Hydrological Variables Research Articles

Enhancing Runoff Simulation Using BTOP-LSTM Hybrid Model in the Shinano River Basin

Runoff simulation is an ongoing challenge in the field of hydrology. Process-based (PB) hydrological models often gain unsatisfactory simulation accuracy due to incomplete physical process representations. While the deep learning (DL) models demonstrate their capacity to grasp intricate hydrological response processes, they still face constraints pertaining to the representative training data and comprehensive hydrological observations. In order to provide unobservable hydrological variables from the PB model to the DL model, this study constructed hybrid models by feeding the output variables of the PB model (BTOP) into the DL model (LSTM) as additional input features. These variables underwent feature dimensionality reduction using the feature selection method (Pearson Correlation Coefficient, PCC) and the feature extraction method (Principal Component Analysis, PCA) before input into LSTM. The results showed that the standalone LSTM performed well across the basin, with NSE values all exceeding 0.70. The hybrid models enhanced the simulation performance of the standalone LSTM. The NSE values increased from 0.75 to nearly 0.80 in a sub-basin. Lastly, if the BTOP output is directly fed into LSTM without feature dimensionality reduction, the model’s accuracy significantly decreases due to noise interference. The NSE value decreased by 0.09 compared to the standalone LSTM in a sub-basin. The results demonstrated the effectiveness of PCC and PCA in removing redundant information within hydrological variables. These findings provide new insights into incorporating physical information into LSTM and constructing hybrid models.

Impact of greenspaces and water bodies on hydrological processes in an urbanizing area: A case study of the Liuxi River Basin in the Pearl River Delta, China

The rapid shifts in landuse and the frequency of flooding events brought by urbanization significantly impact the urban basins, which means that optimizing landscape patterns is crucial for improving urban hydrology. Therefore, the Liuxi River Basin (LRB), an important river in the Pearl River Delta region, was utilized as an example in the study to examine how the landscape configuration of greenspaces and water bodies influences the hydrological components. However, there are few studies on landscape hydrology in the Pearl River Delta (PRD) and the spatial linkages between landscape metrics and hydrological components are not clear. Based on this, this paper aims to explore the mathematical and spatial associations between landscape configuration and hydrological variables in the PRD region. The SWAT model was constructed to simulate hydrological components based on landuse changes from 1980 to 2018, and regressions were established for landscape pattern indices, water morphology parameters, and hydrological components using stepwise multiple linear regression and geographically weighted regression (GWR). According to the results, the LRB continued to develop, although the greenspaces spread out and changed shape considerably. The influence of water bodies on the hydrological component is mainly in evapotranspiration, and some three-dimensional parameters such as relief ratio favor surface runoff. In the case of greenspace, the runoff is less in connected and wide farmland areas. Irregular forest land will promote water yield but suppress evapotranspiration, and the effect of forest’s shape on hydrological components is more obvious in upstream areas because the regression coefficients in this range are higher, such as 90.27. Methodologically, we found that for water yield, the accuracy of GWR was significantly higher (R2 = 0.814) than that of multiple linear regression (R2 = 0.243). In addition, understanding land use changes before conducting landscape hydrology studies can help make the results more interpretable. We supplemented the relevant research gaps in the PRD region and revealed the spatial correlation between landscape metrics and the hydrological cycle. These findings can aid urban planners in improving their comprehension of hydrological processes and landscape design, leading to more sustainable management of the urban basins.

Assessing the Impact of Climate and Land-Use Changes on the Hydrologic Cycle Using the SWAT Model in the Mun River Basin in Northeast Thailand

