systems, the role of groundwater in sustaining streamflow in these systems remains elusive. Changing mountain headwaters, marked by reduced snowpacks, retreating glaciers, shifting precipitation patterns, and rising temperatures, pose a crucial question: what is the resilience of streamflow in these mountains, and what role does groundwater play in this resilience? This is particularly uncertain in tropical high mountains where the seasonality of precipitation and glacier melt govern streamflow generation. A glacio-hydrological model was created using the Cold Regions Hydrological Modelling platform to investigate cryosphere-surface water-groundwater interactions in the Quilcayhuanca Basin, in Peru’s Cordillera Blanca. The model was forced by in-situ meteorological observations and parameterized using numerous data sources and process-based studies in the basin. Model results show that during the dry season, 37% of streamflow is generated from groundwater discharge, increasing to 56% during the lowest flows. Evapotranspiration is the largest mass flux from the basin at the peak of the dry season. Precipitation, temperature, and glacier change scenarios were used to assess the sensitivity of basin hydrology to climate change and glacier retreat. In a warmer, wetter, and nearly deglaciated future, Quilcayhuanca basin streamflow is expected to decrease by 4-19% annually, with a larger volumetric change in overland and vadose zone flow than in groundwater flow. The range in values is more closely linked to uncertainty in precipitation change than temperature change. Despite a strong reduction in snow and ice contribution to streamflow with warming and deglaciation, the concomitant increase in precipitation can limit the changes in streamflow and groundwater flow, showcasing the resilience of the system to shifts in climate and glacier cover.

Temporal changes in streamflow can predict parasitism levels in freshwater prawns better than host traits

Abstract Understanding how changes in the hydrological regime drive parasite loads and dynamics remains a challenging issue in ecological parasitology. Temporal changes in streamflow and rainfall are key hydrological factors that could alter interactions between the parasite and host and affect parasitism levels. However, to investigate the effect of streamflow, rainfall and its mechanisms, it is important to control host traits that can also influence parasitism levels in freshwater systems. Here, we used a cymothoid‐palaemonid prawn model to test the combined effects of streamflow and rainfall accounting for host traits to predict variations in parasite loads in stream ecosystems. We collected the palaemonid prawns monthly for 2 years and measured the variation of rainfall and streamflow (i.e. stream discharge) at the time of prawn sampling. Our best model showed that streamflow can predict parasitism levels in prawns better than host traits. We found a higher prevalence and abundance of parasites in reduced streamflow compared to increased ones. We also found higher ectoparasite loads in females rather than males and occurring in autumn and winter rather than in spring and summer. Our results also showed that ectoparasite loads also suffer an effect of host body size, sex and moult stage, but not host age. Our findings show that reduced streamflow can facilitate host finding that favours parasite transmission in dry seasons and increase parasitism levels in stream systems. Temporal variability in streamflow can have a strong influence on parasite loads and dynamics compared to host traits in stream ecosystems. Identifying how interactions between aquatic invertebrates and parasites respond to variability in the hydrological regime can help to better understand and predict disease outbreaks as habitat reduction and disturbance continue in stream ecosystems.

Regionalization of Climate Elasticity Preserves Dooge's Complementary Relationship

AbstractClimate elasticity of streamflow represents a nondimensional measure of the sensitivity of streamflow to climatic factors. Estimation of such elasticities from observational records has become an important alternative to scenario‐based methods of evaluating streamflow sensitivity to climate. Nearly all previous elasticity studies have used a definition of elasticity known as arc elasticity, which measures changes in streamflow about mean values of streamflow and climate. Using observational records in the western U.S., our findings reveal that elasticity definitions based on power law models lead to both regional and basin specific estimates of elasticity which are physically more realistic than estimates based on arc elasticity. Evaluating the ability of arc and power law elasticity estimators in reproducing Dooge's complementary relationship (DCR) between potential evapotranspiration and precipitation elasticities reveal that power law elasticities estimated from at‐site, panel, and hierarchical statistical models reproduce DCR, whereas corresponding estimators based on arc elasticity cannot reproduce DCR. Importantly, our regional elasticity formulations using panel and hierarchical formulations led to estimates of both regional and basin specific estimates of elasticities, enabling and contrasting streamflow sensitivity to climate across both basins and regions.

