Seasonal Water Research Articles

Using Geochemical Tracers to Determine Seasonal Inputs of Freshwater to a Coastal Estuary: Biscayne Bay, FL

Biscayne Bay is a coastal estuary that historically relied on rainfall and groundwater inputs from the karst Biscayne aquifer. The construction of major canals along the coastline has released point-source freshwater inputs into the bay, detrimentally affecting the Bay’s ecosystem balance. This project investigates the proportional inputs of freshwater between the wet and dry seasons in Deering Estate, adjacent to Biscayne Bay. The objective of this project was accomplished by analyzing the water chemistry of the bay using naturally occurring geochemical tracers. Water sampling occurred from May to August (wet season 2022) and January to March (dry season 2023); at an inland freshwater spring and on Biscayne Bay. Water samples were analyzed for δ18O and δ2H values, and Sr2+/Ca2+ ratios as geochemical tracers. The highest and lowest salinity values observed in the wet and dry seasons, at both the freshwater spring and Biscayne Bay sites, were before and after a major rain event, respectively. The chemical analysis supports that rain is the dominant source of freshwater input into the bay at our sampling location, and the freshwater spring is dominated by groundwater and canal water during the wet season. During the dry season, groundwater and canal water are the dominant source for the sampling location in Biscayne Bay and the dominant source of freshwater input for the freshwater spring. However, all three endmembers contribute seasonally. Understanding freshwater inputs to this crucial estuary will provide important information for current restoration efforts of Biscayne Bay, specifically around the Deering Estate area.

Assessing the Seasonal Water Requirement of Fully Mature Japanese Plum Orchards: A Systematic Review

Assessing the Seasonal Water Requirement of Fully Mature Japanese Plum Orchards: A Systematic Review

Changes in the Water Area of an Inland River Terminal Lake (Taitma Lake) Driven by Climate Change and Human Activities, 2017–2022

Constructed from a dataset capturing the seasonal and annual water body distribution of the lower Qarqan River in the Taitma Lake area from 2017 to 2022, and combined with the meteorological and hydraulic engineering data, the spatial and temporal change patterns of the Taitma Lake watershed area were determined. Analyses were conducted using Planetscope (PS) satellite images and a deep learning model. The results revealed the following: ① Deep learning-based water body extraction provides significantly greater accuracy than the conventional water body index approach. With an impressive accuracy of up to 96.0%, UPerNet was found to provide the most effective extraction results among the three convolutional neural networks (U-Net, DeeplabV3+, and UPerNet) used for semantic segmentation; ② Between 2017 and 2022, Taitma Lake’s water area experienced a rapid decrease, with the distribution of water predominantly shifting towards the east–west direction more than the north–south. The shifts between 2017 and 2020 and between 2020 and 2022 were clearly discernible, with the latter stage (2020–2022) being more significant than the former (2017–2020); ③ According to observations, Taitma Lake’s changing water area has been primarily influenced by human activity over the last six years. Based on the research findings of this paper, it was observed that this study provides a valuable scientific basis for water resource allocation aiming to balance the development of water resources in the middle and upper reaches of the Tarim and Qarqan Rivers, as well as for the ecological protection of the downstream Taitma Lake.

Event-based evaluation of operational ENSO forecasting models in 2002–2020: Implications for seasonal water resources management

Given the practical implications of El Niño-Southern Oscillation (ENSO) forecasting for developing decision support systems and its importance in global climate prediction on seasonal to interannual scales, significant efforts have been made to enhance ENSO forecasts. This study presents an event-based evaluation of ENSO forecasts and discusses the implications of such forecasts to water resources management. Using ENSO forecasts for the period 2002–2020, this study evaluates and compares the results of two types of forecasting models, i.e., statistical and dynamical models, in predicting Sea Surface Temperatures (SSTs). Forecasting skills are evaluated for distinct target seasons via two metrics, spearman correlation and mean squared error. In addition, the performance of the two types of models at different lead times are also evaluated. Results reveal that the forecasting skills of these models are comparable, both of which exhibit higher forecasting skills for the boreal fall-winter season and lower skills for the boreal spring season. Event-based analyses show that dynamical and statistical models under-forecast SST anomaly at the onset month for El Niño events, although the forecasting error diminishes with a reduced forecasting lead for most occasions. For La Niña events, SST anomaly forecasting errors could be positive or negative. It is also difficult for the models to accurately predict the quick shift from one ENSO phase to another. Factors that contribute to such challenges are discussed. The implication of ENSO forecasts for water resources management, mainly streamflow forecasts, for a regional water supply utility in the southeastern United States is also discussed. Results from this study provide insights into ENSO forecasting skills and their practical implications.

