Hydrological Variables Research Articles

Millennial hydrological variability in the continental northern Neotropics during Marine Isotope Stages (MISs) 3–2 (59–15 cal ka BP) inferred from sediments of Lake Petén Itzá, Guatemala

Abstract. Lake Petén Itzá (Guatemala) possesses one of the longest lacustrine sediment records in the northern Neotropics, which enabled study of paleoclimate variability in the region during the last ∼400 000 years. We used geochemical (Ti, Ca/(Ti+Fe) and Mn/Fe) and mineralogical (carbonates, gypsum, quartz, clay) data from sediment core PI-2 to infer past changes in runoff, lake evaporation, organic matter sources and redox conditions in the water column, caused by hydrological changes in the northern Neotropics during Marine Isotope Stages (MISs) 3–2. From 59 to 39 cal ka BP climate conditions were relatively wet, and the lake was marked by higher primary productivity and anoxic bottom waters. This wet environment was interrupted for two periods of possible low water level at 52 and 46 cal ka BP, when our data suggest higher evaporation, high terrestrial organic matter input and persistent oxic conditions. Between 39 and 23 cal ka BP, evaporation and input of terrestrial organic matter increased considerably, lake level declined, and lake bottom waters generally became oxic. These conditions reversed during the Last Glacial Maximum (23.5–18.0 cal ka BP), when runoff and lake productivity increased, and rising lake level caused bottom waters to again become anoxic. Comparison of our hydrologic proxy data with sea surface temperature anomalies between the eastern Pacific and the Caribbean suggests that changes in the intensity of the Caribbean Low-Level Jet (CLLJ) may have influenced long-term changes in runoff during MISs 3–2. Higher intensity of the CLLJ during the onset of MIS 3 and the LGM might have led to greater runoff into the lake, whereas the MIS 3–2 transition experienced a weaker CLLJ and consequently less runoff. A refined, high-resolution age–depth model for the PI-2 sediment core enabled us to identify millennial-scale Greenland interstadials (GIs) 14–2, Greenland stadials (GSs) 14–2 and Heinrich stadials (HSs) 5–1. In general, HSs and GSs were characterized by drier conditions. In contrast to GSs and HSs, GIs were characterized by greater runoff and overall wetter conditions, with the most pronounced GI peaks between 40 and 30 cal ka BP. Whereas GSs 9, 8, 7 and 6 began with abrupt increases in evaporation and ended with gradual increases in humidity, GSs 11 and 10 showed reversed patterns. The Lake Petén Itzá paleohydrology record, along with other regional paleoclimate records, led us to conclude that shifts in the position of the Intertropical Convergence Zone (ITCZ) altered moisture delivery to the lake on millennial timescales. During GSs and HSs, high evaporation from Petén Itzá (dry climate conditions) was associated with a more southerly position of the ITCZ, whereas wetter GIs prevailed during a more northerly ITCZ position. Although abrupt millennial-scale shifts in ITCZ and hydroclimate between GSs/HSs and GIs can be linked to instabilities in the Atlantic Meridional Overturning Circulation (AMOC), longer-term changes were additionally influenced by changes in atmospheric convection linked to modulations of the CLLJ in response to ΔSST between the equatorial Pacific and tropical Atlantic.

Hydrological variations in the Yellow River Source area (NE Tibetan Plateau) during the last 7.4 ka inferred from stable isotopes of ostracod valves

The availability of water resources in the Yellow River Source Area (YRSA) on the NE Tibetan Plateau (TP) is of great significance for the hydrology, ecology, and socioeconomics of the Yellow River Valley downstream. To better understand hydrological variations in the YRSA in the past, we investigated a 170-cm long sediment core from Xingxinghai (XXH), a lake in the YRSA, with robust chronology. The stable oxygen and carbon isotopes of ostracod valves, element concentrations of the bulk sediments, and magnetic susceptibility were analyzed to infer lake-level changes and their driving factors during the last 7.4 cal ka BP. The results show that relatively high lake levels existed in the initial period of the record from 7.4 to 7.0 cal ka BP, and lower levels afterwards until 6.2 cal ka BP. During 6.2–3.5 cal ka BP, highest lake levels prevailed at XXH, followed by lowest levels afterwards. A slight lake-level recovery is inferred at 2.4 cal ka BP. Comparison with other studies from the NE TP shows that the period of highest lake levels at XXH during the second half of the middle Holocene is a distinct phenomenon of wetter conditions in the YRSA. These higher lake levels across the region apparently resulted from higher effective moisture due to insolation-driven cooling and reduced evaporation. Our study shows that ongoing and future global warming may lead to a significant decrease of lake levels and surfaces in the YRSA, and to a reduction in Yellow River discharge and water availability downstream.