Climate change (CC) and land-use change (LUC) will alter a basin’s hydrological processes and water balance. Quantifying their significance is imperative in formulating appropriate countermeasures and management plans. This study assesses projected changes in hydrological variables under CC and LUC scenarios to provide multi-dimensional insight into water balance relevant to an agricultural watershed in Northeast Thailand. The soil and water assessment tool (SWAT) is utilized to simulate hydrological variables (evapotranspiration, soil moisture, surface runoff, and water yield) for the baseline (1981–2010) and the near-future (2021–2050) after calibrating the model. CC projections considering the CMIP6 model ensemble for the high-emission scenario (SSP5-8.5) show the annual rainfall may not change significantly (0.5% increase compared to baseline). However, the surface runoff will likely increase by 33% due to the projected increase in rainfall intensity and extremes. The increase in urban areas due to LUC for the business as usual (BAU) scenario is also expected to boost surface runoff by 38–87%. Similarly, the LUC scenario with forest expansion will increase evapotranspiration by up to 1.3%. While CC is anticipated to raise water yield by 11%, LUC may reduce it by 6%. Under the combined scenario, the yield is expected to increase by 8%, indicating CC as the dominating driver. The results show that although CC governs the runoff from the basin, land-use change will also impact flows at a monthly scale. Importantly, soil moisture in the future will decrease (8–9%) under both stressors, which will reverberate in the basin’s agricultural livelihood and socioeconomic settings unless the appropriate adaptation measures are implemented.

Three-dimensional hydrological thresholds to predict shallow landslides

Past studies have focused on the importance of hydrological variables in analyzing landslide initiation condition. Even though precipitation is the main driver of shallow landslides and debris flows, use of only rainfall-based parameters has shown some limitations. Soil moisture has been used widely to improve threshold detection capabilities. Since soil moisture directly reflects the wetness status of the ground, it can be used to identify pore pressure fluctuations more effectively. This study used rainfall and soil moisture simultaneously to capture landslide initiation conditions in detail. Results showed that continued rainfall on the day of landslide leaded to a sudden increase in soil moisture, and that soil moisture increments (∆SM) were positive in 155 out of 170 landslide cases (91%). Two simple thresholds (daily precipitation over 40 mm, ∆SM over 0) and daily precipitation (P), Antecedent Precipitation Index (API), ∆SM-based three-dimensional threshold planes having 5%, 20% probability levels were applied and compared. With respect to false alarms (FA), P-based threshold was most effective among the single thresholds (FA ranging from 24 to 28 from September 2016 to December 2019 at five validation locations). Combining P- and ∆SM-based thresholds, FA reduced without compromising the detection accuracy (2 to 3 reduction in FA). Additionally combining three-dimensional threshold with 20% probability level, FA reduced significantly (ranging from 12 to 16), at the cost of two detection failures. These findings demonstrate the need for combining precipitation and soil moisture to determine landslide thresholds.

Characterizing CONUS-wide spatio-temporal changes in daily precipitation, flow, and variability of extremes

The hydrological cycle strongly relies on the spatial and temporal variability of precipitation, which has been considerably affected by climate change. Focusing on CONUS, this research extended the analysis by evaluating the variability of daily concentrations of various components of the hydrological cycle in terms of the Gini index and its potential effects on floods and droughts by integrating historical data with an existing database of extreme events. Differences were found in the geographical location of significant correlations among the hydrological variables investigated, implying that the impacts of variability are neither spatially nor temporally uniform. The work also showed the importance of selecting thresholds of precipitation and flow to define “extreme” events. We identified a precipitation deficit threshold at the 5th percentile and an extreme precipitation threshold at the 95th percentile as the most representative. Moreover, for flood, the 95th percentile and for hydrological drought, the 5th percentile were the most representative. Spatially the study showed that, the concentration of precipitation deficit and evapotranspiration had a major impact on the occurrence of hydrological hazards, such as droughts and floods.

Geospatial modeling of hydrological ecosystem services in an ungauged upper Yamuna catchment using SWAT

