Search Results (21)

The snow water equivalent (SWE) in high-altitude regions is crucial for water resource management and disaster risk reduction, yet accurate predictions remain challenging due to complex snowmelt processes, nonlinear meteorological factors, and time-lag effects. This study used snow remote sensing products from the Advanced Microwave Scanning Radiometer (AMSR) as the predictand for evaluating SWE predictions. It applied nine machine learning models—linear regression (LR), decision trees (DT), support vector regression (SVR), random forest (RF), artificial neural networks (ANNs), AdaBoost, XGBoost, gradient boosting decision trees (GBDT), and CatBoost. For each machine learning model, submodels were constructed to predict the SWE for the next 1 to 30 days. The 30 submodels of each machine learning model formed the prediction model for the snow water equivalent over the next 30 days. Through an accuracy evaluation and ensemble forecasting, the snow water equivalent prediction for the next 30 days in the Yalong River above the Ganzi Basin was finally achieved. The results showed that for all models, the average Nash–Sutcliffe Efficiency (NSE) rate was greater than 0.8, the average root mean square error (RMSE) was under 8 mm, and the average relative error (RE) was below 7% across three lead time periods (1–10, 11–20, and 21–30 days). The ensemble average model, combining ANNs, GBDT, and CatBoost, demonstrated superior accuracy, with NSE values exceeding 0.85 and RMSE values under 6 mm. A sensitivity analysis using the Shapley Additive Explanations (SHAP) model revealed that temperature variables (average, minimum, and maximum temperatures) were the most influential factors, while relative humidity (Rhu) significantly affected the SWE by reducing evaporation. These findings provide insights for improving SWE prediction accuracy and support water resource management in high-altitude regions. Full article

Satellite and reanalysis-based precipitation products have played a crucial role in addressing the challenges associated with limited ground-based observational data. These products are widely utilized in hydrometeorological research, particularly in data-scarce regions like the Qinghai–Tibetan Plateau (QTP). This study proposed an ensemble streamflow simulation method using remote sensing precipitation data as input. By employing a 1D Convolutional Neural Networks (1D CNN), streamflow simulations from multiple models are integrated and a Shapley Additive exPlanations (SHAP) interpretability analysis was conducted to examine the contributions of individual models on ensemble streamflow simulation. The method is demonstrated using GPM IMERG (Global Precipitation Measurement Integrated Multi-satellite Retrievals) remote sensing precipitation data for streamflow estimation in the upstream region of the Ganzi gauging station in the Yalong River basin of QTP for the period from 2010 to 2019. Streamflow simulations were carried out using models with diverse structures, including the physically based BTOPMC (Block-wise use of TOPMODEL) and two machine learning models, i.e., Random Forest (RF) and Long Short-Term Memory Neural Networks (LSTM). Furthermore, ensemble simulations were compared: the Simple Average Method (SAM), Weighted Average Method (WAM), and the proposed 1D CNN method. The results revealed that, for the hydrological simulation of each individual models, the Kling–Gupta Efficiency (KGE) values during the validation period were 0.66 for BTOPMC, 0.71 for RF, and 0.74 for LSTM. Among the ensemble approaches, the validation period KGE values for SAM, WAM, and the 1D CNN-based nonlinear method were 0.74, 0.73, and 0.82, respectively, indicating that the nonlinear 1D CNN approach achieved the highest accuracy. The SHAP-based interpretability analysis further demonstrated that RF made the most significant contribution to the ensemble simulation, while LSTM contributed the least. These findings highlight that the proposed 1D CNN ensemble simulation framework has great potential to improve streamflow estimations using remote sensing precipitation data as input and may provide new insight into how deep learning methods advance the application of remote sensing in hydrological research. Full article

