Hydrological Indicators Research Articles

The ecological operation of ChiTan hydropower station based on hydrological alteration using the PCA method

ABSTRACT Water infrastructure affects the quantity, quality, and environment of water. During reservoir construction, only the social-economic benefits were considered and the health of the downstream river was neglected. Due to the severe downstream river system, the operators and managers faced the challenge of the downstream ecosystem. Many scholars are concerned with the reservoir's optimum functioning, which considers social and economic advantages. Numerous significant opinions and research findings are being presented on ecological scheduling at the moment, both in China and overseas, which surely deepen the connotations of ecological scheduling and serve as the research basis and critical reference of this work. The influence of hydropower development on hydrological conditions, the categorization and calculation of environmental runoff, and the building and solution of ecological scheduling models are all discussed and researched in this work. The study examined how the ChiTan hydropower project influenced the Jinxi River's flow. The total hydrologic alteration calculated using RVA is 50.53%. The component analysis is also utilized to eliminate the statistical redundancy in the hydrologic indicators. The indicators’ monthly flow for July and August's mean minimum flow for 7 days are relevant indicators for the Jinxi River basin's hydrologic modification regime.

Assessment of agroclimatic resources in agricultural landscapes of the Turkestan region of the Republic of Kazakhstan Askha

The article presents the scientific results of a study assessing agroclimatic resources in agricultural landscapes located in various natural zones of the Turkestan region of the RK (Republic of Kazakhstan). The research methods included classical and modern methods of mathematical statistics using digital technology and time series graphs to develop a mathematical model for climatic and hydrological indicators. Assessment of changes in indicators of agroclimatic resources in agricultural landscapes for 1941–2020 showed that the sum of air temperature, evaporation from the water surface and radiation balance of the daytime surface, characterising the energy resources of landscapes, increased by 10–15%, which contributes to increase in the total water consumption of agricultural land by 10–12%. Meanwhile, the decreasing tendency of the amount of precipitation by 5–10% in all natural climatic zones of the region has become one of the factors leading to a decrease in the natural moisture supply of the soil and vegetation cover of landscapes by 10–15%, acting as important environment-forming and ecological functions. The combined impact of these environment-forming factors has become the key reason for the increase in the deficit of agricultural land water consumption by 15–20%, the reduction of solar energy costs for the soil-forming process by 10–15% and the increase of the climate aridisation, and has become a signal for the need in the safety of agricultural activities, requiring the development of a set of adaptive measures to mitigate this process in the region.

Participatory multi-criteria decision making for optimal siting of multipurpose artificial reservoirs

Water resources management is a crucial activity due to climate change concerns and increasing water scarcity. The increase of water storage by building new artificial reservoirs is one of the most common solutions to fulfill the community’s needs for potable water, energy, irrigation, and prevention of flood risk. The siting of the reservoirs is usually assessed based on topographical and hydrological considerations. However, there are additional crucial factors, often disregarded in common engineering top-down design approaches, that should be considered to determine suitable locations, such as bio-physical, socio-economic, regulatory, and environmental factors, especially where there are competing interests in the use of territory. Involving the communities in the initial stages of the decision-making process is therefore crucial. In this work, we present a methodology, based on multi-criteria decision-making analysis (MCDA), for identifying the optimal locations for new reservoirs while simultaneously addressing all the aforementioned factors through community engagement. The presented method aims at overcoming two major challenges in existing works, i.e., retrieving quantitative information from the participatory approach and going beyond standard hydro-morphological suitability of a dam site. In a first step, an algorithm assesses a large number of sites using a Digital Elevation Model (DEM). Then, MCDA is conducted to rank all potential locations. The selection criteria are defined quantitatively, based on a territorial analysis combined with hydrological modelling. The criteria include: geometric and morphological aspects (reservoir volume, etc.), hydrological indicators (water balance, potential flood mitigation), anthropization (population, infrastructures, etc.), landscape, archaeological heritage, ecology, environmental components and natural hazards. A web-based survey tool based on the pairwise comparison is developed to engage the community in the decision-making process, allowing them to express their perspectives on the relative significance of each criterion. The application of this methodology is demonstrated through a case study in the Pesa river watershed, Italy, showcasing its effectiveness in identifying the most suitable reservoir locations while targeting the highest environmental preservation and community acceptance. The results show that sole engineering criteria might not reflect the sensibility of the community and that variations of the size of the dam can lead to distinct trade-offs between costs and benefits where interference with impact elements occurs.

