Hydrological Variables Research Articles

Factors Influencing the Variation in Phytoplankton Functional Groups in Fuchunjiang Reservoir

To explore the distribution characteristics, blooming risk mechanism and driving factors of phytoplankton community structure in Fuchunjiang Reservoir. The variation characteristics of phytoplankton, zooplankton and physicochemical indicators in Fuchunjiang Reservoir and its upper and lower reaches were investigated in 2020 and 2021. Based on the phytoplankton functional groups, non-metric multidimensional scale analysis, redundancy analysis and other statistical methods, the seasonal succession characteristics and driving factors of phytoplankton functional groups were analyzed. A total of 18 phytoplankton functional groups were identified, in of which 10 were predominant. The composition of phytoplankton functional groups in the Fuchunjiang Reservoir was significant different. Spatially, the upstream were dominated by group C and P while the represent species were Cyclotella and Aulacoseira,reflecting the mixed meso-eutrophic environments. However, group P was the main group in Fuchunjiang reservoir, and the dominance decreased gradually along the stream direction. Meanwhile, in the downstream, MP has an absolute advantage at Qiantang River estuary. It reflected the environmental characteristics of frequent disturbance and high turbidity of tide-sensing rivers. In addition, the predominant functional groups demonstrated strong seasonal variations. The dominant functional groups were diverse in summer and consisted of P+L0+J+M+S1+H1+MP. In addition to group P (Aulacoseira), which was dominant throughout the year, it also included several groups represented by cyanobacteria and chlorophyta, reflecting the environmental characteristics of changeable habitats and vigorous productivity. In autumn, the succession was dominated by H1 group represented by Dolichospermum and the representative function groups were P and H1, reflecting the hydrological background of reduced flow and static flow. In winter, the increase of Cyclotella led to the predominance of group C, which was dominated by P+C, reflecting the changing conditions of weakened water exchange and intensified eutrophication problems. In spring, the dominant functional groups were gradually enriched and were composed of C, D, P, and MP, which also reflected the changing environmental habitat characteristics which caused by increasing rainfall and air temperature. According to the results of the C-R-S growth strategy, the Fuchunjiang Reservoir has been in the R strategy for a long time, which was consistent with the habitat characteristics of Fuchunjiang Reservoir and its upper and lower reaches with high disturbance and low stress. In addition, C strategy and S strategy appeared in some reaches, reflecting the variability of water quality and hydrology. RDA analysis showed that water temperature, discharge, zooplankton biomass, permanganate index, total nitrogen and total phosphorus were significantly correlated with the seasonal succession of phytoplankton functional groups (P < 0.05), and temperature and flow pattern were probably the most critical factors for the succession. Studies have shown that the impact of hydrometeorological processes on phytoplankton in the Fuchunjiang Reservoir is crucial:high temperature and changing discharge during the summer may lead to cyanobacterial blooms in the Fuchunjiang reservoir; To reduce the risk of algal blooms, it is still necessary to increase the control of nitrogen and phosphorus load in rivers, and fully consider the coordination of water conservancy dispatch methods.

Model and remote-sensing-guided experimental design and hypothesis generation for monitoring snow-soil–plant interactions

In this study, we develop a machine-learning (ML)-enabled strategy for selecting hillslope-scale ecohydrological monitoring sites within snow-dominated mountainous watersheds, with a particular focus on snow-soil–plant interactions. Data layers rely on spatial data layers from both remote sensing and hydrological model simulations. Specifically, a Landsat-based foresummer drought sensitivity index is used to define the dependency of the annual peak plant productivity on the Palmer drought severity index in the early growing season. Hydrological simulations provide the spatiotemporal dynamics of near-surface soil moisture and snow depth. In this framework, a regression analysis identifies the key hydrological variables relevant to the spatial heterogeneity of drought sensitivity. We then apply unsupervised clustering to these key variables, using the Gaussian mixture model, to group hillslopes into several zones that have divergent relationships regarding soil moisture, snow dynamics, and drought sensitivity. Using the datasets collected in the East River Watershed (Crested Butte, Colorado, United States), results show that drought sensitivity is significantly correlated with model-derived soil moisture and snow-free timing over space and time. The relationship is, however, non-linear, such that the correlation decreases above a threshold elevation and in a heavy snow year due to large snowpacks, lateral flow, and soil storage limitations. Clustering is then able to define the zones that have high or low sensitivity to drought, as well as the mid-elevation regions where sensitivity is associated with the topographic aspect and net potential radiation. In addition, the algorithm identifies the most representative hillslopes with road/trail access within each zone for installing monitoring sites. Our method also aims to significantly increase the use of ML and model-simulation results to guide critical zone and watershed monitoring activities.

