Search Results (218)

More than half of the world’s rivers are intermittent, and climate change is increasing their intermittency, affecting water resources and ecosystems. In Taiwan, steep topography and uneven rainfall have led to increased intermittency in some areas, reflecting changes in hydrological conditions. Using streamflow data, this study applied intermittency ratio (IR), modified 6-month dry period seasonality (SD6), and trend analysis, as well as watershed properties and climate indices. Results showed that 92% of stations had low flows for less than 20% of the time. The dry season was mainly from November to April, and intermittency was spatially affected mainly by upstream soil moisture, moderately by potential evapotranspiration and infiltration, and less by actual evapotranspiration and catchment area. Intermittency increased in the east and decreased in the west. It was negatively correlated with upstream soil moisture and strongly associated with rainfall frequency, especially the proportion of days with precipitation less than 1 mm. These patterns highlight regional differences in river responses to climate. Full article

Accurate drought monitoring depends on selecting an appropriate cumulative distribution function (CDF) to model the original data, resulting in the standardized drought indices. In the numerous research studies, while rigorous validation was not made by scrutinizing the model assumptions and uncertainties in identifying theoretical drought CDF models, such oversights lead to biased representations of drought evaluation and characteristics. This research compares the parametric theoretical and empirical CDFs for a comprehensive evaluation of standardized Drought Indices. Additionally, it examines the advantages, disadvantages, and limitations of both empirical and theoretical distribution functions in drought assessment. Three drought indices, Standardized Precipitation Index (SPI), Streamflow Drought Index (SDI), and Standardized Precipitation Evapotranspiration Index (SPEI), cover meteorological and hydrological droughts. The assessment spans diverse applications, covering different climates and regions: Durham, United Kingdom (SPEI, 1868–2021); Konya, Türkiye (SPI, 1964–2022); and Lüleburgaz, Türkiye (SDI, 1957–2015). The findings reveal that theoretical and empirical CDFs demonstrated notable discrepancies, particularly in long-term hydrological drought assessments, where underestimations reached up to 50%, posing risks of misinformed conclusions that may impact critical drought-related decisions and policymaking. Root Mean Squared Error (RMSE) for SPI3 between empirical and best-fitted CDF was 0.087, and between empirical and Gamma it was 0.152. For SDI, it ranged between 0.09 and 0.143. The Mean Absolute Error (MAE) for SPEI was approximately 0.05 for all timescales. Additionally, it concludes that empirical CDFs provide more reliable and conservative drought assessments and are free from the constraints of model assumptions. Both approaches gave approximately the same drought duration with different intensities regarding drought characteristics. Due to the complex process of drought events and different definitions of drought events, each drought event must be studied separately, considering its effects on different sectors. Full article

Machine learning models are increasingly used for streamflow prediction due to their promising performance. However, their data-driven nature makes interpretation challenging. This study explores the interpretability of a Random Forest model trained on high streamflow events from a hydrological perspective, comparing methods for assessing feature influence. The results show that the mean decrease accuracy, mean decrease impurity, Shapley additive explanations, and Tornado methods identify similar key features, though Tornado presents the most notable discrepancies. Despite the model being trained with events of considerable temporal variability, the last observed streamflow is the most relevant feature accounting for over 20% of importance. Moreover, the results suggest that the model identifies a catchment region with a runoff that significantly affects the outlet flow. Accumulated local effects and partial dependence plots may represent first infiltration losses and soil saturation before precipitation sharply impacts streamflow. However, only accumulated local effects depict the influence of the scarce highest accumulated precipitation on the streamflow. Shapley additive explanations are simpler to apply than the local interpretable model-agnostic explanations, which require a tuning process, though both offer similar insights. They show that short-period accumulated precipitation is crucial during the steep rising limb of the hydrograph, reaching 72% of importance on average among the top features. As the peak approaches, previous streamflow values become the most influential feature, continuing into the falling limb. When the hydrograph goes down, the model confers a moderate influence on the accumulated precipitation of several hours back of distant regions, suggesting that the runoff from these areas is arriving. Machine learning models may interpret the catchment system reasonably and provide useful insights about hydrological characteristics. Full article

