Federal University of Uberlândia
Publications
13 801
Citations
193 315
h-index
124
Top-3 journals
Physical Review B
(162 publications)
Lecture Notes in Computer Science
(115 publications)
PLoS ONE
(115 publications)
Top-3 organizations
Universidade Estadual Paulista
(845 publications)
Universidade Estadual de Campinas
(762 publications)
Federal University of Goiás
(697 publications)
Top-3 foreign organizations
University of Florida
(89 publications)
University of California, Davis
(86 publications)
Harvard University
(43 publications)
Most cited in 5 years
Found
Publications found: 21327
A Fully Coupled Numerical Solution of Water, Vapor, Heat, and Water Stable Isotope Transport in Soil
Fu H., Neil E.J., Li H., Si B.
AbstractModeling water stable isotope transport in soil is crucial to sharpen our understanding of water cycles in terrestrial ecosystems. Although several models for soil water isotope transport have been developed, many rely on a semi‐coupled numerical approach, solving isotope transport only after obtaining solutions from water and heat transport equations. However, this approach may increase instability and errors of model. Here, we developed an algorithm that solves one‐dimensional water, heat, and isotope transport equations with a fully coupled method (MOIST). Our results showed that MOIST is more stable under various spatial and temporal discretization than semi‐coupled method and has good agreement with semi‐analytical solutions of isotope transport. We also validated MOIST with long‐term measurements from a lysimeter study under three scenarios with soil hydraulic parameters calibrated by HYDRUS‐1D in the first two scenarios and by MOIST in the last scenario. In scenario 1, MOIST showed an overall NSE, KGE, and MAE of simulated δ18O of 0.47, 0.58, and 0.92‰, respectively, compared to the 0.31, 0.60, and 1.00‰ from HYDRUS‐1D; In scenario 2, these indices of MOIST were 0.33, 0.52, and 1.04‰, respectively, compared to the 0.19, 0.58, and 1.15‰ from HYDRUS‐1D; In scenario 3, calibrated MOIST exhibited the highest NSE (0.48) and KGE (0.76), the smallest MAE (0.90) among all scenarios. These findings indicate MOIST has better performance in simulating water flow and isotope transport in simplified ecosystems than HYDRUS‐1D, suggesting the great potential of MOIST in furthering our understandings of ecohydrological processes in terrestrial ecosystems.
A Half‐Order Derivative Based Model of Lake Heat Storage Change
Liu Y., Tang L., Xing W., Wang J., Wang R., Cui Y., Li Q.
AbstractHeat storage change (HSC) is a crucial component of lake's thermal energy budget. Conventional temperature profile based models of HSC require location specific parameters such as lakebed topography. Based on the half‐order time‐derivative formula of heat fluxes, an analytical model was formulated for estimating HSC from water surface temperature and solar radiation without using geography dependent parameters. The proposed model was tested against field measurements at Poyang Lake, a shallow inland lake, which has pronounced seasonal variations in water level and lake area. Our analysis indicates that the model accurately simulates diurnal HSC with a coefficient of determination of 0.94 and a root mean squared error (RMSE) of 77.5 ± 21.6 Wm−2 for the study period. Larger nighttime RMSE (75.0 ± 26.8 Wm−2) than the daytime value (55.1 ± 19.7 W m−2) is attributable to larger measurement errors of nighttime turbulent fluxes. The estimation of HSC independent of temperature profile and lake‐specific parameters by the proposed model facilitates remote sensing monitoring the HSC of global water bodies.
Experimental Analyses of Pore‐Size Dependent Biomineralization in Porous Media Under Various Flow Rate and Bacterial Density Scenarios
Yang Z., Dou Z., Guadagnini A., Li X., Wang C., Wang J.
