Streamflow Variability Research Articles

Identification of dominant drivers of streamflow spatiotemporal variations in typical mountainous areas in the Hexi Corridor, China

Study regionTypical mountain areas in the Hexi Corridor, China. Study focusWater security and ecosystem sustainability of arid inland river basins are highly dependent on upstream streamflow. However, due to the complex geographical environment and limited observation data in the study region, the attribution of spatiotemporal variations in streamflow influenced by climate change and/or human activities remains unclear. Here, we used partial least squares regression (PLSR) and the Budyko framework to unravel the dominant drivers of spatiotemporal variation in streamflow over the past 30 yr. New hydrological insight for the regionPrecipitation, topographic wetness index, slope, forest land, gross primary productivity, hydrological connectivity, soil organic carbon content, silt content, relative relief, NDVI and gravel content dominated spatial variation in streamflow. Temporal variation of streamflow was sensitive to precipitation and land surface. Specifically, increased precipitation and land surface alteration dominated the increase in streamflow in 50 % of the watersheds and the decrease in streamflow in 33 % of them, respectively. Further, land surface alteration was dominated by expansion of agricultural and built-up areas, weakened hydrological connectivity, increased landscape aggregation and forest cover. Controlling agricultural and built-up areas and the scale of afforestation, and focusing on the dynamics of hydrological connectivity and landscape patterns in the upstream reaches are imperative to maintain the security and sustainability of water resources in the arid inland river basins.

A century of variability of heatwave-driven streamflow in melt-driven basins and implications under climate change

Abstract Meltwater runoff from snow and glaciers in high mountain regions supports the water needs of hundreds of millions of people, but extreme events such as heatwaves modify the timing and magnitude of water available for downstream communities and ecosystems. The streamflow response to heatwaves depends strongly on heatwave timing and temperature, and the amount of snow and glacier ice available to melt. However, as ongoing climate change continues to alter both seasonal melt patterns and the frequency and intensity of heatwaves, it is not well understood how such streamflow responses will evolve relative to the seasonal cycle of streamflow. We address this knowledge gap by using long-term meteorological and hydrological datasets to characterize spatial and temporal heterogeneity in the streamflow sensitivity to heatwaves at six basins with >80 years of observations in Western Canada. We use years with earlier freshets and less snowfall as proxies of anticipated climate change, and apply a metric to describe how the streamflow sensitivity to heatwaves varies across years with different hydro-climatological characteristics. We find that in future proxy years relative to baseline years, nival streamflow is more sensitive to early spring heatwaves but less sensitive to late spring and summer heatwaves. Relative to baseline years, late spring heatwaves in future proxy years generate a smaller fraction of peak streamflow that is diminished as the freshet progresses. Our findings imply that future heatwave-driven peak flows in spring may be lessened by the diminished streamflow sensitivity to heatwaves in late spring, but this may be partially offset by excess melt during future heatwaves that are longer and hotter.

Comparative analysis of lumped and semi-distributed hydrological models in humid Mediterranean environments

ABSTRACT This study conducts a comparative analysis of four hydrological models—GR4J, HBV-light, HEC-HMS, and WEAP—to evaluate their effectiveness in simulating surface water responses in humid Mediterranean climates. The Longaví basin, located in the Maule River basin of central Chile, was selected for its characteristic climate. The models used lumped and semi-distributed approaches to simulate the basin’s hydrological cycle. The study area was divided into three zones, considering climate, soil properties, and altitude. Results showed that GR4J, HEC-HMS, and WEAP performed well during the winter months (June to October), while HBV-light was more effective in summer (November to May). WEAP model, with its extensive parameterization, effectively captured streamflow variability during the rainy season. The Differential Split Sample Test indicated that GR4J and WEAP offered the best fit for analysing climatic variability. These models are highly recommended for climate change impact studies, given their strong performance in capturing seasonal hydrologic responses across similar regions.

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.

How do gridded meteorological datasets perform in a typical data-scarce cryospheric basin?