The Sylhet Division of Bangladesh, highly susceptible to monsoon flooding, requires effective flood risk management to reduce socio-economic losses. Flood frequency analysis is an essential aspect of flood risk management and plays a crucial role in designing hydraulic structures. This study applies regional flood frequency analysis (RFFA) using L-moments to identify homogeneous hydrological regions and estimate extreme flood quantiles. Records from 26 streamflow gauging stations were used, including streamflow data along with corresponding physiographic and climatic characteristic data, obtained from GIS analysis and ERA5 respectively. Most stations showed no significant monotonic trends, temporal correlations, or spatial dependence, supporting the assumptions of stationarity and independence necessary for reliable frequency analysis, which allowed the use of cluster analysis, discordancy measures, heterogeneity tests for regionalization, and goodness-of-fit tests to evaluate candidate distributions. The Generalized Logistic (GLO) distribution performed best, offering robust quantile estimates with narrow confidence intervals. Multiple Non-Linear Regression models, based on catchment area, elevation, and other parameters, reasonably predicted ungauged basin peak discharges (R² = 0.61–0.87; RMSE = 438–2726 m³/s; MAPE = 41–74%) at different return periods, although uncertainty was higher for extreme events. Four homogeneous regions were identified, showing significant differences in hydrological behavior, with two regions yielding stable estimates and two exhibiting greater extreme variability. Full article

Understanding how spatial drought variability influences streamflow is critical for sustainable water management under changing climate conditions. This study developed a novel Combined Drought Index (CDI) and a method to assess spatial drought impacts on different flow components by integrating remote sensing and hydrological modelling frameworks with generic applicability. The CDI was constructed using Principal Component Analysis to merge multiple standardized indicators: the Standardized Precipitation Evapotranspiration Index, Temperature Condition Index, Vegetation Condition Index, and Soil Moisture Condition Index. The developed framework was applied to the Giriulla sub-basin of the Maha Oya River Basin, Sri Lanka. The CDI strongly correlated with standardized streamflow with a Pearson correlation coefficient of 0.74 and successfully captured major drought and flood events between 2015 and 2023. A semi-distributed hydrological model was used to simulate streamflow variations across sub-catchments under varying drought conditions. Results show upstream sub-catchments were more sensitive to droughts, with sharper declines in specific discharge. Spatial drought variability had different impacts under high- and low-flow conditions: wetter sub-catchments contributed more during high flows, while resilience during low flows varied with catchment characteristics. This integrated approach provides a valuable framework that can be generically applicable for enhanced drought impact assessments. Full article

British Columbia, Canada, is recognized for its abundant natural resources, including agricultural and aquaculture products, sustained by its diverse climate and geography. Water resource allocation in BC is governed by the Water Sustainability Act, enacted on 29 February 2016, replacing the historic Water Act. However, limited gauging of streams across the province poses challenges for ensuring water allocation while meeting Environmental Flow Needs. Overallocated watersheds and data-scarce watersheds in need of licensing highlight the need for robust streamflow prediction methods. To address these challenges, we developed a methodology that integrates the Drainage Area Ratio and Nearest Neighbors techniques to predict streamflows efficiently, without incurring additional financial costs. We utilized Digital Elevation Models and flow data from provincially and municipally managed hydrometric stations, as well as from the Water Survey of Canada, to normalize streamflows based on area, slope, and elevation. This approach ensures hydrological predictions that account for variability in hydrological processes resulting from differences in lumped-scale watershed characteristics. The method was validated using streamflow data from hydrometric stations maintained by the aforementioned entities. For validation, each station was iteratively treated as ungauged by temporarily removing it from the dataset and then predicting its streamflow using the proposed methodologies. The results demonstrated that the amalgamated Drainage Area Ratio–Nearest Neighbors approach outperformed the traditional Drainage Area Ratio method, offering reliable predictions for diverse watersheds. This study provides an adaptable and cost-effective framework for enhancing water resource management across BC. Full article

