Search Results (884)

The accuracy of long-term runoff models can be increased through the input of local weather variables and global climate indices. However, existing methods do not effectively extract important information from complex input factors across various temporal and spatial dimensions, thereby contributing to inaccurate predictions of long-term runoff. In this study, local–global–temporal attention mechanisms (LGTA) were proposed for capturing crucial information on global climate indices on monthly, annual, and interannual time scales. The graph attention network (GAT) was employed to extract geographical topological information of meteorological stations, based on remotely sensed elevation data. A long-term runoff prediction model was established based on long-short-term memory (LSTM) integrated with GAT and LGTA, referred to as GAT–LGTA–LSTM. The proposed model was compared to five comparative models (LGTA–LSTM, GAT–GTA–LSTM, GTA–LSTM, GAT–GA–LSTM, GA–LSTM). The models were applied to forecast the long-term runoff at Luning and Pingshan stations in China. The results indicated that the GAT–LGTA–LSTM model demonstrated the best forecasting performance among the comparative models. The Nash–Sutcliffe Efficiency (NSE) of GAT–LGTA–LSTM at the Luning and Pingshan stations reached 0.87 and 0.89, respectively. Compared to the GA–LSTM benchmark model, the GAT–LGTA–LSTM model demonstrated an average increase in NSE of 0.07, an average increase in Kling–Gupta Efficiency (KGE) of 0.08, and an average reduction in mean absolute percent error (MAPE) of 0.12. The excellent performance of the proposed model is attributed to the following: (1) local attention mechanism assigns a higher weight to key global climate indices at a monthly scale, enhancing the ability of global and temporal attention mechanisms to capture the critical information at annual and interannual scales and (2) the global attention mechanism integrated with GAT effectively extracts crucial temporal and spatial information from precipitation and remotely-sensed elevation data. Furthermore, attention visualization reveals that various global climate indices contribute differently to runoff predictions across distinct months. The global climate indices corresponding to specific seasons or months should be selected to forecast the respective monthly runoff. Full article

This study investigates the impact of climate change on hydrological dynamics in the Ruzizi River Basin (RRB) by leveraging a combination of observational historical data and downscaled climate model outputs. The primary objective is to evaluate changes in precipitation, temperature, and water balance components under different climate scenarios. We employed a multi-modal ensemble (MME) approach to enhance the accuracy of climate projections, integrating historical climate data spanning from 1950 to 2014 with downscaled projections for the SSP2-4.5 and SSP5-8.5 scenarios, covering future periods from 2040 to 2100. Our methodology involved calibrating and validating the SWAT model against observed hydrological data to ensure reliable simulations of future climate scenarios. The model’s performance was assessed using metrics such as R², NSE, KGE, and PBIAS, which closely aligned with recommended standards. Results reveal a significant decline in mean annual precipitation, with reductions of up to 37.86% by mid-century under the SSP5-8.5 scenario. This decline is projected to lead to substantial reductions in surface runoff, evapotranspiration, and water yield, alongside a marked decrease in mean monthly stream flow, critically impacting agricultural, domestic, and ecological water needs. The study underscores the necessity of adaptive water resource management strategies to address these anticipated changes. Key recommendations include implementing a dynamic reservoir operation system, enhancing forecasting tools, and incorporating green infrastructure to maintain water quality, support ecosystem resilience, and ensure sustainable water use in the RRB. This research emphasizes the need for localized strategies to address climate-driven hydrological changes and protect future water resources. Full article

