The characterization of changes over the full distribution of precipitation intensities remains an overlooked and underexplored subject, despite their critical importance to hazard assessments and water resource management. Here, we aggregate daily in situ Global Historical Climatology Network precipitation observations within seventeen internally consistent domains in the United States for two time periods (1951-1980 and 1991-2020). We find statistically significant changes in wet day precipitation distributions in all domains – changes primarily driven by a shift from lower to higher wet day intensities. Patterns of robust change are geographically consistent, with increases in the mean (4.5-5.7%) and standard deviation (4.4-8.7%) of wet day intensity in the eastern U.S., but mixed signals in the western U.S. Beyond their critical importance to the aforementioned impact assessments, these observational results can also inform climate model performance evaluations.
Constructed flood mitigation and drainage systems are integral to the development of many estuarine floodplains. These systems function throughout the tidal range, protecting from high water levels while draining excess catchment flows to the low water level. However, drainage can only be achieved under gravity when suitable water levels are available for discharge. Changes to the tidal range and symmetry that occur throughout estuarine waters mean that the window of opportunity for gravity discharge will vary dynamically within and between different catchments. It will also be affected by sea level rise (SLR). Concerns regarding the impacts of SLR have focussed on the acute effects of higher water levels, but SLR will affect the full tidal range and drainage systems will be particularly vulnerable to changes in the low tide. This study introduces the concept of the “drainage window”; to assess how the tidal regime may influence the drainage of estuarine floodplains, and particularly the potential impact of changing tidal regimes under SLR. The results of applying the drainage window to two different estuaries indicate that SLR may substantially reduce the opportunity for discharging many estuarine floodplain drainage systems. Additionally, measures proposed to mitigate flood risks may exacerbate drainage risks. Reduced drainage creates a host of chronic problems that may necessitate changes to existing land uses. A holistic assessment of future changes to all water levels (including low tide water levels) is required to inform strategic land use planning and management.
The use of geophysical characterization of karst systems can provide an economical and non-invasive alternative for extracting information about cavities, sinkholes, pathways for water infiltration as well as the degree of karstification of underlying carbonate rocks. In the present study, three geophysical techniques, namely, Ground Penetrating Radar (GPR), Electrical Resistivity Tomography (ERT) and Very Low Frequency Electromagnetic (VLFEM) were applied at three different and appropriate locations in fluvial karst of a listed environmentally sensitive area of the Rio Vermelho, Mambaí, Goiás, Brazil. In the data acquisition phase, the GPR, direct-current (DC) resistivity and VLFEM profiles were obtained at three different locations in the area. Data were analyzed using commonly adopted processing workflows. Different radar typologies have been assigned to soil and rock typse. The GPR results showed a well-defined lithology of the site based on the amplitude of the signal. On the other hand, the inverted resistivity cross-sections showed a three-layered stratigraphy, pathways of water infiltration and the weathered structures in carbonate (Bambui group). The interpretation of VLFEM as contours of current density resulted from Fraser and Karous-Hjelt filters, indicate the presence of conductive structures (high apparent current density) that may be linked with the weathered carbonate and other conductive and resistive anomalies may be associated with the water-filled and dry cavities (cave). The results encourage the integrated application of geophysical techniques as the reconnaissance for further detailed characterization of the karst areas.
Heterogeneous snow accumulation in the mountains introduces uncertainty to water-supply forecasting in much of the world. Water managers’ awareness of the challenge may account for forecast errors in management decisions. We assess the impact of uncertainty in seasonal-water-supply forecasts on reservoir management using the western slope of the Sierra Nevada of California as a case study. We find that higher forecast uncertainty decreases the volume of water released from reservoirs between April and July, suggesting that water managers hedge against the possibility of lower-than-expected runoff. We modeled April-July water releases as a function of corresponding runoff forecasts, their reported uncertainty, and available storage capacity. An unbalanced (n=416) panel data model with fixed effects suggests that if uncertainty goes up by 10 units, water managers reduce releases by about 6 units, even holding the mean forecast constant. The forecast volume, its uncertainty, available storage capacity, and the interaction between forecasted volume and uncertainty were all statistically significant predictors (p < 0.005) of releases. Increased forecast uncertainty and increased available storage were significantly and inversely associated with April-July release volume, whereas forecast volume and the interaction between forecast uncertainty and forecast volume were significantly and positively associated with release volume. These results support the hypothesis that water managers behave as if they are risk-averse with respect to the possibility of less runoff than forecasted. Thus, reducing operational forecast uncertainty may result in more water being released, without the need for direct coordination with water managers.