Estimating future changes in streamflow and suspended sediment load under CMIP6 multi-model ensemble projections: a case study of Bitlis Creek, Turkey

Estimating future changes in streamflow and suspended sediment load under CMIP6 multi-model ensemble projections: a case study of Bitlis Creek, Turkey

Hydrological response to climate change in Baro basin, Ethiopia, using representative concentration pathway scenarios

Droughts and floods are common in the Baro basin and climate change may exacerbate them. This study aimed to investigate the hydrological response to climate change’s impact in the Baro River basin. Four climate models namely, Hadley Centre Global Environmental Model, version 2 (HadGEM2-ES), Max Planck Institute Earth System Model—Low Resolution (MPI-ESM-LR), Coupled Model Version 5, Medium Resolution (CM5A-MR) and European Community Earth System Model (EC-Earth) dynamically downscaled outputs were obtained from Africa coordinated regional downscaling experiment program. The four climate models were evaluated using a suite of statistical measures such as bias, Root Mean Squared Error, and Coefficient of Variation. The bias of the simulated rainfall varies between − 4.20% and − 25.39% suggesting underestimation. The performance of the models differs subject to the performance measures used for evaluation. Before being used in the climate impact analysis, the climate model data was heavily skewed and needed correction. In terms of bias, HadGEM2-ES performed the worst while EC-Earth performed the best. MPI-ESM-LR was the worst performer in terms of RMSE and CM5A-MR was the best. Changes in the hydrological response to climate change were compared to the baseline scenario (1971–2000) under the Representative Concentration Pathway Scenarios (RCP 4.5) for the medium term (2041–2070). The GCM predictions for the RCP 4.5 scenarios suggested that, in the medium period (2041–2070) the maximum temperature in the Baro River basin will probably rise by 2.1 °C for MPI-ESM-LR and 2.49 °C for CM5A-MR, while the minimum temperature would likely climb by 1.7 °C to EC-Earth and 2.8 °C for HadGEM2-ES. Annual rainfall is expected to fall by 7.02% for CM5A-MR and 17.01% for HadGEM2-ES, while annual evapotranspiration potential is likely to rise. Except from March to May CM5A-MR consistently generated the greatest amount of streamflow change, while MPI-ESM-LR consistently generated the highest magnitude of streamflow change. The annual streamflow reduction is consistent with the annual precipitation reduction and increased annual potential evapotranspiration. Generally, climate change is predicted to have a significant impact on the hydrological response in the Baro River basin under the RCP 4.5 scenario.

Distributed modelling of snow and ice melt in the Naltar Catchment, Upper Indus basin

Energy balance distributed modelling in High Mountain Asia (HMA) is important to examine glaciological and hydrological processes and assess changes in streamflow in the current and future climate. In this study, the Physically based Distributed Snow Land and Ice Model (PDSLIM) using detailed observed meteorological data at hourly scale is employed to simulate the hydrological response of the Naltar catchment, 242.62 km2 in size, (in the Karakoram region in Pakistan to simulate its glaciers’ mass balance as well as daily runoff. The results exhibited overall satisfactory performance in terms of coefficient of determination (R2 = 0.96) and Nash-Sutcliffe Efficiency (NSE=0.95) modelled against satellite-based snow cover areas, for internal model verification, in eight years. The results of runoff simulations compared for external model verification, with observed daily discharge resulted in NSE 0.90 and 0.89 for calibration and validation period respectively. Flow composition analysis revealed that the streamflow regime of Naltar catchment is composed to 40 % by glacier runoff, 42 % by sub-surface runoff and 18 % by surface runoff. The eight year mean value of net mass balance exhibited a slightly negative mass balance (−0.810 ± 0.31 m w.e. a-1) less pronounced than that observed globally in several continental glaciers distinct from the Greenland and Antarctic ice sheets in the current climate. It seems that the ‘Karakoram anomaly’, i.e. the balanced to slightly positive glacier budgets observed in the region in the recent decades, a unique dynamics worldwide, has a moderate impact in the central Karakoram. Overall, the distributed energy-balance model PDSLIM, so far tested in the Alps, results to be a suitable tool to estimate energy and mass balance in the glacierized catchments of Karakoram and Himalaya and to better understand snow and ice melt runoff dynamics and floods in highly complex and glacierized mountain basins in the current and, in our research perspective, in the future climate.