Water distribution based on SAR and optical data to improve hazard mapping

Climate projections foresee intense precipitation and long-term drought events is increasing with consequent rapid changes in surface water bodies in a short period. In areas with drastic hydrological changes, achieving accurate and rapid mapping of these phenomena in combination with hydrologic variability characteristics is a key of effective emergency management and disaster risk reduction plans. This study presents an automatic method for mapping drought and flood hazards, particularly in regions with significant hydrological changes. We use Sentinel-1/2 and Landsat data to extract surface water and classify permanent and seasonal water bodies in historical periods, which serve as the basis for identifying flood or drought areas. The water extraction method combines index-based analysis for optical data and the region-Otsu method for radar data, ensuring accurate identification of water. The effectiveness of this approach is demonstrated through comparisons with existing products in Poyang Lake (China), the Po River Plain (Italy), and the Indus River Plain (Pakistan). Findings show a high similarity between the two, and our results can provide more specific details. Our method is particularly well-suited for areas with fluctuating hydrological conditions, can also map quickly without optical data. By effectively identifying areas affected by drought and flood hazards while mitigating errors from natural hydrological dynamics, this methodology contributes valuable insights to enhance emergency management and disaster risk reduction plans.

Holocene precipitation hydrogen isotopic values on Nilgiri Plateau (southern India) suggest a combined effect of precipitation amount and transport paths

Paleoclimate investigations of the peat deposits in the Nilgiri Plateau, an important paleoclimate archive of India, are mainly restricted to the carbon isotope composition ((δ13C) of plant-derived materials and pollen studies. However, it is unclear whether these proxies reflect past variability in temperature or hydrology. Here, we report the hydrogen and carbon isotopic variability of n-alkanoic acid of chain length 28 (δDC28and δ13CC28, respectively) and demonstrate that the peatland leaf wax hydrogen isotopes provide a sensitive record of past hydrology. The decoupling of δ13CC28and δD of vegetation-corrected rain during the Holocene indicate that δD of the leaf wax compounds mainly respond to past hydrological variability whereas δ13C variations might reflect the temperature-controlled variability of C3 and C4 vegetation. Conforming with the other paleoclimate records from the region, the δDC28variations showed a reducing precipitation trend since the early Holocene. However, a large amplitude of reconstructed δD of rain (~44‰) during the Holocene indicated changes in the moisture source and trajectory could be an additional factor contributing to the orbital-scale δD variability of proxies from the Indian region.

SuDS as a climate change adaptation strategy: Scenario-based analysis for an urban catchment in northern Italy

Urbanization and climate change effects on precipitation are the leading cause of flood risk increase in urban settlements. Sustainable Drainage Systems (SuDS) are effective strategies to improve city resilience toward flooding but usually they are designed to meet performance standards based on historical climate settings. The reported modeling scenario-analysis strives for investigating the potential of SuDS as adaptation strategy to a projected medium-term climate change affecting Sesto Ulteriano (Milan, Northern Italy). Two drainage models, a benchmark scenario reproducing the drainage network current configuration and a designed scenario involving SuDS retrofitting, were tested under historical (observed) and expected future precipitation extremes (projected) to assess and compare hydrological and hydraulic variables essential for the definition of a Precipitation Variability Adaptation Index (PVAI), which measures the adaptation strategy performance. Results indicate that even though SuDS were confirmed as effective flood control measures, their adaptability potential is affected by changes in rainfall severity indeed. In particular, the multi-variable PVAI, a global index able to take into account the effects of different determinants for an overall assessment of SuDS potential as adaptation strategies to climate changes, decreases when rainfall frequency decreases, among the same climate scenario, or when rainfall intensity increases due to climate changes.