Vegetation structure drives mosquito community composition in UK’s largest managed lowland wetland

PurposeThe rising burden of mosquito-borne diseases in Europe extends beyond urban areas, encompassing rural and semi-urban regions near managed and natural wetlands evidenced by recent outbreaks of Usutu and West Nile viruses. While wetland management policies focus on biodiversity and ecosystem services, few studies explore the impact on mosquito vectors.MethodsOur research addresses this gap, examining juvenile mosquito and aquatic predator communities in 67 ditch sites within a South England coastal marsh subjected to different wetland management tiers. Using joint distribution models, we analyse how mosquito communities respond to abiotic and biotic factors influenced by wetland management.ResultsOf the 12 mosquito species identified, Culiseta annulata (Usutu virus vector) and Culex pipiens (Usutu and West Nile virus vector) constitute 47% of 6825 larval mosquitoes. Abundant predators include Coleoptera (water beetles) adults, Corixidae (water boatmen) and Zygoptera (Damselfy) larvae. Models reveal that tier 3 management sites (higher winter water levels, lower agricultural intensity) associated with shade and less floating vegetation are preferred by specific mosquito species. All mosquito species except Anopheles maculipennis s.l., are negatively impacted by potential predators. Culiseta annulata shows positive associations with shaded and turbid water, contrary to preferences of Corixidae predators.ConclusionsTier 3 areas managed for biodiversity, characterised by higher seasonal water levels and reduced livestock grazing intensity, provide favourable habitats for key mosquito species that are known vectors of arboviruses, such as Usutu and West Nile. Our findings emphasise the impact of biodiversity-focused wetland management, altering mosquito breeding site vegetation to enhance vector suitability. Further exploration of these trade-offs is crucial for comprehending the broader implications of wetland management.

Temporal and spatial variations of terrestrial water storage in the northeastern Tibetan Plateau retrieved by GNSS observations

The variation of solid Earth's hydrologic loading could cause the elastic vertical deformation of the crust, and the Global Navigation Satellite System (GNSS) could effectively monitor the vertical displacement of surface loads. However, the widely used Green's function method does not work well in areas where GNSS sites are sparse. Here, the vertical displacement time series of GNSS stations and the Slepian basis function method have been applied to convert displacement signals into spatial spectrum signals. The elastic mass load theory is used to study the changes in terrestrial water storage on the Northeastern Tibetan Plateau (NETP). The temporal and spatial characteristics of seasonal water changes are well-represented by the GNSS, the Gravity Recovery and Climate Experiment (GRACE), and the Global Land Data Assimilation System (GLDAS). Several data points suggest that the change in water storage shows a gradual increase from the northeast to the southwest. The greatest annual amplitude of water storage retrieved by GNSS is ~159 mm, which is greater than the ~47 mm and ~44 mm obtained by GRACE and GLDAS. These results demonstrate that GNSS is capable of capturing small-scale hydrological changes in this region, whereas GRACE and GLDAS data tend to underestimate seasonal variations in water storage. We also used GNSS to describe the hydrological drought conditions in NETP, showing that GNSS could be used as an independent method to characterize hydrological drought events. The findings suggest it could observe water storage with high spatial and temporal resolution and aid in comprehending regional hydrological trends with a sparse GNSS station network.

Predicting and evaluating seasonal water turbidity in Lake Balkhash, Kazakhstan, using remote sensing and GIS

Predicting and evaluating seasonal water turbidity in Lake Balkhash, Kazakhstan, using remote sensing and GIS

Self-reported urinary tract infection and bacterial vaginosis symptoms among indigenous adolescents during seasonal periods of water scarcity: A cross-sectional study in Bandarban Hill District of Bangladesh.

