Hydrological Variables Research Articles

Temperature and palaeolake evolution during a Middle Pleistocene interglacial–glacial transition at the Palaeolithic locality of Schöningen, Germany

The Middle Pleistocene Reinsdorf sequence at the Lower Palaeolithic sites of Schöningen offers the opportunity to reconstruct a rarely well‐preserved post‐Holsteinian environmental transition from an interglacial into a glacial phase along with its highly dynamic interjacent climatic oscillations. Combining biological proxies, element composition and stable isotope ratios of two lakeshore sequences at excavation site 13 II, we demonstrate repeated variations in climate, hydrology and catchment vegetation cover. New ostracod‐based quantitative mean summer and winter air temperature reconstructions with the Mutual Ostracod Temperature Range (MOTR) method provide the first detailed information about the temperature evolution. The interglacial temperature maximum, probably corresponding to Marine Isotope Stage 9e, is followed by a first dry phase and, during the younger part of the Reinsdorf sequence, by a second dry period. Both were marked by lower precipitation/evaporation ratios, reduced vegetation cover in the catchment and increased surface inflows from springs. Temperature reconstructions of these two steppe (open woodland) phases yield very narrow ranges for mean January (−4–0 °C) and July (+17–19 or +17–21 °C) air temperatures, demonstrating that, while summers were similar to those of today, winters were at least 1 °C colder, hinting at a more pronounced continental climate. Precise temperature estimates for the interjacent woodland and steppe (woodland) phase are hindered by generally wider ranges produced by the MOTR method (January mean −4–3 °C, July mean +15–21 °C). The development of a more extensive vegetation cover, reducing surface runoff and erosion in favour of increased river and groundwater discharge, as indicated by a shift in microfossil and stable isotope records, suggests generally more humid climates with higher precipitation/evaporation ratios as well as reduced seasonal temperature variations.

Soil hydro-physical variables and crop residues determinate runoff, soil loss, and glyphosate and AMPA concentration in the aqueous phase under simulated rainfall events.

Soil structural degradation and water erosion processes were observed even in no-tillage schemes in the Pampas region. Within these conservation systems, agrochemical application per hectare is one of the highest globally. Thus, this entails a serious risk of water contamination. The objectives of this study were to (1) test the hypothesis that the hydrological dynamics and sediment concentration related to surface runoff were conditioned by soil structure regardless of the presence of maize (Zea mays L.) crop residue and (2) assess the incidence of maize crop residue on glyphosate and aminomethylphosphonic acid (AMPA) concentration in runoff. The soil under study corresponded to Arroyo Dulce Series (Typic Argiudoll silty loam soil). Rain simulations were performed in the laboratory on undisturbed soil samples. Total runoff and infiltration rate were similar between treatments with C(+) and without C(-) maize crop residues (C(+) 1381.40mL and 14.27mm h-1, C(-): 1529.70mL and 21.67mm h-1). The C(-) treatments showed a higher sediment concentration than C(+) (1.58 and 0.42g 100mL-1, respectively). Glyphosate and AMPA average values in runoff were 15.9 and 33.9µg L-1. High variability of the hydro-physical properties and occurrence of soil structure, particularly platy ones, were detected. The hydrological variables were conditioned mainly by the occurrence of platy structures regardless of crop residue presence. Glyphosate concentration was increased in the first runoff event by the presence of corn residues, while AMPA concentrations were higher in the second runoff event in both residue treatments. In this study, maize residue on the soil surface protected the soil from sediment detachment but did not change runoff or infiltration. Thus, the implementation of agricultural management practices that promote vegetative residue cover has shown positive results to erosion.