A framework for analyzing the most relevant indicators of ecohydrology in a changing environment

ABSTRACT Human activities and climatic changes have altered the hydrological ecosystem of the Min River Basin and affected in-river biodiversity. In this paper, the year of abrupt hydrological change was identified using multiple tests, and the drivers of ecohydrological change were quantified in conjunction with the Budyko coupled hydrothermal equilibrium theory. Combined with ecological flow indicators (ecological surplus (ES) and ecological deficit (ED)) calculated based on discharge hydrographs and multiple hydrological indicators (ES/ED), the degree of river hydrological alteration in the Min River Basin was comprehensively evaluated, and its impacts on in-river biodiversity analyzed. The results of the study showed that: (1) according to the Budyko theory, the influence of human activities on the runoff changes in the Min River Basin reached 56.80%, which was the main influence factor, followed by climatic factors (41.56% for precipitation and 1.64% for evapotranspiration); (2) dam construction has generally resulted in an increase in seasonal ES and a decrease in seasonal ED; (3) the combination of the ecological flow indexes with the ecologically relevant hydrological indicators not only reduces the redundancy between the parameters, but also reflects the essential hydrological information and ecological connotations, and it is an effective method for evaluating ecohydrological mechanisms.

Developing practical models for predicting chlorophyll levels in the Andong Reservoir using empirical and machine learning approaches based on integrated analysis of physical, chemical, and hydrological indicators

Managing excessive phytoplankton biomass is a pressing issue worldwide and requires precise predictions and efficient water quality control. In this study, we employed the random forest approach to build a practical predictive model for phytoplankton biomass as determined by chlorophyll (CHL-a) levels. Using an integrative 15-year dataset, we obtained insights into CHL-a dynamics and identified important factors influencing it. Seasonal dynamics were crucial for shaping water quality, with meteorological and hydrological fluctuations playing pivotal roles. Elevated suspended solids and phosphorus levels during the summer monsoon indicated increased runoff and nutrient loading, contributing to fluctuations in nutrient ratios. CHL-a was also responsive to seasonal variations in nutrient availability, particularly in phosphorus and ammonium-nitrogen (NH4-N). These results suggest the importance of nutrient management strategies for controlling and mitigating eutrophication risk. The random forest model had practical accuracy (R2 = 0.66, RMSE = 1.85) for predicting long-term CHL-a variation and identified phosphate-phosphorus, water temperature, and NH4-N as the most important predictors. However, our findings emphasize the importance of comprehensive analyses to identify the factors driving CHL-a variation. Combining the strengths of machine learning with multifaceted insights would enhance prediction accuracy and efficiency, ultimately facilitating informed decision-making for reservoir management and environmental conservation efforts.

Analysis of intense hydrological periods based on observed and estimated flow series at a reference station on the Paraná River, Argentina

The hydrological variability and especially extreme phenomena such as droughts and floods affect natural and human systems worldwide. The Paraná River sub-basin, belonging to the La Plata basin, is of great regional importance, and in recent years extreme events have been observed that have impacted socio-economic aspects of the community. This study compares the series of a relevant hydrological index, calculated from observed flows and unregulated contributions within Argentine territory, at the same reference station. Intense hydrological periods are analyzed in both cases, for droughts and excess water events, for subsequent comparison.