Water scarcity and poor water quality could lead to suboptimum menstrual hygiene practices, and subsequently urinary tract infection (UTI) and bacterial vaginosis (BV). In this study, we estimate the prevalence of self-reported UTI and BV among indigenous adolescent girls during the water scarcity period in the Bandarban Hill Districts in south-eastern Bangladesh. Using a cross-sectional design, a total of 242 indigenous adolescent girls were selected and interviewed during the seasonal water scarcity period (from February to May 2022) in Bandarban. The difference in prevalence of any self-reported UTI or BV symptoms by respondents' characteristics was assessed by χ 2 test. Multivariable logistic regression model was used to observe the associated factors. The prevalence of self-reported UTI, BV, and any symptoms of UTI or BV among the respondents were 35.54%, 28.93%, and 43.80%, respectively. Ethnicity, studentship status, source of water used for menstrual hygiene, and perceived water quality were significantly associated with the prevalence of any self-reported UTI or BV symptoms. Findings recommend further research to cross-check the validity of self-reported prevalence and investigate if the episodes of UTI or BV could be attributable to water scarcity and poor water quality in study areas during dry period.

Vegetation dynamics regulate baseflow seasonal patterns of the Chaohe watershed in North China

Study regionThe Chaohe watershed is Beijing's primary potable water source. Study focusThe initiation of ecological restoration (ER), combined with the rapid acceleration of climate change (CC), has precipitated severe water shortages in North China. The seasonal responses of baseflow (BF), pivotal for sustaining rivers' fundamental flow and ecological equilibrium, to ER and CC are poorly understood. This study provides a precise depiction of the seasonal variations in BF by leveraging multiple separation methodologies. By applying the BFAST algorithm and a comprehensive sensitivity analysis, we unveil the nuanced seasonal patterns of BF adjustments in reaction to ER and CC. New hydrological insights for the regionBaseflow, primarily influenced by the wet season, constituted 64.21% of the annual aggregate. Considerable decreases in BF during the dry (−32.61%) and wet (−68.21%) seasons pose increasing threats to available water resources. The decrease in sub-surface runoff (−1.91 mm per decade) dominated the reduction of dry season BF. Indeed, vegetation regulated seasonal water distribution, maintaining the essential flow throughout the dry season. In the wet season, the reduction in BF acts as a supplemental water source to fulfill the escalating evapotranspiration needs due to afforestation and a drying climate. This study highlights the persistent hydrological consequences of ER and CC on water resources, emphasizing the crucial function of vegetation in baseflow, a key component for ecological restoration and water resource management in water-limited areas.

Influence of the hydrological regime of lake buyo on fish community of three floodplains in the Tai national park (southwestern Côte d’Ivoire)

Floodplains play an important role in the ecological functioning of the areas on which they depend. These plains are also of socio-economic interest to the local population, particularly in terms of fishing activities. The fish fauna of three Lake Buyo floodplains (Néma, Libériakôbro and Gah) was studied in relation to the lake's hydrological regime. The aim of this study was to evaluate the influence of the hydrological seasons of Lake Buyo on the structure of the fauna considered. Sampling of fish community was carried out monthly during an annual cycle using experimental fishing. The structure of the fish community was analyzed by determining taxonomic composition, taxon abundances (Ar) and the Shannon-Weaver diversity (H') and Pielou equitability (E) index. Temporal dynamics of the studied parameters were analyzed according to three hydrological seasons: flood season, high water season and recession season. Results showed that total specific richness of ichthyofauna of the studied plains was 38 species, and the richness observed at the different hydrological seasons varied significantly (p < 0.05). The Cyprinidae family was dominant in the abundances recorded at all seasons (48.34% ≤ Ar ≤ 85.5%). It should be noted that the studied ichthyofauna exhibited relatively low diversity and equitability with reference to diversity index (1.42 ≤ H' ≤ 1.46 and 0.28≤ E ≤ 0.29).