Assessment of factors affecting hydrologic variability of Wadhvana wetland, Gujarat (India)

Assessment of factors affecting hydrologic variability of Wadhvana wetland, Gujarat (India)

Hydrologic variability governs GHG emissions in rice-based cropping systems of Eastern India

Reducing methane (CH4) emissions is increasingly recognized as an urgent greenhouse gas mitigation priority for avoiding ecosystem ‘tipping points’ that will accelerate global warming. Agricultural systems, namely ruminant livestock and rice cultivation are dominant sources of CH4 emissions. Efforts to reduce methane from rice typically focus on water management strategies that implicitly assume that irrigated rice systems are consistently flooded and that farmers exert a high level of control over the field water balance. In India most rice is cultivated during the monsoon season and hydrologic variability is common, particularly in the Eastern Gangetic Plains (EGP) where high but variable rainfall, shallow groundwater, and subtle differences in topography interact to create complex mosaics of field water conditions. Here, we characterize the hydrologic variability of monsoon season rice fields (n = 207) in the Indian EGP (‘Eastern India’) across two contrasting climate years (2021, 2022) and use the Denitrification Decomposition (DNDC) model to estimate GHG emissions for the observed hydrologic conditions. Five distinct clusters of field hydrology patterns were evident in each year, but cluster characteristics were not stable across years. In 2021, average GHG emissions (8.14 mt CO2-eq ha−1) were twice as high as in 2022 (3.81 mt CO2-eq ha−1). Importantly, intra-annual variability between fields was also high, underlining the need to characterize representative emission distributions across the landscape and across seasons to appropriately target GHG mitigation strategies and generate accurate baseline values. Simulation results were also analyzed to identify main drivers of emissions, with readily identified factors such as flooding period and hydrologic interactions with crop residues and nitrogen management practices emerging as important. These insights provide a foundation for understanding landscape variability in GHG emissions from rice in Eastern India and suggest priorities for mitigation that honor the hydrologic complexity of the region.

The preservation of photosynthetic and hydrological signals in the carbon and hydrogen isotope compositions of n-fatty acids in the seasonal wetland soils of the Okavango Delta (Botswana)

The Okavango wetland (Botswana) is the world’s largest inland delta. A strong seasonality in water input leads to the contraction and extension of wetlands in the floodplains. The extreme evapotranspiration and little precipitation lead to a difference in the hydrogen isotope signature of rain, soil and river water. Biomarkers, such as plant waxes, are stored in the soils and preserved on geological timescales. To understand which signal is preserved in the stable isotope signatures of plant waxes, soils along a 250 m-long transect spanning waterlogged to dry soils were collected over several seasons and three years. In addition, plants, and plant and soil water were collected along this transect. First, carbon isotope ratios (δ13C) of plant waxes (i.e, n-fatty acids) were used to classify their metabolism. δ13C of bulk organic matter and individual n-fatty acids analyzed in the soils show a strong dependance on the type of vegetation found along the transect (C3 versus C4 plants). Hydrogen isotope ratios (δ2H) of water present in soil showed that shallow-rooted C4 grasses use superficial soil water, whereas the xylem water δ2H content in trees growing near the flooded channel indicated the use of river water. In addition, plant hydrogen fractionation between lipids and rain showed a strong influence of carbon metabolisms with larger fractionation for C3 plants compared with C4 grasses. n-fatty acid δ2H ratios in surface soils followed the hydrological variation in the Delta with its floods and dry periods. Hence δ2H of long-chain fatty acids seems to track the river-level variation rather than precipitation.

The response of non-point source pollution to climate change in an orchard-dominant coastal watershed

China is the largest global orchard distribution area, where high fertilization rates, complex terrain, and uncertainties associated with future climate change present challenges in managing non-point source pollution (NPSP) in orchard-dominant growing areas (ODGA). Given the complex processes of climate, hydrology, and soil nutrient loss, this study utilized an enhanced Soil and Water Assessment Tool model (SWAT-CO2) to investigate the impact of future climate on NPSP in ODGA in a coastal basin of North China. Our investigation focused on climate-induced variations in hydrology, nitrogen (N), and phosphorus (P) losses in soil, considering three Coupled Model Intercomparison Project phase 6 (CMIP6) climate scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. Research results indicated that continuous changes in CO2 levels significantly influenced evapotranspiration (ET) and water yield in ODGA. Influenced by sandy soils, nitrate leaching through percolation was the principal pathway for N loss in the ODGA. Surface runoff was identified as the primary pathway for P loss. Compared to the reference period (1971–2000), under three future climate scenarios, the increase in precipitation of ODGA ranged from 15% to 28%, while the growth rates of P loss and surface runoff were the most significant, both exceeding 120%. Orchards in the northwest basin proved susceptible to nitrate leaching, while others were more sensitive to N and P losses via surface runoff. Implementing targeted strategies, such as augmenting organic fertilizer usage and constructing terraced fields, based on ODGA's response characteristics to future climate, could effectively improve the basin's environment.