A long-term monthly surface water storage dataset for the Congo basin from 1992 to 2015

Abstract. The spatio-temporal variation of surface water storage (SWS) in the Congo River basin (CRB), the second-largest watershed in the world, remains widely unknown. In this study, satellite-derived observations are combined to estimate SWS dynamics at the CRB and sub-basin scales over 1992–2015. Two methods are employed. The first one combines surface water extent (SWE) from the Global Inundation Extent from Multi-Satellite (GIEMS-2) dataset and the long-term satellite-derived surface water height from multi-mission radar altimetry. The second one, based on the hypsometric curve approach, combines SWE from GIEMS-2 with topographic data from four global digital elevation models (DEMs), namely the Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Observing Satellite (ALOS), Multi-Error-Removed Improved Terrain (MERIT), and Forest And Buildings removed Copernicus DEM (FABDEM). The results provide SWS variations at monthly time steps from 1992 to 2015 characterized by a strong seasonal and interannual variability with an annual mean amplitude of ∼101±23 km3. The Middle Congo sub-basin shows a higher mean annual amplitude (∼71±15 km3). The comparison of SWS derived from the two methods and four DEMs shows an overall fair agreement. The SWS estimates are assessed against satellite precipitation data and in situ river discharge and, in general, a relatively fair agreement is found between the three hydrological variables at the basin and sub-basin scales (linear correlation coefficient >0.5). We further characterize the spatial distribution of the major drought that occurred across the basin at the end of 2005 and in early 2006. The SWS estimates clearly reveal the widespread spatial distribution of this severe event (∼40 % deficit as compared to their long-term average), in accordance with the large negative anomaly observed in precipitation over that period. This new SWS long-term dataset over the Congo River basin is an unprecedented new source of information for improving our comprehension of hydrological and biogeochemical cycles in the basin. As the datasets used in our study are available globally, our study opens opportunities to further develop satellite-derived SWS estimates at the global scale. The dataset of the CRB's SWS and the related Python code to run the reproducibility of the hypsometric curve approach dataset of SWS are respectively available for download at https://doi.org/10.5281/zenodo.7299823 and https://doi.org/10.5281/zenodo.8011607 (Kitambo et al., 2022b, 2023).

500-year reconstruction of Dez River discharge in southwestern Iran from tree rings

Long-term river discharge records are essential for water resource management, especially in semi-arid regions. However, the short instrumental records in Iran and the limited availability of local paleoclimate proxies to extend these records hinder our understanding of the full range of hydrologic variability. To help overcome these constraints, we have used annually-resolved tree-ring width chronologies from a network of sites spanning the eastern Mediterranean to the Tibetan Plateau to develop the first multi-centennial (∼500 years) river discharge reconstruction for the Dez River basin, a catchment that supports substantial water extraction in southwestern Iran. A Hierarchical Bayesian Regression (HBR) model accompanied by parsimonious predictor selection was used to derive the most robust model output. The selection method retained 26 precipitation-sensitive tree-ring width chronologies, mainly from regions with similar hydroclimate features to the Dez River basin. The resulting reconstruction of the most downstream gauging station accounts for 62% of the variance in river discharge observations, closely matching known historical events and other regional reconstructions. Notably, the underlying recurrence pattern of extreme flow conditions suggests floods were more frequent than low flow extremes (i.e., droughts) during much of the 18th and early 19th centuries. In contrast, during the early 17th century, droughts were more frequent. Worryingly, the frequency of both floods and droughts has increased simultaneously since the beginning of the twentieth century. Our reconstruction could be used to assess current water allocation strategies under conditions similar to past extended dry periods. When combined with future projections, they can help provide more robust assessment scenarios for water management.

Responses of the Siberian crane population to temporal and spatial hydrological variations in stopover sites in northeast China