Gridded meteorological datasets have been widely used in meteorological and hydrological studies, especially in data-scarce regions. However, assessments of these datasets in cryospheric basins, which are highly sensitive to global warming, remain inadequate. Therefore, three representative gridded meteorological datasets, i.e., the China meteorological forcing dataset (CMFD), CN05.1, and the meteorological forcing dataset for the third-pole region (TPMFD), were systematically evaluated in the Nachitai Basin based on comparisons with limited gauge observations, pairwise intercomparisons, and hydrological simulations. All gridded precipitation and temperature datasets were in good agreement with gauge observations on an intra-annual scale, while substantial differences existed among them in terms of precipitation and temperature on an annual scale. Additionally, pairwise correlation analyses indicated that the temporal consistency between the datasets was higher than their spatial consistency. Furthermore, all three datasets achieved satisfactory and reasonable results in simulating glacier area and streamflow variations when used as inputs for the hydrological−hydrodynamic model, exhibiting the robustness of the model in this data-scarce basin. This study provides a more profound understanding of the significance of gridded datasets in data-scarce areas situated on the Tibetan Plateau.

Historical drivers and future streamflow variations of the Kura River in the Western Transcaucasia region of Georgia: Analysis of tree-ring chronologies from 1720 to 2021 CE

In this paper, we conduct a dendrochronological study in the Western Transcaucasia region of Georgia to elucidate the relationship between tree growth and climatic variables (temperature, precipitation, and streamflow) in order to reconstruct the paleohydrology of the Kura River and to assess the impacts of climate change on water resources. We analyzed tree-ring chronologies from 1720 to 2021 CE, and analysis of July–August streamflow revealed five wet phases (1739–1753, 1764–1807, 1811–1831, 1962–1988 and 2000–2019 CE ranging in duration from 11 to 49 years) when streamflow exceeded the long-term mean, and four dry phases (1720–1738, 1832–1881, 1936–1961 and 1989–1999 CE) when streamflow was below average. The most extreme wet and dry years were 1771 and 1851 CE, respectively. Spatial correlation patterns of Kura River reconstructed runoff against sea level pressure (SLP) are negative in the Arctic and positive in mid- to high-latitude Eurasia. The Arctic Oscillation (AO)/North Atlantic Oscillation (NAO), and solar activity are important drivers of hydroclimatic conditions in the Mediterranean region, by modifying the location of high-pressure areas and large-scale atmospheric circulations. Comparison of our Kura streamflow reconstructions and data in the Old World Drought Atlas (OWDA) reveals a moderately positive correlation (0.40) throughout the study period, being strongest over the Southern Caucasus and Eastern Turkey. This validates the drought signals and emphasizes the importance of using multi-proxy approaches, supported by spatially resolved data, to enhance understanding of regional hydroclimatic variability and drought patterns. We additionally conducted future streamflow scenarios, most notably under the high-emission SSP585 scenario, in line with global trends in CMIP6 projections. Our streamflow results indicate a trend towards increasing drought severity, which highlights the need for urgent adaptation and mitigation strategies in water resource management in this region. Ultimately, this study provides valuable information on historical hydroclimatic conditions in the Kura River Basin, which help to develop strategies to mitigate the hydrologic impacts of climate change in the Western Transcaucasia Caucasus region. We further highlight the importance of regional and global teleconnections for understanding regional hydrological dynamics.

Quantifying the Influence of Climatic and Anthropogenic Factors on Multi-Scalar Streamflow Variation of Jialing River, China

Clarifying the impact of driving forces on multi-temporal-scale (annual, quarterly and monthly) runoff changes is of great significance for watershed water resource planning. Based on monthly runoff data and meteorological data of the Jialing River (JLR) during 1982–2020, the Mann–Kendall tendency testing approach was first applied to analyze variation tendencies of multi-timescale runoff. Then, abrupt variation years of runoff were determined using Pettitt and cumulative anomaly mutation testing approaches. The ABCD model was employed for simulating hydrological change processes in the base period and variation period. Finally, influences of climatic and anthropic factors on multi-scalar runoff were computed using the multi-scalar Budyko formula. The following conclusions were drawn in this study: (1) The mutation year of discharge was 1993; (2) the monthly runoff in the JLR presented a “single peak” distribution, and the concentration degree and concentration period in the JLR both showed an insignificant reduction trend; (3) anthropic factors were the dominant factor for spring runoff variations; climatic factors were the dominant factor on annual, summer, fall and winter runoff variations; (4) except for November, climatic factors were the dominant factor causing runoff changes in the other 11 months. This study has important reference value for water resource allocation and flood control decisions in the JLR.