In recent years, renewable, clean energy options such as hydropower, wind energy and solar energy have been attracting more and more attention as high-quality alternatives to fossil fuels, due to the depletion of fossil fuels and environmental pollution. Multi-energy power systems have replaced traditional thermal power systems. However, the output of solar and wind power is highly variable, random and intermittent, making it difficult to integrate it directly into the grid. In this context, a multi-objective model for the short-term operation of wind–solar–hydro–thermal hybrid systems is developed in this paper. The model considers the stability of the system operation, the operating costs and the impact in terms of environmental pollution. To solve the model, an evolutionary cost value region search algorithm is also proposed. The algorithm is applied to a hydro–thermal hybrid system, a multi-energy hybrid system and a realistic model of the wind–solar–hydro experimental base of the Yalong River Basin in China. The experimental results demonstrate that the proposed algorithm exhibits superior performance in terms of both convergence and diversity when compared to the reference algorithm. The integration of wind and solar energy into the power system can enhance the economic efficiency and mitigate the environment impact from thermal power generation. Furthermore, the inherent unpredictability of wind and solar energy sources introduces operational inconsistencies into the system loads. Conversely, the adaptable operational capacity of hydroelectric power plants enables them to effectively mitigate peak loads, thereby enhancing the stability of the power system. The findings of this research can inform decision-making regarding the economic, ecological and stable operation of hybrid energy systems. Full article

The Tibetan medicinal fruit E’seguo originates from two species, Malus toringoides (Rehd.) Hughes and Malus transitoria (Batal.) Schneid, both unique to the Hengduan Mountains. These species are predominantly found in high-altitude regions of Ganzi Prefecture, Sichuan Province, particularly in the Xianshui River and Yalong River basins. Malus toringoides (Rehd.) Hughes is far more abundant in both resource quantity and distribution compared to Malus transitoria (Batal.) Schneid. However, the nutritional and medicinal differences between the two remain unclear, which significantly impacts the development and utilization of E’seguo resources. This study aimed to measure the mineral content, nutritional components, and medicinal properties of E’seguo from 12 different regions of Ganzi Prefecture to explore the quality differences between these two species and across different regions. ICP-MS (Inductively Coupled Plasma Mass Spectrometry) was used to determine the mineral content, ultraviolet-visible spectrophotometry and potentiometric titration to analyze nutritional indicators, and HPLC (High-Performance Liquid Chromatography) to measure the medicinal components L-malic acid and 2-O-β-_D-glucopyranosyl-_L-ascorbic acid (AA-2βG). Results indicate that Malus transitoria (Batal.) Schneid contains higher levels of K, Ca, Zn, Mg, and Cu compared to Malus toringoides (Rehd.) Hughes, which has higher Fe and Mn content. Malus toringoides (Rehd.) Hughes from the Kangding and Litang regions showed the highest mineral content, with mineral elements primarily influencing polysaccharide levels, according to Mantel analysis. Nutritional and medicinal analyses revealed that Malus toringoides (Rehd.) Hughes outperformed Malus transitoria (Batal.) Schneid in all metrics except for the sugar-acid ratio. Given the mineral content and taste, Malus transitoria (Batal.) Schneid is better suited for consumption, while Malus toringoides (Rehd.) Hughes has superior medicinal properties, making it more appropriate for medicinal use. In the Malus transitoria (Batal.) Schneid regions, both Luhuo and Daofu are in the Xianshui River basin, with Daofu County producing the higher quality fruit. Among the nine Malus toringoides (Rehd.) Hughes regions, the M10 (Tuoba Township, Ganzi County) near the Yalong River had the highest overall score, followed by M7 (Yade Township, Luhuo County) and M6 (Keke, Xiala Tuo Town, Luhuo County), both of which are near the Xianshui River. In summary, Malus transitoria (Batal.) Schneid generally has higher mineral content, but Malus toringoides (Rehd.) Hughes has larger fruit and higher medicinal value, making the latter more suitable as a medicinal resource. At the same time, the medicinal quality of Xianshui River fruit was higher in the two watersheds of Malus toringoides (Rehd.) Hughes. Full article