The Siberian crane (Grus leucogeranus), a critically endangered species, is used as an indicator species for ecosystem stability because of its sensitivity to the environment. The distribution of Siberian cranes has shown dramatic fluctuations on an order of magnitude at the main stopover site (Jilin Momoge National Nature Reserve) since the present century, which poses a great challenge to the conservation of endangered species. Research on the threshold values of key factors of the Siberian crane in stopover habitats and the relationship between the Siberian crane and hydrological temporal and spatial fluctuations can promote endangered species conservation. The response of population changes and distribution patterns of Siberian cranes to hydrological temporal and spatial patterns of wetlands was analyzed from 1984 to 2020. The results showed that population changes and distribution patterns of Siberian cranes were highly correlated with hydrological temporal and spatial patterns in the study area, and their variation trend was the same. However, there were obvious stage migration changes in the core activity area of the Siberian crane (1984–2000, 2001–2010 and 2010–2020). The habitat characteristics of fragmentation, dispersion and complex shape of water bodies changed from steady to dramatic fluctuations and then leveled off in the core activity area of Siberian cranes during the 37 years of migration transit. This was closely related to the early agricultural reclamation and the later ecological water replenishment project in the study area. In addition, the trend of the water pattern index of the spatial distribution of Siberian crane active habitat was also closely related to their habitat selection preference. When the perimeter area fractal dimension of water (1.27–1.40), landscape division index (0.73–0.95) and aggregation index (80.97–91.36) are within a certain stable threshold, the stopover habitat of the Siberian crane is suitable. This study will provide scientific guidance for the conservation of the Siberian crane population and the development of wetland restoration projects.

Energy efficiency in transient surface runoff and sediment fluxes on hillslopes – a concept to quantify the effectiveness of extreme events

Abstract. Surface runoff over time shapes the morphology of the landscape. The resulting forms and patterns have been shown to follow distinct rules, which hold throughout almost all terrestrial catchments. Given the complexity and variety of the Earth's runoff processes, those findings have inspired researchers for over a century, and they resulted in many principles and sometimes proclaimed laws to explain the physics that govern the evolution of landforms and river networks. Most of those point to the first and second law of thermodynamics, which describe conservation and dissipation of free energy through fluxes depleting their driving gradients. Here we start with both laws but expand the related principles to explain the co-evolution of surface runoff and hillslope morphology by using measurable hydraulic and hydrological variables. We argue that a release of the frequent assumption of steady states is key, as the maximum work that surface runoff can perform on the sediments relates not only to the surface structure but also to “refueling” of the system with potential energy by rainfall events. To account for both factors, we introduce the concept of relative dissipation, relating frictional energy dissipation to the energy influx, which essentially characterizes energy efficiency of the hillslope when treated as an open, dissipative power engine. Generally, we find that such a hillslope engine is energetically rather inefficient, although the well-known Carnot limit does not apply here, as surface runoff is not driven by temperature differences. Given the transient and intermittent behavior of rainfall runoff, we explore the transient free energy balance with respect to energy efficiency, comparing typical hillslope forms that represent a sequence of morphological stages and dominant erosion processes. In a first part, we simulate three rainfall runoff scenarios by numerically solving the shallow water equations, and we analyze those in terms of relative dissipation. The results suggest that older hillslope forms, where advective soil wash erosion dominates, are less efficient than younger forms which relate to diffusive erosion regimes. In the second part of this study, we use the concept of relative dissipation to analyze two observed rainfall runoff extremes in the small rural Weiherbach catchment. Both flood events are extreme, with estimated return periods of 10 000 years, and produced considerable erosion. Using a previously calibrated, distributed physics-based model, we analyze the free energy balance of surface runoff simulated for the 169 model hillslopes and determine the work that was performed on the eroded sediments. This reveals that relative dissipation is largest on hillslope forms which relate to diffusive soil creep erosion and lowest for hillslope profiles relating to advective soil wash erosion. We also find that power in surface runoff and power in the complementary infiltration flux are during both events almost identical. Moreover, there is a clear hierarchy of work, which surface runoff expended on the sediments and relative dissipation between characteristic hillslope clusters. For hillslope forms that are more energy efficient in producing surface runoff, on average, a larger share of the free energy of surface runoff performs work on the sediments (detachment and transport) and vice versa. We thus conclude that the energy efficiency of overland flow during events does indeed constrain erosional work and the degree of freedom for morphological changes. We conjecture that hillslope forms and overland dynamics co-evolve, triggered by an overshoot in power during intermittent rainfall runoff events, towards a decreasing energy efficiency in overland flow. This implies a faster depletion of energy gradients during events and a stepwise downregulation of the available power to trigger further morphological development.