The Okavango River is a transboundary waterway that flows through Angola, Namibia, and Botswana, forming a significant alluvial fan in northwestern Botswana. This fan creates a Delta that plays a vital role in the country’s GDP through tourism. While research has primarily focused on the Delta, the river’s catchment area in the Angolan highlands—its main water source and critical for downstream flow—has been largely overlooked. The basin is under pressure from development, water abstraction, and population growth in the surrounding areas, which negatively affect the environment. These challenges are intensified by climate change, leading to increased water scarcity that necessitates improved management strategies. Currently, there is a lack of published research on the basin’s hydrology, leaving many hydrological parameters related to streamflow in the catchments inadequately understood. Most existing studies have employed single-site calibration methods, which fail to capture the diverse characteristics of the basin’s catchments. To address this, a SWAT model has been developed to simulate the hydrologic behaviour of the basin using sequential multisite calibration with data from five gauging stations, including the main river systems: Cubango and Cuito. The SUFI2 program was used for sensitivity analysis, calibration, and validation. The initial sensitivity analysis identified several key parameters: the Soil Evaporation Compensation Factor (ESCO), the SCS curve number under moisture condition II (CN2), Saturated Hydraulic Conductivity (SOL_K), and Moist Bulk Density (SOL_BD) as the most influential. The calibration and validation results were generally satisfactory, with a coefficient of determination ranging from 0.47 to 0.72. Analysis of the water balance and parameter sensitivities revealed the varied hydrologic responses of different sub-watersheds with distinct soil profiles. Average annual precipitation varies from 1116 mm upstream to 369 mm downstream, with an evapotranspiration-to-precipitation ratio ranging from 0.47 to 0.95 and a water yield ratio between 0.51 and 0.03, thereby revealing their spatial gradients, notably increasing evapotranspiration and decreasing water yield downstream. The SWAT model’s water balance components provided promising results, with soil moisture data aligned with the TerraClimate dataset, achieving a coefficient of determination of 0.63. Additionally, the model captured the influence of the El Niño–Southern Oscillation (ENSO) on local hydrology. However, limitations were noted in simulating peak and low flows due to sparse gauge coverage, data gaps (e.g., groundwater abstraction, point sources), and the use of coarse-resolution climate inputs. Full article

Streamflow prediction is vital for effective water resource management, enabling a better understanding of hydrological variability and its response to environmental factors. This study presents a spatio-temporal graph neural network (STGNN) model for streamflow prediction in the Upper Colorado River Basin (UCRB), integrating graph convolutional networks (GCNs) to model spatial connectivity and long short-term memory (LSTM) networks to capture temporal dynamics. Using 30 years of monthly streamflow data from 20 monitoring stations, the STGNN predicted streamflow over a 36-month horizon and was evaluated against traditional models, including random forest regression (RFR), LSTM, gated recurrent units (GRU), and seasonal auto-regressive integrated moving average (SARIMA). The STGNN outperformed these models across multiple metrics, achieving an R² of 0.78, an RMSE of 0.81 mm/month, and a KGE of 0.79 at critical locations like Lees Ferry. A sequential analysis of input–output configurations identified the (36, 36) setup as optimal for balancing historical context and forecasting accuracy. Additionally, the STGNN showed strong generalizability when applied to other locations within the UCRB. These results underscore the importance of integrating spatial dependencies and temporal dynamics in hydrological forecasting, offering a scalable and adaptable framework to improve predictive accuracy and support adaptive water resource management in river basins. Full article

Recent advances in hydrological modling have led to the generation of numerous global or regional runoff datasets, which have been widely used in hydrological analysis. However, it is not yet clear how their accuracy and reliabilities are. In this study, using observed gauge streamflow data at four stations (Hequ, Fugu, Wubu, and Longmen) in the middle reaches of the Yellow River as reference, we compare and evaluate the accuracy of three runoff gridded dataset products (GloFAS, GRFR v1.0, and WGHM) at four temporal scales: daily, monthly, annual, and wet/dry seasons. The results indicate the following: (1) As the temporal scale increases, the simulated streamflow accuracy of the three datasets gradually improves. The GloFAS dataset performs the best at daily scale, while the WGHM dataset outperforms the other two at monthly and annual scales. (2) The three datasets all tend to overestimate the total streamflow at the main stations. (3) Comparing the two hydrological scenarios of wet and dry seasons, all three datasets exhibit better performance during the wet season. (4) The capture of peak streamflow is influenced by dataset type, temporal scale, and station characteristics. In general, the three datasets perform better at stations with higher base streamflow, such as Longmen and Wubu stations. Additionally, this study discusses the possible reasons for their different performances, which can be mainly attributed to three aspects: the quality of meteorological input datasets, missing or simplified simulation processes, and incorrect model structure and parameterization. Future research will consider revising the datasets to obtain more accurate data sources and further enhance the accuracy of watershed streamflow simulations. Full article