Hydrological ecosystem services (HES) are vital for resource allocation, conservation prioritization, and climate change adaptation. However, research gaps persist due to limited understanding of complex hydrological systems and inadequate ground-station data, especially in ungauged watersheds with complex terrains. The present study addresses these gaps by estimating and mapping HES descriptors using regionalization techniques in an ungauged Aglar watershed. Additionally, it conducts a temporal analysis of hydrological fluxes using Soil and Water Assessment Tool (SWAT). Aglar is a constituent sub-watershed of a gauged watershed named Bausan having two discharge observation stations located at Naugaon and Bausan sites in Uttarakhand, India. A nested parameterization approach was adopted to represent the hydrological variabilities over the Bausan watershed with one outlet at Aglar. The stream flow derived from the calibrated Bausan watershed was used as synthetic observations in SWAT model setup for Aglar and found to yield very good statistical results. Calibration yielded coefficient of determination (R2) = 0.91, Nash Sutcliff Efficiency (NSE) = 0.91, and standardized root mean square error (RSR) = 0.29, while the validation yielded R2 = 0.68, NSE = 0.50, and RSR = 0.37. Parameters related to base flow or stream flow were the most sensitive in the model's output. Water balance analysis reveals 36% of precipitation transformed into stream flow, with direct runoff accounting for 72% and base flow for 28%. Forest cover contributed approximately 50.68% of precipitation through evapotranspiration. The study identifies a maximum sediment load of approximately 26 t/ha, indicating fragility of the landscape. Non-parametric tests such as Mann Kendall and Sen's slope indicated increasing trends in surface runoff, lateral flow, water yield, and groundwater recharge. The analysis of the empirical cumulative distribution function demonstrated that all hydrological components exhibit trends similar to precipitation. Spatially and temporally, variations in HES provisioning were observed, with forests surpassing non-forest areas. These findings emphasize the value of HES descriptors in analyzing spatiotemporal changes in HES provisioning and offer valuable insights for policymakers for future policy dialogues. This study lays the groundwork for further investigations into the hydrological processes of ungauged watersheds.

Bayesian model averaging by combining deep learning models to improve lake water level prediction

Water level (WL) is an essential indicator of lakes and sensitive to climate change. Fluctuations of lake WL may significantly affect water supply security and ecosystem stability. Accurate prediction of lake WL is, therefore, crucial for water resource management and eco-environmental protection. In this study, three deep learning (DL) models, including long short-term memory (LSTM), the gated recurrent unit (GRU), and the temporal convolutional network (TCN), were used to predict WLs at five stations of Poyang Lake for different forecast periods (1-day ahead, 3-day ahead, and 7-day ahead). The forecast results of the three DL models were synthesized through Bayesian model averaging (BMA) to improve prediction accuracy, and Monte Carlo sampling method was used to calculated the 90 % confidence intervals to analyze the model uncertainty. All the three DL models achieved satisfactory prediction accuracy. GRU performed best in most forecast scenarios, followed by TCN and LSTM. None of the models, however, consistently provided the optimal results in all forecast scenarios. Lake WL prediction accuracy of BMA had a further improvement in metrics of NSE and R2 in 80 % of the forecast scenarios and ranked at least top two in all forecast scenarios. The uncertainty analysis showed that the containing ration (CR) values were above 84 % while the relative bandwidth (RB) maintained reliable performance over the 7-day ahead prediction. The proposed framework in the present study can realize satisfactory WL forecast accuracy while avoiding complex comparison and selection of DL models, and it can also be easily applied to the prediction of other hydrological variables.

What causes site-specific intra-event variations of stable isotopes in precipitation in Beijing?

Stable oxygen isotope composition (δ18Op) and deuterium excess (dp) in precipitation carry important climate and hydrological information on the history of water from evaporation, transport to precipitation. This study aims to quantify the effect of potential factors on site-specific intra-event δ18Op and dp dynamics during three precipitation events in a mid-latitude region Beijing, China. The tested factors included moisture source (Ms) and upstream rainout fraction (Up) obtained by HYSPLIT backward trajectory model, radar reflectivity factor (Ze) that corresponds to raindrop size by weather radar, sub-cloud evaporation fractionation (fe) by an isotope-evaporation model, local precipitation amount (Lp) and precipitation intensity (I) by rain gauge, and relative location of the sampling sites (downwind or upwind). Intra-event δ18Op had an amplitude of 10.3‰, and 84% of the variance could be explained, in the descending order of importance, by Ms, site, Ze, fe and I. Intra-event dp had an amplitude of 16.5‰, and 57% of the variance could be explained by fe, site, Ms and I, in the descending order of importance. Absolute values of intra-event δ18Op and dp differed between two sites, 30 km apart, by up to 4.7‰ and 9.7‰, respectively. This site-specificity in δ18Op and dp was mainly caused by differences in rainout effects and I. This work provides new insights into separating the effects of various climatic and hydrological variables on high-frequency dynamics of δ18Op and dp.