Enforcing Water Balance in Multitask Deep Learning Models for Hydrological Forecasting

Abstract Accurate prediction of hydrological variables (HVs) is critical for understanding hydrological processes. Deep learning (DL) models have shown excellent forecasting abilities for different HVs. However, most DL models typically predicted HVs independently, without satisfying the principle of water balance. This missed the interactions between different HVs in the hydrological system and the underlying physical rules. In this study, we developed a DL model based on multitask learning and hybrid physically constrained schemes to simultaneously forecast soil moisture, evapotranspiration, and runoff. The models were trained using ERA5-Land data, which have water budget closure. We thoroughly assessed the advantages of the multitask framework and the proposed constrained schemes. Results showed that multitask models with different loss-weighted strategies produced comparable or better performance compared to the single-task model. The multitask model with a scaling factor of 5 achieved the best among all multitask models and performed better than the single-task model over 70.5% of grids. In addition, the hybrid constrained scheme took advantage of both soft and hard constrained models, providing physically consistent predictions with better model performance. The hybrid constrained models performed the best among different constrained models in terms of both general and extreme performance. Moreover, the hybrid model was affected the least as the training data were artificially reduced, and provided better spatiotemporal extrapolation ability under different artificial prediction challenges. These findings suggest that the hybrid model provides better performance compared to previously reported constrained models when facing limited training data and extrapolation challenges.

Holocene stable oxygen isotope record from the North Caspian Rybachya core

The first results of 22 δ18Oost measurements on ostracod shells from the Rybachya core in the North Caspian Sea are presented. The stable oxygen isotope record here confirms previous studies of Upper Quaternary and Holocene sediments of the Rybachya core area, where it was suggested that a paleovalley was formed during the Mangyshlak regressive stage and then gradually filled during the Holocene transgressive stage. We recorded three local isotopic stages, each characterized by δ18Oost values. The hydrological variability and sea level changes associated with the transgressive-regressive rhythm of the Caspian Basin and the climatic changes in the Northern Hemisphere led to the δ18Oost changes obtained in this work. Relatively depleted δ18Oost values from sediments filling the paleovalley allowed to interpret the isotope record in terms of freshwater influx. The average δ18O enrichment of ostracod shells is taken as evidence for the isolation of the paleovalley during the Caspian Sea level retreat. However, the higher δ18Oost values obtained for the last (modern) stage of Caspian history are under the strong control of host water salinity, which is higher than during the paleovalley filling episode.

Comparative Analysis of Nonlinear Methods for Multivariable Water Level Prediction: The Case Study of the Atrato River