The Water–Ecosystem–Agriculture (WEA) relationship is pivotal to the sustainable development of arid and semi-arid areas. The WEA nexus in these areas is essential for making policies towards sustainable development. This study aims to explore the WEA nexus in three large inland river basins (Heihe River Basin, Shiyang River Basin, and Shule River Basin) in the Hexi Corridor, Northwest China, using an integrated hydrological modeling approach. The integrated model was calibrated and validated against observed streamflow data, achieving Nash–Sutcliffe Efficiencies ranging from 0.83 to 0.94 in the validation period. The major findings are as follows. First, altering the amount of irrigation water significantly affects hydrological and ecological processes in both midstream and downstream areas, influencing the WEA nexus. For example, a 20% reduction in irrigation demand led to a 0.46 billion m³/year recovery in midstream groundwater storage and a 4.3% increase in downstream ecosystem health, but resulted in a 5.4% decrease in midstream agricultural productivity. Second, intense trade-offs among agricultural productivity, ecosystem health, and groundwater sustainability were identified. These trade-offs are highly sensitive to water management strategies, particularly those affecting groundwater sustainability. Third, implementing stricter groundwater-level drawdown constraints significantly improved groundwater sustainability and ecosystem health. Fourth, this study highlighted unique WEA nexus characteristics in each of the three basins. This study provides insights into the understanding the complex WEA nexus, and the quantitative results underscore the trade-offs and synergies within the WEA nexus, providing a foundation for informed decision-making in water resource management. Full article

The standardized hydrological index (SHI) is the standardized but not normalized (normal probability variate) value of the streamflow used to characterize a hydrological drought, akin to the standardized precipitation index (SPI, which is both standardized and normalized) in the realm of the meteorological drought. The time series of the SHI can be used as a platform for deriving the longest duration, L_T, and the largest magnitude, M_T (in standardized form), of a hydrological drought over a desired return period of T time units (year, month, or week). These parameters are predicted based on the SHI series derived from the annual, monthly, and weekly flow sequences of Canadian rivers. An important point to be reckoned with is that the monthly and weekly sequences are non-stationary compared to the annual sequences, which fulfil the conditions of stochastic stationarity. The parameters, such as the mean, standard deviation (or coefficient of variation), lag 1 autocorrelation, and conditional probabilities from SHI sequences, when used in Markov chain-based relationships, are able to predict the longest duration, L_T, and the largest magnitude, M_T. The product moment and L-moment ratio analyses indicate that the monthly and weekly flows in the Canadian rivers fit the gamma probability distribution function (pdf) reasonably well, whereas annual flows can be regarded to follow the normal pdf. The threshold level chosen in the analysis is the long-term median of SHI sequences for the annual flows. For the monthly and weekly flows, the threshold level represents the median of the respective month or week and hence is time varying. The runs of deficit in the SHI sequences are treated as drought episodes and thus the theory of runs formed an essential tool for analysis. This paper indicates that the Markov chain-based methodology works well for predicting L_T on annual, monthly, and weekly SHI sequences. Markov chains of zero order (MC0), first order (MC1), and second order (MC2) turned out to be satisfactory on annual, monthly, and weekly scales, respectively. The drought magnitude, M_T, was predicted satisfactorily via the model M_T = I_d × L_c, where I_d stands for drought intensity and L_c is a characteristic drought length related to L_T through a scaling parameter, ɸ (= 0.5). The I_d can be deemed to follow a truncated normal pdf, whose mean and variance when combined implicitly with L_c proved prudent for predicting M_T at all time scales in the aforesaid relationship. Full article

As climate change intensifies, extreme drought events have become more frequent, and investigating the mechanisms of watershed drought has become highly significant for basin water resource management. This study utilizes the WRF-Hydro model in conjunction with standardized drought indices, including the standardized precipitation index (SPI), standardized soil moisture index (SSMI), and Standardized Streamflow Index (SSFI), to comprehensively investigate the spatiotemporal characteristics of drought in the Huai River Basin, China, from 2012 to 2018. The simulation performance of the WRF-Hydro model was evaluated by comparing model outputs with reanalysis data at the regional scale and site observational data at the site scale, respectively. Our results demonstrate that the model showed a correlation coefficient of 0.74, a bias of −0.29, and a root mean square error of 2.66% when compared with reanalysis data in the 0–10 cm soil layer. Against the six observational sites, the model achieved a maximum correlation coefficient of 0.81, a minimum bias of −0.54, and a minimum root mean square error of 3.12%. The simulation results at both regional and site scales demonstrate that the model achieves high accuracy in simulating soil moisture in this basin. The analysis of SPI, SSMI, and SSFI from 2012 to 2018 shows that the summer months rarely experience drought, and droughts predominantly occurred in December, January, and February in the Huai River Basin. Moreover, we found that the drought characteristics in this basin have significant seasonal and interannual variability and spatial heterogeneity. On the one hand, the middle and southern parts of the basin experience more frequent and severe agricultural droughts compared to the northern regions. On the other hand, we identified a time–lag relationship among meteorological, agricultural, and hydrological droughts, uncovering interactions and propagation mechanisms across different drought types in this basin. Finally, we concluded that the WRF-Hydro model can provide highly accurate soil moisture simulation results and can be used to assess the spatiotemporal variations in regional drought events and the propagation mechanisms between different types of droughts. Full article