Selective logging, fragmentation, and understory fires directly degrade forest structure and composition. However, studies addressing the effects of forest degradation on carbon, water, and energy cycles are scarce. Here, we integrate field observations and high-resolution remote sensing from airborne lidar to provide realistic initial conditions to the Ecosystem Demography Model (ED–2.2) and investigate how disturbances from forest degradation affect gross primary production (GPP), evapotranspiration (ET), and sensible heat flux (H). We used forest structural information retrieved from airborne lidar samples (13,500 ha) and calibrated with 817 inventory plots (0.25 ha) across precipitation and degradation gradients in the Eastern Amazon as initial conditions to ED-2.2 model. Our results show that the magnitude and seasonality of fluxes were modulated by changes in forest structure caused by degradation. During the dry season and under typical conditions, severely degraded forests (biomass loss ≥ 66%) experienced water-stress with declines in ET (up to 34%) and GPP (up to 35%), and increases of H (up to 43%) and daily mean ground temperatures (up to 6.5°C) relative to intact forests. In contrast, the relative impact of forest degradation on energy, water, and carbon cycles markedly diminishes under extreme, multi-year droughts, as a consequence of severe stress experienced by intact forests. Our results highlight that the water and energy cycles in the Amazon are not only driven by climate and deforestation, but also the past disturbance and changes of forest structure from degradation, suggesting a much broader influence of human land use activities on the tropical ecosystems.
Direction and depths of hyporheic exchange fluxes at the groundwater - surface water interface are drivers of biogeochemical processes influencing nutrient cycling and water quality. Model concepts on the dynamic relationship between hyporheic exchange fluxes and exchange depth are typically based on the assumption of a linear relationship between both measures. Here, we quantify seasonal and episodic variations of hyporheic exchange fluxes and hyporheic exchange depths with methods of heat tracing. Numerically (FLUX-BOT) and analytically (VFLUX; method based on temperature amplitude dampening developed by Hatch et al., 2006) working program scripts were used to solve the one-dimensional conduction-advection-dispersion equation and compute hyporheic flux rates from three vertical sediment water temperature profiles recorded continuously in a small low mountain creek between 2011 and 2017. By comparing the behavior of two differing water temperature-based modelling approaches, dissimilarities in the sensitivity to sediment thermal properties were identified. These differences in parameter responsivity explain deviating behavior of the models regarding exchange flux and depth calculations. We show that the vertical extension of hyporheic exchange depth has a distinctive seasonal pattern over seven years, which differs between the chosen models. Surface water levels, groundwater levels and stream discharges show significant correlations with both flux direction and hyporheic zone extension. In contrast to the numerical modelling approach, analytically derived flux data allowed for establishing a significant relationship between the hydraulic gradient observed at a nearby groundwater well and simulated hyporheic exchange depths.
Global climate model projections suggest that 21st century climate change will bring significant drying in the midlatitudes. Recent glacier modeling suggests that runoff from glaciers will continue to provide substantial freshwater in many drainage basins, though the supply will generally diminish throughout the century. In the absence of dynamic glacier ice within global climate models (GCMs), a comprehensive picture of future basin-scale water availability for human and ecosystem services has been elusive. Here, we leverage the results of existing GCMs and a global glacier model to compute the effect of glacial runoff on the Standardized Precipitation-Evapotranspiration Index (SPEI), an indicator of basin-scale water availability. We find that glacial runoff tends to increase mean SPEI and reduce interannual variability, even in basins with relatively little glacier cover. However, in many basins we find inter-GCM spread comparable to the amplitude of the ensemble mean glacial effect, which suggests considerable structural uncertainty.
Deep learning (DL) methods have shown great promise for accurately predicting hydrologic processes but have not yet reached the complexity of traditional process-based hydrologic models (PBHM) in terms of representing the entire hydrologic cycle. The ability of PBHMs to simulate the hydrologic cycle makes them useful for a wide range of modeling and simulation tasks, for which DL methods have not yet been adapted. We argue that we can take advantage of each of these approaches to couple DL methods into PBHMs as individual process parameterizations. We demonstrate that this is viable by developing DL process parameterizations for turbulent heat fluxes and couple them into the Structure for Unifying Multiple Modeling Alternatives (SUMMA), a modular PBHM modeling framework. We developed two DL parameterizations and integrated them into SUMMA, resulting in a one way coupled implementation (NN1W) which relies only on model inputs and a two-way coupled implementation (NN2W), which also incorporates SUMMA-derived model states. Our results demonstrate that the DL parameterizations are able outperform calibrated standalone SUMMA benchmark simulations. Further we demonstrate that the two-way coupling can simulate the long-term latent heat flux better than the standalone benchmark. This shows that DL methods can benefit from PBHM information, and the synergy between these modeling approaches is superior to either approach individually.