This paper presents an innovative multivariate forecasting application for hydrological variables, using an online model based on backpropagation (BP) neural networks. The effectiveness of this approach is verified by using the Nonlinear Autoregressive with Exogenous Inputs (NARX) and the linear Autoregressive with Exogenous Inputs (ARX) methods. The proposed methodology is implemented in two hydrological stations in the department of Chocó, Colombia, located along the Atrato River. The BP neural network structure is applied to establish correlations between water level, flow, and precipitation. This structure is designed to handle the dynamic complexity of the hydrological variables and their interconnections. In addition, a short‐term water level prediction is developed for incorporation into a flood early warning system. Validation of the approach is performed by comparing various estimation errors using regression metrics such as root mean square error (RMSE), relative mean square error (MSRE), mean absolute error (MAE), relative mean absolute error (MARE), and Nash–Sutcliffe efficiency coefficient (NSE). The computation time and the accuracy of the estimated weights versus real data are also considered. The results show that the proposed structure is the most suitable for accurately predicting water levels. It is shown that the backpropagation neural network structure of the proposed model is more effective for water level prediction. This is observed when evaluating the real data compared to the estimated data, obtaining values corresponding to the regression metrics. In particular, for output 2, the RMSE is 0.0742. Compared to the NARX model (RMSE of 0.0985) and the ARX model (RMSE of 0.1265), it is observed that the backpropagation neural network model improves the estimation by 32.6 over the NARX model and by 70.4% over the ARX model. As for the NSE coefficient for output 2 of the model, the backpropagation neural network is the closest to 1 compared to the other two models. For output 2, the backpropagation model obtains a value of 0.9966, the NARX model 0.9958, and the ARX model 0.9914. Another evaluation metric applied to this model is the Kling–Gupta efficiency coefficient (KGE), which is used to evaluate the performance of hydrological models. For the output of the two hydrological stations located in the Atrato River, we observed that for the backpropagation model a value of 0.9992 was obtained, for the NARX model a value of 0.9988 was obtained, and for the linear ARX model with nonlinear structure a value of 0.9984 was obtained. This indicates that the backpropagation neural network model provides a better estimate, as its value is closer to 1. Furthermore, the computational time executed in the algorithm of the three proposed nonlinear models is calculated, where the backpropagation obtains a calculated value of 350.9 microseconds, for the NARX model the computational time is 5908.7 microseconds and for the ARX nonlinear model, the computational time is 5172.6 microseconds. The model put forward demonstrates its effectiveness in accurately predicting water levels at two key hydrological stations along Colombia’s Atrato River. By incorporating intricate interactions among hydraulic structures and dynamic hydrological conditions, it enhances prediction accuracy. This not only aids in disaster risk management but also streamlines planning processes, particularly within flood early warning systems.

Extreme Learning Machine Predicts High-Frequency Stream flow and Nitrate-N Concentrations in a Karst Agricultural Watershed

Highlights A novel high-frequency dataset of streamflow, nitrate-N concentration, and soil moisture at depths throughout and below the root zone highlighted tight connectivity between soil moisture dynamics and nitrate-N concentrations spanning event to seasonal timescales. A TELM model successfully predicted both flow rate and nitrate exports from these complex systems because the time lag associated with the soil conditions was represented in model training. The inclusion of soil moisture and temperature data below the effective root zone was important to accurately capture the storm event hysteresis dynamics observed in the exported nitrate signals. Abstract. Efforts to reduce nitrogen contributions from karst agroecosystems have had variable success, in part due to an incomplete understanding of nitrogen source, fate, and transport dynamics in karst watersheds. Recent advancements in environmental sensors and data-driven artificial intelligence models may be useful in improving our understanding of system behavior and the linkages between soil hydrologic processes and karst nitrate loading dynamics. We collected 35 months of high-resolution streamflow, nitrate-N concentration, soil moisture and temperature (from 10-100 cm depths), and meteorological data in a karst agricultural watershed in the Inner-Bluegrass region of Central Kentucky. Two-layer extreme learning machine (TELM) models were developed to predict nitrate-N concentrations and flow rates as a function of meteorological and soil parameter inputs. Results suggest tight linkages between soil moisture gradients at different depths and nitrate-N concentrations at the watershed outlet. TELM modeling results supported visual observations from the high-frequency data and suggest that inclusion of both soil moisture and temperature parameters at all soil depths improved predictions of both flow rate and nitrate-N concentration (with optimal NSE values of 0.93 and 0.94, respectively, when all inputs were considered). Hysteresis analysis suggested that inclusion of the deepest soil layer (100 cm) was necessary to predict hysteresis observed during storm events. The findings of the study highlight the importance of variable activation of matrix waters in preferential flows throughout events and seasons and its subsequent impacts on nitrate-N concentrations. Results suggest that management models should incorporate vertical variability in soil hydrology to accurately characterize nitrate source and transport dynamics. Further, the results of hysteresis analysis underscore the importance of inclusion of hysteresis indices, in addition to typical model evaluation statistics, to ensure accurate representation of nutrient flow pathways. Keywords: Extreme learning machine, Karst agroecosystem, Nitrate, Water resources.