The regulation of upstream cascade reservoirs has significantly altered the downstream hydrologic regime and should be taken into account in design flood estimation. The current flood regional composition (FRC) methods do not consider the unfavorable situations for reservoir flood control operation. In this paper, a novel framework, the most unfavorable flood regional composition (MUFRC) method, was proposed based on flood risk analysis to estimate design flood in the cascade reservoir operation period. The cascade reservoirs in the Yalong River basin were selected as a case study. The results indicated that (1) the proposed MUFRC method would allocate more flood volume to the downstream uncontrolled sub-basin, and the precise definition of flood disaster loss could have a significant impact on the MUFRC method for the rational estimation of design flood. (2) The 1000-year design flood peak, and 3-day and 7-day flood volumes at the outlet section estimated by the MUFRC method are 15,400 m³/s, 3.91, and 8.42 billion m³, respectively, which are higher than the values estimated by other FRC methods. (3) The flood control water level in the downstream reservoir can be adjusted for the reduction in design floods in the operation period, which can additionally generate 460 million kW·h (+1.82%) of hydropower during the flood season. A comparison study and sensitivity analysis further proved that the MUFRC method can rationally allocate flood volume while balancing the flood risk and comprehensive utilization benefits, which is worth further study and practical application. Full article

Studying the spatiotemporal evolution and driving forces behind construction land amidst the intricate ecological and geological setting on the eastern edge of the Qinghai–Tibet Plateau offers invaluable insights for local sustainable development in a landscape transition zone and ecologically fragile area. Using construction land data from four phases, spanning 1990 to 2020, in Mianning County, this study employs methodologies like the Landscape Expansion Index (LEI) and land use transfer matrix to delineate the spatiotemporal evolution characteristics of construction land. A comprehensive set of 12 influencing factors across five categories—geomorphology, geological activity, climate, river and vegetation environment, and social economy—were examined. The Geographically Weighted Regression (GWR) model was then employed to decipher the spatial distribution pattern of construction land in 1990 and 2020, shedding light on the driving mechanisms behind its changes over the three decades. The research reveals distinct patterns of construction land distribution and evolution in Mianning County, shaped by the ecological and geological landscape. Notably, the Anning River wide valley exhibits a concentrated and contiguous development mode, while the Yalong River deep valley showcases a decentralized development pattern, and the Dadu River basin manifests an aggregation development mode centered around high mountain lakes. Over the study period, all three river basins witnessed varying degrees of construction land expansion, transitioning from quantitative expansion to qualitative enhancement. Edge expansion predominantly characterizes the expansion mode, complemented by leapfrog and infilling modes, accompanied by conversions from cropland and forest land to construction land. An analysis of the spatial pattern and drivers of construction land change highlights human-induced factors dominating the Anning River Basin, contrasting with natural factors prevailing in the Yalong River Basin and the Dadu River Basin. Future efforts should prioritize climate change considerations and environmental capacity, aiming for an ecologically resilient spatial pattern of construction land. Full article

The stability of riverbank slopes is crucial in watershed ecology. The morphology and tensile strength properties of plant roots play a significant role in slope stability, which is of great importance for the ecological stability of riverbanks. The Jinsha and Yalong River basins are the largest hydropower bases in China and are in the ecologically fragile areas of the dry and hot river valleys, yet fewer studies are available on these basins. Further studies on the growth morphology and root mechanical properties of plant roots in the riparian zone at different elevations have not been reported. Therefore, we selected the dominant species of Cynodon dactylon root as the research subject, analyzed the root morphology, and conducted indoor single-root tensile tests to study its root structure and mechanical properties at various elevations. The results showed that the root morphology of Cynodon dactylon was positively correlated with elevation. Compared to low elevations (L and M), the root length increased by 57.3% and 21.47%, the root diameter increased by 24.85% and 13.92%, the root surface area increased by 93.5% and 67.37%, and the total root volume increased by 119.91% and 107.36%. As the elevation gradient increased, the flooding time decreased, leading to more developed plant roots for Cynodon dactylon. The Young’s modulus ranged from 148.43 to 454.18 MPa for Ertan Cynodon dactylon roots and 131.31 to 355.53 MPa for Guanyingyan Cynodon dactylon roots. The maximum tensile strength, ultimate tensile strength, ultimate elongation, and Young’s modulus of the plant root of the Cynodon dactylon showed a power function relationship with the diameter. The maximum tensile strength increased as the diameter increased, while the remaining properties decreased following a power function relationship. The maximum tensile strength, ultimate tensile strength, and Young’s modulus of Cynodon dactylon were positively correlated with elevation, while the ultimate elongation was negatively correlated with elevation. The results elucidate the influence of elevation on the root morphology and mechanical properties of dominant riparian species. This provides a theoretical basis for managing and protecting riparian slopes in ecologically fragile areas. Full article