Identifying fluid flow maldistribution in planar geometries is a well-established problem in subsurface science/engineering. Of particular importance to the thermal performance of Engineered (or “Enhanced”) Geothermal Systems (EGS) is identifying the existence of non-uniform (i.e., heterogeneous) permeability and subsequently predicting advective heat transfer. Here, machine learning via a Genetic Algorithm (GA) identifies the spatial distribution of an unknown permeability field in a two-dimensional Hele-Shaw geometry (i.e., parallel-plates). The inverse problem is solved by minimizing the L2-norm between simulated Residence Time Distribution (RTD) and measurements of an inert tracer breakthrough curve (BTC) (C-Dot nanoparticle). Principal Component Analysis (PCA) of spatially-correlated permeability fields enabled reduction of the parameter space by more than a factor of ten and restricted the inverse search to reservoir-scale permeability variations. Thermal experiments and tracer tests conducted at the mesoscale Altona Field Laboratory (AFL) demonstrate that the method accurately predicts the effects of extreme flow channeling on heat transfer in a single bedding-plane rock fracture. However, this is only true when the permeability distributions provide adequate matches to both tracer RTD and frictional pressure loss. Without good agreement to frictional pressure loss, it is still possible to match a simulated RTD to measurements, but subsequent predictions of heat transfer are grossly inaccurate. The results of this study suggest that it is possible to anticipate the thermal effects of flow maldistribution, but only if both simulated RTDs and frictional pressure loss between fluid inlets and outlets are in good agreement with measurements.
Hydrological interactions between vegetation, soil, and topography are complex, and heterogeneous in semi-arid landscapes. This along with data scarcity poses challenges for large-scale modelling of vegetation-water interactions. Here, we exploit metrics derived from daily Meteosat data over Africa at ca. 5 km spatial resolution for ecohydrological analysis. Their spatial patterns are based on Fractional Vegetation Cover (FVC) time series and emphasise limiting conditions of the seasonal wet to dry transition: the minimum and maximum FVC of temporal record, the FVC decay rate and the FVC integral over the decay period. We investigate the relevance of these metrics for large scale ecohydrological studies by assessing their co-variation with soil moisture, and with topographic, soil, and vegetation factors. Consistent with our initial hypothesis, FVC minimum and maximum increase with soil moisture, while the FVC integral and decay rate peak at intermediate soil moisture. We find evidence for the relevance of topographic moisture variations in arid regions, which, counter-intuitively, is detectable in the maximum but not in the minimum FVC. We find no clear evidence for wide-spread occurrence of the “inverse texture effect”’ on FVC. The FVC integral over the decay period correlates with independent data sets of plant water storage capacity or rooting depth while correlations increase with aridity. In arid regions, the FVC decay rate decreases with canopy height and tree cover fraction as expected for ecosystems with a more conservative water-use strategy. Thus, our observation-based products have large potential for better understanding complex vegetation–water interactions from regional to continental scales.
The recent development of the TOUGH3 code allows for a faster and more reliable fluid flow simulator. At the same time, new versions of FLAC3D are released periodically, allowing for new features and faster execution. In this paper, we present the first implementation of the coupling between TOUGH3 and FLAC3Dv6/7, maintaining parallel computing capabilities for the coupled fluid flow and geomechanical codes. We compare the newly developed version with analytical solutions and with the previous approach, and provide some performance analysis on different meshes and varying the number of running processors. Finally, we present two case studies related to fault reactivation during CO2 sequestration and nuclear waste disposal. The use of parallel computing allows for meshes with a larger number of elements, and hence more detailed understanding of thermo-hydro-mechanical processes occurring at depth.
