To understand an integral environmental health dynamic a symbiotic observation of water and its socio-environmental interactions should be knitted. Assuming circularity of water can provide related knowledge. A robust scientific evidence baseline is essential to allow health impact evaluation, in order to inform collective decisions on infrastructure development. Mixed methods are used to elaborate an analytical process to combine different heterogeneous data sources. Three analytical levels were defined: Population health, socioeconomic status infrastructure and natural resources specifically river basin. In 2017, water quality perception was surveyed in Drake, Osa Peninsula, Southern of Costa Rica. Then in 2018, a socioeconomic and general health census strategy was undertaken. A water microbiology survey was applied to assess river basin quality. Interaction between population health economics and river basin was observed on aqueducts. This technology play an essential role enabling communities for health improvement and address reduction of socio-economic inequalities by means of community-specific tools for social learning. Since water filtering was identified missing in overall water systems, a water bio-sand filter was designed and tested as a novel conservation technology to cultivate drinkable water at a very low cost. Drake’s inhabitants perceived the need for technologies to treat drinkable water. Conservation culture should be considered for the design of new aqueduct communal systems. An integral ecosystem health assessment index (IEHAI) is proposed as a baseline specification model to improved water resources research.

Hydroclimatic flood generating processes, such as excess rain, short rain, long rain, snowmelt and rain-on-snow, underpin our understanding of flood behaviour. Knowledge about flood generating processes helps to improve modelling decisions, flood frequency analysis, estimation of climate change impact on floods, etc. Yet, not much is known about how climate and catchment attributes influence the distribution of flood generating processes. With this study we aim to offer a comprehensive and structured approach to close this knowledge gap. We employ a large sample approach (671 catchment in the conterminous United States) and test attribute influence on flood processes with two complementary approaches: firstly, a data-based approach which compares attribute probability distributions of different flood processes, and secondly, a random forest model in combination with an interpretable machine learning approach (accumulated local effects). This machine learning technique is new to hydrology, and it overcomes a significant obstacle in many statistical methods, the confounding effect of correlated catchment attributes. As expected, we find climate attributes (fraction of snow, aridity, precipitation seasonality and mean precipitation) to be most influential on flood process distribution. However, attribute influence varies both with process and climate type. We also find that flood processes can be predicted for ungauged catchments with relatively high accuracy (R2 between 0.45 and 0.9). The implication of these findings is that flood processes should be taken into account for future climate change impact studies, as impact will vary between processes.

Sediment flows generate ground vibrations by exerting basal force fluctuations on the riverbed, which motivates the use of seismology to indirectly measure flow properties. Linking the force fluctuations and properties of highly concentrated sediment flows, however, remains particularly challenging due to complexities that arise from grain-to-grain interactions. Here we conduct downscaled flume experiments designed to investigate the influence of grain scale processes on the generation of force fluctuations for stratified sediment flows associated with significant grain sorting. We demonstrate that, under such flow conditions, the amplitude of force fluctuations decreases as the volumetric solid concentration increases. We suggest that this dependency reflects the negative relationship between volumetric solid concentration and particle agitation, which in turn controls the amplitude of force fluctuations. We therefore advance that volumetric solid should be incorporated in seismic models as a key parameter describing the particle agitation of highly concentrated sediment flows.

Channel avulsions on river deltas are the primary means to distribute sediment and build land at the coastline. Many studies have detailed how avulsions generate delta lobes, whereby multiple lobes amalgamate to form a fan-shaped deposit. Physical experiments demonstrated that a condition of sediment transport equilibrium can develop on the topset, characterized by neither deposition nor erosion of sediment, and material is dispersed to the foreset. This alluvial grade condition assumes steady subsidence and uniform basin depth. In nature, however, alluvial grade is disrupted by variable subsidence, and progradation of lobes into basins with variable depth: conditions that are prevalent for tectonically active margins. We explore sediment dispersal and deposition patterns across scales using measurements of delta and basin morphology compiled from field surveys and remote sensing, collected over 150 years, from the Selenga Delta (Baikal Rift Zone), Russia. Tectonic subsidence events, associated with earthquakes on normal faults crossing the delta, displace portions of the topset several meters below mean lake level. This allogenic process increases regional river gradient and triggers lobe-switching avulsions. The timescale for these episodes is shorter than the predicted autogenic lobe avulsion timescale. During quiescent periods between subsidence events, channel-scale avulsions occur relatively frequently because of in-channel sediment aggradation, dispersing sediment to regional lows of the delta. The hierarchical avulsion processes, arise for the Selenga Delta, preserves discrete stratal packages that contain predominately deep channels. Exploring the interplay between discrete subsidence and sediment accumulation patterns will improve interpretations of stratigraphy from active margins and basin models.