Accuracy in monthly runoff forecasting is of great significance in the full utilization of flood and drought control and of water resources. Data-driven models have been proposed to improve monthly runoff forecasting in recent years. To effectively promote the prediction effect of monthly runoff, a novel hybrid data-driven model using particle swarm optimization coupled with flower pollination algorithm-based deep belief networks (PSO-FPA-DBNs) was proposed, which selected the optimal network depth via PSO and searched for the optimum hyper parameters (the number of neurons in the hidden layer and the learning rate of the RBMs) in the DBN using FPA. The methodology was divided into three steps: (i) the Comprehensive Basin Response (COM) was constructed and calculated to characterize the hydrological state of the basin, (ii) the information entropy algorithm was adopted to select the key factors, and (iii) the novel model was proposed for monthly runoff forecasting. We systematically compared the PSO-FPA-DBN model with the traditional prediction models (i.e., the backpropagation neural network (BPNN), support vector machines (SVM), deep belief networks (DBN)), and other improved models (DBN-PLSR, PSO-GA-DBN, and PSO-ACO-DBN) for monthly runoff forecasting by using an original dataset. Experimental results demonstrated that our PSO-FPA-DBN model outperformed the peer models, with a mean absolute percentage error (MAPE) of 18.23%, root mean squared error (RMSE) of 230.45 m³/s, coefficient of determination (DC) of 0.9389, and qualified rate (QR) of 64.2% for the data from the Yalong River Basin. Also, the stability of our PSO-FPA-DBN model was evaluated. The proposed model might adapt effectively to the nonlinear characteristics of monthly runoff forecasting; therefore, it could obtain accurate and reliable runoff forecasting results. Full article

Climate change and human activities are two important factors in the changing environment that affect the variability of the hydrological cycle and river regime in the Yalong River basin. This paper analyzed the hydrological alteration and extremes in the Yalong River basin based on multi-source satellite data, and projected the hydrological response under different future climate change scenarios using the CwatM hydrological model. The results show that: (1) The overall change in hydrological alteration at Tongzilin station was moderate during the period of 1998–2011 and severe during the period of 2012–2020. (2) Precipitation (average 781 mm/a) is the dominant factor of water cycle on a monthly scale, which can explain the temporal variability of runoff, evaporation, and terrestrial water storage, while terrestrial water storage is also simultaneously regulated by runoff and evaporation. (3) The GRACE data are comparable with regional water resource bulletins. The terrestrial water storage is mainly regulated by surface water (average 1062 × 10⁸ m³), while the contribution of groundwater (average 298 × 10⁸ m³) is relatively small. (4) The evaporation and runoff processes will intensify in the future due to climate warming and increasing precipitation (~10%), and terrestrial water storage will be depleted. The magnitude of change will increase with the enhancement of emission scenarios. Full article