Integrating UAV data to explore the relationship between microtopographic variation and Spartina alterniflora expansion during its early invasion

As a major invasive species in coastal China, Spartina alterniflora (S. alterniflora) has seriously threatened the ecological functions of coastal wetlands. Based on multisource high-precision geospatial data obtained by unmanned aerial vehicles (UAVs), the shape and distribution of S. alterniflora patches were extracted and analysed by using object-oriented classification and the landscape pattern index in this study. Considering microtopographic variations, the trend-surface subregions were delimited. In this way, we examined the relationship between S. alterniflora distribution and expansion and the variation in microtopography and tidal hydrology in the early invasion stage. The results showed that the microtopography silted up acceleration from land to sea, and the inundation time was reduced significantly in the study area for 2020–2022. In 2020, the average area of S. alterniflora patches showed a decreasing trend from land to sea. The distribution of patches on both sides of the sea and land was relatively concentrated and dispersed in the middle. By 2022, S. alterniflora presented a rapid expansion, with the total area expanding from 8488.57 m2 to 18470.72 m2. With the variation in microtopography, the characteristics of S. alterniflora patch expansion presented regional differences: Due to the small microtopographic and tidal hydrological variations, the patches fluctuated outwards slightly (average variation distance was 0.93 m) near the dike. The patches in the seaside front area had an annular expansion, and the expansion speed positively correlated with the area of the original patch. In the central region, the small patches merged and filled to form an S. alterniflora belt, while a large number of new small patches appeared. In general, the heterogeneous environment formed by microtopographic variations was conducive to the colonization and growth of S. alterniflora, but the excessive slope could inhibit expansion. This study will provide a theoretical basis for coastal ecological management and rapid control of S. alterniflora expansion.

The relative importance of environmental heterogeneity and dispersal limitation on spatial patterns of phytoplankton communities varies across seasons

AbstractEnvironmental heterogeneity and dispersal limitation structure community assembly, but there is limited knowledge on how their effects vary on temporal scale. This study compiled a series of data, including phytoplankton, seawater depth and chemistry from 104 sites, to explore the spatial patterns and their drivers of phytoplankton communities in the Beibu Gulf and adjacent waters, located in the northwestern South China Sea. Our results showed that (1) the beta diversity values ranged from 0.2 to 0.84 (0.5 average), and turnover dominated. (2) The relative contributions of environmental heterogeneity and dispersal limitation to beta diversity varied seasonally, wherein former dominated in summer and winter, latter dominated in spring and autumn. (3) Hydrologic distance, pH, alkalinity, and total nitrogen were the crucial factors shaping the beta diversity pattern. This study detailed the temporal variation of the relative roles of environmental heterogeneity and dispersal limitation in shaping the patterns of phytoplankton communities, which may reflect the dynamical nature of ocean, characterized by seasonal variation in wind and hydrology. It also clarified the intrinsic linkage between these two processes in the ocean, which provides a novel insight to understanding the ocean biogeography and informs decision making for area‐based marine biodiversity conservation.

Application of coupling mechanism and data-driven models in the Hanjiang river basin

Widely confirmed and applied, data-driven models are an important method for watershed runoff predictions. Since decomposition methods such as time series decomposition cannot automatically handle the decomposition process of date changes and less consideration of influencing factors before decomposition, resulting in insufficient correlation analysis between influencing factors and forecast objects, we propose a method based on hydrological model decomposition to generate time series state variables (broadening the range of influencing factors to be considered). In this study, we constructed hydrological models wherein rainfall and other hydrological elements are decomposed into hydrological and hydrodynamic characteristic state variables to expand the range of the prediction factors. A data-driven model was then built to perform runoff predictions in the Han River Basin. The results showed that compared with the single prediction model, the prediction results based on the coupling model were superior, the performance evaluation grade of the coupling model was high, and the coupling model had a higher stability.