Year-to-year variability of precipitation and temperature has significant consequences for water management decision making. “Whiplash” is a term which describes this variability at its most severe, referring to events at various timescales in which the hydroclimate switches between extremes. Tree-rings in semi-arid environments like the Truckee-Carson River Basin (California/Nevada watersheds with headwaters in the Sierra Nevada) can provide proxy records of hydroclimate as their annual growth is tied directly to limitations in water-year rainfall and temperature, but traditional metrics of reporting explained variance do not distinguish a reconstruction’s sensitivity to whiplash events. In this study, a pool of total ring width indices from five snow-adapted conifer species (Abies magnifica, Juniperus occidentalis, Pinus ponderosa, Pinus jeffreyi, Tsuga mertensiana) were used to develop a series of standardized reconstructions of water-year PRISM precipitation (P12) using stepwise linear regression. A nonparametric analysis approach was then used to determine positive and negative whiplash events in reconstructed and instrumental precipitation records. Hypergeometric distribution of the resulting timeseries datasets illustrates relationships between reconstructions and recorded whiplash events and allows for determination of patterns in tree-ring growth response. The results of this study suggest that ring-width indices from the assessed conifer species in the snow-belt of the Sierra Nevada are often able to record consecutive years of opposing extreme precipitation and report such events through derived models. Negative WL events are tracked more consistently across species in site-specific reconstructions of P12 than positive ones. It appears that residual effects of a preceding year’s drought or pluvial do not necessarily suppress records of WL, though sensitivity to precursor conditions in tracking of WL events may differ across species, and the absolute WL events captured in a reconstruction vary.

In the present study, we extend the analysis of the dispersion processes induced by tidal flow in weakly-dissipative estuaries discussed in the companion paper. Here we focus the attention on the flow induced by more realistic tidal waves provided by different combinations of semi-diurnal and diurnal constituents. We employ a large-scale physical model of a system composed by a large basin (open sea) and a compound tidal channel, where tides are produced as volume waves with prescribed shapes. Two-dimensional superficial velocity fields are used to study the main Eulerian and Lagrangian properties of the flow, in terms of absolute and relative particle statistics. The results suggest that the mixed character of the tides strongly influences the shape of the macro-vortices generated at the tidal inlet, whereas the overall residual currents seem to be less sensitive. Moreover, for the present tidal setting, longitudinal dispersion, the dominant dispersion process, is enhanced when the semi-diurnal constituents prevail. Finally, multiple particle statistics show regimes typical of non-local dynamics for particle separation larger than a typical injection length scale, which is the size of the tidal inlet. Non-local dynamics imply that the dispersion is dominated by flow structures larger than the mean separation length, i.e. the tidal wavelength and the size of the macro-vortices. The present results together with those discussed in Part 1, offer a thorough insight in the main dispersion processes induced by tidal flows, which are extremely relevant in the case of estuarine dynamics.

Water stored in lakes and underground is a crucial component of the global hydrological cycle, with impacts on climate and sea level. However, long-term changes in the global distribution of this water are not well understood. Here we present the Water Table Model (WTM), which is capable of computing water-table elevation at large spatial scales and over long temporal scales. The WTM comprises two components: groundwater and dynamic lakes. The inclusion of a dynamic lake component allows us to incorporate surface-water movement and evaporation into water-table elevation estimates. We share sample results from both an artificial topography, and for the North American continent. These results indicate the close interactions between changes to water levels in lakes and the surrounding groundwater tables. The open-source code for the WTM is available on Github and Zenodo.

