Search Results (1,015)

Modern hydrological modeling frequently incorporates global remote sensing or reanalysis products for multivariate calibration. Although these datasets significantly contribute to model accuracy, the inherent uncertainties in the datasets and multivariate calibration present challenges in the modeling process. To address this issue, this study introduces an adaptive, process-wise fitting framework for the iterative multivariate calibration of hydrological models using global remote sensing and reanalysis products. A distinctive feature is the “kinship” concept, which defines the relationship between model parameters and hydrological processes, highlighting their impacts and connectivity within a directed graph. The framework subsequently develops an enhanced particle swarm optimization (PSO) algorithm for stepwise calibration of hydrological processes. This algorithm introduces a learning rate that reflects the parameter’s kinship to the calibrated hydrological process, facilitating efficient exploration in search of suitable parameter values. This approach maximizes the performance of the calibrated process while ensuring a balance with other processes. To ease the impact of inherent uncertainties in the datasets, the Extended Triple Collocation (ETC) method, operating independently of ground truth data, is integrated into the framework to assess the simulation of the calibrated process using remote sensing products with inherent data uncertainty. This proposed approach was implemented with the SWAT model in both arid and humid basins. Five calibration schemes were designed and evaluated through a comprehensive comparison of their performance in three repeated experiments. The results highlight that this approach not only improved the accuracy of ET simulation across sub-basins but also enhanced the precision of streamflow at gauge stations, concurrently reducing parameter uncertainty. This approach significantly advances our understanding of hydrological processes, demonstrating the potential for both theoretical and practical applications in hydrology. Full article

Studying the response of runoff to climate change and land use/cover change has guiding significance for watershed land planning, water resource planning, and ecological environment protection. Especially in the Yellow River Basin, which has a variable climate and fragile ecology, such research is more important. This article takes the Huangfuchuan River Basin (HFCRB) in the middle reaches of the Yellow River as the research area, and analyzes the impact of climate change scenarios and land use/cover change scenarios on runoff by constructing a SWAT model. Using CMIP6 GCMs to obtain future climate data and the CA–Markov model to predict future land use data, the two are coupled to estimate the future runoff process in the HFCRB, and the uncertainty of the estimated runoff is decomposed and quantified. The results were as follows: ① The SWAT model has good adaptability in the HFCRB. During the calibrated period and the validation period, R² ≥ 0.84, NSE ≥ 0.8, and |PBIAS| ≤ 17.5%, all of which meet the model evaluation criteria. ② There is a negative correlation between temperature and runoff, and a positive correlation between precipitation and runoff. Runoff is more sensitive to temperature rise and precipitation increase. ③ The impact of land use types on runoff is in the order of cultivated land > grassland > forest land. ④ The variation range of runoff under the combined effects of future climate change and LUCC is between that of single climate change or LUCC scenarios. The increase in runoff under SSP126, SSP245, and SSP585 scenarios is 10.57%, 25.55%, and 31.28%, respectively. Precipitation is the main factor affecting the future runoff changes in the HFCRB. Model uncertainty is the main source of uncertainty in runoff prediction. Full article

During aging, subcutaneous white adipose tissue (sWAT) thickness and the adipogenic potential of adipose-derived stem cells (ASCs) decline. Poly-D,L-lactic acid (PDLLA) fillers are commonly used to restore diminished facial volume. Piezo1 increases polarizing macrophages towards the M2 phenotype, which promotes the secretion of fibroblast growth factor 2 (FGF2), thereby increasing ASC survival. This study evaluated whether PDLLA enhances adipogenesis in ASCs by modulating M2 polarization in an in vitro senescence model and in aged animals. Lipopolysaccharide (LPS)-induced senescent macrophages showed decreased Piezo1, which was upregulated by PDLLA. CD163 (an M2 marker) and FGF2 were downregulated in senescent macrophages but were upregulated by PDLLA. We evaluated whether reduced FGF2 secretion from senescent macrophages affects ASCs by applying conditioned media (CM) from macrophage cultures to ASCs. CM from senescent macrophages decreased ERK1/2 and proliferation in ASCs, both of which were restored by CM from PDLLA-stimulated senescent macrophages. Adipogenesis inducers (PPAR-γ and C/EBP-α) were downregulated by CM from senescent macrophages but upregulated by CM from PDLLA-stimulated senescent macrophages in ASCs. Similar patterns were observed in aged animal adipose tissue. PDLLA increased Piezo1 activity, M2 polarization, and FGF2 levels. PDLLA also enhanced ERK1/2, cell proliferation, PPAR-γ, and C/EBP-α expression, leading to increased adipose tissue thickness. In conclusion, our study showed that PDLLA increased adipose tissue thickness by modulating adipogenesis. Full article