Copulas and hydro-economic models for assessing the impacts of climate change in irrigated agriculture

Climate change significantly affects water resources through alterations in rainfall patterns, reduced rainfall quantities, rising temperatures, and prolonged drought cycles. Consequently, the escalating demand for water coupled with diminishing water availability is anticipated to lead to a critical water scarcity issue in the future unless proactive and innovative strategies are implemented.The impact of climate change on water resources is globally recognized as an increasingly vital concern, given its intricate interconnections with various sectors, notably agriculture, energy, and the provision of drinking water. Consequently, the scientific community has dedicated substantial efforts to devise optimal water resource management strategies in response to the challenges posed by climate change. Over the past four decades, hydro-economic models (HEM) have been instrumental in proposing solutions to adapt to evolving climatic conditions.This paper presents a newhydro-economic optimization model accounting for climatic uncertainties. The problem is formulated as a chance-constrained program in which the dependence structure between hydrologic and meteorologic variables is modeled using copula theory. The novelty of the approach lies in the capacity of the model to optimize the water resources, taking into account the dependence between agronomic, socio-economic, and hydrologic systems and climatic uncertainties.

An Investigation of the Spatial and Temporal Characteristics of Extreme Dry and Wet Events across NLDAS-2 Models

Abstract Extreme hydrological events (including droughts and floods) produce severe social and economic impacts. Monitoring hydrological processes from remote sensing is necessary to improve understanding and preparedness for these events, with current missions focusing on a range of hydrological variables (i.e., SWOT, SMAP, and GRACE). This study uses output from three state-of-the-art land surface assimilation models and an event clustering algorithm to identify the characteristic spatial and temporal scales of large-scale extreme dry and wet events in the contiguous United States for three major hydrological processes: precipitation, runoff, and soil moisture. We also examine the sensitivity of extreme event characteristics to model resolution and assess intermodel differences. We find that models generally agree in terms of the mean characteristics of events: precipitation dry events are of shorter duration in comparison with soil moisture and runoff events, and more intense events tend to be smaller in area. We also find that mean spatial and temporal characteristics are highly dependent on model resolution—important in the context of detecting and monitoring these events. Results from this study can be used to inform land surface model development, extreme hydrology event detection, and sampling requirements of upcoming remote sensing missions in hydrology. Significance Statement Understanding the fundamental characteristics of dry and wet extreme events (droughts and floods) is of importance for improving our preparedness and response to events, as well as for designing satellite observing systems that can adequately monitor them. Here we use output from land surface models to determine the average size and duration of large-scale extreme events for the contiguous United States using fine temporal data. We find that events that are most extreme—the most severe floods and droughts—tend to be shorter in duration and smaller in size. We also present an assessment of how three commonly used land surface models detect extreme hydrological events, which is important for assessments based on models. These findings are important for understanding the proportion of events that may be not adequately resolved by current hydrology remote sensing missions.

Spatiotemporal Mapping of Surface Water Using Landsat Images and Spectral Mixture Analysis on Google Earth Engine

Ensuring water resource security and enhancing resilience to extreme hydrological events demand a comprehensive understanding of water dynamics across various scales. However, monitoring water bodies with highly seasonal hydrological variability, particularly using medium-resolution satellite imagery such as Landsat 4-9, presents substantial challenges. This study introduces the Normalized Difference Water Fraction Index (NDWFI) based on spectral mixture analysis (SMA) to improve the detection of subtle and dynamically changing water bodies. First, the effectiveness of NDWFI is rigorously assessed across four challenging sites. The findings reveal that NDWFI achieves an average overall accuracy (OA) of 98.2% in water extraction across a range of water-covered scenarios, surpassing conventional water indices. Subsequently, using approximately 11,000 Landsat satellite images and NDWFI within the Google Earth Engine (GEE) platform, this study generates a high-resolution surface water (SW) map for Jiangsu Province, China, exhibiting an impressive OA of 95.91% ± 0.23%. We also investigate the stability of the NDWFI threshold for water extraction and its superior performance in comparison to existing thematic water maps. This research offers a promising avenue to address crucial challenges in remote sensing hydrology monitoring, contributing to the enhancement of water security and the strengthening of resilience against hydrological extremes.