The Arctic hydrological system is an interconnected system that is experiencing rapid change. It is comprised of permafrost, snow, glacier, frozen soils, and inland river systems. Permafrost degradation, trends towards earlier snow melt, a lengthening snow-free season, soil ice melt, and warming frozen soils all challenge hydrologic simulation under climate change in the Arctic. In this study, we provide an improved representation of the hydrologic cycle across a regional Arctic domain using a generalizable optimization methodology and workflow for the community. We applied the Community Terrestrial Systems Model (CTSM) across the US state of Alaska and the Yukon River Basin at 4-km spatial resolution. We highlight several potentially useful high-resolution CTSM configuration changes. Additionally, we performed a multi-objective optimization using snow and river flow metrics within an adaptive surrogate-based model optimization scheme. Four representative river basins across our study domain were selected for optimization based on observed streamflow and snow water equivalent observations at ten SNOTEL sites. Fourteen sensitive parameters were identified for optimization with half of them not directly related to hydrology or snow processes. Across fifteen out-of-sample river basins, thirteen had improved flow simulations after optimization and the median Kling-Gupta Efficiency of daily flow increased from 0.40 to 0.63. In addition, we adapted the Shapley Decomposition to disentangle each parameter’s contribution to streamflow performance changes, with the seven non-hydrological parameters providing a non-negligible contribution to performance gains. The snow simulation had limited improvement, likely because snow simulation is influenced more by meteorological forcing than model parameter choices.

Despite the necessity of Global Climate Models (GCMs) sub-selection in the dynamical downscaling experiments, an objective approach for their selection is currently lacking. Building on the previously established concepts in GCMs evaluation frameworks, we relatively rank 37 GCMs from the 6th phase of Coupled Models Intercomparison Project (CMIP6) over four regions representing the contiguous United States (CONUS). The ranking is based on their performance across 60 evaluation metrics in the historical period (1981–2014). To ensure that the outcome is not method-dependent, we employ two distinct approaches to remove the redundancy in the evaluation criteria. The first approach is a simple weighted averaging technique. Each GCM is ranked based on its weighted average performance across evaluation measures, after each metric is weighted between zero and one depending on its uniqueness. The second approach applies empirical orthogonal function analysis in which each GCM is ranked based on its sum of distances from the reference in the principal component space. The two methodologies work in contrasting ways to remove the metrics redundancy but eventually develop similar GCMs rankings. While the models from the same institute tend to display comparable skills, the high-resolution model versions distinctively perform better than their lower-resolution counterparts. The results from this study should be helpful in the selection of models for dynamical downscaling efforts, such as the COordinated Regional Downscaling Experiment (CORDEX), and in understanding the strengths and deficiencies of CMIP6 GCMs in the representation of various background climate characteristics across CONUS.

Since the 1970s, agricultural production in central Argentina has shifted away from perennial crops and grasses towards annual crops, largely soy. In this work we use observations and modeling to understand how this shift in land cover has affected the sub-surface, surface and atmospheric fluxes of moisture and energy in a flat agricultural area. We analyze the flux tower data from a paired site at Marcos Juarez in central Argentina during the period of the RELAMPAGO field campaign (2018-2019). When compared to perennial alfalfa, the observations over soy show lower evapotranspiration and specific humidity, higher sensible heat, higher outgoing shortwave radiation and soil temperature. Furthermore, water table depth is shallower below the soy than the alfalfa sites. To better understand the long-term temporal behavior from 1970s to present, the Noah-MP land surface model was calibrated at both soy and alfalfa sites based on RELAMPAGO data. Long-term simulation of the calibrated model suggests that ~95% of precipitation is evaporated in the alfalfa site with negligible recharge and runoff. In the case of soy, ET is about 68% of precipitation, leaving nearly 28% for recharge and 4% for runoff. Observed increases in streamflow and decreases in water table depth over time are likely linked to shifts in land cover. The changes in water table depth are enhanced in El Nino years. Furthermore, the partitioning of net radiation shifts from latent heat to sensible heat resulting in a 250% increase in Bowen ratio (from 0.2 to 0.7).

FSO is a symbolic regression method that allows for automatic estimation of the structure and parameterization of transfer functions from catchment data. The FSO method transforms the search for an optimal transfer function into a continuous optimization problem using a text generating neural network (variational autoencoder). mHM is a widely applied distributed hydrological model, which uses transfer functions for all its parameters. For this study, we estimate transfer functions for the parameters saturated hydraulic conductivity and field capacity. To avoid the influence of parameter equifinality, the remaining mHM parameter values are optimized simultaneously. The study domain consists of 229 basins, including 7 major basins for Training and 222 smaller basins for validation, distributed across Germany. 5 years of data are used for training und 35 years for validation. By validating the estimated transfer functions in a set of validation basins in a different time period, we can examine the FSO estimated transfer functions influence on model performance, scalability and transferability. We find that transfer functions estimated by FSO lead to a robust performance when being applied in an ungauged setting. The median KGE of the validation basins in the validation time period is 0.73, while the median KGE of the 7 training basins in training time is 0.8. These results look promising, especially since we are only using 5 years of training data, and show the general applicability of FSO for distributed hydrological models.