Hybrid Deep Learning and S2S Model for Improved Sub-Seasonal Surface and Root-Zone Soil Moisture Forecasting

Surface soil moisture (SSM) and root-zone soil moisture (RZSM) are key hydrological variables for the agricultural water cycle and vegetation growth. Accurate SSM and RZSM forecasting at sub-seasonal scales would be valuable for agricultural water management and preparations. Currently, weather model-based soil moisture predictions are subject to large uncertainties due to inaccurate initial conditions and empirical parameterization schemes, while the data-driven machine learning methods have limitations in modeling long-term temporal dependences of SSM and RZSM because of the lack of considerations in the soil water process. Thus, here, we innovatively integrate the model-based soil moisture predictions from a sub-seasonal-to-seasonal (S2S) model into a data-driven stacked deep learning model to construct a hybrid SSM and RZSM forecasting framework. The hybrid forecasting model is evaluated over the Yangtze River Basin and parts of Europe from 1- to 46-day lead times and is compared with four baseline methods, including the support vector regression (SVR), random forest (RF), convolutional long short-term memory (ConvLSTM) and the S2S model. The results indicate substantial skill improvements in the hybrid model relative to baseline models over the two study areas spatiotemporally, in terms of the correlation coefficient, unbiased root mean square error (ubRMSE) and RMSE. The hybrid forecasting model benefits from the long-lead predictive skill from S2S and retains the advantages of data-driven soil moisture memory modeling at short-lead scales, which account for the superiority of hybrid forecasting. Overall, the developed hybrid model is promising for improved sub-seasonal SSM and RZSM forecasting over global and local areas.

Trends, Shifting, or Oscillations? Stochastic Modeling of Nonstationary Time Series for Future Water‐Related Risk Management

AbstractHydrological time series often present nonstationarities such as trends, shifts, or oscillations due to anthropogenic effects and hydroclimatological variations, including global climate change. For water managers, it is crucial to recognize and define the nonstationarities in hydrological records. The nonstationarities must be appropriately modeled and stochastically simulated according to the characteristics of observed records to evaluate the adequacy of flood risk mitigation measures and future water resources management strategies. Therefore, in the current study, three approaches were suggested to address stochastically nonstationary behaviors, especially in the long‐term variability of hydrological variables: as an overall trend, shifting mean, or as a long‐term oscillation. To represent these options for hydrological variables, the autoregressive model with an overall trend, shifting mean level (SML), and empirical mode decomposition with nonstationary oscillation resampling (EMD‐NSOR) were employed in the hydrological series of the net basin supply in the Lake Champlain‐River Richelieu basin, where the International Joint Committee recently managed and significant flood damage from long consistent high flows occurred. The detailed results indicate that the EMD‐NSOR model can be an appropriate option by reproducing long‐term dependence statistics and generating manageable scenarios, while the SML model does not properly reproduce the observed long‐term dependence, that are critical to simulate sustainable flood events. The trend model produces too many risks for floods in the future but no risk for droughts. The overall results conclude that the nonstationarities in hydrological series should be carefully handled in stochastic simulation models to appropriately manage future water‐related risks.

A New Approach to Predict Tributary Phosphorus Loads Using Machine Learning- and Physics-Based Modeling Systems.

Tributary phosphorus (P) loads are one of the main drivers of eutrophication problems in freshwater lakes. Being able to predict P loads can aid in understanding subsequent load patterns and elucidate potential degraded water quality conditions in downstream surface waters. We demonstrate the development and performance of an integrated multimedia modeling system that uses machine learning (ML) to assess and predict monthly total P (TP) and dissolved reactive P (DRP) loads. Meteorological variables from the Weather Research and Forecasting (WRF) Model, hydrologic variables from the Variable Infiltration Capacity model, and agricultural management practice variables from the Environmental Policy Integrated Climate agroecosystem model are utilized to train the ML models to predict P loads. Our study presents a new modeling methodology using as testbeds the Maumee, Sandusky, Portage, and Raisin watersheds, which discharge into Lake Erie and contribute to significant P loads to the lake. Two models were built, one for TP loads using 10 environmental variables and one for DRP loads using nine environmental variables. Both models ranked streamflow as the most important predictive variable. In comparison with observations, TP and DRP loads were predicted very well temporally and spatially. Modeling results of TP loads are within the ranges of those obtained from other studies and on some occasions more accurate. Modeling results of DRP loads exceed performance measures from other studies. We explore the ability of both ML-based models to further improve as more data become available over time. This integrated multimedia approach is recommended for studying other freshwater systems and water quality variables using available decadal data from physics-based model simulations.