Analysis of flow regime classification in the Omo-Gibe River Basin: insights into fluid dynamics in Ethiopia

ABSTRACT The study investigates flow regime in the Omo-Gibe River Basin to address hydrological complexity caused by precipitation and catchment features. Despite employing various methodologies, daily flow data highlight the need for a more comprehensive understanding of flow variability. The study aims to scrutinize flow regime classification, emphasizing the challenges posed by the basin's unique hydrological dynamics, with the ultimate goal of improving water management practices in the region. Using XLSTAT (Excel statistics software), the average base flow index (60.66%), zero flow index (0.25%), coefficient of variation (1.56%), and flashiness index (0.276%) were determined to be the primary hydrological indices that contributed to streamflow characterization. Finally, flow regime classification was described as non-perennial (13%) or perennial (87%) using the shape of the flow duration curve and this hydrological index. However, the magnitude of extreme flow events was judged depending on flow duration curve and calibrated by the flashiness index computed in the study. The study's findings serve as an input for streamflow regionalization and the foundation for future research on the ecology and hydrology of Ethiopia's river basins as well as the management of the water resources throughout the Omo-Gibe River Basin.

Characterization of Water Bodies through Hydro-Physical Indices and Anthropogenic Effects in the Eastern Northeast of Brazil

Brazil, despite possessing the largest renewable freshwater reserves in the world (8.65 trillion m3 annually), faces growing challenges in water management due to increasing demand. Agriculture, responsible for 68.4% of water consumption, is one of the main drivers of this demand, especially in the São Francisco River Basin, where irrigation accounts for 81% of total water withdrawals. Water bodies play a crucial role in sustaining ecosystems and supporting life, particularly along the East-West axis of Alagoas, a water-rich region in the ENEB. This study aimed to map and quantify the spatiotemporal variations of water bodies in the ENEB region and assess the impacts of human activities using MODIS satellite data, applying hydrological indices such as NDWI, MNDWI, and AWEI. Between 2003 and 2022, significant variations in the extent of water bodies were observed, with reductions of up to 100 km2 during dry periods and expansions of up to 300 km2 during wet seasons compared to dry periods. AWEI and MNDWI proved to be the most effective indices for detecting water bodies with MODIS data, providing accurate insights into water dynamics. Additionally, the MapBiomas Rios dataset, despite being resampled from a 30 m to a 500 m resolution, offered the most accurate representation of water bodies due to its methodology for data acquisition. Changes in albedo and surface temperature were also detected, highlighting the influence of climate change on the region’s water resources. These findings are crucial for guiding the sustainable management of water resources, not only in Alagoas but also in other regions of Brazil and similar semi-arid areas around the world. The study demonstrates the hydrological variability in the state of Alagoas, indicating the need for adaptive strategies to mitigate the impacts of climate change and anthropogenic pressures, supporting the need for informed decision-making in water resource management at both local and national levels.

The Impact of Urbanization on Surface Runoff and Flood Prevention Strategies: A Case Study of a Traditional Village

Increasingly severe flooding disasters have caused heavy casualties and property losses worldwide. Traditional Chinese villages that rarely experienced flooding disasters in the past have begun to frequently suffer from floods due to unreasonable reconstruction activities such as ground hardening and pond filling caused by urbanization. However, previous studies on hydrological changes and flood disasters caused by reconstruction activities in rural areas are scarce, especially lacking in quantitative analysis and research. In this study, the Storm Water Management Model (SWMM) is used to construct two hydrological models before and after the reconstruction of Hezhai Village, a traditional Chinese village. By simulating and comparing the changes in hydrological indicators of the two models, this study quantitatively analyzes how reconstruction activities caused changes in surface runoff and flooding disasters in Hezhai Village. The results show that the increase in the impervious ratio in the village has obvious effects on the total runoff, peak runoff, and runoff coefficient. And the reconstruction of ponds and canals has a notable impact on flooding. This study further delves into the logic of flooding at ponds and ordinary nodes and analyzes the specific reasons for flooding in Hezhai Village. Based on this, the paper provides recommendations for the optimization of the reconstruction of Hezhai Village.

