Search Results (50)

Precipitation, as a key input in the water cycle, directly influences the formation and change process of runoff. Meanwhile, the return runoff intuitively reflects the available quantity of water resources in a river basin. An in-depth analysis of the evolution laws and response relationships between precipitation and return runoff over a long time scale serves as an important support for exploring the evolution of hydrometeorological conditions and provides an accurate basis for the scientific planning and management of water resources. Taking Lanzhou Station on the upper Yellow River as a typical case, this study proposes the VSSL (LSTM Fusion Method Optimized by SSA with VMD Decomposition) deep learning precipitation element series extension method and the SSVR (SVR Fusion Method Optimized by SSA) machine learning runoff element series extension method. These methods achieve a reasonable extension of the missing data and construct 100-year precipitation and return runoff series from 1921 to 2020. The research results showed that the performance of machine learning and deep learning methods in the precipitation and return runoff test sets is better than that of traditional statistical methods, and the fitting effect of return runoff is better than that of precipitation. The 100-year precipitation and return runoff series of Lanzhou Station from 1921 to 2020 show a non-significant upward trend at a rate of 0.26 mm/a and 0.42 × 10⁸ m³/a, respectively. There is no significant mutation point in precipitation, while the mutation point of return runoff occurred in 1991. The 100-year precipitation series of Lanzhou Station has four time-scale alternations of dry and wet periods, with main periods of 60 years, 20 years, 12 years, and 6 years, respectively. The 100-year return runoff series has three time-scale alternations of dry and wet periods, with main periods of 60 years, 34 years, and 26 years, respectively. During the period from 1940 to 2000, an approximately 50-year cycle, precipitation and runoff not only have strong common-change energy and significant interaction, but also have a fixed phase difference. Precipitation changes precede runoff, and runoff responds after a fixed time interval. Full article

Evapotranspiration (ET) is a critical component of the hydrological cycle. The eddy covariance data at 40 flux stations in different climatic regions in China were used to evaluate the accuracy of five reanalysis actual ET datasets (ERA5, ERA5-LAND, GLDAS-2.1, MERRA-2, TerraClimate) and four remote sensing estimation ET datasets (ETMonitor, GLEAM4.2a, PML_V2, SiTHv2), which are widely used by the hydrometeorological and climatological communities, in terms of the root mean square error, Pearson correlation coefficient, mean absolute deviation, and Taylor skill score. The results show that remote sensing products outperform reanalysis datasets. Among them, ETMonitor has the highest accuracy, followed by PML_V2 and SiTHv2. TerraClimate and MERRA-2 have the least agreement with the observations at flux sites across nearly all evaluation metrics. All products can capture the seasonality of ET in China, but underestimate ET in northwest China and overestimate ET in southern China throughout the year. We tried to merge three optimal data products (ETMonitor, PML_V2, and SiTHv2) using the triple collocation analysis method to improve the ET estimation, but the results showed that the improvement by the data fusion approach is marginal. The estimation of the multi-year average evapotranspiration during the period from 2001 to 2020 ranges from 397.8 mm/year (GLEAM4.2a) to 504.8 mm/year (ERA5-Land) in China. From 2001 to 2020, annual evapotranspiration in China generally increased, but with varying rates across different products. MERRA-2 showed the largest annual increase rate (3.71 mm/year), while SiTHv2 had the smallest (0.17 mm/year). There are no significant changes in the seasonality of ET by most ET products from 2001 to 2020, except for PML_V2 and SiTHv2, which indicate an increase in seasonality in terms of the evapotranspiration concentration index. This ET intercomparison addresses a key knowledge gap in terrestrial water flux quantification, aiding climate and hydrological research. Full article

Moisture transports play a key role in maintaining the hydrometeorological cycle and forming its climate variability over the Tibetan Plateau (TP), also known as the “Asian water tower”. This study focuses on understanding the interannual variability mode characteristics of moisture transport in the TP in boreal summer, using satellite-based analysis and reanalysis data from 1983 to 2022 with a combined empirical orthogonal function (EOF) analysis. We identified the first two primary interannual modes of TP summer water vapor fluxes, which are primarily characterized by zonal and meridional dipole patterns, respectively. The zonal pattern of the TP water vapor flux dominates the TP and East Asian summer rainfall variability, while the meridional pattern of the TP water vapor flux tends to be a result of the South Asian summer rainfall and its circulation anomalies. The tropical Indo-Pacific sea surface temperature (SST) variations, such as El Niño and Indian Ocean SST modes, have significantly delayed relationships with the interannual variability modes of the summer water vapor fluxes over the TP, indicating a significant modulation effect of the low-latitude oceanic variability on the interannual variations in TP summer moisture transport. These results deepen our understanding of the relationship between TP moisture transport and summer monsoonal rainfall variability, as well as the influence of the tropical oceans. Full article