The uncertainty associated with the use of copulas in multivariate analysis

ABSTRACT The dependency structure between hydrological variables is of critical importance to hydrological modelling and forecasting. When a copula capturing that dependence is fitted to a sample, information on the uncertainty of the fit is needed for subsequent hydrological calculations and reasoning. A new method is proposed to report inferential uncertainty in a copula parameter. The method is based on confidence curves constructed with the use of a pseudo maximum likelihood estimator for the copula parameter. The method was tested on synthetic data and then used as a tool in two hydrological examples. The first examines the probability of major floods in two locations on the Rhine River and its tributaries in the same calendar year. In the second example, rainfall–runoff from a karst region in Tunisia was analysed to determine a confidence interval for the delay between precipitation and runoff.

Water productivity maximization and ecosystem monitoring to estimate tourism economic value

Abstract Water supply from a common pool resource based on productivity indicators for different uses is one of the goals of planning in dry areas. Productivity indicators are defined based on time, geographical location and hydrological conditions in the form of food security, economic benefits and ecosystem restoration. This study was conducted in order to evaluate the contrast between economic criteria and food security in the exploitation of water resources in Lu'an city in Anhui province of China. Probabilistic modeling based on the prediction of uncertain values using the Latin hypercube technique was used for hydrological variables and water resources. The method of data mining and trend analysis of dependent variables was also simulated to estimate economic values in the water cycle. Statistical information of 32 years from 1991 to 2022 has been collected and used as an annual average per population. The results showed that the economic value of water consumption in the tourism industry has increased compared to agriculture. The total water provided for food security is equal to 6.5 m3 per person, the excess of which can be allocated to other uses through weighting indicators based on ecosystem and quality.

Blue and green water accounting for climate change adaptation in a water scarce river basin

Understanding how climate change influences the portioning of rainfall into blue water (BW) and green water (GW) is vital for establishing adaptive water management strategies in water-scarce regions. This study presents a conceptual framework to investigate climate change impacts on blue and green water accounting by integrating a hydrological model with a multi-criteria decision-making approach to prioritize watersheds for climate change adaptation. A Soil and Water Assessment Tool (SWAT) is adopted to investigate the hydrologic variability of BW and GW for baseline (1995–2019) and future (2020–2100) periods in a semi-arid Dharoi catchment of the Sabarmati River basin, India. The future climate data, derived from five general circulation models, are ensembled using a multi-model ensemble mean approach under two RCP (representative concentration pathways) scenarios. Several non-parametric tests are employed to ascertain statistically significant changes in the hydrologic variables between future and baseline periods. Further, Analytic Hierarchy Process (AHP) is applied considering seven attributes related to climate change, land use, and demographic parameters for watershed prioritization. Higher weights are assigned to the climate change attributes, followed by land use and demographic attributes. The SWAT results indicate a decrease in rainfall, BW, and GWS (green water storage) and an increase in temperature and GWF (green water flow) while traversing from the beginning of 2040 till the end of the 21st century under both RCPs. Thus, 2040 is established as a ‘tipping point’ for climate-induced changes in the Dharoi catchment, wherein a distinct reversal in the hydroclimatic regime is evident before and after 2040. Further, significant warming is observed by the end of 21st century under RCP4.5 and RCP8.5, contributing to a reduction in rainfall (up to 24.8%) and a decline in BW (up to 78.8%) across two-thirds of the watersheds in the Dharoi catchment. The AHP analysis identifies three out of ten watersheds as vulnerable and assigned ‘high priority’ for implementing adaptation measures to counter climate change-induced water scarcity.

Unravelling the Role of Vegetation Dynamics in the Execution of ArcSWAT Hydrological Modeling for Cumulative Streamflow of a Tibetan Watershed