Exploring climate-change impacts on streamflow and hydropower potential: insights from CMIP6 multi-GCM analysis

ABSTRACT The generation of hydropower is profoundly influenced by shifts in streamflow patterns induced by climate change. This research examines changes in streamflow and the potential surge in hydropower generation over a span of 35 years (2015–2050) at the Bhakra Dam site within the Upper Sutlej River basin. Employing a deep learning methodology, particularly the long short-term memory (LSTM) model, in conjunction with Coupled Model Intercomparison Project (CMIP) 6 multi-global climate model (GCM), facilitates a thorough analysis of these dynamics. Six out of 14 bias-corrected statistically downscaled datasets (0.25° × 0.25° grid resolution) from CMIP6 multi-GCM were selected based on entropy and combined compromise solution techniques. This innovative approach is utilized to assess streamflows and project potential increases in hydropower at the Bhakra Dam site under shared socioeconomic pathways (SSP) scenarios, specifically SSP245 and SSP585. The results indicate a maximum increase of approximately 15% and 17% in mean monthly streamflow under SSP245 and SSP585, respectively. Moreover, dependability flows calculated at Q50, Q75, and Q90 show respective rises of 13%, 16%, and 17% under SSP245 and 21%, 17%, and 18% under SSP585. The projected hydropower potential exhibits an increase of up to 15.9% and 17.3% under SSP245 and SSP585, respectively.

Higher-order internal modes of variability imprinted in year-to-year California streamflow changes

Climate internal variability plays a crucial role in the hydroclimate system, and this study quantifies its predictability on streamflow in California using historical observations, climate simulations, and various machine learning (ML) models. Here we demonstrate that while 5% of the year-to-year variability in seasonal peak streamflow can be attributed to the well-known climate variability indices, the explained variance surpasses 30% when higher-order empirical orthogonal functions of these indices are retained in the analysis. Notably, the results highlight the significant influence of the 5th empirical mode of the Pacific North American pattern and of the Pacific Decadal Oscillation in shaping the streamflow variability, which is consistent across all the tested ML models. A deeper investigation reveals a clear and monotonic quasi-linear response of streamflow to these dominant patterns, emphasizing the substantial role played by higher-order internal modes of variability in shaping regional hydroclimate systems, which contributes to bridging the gap between the well-known variability domains and local climate systems.

The Impacts of Dams on Streamflow in Tributaries to the Lower Mekong Basin

The Lower Mekong Basin has had extensive hydropower dam development, which changes its hydrologic conditions and threatens the exceptional aquatic biodiversity. This study quantifies the degree of hydrologic change between pre-impact (1965–1968) and post-impact (2018–2021) peak hydropower development in two major tributaries of the Lower Mekong Basin—the Sekong River, with the fewest dams, and the Sesan River, with the most dams. Both rivers have historically supported migratory fishes. We used daily pre- and post-impact data and the Indicators of Hydrologic Alteration framework to evaluate streamflow changes from dam development. We found significant changes in low- and high-magnitude flows in the pre- and post-impact periods of dam development. For the Sekong River, minimum flow had large fluctuations, with increases of 290% to 412% compared to the pre-impact period, while the Sesan River’s minimum flow ranged from 120% to 160% more than pre-impact. Dry season flows increased by 200 ± 63% on average in the Sekong River, which was caused by releases from upstream dams. Meanwhile, the Sesan River’s dry season flows increased by 100 ± 55% on average. This study indicates that seasonal flow changes and extreme flow events occurred more frequently in the two basins following dam construction, which may threaten the ecosystem’s function.