Considering the possibility of increasing water supply in China in the short term and the long-term threat posed by shrinking glaciers, this paper studied the spatiotemporal evolution of runoff in typical arid areas and the influence of hydrometeorological elements on runoff, aiming to clarify the hydrological cycle law and provide a basis for adjusting water resource management strategies to cope with future uncertain changes. Based on hydrological data from 1956 to 2020, the spatial and temporal variation in runoff were discussed by means of wavelet analysis, MK test, RS analysis, and spatial interpolation. The influencing factors of runoff evolution in the Shule River Basin were determined. The results showed that the runoff in the Shule River Basin showed an increasing trend in the past 60 years. Five hydrological stations (Changmabao Station, Panjiazhuang Station, Shuangtabao Reservoir, Dangchengwan Reservoir, and Danghe Reservoir) were selected as the research objects. Among them, the runoff of Changmabao Station increased the most, which was 1.202 × 10⁸ m³/10 a. Future projections suggest a continued rise in runoff, particularly at Shuangtabao Reservoir. The runoff exhibited positive persistence and varying degrees of mutation, with most mutations occurring in the early 21st century. The runoff in the basin has a periodicity of multiple time scales (there are 2–3 main cycles), and the main cycle of annual runoff is concentrated in 58 years. This comprehensive analysis provides valuable insights for the sustainable management of water resources in inland river basins amidst changing environmental conditions. The spatial variation in runoff in summer and autumn and the whole year showed a significant southeast to northwest decreasing pattern. During the study period, accelerated glacier melting caused by rising temperatures had the most significant impact on runoff change (p < 0.01), and the upstream of the study area also complied with this rule (temperature contribution rate [25.96%] > precipitation contribution rate [23.91%]). The contribution of temperature and precipitation changes caused by human activities in the middle stream to runoff was relatively large, which showed that the contribution rate of temperature in Guazhou Station to runoff was 34.23% and the contribution rate of precipitation in Dangchengwan to runoff was 60.27%. The research results provide a scientific basis for the rational and efficient utilization of water resources in the arid area of Northwest China. Full article

Understanding the hydrometeorological processes of the Yarlung Tsangpo River Basin, located on the “Third Pole” Qinghai–Tibet Plateau, is crucial for effective water resource management and climate change adaptation. This study provides a comprehensive analysis of the basin’s hydrometeorological characteristics using long-term observational data from six representative stations across the upper, middle, and lower reaches. We examined trends, periodicity, variability, and correlations of key elements—precipitation, temperature, evaporation, and discharge—employing methods such as linear regression, Mann–Kendall tests, wavelet analysis, and Kendall rank correlation coefficient tests. The results indicated that precipitation and discharge exhibited non-significant upward trends, with fluctuations across decades, while temperature showed a significant increase of 0.39 °C per decade, surpassing the national and global rates. Evaporation generally decreased with increasing precipitation; however, at Lazi Station, evaporation significantly increased due to low precipitation and rising temperatures causing decreased relative humidity. Periodic analysis revealed cycles at multiple temporal scales, particularly at 2–5 years, 10 years, and over 20 years. Correlation analysis demonstrated a strong positive relationship between precipitation and discharge, and a negative correlation between evaporation and discharge. The hydrometeorological characteristics are significantly influenced by climatic factors, especially precipitation and temperature, with the warming trend potentially affecting water’s availability and distribution. These findings offer valuable insights for water resource management and highlight the need for continuous monitoring to understand hydrological responses to climatic and anthropogenic changes in this critical region. Full article