Impact of Hillslope Agriculture on Soil Compaction and Seasonal Water Dynamics in a Temperate Vineyard

Major losses of agricultural production and soils are caused by erosion, which is especially pronounced on hillslopes due to specific hydrological processes and heterogeneity. Therefore, the aim of this study was to assess the impact of agricultural management on the compaction, infiltration, and seasonal water content dynamics of the hillslope. Measurements were made at the hilltop and footslope, i.e., soil water content and potential were measured using sensors, wick lysimeters were used to quantify water flux, while a mini-disk infiltrometer was used to measure the infiltration rate and calculate the unsaturated hydraulic conductivity (K_unsat). Soil texture showed differences between hillslope positions, i.e., at the hilltop after 50 cm depth, the soil is classified as silty clay loam, and from 75 cm onward, the soil is silty clay, while at the footslope, the soil is silt loam even at the deeper depths. The results show a higher K_unsat at the footslope as well as higher average water volumes collected in wick lysimeters compared to the hilltop. Average water volumes showed a statistically significant difference at p < 0.01 between the hilltop and the footslope. The soil water content and water potential sensors showed higher values at the footslope at all depths, i.e., 8.0% at 15 cm, 8.4% at 30 cm, and 27.3% at 45 cm. The results show that, even though the vineyard is located in a relatively small area, soil heterogeneity is present, affecting the water flow along the hillslope. This suggests the importance of observing water movement in the soil, especially today when facing extreme weather (e.g., short-term high-intensity rainfall events) in order to protect soil and water resources.

Long-Term Changes in Water and Ion Flows of the Pechora River, the Longest Full-Water European Arctic River

Long-term series of annual and seasonal water flow and major ions in the Pechora River were analyzed. Long-term phases of increased and decreased water flow were identified, ranging in duration from 11 to 49 years, and the major characteristics of these phases were determined. Changes in the sequence and boundaries of contrast phases in the annual and snowmelt spring–summer flood runoff were found to coincide. The difference between the mean seasonal water runoff during the phases of increased and decreased flow varied from 12 to 41%. The ion flow values of contrast phases typically differed by 9 to 36%, which is less than for water flow. This is due to the inverse dependence between ion concentrations and water discharge. Such peculiar negative feedback stabilizes the rates of chemical denudation in the river catchments to some extent and, thus, the discharge of major ions into seas, even during significant variations in water.

Comparative metabolome profiling of two contrasting wheat cultivars in late-season water deficit

Abiotic stresses, including water deficit, cause myriad changes in plant metabolism. Metabolomics is a powerful tool for understanding plant resistance to such stresses. Therefore, this research was conducted to evaluate the effects of after anthesis water deficit (WD: from anthesis to maturity), i.e., maintaining moisture at field capacity (well-watered: WW) and 50% field capacity, on metabolome profiling (amino acids, hormones, nucleotides, soluble and insoluble sugars, sugar alcohols, and enzymes) in grains of two contrasting wheat cultivars grown in a greenhouse at Razi University as a factorial experiment. Based on the results, WD decreased grain yield (57%), 1000 grain weight (50%), and grain plant-1 (9%) in the sensitive cultivar (SC), while it had no significant effect on the tolerant cultivar (TC). In WW, the concentration of IAA, glucose, and fructose, as well as the activity of three invertase isoforms (vacuolic, cytosolic, and cell wall bound), were significantly higher at 10 days after anthesis compared to 17 and 31 days. In WD, these traits were significantly reduced in SC. ABA and sucrose concentration and sucrose synthase activity were significantly higher 17 days after anthesis than 10 days after anthesis. WD. In contrast to ABA, sucrose concentration decreased significantly in growing grains of SC in WD. The ATP/ADP ratio, inositol, glycerol and erythritol contents of TC were significantly higher in WD than in SC. Among the different amino acids, asparagine, glutamine and lysine were the highest, while ACC and methionine were the lowest. Contrary to SC, WD significantly increased the sum of the amino acid concentrations determined in the grains of TC, i.e. asparagine (22%), aspartic acid (48%), glycine (48%), ACC (79%) and valine (133%). These findings could help us to understand the resistance mechanisms to late season water deficit, which can increase sink strength (sink size and sink activity) and achieve higher yield quantity and quality in wheat.