Combining the digital filtering method with the SWAT model to simulate spatiotemporal variations of baseflow in a mountainous river basin

Study regionGanjiang River Basin, a typical mountainous river basin which located on the south bank of the middle-lower Yangtze River. The Ganjiang River is the seventh largest tributary of the Yangtze River. Study focusBaseflow, a key recharge source for the river streamflow. This study combined the digital filtering method with the SWAT model to examine the temporal and spatial patterns of baseflow across the Ganjiang River Basin, and quantitatively assessed land use change impact on baseflow. New hydrological insights for the regionBaseflow in the Ganjiang River Basin shows a "single peak" intra-annual distribution. Monthly variations of streamflow and baseflow across the basin are different. The variation of baseflow index is generally opposite to that of streamflow. A positive correlation has been noted between the annual baseflow and streamflow, while a negative correlation was found between the annual baseflow index and precipitation. Due to the potential influence of basin topography, river flow direction and rock layer distribution, baseflow and baseflow modulus showed a spatially increasing trend from south to north, with the northwest region having extremely strong groundwater recharge. In comparison to the basic scenario, under extreme land use scenarios of forest, grassland, and cropland, baseflow may experience an increase of 14.7 % and 2.9 %, while witness a decrease of 13.9 %. All results improve the understanding of baseflow spatiotemporal variations in river basins.

Spatiotemporal variability of streamflow under current and projected climate scenarios of Andit Tid watershed, central highland of Ethiopia

This study examined the impact of climate change on streamflow in the Andit Tid watershed using climate models of dynamically downscaled Ethiopia’s CORDEX. The Arc SWAT and ArcGIS 10.5 software assessed the spatial and temporal distribution of streamflow, incorporating geospatial data like land use maps, digital elevation models, soil maps, and climate data. The SWAT model was calibrated and validated using SWAT-CUP with the SUFI-2 algorithm. The Canadian Centre for Climate Modeling and Analysis, Canada (CCCma (RCA4) model was selected for future projections after validation. From 1991 to 2021, the average streamflow rate was 0.0374 m3/s (247 mm), with R2 values of 0.83 for calibration and 0.72 for validation. Hotspots with active gullies and slopes over 20% were identified mainly in cultivated lands. Future projections indicated a comparable streamflow rate to current conditions at 0.0322 m3/s (212.6 mm). A decline in streamflow is projected: 7.2% and 30.2% decreases in the near and far future under RCP 4.5, and 32.3% decreases and 5% increases under RCP 8.5 scenarios. These variations were attributed to differences in catchment characteristics and climate variability. Further research is needed to validate these findings by incorporating additional biophysical variables. This study provides insights into hydrological planning and management in the Andit Tid watershed and similar regions facing climate variability.

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.

Comprehensive Hydrological Analysis of the Buha River Watershed with High-Resolution SHUD Modeling

This study utilizes the Simulator of Hydrologic Unstructured Domains (SHUD) model and the China Meteorological Forces Dataset (CMFD) to investigate the hydrological dynamics of the Buha River watershed, a critical tributary of Qinghai Lake, from 1979 to 2018. By integrating high-resolution terrestrial and meteorological data, the SHUD model simulates streamflow variations and other hydrological characteristics, providing valuable insights into the region’s water balance and runoff processes. Key findings reveal a consistent upward trend in precipitation and temperature over the past four decades, despite minor deviations in daily precipitation intensity and relative humidity data. The SHUD model demonstrates high accuracy on a monthly scale, with Nash–Sutcliffe Efficiency (NSE) values of 0.72 for the calibration phase and 0.61 for the validation phase. The corresponding Kling–Gupta Efficiency (KGE) values are 0.73 and 0.49, respectively, underscoring the model’s applicability for hydrological forecasting and water resource management. Notably, the annual runoff ratios for the Buha River fluctuate annually between 0.11 and 0.21, with significant changes around 2007 correlating with a shift in Qinghai Lake’s water levels. The analysis of water balance indicates a net leakage over long-term periods, with spatial alterations in leakage and replenishment along the river. Furthermore, snow accumulation, which increases with altitude, significantly contributes to streamflow during the melting season. Despite the Buha River basin’s importance, research on its hydrology remains limited due to data scarcity and minimal human activity. This study enhances the understanding of the Buha River’s hydrological processes and highlights the necessity for improved dataset accuracy and model parameter optimization in future research.