AbstractWe document results of a set of laboratory experiments aimed at exploring impacts of injection rate and bacterial density on biomineralization across water‐saturated porous media. The study relies on a Low‐Field Nuclear Magnetic Resonance technology and the ensuing transverse spin‐spin relaxation time distributions. The latter is documented to provide a robust quantification of temporal histories of pore size distributions during biomineralization. As such, our work explores and quantifies pore‐size dependent biomineralization across the three‐dimensional pore space. The study also provides a quantitative analysis of alterations in porosity and permeability induced by biomineralization, together with a quantification of (time‐averaged) rates of pore volume change. A plugging ratio efficiency index is introduced to quantify the strength of pore‐size‐related biomineralization. Our results reveal that biomineralization induces significant alterations in the pore size distribution within a porous medium, these changes being modulated by bacterial density and injection rate. We find that CaCO3 mainly precipitates in macropores, consistent with the presence of favorable local hydrodynamic conditions and large surface areas therein. Precipitated CaCO3 volume is found to increase with bacterial density. High bacterial densities amplify rate of pore volume change within macropores and adequate plugging ratio of biomineralization and contribute to a significant permeability reduction. Otherwise, a diminished strength of biomineralization in mesopores and micropores is documented for the highest injection rates considered.
Quantifying Dynamic Linkages Between Precipitation, Groundwater Recharge, and Streamflow Using Ensemble Rainfall‐Runoff Analysis
Gao H., Ju Q., Zhang D., Wang Z., Hao Z., Kirchner J.W.
AbstractUnderstanding streamflow generation at the catchment scale requires quantifying how different components of the system are linked, and how they respond to meteorological forcing. Here we present a proof‐of‐concept study characterizing and quantifying dynamic linkages between precipitation, groundwater recharge, and streamflow using a data‐driven nonlinear deconvolution and demixing approach, Ensemble Rainfall‐Runoff Analysis (ERRA). Streamflow in our mesoscale, intensively farmed test catchment is flashy, but occurs at time lags that are too long to be plausibly attributed to overland flow. Instead, ERRA's estimates of the impulse responses of groundwater recharge to precipitation, and of streamflow to groundwater recharge, imply that this intermittent streamflow is primarily driven by precipitation infiltrating to recharge groundwater, followed by discharge of groundwater to streamflow. ERRA reveals that streamflow increases nonlinearly with increasing precipitation intensity or groundwater recharge, and exhibits almost no response to precipitation or recharge rates of less than 10 mm d−1. Groundwater recharge is both nonlinear, increasing more‐than‐proportionally with precipitation intensity, and nonstationary, increasing with antecedent wetness. Simulations with the infiltration model Hydrus‐1D can reproduce the observed water table time series reasonably well (NSE = 0.70). However, ERRA shows that the model's impulse response is inconsistent with the real‐world impulse response estimated from measured precipitation and groundwater recharge, illustrating that conventional goodness‐of‐fit statistics can be weak tests of model realism. Thus, our proof‐of‐concept study demonstrates how impulse responses estimated by ERRA can help clarify linkages between precipitation and streamflow at the catchment scale, quantify nonlinearity and nonstationarity in hydrologic processes, and critically evaluate simulation models.
Roughness and Energy Losses Induced by Mussel Growth on the Walls of Hydraulic Structures and Application to a Water Transfer Project
Zhang J., Xu M., Huber B., Grünzner M., Blanckaert K.
AbstractMussel biofouling increases energy losses in hydraulic structures. The first contribution of this paper is the quantification of the mussel‐induced equivalent sand roughness ks as function of the mussel attachment density N and the shell length L. Laboratory experiments reveal that ks/L ≈ 1.5 for a continuous regular layer of mussels, which is found for N L2 > 1.2. For 0.5 < N L2 < 1.2, the mussels form a continuous irregular roughness layer with increased values of ks/L of up to 2.4. These geometrical irregularities are interpreted as macro‐roughness elements, that is, roughness elements with a spatial scale larger than that of an individual mussel. For N L2 < 0.5, the density of the irregularities is too low to act as macro‐roughness elements leading to ks/L < 1.5. The second contribution is the establishment of a threshold criterion for the importance of filtering activity on ks based on data from the here reported experiments and data reported in literature in other configurations and/or with other mussel species. It is found that laboratory conditions are often close to the threshold value but that mussel filtering is always negligible in large hydraulic structures. The third contribution is the development of a method based on 3‐D numerical simulations for estimating a Darcy‐Weisbach friction factor f for walls that are only partially covered with patches of mussels. An application example illustrates how the thus obtained f can be used in a 1‐D model for quantifying the additional energy losses in large water transfer projects.