Fire regimes are influenced by both exogenous drivers (e.g., increases in atmospheric CO2; and climate change) and endogenous drivers (e.g., vegetation and soil/litter moisture), which constrain fuel loads and fuel aridity. Herein, we identified how exogenous and endogenous drivers can interact to affect fuels and fire regimes in a semiarid watershed in the inland northwestern U.S. throughout the 21st century. We used a coupled ecohydrologic and fire regime model to examine how climate change and CO2 scenarios influence fire regimes over space and time. In this semiarid watershed we found that, in the mid-21st century (2040s), the CO2 fertilization effect on vegetation productivity outstripped the effects of climate change-induced fuel decreases, resulting in greater fuel loading and, thus, a net increase in fire size and burn probability; however, by the late-21st century (2070s), climatic warming dominated over CO2 fertilization, thus reducing fuel loading and fire activity. We also found that, under future climate change scenarios, fire regimes will shift progressively from being flammability to fuel-limited, and we identified a metric to quantify this shift: the ratio of the change in fuel loading to the change in its aridity. The threshold value for which this metric indicates a flammability versus fuel-limited regime differed between grasses and woody species but remained stationary over time. Our results suggest that identifying these thresholds in other systems requires narrowing uncertainty in exogenous drivers, such as future precipitation patterns and CO2 effects on vegetation.
The difference between precipitation and evaporation has been extensively used to characterize the water cycle’s response to global warming. However, when it comes to the global scale, the information provided by this metric is inconclusive. Herein, we discuss how the sum of precipitation and evaporation could complement the assessment of global water cycle intensification. To support our argument, we present a brief yet robust correlation analysis of both metrics in four reanalysis data sets (20CR v3, ERA-20C, ERA5, and NCEP/NCAR R1). Additionally, by combining the two metrics, we investigate how well the global water cycle fluxes are represented in the four reanalyses. Among them, we observe four different responses to the temperature increase between 1950-2010, with ERA5 showing the best agreement with the intensification hypothesis. We argue that these discrepancies would remain elusive with the traditional approach, which makes the utilization of the sum of precipitation and evaporation a valuable addition to our methodological toolbox for the assessment of the global water cycle intensification.
We investigate if the commonly neglected riverine detrital carbonate fluxes might balance several chemical mass balances of the global ocean. Particulate inorganic carbon (PIC) concentrations in riverine suspended sediments, i.e., carbon contained by these detrital carbonate minerals, was quantified at the basin and global scale. Our approach is based on globally representative datasets of riverine suspended sediment composition, catchment properties and a two-step regression procedure. The present day global riverine PIC flux is estimated at 3.1 ± 0.3 Tmol C/y (13% of total inorganic carbon export and 4 % of total carbon export), with a flux-weighted mean concentration of 0.26 ± 0.03 wt%. The flux prior to damming was 4.1 ± 0.5 Tmol C/y. PIC fluxes are concentrated in limestone-rich, rather dry and mountainous catchments of large rivers in Arabia, South East Asia and Europe with 2.2 Tmol C/y (67.6 %) discharged between 15 °N and 45 °N. Greenlandic and Antarctic meltwater discharge and ice-rafting additionally contribute 0.8 ± 0.3 Tmol C/y. This amount of detrital carbonate minerals annually discharged into the ocean implies a significant contribution of calcium (~ 4.75 Tmol Ca/y) and alkalinity fluxes (~ 10 Tmol(eq)/y) to marine mass balances and moderate inputs of strontium (~ 5 Gmol Sr/y), based on undisturbed riverine and cryospheric inputs and a dolomite/calcite ratio of 0.1. Magnesium fluxes (~ 0.25 Tmol Mg/y), mostly hosted by less-soluble dolomite, are rather negligible. These unaccounted fluxes help elucidating respective marine mass balances and potentially alter conclusions based on these budgets.
Floods are convincingly the most frequent and widespread natural hazard across the world. With an ample amount of literature forecasting increase in its frequency and magnitude further in the future, highly credible and efficient algorithms and tools are crucial for real-time flood monitoring. In this study, a highly efficient tool, Multi-Mission Flood Mapper, has been developed to delineate flood inundation extent without any human intervention from SAR images captured by multiple microwave SAR satellite missions, including ALOS PALSAR CEOS, ALOS 2 CEOS, COSMO-SkyMed, ENVISAT ASAR, ERS 1/2 CEOS, ERS 1/2 SAR(.E1, .E2), ICEYE, JERS CEOS, KOMPSAT-5, PAZ, RADARSAT-1 & -2, RCM, SAOCOM, SeaSat, Sentinel-1, TerraSAR-X, and TanDEM-X. The efficacy of the developed tool is assessed by performing a test on a significant number of flood events in India having diverse flooding patterns and landforms. To manifest the performance of the tool, the step-by-step processing at the backend of the tool is discussed in detail in this study by taking a flood event along the Ganga River in India as a case study. The algorithm of the tool includes various processing steps: pre-processing that incorporate applying orbit file, calibrate to sigma naught, speckle filtering, terrain correction and linear to decibel conversion; thematic analysis that involves multi-segmentation and Otsu’s thresholding techniques; post-processing that consists of the elimination of hill shadows, applying majority filter, and masking out permanent water bodies. Thus derived flood inundation layer is observed to be highly accurate compared to the master image. The total time taken by the tool for processing is about 4 minutes for the given image. The developed tool would be beneficial for rapid flood inundation map generation on a timely basis for flood monitoring and relief management during a disaster. In addition, the flood inundation layers can also be used for calibration/validation of hydrological/hydraulic models, geospatial planning, and generating flood hazard maps. Also, the Multi-Mission Flood Mapper tool is facilitated with a user-friendly Graphical User Interface (GUI), making it look simple and easy to use.