Index-Based Alteration of Long-Term River Flow Regimes Influenced by Land Use Change and Dam Regulation

The growing population and expansion of rural activities, along with changing climatic patterns and the need for water during drought periods, have led to a rise in the water demand worldwide. As a result, the construction of water storage structures such as dams has increased in recent years to meet the water needs. However, dam construction can bring significant alterations to the natural flow regime of rivers, and it is therefore essential to understand the potential effects of human structures on the hydrological regime of rivers to reduce their destructive impacts. This study analyzes the hydrological changes in the Shahrchai River in response to the Shahrchai Dam construction in Urmia, Iran. The study period was from 1950 to 2017 at the Urmia Band station. The Indicators of Hydrological Alteration (IHA) were used to analyze the hydrological changes before and after regulating, accounting for land use changes and climatic factors. The results revealed the adverse effects of the Shahrchai Dam on the hydrological indices. The analysis showed an increase in the average flow rate during the summer season and a decrease in other seasons. However, the combined effects of water transferring for drinking purposes, a decrease in permanent snow cover upstream of the dam, and an increase in water use for irrigation and agricultural purposes resulted in a decrease in the released river flow. Furthermore, the minimum and maximum daily flow rates decreased by approximately 85% and 65%, respectively, after the construction of the Shahrchai Dam. Additionally, the number of days with maximum flow rates increased from 117 days in the pre-dam period to 181 days in the post-dam period. As a concluding remark, the construction of the Shahrchai Dam, land use/cover changes, and a decrease in permanent snow cover had unfavorable effects on the hydrological regime of the river. Therefore, the hydrological indicators should be adjusted to an acceptable level compared to the natural state to preserve the river ecosystem. The findings of this study are expected to guide water resource managers in regulating the sustainable flow regime of permanent rivers.

Evaluating machine learning models in predicting GRI drought indicators (case study: Ajabshir area)

Examining the condition of groundwater resources and the impact of droughts is valuable for effective water resources management. Today, machine learning (ML) models are recognized as one of the useful tools in time series predictions. In this study, the groundwater condition of one of the most important aquifers in northwest Iran was investigated using MODFLOW, followed by estimating the groundwater resource index (GRI) utilizing the multivariate adaptive regression spline (MARS) and least squares support vector regression (LSSVR) for a period between 2001 and 2019. Meteorological and hydrological drought indicators along with precipitation and flow rate were used as input variables for prediction. The simulation results revealed a groundwater level decrease since the aquifer withdrawal amount is more than the recharge amount. Besides, results showed that there is a limited interaction between surface water and groundwater resources, mainly caused by the decrease in the river flow and aquifer groundwater level drop. Both ML models performed well in GRI estimation, using groundwater flow, streamflow drought index, standardized precipitation index, and runoff as input variables. The performance of the MARS model with RMSE, MAE, and NSE error evaluation criteria of 0.37, − 0.19, and 0.83, respectively, exerted slightly better results than LSSVR with RMSE, MAE, and NSE of 0.48, − 0.06, and 0.80, respectively. The findings reveal the appropriate performance of both models in forecasting drought indicators, highlighting the necessity of using ML models in hydrology and drought prediction problems.

Sequential Changes in Coastal Plain Rivers Influenced by Rising Sea-Level

Coastal backwater effects on low-gradient coastal plain rivers extend well upstream of the head of the estuary and propagate upstream as sea-level rises. Hydrological, geomorphological, and ecological indicators can serve as sentinels of the upriver encroachment. Analyzing the along-river spatial distribution of these indicators as a space-for-time substitution allows the prediction of sequential changes. Interpretation of results from 20 rivers in Virginia and the Carolinas shows that backwater effects at the leading edge result in higher river stages, increasing floodplain inundation, and raising water tables. Lower slopes and flow velocities reduce sediment transport, reducing river sediment input and floodplain deposition. This inhibits natural levee development, reducing bank heights. These factors combine to increase the frequency and duration of inundation, resulting in semi-permanently flooded wetlands. Anaerobic conditions limit organic decomposition, and ponding allows transported and suspended organic matter to settle, leading to organic muck and peat floodplain soils. This accumulation, coupled with general valley-filling, buries alluvial terrace remnants. Finally, vegetation changes driven by salinity increases occur, resulting in swamp conversions to brackish marsh. Backwater encroachment is strongly controlled by channel bed slope, with relatively steeper channels experiencing slower rates of tidal extension. With accelerating sea-level rise (SLR), the lowest-sloping channels could experience encroachment rates of >1 km yr−1. Hydrological changes associated with SLR are most rapid at the leading, upriver end—averaging 71 km upstream of the head of the estuary in the study rivers at present—and at the lowermost, downstream end of the fluvial-estuarine transition zone.