Evaluation of the support vector regression (SVR) and the random forest (RF) models accuracy for streamflow prediction under a data-scarce basin in Morocco

Streamflow prediction is a key variable for water resources management. It becomes more important in semi-arid regions such as the Tensift river basin in Morocco, where water resources are facing a severe drought and the demand is continuously increasing. The present analysis focuses on evaluating Machine Learning techniques, namely support vector regression (SVR) and Random Forest (RF) against the multiple linear regression (MLR) for daily streamflow forecasting in the mountainous sub-basin of Rheraya between 2003 and 2016. The results show that SVR performed best, followed by RF and MLR. In measurable terms and regarding mean performance, SVR exhibited the higher Nash–Sutcliffe efficiency score (NSE = 0.59) and a lower root mean squared error (RMSE = 1.18 m3s-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {m}^3\\,\ ext {s}^{-1}$$\\end{document}) compared to RF (NSE = 0.53, RMSE = 1.18 m3s-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {m}^3\\,\ ext {s}^{-1}$$\\end{document}) and MLR (NSE = 0.54, RMSE = 1.01 m3s-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {m}^3\\,\ ext {s}^{-1}$$\\end{document}). Furthermore,the available time series was too short to properly capture the full range of streamflow variability, which reduced the prediction performance outside of the calibration conditions. These findings suggest that ML algorithms, particularly SVR, can provide accurate streamflow estimation useful for water resources management when trained on a representative period. The results highlight the capacity of Machine Learning algorithms, specifically SVR, to augment streamflow prediction for enhanced water resource management in arid regions.

Ubiquitous Increases in Streamflow and Flooding Magnitude Across the Yellow River Basin

AbstractGlobal human‐induced warming has intensified water circulation in the atmospheric environment and altered the streamflow generation regime. The VIC hydrological model approach for impact assessment of climate change and human activities mainly focuses on variations in streamflow, but ignores other critical flooding characteristics induced by extreme streamflow, especially bivariate flooding characteristics. In this work, the copula functions are employed to structure the flooding risk under the shared socioeconomic pathway (SSP) across the Yellow River basin (YRB). This is based on the multi‐model ensemble (MME) and Delta downscaling outputs (Delta‐MME) of the CMIP6 global climate models (GCMs), as well as the flooding characteristics simulated by VIC hydrological model. Compared to the reference period (1995–2014), Delta‐MME reveals a significant warming and humidifying trend under three SSPs over the YRB. Despite uncertainties originating from climate variables and hydrological model, multiple findings underscore the substantial influence of climate change on the flooding generation regime in YRB. This includes: (a) an increase in the streamflow under all SSPs; (b) a larger flooding peak (Q) and volume (W) under SSP585, with Q and W at the Huayuankou hydrologic station (HYK) increasing by 52.7% and 44.8%, respectively; (c) an advancement in the bivariate flooding risk, particularly in SSP585 where flooding co‐occurrence return period at HYK may be more than 50 times earlier. This study underscores that the urgent need to enhance social resilience to climate change in the YRB.

Untangling the implications of climate-forcing and human-induced drivers in streamflow variability: the Magdalena River, Colombia

ABSTRACT A comprehensive study was conducted to analyse the impact of different drivers on streamflow downstream of the Magdalena River (MR), Colombia, between 1971 and 2020. The study considered El Niño Southern Oscillation (ENSO) events as a climate-forcing driver and evaporation from the reservoir network as a human-induced driver. Streamflow data was linked to previously categorized ENSO events, then standardized and classified based on date and year to evaluate inter- and intra-annual changes in streamflow. Daily evaporation losses were calculated using meteorological records and the aerodynamic method. The study also grouped 10-year periods of evaporation and streamflow to assess their ranges, rates, and water volumes. The results showed a decrease in means, volume, and maximum streamflow, while evaporation and minimum streamflow increased, especially during positive ENSOs. These changes in streamflow could have a significant impact on river-dominated dependent ecosystems, such as the MR downstream wetland complex, putting these ecosystems at risk.