Complexity evaluation of hydro-climatic datasets is a challenging but essential pre-requisite for accurate modeling and subsequent planning. Changes in climate and anthropogenic interventions amplify the complexity of hydro-climatic time-series. Understanding persistence and fractal features may help us to develop new and robust modeling frameworks which can work well under non-stationary and non-linear environments. Classical fractal hydrology, rooted in statistical physics, has been developed since the 1980s and the modern alternatives based on de-trending, complex network, and time–frequency principles have been developed since 2002. More specifically, this review presents the procedures of Multifractal Detrended Fluctuation Analysis (MFDFA) and Arbitrary Order Hilbert Spectral Analysis (AOHSA), along with their applications in the field of hydro-climatology. Moreover, this study proposes a complex network-based fractal analysis (CNFA) framework for the multifractal analysis of daily streamflows as an alternative. The case study proves the efficacy of CNMFA and shows that it has the flexibility to be applied in visibility and inverted visibility schemes, which is effective in complex datasets comprising both high- and low-amplitude fluctuations. The comprehensive review showed that more than 75% of the literature focuses on characteristic analysis of the time-series using MFDFA rather than modeling. Among the variables, about 70% of studies focused on analyzing fine-resolution streamflow and rainfall datasets. This study recommends the use of CNMF in hydro-climatology and advocates the necessity of knowledge integration from multiple fields to enhance the multifractal modeling applications. This study further asserts that transforming the characterization into operational hydrology is highly warranted. Full article

Anthropogenic climate change and changes to land use and land management practices can have significant impacts on streamflow and sediment transport. In this study, we investigated long-term precipitation, streamflow, and suspended sediment load patterns within the Hangman Creek watershed, draining from the Rocky Mountains in Idaho to Washington, to identify the magnitude of changes with the goal of better understanding the links between these processes and the potential effects of agricultural conservation practices (ACPs) implemented since the 1990s. Comparing the study periods of 1991 to 2020 with 1961 to 1990, 1991 to 2020 had lower streamflow/precipitation ratios in the highest flow months such as February and March. Most streamflow occurred during winter and spring, so did suspended sediment. In addition, 2018 had much lower suspended sediment load compared to earlier years (1999 and 2000) during high flow seasons (January to April) given that streamflow was higher in 2018 than in 1999 and 2000. These changes may be attributed to the adoption of agricultural conservation practices because land cover remained almost unchanged from 2001 to 2021 and ACP adoption increased. Finally, the flow frequency analysis showed a strong linkage between higher streamflow events and increased suspended sediment load, with between 81% and 96% of total annual suspended sediment loads transported during the highest 10% of flows. Full article

Climate change often affects streamflow, which can be categorized into immediate and lag responses. Historically, the phenomenon of lag responses, known as hydrological memory, has often been overlooked. This study aims to determine whether hydrological memory characteristics exist in Taiwan’s catchments and to identify the lag time in streamflow response. Using data from 67 catchments across Taiwan with a length of over 30 years, the study examines the response of streamflow to precipitation and potential evapotranspiration across different time scales. Streamflow elasticity was employed to quantify the sensitivity of catchment streamflow. Sensitivity analysis results indicate that the month scale better explains the sensitivity of streamflow to climatic factors compared to the year scale. Therefore, memory characteristics are discussed using the month scale. Only 19.4% of the studied catchments exhibit significant hydrological memory, making it a rare phenomenon in Taiwan. The conceptual model of hydrological memory shows that extreme precipitation and other hydrological climate anomalies primarily impact river streamflow generation 33 days (1.11 months) later, with the influence of precipitation on streamflow recharge lag up to 50 days (1.67 months). Catchments with hydrological memory characteristics are predominantly located in southwestern Taiwan, mainly in catchments smaller than 500 km², with generally lower baseflow indices and a higher proportion of streamflow contributions. These characteristics are less common in high-elevation areas. The results of this study highlight that streamflow response to climatic factors exhibits a lag time, illustrating the memory characteristics of Taiwan’s catchments. This understanding will aid in the prediction of hydrological phenomena and provide valuable references for hydrological modeling and the development and management of water resources. Full article