Based on the meteorological and hydrological data of the Yalong River Basin from 1960 to 2019, meteorological and hydrological droughts were analyzed using the standardized precipitation evapotranspiration index (SPEI) and standardized runoff index (SRI); then, the spatio-temporal evolution and propagation characteristics of the droughts were studied on multiple time scales. The results showed that, firstly, on the annual scale, the frequencies of meteorological and hydrological droughts in the basin were 28.3% and 34.0%, respectively, in the past 60 years. From upstream to downstream, the longer the alternating period of dry and wet periods, the more significant the frequency of droughts. Secondly, on a seasonal scale, the frequency of meteorological droughts is high in autumn, and the frequency of hydrological drought is high in autumn and winter. The frequency of drought in different seasons decreases from the upper reaches to the lower reaches of the basin. Thirdly, on a monthly scale, the severe and exceptional meteorological drought frequencies are high from March to May, and the severe and exceptional hydrological drought frequencies are high in January, March, October, and December. The frequency of hydrological droughts is much higher than that of meteorological droughts, especially with respect to severe and exceptional drought. Meteorological and hydrological droughts spread in the same period without lag, but they tend to expand. The propagation time of drought is short in summer and autumn, but long in spring and winter. The conclusions can serve as a decision-making basis for the water diversion planning of the west route of China’s South-to-North Water Diversion Project and the cascade hydropower operation of the basin. Full article

To address the water-related issues faced by humans, the planning and construction of dams, water diversion projects, and other water infrastructures have been continuously adopted by decision makers worldwide. This is especially the case for the Yalong River Basin (YRB) in China, which is expected to be one of the most regulated rivers due to reservoir construction and the planned South-to-North Water Diversion project. To understand the potential impact of these water infrastructures on the water resources and hydropower production of the basin and downstream areas, we employ a land surface–hydrologic model with explicit representations of dam operation and water diversions in order to quantify the impact of reservoir operation and water diversion on the future water and energy security of the YRB. In particular, a conceptual reservoir operation scheme and a hydropower-optimized reservoir operation scheme are employed to predict the future release, storage and hydropower generation of the YRB, respectively. Results indicate that reservoirs can have noticeable, cumulative effects in enhancing the water security by reducing the wet season streamflow by 19% and increasing the dry season streamflow by 66%. The water diversion can result in an overall decrease in the streamflow, while the downstream reservoirs are expected to fully mitigate the decline in the dry season streamflow. The hydropower production is likely to decrease by 16% and 10% with conventional and optimized operation schemes, respectively, which suggests that the adaptation of operation rules alone cannot reverse the decline in the electricity production. Our findings can provide implications for sustainable water resource management. Full article

Identifying the distribution dynamics of invasive alien species can help in the early detection of and rapid response to these invasive species in newly invaded sites. Ageratina adenophora, a worldwide invasive plant, has spread rapidly since its invasion in China in the 1940s, causing serious damage to the local socioeconomic and ecological environment. To better control the spread of this invasive plant, we used the MaxEnt model and ArcGIS based on field survey data and online databases to simulate and predict the spatial and temporal distribution patterns and risk areas for the spread of this species in China, and thus examined the key factors responsible for this weed’s spread. The results showed that the risk areas for the invasion of A. adenophora in the current period were 18.394° N–33.653° N and 91.099° E–121.756° E, mainly in the tropical and subtropical regions of China, and densely distributed along rivers and well-developed roads. The high-risk areas are mainly located in the basins of the Lancang, Jinsha, Yalong, and Anning Rivers. With global climate change, the trend of continued invasion of A. adenophora is more evident, with further expansion of the dispersal zone towards the northeast and coastal areas in all climatic scenarios, and a slight contraction in the Yunnan–Guizhou plateau. Temperature, precipitation, altitude, and human activity are key factors in shaping the distribution pattern of A. adenophora. This weed prefers to grow in warm and precipitation-rich environments such as plains, hills, and mountains; in addition, increasing human activities provide more opportunities for its invasion, and well-developed water systems and roads can facilitate its spread. Measures should be taken to prevent its spread into these risk areas. Full article