The effect of environmental variations on the production of the principal agricultural products in Colombia.

The agricultural sector of Colombia supports the national economy and food security due to the rich lands for cultivation. Although Colombia has a vast hydrological basin, climate change can impact agricultural productivity, generating economic and social adverse effects. For this, we evaluated the impact of some environmental variables on the production of the most sold crops using production, climatic, and hydrological data of the 1121 municipalities from 2007 to 2020. We modeled the production of coffee, rice, palm oil, sugarcane, and corn, adopting a Bayesian spatio-temporal model that involved a set of environmental variables: average temperature, minimum temperature, maximum temperature, evapotranspiration, precipitation, runoff, soil moisture, vapor pressure, radiation, and wind speed. We found that increases in the average temperatures can affect coffee (-0.2% per °C), rice (-3.76% per °C), and sugarcane (-0.19% per °C) production, meanwhile, these increases can boost palm oil (+2.55% per °C) and corn (+1.28% per °C) production in Colombia. This statement implies that the agricultural sector needs to substitute land use, promoting the production of palm oil and corn. Although our results did not find a significant effect of hydrological variables in any crop, suggesting that the abundance of water in Colombia might balance the impact of these variables. The increases in vapor pressure impact all the crops negatively (between -11.2% to -0.43% per kPa), except rice, evidencing that dry air conditions affect agricultural production. Colombia must manage the production location of the traditional products and implement agro-industrial technologies to avoid the climate change impact on crops.

LSTM-Based Data Integration to Improve Snow Water Equivalent Prediction and Diagnose Error Sources

Abstract Accurate prediction of snow water equivalent (SWE) can be valuable for water resource managers. Recently, deep learning methods such as long short-term memory (LSTM) have exhibited high accuracy in simulating hydrologic variables and can integrate lagged observations to improve prediction, but their benefits were not clear for SWE simulations. Here we tested an LSTM network with data integration (DI) for SWE in the western United States to integrate 30-day-lagged or 7-day-lagged observations of either SWE or satellite-observed snow cover fraction (SCF) to improve future predictions. SCF proved beneficial only for shallow-snow sites during snowmelt, while lagged SWE integration significantly improved prediction accuracy for both shallow- and deep-snow sites. The median Nash–Sutcliffe model efficiency coefficient (NSE) in temporal testing improved from 0.92 to 0.97 with 30-day-lagged SWE integration, and root-mean-square error (RMSE) and the difference between estimated and observed peak SWE values dmax were reduced by 41% and 57%, respectively. DI effectively mitigated accumulated model and forcing errors that would otherwise be persistent. Moreover, by applying DI to different observations (30-day-lagged, 7-day-lagged), we revealed the spatial distribution of errors with different persistent lengths. For example, integrating 30-day-lagged SWE was ineffective for ephemeral snow sites in the southwestern United States, but significantly reduced monthly-scale biases for regions with stable seasonal snowpack such as high-elevation sites in California. These biases are likely attributable to large interannual variability in snowfall or site-specific snow redistribution patterns that can accumulate to impactful levels over time for nonephemeral sites. These results set up benchmark levels and provide guidance for future model improvement strategies.

Consistent Modeling of Transport Processes and Travel Times—Coupling Soil Hydrologic Processes With StorAge Selection Functions

AbstractUnderstanding the transport processes and travel times of pollutants in the subsurface is crucial for an effective management of drinking water resources. Transport processes and soil hydrologic processes are inherently linked to each other. In order to account for this link, we couple the process‐based hydrologic model RoGeR with StorAge Selection (SAS) functions. We assign to each hydrological process a specific SAS function (e.g., power law distribution function). To represent different transport mechanisms, we combined a specific set of SAS functions into four transport model structures: complete‐mixing, piston flow, advection‐dispersion and advection‐dispersion with time‐variant parameters. In this study, we conduct modeling experiments at the Rietholzbach lysimeter, Switzerland. All modeling experiments are benchmarked with HYDRUS‐1D. We compare our simulations to the measured hydrologic variables (percolation and evapotranspiration fluxes and soil water storage dynamics) and the measured water stable isotope signal (18O) in the lysimeter seepage for a period of ten years (1997–2007). An additional virtual bromide tracer experiment was used to benchmark the models. Additionally, we carried out a sensitivity analysis and provided Sobol indices for hydrologic model parameters and SAS parameters. Our results indicate that the advection‐dispersion transport model produces the best results. And thus, advective‐dispersive transport processes play a dominant role at Rietholzbach lysimeter. Our modeling approach provides the capability to test hypotheses of different transport mechanisms and to improve process understanding and predictions of transport processes. Overall, the combined model allows a very effective simulation of combined flux and transport processes at various temporal and spatial scales.