Changing precipitation patterns in the Arctic is a key indicator of climate change, in addition to increasing land and ocean temperatures, but these patterns are not uniform across the circumpolar region. This regional analysis focuses on the Yukon–Kuskokwim Delta in southwestern Alaska and addresses the following questions: (1) What is the baseline hydroclimatology during the growing season on the Yukon–Kuskokwim Delta? (2) What are the seasonal and intraseasonal trends of the hydroclimate variables in the YKD? (3) What are the implications of documented trends for the study region? Utilizing ECMWF’s ERA5 reanalysis dataset, we conducted a seasonal analysis for May through September for the years 1982–2022. While no strong trend emerged for total precipitation over the 41-year study period, differing trends were observed for large-scale and convective precipitation. The decline in large-scale precipitation is supported by a decrease in storm counts in the Bering Sea, as well as declining vertically integrated moisture convergence and moisture flux. By contrast, the increase in convective precipitation underscores the growing importance of the local hydrologic cycle, further supported by a significant rise in evaporation. These enhanced local hydroclimatological cycles have significant implications for wildfires and subsistence activities. Full article

The Magdalena department, influenced by southern trade winds and ocean currents from the Atlantic and Pacific, is a climatically vulnerable region. This study assesses the Magdalena Department’s precipitation trends and stationary patterns by analyzing multi-year monthly records from 55 monitoring stations from 1990 to 2022. To achieve this, the following methods were used: (i) homogeneous regions were established by an unsupervised clustering approach, (ii) temporal trends were quantified using non-parametric tests, (iii) stationarity was identified through Morlet wavelet decomposition, and (iv) Sea Surface Temperature (SST) in four Niño regions was correlated with stationarity cycles. Silhouette’s results yielded five homogeneous regions, consistent with the National Meteorological Institute (IDEAM) proposal. The Department displayed decreasing annual trends (−32–−100 mm/decade) but exhibited increasing monthly trends (>20 mm/decade) during the wettest season. The wavelet decomposition analysis revealed quasi-bimodal stationarity, with significant semiannual cycles (~4.1 to 5.6 months) observed only in the eastern region. Other regions showed mixed behavior: non-stationary in the year’s first half and stationary in the latter half. Correlation analysis showed a significant relationship between SST in the El Niño 3 region (which accounted for 50.5% of the coefficients), indicating that strong phases of El Niño anticipated precipitation responses for up to six months. This confirms distinct rainfall patterns and precipitation trends influenced by the El Niño–Southern Oscillation (ENSO), highlighting the need for further hydrometeorological research in the area. Full article

Environmental pollution indicated by the presence of $P{M}_{2.5}$ particulate matter varies based on prevailing atmospheric conditions described by certain meteorological variables. Consequently, it is important to understand atmospheric behavior in areas such as the Aburrá Valley, which experiences recurrent pollution events twice a year. This study examines the behavior of specific meteorological variables and $P{M}_{2.5}$ particulate matter in the Aburrá Valley. By using statistical analysis tools such as correlation coefficients, principal component analysis (PCA), and multiple linear regression models, the research identifies relationships between $P{M}_{2.5}$ and daily cycles of temperature, rainfall, radiation, and wind speed and direction. Datasets were analyzed considering periods before and after the COVID-19 lockdown (pre-pandemic and pandemic, respectively), and specific pollution events were also analyzed. Furthermore, this work considers the relationships between $P{M}_{2.5}$ and meteorological variables, contrasting the pre-pandemic and pandemic periods. This study characterizes diurnal cycles of meteorological variables and their relationship with $P{M}_{2.5}$. There are consistent patterns among temperature, atmospheric boundary layer (ABL) height, and solar radiation, whereas precipitation and relative humidity show the opposite behavior. $P{M}_{2.5}$ exhibits similar relative frequency functions during both daytime and nighttime, regardless of rainfall. An inverse relationship is noted between $P{M}_{2.5}$ levels and ABL height at different times of the day. Moreover, the PCA results show that the first principal component explains around 60% of the total variance in the hydrometeorological data. The second PC explains 10%, and the rest of the variance is distributed among the other three to eight PCs. In this sense, there is no significant difference between the two PCAs with hydrometeorological data from a pre-pandemic period and a COVID-19 pandemic period. Multiple regression analysis indicates a significant and consistent dependence of $P{M}_{2.5}$ on temperature and solar radiation across both analyzed periods. The application of Generalized Additive Models (GAMs) to our dataset yielded promising results, reflecting the complex relationship between meteorological variables and $P{M}_{2.5}$ concentrations. The metrics obtained from the GAM were as follows: Mean Squared Error (MSE) of 98.04, Root Mean Squared Error (RMSE) of 9.90, R-squared (R²) of 0.24, Akaike Information Criterion (AIC) of 110,051.34, and Bayesian Information Criterion (BIC) of 110,140.63. In comparison, the linear regression model exhibited slightly higher MSE (100.49), RMSE (10.02), and lower R-squared (0.22), with AIC and BIC values of 110,407.45 and 110,460.67, respectively. Although the improvement in performance metrics from GAM over the linear model is not conclusive, they indicate a better fit for the complexity of atmospheric dynamics influencing $P{M}_{2.5}$ levels. These findings underscore the intricate interplay of meteorological factors and particulate matter concentration, reinforcing the necessity for advanced modeling techniques in environmental studies. This work presents new insights that enhance the diagnosis, understanding, and modeling of environmental pollution, thereby supporting informed decision-making and strengthening management efforts. Full article