Surface water availability in arid regions like the Riyadh region of Saudi Arabia is a significant concern due to its low and highly variable rainfall. This study represents the first comprehensive attempt to estimate surface runoff in the Riyadh region by integrating satellite data with field measurements, including dam observations, for enhanced accuracy. Utilizing the advanced Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Dynamic Infrared Rain Rate near-real-time (PDIR-Now) dataset, the study covers a 23-year period from 2001 to 2023. The research aimed to determine runoff coefficients, which are critical for predicting how much rainfall contributes to surface runoff. Analysis of annual runoff volumes and rainfall data from 39 dams, divided into calibration and validation sets, led to a runoff coefficient of 0.059, indicating that 5.9% of rainfall contributes to runoff. The calibration process, validated by statistical measures such as mean bias (0.23 mm) and RMSE (0.94 mm), showed reasonable model accuracy but also highlighted areas for refinement. With an average annual rainfall of 89.6 mm, resulting in 1733.1 million cubic meters (mil. m³) of runoff, the study underscores the importance of localized calibration and ongoing model refinement to ensure sustainable water management in the face of environmental and climatic challenges. Full article

Runoff prediction is of great importance to water utilization and water-project regulation. Although sun activity has been considered an important factor in runoff, little modeling has been constructed. This study put forward a forecast heuristic combining back propagation neural network (BPNN) and particle swarm optimization (PSO) for annual runoff based on sunspot number and applied it to the Yellow River of China for the period 1956–2016 and assessed the contribution of the sunspot number by placing sole BPNN modeling on the time series as a contrast. First, the heuristic is made up of BPNN calibration and PSO optimization: (1) we use historical data to calibrate BPNN models and obtain a prediction of the sunspot number for training and testing stages; (2) we use the PSO to minimize the difference between the predicted runoff of both BPNN and a linear equation for forecasting stage. Second, the application offers interesting findings: (1) while BPNN calibration obtains first-class forecasting with the ratio >85% with <20% absolute error in training and testing stages, the PSO can achieve similar performance in the forecasting stage; (2) the heuristic can achieve better prediction in years with a lower sunspot number; (3) besides the influence of the sun activity, atmospheric circulation, water usage, and water-project regulation do play important roles on the measured or natural runoff to some extent. This study could provide useful insights into further forecasting of measured and natural runoff under this forecast heuristic in the world. Full article

Urban and storm water retention ponds eventually become eutrophic after years of receiving runoff water. In 2020, a novel biological and chemical treatment was initiated to remove accumulated nutrients from an urban retention pond that had severe algae and weed growth. Our approach installed two 6.1 m × 6.1 m floating treatment wetlands (FTWs) and two airlift pumps that contained slow-release lanthanum composites, which facilitated phosphate precipitation. Four years of treatment (2020–2023) resulted in median nitrate-N concentrations decreasing from 23 µg L⁻¹ in 2020 to 1.3 µg L⁻¹ in 2023, while PO₄-P decreased from 42 µg L⁻¹ to 19 µg L⁻¹. The removal of N and P from the water column coincided with less algae, weeds, and pond muck (sediment), and greater dissolved oxygen (DO) concentrations and water clarity. To quantify the sustainability of this bio-chemical approach, we focused on quantifying nitrate removal rates beneath FTWs. By enclosing quarter sections (3.05 × 3.05 m) of the field-scale FTWs inside vinyl pool liners, nitrate removal rates were measured by spiking nitrate into the enclosed root zone. The first field experiment showed that DO concentrations inside the pool liners were well below the ambient values of the pond (<0.5 mg/L) and nitrate was quickly removed. The second field experiment quantified nitrate loss under a greater range of DO values (<0.5–7 mg/L) by including aeration as a treatment. Nitrate removal beneath FTWs was roughly one-third less when aerated versus unaerated. Extrapolating experimental removal rates to two full-sized FTWs installed in the pond, we estimate between 0.64 to 3.73 kg of nitrate-N could be removed over a growing season (May–September). Complementary laboratory mesocosm experiments using similar treatments to field experiments also exhibited varying nitrate removal rates that were dependent on DO concentrations. Using an average annual removal rate of 1.8 kg nitrate-N, we estimate the two full-size FTWs could counter 14 to 56% of the annual incoming nitrate load from the contributing watershed. Full article