Hydrological Projections under CMIP5 and CMIP6: Sources and Magnitudes of Uncertainty

Abstract Projections of future hydrological conditions rely largely on global climate models, but model performance varies greatly. In this study, we investigated projected changes in runoff (R), precipitation (P), evapotranspiration (ET), and soil moisture (SM) based on the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6) and quantified the uncertainties of their projected changes on annual and seasonal scales. The results indicate that all four hydrological variables show an increase over most of the global land: annual projections of R, P, ET, and SM from CMIP6 increase in 72%, 81%, 82%, and 66% of the global land area, respectively, under a high emissions scenario during the period 2080–99 relative to 1970–99. We estimated the uncertainties in CMIP6 from different sources on an annual scale and found that model uncertainty dominates the total projected uncertainties during the twenty-first century [76% (R), 73% (P), 89% (ET), and 95% (SM) in the 2090s], and the contribution of internal variability decreases with time. The low-latitude regions have the greatest uncertainty in hydrological projections. In CMIP6, the uncertainty of projected changes in P contributes the most to the uncertainty of projected changes in R, with a contribution of 93% on annual scale, followed by ET and SM. Overall, the performances of the CMIP5 and CMIP6 models are similar in terms of hydrological changes and the composition of their uncertainties. This study provides a theoretical reference for the further improvement and development of hydrological components in global climate models.

Towards Groundwater-Level Prediction Using Prophet Forecasting Method by Exploiting a High-Resolution Hydrogeological Monitoring System

Forecasting of water availability has become of increasing interest in recent decades, especially due to growing human pressure and climate change, affecting groundwater resources towards a perceivable depletion. Numerous research papers developed at various spatial scales successfully investigated daily or seasonal groundwater level prediction starting from measured meteorological data (i.e., precipitation and temperature) and observed groundwater levels, by exploiting data-driven approaches. Barely a few research combine the meteorological variables and groundwater level data with unsaturated zone monitored variables (i.e., soil water content, soil temperature, and bulk electric conductivity), and—in most of these—the vadose zone is monitored only at a single depth. Our approach exploits a high spatial-temporal resolution hydrogeological monitoring system developed in the Conero Mt. Regional Park (central Italy) to predict groundwater level trends of a shallow aquifer exploited for drinking purposes. The field equipment consists of a thermo-pluviometric station, three volumetric water content, electric conductivity, and soil temperature probes in the vadose zone at 0.6 m, 0.9 m, and 1.7 m, respectively, and a piezometer instrumented with a permanent water-level probe. The monitored period started in January 2022, and the variables were recorded every fifteen minutes for more than one hydrologic year, except the groundwater level which was recorded on a daily scale. The developed model consists of three “virtual boxes” (i.e., atmosphere, unsaturated zone, and saturated zone) for which the hydrological variables characterizing each box were integrated into a time series forecasting model based on Prophet developed in the Python environment. Each measured parameter was tested for its influence on groundwater level prediction. The model was fine-tuned to an acceptable prediction (roughly 20% ahead of the monitored period). The quantitative analysis reveals that optimal results are achieved by expoiting the hydrological variables collected in the vadose zone at a depth of 1.7 m below ground level, with a Mean Absolute Error (MAE) of 0.189, a Mean Absolute Percentage Error (MAPE) of 0.062, a Root Mean Square Error (RMSE) of 0.244, and a Correlation coefficient of 0.923. This study stresses the importance of calibrating groundwater level prediction methods by exploring the hydrologic variables of the vadose zone in conjunction with those of the saturated zone and meteorological data, thus emphasizing the role of hydrologic time series forecasting as a challenging but vital aspect of optimizing groundwater management.