Wetland water balance estimation in an arid region using remote sensing technology and hydrological modelling

Understanding the hydrological processes associated with wetland dynamics is fundamental to studying climate change impacts and the global water cycle. This study simulates the variation of water balance in the Ghorra Playa, Eastern Tunisia, over the period extending from September 2017 to August 2020. The playa is a seasonal habitat for a number of migratory bird species and its watershed is of vital agricultural importance. Water balance estimations were performed using two different approaches: remote sensing-based analyses (Sentinel-2B, GPM and FEWS-NET) and modelling founded on field data. The playa presents an average annual water balance of 1.1 million cubic meters. Water inflows come from direct rainfall mostly in the fall and spring seasons. Groundwater flow into the playa significantly influenced the pattern of water flux (81 %). The annual, seasonal and monthly water budget simulations show reasonably good agreement between the remote sensing-based analysis and the hydrological modelling exercise.

Evaluation of water quality and trophic status in relation to seasonal water mixing in a highland Lake Ardibo, Ethiopia

Abstract The aim of the present study was to evaluate the spatio-temporal variability of various physical and chemical parameters of water quality and to determine the trophic state of Lake Ardibo. Water samples were collected from October 2020 to September 2021 at three sampling stations in four different seasons. A total of 14 physico-chemical parameters, such as water temperature, pH, dissolved oxygen (DO), electrical conductivity, turbidity, alkalinity, Secchi-depth, nitrate, ammonia, silicon dioxide, soluble reactive phosphorus, total phosphorus, chloride, and fluoride were measured using standard methods. The results demonstrated that seasonal variation existed throughout the study period. Except for turbidity, the water quality of the lake varied significantly within the four seasons (ANOVA, p < 0.05). DO levels decreased significantly during the dry season following water mixing events. Chlorophyll-a measurements exhibit significant seasonal differences ranging from 0.58 μg L−1 in the main-rainy season to 8.44 μg L−1 in the post-rainy period, indicating a moderate algal biomass production. The overall category of Lake Ardibo was found to be under a mesotrophic state with medium biological productivity. A holistic lake basin approach management is suggested to maintain water quality and ecological processes and to improve the lake ecosystem services.

Catchment-scale thawing and greening decreases long-term nitrogen export in NE Greenland

Climate change is expected to alter nitrogen (N) export from Arctic rivers, with potential implications for fragile coastal ecosystems and fisheries. Yet, the directionality of change is poorly understood, as increased mobilization of N in a ‘thawing’ Arctic is countered by higher rates of vegetative uptake in a ‘greening’ Arctic, particularly in the understudied region of Greenland. We use an unprecedented dataset of long-term (n = 18 years) river chemistry, streamflow, and catchment-scale changes in snow and vegetation to document changing riverine N loss in Greenland. We documented decreasing inorganic and organic N loads, linked to decreasing snow stores, warming soils, and enhanced plant uptake. Higher variability in N export across years also points to the increasing role of high flow events in driving downstream N loss. This alteration in N cycling may significantly reduce both inorganic and organic N transport across the terrestrial-aquatic boundary during the open water season in a rapidly warming Greenland.

Quantifying regional variability of machine-learning-based snow water equivalent estimates across the Western United States

Seasonal snow-derived water is a critical component of the water supply in the mountains and downstream regions, and the accurate characterization of available water in the form of snow-water-equivalent (SWE), peak SWE, and snowmelt onset are essential inputs for water management efforts. Arising from recent advancements in artificial intelligence (AI) and machine learning (ML), we introduce a large-scale ML SWE model leveraging publicly available data sources and open-source software. The model demonstrates the application of a limited feature space in a relatively simple ML architecture without the need for process-based formulations to effectively estimate spatially continuous SWE at a daily temporal resolution. Beginning with in situ SWE measurements (i.e., SNOTEL), lidar-derived terrain features, and temporal variables, we employ localized feature engineering and optimization via gradient-boosting decision trees to identify regionally unique drivers of snowpack dynamics and use the optimal features to train regionally independent artificial neural networks to estimate regional SWE at a 1 km spatial resolution. The model results yield respectable skill in reconstructed 1 km gridded SWE magnitudes in a hindcast simulation of the 2019 water year that is independent of the training and testing data. Comparing model estimates to over 6200 observations, the model demonstrates a weighted RMSE of 15.4 cm, Kling-Gupta Efficiency metric of 0.86, and a percent bias of 0.71% across 23 snow-influenced regions in the western U.S. The model simulation produces peak SWE estimates within 10 cm for twenty of the twenty-three regions, demonstrating capability in effectively capturing regional snow accumulation processes. The demonstration of low-error ML workflows capable of providing near-real-time, spatially continuous SWE estimates at a high spatial resolution provides proof-of-concept and a foundation to effectively update snow state variables that drive water supply forecasts in snow-dominated regions.