Forest cover lessens hurricane impacts on peak streamflow

AbstractCyclonic storms (i.e., hurricanes) are powerful disturbance events that often cause widespread forest damage. Storm‐related canopy damage reduces rainfall interception and evapotranspiration, but impacts on streamflow regimes are poorly understood. We quantify streamflow changes in Puerto Rico following Hurricane Maria in September 2017, and evaluate whether forest cover and storm‐related canopy damage account for the differences. Streams are particularly vulnerable to flooding in early post‐disturbance stages during hurricane season, so we focus on 3 months (Oct–Dec) following the hurricane. To discern changes in rainfall responses, we partitioned streamflow into baseflow and quickflow using a digital filter. We collected 2010–2017 streamflow and rainfall data from 18 watersheds and compared the relative magnitude of post‐ to pre‐hurricane double mass curve slopes of baseflow and quickflow volumes against rainfall. Several watersheds displayed higher post‐hurricane quickflow and baseflow, however, the response was variable. The magnitude of quickflow increase was greater in watersheds with high forest damage. Under the same level of relative damage, watersheds with low initial forest cover had greater quickflow increases than highly forested ones. Conversely, baseflow generally increased, but increases were greater in highly forested watersheds and smaller in highly damaged watersheds. These results suggest that post‐storm baseflow increases were due to recharge of hurricane‐related rainfall, as well as forest transpiration interruption and soil disturbance enhancing recharge of post‐hurricane rainfall, while increases to quickflow are related to loss of canopy rainfall interception and higher soil saturation decreasing infiltration. Our research demonstrates that forest damage from disturbance lowers quickflow and elevates baseflow in highly forested watersheds, and elevates quickflow and lowers baseflow in less‐forested watersheds. Less‐forested watersheds may be closer to the forest cover loss threshold needed to elicit a streamflow response following disturbance, suggesting higher flooding potential downstream, and a lower storm‐related forest disturbance threshold than in heavily forested watersheds.

Streamflow response to land use/land cover change in the tropical Andes using multiple SWAT model variants

Study regionTropical watershed in the Colombian Andes, the Chico River (CR) watershed. Study focusHydrological models are widely used to project the impacts of LULC (Land Use/Land Cover) change on water budget. However, their ability to produce reliable predictions depends on how accurately they represent the role of vegetation in the watershed’s water balance. We analyze how different representations of Leaf Area Index (LAI) affect streamflow responses to LULC change using the Soil and Water Assessment Tool (SWAT) model. We also examine streamflow response to 100 % pasture cover (PAS), 100 % forest cover (FOR), and a control scenario using the original SWAT model, SWAT-T, and a proposed new variant (SWAT-Tb), which improves LAI bimodal representation for tropical regions. New hydrological insights for the regionSWAT-T and SWAT-Tb reproduce observed LAI and streamflow in the CR watershed. However, SWAT-T restricts LAI simulation to unimodal seasonality, while the original SWAT reproduces streamflow but not LAI seasonality. Results using SWAT-T (unimodal LAI) and SWAT-Tb (constant and bimodal LAI) show streamflow increases during the dry seasons for the FOR scenario and decreases for the PAS scenario. Conversely, the original SWAT, with its default LAI representation, tends to underestimate and overestimate streamflow changes in the FOR and PAS scenarios, respectively. Our results highlight that an unrealistic LAI representation can mislead LULC change impact assessments on streamflow in the tropical Andes.

Streamflow in the United States: Characteristics, trends, regime shifts, and extremes

Long-term streamflow observations contain essential information for understanding hydrological changes and managing water resources. A continental-scale dataset or analysis of temporal streamflow change is still lacking across hydrologic gauges in the Conterminous United States (CONUS). Here, we compiled 70 years of streamflow records from 1951 to 2021 at ~ 8000 hydrologic stations across the CONUS and characterized temporal trends, regime shifts, and extreme events using a Bayesian time series analysis algorithm. We found that the occurrences of sudden streamflow changes (e.g., regime shifts and extreme events) have been increasing with time across the CONUS. In addition, we derived 181 streamflow indicators that are valuable for hydrological and biological applications, such as the duration and frequency of high or low streamflow events. The Mississippi River Basin, especially the middle and lower parts, was a hot spot of high-frequency high-flow events. Overall, we anticipate the dataset generated here offers valuable information for understanding and quantifying changes in water resources across the CONUS.