There have been several incidences of flood recently, which are believed to be aggravated by increased climatic variables as a result of perceived changes in climatic conditions (due to climate change) in the Cross River Basin. The basin is the most extensively developed and used river basin in the management of the water resources of the Cross River and Akwa Ibom States in Nigeria. In this paper, 30 years (from 1992 to 2021) of hydro-meteorological data (annual average rainfall, maximum and minimum temperatures, hu midity, duration of sunlight (sunshine hours), evaporation, wind speed, soil temperature, cloud cover, solar radiation, and atmospheric pressure) from four stations in the Cross River Basin were obtained from the Nigerian Meteorological Agency (NIMET), Abuja and subjected to trend detection analysis using the Mann–Kendall test to determine the trend in climatic parameters. The results indicate that there is a significant upward trend in annual rainfall in Ogoja but a downward trend in Calabar. The evaporation trend is significantly downward in Eket, whereas in Calabar, there is an upward trend in solar radiation. Generally, there is a significant rise in annual maximum temperature across the basin. Serial correlation and segmented regression analyses were performed to measure the impact of fluctuations in monthly and long-term Tahiti and Darwin’s Sea level pressures on the climatic variables at the Cross River Basin catchment. These analyses were necessary to determine the extent of the influence of the El Nino Southern Oscillation climatic cycle. The analyses show no significant association between the El Niño Southern Oscillation (ENSO) and rainfall or between the ENSO and runoff in the catchment. This implies that the impact of the ENSO on rainfall and runoff in the Cross River Basin catchment is not considerable. The intercepts derived from the segmented regression in Eket and Ogoja show significant positive trends in both areas for rainfall and runoff. The trends in intercepts suggest that there are external factors influencing rainfall and runoff other than ENSO events, thus strengthening the assertion of climate change. Results from this study will facilitate the understanding of the variability in climatic parameters by stakeholders in the basin, researchers, policymakers, and water resource managers. Full article

Soil moisture is a critical variable in the hydrological cycle and the climate system, significantly impacting water resources, ecosystem functioning, and the occurrence of extreme events. However, soil moisture data are often scarce, and soil water dynamics are not fully understood in mountainous regions such as the tropical Andes of Ecuador. This study aims to model and predict soil moisture dynamics using in situ-collected hydrometeorological data for training and data-driven machine-learning techniques. Our results highlight the fundamental role of vegetation in controlling soil moisture dynamics and significant differences in soil water balance related to vegetation types and topography. A baseline model was developed to predict soil moisture dynamics using neural network techniques. Subsequently, by employing transfer-learning techniques, this model was effectively applied to different soil horizons and profiles, demonstrating its generalization capacity and adaptability. The use of neural network schemes and knowledge transfer techniques allowed us to develop predictive models for soil moisture trained on in situ-collected hydrometeorological data. The transfer-learning technique, which leveraged the knowledge from a pre-trained model to a model with a similar domain, yielded results with errors on the order of $1\times {10}^{-6}<ϵ<1\times {10}^{-3}$. For the training data, the forecast of the base network demonstrated excellent results, with the lowest magnitude error metric RMSE equal to $4.77\times {10}^{-6}$, and NSE and KGE both equal to 0.97. These models show promising potential to accurately predict short-term soil moisture dynamics with potential applications for natural hazard monitoring in mountainous regions. Full article