Water Level Fluctuations Modulate the Microbiomes Involved in Biogeochemical Cycling in Floodplains.

Drastic changes in hydrological conditions within floodplain ecosystems create distinct microbial habitats. However, there remains a lack of exploration regarding the variations in microbial function potentials across the flooding and drought seasons. In this study, metagenomics and environmental analyses were employed in floodplains that experience hydrological variations across four seasons. Analysis of functional gene composition, encompassing nitrogen, carbon, and sulfur metabolisms, revealed apparent differences between the flooding and drought seasons. The primary environmental drivers identified were water level, overlying water depth, submergence time, and temperature. Specific modules, e.g., the hydrolysis of β-1,4-glucosidic bond, denitrification, and dissimilatory/assimilatory nitrate reduction to ammonium, exhibited higher relative abundance in summer compared to winter. It is suggested that cellulose degradation was potentially coupled with nitrate reduction during the flooding season. Phylogenomic analysis of metagenome-assembled genomes (MAGs) unveiled that the Desulfobacterota lineage possessed abundant nitrogen metabolism genes supported by pathway reconstruction. Variation of relative abundance implied its environmental adaptability to both the wet and dry seasons. Furthermore, a novel order was found within Methylomirabilota, containing nitrogen reduction genes in the MAG. Overall, this study highlights the crucial role of hydrological factors in modulating microbial functional diversity and generating genomes with abundant nitrogen metabolism potentials.

Detection and attribution of hydrological changes in different climatic and geomorphic regions of China

Through large-scale hydrological simulation, understanding the impact of different climatic and geomorphic conditions on hydrological variables is valuable for water and land management. However, the related study is still a challenge due to strong environmental diversity in large scale region. The physically-based, national-scale hydrological model in China was developed and validated, which considered the spatial heterogeneity of climatic and geomorphic conditions. Using the model, hydrological differences during the period 1956–2020 in 21 representative basins located in nine climatic zones and four geomorphic regions were quantified. Results showed that: 1) mean annual precipitation was strongly positively correlated with mean actual evapotranspiration, and both increased gradually from north to south. Interestingly, as annual precipitation increases, precipitation tended to be more evenly distributed. In recent decades, the northern river basins have been warming and drying, while the Heihe River basin and the cold northeast regions were under climatic warming and wetting; 2) the spatial distribution of streamflow was consistent with precipitation, but their trends were different. In cold regions affected by frozen soil, the streamflow tended to increase. On the contrary, the basins located in the Warm Temperate Zone with intense human activities and fragile ecosystem had a significant decrease in natural streamflow. As for the streamflow components, the frozen soil and karst structures contributed to the increase of the baseflow index (BFI); 3) The streamflow increase or reduction in 86% of the basins was dominated by climate change, as the contribution rate varied from 51.4% to 95.7%. Affected by the Grain to Green Programme, the streamflow of the Weihe River basin reduced significantly while the BFI increased. However, the reduction of forest, grassland and wetland areas dominated streamflow increase in the Huaihe, and Hulan River basins, and the rates were 65.3% and 66.1%, respectively.

‘Paleofloodscapes’: Application of sediment source fingerprinting to track flood regime change over space and time at the Peace-Athabasca Delta, Canada