Climate change is projected to bring substantial changes to hydroclimatic extremes, which will affect natural river regimes and have wide-ranging impacts on human health and ecosystems, particularly in Central Highland Vietnam. This study focuses on understanding and quantifying the projected impacts of climate change on streamflow in the Kon-Ha Thanh River basin, using the Soil and Water Assessment Tool (SWAT) between 2016 and 2099. The study examined projected changes in streamflow across three time periods (2016–2035, 2046–2065, and 2080–2029) under two scenarios, Representative Conversion Pathways (RCPs) 4.5 and 8.5. The model was developed and validated on a daily scale with the model performance, yielding good performance scores, including Coefficient of Determination (R²), Nash-Sutcliffe Efficiency (NSE), and Root Mean Squared Error (RMSE) values of 0.79, 0.77, and 50.96 m³/s, respectively. Our findings are (1) streamflow during the wet season is projected to increase by up to 150%, particularly in December, under RCP 8.5; (2) dry season flows are expected to decrease by over 10%, beginning in May, heightening the risk of water shortages during critical agricultural periods; and (3) shifts in the timing of flood and dry seasons are found toward 2099 that will require adaptive measures for water resource management. These findings provide a scientific foundation for incorporating climate change impacts into regional water management strategies and enhancing the resilience of local communities to future hydroclimatic challenges. Full article

This national-scale assessment explores the anticipated impact of climate change on stream temperature in Poland. Utilizing an ensemble of six EURO-CORDEX projections (2006 to 2100) under Representative Concentration Pathways (RCPs) 4.5 and 8.5, the study employs the Soil and Water Assessment Tool (SWAT) to simulate stream temperature regimes. Validation against observed stream temperatures at 369 monitoring points demonstrates the reliability and accuracy of the SWAT model performance. Projected changes in air temperature reveal distinct seasonal variations and emission scenario dependencies. The validated stream temperature model indicates a uniform warming tendency across Poland, emphasizing the widespread nature of climate change impacts on aquatic ecosystems. Results show an increase in country-averaged stream temperature from the baseline (16.1 °C), with a rise of 0.5 °C in the near future (NF) and a further increase by 1 °C in the far future (FF) under RCP4.5. Under RCP8.5, the increase is more pronounced, reaching 1 °C in the NF and a substantial 2.6 °C in the FF. These findings offer essential insights for environmental management, emphasizing the need for adaptive strategies to mitigate adverse effects on freshwater ecosystems. However, as a preliminary study, this work uses a simplified temperature model that does not account for detailed hydrological processes and spatial variability, making it a good starting point for more detailed future research. Full article

Joint multivariate distribution and calculation of return period are essential in enhancing drought risk assessment and promoting the sustainable development of water resources. Aiming to address the increasingly serious drought situation in the Yellow River Basin, this study first utilized the Soil and Water Assessment Tool (SWAT) distributed hydrological model combined with the Standardized Precipitation Evapotranspiration Index (SPEI), the Standardized Soil Moisture Index (SSMI), and the Standard Water Yield Index (SWYI); the duration, peak, and severity of meteorological, agricultural, and hydrological droughts were analyzed. Based on the selected copula function, a three-dimensional joint distribution of drought duration (D), drought severity (S), and maximum severity (M) was constructed. The corresponding copula joint probability was calculated, leading to the three-dimensional joint return period and concurrent return period of meteorological drought, agricultural drought, and hydrological drought. The findings reveal several key trends: (1) Meteorological drought intensifies over time. Although drought areas eased after the 1990s, the overall drought trend continues to rise. Agricultural drought has intensified in arid regions but eased in semi-humid areas after the 2000s. Hydrological drought was severe in the upstream regions during the 1990s but eased in the 2000s, while it was particularly severe in the midstream and downstream regions during the 2000s. (2) Meteorological droughts are more severe in arid and semi-arid temperate regions and milder in semi-humid cold temperate regions. Agricultural droughts are extreme in arid and semi-arid cold temperate regions. Hydrological drought events are fewer but more severe in semi-arid temperate regions and have the lowest probability of occurrence in semi-humid cold temperate regions. (3) The overall probability of the occurrence of meteorological drought is between 55.7% and 69%; that of agricultural drought is between 73.1% and 91.7%, and that of hydrological drought is between 66.9% and 84%. Drought risk assessment provides scientific references for the analysis of the uncertainty of water supply in the basin and the formulation of effective risk management strategies. Full article