Drought is a common hydrometeorological phenomenon and a pervasive global hazard. To monitor global drought-wetness conditions comprehensively and promptly, this research proposed a spatial distance drought index (SDDI) which was constructed by four drought variables based on multisource remote sensing (RS) data, including the normalized difference vegetation index (NDVI), land surface temperature (LST), soil moisture (SM), and precipitation (P), using the spatial distance model (SDM). The results showed that the consistent area of SDDI with the 1-month and 3-month standardized precipitation-evapotranspiration index (SPEI1 and SPEI3), and the self-calibrating Palmer drought severity index (scPSDI) accounted for 85.5%, 87.3%, and 85.1% of the global land surface area, respectively, indicating that the index can be used to monitor global drought-wetness conditions. Over the past two decades (2001–2020), a discernible spatial distribution pattern has emerged in global drought-wetness conditions. This pattern was characterized by the extreme drought mainly distributed deep within the continent, surrounded by expanding moderate drought, mild drought, and no drought areas. On the annual scale, the global drought-wetness conditions exhibited an upward trend, while on the seasonal and monthly scales, it fluctuated steadily within a certain cycle. Through this research, we found that the sensitive areas of drought-wetness conditions were mainly found on the east coast of Australia, the Indus Basin of the Indian Peninsula, the Victoria and Katanga Plateau areas of Africa, the Mississippi River Basin of North America, the eastern part of the Brazilian Plateau and the Pampas Plateau of South America. Full article

There is currently a context of climate change due to the way modern cities are developed, and they are made up mainly of impermeable surfaces and concrete buildings that change the hydrological cycle, causing (i) an increase in temperatures, (ii) the accumulation of stormwater on different surfaces, (iii) overflow in drainage systems, and (iv) the alteration of ventilation patterns, among others. This article presents a case study on the implementation of a permeable interlocking concrete paving (PICP) system, and it develops physical–mathematical modeling using software for the design of a parking lot that currently does not have adequate paving and urban drainage, resulting in sporadic flooding due to heavy rainfall in the city of Temuco, La Araucanía region, Chile. This article’s contribution highlights the application of new technology in Chile, discussing road infrastructure solutions based on sustainable urban drainage systems (SUDSs), which seek to implement feasible alternatives in urban sectors to improve human livelihood. The factors studied include structural and hydrological properties, along with the infiltration analysis of the system according to historical rainfall records in the area. This research concludes that the permeable pavement system with a drainage pipe and smooth roughness coefficient performs satisfactorily for an extreme hydrometeorological event corresponding to 140 mm considering 24 h of rainfall with a return period of 100 years equivalent to an inflow of 673 m³/day. Finally, the results indicate that, at least in the conditions of the city of Temuco, the use of permeable interlocking concrete pavement (PICP) proves to be a sustainable and feasible alternative to implementing measures of adaptation and mitigation against climate change, reducing the city’s flooding zones and allowing the irrigation of urban green areas. Full article

The most crucial losses in the hydrological cycle occur due to evaporation (EP). As a result, the accurate attainment of this complex phenomenon is critical in studies on irrigation, efficiency in the basins, dams, continuous hydrometeorological simulations, flood frequency, and water budget analysis. However, EP data sets are expensive, difficult to sustainably measure, and scarce, also, predictions are challenging tasks due to the wide range of parameters involved in these processes. In this study, the data gaps are filled with Class A evaporation pan observations through building a new meteorological station during seasons with no gauge measurements available for a three-year time period. These observations demonstrate high correlations with the readings from the Meteorology Airport Station, with a PCC of 0.75. After the continuous EP time series was completed over Kahramanmaras, these values were retrieved non-linearly via an artificial intelligence model using multi-meteorological parameters. In the study, the simulation performance is evaluated with the help of eight different statistical metrics in addition to graphical representations. The evaluation reveals that, when compared to the other EP functions, using both temperature and wind-driven simulations has the highest correlation (PCC = 0.94) and NSCE (0.87), as well as the lowest bias (P_Bias = −1.65%, MAE = 1.27 mm d⁻¹, RMSD = 1.6 mm d⁻¹, CRMSE = 24%) relative to the gauge measurements, while they give the opposite results in the solely precipitation-based models (PCC = 0.42, NSCE = 0.17, P_Bias = −6.44%, MAE = 3.58 mm d⁻¹, RMSD = 4.2 mm d⁻¹, CRMSE = 62%). It has been clearly seen that the temperature parameter is the most essential factor, while precipitation alone may be insufficient in EP predictions; additionally, wind speed and relative humidity would improve the prediction performance in artificial intelligence techniques. Full article