HRU-based Downscaling of GRACE-TWS to Quantify the Hydrogeological Fluxes and Specific Yield in the Lower Middle Ganga Basin

Precise volumetric assessments of different hydrological variables, such as precipitation, evapotranspiration, and groundwater components, are necessary for comprehensive water resource management. This presents several challenges, including topographical complexity and economic limitations, mainly when aiming for high temporal and spatial resolution.The satellite mission Gravity Recovery and Climate Experiment (GRACE) has greatly improved the ability to quantify variations in GWS. We have described a two-stage downscaling system that integrates SWAT Hydrological Response Units (HRUs) with GRACE-derived Terrestrial Water Storage Anomalies (GRACE-TWSA) using Artificial Neural Networks (ANN). Using data from the Global Land Data Assimilation System (GLDAS), GWS was derived with GRACE-TWSA and further downscaled to the HRU level. The GWS trend in most of the study areas has not shown any significant trend from 2001 to 2014. The mean increasing trend was 2.16 mm/year and the mean decreasing was 2.28 mm/year. Using a decision-tree-based CatBoost model, the GWS has been used as an independent variable to determine distributed groundwater levels within the study area. A strong correlation between measured groundwater levels and GWS was observed, and a regularised robust optimization was used to determine the Specific yield. The surface water and groundwater budgeting indicate that most Gangetic area blocks are water-stressed. The study offers an extensive approach to integrated water resource management by providing insights into groundwater availability, aquifer storage characteristics, and budgeting with HRU scale GWS estimates.

Fuzzy rule–based control of multireservoir operation system for flood and drought mitigation in the Upper Mun River Basin

Strategic reservoir operation, a primary water management measures, plays a significant role in mitigating floods and droughts. Since the reservoir operation involves making complicated decisions on uncertain hydrological variables driven by climate variability, therefore, constructive tool for decision making like fuzzy logic is essential to optimize reservoir management and ensure water security. This study demonstrated fuzzy logic application to multiple reservoir operation in tropical region like Thailand. A Fuzzy Rule–Based Model (FRBM) exploiting FL approach was developed to control the upstream reservoir operation in the Upper Mun River Basin (UMRB) using the data from 2008 to 2021. Implementing FRBM for UMRB was conducted by identifying two key variables; available water storage and 7–day ahead predicted inflow, as fuzzy inputs. The fuzzy output of the system is the release fraction determined by three operational condition modules; flood, neutral, and drought. For flood module, fuzzy release is primarily determined by the predicted inflow. However, the determination of reservoir release for drought and neutral modules is influenced by the targeted water demand. The results of base case illustrate the capability of FRBM in increasing reservoir storages at the start of dry season by 123.56 MCM/yr in UMRB due to the new daily release schemes generated. This allows supplying water closer to the theoretical agricultural needs and gross irrigation water requirement potentially reducing the risk of water shortfall during consecutive dry years. Whereas, the maximum fuzzy release is constrained corresponding to safe channel capacity of tributaries and Upper Mun river, therefore, downstream flooding is accordingly prevented.

The Duration of Dry Events Promotes PVC Film Fragmentation in Intermittent Rivers.