Enhancing Hectare‐Scale Groundwater Recharge Estimation by Integrating Data From Cosmic‐Ray Neutron Sensing Into Soil Hydrological Modeling
Scheiffele L.M., Munz M., Francke T., Baroni G., Oswald S.E.
AbstractVadose zone models, calibrated with state variables, may offer a robust approach for deriving groundwater recharge. Cosmic‐ray neutron sensing (CRNS) provides soil moisture over a large support volume (horizontal extent of hectares) and offers the opportunity to estimate water fluxes at this scale. However, the horizontal and vertical sensitivity of the method results in an inherently weighted water content, which poses a challenge for its application in soil hydrologic modeling. We systematically assess calibrating a soil hydraulic model in HYDRUS 1D at a cropped field site. Calibration was performed using different field‐scale soil moisture time series and the ability of the model to represent root zone soil moisture and derive groundwater recharge was assessed. As our benchmark, we used a distributed point sensor network from within the footprint of the CRNS. Models calibrated on CRNS data or combinations of CRNS with deeper point measurements resulted in cumulative groundwater recharge comparable to the benchmark. While models based exclusively on CRNS data do not represent the root zone soil moisture dynamics adequately, combining CRNS with profile soil moisture overcomes this limitation. Models calibrated on CRNS data also perform well in timing the downward flux compared to an independent estimate based on soil water tension measurements. However, the latter provides quantitative groundwater recharge estimates spanning a wide range of values, including unrealistic highs exceeding local annual precipitation. Conversely, modeled groundwater recharge based on the distributed sensor network or on CRNS resulted in estimates ranging between 30% and 40% of annual precipitation.
Catchments Amplify Reservoir Thermal Response to Climate Warming
Gai B., Kumar R., Hüesker F., Mi C., Kong X., Boehrer B., Rinke K., Shatwell T.
AbstractLentic waters integrate atmosphere and catchment processes, and thus ultimately capture climate signals. However, studies of climate warming effects on lentic waters usually do not sufficiently account for a change in heat flux from the catchment through altered inflow temperature and discharge under climate change. This is particularly relevant for reservoirs, which are highly impacted by catchment hydrology and may be affected by upstream reservoirs or pre‐dams. This study explicitly quantified how the catchment and pre‐dams modify the thermal response of Rappbode Reservoir, Germany's largest drinking water reservoir system, to climate change. We established a catchment‐lake modeling chain in the main reservoir and its two pre‐dams utilizing the lake model GOTM, the catchment model mHM, and the stream temperature model Air2stream, forced by an ensemble of climate projections under RCP2.6 and 8.5 warming scenarios. Results exhibited a warming of 0.27/0.15°C decade−1 for the surface/bottom temperatures of the main reservoir, with approximately 8%/24% of this warming attributed to the catchment warming, respectively. The catchment warming amplified the deep water warming more than at the surface, contrary to the atmospheric warming effect, and advanced stratification by about 1 week, while having a minor impact on stratification intensity. On the other hand, pre‐dams reduced the inflow temperature into the main reservoir in spring, and consequently lowered the hypolimnetic temperature and postponed stratification onset. This shielded the main reservoir from climate warming, although overall the contribution of pre‐dams was minimal. Altogether, our study highlights the importance of catchment alterations and seasonality when projecting reservoir warming, and provides insights into catchment‐reservoir coupling under climate change.
An Analytical Framework for Risk Evaluation and Design of Infiltration Basins for Managed Aquifer Recharge
Fiori A., de Barros F.P., Bellin A.