Disentangling climate change & land use change effects on river flows: A probabilistic approach

Determining the respective attribution proportions of climate change and land use change to streamflow variations in river systems is of increasing interest to researchers and practitioners tasked with managing river basins. This paper proposes an extension to established techniques of attributing the relative proportions of climate change (CC) and land use change (LUC) drivers to streamflow change by instead considering these proportions as distributed through a probability density function (pdf), rather than as a point value. The novel method is demonstrated for the River Tweed in the UK. Results are determined by the flow, temperature and precipitation data, and upon the algorithms used to identify change points in these vectors. The ratio of the LUC and CC attribution proportions (Land Use and Climate Change Attribution Proportions, LCAP) is more appropriately expressed as a vector of values, each associated with its own probability value within a probability density function. The paper demonstrates that the LCAP ratio pdf can vary considerably over time and that it is possible to track physical changes in the catchment in the evolution of the probability density function. Results show that the land-use change/climate change attribution proportion (LCAP) ratio varies over time and can be expressed as probabilistic estimate. It can be concluded, with a high degree of confidence, that for the Tweed catchment the LUC is a significant driver of streamflow change. Hence this finding may have implications for future catchment flood management utilising nature based solutions (NbS) to reverse landscape degradation and mitigate effects of climate change, provided that the economic and social costs are outweighed by the benefits.

Wastewater discharges and urban land cover dominate urban hydrology signals across England and Wales

Urbanisation is an important driver of changes in streamflow. These changes are not uniform across catchments due to the diverse nature of water sources, storage, and pathways in urban river systems. While land cover data are typically used in urban hydrology analyses, other characteristics of urban systems (such as water management practices) are poorly quantified which means that urbanisation impacts on streamflow are often difficult to detect and quantify. Here, we assess urban impacts on streamflow dynamics for 711 catchments across England and Wales. We use the CAMELS-GB dataset, which is a large-sample hydrology dataset containing hydro-meteorological timeseries and catchment attributes characterising climate, geology, water management practices and land cover. We quantify urban impacts on a wide range of streamflow dynamics (flow magnitudes, variability, frequency, and duration) using random forest models. We demonstrate that wastewater discharges from sewage treatment plants and urban land cover dominate urban hydrology signals across England and Wales. Wastewater discharges increase low flows and reduce flashiness in urban catchments. In contrast, urban land cover increases flashiness and frequency of medium and high flow events. We highlight the need to move beyond land cover metrics and include other features of urban river systems in hydrological analyses to quantify current and future drivers of urban streamflow.

Past, present and future changes in the annual streamflow of the Lancang-Mekong River and their driving mechanisms

The rapid development of the Greater Mekong Subregion (GMS) makes it essential to understand the major mechanisms controlling the streamflow, especially for the Lancang-Mekong River (abbr. Mekong River). We used instrumental annual streamflow data (1960–2007) from Chiang Saen hydrological station and several gridded hydroclimatic datasets to explore the hydroclimatic evolution of the Mekong River, together with its driving mechanisms. We found that changes in the Mekong streamflow are consistent with precipitation changes, and the Mekong is thus a precipitation-dominated river that is susceptible to the effects of ongoing climate change. The instrumental record of Mekong annual streamflow is closely related to hydroclimatic changes, especially those related to moisture, within the area from the Hengduan Mountains to the Chiang Saen Station. Based on these gridded records, we extended the Mekong annual streamflow record to cover 1891–2021 using nested multiple linear regression fitting. The fitted streamflow explained up to 57.6 % of the instrumental changes and it indicates that the major 2019 drought was not unique over the past century. Despite extremely low precipitation and high temperatures, it is likely that the effects of drought can be mitigated via hydraulic engineering regulation. Climatological analyses showed that the Mekong annual streamflow is driven by the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), which is consistent with observed quasi-interannual cycles of 3–4 years. A multi-model ensemble of CMIP6 revealed that the Mekong annual streamflow will experience an upward trend in the future, accompanied by the more extreme impacts of ENSO and the IOD.