The majority of microplastics (MPs) found in the environment originate from plastic fragmentation occurring in the environment and are influenced by environmental factors such as UV irradiation and biotic interactions. However, the effects of river drying on plastic fragmentation remain unknown, despite the global prevalence of watercourses experiencing flow intermittence. This study investigates, through laboratory experiments, the coupled effects of drying duration and UV irradiation on PVC film fragmentation induced by artificial mechanical abrasion. This study shows that PVC film fragmentation increases with drying duration through an increase in the abundance and size of formed MPs as well as mass loss from the initial plastic item, with significant differences for drying durations >50% of the experiment duration. The average abundance of formed MPs in treatments exposed to severe drying duration was almost two times higher than in treatments nonexposed to drying. Based on these results, we developed as a proof of concept an Intermittence-Based Plastic Fragmentation Index that may provide insights into plastic fragmentation occurring in river catchments experiencing large hydrological variability. The present study suggests that flow intermittence occurring in rivers and streams can lead to increasing plastic fragmentation, unraveling new insights into plastic pollution in freshwater systems.

Evaluation and Uncertainty Analysis of the Land Surface Hydrology in LS3MIP Models Over China

AbstractThe Land Surface, Snow and Soil moisture Model Intercomparison Project (LS3MIP) offers valuable land surface hydrology products from the land modules of current Earth system models (ESMs). Historical hydrological variables from six ESMs driven by four meteorological forcing data sets (GSWP, WFDEI, CRU‐NCEP, and Princeton) in Land Model Intercomparison Project (LMIP) have been extensively evaluated with various high‐quality reference data sets over Chinese mainland. Compared with the reference data sets, the multi‐model ensemble means (MMEs) of most hydrological variables are underestimated, while their annual trends show high spatial consistency, with sign consistency over 56%–85% of land area. After computing and ranking four statistical metrics (bias, correlation coefficient, normalized standard deviation, and unbiased root‐mean‐square biases) between simulations and references, it is found that the CLM5 has the best performance, while the GSWP3 exhibits the highest quality. Furthermore, the analysis of variance method (ANOVA) is then used to trace sources (model, atmospheric forcing data sets and their interactions) of the uncertainty of those modeling hydrological variables for 1900–2012 (1948–2012 for runoff) over China. The results indicate that the total uncertainty and its composition vary with time and decrease significantly in recent decades, reflecting the enhanced forcing data quality. Larger forcing uncertainty existed during the early twentieth century because less available observation data sets have been adopted to constrain climate variables. For all modeling hydrological variables, the model uncertainty plays the dominant role, suggesting that the quality of LMIP products largely relies on Land surface models.

Assessing the Response Mechanisms of Elevated CO2 Concentration on Various Forms of Nitrogen Losses in the Golden Corn Belt

AbstractNitrogen (N) loss is a significant source of water quality pollution in alluvial watersheds. However, the mechanisms linking N loss and elevated CO2 concentration (eCO2) are not well recognized. In this study, we comprehensively calibrated the SWAT model equipped with a dynamic CO2 input and response module to investigate the response mechanisms between multiform N losses and eCO2 in a representative large‐scale watershed. Results revealed nitrate loss under eCO2 exceeding 100% in some upstream zones under the SSP5‐8.5 scenario (P < 0.05) compared to the constant CO2 concentration. This was directly related to the great increase in hydrological variables, which were the carriers of N losses, and the intensive inputs of N fertilizer. Results also found that nitrate leaching was greater than the other two processes for future periods, peaking at 309.3%, as compared to the baseline period. The findings suggested reducing fertilizer inputs by 10%–20% was promising, especially for reducing nitrate loss through runoff and leaching by up to 17.7% and 12.2%. This study explored the mechanisms of increased N loss in response to eCO2 and provided scientific evidence for early warning and making decisions to improve water quality at a large watershed scale.