Monitoring vegetation change and hydrological variation is crucial as they are useful means of appraising the ecological environment and managing water resources in water-resource-sensitive regions. The leaf area index (LAI) describes water consumption in hydrologic processes and is an important vegetation variable for water budgeting in catchments. As part of the Soil and Water Assessment Tool (SWAT), LAI is a significant parameter, which links vegetation dynamics with the hydrological cycle. In the current study, we have aimed to describe the Lhasa River (LR) cumulative streamflow based on simulation scenarios obtained with the SWAT model. After dispensing a heterogeneous LAI time series developed by MODIS NDVI as a source of data at the HRU level (SWAT-synthetic LAI scenario), the study has produced a better representation of LR cumulative streamflow in terms of the selected evaluation criteria, encompassing the SWAT-baseline (SWAT-B scenario)-simulated and SWAT-built-in LAI-influenced (SWAT-LAI scenario) LR cumulative streamflow. The study has revealed a close relationship between the observed and the SWAT-SLAI-scenario-generated LR streamflow, with a similar MK trend for the study time span. The LAI has been found to share a close relationship with LR streamflow, as both the LAI and LR streamflow are found to be influenced by the rainfall received in the Lhasa River Basin (LRB). The study is instrumental in understanding the association between LR streamflow, vegetation change, and the climatic conditions of the Lhasa River Basin (LRB).

Fecal bacteria contamination in the Adriatic Sea: Investigating environmental factors and modeling to manage recreational coastal waters

This study is based on assessing fecal indicator bacteria contamination along meteorological, hydrological and physical-chemical variables after high rainy events during the summer period. The study focused on four different coastal sites in the western and eastern Adriatic coast characterized by various geomorphological and hydrological features, levels of urbanization and anthropogenic pressures, with the aim of finding appropriate and effective solutions to ensure the safety and sustainability of tourism and public health. Detailed in-situ survey revealed a wide range of fecal indicator bacterial (FIB) across the different river mouths with concentrations of E. coli ranging from 165 to 6700 CFU 100 mL−1. It was found that nitrogen compounds track microbial load and acted as tracers for fecal contaminants. Further, a modelling tool was also used to analyze the spatial and temporal distribution of fecal pollution at these coastal sites. The integrated monitoring through high frequent survey in river waters and modeling framework allowed for the estimation of fecal indicator bacterial load at the river mouth and examination of fecal pollutant dispersion in recreational waters, considering different scenarios of fecal dispersion along the coast. This study formed the basis of a robust decision support system aimed at improving the management of recreational areas and ensuring the protection of water bodies through efficient management of bathing areas.

Temperature series analysis of the Hirfanli Dam Basin with the Mann-Kendall and Sequential Mann-Kendall tests

Global warming is a result of the greenhouse effect caused by the gases released into the atmosphere; It is usually expressed as an increase in the measured average temperatures. In addition to this, extreme weather events such as droughts, floods, and severe hurricanes become more frequent, and the specified extreme events become more severe. The effects of global climate change on hydrological and meteorological variables are increasing day by day. Therefore, hydro-meteorological parameters should be examined carefully. In this study, the effects of global climate change on the Hirfanli Dam Basin temperature series were investigated using the Mann-Kendall Test and Sequential Mann–Kendall Test. The annual mean temperature series of six stations recorded between 1965 and 2017 were analyzed and evaluated. It has been determined that the annual mean temperature has increased throughout the basin and significant increases started since the 1990s. Researches analysing the effects of global climate change on hydro-meteorological parameters related to the Hirfanli Dam Basin should be increased. These studies may be the investigation of the trends of climate parameters with different methods, as well as their relations with global atmospheric indices such as the North Atlantic Oscillation and Southern Oscillation. In the Hirfanlı Dam Basin, which shows semi-arid climate characteristics, especially drought disaster should be investigated. In addition to drought detection, these studies should be conducted with an integrated disaster management perspective in order to prepare for drought disasters. With the perspective of integrated disaster management, it will be more resilience to disasters with plans in which all components in the basin are together and effective against many disasters, especially drought.