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. Full article

The Xiao Bei mainstream, located in the middle reaches of the Yellow River, plays a vital role in regulating the quality of river water. Our study leveraged 73 years of hydrological data (1951–2023) to investigate long-term runoff trends and seasonal variations in the Xiao Bei mainstream and its two key tributaries, the Wei and Fen Rivers. The results indicated a significant decline in runoff over time, with notable interannual fluctuations and an uneven distribution of runoff within the year. The Wei and Fen Rivers contributed 19.75% and 3.59% of the total runoff to the mainstream, respectively. Field monitoring was conducted at 11 locations along the investigated reach of Xiao Bei, assessing eight water quality parameters (temperature, pH, dissolved oxygen (DO), chemical oxygen demand (COD), ammonia nitrogen (NH₃-N), total phosphorus (TP), permanganate index (COD_Mn), and 5-day biochemical oxygen demand (BOD₅)). Our long-term results showed that the water quality of the Xiao Bei mainstream during the monitoring period was generally classified as Class III. Water quality parameters at the confluence points of the Wei and Fen Rivers with the Yellow River were higher compared with the mainstream. After these tributaries merged into the mainstream, local sections show increased concentrations, with the water quality parameters exhibiting spatial fluctuations. Considering the mass flux process of transmission of the quantity and quality of water, the annual NH₃-N inputs from the Fen and Wei Rivers to the Yellow River accounted for 11.5% and 67.1%, respectively, and TP inputs accounted for 6.8% and 66.18%. These findings underscore the critical pollutant load from tributaries, highlighting the urgent need for effective pollution management strategies targeting these tributaries to improve the overall water quality of the Yellow River. This study sheds light on the spatiotemporal changes in runoff, water quality, and pollutant flux in the Xiao Bei mainstream and its tributaries, providing valuable insights to enhance the protection and management of the Yellow River’s water environment. Full article

Investigations into the impacts of climate change on water conservation capacity in the upper Mekong River Basin (UMRB) are important for the region’s sustainability. However, quantitative studies on isolating the individual contribution of climate change to water conservation capacity are lacking. In this study, various data-driven SWAT models were developed to quantitatively analyze the unique impact of climate change on water conservation capacity in the UMRB. The results reveal the following: (1) From 1981 to 2020, the annual water conservation capacity ranged from 191.6 to 392.9 mm, showing significant seasonal differences with the values in the rainy season (218.6–420.3 mm) significantly higher than that in the dry season (−57.0–53.2 mm). (2) The contribution of climate change to water conservation capacity is generally negative, with the highest contribution (−65.2%) in the dry season, followed by the annual (−8.7%) and the rainy season (−8.1%). (3) Precipitation, followed by evaporation and surface runoff, emerged as the critical factor affecting water conservation capacity changes in the UMRB. This study can provide insights for water resources management and climate change adaptations in the UMRB and other similar regions in the world. Full article

Frequent soil erosion disasters in the middle reaches of the Yellow River (MRYR) have a profound effect on the sediment load of the river. This paper addresses the intertwined effects of human activities and climate change on river runoff and sediment load. Therefore, runoff and sediment loads from hydrological stations along the main and tributary rivers within the MRYR were used. The Mann–Kendall (M–K) trend test and the double mass curve analysis, among other analytical tools, were used to examine the erosion patterns of these rivers from 1950 to 2022, as well as the main factors driving these changes. The results showed that the runoff depth of the Yan River tended to decrease, and there was a significant decrease in the mainstream and nine other tributaries, with a significant decrease in the sediment transport modulus for both the mainstream and tributaries. In the main river, human activities contributed between 69.99% and 94.69% to the runoff and between 88.52% and 98.49% to the sediment load, while in the tributaries, the contribution of human activities was greater. The annual runoff and annual sediment load in the MRYR showed a decreasing trend, with a discernible impact of human activities. The results of this research are of great significance for erosion control and the restoration of the ecological balance in the Yellow River Basin. Full article