This article comprises three independent commentaries about the state of ICON principles in hydrology and discusses the opportunities and challenges of adopting them. Each commentary focuses on a different perspective as follows: (i) field, experimental, remote sensing, and real-time data research and application (Section 1); (ii) Inclusive, equitable, and accessible science: Involvement, challenges, and support of early career, marginalized racial groups, women, LGBTQ+, and/or disabled researchers (Section 2); and (iii) an ICON perspective on machine learning for multiscale hydrological modeling (Section 3). Hydrologists depend on data monitoring, analyses, and simulations from these diverse scientific disciplines to ensure safe, sufficient, and equal water distribution. These hydrologic data come from but are not limited to primary (in-situ: lab, plots, and field experiments) and secondary sources (ex-situ: remote sensing, UAVs, hydrologic models) that are typically openly available and discoverable. Hydrology-oriented organizations have pushed our community to increase coordination of the protocols for generating data and sharing model platforms. In addition, networking at all levels has emerged with an invigorated effort to activate community science efforts that complement conventional data collection methods. With increasing amounts of data, it has become difficult to decipher various complex hydrologic processes. However, machine learning, a branch of artificial intelligence, provides accurate and faster alternatives to understand different biogeochemical and hydrological processes better. Diversity, equity, and inclusivity are essential in terms of outreach and integration of peoples with historically marginalized identities into this professional discipline and respecting and supporting the local environmental knowledge of water users.
Poleward ocean heat transport is a key process in the earth system. We detail and review the northward Atlantic Water (AW) flow, Arctic Ocean heat transport, and heat loss to the atmosphere since 1900 in relation to sea ice cover. Our synthesis is largely based on a sea ice-ocean model forced by a reanalysis atmosphere (1900-2018) corroborated by a comprehensive hydrographic database (1950-), AW inflow observations (1996-), and other long-term time series of sea ice extent (1900-), glacier retreat (1984-) and Barents Sea hydrography (1900-). The Arctic Ocean, including the Nordic and Barents Seas, has warmed since the 1970s. This warming is congruent with increased ocean heat transport and sea ice loss and has contributed to the retreat of marine-terminating glaciers on Greenland. Heat loss to the atmosphere is largest in the Nordic Seas (60% of total) with large variability linked to the frequency of Cold Air Outbreaks and cyclones in the region, but there is no long-term statistically significant trend. Heat loss from the Barents Sea (~30%) and Arctic seas farther north (~10%) is overall smaller, but exhibit large positive trends. The AW inflow, total heat loss to the atmosphere, and dense outflow have all increased since 1900. These are consistently related through theoretical scaling, but the AW inflow increase is also wind-driven. The Arctic Ocean CO2 uptake has increased by ~30% over the last century - consistent with Arctic sea ice loss allowing stronger air-sea interaction and is ~8% of the global uptake.
Future precipitation changes are controlled by the atmospheric energy budget, with temperature, water vapor, and absorbing aerosols playing dominant roles in driving radiative changes. Atmospheric energy budgets are calculated for different Shared Socioeconomic Pathways (SSPs) using ScenarioMIP projections from phase 6 of the Climate Model Intercomparison Project and are used to quantify the influence of 21st century aerosol cleanup on precipitation. Absorbing aerosol influences on shortwave absorption are isolated from the effects of water vapor. Apparent hydrologic sensitivity is ~40% higher for the “Middle of the Road” (SSP2-4.5) scenario with aerosol cleanup than for the “Regional Rivalry” (SSP3-7.0) scenario that maintains aerosol. Regionally, cleanup-induced changes in the atmospheric energy budget are of a similar magnitude to the precipitation increases themselves and are larger than the influence of changes in atmospheric circulation. Policy choices about future absorbing aerosol emissions will therefore have major impacts on global and regional precipitation changes.