Comparative analysis of bias correction techniques for future climate assessment using CMIP6 hydrological variables for the Indian subcontinent

Comparative analysis of bias correction techniques for future climate assessment using CMIP6 hydrological variables for the Indian subcontinent

Performance evaluation of CMIP6 models for application to hydrological modelling studies – A case study of Australia

Accurate estimation of climate change impacts on catchment hydrology is essential for effective future water management. The efficacy of such estimations is dependent on proper climate model selection. In this study, an attempt was made to formulate a methodology for climate model selection, evaluating eight climate models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The models were assessed for their ability to simulate variables used in hydrological studies and large-scale atmospheric circulation influencing rainfall in Australia. Five statistical indicators Root Mean Square Error (RMSE), Spatial Correlation (SC), Percentage Bias (Pbias), Normalized Root Mean Square Error (NRMSE), and Nash-Sutcliffe Efficiency (NSE) were used to evaluate the performance, and the models were ranked through Compromise Programming (CP), a multiple criteria decision making technique. Results show that HadGEM3-GC31-LL performed well in most of the categories considered and was top top-ranked model overall followed by GFDL-ESM4, CESM2-CAM6-RT, and CanESM5 for Australia. Conversely, MIROC6 consistently ranked lower in most of the categories. In the context of simulating hydrological variables, CESM2-CAM6-RT, HadGEM3-GC31-LL, and GFDL-ESM4 emerged as the top three models. The robustness of the proposed methodology suggests its applicability for model selection, making it a replicable approach for climate change impact assessment studies in diverse regions.

Effects of hydraulic conductivity on simulating groundwater–land surface interactions over a typical endorheic river basin

Hydraulic conductivity is the key variable in subsurface configuration that is important in modeling groundwater and its interactions with land surface processes. However, an acquisition of quantitatively distributed hydraulic conductivity is challenging due to limitations on data availability, accuracy and resolution, which inhibits an advanced understanding of hydrologic and energy cycles at the basin scale. This study investigated the role of hydraulic conductivity in simulating groundwater-land surface interactions over a typical endorheic river basin in northwestern China. A 3D variably saturated groundwater model coupled to a land surface model was configured to account for interactions between groundwater dynamics and land energy fluxes. Four scenarios of hydraulic conductivity were tested and inter-compared, adopting both homogeneous and heterogeneous parameterization strategies based on Global Soil Dataset for use in Earth System Models (GSDE) and HYdrogeology MaPS (GLHYMPS). The overall objectives are to diagnose effects of hydraulic conductivity on simulating groundwater–land surface interactions, with a particular focus on differences in spatial distributions and statistical characteristics between scenarios, as well as the dynamics and feedback mechanisms between groundwater and land surface energy fluxes. Comprehensive simulations and analyses were conducted to discern spatial distributions and statistical characteristics of key hydrological variables (streamflow, water table depth, latent heat flux, sensible heat flux) under different scenarios. Results showed that the hydraulic conductivity of the saturated zone had a significant control over water table depth, while the combine hydraulic conductivity in the vadose zone and the saturated zone governed the evolution of streamflow. Moreover, the water table critical range—where the land energy budget is highly sensitive to groundwater table—was 0.1 m–5 m and the area might expand approximately 4.5 % after considering the heterogeneity of hydraulic conductivity. Compared to the homogeneous scenario, the spatial heterogeneity of hydraulic conductivity amplified water table depth variability while diminishing land energy fluxes variability.

Dams alter the control pattern of watershed land use to riverine nutrient distribution: Comparison of three major rivers under different hydropower development levels in Southwestern China

Land use plays a critical role in managing water quality in a watershed, as it governs the import and distribution of nutrients. In addition to the land use, some rivers in Southwest China are encountering a new environmental stressor of damming, which is being driven by the national strategy of hydropower development. However, the coupling effect of land use and dams on nutrients remains poorly understood, challenging the effective management of riverine water quality. Therefore, this study examined the nutrients in the Nu, Yarlung Tsangpo (YT), and Lancang (LC) Rivers, which have no dam, 1 dam, and 11 dams, respectively, during different regulatory periods (spring and fall) to identify variations in nutrient control patterns influenced by land use and dams. The findings suggested that an increase in hydropower development contributed to a notable shift in nutrient patterns from land use regulation towards dam regulation and coupling effects. Land use dominated the nutrient variations of the Nu (27.4 %–32.8 %) and low hydropower development YT (25.2 %–30.9 %) Rivers during both seasons, but the primary contributors to the nutrient variations of the high hydropower development LC River were dams (17.9 %–41.6 %) and coupling effects (16.5 %–29.0 %). Dams transform nutrient levels and compositions through internal reservoir cycling, decoupling land use and nutrients. Partial least-squares structural equation model analysis further suggested that the coupling effects of the LC River were seasonal-specific, which was primarily attributed to hydrological variations that affected their interactions. During spring, the reservoir underwent a drainage mode characterized by high-level nutrients in the bottom water. Combined with the import of riverine nutrients, it exacerbated the increase of nutrients (synergistic effect). In contrast, the reservoir transitioned into a storage mode where it intercepted nutrients from the upstream and watershed during the fall, leading to a reduction in the previously observed increasing trend and an increase in nutrient variability (antagonism effect).