Impact of revegetation and agricultural intensification on water storage variation in the Yellow River Basin

The Yellow River Basin (YRB) is experiencing a critical water resource shortage due to climate change and human activities. Thoroughly understanding water resource dynamics under the dual pressures of revegetation and agricultural intensification presents significant challenges. Development of satellite-based observations for vegetation and water storage has greatly facilitated large-scale and high-resolution watershed analysis, and enables understanding their dynamics. In this study, the effects of revegetation and agricultural intensification were assessed by incorporating multiple vegetation indices and phenological methods. Regression models were developed to determine how much water variation could be attributed to vegetation change driven by these human activities, using an expanded set of vegetation features to analyze seasonal water storage variation. The results indicate that both revegetation and agricultural intensification played important roles in the decline of water storage in the YRB and showed significant heterogeneity across the basin. In semi-humid and semi-arid regions, revegetation can explain over 60 % of groundwater reduction throughout the year, while in arid areas, the primary impact was particularly revealed in summer soil moisture, with the explanation rate reaching 70 %. Water storage in the downstream croplands decreased at a rate of 18 mm·yr−1 due to a combination of intensified irrigation and reduced precipitation. In the upstream croplands, crop structure changes and irrigation reduction led to a reduction in spring and winter water storage at a rate of 2 mm·yr−1. Consequently, the security of water, food, and ecosystems is challenged. This study benefits sustainable management of the YRB by providing comprehensive vegetation features and their impact on water storage.

Isotopic evidence for seasonal water sources in tree xylem and forest soils

Isotopic evidence for seasonal water sources in tree xylem and forest soils

Benchmarking multimodel terrestrial water storage seasonal cycle against Gravity Recovery and Climate Experiment (GRACE) observations over major global river basins

Abstract. The increasing reliance on global models for evaluating climate- and human-induced impacts on the hydrological cycle underscores the importance of assessing the models' reliability. Hydrological models provide valuable data on ungauged river basins or basins with limited gauge networks. The objective of this study was to evaluate the reliability of 13 global models using the Gravity Recovery and Climate Experiment (GRACE) satellite's total water storage (TWS) seasonal cycle for 29 river basins in different climate zones. Results show that the simulated seasonal total water storage change (TWSC) does not compare well with GRACE even in basins within the same climate zone. The models overestimated the seasonal peak in most boreal basins and underestimated it in tropical, arid, and temperate zones. In cold basins, the modeled phase of TWSC precedes that of GRACE by up to 2–3 months. However, it lagged behind that of GRACE by 1 month over temperate and arid to semi-arid basins. The phase agreement between GRACE and the models was good in the tropical zone. In some basins with major underlying aquifers, those models that incorporate groundwater simulations provide a better representation of the water storage dynamics. With the findings and analysis of our study, we concluded that R2 (Water Resource Reanalysis tier 2 forced with Multi-Source Weighted Ensemble Precipitation (MSWEP) dataset) models with optimized parameterizations have a better correlation with GRACE than the reverse scenario (R1 models are Water Resource Reanalysis tier 1 and tier 2 forced with the ERA-Interim (WFDEI) meteorological reanalysis dataset). This signifies an enhancement in the predictive capability of models regarding the variability of TWSC. The seasonal peak, amplitude, and phase difference analyses in this study provide new insights into the future improvement of large-scale hydrological models and TWS investigations.