Climate change and human activities were identified as the primary drivers of streamflow in arid alpine regions. However, limitations in observational data have resulted in a limited understanding of streamflow changes in these water sources, which hinders efforts to adapt to ongoing climate change and to formulate effective streamflow management policies. Here, we use the four main tributaries in the upper reach of the Shiyang River in China as a case study to investigate the long-term trends in streamflow within arid alpine water sources, quantifying the individual contributions of climate change and human activities to these changes. The findings revealed that temperatures and precipitation in arid alpine regions have risen over the past 40 years. Although the warming trend has been significant, it has slowed in recent years. Nevertheless, three-quarters of the rivers are experiencing a decline in streamflow. The land types within the watershed remain relatively stable, with land use and cover change (LUCC) primarily occurring in the Gulang River watershed. Climate change has significantly affected streamflow change in high and rugged terrains, with an influence exceeding 70%. For example, Jingta River showed an impact of 118.79%, Zamu River 84.00%, and Huangyang River 71.43%. Human-driven LUCC, such as the expansion of cultivated and urban land, have led to increased water consumption, resulting in reduced streamflow. This effect is particularly pronounced in the low-lying and gently undulating areas of the Gulang River, where LUCC account for 78.68% of the change in streamflow. As human activities intensify and temperatures continue to rise, further declines in streamflow are projected, highlighting the urgent need for effective water resource management. These insights highlight the urgent need for targeted mitigation and adaptation strategies to confront the water scarcity challenges faced by these vulnerable regions. Full article

The Kabul River Basin (KRB) is witnessing frequent flood and drought events that influence food production and distribution. The KRB is one of the world’s poorest regions regarding food security. Food security issues in the KRB include shifts in short-term climate cycles with significant river flow variations that result in inadequate water distribution. Due to the lack of hydro-infrastructure, low irrigation efficiency, and continuing wars, the Afghanistan portion of the KRB has experienced low agricultural land expansion opportunities for food production. This research assesses the relationship between flood mitigation, flow balances, and food production and, cumulatively, assesses the social and economic well-being of the population of the KRB. SWAT modeling and climate change (CCSM4) implications are utilized to assess how these relationships impact the social and economic well-being of the population in the KRB. The intricacies of transboundary exchange and cooperation indicate that the conservation of ~38% of the water volume would nearly double the low flows in the dry season and result in the retention of ~2B m³/y of water for agricultural developmental use. Results show that the peak flood flow routing in reservoirs on the Afghanistan side of the KRB would have a substantial positive impact on agricultural products and, therefore, food security. Water volume conservation has the potential to provide ~44% more arable land with water, allowing a ~51% increase in crop yield, provided that improved irrigation efficiency techniques are utilized. Full article

In hydrological modeling, the accuracy of precipitation data and the reflection of the model’s physical mechanisms are crucial for accurately describing hydrological processes. Identifying reliable data sources and exploring reasonable hydrological evolution mechanisms for hydrology and water resources research in high-altitude mountainous regions with sparse stations and limited data constitute a significant challenge and focus in the field of hydrology. This study focuses on the Yarkant River Basin in Xinjiang, which originates from glaciers and contains a substantial amount of meltwater runoff. A dynamic glacier melt module considering the synergistic effects of multiple meteorological factors was developed and integrated into the original Soil and Water Assessment Tool (SWAT) model. Four precipitation datasets (ERA5-land, MSWEP, CMA V2.0, and CHM-PRE) were selected to train the model, including remote sensing precipitation products and station-interpolated precipitation data. The applicability of the improved SWAT model and precipitation datasets in the source region of the Yarkant River was evaluated and analyzed using statistical indicators, hydrological characteristic values, and watershed runoff simulation effectiveness. The optimal dataset was further used to analyze glacier evolution characteristics in the basin. The results revealed the following: (1) The improved model fills the gap in glacier runoff simulation with respect to the original SWAT model, with the simulation results more closely aligning with the actual runoff variation patterns in the study area, better describing the meltwater runoff process. (2) CMA V2.0 precipitation data has the best applicability in the study area. This is specifically reflected in the rationality of the spatial and temporal distribution patterns of the inverted precipitation, the accuracy observed in capturing precipitation events and actual precipitation characteristics, the goodness of fit in driving hydrological models, and the observed precision in reflecting the composition of watershed runoff, all of which are superior to those pertaining to other precipitation products. (3) The glacier melt calculated using the improved SWAT model informed by CMA V2.0 shows that during the study period, the basin formed a pattern with a positive–negative glacier balance demarcation at 36.5° N, featuring melting at higher latitudes and accumulation at lower latitudes. The results of this study are of significant importance for hydrometeorological applications and hydrological and water resources research in this region. Full article