Open water evaporation from reservoirs and lakes is becoming increasingly important for water management under a changing climate and increasing demands from growing populations. Remotely sensed evapotranspiration (ET) data have significantly advanced from MODIS to Landsat to ECOSTRESS. Here, we evaluate remotely sensed open water evaporation from NASA JPL’s ET data production team (e.g., ECOSTRESS) against in situ measurements of evaporation from multiple sites around the world.

Extreme precipitation events from the western disturbances have significant impacts on the water management and ecosystem services in the Upper Indus basin, a part of the Hindukush Himalayan region. Further, there are changes in the duration, intensity, spatial extent and frequency of extreme events due to climate change. In this domain, a few studies have assessed the impact of climate change in the study area with limited data. There will be high uncertainty in the outcomes obtained from the investigation of extreme events with limited data. Therefore, a comprehensive analysis of extreme precipitation events from western disturbances considering high resolution with long-term data is required in the study area. Accordingly, In the present study, Precipitation based ETCCDI Indices are calculated for every year, and non-parametric Mann-Kendall test is applied Sen slope is calculated to detect the changes in the monthly precipitation during the winter season for the period 1901-2019. The findings of the present study reveal the northwest region has an increasing trend in RX1day extreme precipitation of 1.85 mm per decade in December, and the rate has amplified due to the effect of western disturbances in January and February. Also, the pattern of RX5day extreme precipitation is consistent in January and February. Overall the wetness in increasing over the north-west part of the study area In recent decades, the northwest region of the upper Indus basin had faced more extreme events with severe impacts due to western disturbances, and outcomes from the study can improve the understanding of extreme events.

The majority of marine plastic pollution originates from land-based sources with the dominant transport agent being riverine. Despite the widespread recognition that rivers dominate the global flux of plastics to the ocean, there is a key knowledge gap regarding the nature of the flux, the behaviour of microplastics (<5mm) in transport and its pathways from rivers into the ocean. To predict transport, fate and biological interactions of microplastics in aquatic environments at a global scale, the factors that control these processes must be identified and understood. Currently, there remains a large knowledge gap around prediction of microplastic transport in rivers, especially in regards to how biofilm formation influence particle settling velocities. This prevents progress in understanding microplastic fate and hotspot formation, as well as curtailing the evolution of effective mitigation and policy measures. A settling experiment was therefore undertaken to understand how different factors, including salinity, suspended sediment concentration and biofilm formation influence microplastic particle settling velocity. The results presented herein explore the role of biofilms on the generation of microplastic flocs and the impact on buoyancy and settling velocities. Five different polymers were tested and compared including fragments and fibres. Settling velocities were then combined with observed flow velocity data from the Mekong River, one of the top global contributors to marine plastic pollution, allowing predictions of areas of microplastic fallout and hotspots. The results highlight potential areas of highest ecological risk related to the dispersal and distribution of microplastics across the river-delta-coast system including the Tonle Sap Lake. Future work involves supporting predicted hotspots with aligned fieldwork from the Mekong River that details the particulate flux and transport of microplastic, throughout the vertical velocity profile.

Gravity currents frequently occur when excess suspended sediments are flushed along a river and discharged into greater natural water environments such as lakes, reservoirs, and estuaries. Gravity and turbidity currents have been broadly investigated, but the effect of aquatic vegetation on their propagation in natural waters still presents several open questions. We conducted a series of laboratory experiments to investigate how flexible vegetation affects the propagation and flow structure of gravity currents on a constant slope. We used both rigid cylinders and flexible synthetic plants to mimic natural submerged vegetation canopies. By varying density configurations of the vegetation array and comparing the outcomes of rigid cylinders and flexible plants, the data showed distinct patterns based on array density and plant morphology. A two-layer current was created when the array density is large enough to redirect the flow, as opposed to sparser conditions where the denser fluid passes swiftly through the array. Flexible vegetation further suppresses the propagation speed of gravity currents compared to arrays of rigid cylinder with the same density, highlighting the importance of the multi-scale processes driven by complex plant morphologies that are not represented by rigid cylinder arrays.