The eastern Hengduan Mountains are located in the transition zone between the Qinghai-Tibet Plateau and the Sichuan Basin and are important for global biodiversity and water conservation in China. However, their landscape pattern vulnerability index (LVI) and its influencing factors have not been systematically studied. Therefore, the spatial distribution patterns, LVI, and the landscape artificial disturbance intensity (LHAI) of Ganzi Prefecture were analyzed using ArcGIS software based on landscape data and Digital Elevation Model (DEM) digital elevation data. Then, the LVI response to LHAI and elevation was discussed. The results showed that Ganzi Prefecture was dominated by low- and middle-LVI areas, together accounting for 56.45% of the total area. LVI values were highest in the northern regions, followed by the southern and eastern regions. Batang and Derong counties had the highest LVI values. Most areas in Ganzi Prefecture had very low- or low-LHAI values, accounting for 81.48% of the total area, whereas high-LHAI areas accounted for 2.32% of the total area. Both the LVI and LHAI of Ganzi Prefecture had clustered distributions. Spearman analysis indicated that when elevation exceeded 4500 m, it was the most important factor affecting LVI and LHAI. In the range of 4500–5400 m, the relationship between elevation and LVI shifted from a weak positive correlation to a negative correlation, whereas LHAI was positively correlated with elevation. In addition, LVI also responded significantly to LHAI. However, the relationship kept changing as elevation increased. Hence, the ecological vulnerability of high elevation areas above 4500 m deserves greater attention. In addition, pasture areas in the upstream reaches of the Yalong River in the northern region, the coastal area in the downstream reaches of the Jinsha River in the southern region, and the eastern mining area, should be prioritized for protection and restoration. This research provides a basis for appropriate environmental planning mechanisms and policy protections at the landscape level. Full article

Satellite precipitation estimates (SPEs) are promising alternatives to gauge observations for hydrological applications (e.g., streamflow simulation), especially in remote areas with sparse observation networks. However, the existing SPEs products are still biased due to imperfections in retrieval algorithms, data sources and post-processing, which makes the effective use of SPEs a challenge, especially at different spatial and temporal scales. In this study, we used a distributed hydrological model to evaluate the simulated discharge from eight quasi-global SPEs at different spatial scales and explored their potential scale effects of SPEs on a cascade of basins ranging from approximately 100 to 130,000 km². The results indicate that, regardless of the difference in the accuracy of various SPEs, there is indeed a scale effect in their application in discharge simulation. Specifically, when the catchment area is larger than 20,000 km², the overall performance of discharge simulation emerges an ascending trend with the increase of catchment area due to the river routing and spatial averaging. Whereas below 20,000 km², the discharge simulation capability of the SPEs is more randomized and relies heavily on local precipitation accuracy. Our study also highlights the need to evaluate SPEs or other precipitation products (e.g., merge product or reanalysis data) not only at the limited station scale, but also at a finer scale depending on the practical application requirements. Here we have verified that the existing SPEs are scale-dependent in hydrological simulation, and they are not enough to be directly used in very fine scale distributed hydrological simulations (e.g., flash flood). More advanced retrieval algorithms, data sources and bias correction methods are needed to further improve the overall quality of SPEs. Full article

In the short-term operation of the power generation of cascade reservoirs, uncertainty factors such as inflow forecast errors could cause various types of risks. The inflow to a downstream reservoir is not only affected by inflow forecast errors from upstream reservoirs but also the forecast errors associated with inflow to the stream segment between the reservoirs, such as from a tributary. The inflow forecast errors of different forecast periods may also be correlated. To address this multivariate problem, the inflow forecast error variables were jointly fitted in this study using the Gaussian mixture model (GMM) and a t-Copula function based on the analysis of the error distribution characteristics in different forecast periods. Therefore, a stochastic model that coupled with the GMM and t-Copula to calculate inflow forecast errors in multiple forecast periods was established. Furthermore, according to the simulation results of the stochastic model and the predicted runoff series, a set of simulated runoff processes were obtained. Then they were combined with the existing power generation plan to carry out the risk analysis for short-term operation of the power generation in a cascade reservoir. The approach was evaluated using the Jinguan cascade hydropower system within the Yalong River basin as a case study. For this case study, the risk analysis for short-term operation of the power generation was analyzed based on stochastic simulation of the inflow forecast errors; the results show the feasibility and effectiveness of the proposed methods. Full article