Exploring the Use of European Weather Regimes for Improving User-Relevant Hydrological Forecasts at the Subseasonal Scale in Switzerland

Abstract Across the globe, there has been an increasing interest in improving the predictability of subseasonal hydrometeorological forecasts, as they play a valuable role in medium- to long-term planning in many sectors, such as agriculture, navigation, hydropower, and emergency management. However, these forecasts still have very limited skill at the monthly time scale; hence, this study explores the possibilities for improving forecasts through different pre- and postprocessing techniques at the interface with a Precipitationn–Runoff–Evapotranspiration Hydrological Response Unit Model (PREVAH). Specifically, this research aims to assess the benefit of European weather regime (WR) data within a hybrid forecasting setup, a combination of a traditional hydrological model and a machine learning (ML) algorithm, to improve the performance of subseasonal hydrometeorological forecasts in Switzerland. The WR data contain information about the large-scale atmospheric circulation in the North Atlantic–European region, and thus allow the hydrological model to exploit potential flow-dependent predictability. Four hydrological variables are investigated: total runoff, baseflow, soil moisture, and snowmelt. The improvements in the forecasts achieved with the pre- and postprocessing techniques vary with catchments, lead times, and variables. Adding WR data has clear benefits, but these benefits are not consistent across the study area or among the variables. The usefulness of WR data is generally observed for longer lead times, e.g., beyond the third week. Furthermore, a multimodel approach is applied to determine the “best practice” for each catchment and improve forecast skill over the entire study area. This study highlights the potential and limitations of using WR information to improve subseasonal hydrometeorological forecasts in a hybrid forecasting system in an operational mode.

Controls of Variability in the Laurentian Great Lakes Terrestrial Water Budget

AbstractThe land surface hydrology of the North American Great Lakes region regulates ecosystem water availability, lake levels, vegetation dynamics, and agricultural practices. In this study, we analyze the Great Lakes terrestrial water budget using the Noah‐MP land surface model to characterize the catchment hydrological regimes and identify the dominant quantities contributing to the variability in the land surface hydrology. We show that the Great Lakes domain is not hydrologically uniform and strong spatiotemporal differences exist in the regulators of the hydrological budget at daily, monthly, and annual timescales. Subseasonally, precipitation and soil moisture explain nearly all the terrestrial water budget variability in the southern basins, while the northern latitudes are snow‐dominated regimes. Seasonal assessments reveal greater differences among the basins. Precipitation, evaporation, and runoff are the dominant sources of variability at lower latitudes, while at higher latitudes, terrestrial water storage in the form of ground snowpack and soil moisture has the leading role. Differences in land cover categorizations, for example, croplands, forests, or urban zones, further induce spatial differences in the hydrological characteristics. This quantification of variability in the terrestrial water cycle embedded at different temporal scales is important to assess the impacts of changes in climate and land cover on catchment sensitivities across the diverse hydroclimate of the Great Lakes region.

Attribution assessment of hydrological trends and extremes to climate change for Northern high latitude catchments in Norway

The northern high latitudes have experienced the strongest warming in the world and substantial changes in streamflow and hydrological extremes. However, there have been limited attribution studies of changes in streamflow and hydrological extremes in this region. This study provides the first trend detection and attribution assessment on 33 hydrological variables for 50 Norwegian catchments in the period 1961–2019, using observed and simulated runoff data from four hydrological models driven by factual (observed) and counterfactual forcing data. Significant increasing trends are detected in observed annual, spring and winter runoff in most catchments and significant trends towards earlier spring floods are found in 40% of catchments. The four hydrological models show similarly good performance in terms of daily discharge in both calibration and validation periods, and they can reproduce 62% of the observed significant trends considering both trend direction and significance. The counterfactual forcing data were generated by the ATTRICI model, which removed all warming trends and most significant trends in precipitation in the factual time series. Ninety-four percent of the simulated significant trends driven by the factual forcing data are insignificant under counterfactual conditions, with trend slopes approaching zero. Thus, based on the model performance in trend reproduction and the difference of significant trends under factual and counterfactual conditions, we conclude that about 58% of the observed significant trends in Norwegian catchments can be attributed mainly to climate change. The comparisons of the historical extreme events under factual and counterfactual conditions show that more than 65% of floods and droughts in the 2010s could have been magnified by climate change.