AbstractManaged Aquifer Recharge (MAR) plays an important role in improving and supplementing groundwater storage. Many natural factors, ranging from climatic conditions to soil characteristics, can impact the efficiency of an infiltration basin. Other factors, such as engineered variables, will also influence the basin performance and the risks associated with groundwater contamination. The latter depends on the interplay between the hydraulic characteristics of the system and the soil and solute properties. The design of infiltration basins has been performed so far with the main objective of mitigating the tendency of the basin to reduce the infiltration rate with time due to clogging of the basin's bottom. Less attention has been paid to the risk of groundwater contamination by the infiltrating water. To understand the complex interplay between natural and engineering parameters on MAR efficiency and the contamination risk, we propose a risk‐oriented analytical framework. The framework allows to investigate the interplay between soil parameters, engineering design and climatic factors on the efficiency of an infiltration basin. Our framework relies on novel analytical solutions that relates the geometrical and hydrological features of the infiltration basin to its efficiency and groundwater contamination risk. The solutions incorporates the randomness associated with inflows (precipitation) and soil properties. We explore the trade‐off between efficiency and the risk of contamination and delineate a design procedure that balances these two opposing needs. Although the framework relies on simplifying assumptions, it provides a computationally efficient manner to obtain physical insights and relate model input parameters to decision making.
ENSO Enhances Seasonal River Discharge Instability and Water Resource Allocation Pressure
Zhu M., Yu D., Yu Y., Zheng Y., Li S., Cai X., Chen N.
AbstractThe El Niño‐Southern Oscillation (ENSO) significantly disrupts Pacific Ocean watershed hydrology, affecting water supply reliability. However, the specific ways in which ENSO affects seasonal river discharge remain underexplored, presenting a significant gap in our understanding of climate‐water interactions. Our study reveals that ENSO exacerbates river discharge variability, evident in the dynamics of maximum rise (Dr) and fall (Df) in standardized discharge, and their duration (M). Notably, ENSO augments Dr but shortens M in major rivers like the Yangtze. Employing a novel metric, the Discharge Instability Index (DII), we find that DII surges by at least 69% in El Niño years, particularly in southwestern North American watersheds. Vegetation and precipitation emerge as pivotal in shaping the discharge response to ENSO. Predictive modeling with DII suggests an escalation in discharge instability under climate warming, with a 0.11%–9.46% increase. This insight calls for water managers to integrate ENSO‐induced seasonal variations into strategic planning, blending immediate actions like dam regulation with long‐term initiatives such as afforestation, to counteract climate‐induced water scarcity.
Dilute Species Transport During Generalized Newtonian Fluid Flow in Porous Medium Systems
Bowers C.A., Miller C.T.
AbstractDilute species transport in generalized Newtonian fluids (GNFs) is typically described using explanatory empirical approaches assuming a traditional Fickian form, which is an approach that lacks predictive ability for systems and conditions not specifically investigated. Dilute species transport was investigated for a wide range of Cross and Carreau fluids flowing through a set of monodisperse and polydisperse sphere pack porous media. Both microscale and macroscale simulations were performed to demonstrate that GNF fluid flow can be predicted based upon Newtonian characterization of the media and rheological characterization of the fluid. Dilute species transport was shown to have a Fickian limit with dispersivity dependent on the porous media, fluid properties, and the flow rate in a nonlinear fashion. Dimensionless analysis and symbolic regression was used to deduce an explanatory and predictive function to describe dispersivity in terms of relevant system properties, enabling prediction of dilute species transport for GNFs flowing through porous media that does not require any non‐Newtonian experiments or parameter estimation.
Temporal Variability in Reservoir Surface Area Is an Important Source of Uncertainty in GHG Emission Estimates
Hansen C.H., Iftikhar B., Pilla R.M., Griffiths N.A., Matson P.G., Jager H.I.