Small-scale open-pit, non-artisanal mining of low-value ores is an understudied practice despite its widespread occurrence and potential impact on freshwater resources due to mining-induced land-use/cover changes (LUCCs). This research investigates the long-term impacts of andesite mining in Pasuruan, Indonesia, on the Umbulan Spring’s water discharge within its watershed. System Dynamics (SD) modeling captures the systemic and systematic impact of mining-induced LUCCs on discharge volumes and groundwater recharge. Agricultural and reservoir-based land reclamation scenarios then reveal post-mining temporal dynamics. The no-mining scenario sees the spring’s discharge consistently decrease until an inflection point in 2032. With mining expansion, reductions accelerate by ~1.44 million tons over two decades, or 65.31 thousand tons annually. LUCCs also decrease groundwater recharge by ~2.48 million tons via increased surface runoff. Proposed post-mining land interventions over reclaimed mining areas influence water volumes differently. Reservoirs on reclaimed land lead to ~822.14 million extra tons of discharge, 2.75 times higher than the agricultural scenario. Moreover, reservoirs can restore original recharge levels by 2039, while agriculture only reduces the mining impact by 28.64% on average. These findings reveal that small-scale non-artisanal andesite mining can disrupt regional hydrology despite modest operating scales. Thus, evidence-based guidelines are needed for permitting such mines based on environmental risk and site water budgets. Policy options include discharge or aquifer recharge caps tailored to small-scale andesite mines. The varied outputs of rehabilitation scenarios also highlight evaluating combined land and water management interventions. With agriculture alone proving insufficient, optimized mixes of revegetation and water harvesting require further exploration. Full article

Determining the relative roles of climatic versus anthropogenic factors in runoff alterations is important for sustainable water resource utilization and basin management. The Danjiang River watershed is a crucial water resource area of the middle route of the South-to-North Water Transfer Project. In this study, four widely used quantitative methods, including the simple linear regression, the double mass curve, the paired year with similar climate conditions, and an elasticity method based on the Budyko framework were applied to detect the relative contribution of climatic and anthropogenic factors to runoff variation in the Danjiang River watershed. The calculation processes of each method were systematically explained, and their characteristics and applications were summarized. The results showed that runoff decreased significantly (p < 0.05) with an average change rate of −3.88 mm year⁻¹ during the period of 1960–2017, and a significant change year was detected in 1989 (p < 0.05). Generally, consistent estimates could be derived from different methods that human activity was the dominant driving force of significant runoff reduction. Although the impacts of human activity estimated by the paired year with similar climate conditions method varied among paired years, the other three methods demonstrated that human activity accounted for 80.22–92.88% (mean 86.33%) of the total reduction in the annual runoff, whereas climate change only contributed 7.12–19.78% (mean 13.67%). The results of this study provide a good reference for estimating the effects of climate change and human activities on runoff variation via different methods. Full article

Shallow coal mining in gully regions has resulted in significant subsidence hazards and increased the risk of surface water inflow into mining panels, compromising the sustainability of surface water management and underground resource exploitation. In this study, the chain disaster process caused by shallow coal seam mining and heavy rainfall is quantitatively analyzed. The findings reveal that shallow coal seam mining leads to the formation of caved and fractured zones in the vertical direction of the overlying rock. The fractured zone can be further classified into a compression subsidence zone and a shear subsidence zone in the horizontal direction. The shear subsidence zone is responsible for generating compression and shear deformations, intercepting rainfall runoff, and potentially triggering landslides, necessitating crack landfill treatments, which are critical for promoting sustainable mining practices. The HEC-RAS program was utilized to integrate annual maximum daily rainfall data across different frequencies, enabling the establishment of a dynamic risk assessment model for barrier lakes. Numerical simulations based on unsaturated seepage theory provide insights into the infiltration and seepage behavior of rainfall in the study area, indicating a significant increase in saturation within lower gully terrain. Precipitation infiltration was found to enhance the saturation of the shallow rock mass, reducing matric suction in unsaturated areas. Finally, the disaster chain is discussed, and recommendations for managing different stages of risk are proposed. This study offers a valuable reference for the prevention and control of surface water damage under coal mining conditions in gully regions. Full article