Excessive nitrate loading from agricultural runoff leads to substantial environmental and economic harm, and although hydrological models are used to mitigate these effects, the influence of various satellite precipitation products (SPPs) on nitrate load simulations is often overlooked. This study addresses this research gap by evaluating the impacts of using different satellite precipitation products—ERA5, IMERG, and gridMET—on flow and nitrate load simulations with the Soil and Water Assessment Tool Plus (SWAT+), using the Tar-Pamlico watershed as a case study. Although agricultural activities are higher in the summer, this study found the lowest nitrate load during this season due to reduced runoff. In contrast, the nitrate load was higher in the winter because of increased runoff, highlighting the dominance of water flow in driving riverine nitrate load. This study found that although IMERG predicts the highest annual average flow (120 m³/s in Pamlico Sound), it unexpectedly results in the lowest annual average nitrate load (1750 metric tons/year). In contrast, gridMET estimates significantly higher annual average nitrate loads (3850 metric tons/year). This discrepancy underscores the crucial impact of rainfall datasets on nitrate transport predictions and highlights how the choice of dataset can significantly influence nitrate load simulations. Full article

In order to anticipate residual errors and improve accuracy while reducing uncertainties, this work integrates the long short-term memory (LSTM) with the Soil and Water Assessment Tool (SWAT) to create a deep learning (DL) model that is guided by physics. By forecasting the residual errors of the SWAT model, the SWAT-informed LSTM model (LSTM-SWAT) differs from typical LSTM approaches that predict the streamflow directly. Through numerical tests, the performance of the LSTM-SWAT was evaluated with both LSTM-only and SWAT-only models in the Upper Heihe River Basin. The outcomes showed that the LSTM-SWAT performed better than the other models, showing higher accuracy and a lower mean absolute error (MAE = 3.13 m³/s). Sensitivity experiments further showed how the quality of the training dataset affects the performance of the LSTM-SWAT. The results of this study demonstrate how the LSTM-SWAT may improve streamflow prediction greatly by remote sensing and in situ observations. Additionally, this study emphasizes the need for detailed consideration of specific sources of uncertainty to further improve the predictive capabilities of the hybrid model. Full article

The increasing frequency of extreme rainfall events poses significant challenges to sustainable water resource management, leading to severe natural disasters. To mitigate these challenges, understanding the hydrological characteristics of watersheds, especially baseflow, is critical for enhancing watershed resilience and supporting sustainable water quality and resource management. However, conventional watershed models often neglect the accurate simulation of baseflow recession. This study proposes a method for calculating and applying the alpha factor for each hydrologic response unit (HRU) in the Soil and Water Assessment Tool (SWAT), considering both temporal and spatial variability in baseflow. The study watershed has undergone significant development, increasing the need for effective water management strategies that promote long-term sustainability. The alpha factor was computed using BFlow2021, and its effectiveness was evaluated by comparing recession and baseflow estimates under different methods. The results indicate that incorporating monthly HRU-specific alpha factors significantly improves model predictions of recession characteristics, highlighting the need for a more spatially and temporally detailed approach in hydrological modeling. The proposed methodology can help clarify the connection between recession and baseflow and can be applied to ungauged stations, offering a valuable tool for sustainable watershed and water quality management. Full article