Understanding the variability of large-scale statistical downscaling methods under different climate regimes

Large-scale downscaling plays an important role in assessing global impacts on hydrological sphere due to climate changes. In such downscaling efforts, it is essential to consider the various climate regimes. Although previous studies have indirectly suggested that the accuracy of downscaling might differ among climate regimes, research that systematically understands or quantifies the variability of this accuracy remains scarce. This study addresses this gap by systematically quantifying the performance of five different large-scale downscaling methods across various climate regimes in the context of downscaling hydroclimatic indicators. Our findings indicate that large-scale downscaling yields the highest accuracy on average when applied to temperature, precipitation, and runoff in tropical, arid, and temperate climate regimes, respectively, while showing poor accuracy in polar regimes for all variables. The maximum difference of normalized root mean squared errors for hydroclimate indicators is 69 % across climate zones, and the spatial distribution of downscaling accuracy aligns with spatial distribution of climate zones. The variation of downscaling accuracy is particularly significant in temperature, precipitation, and seasonal runoff indicators. Furthermore, linkages between accuracy of climate and hydrological indicators differ by climate zones. The underlying reasons for the different accuracy of downscaling are spatially different accuracy of global climate models (GCMs) and interaction of downscaling structure and climate regimes. This study articulated the source of spatially different accuracy/uncertainties for large-scale downscaling that have never been addressed before. The findings of this study provide valuable support in selecting appropriate downscaling methods, ultimately enhancing the spatial reliability and accuracy of large-scale downscaling methods.

Spatial-Temporal Dynamics of Water Resources in Seasonally Dry Tropical Forest: Causes and Vegetation Response

Seasonally Dry Tropical Forests (SDTFs) are situated in regions prone to significant water deficits. This study aimed to evaluate and quantify the dynamics and spatial patterns of vegetation and water bodies through the analysis of physical–hydrological indices for a remnant of FTSD between 2013 and 2021. Basal area, biomass, and tree number were monitored in 80 permanent plots located in two areas of an SDTF remnant with different usage histories. To assess vegetation and water resource conditions, geospatial parameters NDVI, NDWIveg, NDWI, and MNDWI were estimated for the period from 2013 to 2021. The observed patterns were evaluated by simple linear regression, principal component analysis (PCA), and principal component regression (PCR). Area 2 presented higher values of basal area, biomass, and number of trees. In area 1, there was an annual increase in basal area and biomass, even during drought years. The NDVI and NDWIveg indicated the vulnerability of vegetation to the effects of droughts, with higher values recorded in 2020. NDWI and MNDWI detected the water availability pattern in the study area. Physical–hydrological indices in the dynamics of tree vegetation in dry forests are influenced by various factors, including disturbances, soil characteristics, and precipitation patterns. However, their predictive capacity for basal area, biomass, and tree number is limited, highlighting the importance of future research incorporating seasonal variability and specific local conditions into their analyses.

Study on the Driving Mechanism of Ecohydrological Regime in the Wandering Section of the Lower Yellow River