Multivariate Hydrological Modeling Based on Long Short-Term Memory Networks for Water Level Forecasting

In the Department of Chocó, flooding poses a recurrent and significant challenge due to heavy rainfall and the dense network of rivers characterizing the region. However, the lack of adequate infrastructure to prevent and predict floods exacerbates this situation. The absence of early warning systems, the scarcity of meteorological and hydrological monitoring stations, and deficiencies in urban planning contribute to the vulnerability of communities to these phenomena. It is imperative to invest in flood prediction and prevention infrastructure, including advanced monitoring systems, the development of hydrological prediction models, and the construction of hydraulic infrastructure, to reduce risk and protect vulnerable communities in Chocó. Additionally, raising public awareness of the associated risks and encouraging the adoption of mitigation and preparedness measures throughout the population are essential. This study introduces a novel approach for the multivariate prediction of hydrological variables, specifically focusing on water level forecasts for two hydrological stations along the Atrato River in Colombia. The model, utilizing a specialized type of recurrent neural network (RNN) called the long short-term memory (LSTM) network, integrates data from hydrological variables, such as the flow, precipitation, and level. With a model architecture featuring four inputs and two outputs, where flow and precipitation serve as inputs and the level serves as the output for each station, the LSTM model is adept at capturing the complex dynamics and cross-correlations among these variables. Validation involves comparing the LSTM model’s performance with linear and nonlinear Autoregressive with Exogenous Input (NARX) models, considering factors such as the estimation error and computational time. Furthermore, this study explores different scenarios for water level prediction, aiming to utilize the proposed approach as an effective flood early warning system.

Spatiotemporal distribution and inter-media transfer of polycyclic aromatic hydrocarbons in Shanghai, China: Historical patterns and future trends

Polycyclic Aromatic Hydrocarbons (PAHs) represent pervasive pollutants, posing health risks in urban environments. It is essential to comprehend the spatiotemporal distributions, composition profiles, and inter-media transfer processes of PAHs in various environmental compartments, influenced by both natural changes and anthropogenic activities. This study integrates historical and future spatiotemporally changing environmental parameters, including climate data, GDP, population data, land-use types, and hydrological variables, into the Multimedia Urban Model (MUM). This integration enables the simulation of spatiotemporal distributions and inter-media transfer fluxes of PAHs among six different media from the 2010s to the 2100s under two distinct Shared Socio-economic Pathways (SSP) scenarios in the megacity of Shanghai, China. The MUM model, featuring diverse gridded parameters, effectively captures PAH concentrations and movement across environmental compartments. Results indicate a decreasing trend in PAHs concentrations in the 2100s compared to the 2010s, with PAH concentrations in water, sediment, vegetation, and organic film covering impermeable surfaces under the SSP3–7.0 scenario higher than those of the SSP1–2.6 scenario. Low molecular weight PAHs dominate in the sediment, water, and air, whereas high molecular weight PAHs prevail in the organic film, vegetation, and soil. Sediment and soil serve as the predominant sinks for PAHs. The primary transport processes for PAH movement include air-film, air-soil, film-water, soil-air, and water-air. Almost all transfer fluxes exhibit a declining trend in future periods except for the air-film transport pathway. The principal input and removal routes for PAHs in Shanghai involve the advection of air and water. The study provides essential insights into the environmental behavior of PAHs and informs targeted pollution control in Shanghai. Additionally, it serves as a technical reference for similar pollution prediction research.