Temporal variations of chemical and isotopic compositions in the river draining the Changbaishan volcanic area: The role of hydrological changes and volcanic activities

AbstractVolcanic activities are known to release a substantial amount of deep carbon to the Earth's surface, thereby influencing the global carbon cycle on geological timescales. It is crucial to clarify the contribution of deep carbon and its changes under hydrological variations in rivers draining volcanic areas. This study reported the temporal variations of chemical compositions and isotopic compositions (δ13C and Δ14C) of dissolved inorganic carbon (DIC) from the Erdaobai River, which drains the Changbaishan volcanic area. The results showed that hydrothermal fluids had a considerable impact on the chemical and carbon isotopic compositions in this study area. The chemical mass balance model indicated that hydrothermal fluids composed 4.6 ± 0.7% of the water budget for the Erdaobai River. Additionally, the isotopic mixing model of Δ14CDIC showed that hydrothermal DIC accounted for 83.6 ± 1.9% and 40.8 ± 3.7% of the DIC budget for the upstream and downstream, respectively, and the proportion of hydrothermal contribution also varied in a narrow range in response to hydrological changes. However, the DIC and dissolved organic carbon fluxes showed significant seasonal variations, verifying the controlling role of hydrological processes. This study highlighted the impact of hydrological processes and deep carbon release on the carbon budget in the volcanic catchment, which is of great significance for understanding atmospheric CO2 balance and climate change.

Influence of Underlying Surface Datasets on Simulated Hydrological Variables in the Xijiang River Basin

Abstract Hydrological models play an important role in water resources management and extreme events forecasting, and they are sensitive to the underlying conditions. This study aims to evaluate the impact of different soil-type maps and land-use maps on hydrological simulations and watershed responses by applying the WRF-Hydro (Weather Research and Forecasting Model Hydrological modeling system) distributed hydrological model to the Xijiang River basin. WRF-Hydro runs for four different scenarios for the period 1992–2013. FAO (Food and Agriculture Organization) and GSDE (Global Soil Dataset for Earth System Science) soil-type maps, and MODIS (Moderate-Resolution Imaging Spectroradiometer) and CNLUCC (China Land Use Land Cover Remote Sensing Monitoring Dataset) land-use maps are used in this study. These soil-type maps and land-use maps are freely combined to form four scenarios. It is found that soil moisture and surface runoff are sensitive to soil-type maps, and absorbed shortwave radiation is found to be the least sensitive to soil-type maps. Absorbed shortwave radiation and heat flux are sensitive to land-use maps. The model performance of simulating soil moisture has increased when the soil-type map changes from FAO to GSDE and the land-use map changes from MODIS to CNLUCC for most stations. When the soil-type map changes from FAO to GSDE and the land-use map changes from MODIS to CNLUCC, the biases of simulating streamflow decrease. This study shows that the performance of the offline WRF-Hydro is significantly influenced by soil-type and land-use maps, and better simulation results can be obtained with more realistic underlying surface maps. Significance Statement The purpose of this study is to evaluate the impacts of land-use and soil-type maps on hydrological processes at the watershed scale by applying a distributed hydrological model WRF-Hydro model for the Xijiang River basin and reveal the importance of choosing land-use and soil-type maps. In this study, two soil-type maps and two land-use maps are used. It is found that soil moisture and surface runoff are sensitive to soil-type maps, and absorbed shortwave radiation and heat flux are sensitive to land-use maps. When using GSDE soil-type and CNLUCC land-use maps, the performance of the model is improved. The underlying conditions should be considered when applying the models in practice.

Spatial and Temporal Dynamics of Key Water Quality Parameters in a Thermal Stratified Lake Ecosystem: The Case Study of Lake Mead