Climate change and human activities exert significant impacts on runoff generation and convergence mechanisms. Understanding the evolution mechanisms and driving forces of runoff is crucial for the sustainable development of water resources. This study is based on the runoff data of the Huayuankou (HYK), Jiahetan (JHT), and Gaocun (GC) stations in the lower reaches of the Yellow River from 1951 to 2019. The objectives are to identify and quantify the hydrological pattern and its driving mechanism of the three stations by the Mann–Kendall test, cumulative deviation method, wavelet analysis, the IHA-RVA method, SCRCO method, and the Budyko method. Our study revealed that the runoff disturbance points at all three stations occurred in 1985. During the two periods before and after the disturbance, the trends and periodicities within the year exhibited high consistency, showing an overall downward trend, with runoff increasing in October and decreasing in June and the primary cycles being 5 and 7 years. All three stations experienced high-degree changes in their hydrological situations, with the high-degree change occupying the largest proportion. At the HYK, JHT, and GC stations, human activities accounted for 66.05%, 71.94%, and 74.38% of runoff changes, respectively. Furthermore, we verified the attribution conclusion of runoff at HYK using the Budyko model, confirming that human activities are the primary factor influencing runoff. Finally, we explored the interactive relationships along the spatial trajectory of runoff at the three stations, analyzed 32 hydrological indicators, and detailed the land use changes in the Yellow River Basin. Our research findings complement the understanding of hydrological change mechanisms in the lower reaches of the Yellow River Basin and provide a scientific basis for future water resource management and flood prevention measures.

A grid-wise approach for accurate computation of Standardized Runoff Index (SRI)

The Standardized Runoff Index (SRI) is a major indicator for evaluating hydrological drought conditions, accomplished by comparing the current runoff data with retrospective runoff conditions of an area for the same period. This hydrological drought indicator facilitates the characterisation of runoff variations across diverse regions. This study introduces a refined methodology for accurate computation of SRI by employing a grid-wise approach. Distinct probability distributions were fitted to each grid within the study area, diverging from the conventional practice of using a single probability distribution for the entire basin or sub-basin. The research endeavours to assess the efficacy of the grid-wise approach in improving the representation of drought characteristics when compared to the traditional areal approach. A comparative analysis between the performances of SRI computed through grid-wise fitting (where the probability distribution dynamically adapts to each grid) and the areal fitting approach (employing a uniform distribution across all grids) was conducted within the Godavari Basin, India. The findings in this study underscore that the misrepresentation of extreme events is inevitable for large heterogeneous basins like Godavari when the traditional areal approach was employed for SRI computation. Consequently, the grid-wise fitting emerges as a more accurate method for computing the SRI, particularly in characterising extreme dry or wet events.

Xenobiotics and pollution of Lake Ladoga

Advances in applied chemistry have led to the emergence of a new group of organic toxicants - xenobiotics, which have found wide application in industry, agriculture and everyday life, and quickly spread through the biosphere, exerting a prolonged effect on living organisms, including humans. Research was carried out in the waters of the Shlisselburg, Volkhov and Svirskaya lips of the littoral zone of the southern part of Lake Ladoga in the period 2020-2023, using generally accepted methods of biological and chemical analytical analysis of fish and their habitat. 1500 specimens were examined. Fish and 3000 larvae. Chemical-analytical studies of samples of water, bottom sediments, atmospheric precipitation and fish for metals were carried out in the laboratory of fishery ecology of the St. Petersburg branch of the Federal State Budgetary Institution "VNIRO" ("GosNIORH" named after L.S. Berg) on an atomic absorption spectrophotometer AA - 7000 (Shimadzu) according to approved methods (PNDF., 2010; M02-2406., 2013 and M02-902-125, 2005), and in the research laboratory of food and natural objects "ANALECT" of the Institute of the Ministry of Health of the Russian Federation, using the method of atomic absorption spectrometry according to approved methods. Hydrochemical and hydrological indicators were determined using an automatic multiparameter probe Aqua TROLL 500. The content of metals in the fish habitat at the surveyed equators, as dangerous toxicants and previously significant for Lake Ladoga, showed their low content and especially such highly toxic ones as lead and cadmium. Minor excesses were noted for copper and manganese, but they could not in themselves be the cause of pathological manifestations of toxicosis in fish and disruption of the process of their natural reproduction.