Enhanced Modulation of Streamflow Flash Droughts by Reservoir Operations in India

AbstractStreamflow flash droughts (SFDs) occur due to a rapid decline in streamflow and cause major challenges associated with water availability for downstream ecosystems, hydropower generation, and irrigation water demand. Human interventions such as reservoir operations and reservoir storage can considerably influence streamflow variability. However, the crucial role of dams/reservoirs on the occurrence of SFDs in India remains unexamined. Using a combination of hydrological and hydrodynamical models, we examined the role of reservoirs on SFDs in India during 1971–2020. Reservoirs play a considerable role in the occurrence of SFDs during the summer monsoon season (June‐September) in India. The frequency and severity of SFDs in the presence of reservoirs are substantially lesser than in the absence of them. In addition, high and low antecedent reservoir storage conditions before meteorological flash droughts (MFDs) do not support the rapid decline of streamflow (i.e., SFDs) downstream of reservoirs, while the medium reservoir storage conditions prior to MFDs favor the development of SFDs. Similar to conventional (or long‐term) streamflow droughts (SDs), SFDs are more frequent in the El Nino phase than in the La Nina phase. Unlike the long‐term streamflow droughts, the implementation of dams considerably reduces the frequency of SFDs during both the negative and positive phases of El Nino Southern Oscillation. Overall, our findings demonstrate the considerable role of human interventions in flash drought occurrence, as SFDs are triggered by MFDs but modulated by reservoir storage and operations.

Interbasin river mapping between Californian and Turkish rivers for climate change assessment

ABSTRACT A climate change assessment for streamflow availability of the selected rivers in Turkey is presented. Using an Index Basin Mapping (IBM) approach, climate change information is transferred across hydrologically similar rivers. This approach maps rivers of interest without downscaled climate information to index rivers where climate projections are available. Then, monthly perturbation ratios of index rivers, conveying projected climatic changes, are applied to the mapped rivers. Climate change effects on monthly streamflow availability and timing in eight selected rivers are evaluated under 20 scenarios produced from 10 General Circulation Models (GCMs) under medium and high emission cases. Results show that winter streamflow availability will increase due to more precipitation falling as rainfall rather than snowfall. Spring and summer streamflow availability will decrease due to reduced snowmelt runoff. Monthly streamflow variability will increase in all evaluated rivers. Out of eight selected rivers, the Çoruh, Yeşilırmak and Zamantı Rivers will be the most affected by climatic changes, with 14.6, 4.1, and 5.6% reductions in overall water availability under the high emission ensemble scenario. Overall water availability is projected increase in the Ceyhan and Göksu Rivers with climate change. Increased monthly streamflow variability can complicate water management in the region.

Response of Streamflow to Future Land Use and Cover Change and Climate Change in the Source Region of the Yellow River

This study utilizes meteorological and leaf area index (LAI) data for three shared socioeconomic pathways (SSP1–2.6, SSP2–4.5, and SSP5–8.5) from four general circulation models (GCMs) of the sixth climate model intercomparison project (CMIP6) spanning from 2015 to 2099. Employing calibrated data and incorporating future land use data under three SSPs, the distributed hydrology soil vegetation model (DHSVM) is employed to simulate streamflow in the source region of the Yellow River (SRYR). The research aims to elucidate variations in streamflow across different future scenarios and to estimate extreme streamflow events and temporal distribution changes under future land use and cover change (LUCC) and climate change scenarios. The main conclusions are as follows: The grassland status in the SRYR will significantly improve from 2020 to 2099, with noticeable increases in temperature, precipitation, and longwave radiation, alongside a pronounced decrease in wind speed. The probability of flooding events increases in the future, although the magnitude of the increase diminishes over time. Both LUCC and climate change contribute to an increase in the multi-year average streamflow in the region, with respective increments of 48.8%, 24.5%, and 18.9% under SSP1–2.6, SSP2–4.5, and SSP5–8.5. Notably, the fluctuation in streamflow is most pronounced under SSP5–8.5. In SSP1–2.6, the increase in streamflow during the near future (2020–2059) exceeds that of the distant future (2059–2099). Seasonal variations in streamflow intensify across most scenarios, leading to a more uneven distribution of streamflow throughout the year and an extension of the flood season.

Streamflow Variation under Climate Conditions Based on a Soil and Water Assessment Tool Model: A Case Study of the Bailong River Basin

We coupled the global climate models (GCMs) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and Future Land Use Simulation model (FLUS) to evaluate land use change in the Bailong River Basin (BRB) under three shared socioeconomic pathway and representative concentration pathway scenarios (SSP1–2.6, SSP2–4.5, SSP5–8.5). Additionally, we used calibrated soil and water assessment tools (SWATs) to evaluate the streamflow in the BRB from 2008 to 2100 under the combined influence of climate and land use changes. The results indicate that (1) under the SSP126-EP scenario, forests have been well preserved, and there has been an increase in the combined area of forests and water bodies. The SSP245-ND scenario has a similar reduction pattern in agricultural land as SSP126-EP, with relatively good grassland preservation and a moderate expansion rate in built-up land. In contrast, the SSP585-EG scenario features a rapid expansion of built-up land, converting a significant amount of farmland and grassland into built-up land. (2) From 2021 to 2100, the annual average flow increases under all three scenarios, and the streamflow change is most significant under SSP5–8.5. (3) Compared to the baseline period, the monthly runoff increases, with the most significant increase occurring during the summer months (June to August). This study offers a thorough assessment of potential future changes in streamflow. Its findings are expected to be applied in the future to improve the management of water resources at a local level.