Modeling slough crayfish populations in response to hydrologic variability

Understanding and predicting animal population dynamics as a function of hydrologic variation is critical for the management of freshwater ecosystems. Crayfish are important fauna supported by the hydro-dynamic freshwater wetlands of the Everglades. We modeled the complex relationships between slough crayfish ( Procambarus fallax) population densities and hydrologic conditions using a spatially and temporally extensive 21-year dataset. We applied linear mixed–effect models, a classification and regression tree (CART), and random forest (RF) algorithms to develop predictions and eco-hydrologic interpretations. The RF model demonstrated the greatest predictive ability ( R2 = 0.56) followed by linear mixed–effect models ( R2 = 0.45) and the regression tree ( R2 = 0.29). Primary effects of hydrologic terms were similar among models, but the RF model identified important nonlinear and threshold relationships. Lower average depths (appr. 30–60 cm) over the year prior to the sample, in conjunction with relatively long periods of inundation, and more recent depths >30 cm, were associated with greater crayfish densities. The three methods revealed consistent understanding of crayfish eco-hydrologic relations and provide insight for natural resource management.

Assessing the effectiveness of reservoir operation and identifying reallocation opportunities under climate change

Reservoirs are designed and operated to mitigate hydroclimatic variability and extremes to fulfill various beneficial purposes. Existing reservoir infrastructure capacity and operation policies derived from historical records are challenged by hydrologic regime change and storage reduction from sedimentation. Furthermore, climate change could amplify the water footprint of reservoir operation (i.e. non-beneficial evaporative loss), further influencing the complex interactions among hydrologic variability, reservoir characteristics, and operation decisions. Disentangling and quantifying these impacts is essential to assess the effectiveness of reservoir operation under future climate and identify the opportunities for adaptive reservoir management (e.g. storage reallocation). Using reservoirs in Texas as a testing case, this study develops data-driven models to represent the current reservoir operation policies and assesses the challenges and opportunities in flood control and water supply under dynamically downscaled climate projections from the Coupled Model Intercomparison Project Phase 6. We find that current policies are robust in reducing future flood risks by eliminating small floods, reducing peak magnitude, and extending the duration for large floods. Current operation strategies can effectively reduce the risk of storage shortage for many reservoirs investigated, but reservoir evaporation and sedimentation pose urgent needs for revisions in the current guidelines to enhance system resilience. We also identify the opportunities for reservoir storage reallocation through seasonal-varying conservation pool levels to improve water supply reliability with negligible flood risk increase. This study provides a framework for stakeholders to evaluate the effectiveness of the current reservoir operation policy under future climate through the interactions among hydroclimatology, reservoir infrastructure, and operation policy.

Characterizing the multisectoral impacts of future global hydrologic variability

There is significant uncertainty in how global water supply will evolve in the future, due to uncertain climate, socioeconomic, and land use change drivers and variability of hydrologic processes. It is critical to characterize the potential impacts of uncertainty in future water supply given its importance for food and energy production. In this work, we introduce a framework that integrates stochastic hydrology and human-environmental systems to characterize uncertainty in future water supply and its multisector impacts. We develop a global stochastic watershed model and demonstrate that this model can generate a large ensemble of realizations of basin-scale runoff with global coverage that preserves the mean, variance, and spatial correlation of a historical benchmark. We couple this model with a well-known human-environmental systems model to explore the impacts of runoff variability on the water and agricultural sectors across spatial scales. We find that the impacts of future hydrologic variability vary across sectors and regions. Impacts are felt most strongly in the water and agricultural sectors for basins that are expected to have unsustainable water use in the future, such as the Indus River basin. For this basin, we find that the variability in future irrigation water withdrawals and irrigated cropland increase over time due to uncertainty in renewable water supply. We also use the Indus basin to show how our stochastic ensemble can be leveraged to explore the global multisector consequences of local extreme runoff conditions. This work introduces a novel technique to explore the propagation of future hydrologic variability across human and natural systems and spatial scales.