AbstractEbullitive methane (CH4) emissions in lentic ecosystems tend to concentrate at river‐lake interfaces and within shallow littoral zones. However, inconsistent definitions of the littoral zone and static representations of the lake or reservoir surface area contribute to major uncertainties in greenhouse gas (GHG) emissions estimates, particularly in reservoirs with large water‐level fluctuations. This study examines temporal variation in littoral and total surface areas of US reservoirs and demonstrates how different methods and data sources lead to discrepencies in reservoir GHG emissions at large scales and over time. We also explore variability in remotely sensed water occurrence according to maximum surface area, reservoir purposes, and hydrologic regions. Notably, the largest relative variability in surface area is exhibited by small reservoirs with a maximum surface area <1 km2 and non‐hydroelectric reservoirs. Additionally, we use a case study of measured CH4 emissions from the southeastern United States (Douglas Reservoir) to illustrate the effects of varying surface area on reservoir‐wide GHG estimates. Upscaled CH4 emissions in Douglas Reservoir differed by nearly two‐fold depending on the source of total surface area data and whether estimates accounted for seasonal fluctuations in surface area. During seasonal drawdown in Douglas Reservoir, relative littoral area varies non‐linearly; periods of lower pool elevation (and thus larger relative littoral area) likely contribute disproportionately high CH4 emission rates compared to the commonly sampled summer season when water levels are at full‐pool elevation. Improved GHG monitoring and upscaling techniques require accounting for temporal variability in reservoir surface extent and littoral area.
Temperature Overshoot Would Have Lasting Impacts on Hydrology and Water Resources
Marshall A., Grubert E., Warix S.
AbstractModels of climate change impacts could be missing significant risks to hydrologic and water infrastructure systems through a shared feature: the idea that temperatures rise monotonically. By contrast, temperature overshoot pathways describe non‐monotonic warming trajectories, in which global temperatures first exceed a given target before declining to that target. Risks from overshoot pathways are qualitatively different from risks associated with monotonic warming trajectories, and are likely underestimated in current research and policy. Models suggest overshoot may be almost unavoidable if the more stringent Paris Agreement target limiting warming to 1.5°C over preindustrial levels is to be met by 2100. While overshoot has been relatively widely described in the climate literature, the impacts of overshoot on individual system characteristics have not. We suggest that failure to consider disparities between monotonic and overshoot warming impacts on hydrology and water resources presents particular risks due to divergent adaptation needs. Processes with decadal hysteresis are especially vulnerable. These include glacial contributions to streamflow; hydrologic consequences of vegetation change; altered groundwater; higher water use for fossil fuel combustion and carbon dioxide removal; and water infrastructure and policy that depends on climate conditions. We argue that risks of overshoot cannot be fully captured in current integrated assessment models and that overshoot needs to be specifically evaluated to adequately characterize risk in the water system. We consider how current modeling tools could be adapted to evaluate overshoot consequences, but also recognize that decisions must be made even without perfect knowledge.
Root‐Zone Water‐Storage Capacity and Uncertainty: An Intrinsic Factor Affecting Agroecosystem Resilience to Drought
Romano N., Mazzitelli C., Nasta P.
AbstractMapping ecosystem function indicators helps identify areas susceptible to drought, heat stress, and reduced agricultural production. This information can be used to prioritize areas for targeted interventions to tackle adverse climatic conditions and changes in land use. Root‐zone water‐storage capacity (SR) is a commonly used variable of agroecosystem functioning, representing the maximum value of water stored within the root zone and accessible to vegetation for its productive growth. Mapping SR over large spatial scales is only feasible through an oversimplification of real‐world conditions. Under such circumstances, we propose to resort to soil‐hydraulic‐energy indices, namely the integral mean water capacity (IMWC) and the integral energy (IE) and an effective root‐zone depth (zR). Accordingly, a more efficient and environmentally sensitive, albeit still simplistic, determination of the root‐zone water‐storage capacity is computed as SR,IMWC = zR × IMWC, and validated against soil moisture measurements carried out along a transect. Subsequently, the SR,IMWC indicator was mapped in Campania, a 13,700 km2 region in southern Italy. This study also addressed the issue of the propagation of epistemic uncertainty in input soil hydraulic parameters to the output response variable IMWC. This was accomplished using a Monte Carlo simulation technique that generated several equiprobable stochastic realizations from the multivariate set of data inputs. Finally, we assessed the potential utility of the integral capacity energy (ICE) composite indicator, computed as the ratio IMWC/IE in %, as a scoring parameter to identify Priority Intervention Areas (PIAs) where resilience to environmental challenges, including water scarcity, drought events, and post‐fire conditions, could be enhanced.