Daily Streamflow Forecasting Using Networks of Real-Time Monitoring Stations and Hybrid Machine Learning Methods

Considering the increased risk of urban flooding and drought due to global climate change and rapid urbanization, the imperative for more accurate methods for streamflow forecasting has intensified. This study introduces a pioneering approach leveraging the available network of real-time monitoring stations and advanced machine learning algorithms that can accurately simulate spatial–temporal problems. The Spatio-Temporal Attention Gated Recurrent Unit (STA-GRU) model is renowned for its computational efficacy in forecasting streamflow events with a forecast horizon of 7 days. The novel integration of the groundwater level, precipitation, and river discharge as predictive variables offers a holistic view of the hydrological cycle, enhancing the model’s accuracy. Our findings reveal that for a 7-day forecasting period, the STA-GRU model demonstrates superior performance, with a notable improvement in mean absolute percentage error (MAPE) values and R-square (R2) alongside reductions in the root mean squared error (RMSE) and mean absolute error (MAE) metrics, underscoring the model’s generalizability and reliability. Comparative analysis with seven conventional deep learning models, including the Long Short-Term Memory (LSTM), the Convolutional Neural Network LSTM (CNNLSTM), the Convolutional LSTM (ConvLSTM), the Spatio-Temporal Attention LSTM (STA-LSTM), the Gated Recurrent Unit (GRU), the Convolutional Neural Network GRU (CNNGRU), and the STA-GRU, confirms the superior predictive power of the STA-LSTM and STA-GRU models when faced with long-term prediction. This research marks a significant shift towards an integrated network of real-time monitoring stations with advanced deep-learning algorithms for streamflow forecasting, emphasizing the importance of spatially and temporally encompassing streamflow variability within an urban watershed’s stream network.

Multi-Method Comparative Analysis of Hydroclimatic Trends and Variability in Dry Creek Catchment, South Australia

AbstractThe trend and variability of hydroclimatic variables over time are apparent in seasonal creeks, especially those located in urbanized areas. Understanding hydro-climatic trends in urban areas is crucial for the sustainable management of water resources and the environment. This study aimed to explore the spatiotemporal variability and trends of hydroclimate variables as well as the potential connection between rainfall and streamflow in Dry Creek catchment, South Australia. The trend-free pre-whitening Mann–Kendall (TFPW-MK) test and Innovative Trend Analysis (ITA) were utilized to examine the monotonic and nonmonotonic trends, respectively, and multiple statistical tests were employed to examine the change points in the hydroclimatic time series. Sen’s slope, Simple Linear Regression (SLR), and ITA were used as alternative approaches to assess the magnitudes of change and overcome the limitations in the underlying assumptions of the various methodologies. The variability in the hydroclimate time series was estimated using several indices, such as the coefficient of variation, seasonality indices, flashiness index, and mean zero flow index. The analyses revealed important findings, notably the high variability of rainfall and streamflow during dry periods. Streamflow displayed greater variability compared to rainfall, with high CV values recorded both seasonally and annually. Furthermore, there was a significant upward trend in seasonal rainfall during winter. Additionally, the maximum and mean temperatures demonstrated a statistically significant increase, which can be attributed to global warming and significant urbanization in the catchment area. Comparative analysis has confirmed that the ITA has superior detection capabilities for nonmonotonic trends, outperforming other methods. It excels at presenting graphical representations that accurately depict trends, effectively differentiating between low, medium, and high values. The strong relationship between rainfall and streamflow demonstrated by the Tanh curve suggests that rainfall is the most reliable predictor of streamflow. The outcomes of this investigation are expected to support local governmental organizations and decision-makers in comprehending the spatial and temporal features of rainfall, as well as its correlation with streamflow. This information will further assist in developing flood and drought mitigation strategies backed by empirical evidence. Graphical Abstract