The hydrologic cycle is a complex and dynamic system of interacting processes. Hydrologists seeking to understand and predict these systems develop models of varying complexity, and compare their output to observations to evaluate their performance or diagnose shortcomings within the models. Often, these analyses take into account only single variables or isolated aspects of the hydrologic system. To explore how process interactions affect model performance we have developed a general framework based on information theory and conditional probabilities. We compare how conditional mutual information and mean square errors are related in a variety of hydrometeorological conditions. By exploring different regions of phase space we can quantify model strengths and weaknesses in terms of both process accuracy as well as classical performance. By considering a range of conditions we can evaluate and compare models outside of their average behavior. We apply this analysis to physically-based models (based on SUMMA), statistical models, and observations from FluxNet towers at a number of hydro-climatically diverse sites. By focusing on how the turbulent heat fluxes are affected by shortwave radiation, air temperature, and relative humidity we go beyond simple error metrics and are able to reason about model behavior in a physically motivated way. We find that the statistically based models, while showing better performance in the mean field, often do not represent the underlying physics as well as the physically based models. The statistically based model’s over-reliance on shortwave radiation inputs limits their ability to reproduce more complex phenomena.

The motivation and objective of the EarthScope Transportable Array (TA) is to record earthquake signals and image the structure of the North American plate, however the observations collected by this National Science Foundation funded project have enabled unanticipated discoveries, innovative data analysis techniques, and ongoing investigations across many disciplines in the Earth and space sciences. The Transportable Array utilized a survey approach to collect data in which high-quality stations were systematically installed in a dense geospatial grid. From the very beginning of the deployment, this strategy allowed for data-driven discovery, such as using seismic data to map out extensive travel time curves for acoustic waves in the atmosphere (Hedlin et al., 2010). While the emplacement of the seismic sensors was kept uniform along with the core components for power and communications, the Transportable Array station design evolved over time to include additional barometric pressure and infrasound sensors and, eventually, meteorological sensors measuring external temperature, wind, and precipitation. As the array rolled across the Lower 48 and the TA became more recognized outside of seismology, collaborations were forged and strengthened with researchers in the infrasound and meteorological communities. Along with standard approaches using direct measurements, inventive techniques were used to apply environmental data for observing tectonic phenomena as well as applying seismic data for observing environmental phenomena. The value of integrated scientific infrastructure became even more apparent with the Transportable Array deployment in Alaska and western Canada, with autonomous and telemetered stations occupying sites within large swaths of previously unmonitored and inaccessible terrain. The majority of Alaska TA stations collect weather data and a subset also include a detached soil temperature probe. As a result, data collected by the Alaska Transportable Array have been used to observe throughout the ‘spheres: the lithosphere (earthquakes, volcanoes, landslides), the cryosphere (sea ice), the hydrosphere (precipitation, fire preparation), the atmosphere and biosphere (weather forecasting, storm systems, bolides), and even into the magnetosphere (space weather).

The coincidence of fluvial and coastal flooding can lead to compound floods with substantial impacts on human life, property, and infrastructure. Low-lying coastal areas are particularly vulnerable to compound flooding because of exposure to multiple drivers such as extreme coastal high tides, storm surge, and fluvial flooding. In this study, we develop a bivariate non-stationary flood risk assessment that accounts for compound flooding from fluvial and coastal events with consideration of impacts of sea level rise (SLR). Extreme river discharge values were identified using peak over threshold method and were paired with the corresponding highest sea-water level within ±1 days of these events across the coastal contiguous United States. The statistical dependence between the paired data was assessed using Kendall’s rank correlation coefficient. For the locations with significant dependence, the best copula fit was used for bivariate dependence analysis by assuming non-stationarity in the marginal distribution of sea-water level data. The mixture Normal-Generalized Pareto Distribution model with SLR as the covariate is used to incorporate the non-stationary coastal flood frequency. The future risk was assessed using the notation of failure probability, which refers to the probability of occurrence of at least one major coastal flooding (i.e., water level exceed the major coastal flood threshold) or 100-year fluvial flood for a given design life. Failure probability was formulated to allow for changing exceeding probabilities over time. The results indicate that the joint exceedance probability of fluvial or coastal flooding can be higher when the dependence is considered. Ignoring the compounding effects may inappropriately underestimate the flood probability at locations that flood hazard can be influenced by the interaction of fluvial and coastal events. Moreover, with rising sea levels, the probability of exceedances of sea-water level over the flood threshold increases and consequently the compound flood probability increases as well. In the locations with less dependency between extreme river discharge and sea-water level, the frequency amplification of fluvial and major coastal flood events is higher.