Lake Mead located in the Arizona–Nevada region of the Mohave Dessert is a unique and complex water system whose flow follows that of a warm monomictic lake. Although monomictic lakes experience thermal stratification for almost the entire year with a period of complete mixing, the lake on occasion deviates from this phenomenon, undergoing incomplete turnovers categorized with light stratifications every other year. The prolonged drought and growing anthropogenic activities have the potential to considerably impact the quality of the lake. Lake Mead and by extension the Boulder Basin receive cooler flow from the Colorado River and flow with varying temperatures from the Las Vegas Wash (LVW), which impacts its stratification and complete turnovers. This study analyzes four key water quality parameters (WQPs), namely, total dissolved solids (TDS), total suspended solids (TSS), temperature, and dissolved oxygen (DO), using statistical and spatial analyses to understand their variations in light of the lake stratifications and turnovers to further maintain its overall quality and sustainability. The study also evaluates the impacts of hydrological variables including in and out flows, storage, evaporation, and water surface elevation on the WQPs. The results produced from the analysis show significant levels of TDS, TSS, and temperature from the LVW and Las Vegas Bay regions compared with the Boulder Basin. LVW is the main channel for conveying effluents from several wastewater treatment facilities into the lake. We observed an increase in the levels of TDS, TSS, and temperature water quality in the epilimnion compared with the other layers of the lake. The metalimnion and the hypolimnion layer, however, showed reduced DO due to depletion by algal blooms. We observed statistically significant differences in the WQPs throughout various months, but not in the case for season and year, an indication of relatively consistent variability throughout each season and year. We also observed a no clear trend of influence of outflows and inflows on TDS, temperature, and DO. TSS concentrations in the lake, however, remained constant, irrespective of the inflows and outflows, possibly due to the settling of the sediments and the reservoir capacity.

Assessing the impacts of reservoirs on downstream hydrological frequency based on a general rainfall-reservoir index

When large reservoirs are built and put into operation, the downstream hydrological processes will be altered significantly, and ecology and agricultural irrigation water of the basin will be affected to some extent. The reservoir index (RI) and the sediment trapping efficiency (TE) of reservoirs are defined to quantify the reservoir impacts on the water flow and sediment by considering the static storage capacity. However, the regulating effect of reservoirs on hydrological variables is not only related to static storage capacity, but also to dynamic reservoir operation. Thus, in this paper, a general rainfall-reservoir index (GRRI) is developed by coupling reservoir regulation indicator (RR, including RI and TE) and effective rainfall affecting the dynamic operation of reservoirs, and the GRRI is used as the covariate to carry out the nonstationary frequency analysis of flood (Q) and annual sediment load (S) at Gaochang (GC) station in Min River, Wulong (WL) station in Wu River, Ankang (AK), Huangjiagang (HJG) and Huangzhuang (HZ) station in Han River, and Cuntan (CT) station on the main stream of the upper Yangtze River. It is found that Q and S at six stations have obvious changes induced by reservoirs, the mean of Q decreases by 22.8%–60.6%, and S drops by 47.7%–89.5% after the change-point of time series. The nonstationary probability distribution models with GRRI as the covariate have better fitting effects than nonstationary models with RR as the covariate. With the incorporation of the impacts of effective rainfall, the GRRI can more accurately capture the occurrence of nonstationarity in the downstream hydrological frequency. These results might be helpful for exploring the impact mechanism of the reservoir regulation on the downstream hydrological variables as well as ecological management of basin.

Implications of changing trends in hydroclimatic and water quality parameters on estuarine habitats in the Gulf Coastal Plain

Florida’s low elevation and geographic location make it particularly vulnerable to climate change effects such as sea level rise, increased intensity and frequency of storm events, and altered precipitation. Climate change is expected to exacerbate hydrological cycling with potential widespread implications for estuarine habitats that thrive under specific salinity regimes. We used historical data from sites in the eastern Gulf Coastal Plain, USA to examine trends and trend variability of several climatic, hydrologic, and estuarine water quality variables which have implications on seagrass and oyster habitat extent in downstream estuarine environments. We analyzed temperature, precipitation, low-flow and high-flow metrics (including the highest or lowest daily, 7-day average, and 30-day average) for each season annually over the period 1985–2020. We also analyzed estuarine water clarity metrics and salinity within waterbody segments of four estuary systems within the study area. Hydroclimate results showed that temperature increased at most sites. While there was variation in streamflow, the overall trend was declining streamflow. Declining trends were observed in most water clarity metrics, indicating improved clarity, especially in winter. Salinity generally declined across the study area. While overall streamflow decreased, main river stems to the estuaries had increasing trends in maximum streamflow characteristics, likely contributing to the decrease in estuarine salinity across the region. These results indicate that trends in streamflow (both magnitude and timing) in the watershed affect downstream estuarine water quality. These results have important implications on seagrass and oyster restoration and management efforts in the region, indicating that it is important to understand changing climatic and hydrologic conditions and how they may impact the estuarine resources.