Since July 2022, the Yangtze River basin has experienced the most severe hydro-meteorological drought since record collection started in 1961, which has greatly affected the ecological environment of the Dongting Lake (DTL) basin. To investigate the effects of drought events on the eutrophication and phytoplankton community structure of DTL, the lake was sampled twice in August and September 2022 based on the water level fluctuations resulting in 47 samples. Furthermore, we combined the comprehensive trophic level index (TLI) and phytoplankton Shannon–Wiener diversity index (H) to characterize and evaluate the eutrophication status. The key influencing factors of the phytoplankton community were identified using redundancy analysis (RDA), hierarchical partitioning, and the Jaccard similarity index (J). Our results showed that the TLI of DTL changed from light–moderate eutrophication status (August) to mesotrophic status (September), whereas the H changed from light or no pollution to medium pollution. The phytoplankton abundance in August (122.06 × 10⁴ cells/L) was less than that in September (351.18 × 10⁴ cells/L) in DTL. A trend in phytoplankton community succession from Bacillariophyta to Chlorophyta and Cyanophyta was shown. The combination of physiochemical and ecological assessment more accurately characterized the true eutrophic status of the aquatic ecosystem. The RDA showed that the key influencing factors in the phytoplankton community were water temperature (WT), pH, nitrogen and phosphorus nutrients, and the permanganate index (COD_Mn) in August, while dissolved oxygen (DO) and redox potential (ORP) were the key factors in September. Hierarchical partitioning further indicated that temporal and spatial variations had a greater impact on the phytoplankton community. And the J of each region was slightly similar and very dissimilar, from August to September, which indicated a decreased hydrological connectivity of DTL during drought. These analyses indicated that the risk to the water ecology of DTL intensified during the summer–autumn drought in 2022. Safeguarding hydrological connectivity in the DTL region is a prerequisite for promoting energy flow, material cycle, and water ecosystem health. Full article

Evapotranspiration (ET) is an important approach for enabling water and energy exchange between the atmosphere and the land, and it has a very close relationship with terrestrial water resources and the ecological environment. Therefore, it is of great scientific to accurately quantify the spatiotemporal change in ET and its impact factors to understand the terrestrial water change pattern, maintaining water resource security and protecting the ecological environment. Our goal is to study the spatiotemporal characteristics of ET in the Indus River basin (IRB) and their driving factors. In our study, we first integrated the multi-source satellite gravimetry observations using the generalized three-cornered hat and least square methods to obtain the high-precision and continuous spatiotemporal evolution features of ET in the IRB from 2003 to 2021. Finally, we combined nine hydrometeorological and land cover type data to analyze the factors influencing ET. The results indicate that the algorithm used in our study can improve the ET accuracy by 40%. During the study period, ET shows a significant increasing trend (0.64 ± 0.73 mm/a), and the increasing rate presents spatial distribution characteristics of high variability in the northern areas and low variability in the southern areas of the study region. ET has a close relationship with precipitation, specific humidity, total canopy water storage, surface temperature and wind speed (with a correlation coefficients greater than 0.53 and variable importance of projection greater than 0.84). Among these factors, precipitation, specific humidity and surface temperature have significant correlations with ET (correlation coefficients greater than 0.85 and variable importance of projection greater than 1.42). And wind speed has a more significant positive effect on ET in the densely vegetated regions. The impacts of climate change on ET are significantly greater than those of land cover types, especially for similar land cover types. Ice and snow are significantly different to other land cover types. In this region, ET is only significantly correlated with precipitation, specific humidity and snow water equivalent (variable importance of projection greater than 0.81), and the impacts of precipitation and specific humidity on ET have been significantly weakened, while that of snow water equivalent is significantly enhanced. Our results contribute to furthering the understanding of the terrestrial water cycle in subtropical regions. Full article