Real‐Time Flood Inundation Modeling With Flow Resistance Parameter Learning
Young A., Albertson J.D., Moretti G., Orlandini S.
AbstractEmergency response to flood plain inundations requires real‐time forecasts of flow depth, velocity, and arrival time. Detailed and rapid flood inundation forecasts can be obtained from numerical solution of 2D unsteady flow equations based on high‐resolution topographic data and geomorphologically informed unstructured meshes. However, flow resistance parameters representing the effects of land surface topography unresolved by digital terrain model data remain uncertain. In the present study, flow resistance parameters representing the effects of roughness, vegetation, and buildings are determined hydraulically in real‐time using flow depth observations. A detailed numerical reproduction of a real flood has been largely corroborated by observations and subsequently used as a surrogate of the ground truth target. In synthetic numerical experiments, flow depth observations are obtained from a network of in‐situ flow depth sensors assigned to hydraulically relevant locations in the flood plain. Starting from a generic resistance parameter set, the capability of a tandem 2D surface flow model and Bayesian optimization technique to achieve convergence to the target resistance parameter set is tested. Convergence to the target resistance parameter set was obtained with 50 or fewer tandem flow + optimization iterations for each forecasting cycle in which the difference between simulated and observed flow depths is minimized. The flood arrival time errors across a 52 flood plain inundation area were reduced by 3.13 hr with respect to results obtained without optimization from a fixed range of flow resistance parameters. Performance metrics like critical success index and probability of detection reach values above 90% across the flood plain.
Improved Correction of Extreme Precipitation Through Explicit and Continuous Nonstationarity Treatment and the Metastatistical Approach
Vidrio‐Sahagún C.T., He J., Pietroniro A.
AbstractClimate models simulate extreme precipitation under nonstationarity due to continuous climate change. However, systematic errors in local‐scale climate projections are often corrected using stationary or quasi‐stationary methods without explicit and continuous nonstationarity treatment, like quantile mapping (QM), detrended QM, and quantile delta mapping. To bridge this gap, we introduce nonstationary QM (NS‐QM) and its simplified version for consistent nonstationarity patterns (CNS‐QM). Besides, correction approaches for extremes often rely on limited extreme‐event records. To leverage ordinary‐event information while focusing on extremes, we propose integrating the simplified Metastatistical extreme value (SMEV) distribution into NS‐QM and CNS‐QM (NS‐QM‐SMEV and CNS‐QM‐SMEV). We demonstrate the superiority of NS‐ and CNS‐QM‐SMEV over existing methods through a simulation study and show several real‐world applications using high‐resolution‐regional and coarse‐resolution‐global climate models. NS‐QM and CNS‐QM reflect nonstationarity more realistically but may encounter challenges due to data limitations like estimation errors and uncertainty, particularly for the most extreme events. These issues, shared by existing approaches, are effectively mitigated using the SMEV distribution. NS‐ and CNS‐QM‐SMEV offer lower estimation error, approximate unbiasedness, reduced uncertainty, and improved representation of the entire distribution, especially for samples of ∼70 years, and greater superiority with larger samples. We show existing methods may perform competitively for short samples but exhibit substantial biases in quantile‐quantile matching due to bypassing nonstationarity modeling. NS‐ and CNS‐QM‐SMEV avoid these biases, adhering better to their theoretical functioning. Thus, NS‐ and CNS‐QM‐SMEV enhance the correction of extremes under nonstationarity. Yet, properly identifying nonstationarity patterns is crucial for reliable implementations.