Against the backdrop of global warming, assessing the effects of climate change on hydrological processes is crucial for local water resource management. Variations in temperature, precipitation, and runoff at four different timescales in the Fuhe River Basin were evaluated based on observational data collected from 1960 to 2020 using the Mann–Kendall test. The findings indicated significant increases in average temperatures for the annual, flood season, and non-flood season periods, rising by 0.0197, 0.0145, and 0.0278 °C every annum, respectively (p < 0.01). Precipitation exhibited non-significant upward trends at all timescales (p > 0.1). The trend in flood season runoff was also non-significantly upward, whereas annual runoff and non-flood season runoff displayed non-significant downward trends (p > 0.1). Flood season temperature decreased with increasing altitude, exhibiting a significant Pearson correlation coefficient of −0.744 at the 0.01 level. Conversely, annual, flood, and non-flood season precipitation significantly increased with increasing altitude, with Pearson correlation coefficients of 0.678 at the 0.01 level, 0.695 at the 0.01 level, and 0.558 at the 0.05 significance level, respectively. Precipitation and runoff exhibited similar trends throughout the year, increasing initially and then decreasing over time, reaching maximum values in June. Climate change is likely responsible for the hydrological alterations in the study basin. The findings of the study could provide references for water resource management decisions in the Fuhe River Basin. Full article

Urbanization in the Haihe River Basin in northern China, particularly the upstream mountainous basin of Baiyangdian, has significantly altered land use and runoff processes. The runoff is a key water source for downstream areas like Baiyangdian and the Xiong’an New Area, making it essential to understand these changes’ implications for water security. However, the exact implications of these processes remain unclear. To address this gap, a simulation framework combining SWAT+ and CLUE-S was used to analyze runoff responses under different land use scenarios: natural development (ND), farmland protection (FP), and ecological protection (EP). The model simulation results were good, with NSE above 0.7 for SWAT+. The Kappa coefficient for CLUE-S model validation was 0.83. The further study found that from 2005 to 2015, urban construction land increased by 11.50 km² per year, leading to a 0.5–1.3 mm rise in annual runoff. Although urban expansion continued, the other scenarios, which emphasized farmland and forest preservation, slowed this growth. Monthly runoff changes were most significant during the rainy season, with annual runoff in ND, FP, and EP varying by 8.9%, 10.9%, and 7.7%, respectively. While the differences in annual runoff between scenarios were not dramatic, these findings provide a theoretical foundation for future water resource planning and management in the upstream mountainous area of Baiyangdian and offer valuable insights for the sustainable development of Xiong’an New Area. Additionally, these results contribute to the broader field of hydrology by highlighting the importance of considering multiple land use scenarios in runoff change analysis. Full article

The floodplain transverse slope is a significant parameter reflecting the degree of development of a secondary suspended river, as well as a crucial index of the flood risk in the river channel. Clarifying the factors that influence the evolution of the floodplain transverse slope has always been a hot and difficult topic for researchers working on the Yellow River management. We took the severe section of the secondary suspended river from Dongbatou to Gaocun in the lower Yellow River as the research object, selecting the annual runoff, annual sediment load, annual sediment coefficient, and the intensity of flood-season flow scouring at the Huayuankou station in the downstream as the water–sediment indexes. The correlation between different water–sediment indexes and the floodplain transverse slope under three modes: interannual, flood season, and flood-season overbank was studied through methods such as cross-wavelet transform and wavelet coherence analysis. The results showed that under the three modes, the annual sediment load and annual sediment coefficient had a high correlation with the evolution cycle of the transverse slope, followed by the intensity of flood-season flow scouring, and the annual runoff had the lowest correlation. Meanwhile, the change in the transverse slope had a good correlation with the flood-season overbank mode, indicating there was a high similarity between the water–sediment characteristics of floodplain flooding and the evolution cycle of the transverse slope; that is, the change in the transverse slope is greatly influenced by floodplain flooding events. Full article