The Nyabarongo river catchment in Rwanda has experienced significant changes in its land use and land cover (LULC) in recent decades, with profound implications for non-point source pollution. However, there are limited studies on non-point pollution caused by nutrient loss associated with land use and land cover changes in the catchment. This study investigates the spatiotemporal impacts of these changes on water quality considering nitrogen and phosphorus within the catchment from 2000 to 2020 and 2030 as a projection. The SWAT model was used in analysis of hydrological simulations, while the CA–Markov model was used for the future projection of LULC in 2030. The results revealed (1) the important changes in LULC in the study area, where a decrease in forestland was observed with a considerable increase in built-up land, grassland, and cropland; (2) that the R² and NSE of the TN and TP in the runoff simulation in the catchment were all above 0.70, showing good applicability during calibration and validation periods; (3) that from 2000 to 2020 and looking to the projection in 2030, the simulated monthly average TN and TP levels have progressively increased from 15.36 to 145.71 kg/ha, 2.46 to 15.47 kg/ha, 67.2 to 158.8 kg/ha, and 9.3 to 17.43 kg/ha, respectively; and (4) that the most polluted land use types are agriculture and urban areas, due to increases in human activities as a consequence of population growth in the catchment. Understanding the patterns and drivers of these changes is critical for developing effective policies and practices for sustainable land management and protection of water resources. Full article

In recent years, remote sensing data have revealed considerable potential in unraveling crucial information regarding water balance dynamics due to their unique spatiotemporal distribution characteristics, thereby advancing multi-objective optimization algorithms in hydrological model parameter calibration. However, existing optimization frameworks based on the Soil and Water Assessment Tool (SWAT) primarily focus on single-objective or multiple-objective (i.e., two or three objective functions), lacking an open, efficient, and flexible framework to integrate many-objective (i.e., four or more objective functions) optimization algorithms to satisfy the growing demands of complex hydrological systems. This study addresses this gap by designing and implementing a multi-objective optimization framework, Py-SWAT-U-NSGA-III, which integrates the Unified Non-dominated Sorting Genetic Algorithm III (U-NSGA-III). Built on the SWAT model, this framework supports a broad range of optimization problems, from single- to many-objective. Developed within a Python environment, the SWAT model modules are integrated with the Pymoo library to construct a U-NSGA-III algorithm-based optimization framework. This framework accommodates various calibration schemes, including multi-site, multi-variable, and multi-objective functions. Additionally, it incorporates sensitivity analysis and post-processing modules to shed insights into model behavior and evaluate optimization results. The framework supports multi-core parallel processing to enhance efficiency. The framework was tested in the Meijiang River Basin in southern China, using daily streamflow data and Penman–Monteith–Leuning Version 2 (PML-V2(China)) remote sensing evapotranspiration (ET) data for sensitivity analysis and parallel efficiency evaluation. Three case studies demonstrated its effectiveness in optimizing complex hydrological models, with multi-core processing achieving a speedup of up to 8.95 despite I/O bottlenecks. Py-SWAT-U-NSGA-III provides an open, efficient, and flexible tool for the hydrological community that strives to facilitate the application and advancement of multi-objective optimization in hydrological modeling. Full article

Non-point source (NPS) pollution has a complex formation mechanism, and identifying its primary controlling factors is crucial for effective pollution treatment. In this study, the Baixi Reservoir Watershed, characterized by low-intensity development, was selected as the study area. A new methodology combining the Soil and Water Assessment Tool (SWAT) with the Random Forest (RF) algorithm was proposed to comprehensively identify the primary controlling factors of NPS pollution and analyze the interaction between factors. The results of the validated SWAT model showed that the annual intensity of total nitrogen (TN) load range was 0.677–11.014 kg ha⁻¹ yr⁻¹, and the total phosphorus (TP) load per unit area range was 0.020–0.110 kg ha⁻¹ yr⁻¹. Loads of sediment, TP, and TN exhibited significant seasonal variations, particularly in the Baixi basin, where sediment yield had the highest absolute change rate, with a value of up to 232.26. Random Forest models for TN and TP displayed high accuracy (R² > 0.99) and robust generalization ability. Fertilization, sediment yield, and terrain slope were identified through RF models as the primary factors affecting TN and TP. By graphing partial dependency plots (PDPs) based on the results of the RF models to analyze the interaction between factors, the findings suggest a strong synergistic effect of two combined factors: fertilization and sediment yield. When fertilizer application exceeds 15 kg ha⁻¹ yr⁻¹ and sediment yield exceeds 3 kg ha⁻¹ yr⁻¹, there is a sharp increase in nitrogen and phosphorus load. Through the identification and analysis of the primary controlling factors of NPS pollution, this study provides a solid scientific foundation for developing effective watershed management strategies. Full article