Paleoenvironmental changes on the central Tibetan Plateau over the last two millennia inferred from sedimentological proxies and brGDGTs in Bamu Co

Understanding multi-centennial climate variations and their impact on lake catchment productivity over the last 2000 years (the Common Era), is crucial for assessing the role of natural climatic factors in driving productivity changes, and for predicting productivity changes under recent global warming scenarios. However, the climate patterns on the Tibetan Plateau (TP), including temperature and hydrological variations, during the Common Era, and their possible links with productivity, are poorly understood. We present a quantitative reconstruction of mean annual air temperature (MAAT), together with grain size and other sedimentary records, from a non-glacial lake sediment archive on the central TP, with the objective of tracking changes in climate and lake catchment productivity over the past two millennia. Our results reveal an overall cooling trend in temperature reconstructed from branched glycerol dialkyl glycerol tetraethers (brGDGTs), with multi-centennial-scale fluctuations superimposed. These fluctuations include relatively warm periods during 200 BCE–400 CE and the Medieval Climate Anomaly (1050–1400 CE), and cold periods centered on ∼500–1000 CE and the Little Ice Age (∼1500–1900 CE). A rapid warming trend since 1900 CE corresponds to the Current Warm Period. The variations in sedimentary grain size reflect lake level changes associated with changes in the summer monsoon. Paleoproductivity inferred from total organic carbon (TOC) shows a similar pattern to the paleoclimate records, suggesting that millennial-scale productivity was influenced by both temperature and the catchment hydrology. In recent decades, human activities exerted a more substantial influence on lake productivity. Additionally, we suggest that the use of brGDGTs for temperature reconstruction necessitates the investigation of the temperature relationship of various fractions of brGDGTs, specifically tetramethyl-brGDGTs; and that careful selection of the temperature calibration equation is needed to minimize inaccuracies in paleotemperature reconstruction.

Drought-induced changes in hydrological and phenological interactions modulate waterbird habitats dynamics

Floodplain wetlands are essential for the survival and reproduction of waterbirds, but droughts can negatively impact their food availability and habitat conditions, leading to a decline in waterbird abundance and diversity. To better understand the impact of hydrological and phenological variables on floodplain waterbird habitats during drought conditions, we employed hydrological modeling and remote sensing techniques to calculate fine-scale environmental variables. We constructed habitat suitability models using nine years of field surveys of waterbirds and identified the impact of the interaction effects between hydrological and phenological variables on waterbirds under drought and non-drought conditions. The results showed that hydrological and phenological variables can provide useful information for quantifying the habitat suitability of waterbirds. Drought-induced early water recession negatively impacted the quality of waterbird habitats, reducing their habitat suitability. Sedge eaters exhibited the most significant reduction of 34.47% in the total area of the lake, followed by seed eaters, fish eaters and invertebrate eaters. Tuber eaters had the largest increasing trend, with a significant increase area of 7.44% in total area. Our findings suggest that various feeding guilds exhibit distinct interactions in response to drought, which can be strengthened or weakened by alterations resulting from drought. For sedge eaters, seed eaters, and fish eaters, drought could result in a faster decline in habitat suitability by strengthening the interaction effects between variables. In contrast, for invertebrate eaters and tuber eaters, drought could weaken interaction effects between variables, resulting in a slower decline in habitat quality. The results provide valuable insights into the spatial distribution of overwintering waterbirds and can inform conservation efforts in wetlands.

Interannual hydrological variability in the Mississippi River Basin based on contemporary geodetic measurements and land surface models

The impact of climate change on terrestrial water storage (TWS) is critical for understanding the global hydrological process. The Global Positioning System (GPS) measurements, Gravity Recovery and Climate Experiment (GRACE), and GRACE Follow-On (GFO) data were used to measure the processes involved in the hydrological process in the Mississippi River Basin (MRB) from 2002 to 2021. The common-mode component (CMC) of the GPS network in the MRB obtained using principal component analysis (PCA) was utilized to study the hydrological loading deformation. The data from GRACE/GFO and hydrological models revealed a long-term trend of reduction in the TWS and obvious depletion of the groundwater storage (GWS) in the southwestern part of the MRB. Both GRACE/GFO and the GPS can accurately detect elastic land deformations in the MRB due to hydrological anomalies caused by El Niño/ Southern Oscillation (ENSO), while the sensitivity of GPS in Tennessee regions to net precipitation around 2016 is higher than that of GRACE/GFO. Furthermore, numerous interannual fluctuations and long-term periodic signals observed in both GPS and GRACE/GFO measurements are found to be intimately connected with hydrological processes and ENSO. The findings demonstrate that modern geodetic methods are useful in monitoring the impacts of climate change on hydrological process.