НОРМИРОВАНИЕ ВОДОПОТРЕБЛЕНИЯ СЕЛЬСКОХОЗЯЙСТВЕННЫХ УГОДИЙ И КУЛЬТУР С УЧЕТОМ ПРОСТРАНСТВЕННО-ВРЕМЕННОЙ ИЗМЕНЧИВОСТИ ГИДРОМЕТЕОРОЛОГИЧЕСКИХ ФАКТОРОВ

The article presents the developed calculation methodology for environmental regulation of water consumption of agricultural land and crops, taking into account the spatiotemporal variability of climatic, hydrological, soil and biological indicators of the natural system. It is noted that the human pressure in the form of a deficit in water consumption of agricultural land and crops should be based on the principles of energy balance of heat, moisture and nutrients, taking into account natural conditions that ensure targeted regulation and management of soil-forming processes in hydro-agrolandscapes. The developed model and algorithm for assessing water consumption in agriculture are built using two models for rationing water consumption - agricultural land and crops, which makes it possible to determine the most important criteria for environmental management in irrigated agriculture and is of great importance in ensuring food security of the population.

Invasibility framework to predict the early colonization of alien Sonneratia in mangrove: Implications for coastal area management

Invasibility, or an ecosystem's susceptibility to invasion, plays a critical role in managing biological invasions but is challenging to quantify due to its dependence on specific ecosystem variables. This limitation restricts the practical application of this concept in the control of alien species. This study aims to simplify invasibility into measurable components and develop an applicable framework to predict early colonization of alien plants within the coastal mangrove ecosystem. We used the unchanneled path length (UPL), a widely applied hydrological connectivity-related indicator, to assess the accessibility of the mangrove. The enhanced vegetation index (EVI), positively correlated with above-ground biomass, was used to evaluate the potential competitive intensity. Firstly, building on existing studies, we developed a four-quadrant concept model integrating the effects of EVI and UPL on the early colonization of the alien species Sonneratia apetala. Our results revealed significant differences in EVI and UPL values between colonized and uncolonized areas, with colonized regions displaying markedly lower values (P < 0.001). Additionally, logistic regression showed a significant negative association between the probability of successful colonization by S. apetala and both indicators (P < 0.001). These results validate the effectiveness of our conceptual model. Furtherly, we identified four key niche opportunities for exotic species in mangrove: mudflats outside the mangrove forest, tidal creeks, canopy gaps, and unmanaged abandoned aquaculture ponds. Overall, this study provides important insight into the ecological processes of alien S. apetala colonization and practical information for management of coastal areas susceptible to invasion. Additionally, it presents a case study on the practical application of the concept of invasibility in the management of alien species.

A novel GNSS and precipitation-based integrated drought characterization framework incorporating both meteorological and hydrological indicators

The Global Navigation Satellite System (GNSS) has become instrumental in developing drought indices, particularly meteorological drought indicators derived from atmospheric precipitable water vapor and hydrological drought indicators based on inverted terrestrial water storage changes. However, these indices traditionally focus on individual aspects of droughts, either meteorological or hydrological droughts, and do not fully capture the integrated nature of drought phenomena. Addressing this gap, this study proposes a novel integrated drought characterization framework using the Gringorten plotting position to derive joint probabilities for GNSS-derived meteorological and hydrological drought indicators. This leads to the creation of a comprehensive multivariate drought severity index (GNSS-MDSI). The analysis across the western United States indicates significant spatial variability in multiyear average precipitation efficiency, ranging from 7.51% to 28.1%. This variability corresponds with marked differences in seasonal terrestrial water storage changes, which oscillate between 25 and 123 mm. Applying this framework in eight states, 9–13 comprehensive drought events from January 2006 to December 2021, with durations spanning from 3 to 54 months, were identified. The GNSS-MDSI not only captured these comprehensive drought periods across various temporal and spatial scales but also aligned closely with drought classifications provided by the US Drought Monitor. These results underscore the utility of this framework in providing a more nuanced and multifaceted perspective on drought conditions, surpassing the capabilities of single-indicator systems. Overall, this study presents an innovative framework for drought monitoring by integrating two GNSS-derived drought indicators, enabling precise and comprehensive delineation of drought characteristics, and offering a geodesy-based solution for integrated global and regional drought monitoring.