Deltas are hydrologically dynamic landscapes where river floodwaters create a mosaic of productive ecosystems that provide important services. The flood regime, however, is vulnerable to upstream anthropogenic activities, climate change and geomorphic processes. Deciphering the roles of multiple potential stressors on flood regime change is critical for developing appropriate adaptive and mitigative strategies but requires knowledge of hydrological variability at broader scales of space and time than is typically available from instrumental and observational records. At the globally recognized Peace-Athabasca Delta (Canada), the timing, magnitude and causes of reduced flooding and drawdown of perched basin water levels remain an intense focus of investigation. Here we employ novel ‘paleofloodscapes’, generated from geospatial interpolation of Bayesian mixing model fingerprinting of sediment elemental concentrations, to quantify variation in the delta's flood regime during the past ~140 years. Results reveal that flooding of the delta began to decline several decades before hydroelectric regulation of Peace River flow, not coincident with it, and the influence of floodwaters from the unregulated Athabasca River has declined more than the regulated Peace River. A key discovery is that widespread flooding of perched basins occurs when ice-jam events on the river(s) coincide with a relatively high water-plane in the delta's open-drainage network. Without knowledge of open-drainage water levels, inferred change to the flood regime of perched basins may be inaccurate when derived solely from analyses of Peace River hydrometric data and climatic records. The paleofloodscapes illustrate that rising sediment delivery caused by a natural river avulsion in 1982 may undermine the intended purpose of a proposed weir installation. The most recent paleofloodscape, developed from lake surface sediment sampling shortly after widespread flooding, demonstrates the value of the approach as a landscape hydrological monitoring tool, and is readily transferrable to other floodplains to track flood regime change.

Spatiotemporal variability and trend detection of hydrological and climatic variables of Modjo catchment, central Ethiopia

Spatiotemporal variability and trend detection of hydrological and climatic variables of Modjo catchment, central Ethiopia

Land Use and Land Cover Trends and Their Impact on Streamflow and Sediment Yield in a Humid Basin of Brazil’s Atlantic Forest Biome

Understanding the trends in land use and land cover (LULC) is crucial for modeling streamflow and sediment yield, particularly in hydrological basins. This study examined the impact of LULC on the dynamics of streamflow and sediment yield within a humid tropical basin of the Atlantic Forest biome in Brazil, focusing on the period from 2000 to 2016. Changes in LULC were analyzed using annual MapBiomas data products for the same period. The Soil and Water Assessment Tool (SWAT) model was deployed to simulate streamflow and sediment yield based on LULC changes. To investigate temporal trends in LULC, a suite of non-parametric statistical tests, including the Mann–Kendall, Pettitt, and Sen’s slope estimator tests, was employed. Ecological diversity indices such as Shannon–Weaver, Simpson, and Pielou were applied to assess forest fragmentation, along with the Forest Fragmentation Index. The results revealed a growing trend in urban and sugarcane areas, coupled with a decline in dense vegetation, mangroves, and other forms of dense vegetation. With regard to the correlation between land uses and hydrological variables, the findings indicate minor variations in hydrological balance, attributable to the not-so-significant changes among the studied land-use scenarios, except for sediment yield estimates, which showed more considerable alterations. Notably, the estimates for 2000 and 2013–2016 were the most divergent. In a broader scientific context, this research conclusively establishes that the incorporation of dynamic LULC data into the SWAT model augments the precision and robustness of simulations pertaining to agricultural watersheds, thereby enabling a more comprehensive hydrological characterization of the study area.

Impacts of hydroclimate change on climate-resilient agriculture at the river basin management

Abstract This paper focuses on exploring the potential of Climate resilient agriculture (CRA) for river basin-scale management. Our analysis is based on long-term historical and future climate and hydrological datasets within a GIS environment, focusing on the Ajoy River basin in West Bengal, Eastern India. The standardized anomaly index (SAI) and slope of the linear regression (SLR) methods were employed to analyse the spatial pattern of the climate variables (precipitation, Tmax and Tmin) and hydrological variables (actual evapotranspiration (AET), runoff (Q), vapor pressure deficit (VPD), potential evapotranspiration (PET), and climate water deficit (DEF)) using the TerraClimate dataset spanning from 1958 to 2020. Future climate trend analysis spanning 2021 to 2050 was conducted using the CMIP6 based GCMs (MIROC6 and EC-Earth3) dataset under shared socio-economic pathway (SSP2-4.5, SSP5-8.5 and historical). For spatiotemporal water storage analysis, we relied on Gravity Recovery and Climate Experiment (GRACE) from the Center for Space Research (CSR) and the Jet Propulsion Laboratory (JPL) data, covering the period from 2002 to 2021. Validation was performed using regional groundwater level data, employing various machine learning classification models. Our findings revealed a negative precipitation trend (approximately −0.04 mm/year) in the southern part, whereas the northern part exhibited a positive trend (approximately 0.10 mm/year).