Hydrologic-Land Surface Models (H-LSMs) have been progressively developed to a stage where they represent the dominant hydrological processes for a variety of hydrological regimes and include a range of water management practices, and are increasingly used to simulate water storages and fluxes of large basins under changing environmental conditions across the globe. However, efforts for comprehensive evaluation of the utility of H-LSMs in large, regulated watersheds have been limited. In this study, we evaluated the capability of a Canadian H-LSM, called MESH, in the highly regulated Saskatchewan River Basin (SaskRB), Canada, under the constraint of significant precipitation uncertainty. A comprehensive analysis of the MESH model performance was carried out in two steps. First, the reliability of multiple precipitation products was evaluated against climate station observations and based on their performance in simulating streamflow across the basin when forcing the MESH model with a default parameterization. Second, a state-of-the-art multi-criteria calibration approach was applied, using various observational information including streamflow, storage and fluxes for calibration and validation. The first analysis shows that the quality of precipitation products had a direct and immediate impact on simulation performance for the basin headwaters but effects were dampened when going downstream. The subsequent analyses show that the MESH model was able to capture observed responses of multiple fluxes and storage across the basin using a global multi-station calibration method. Despite poorer performance in some basins, the global parameterization generally achieved better model performance than a default model parameterization. Validation using storage anomaly and evapotranspiration generally showed strong correlation with observations, but revealed potential deficiencies in the simulation of storage anomaly over open water areas. Keywords: Precipitation Uncertainty, Hydrologic-Land Surface Models, multi-criteria calibration, storage and fluxes validation, Saskatchewan River Basin, Canada

The state and dynamics of river chemistry are influenced by both anthropogenic and natural catchment characteristics. However, understanding key controls on catchment mean concentrations and export patterns comprehensively across a wide range of climate zones is still lacking, as most of this research is focused on temperate regions. In this study, we investigate the catchment controls on mean concentrations and export patterns (concentration–discharge relationship, C–Q slope) of river chemistry, using a long-term data set of up to 507 sites spanning five climate zones (i.e., arid, Mediterranean, temperate, subtropical, tropical) across the Australian continent. We use Bayesian model averaging (BMA) and hierarchical modelling (BHM) approaches to predict the mean concentrations and export patterns and compare the relative importance of 26 catchment characteristics (e.g., topography, climate, land use, land cover, soil properties and hydrology). Our results demonstrate that mean concentrations result from the interaction of catchment intrinsic and anthropogenic factors (i.e., land use, topography and soil), while export patterns are more influenced by catchment intrinsic characteristics only (i.e., topography). We also found that incorporating the effects of climate zones in a BHM framework improved the predictability of both mean concentrations and C–Q slopes, suggesting the importance of climatic controls on hydrological and biogeochemical processes. Our study provides insights into the contrasting effects of catchment controls across different climate zones. Investigating those controls can inform sustainable water quality management strategies that consider the potential changes in river chemistry state and export behaviour.

Subtle elastic rock deformation during aquifer testing may bear hydraulic parameter (permeability and compressibility) information owing to the poroelastic hydromechanical coupling effect. Here we report that such in situ rock deformations (~50 µε) during an aquifer pumping test are successfully measured along a vertical well by a high-resolution fiber optic distributed strain sensing (DSS) tool with an accuracy of 0.5 µε. We investigate the feasibility of hydraulic parameter estimation at meter scale using DSS data through a coupled hydromechanical model. Both synthetic and field cases are tested with sensitivity analysis. The results indicate that the simultaneous estimation of permeability and compressibility using DSS data is possible at low noise levels. However, only non-global near-optimal solutions can be obtained using the applied gradient-based inversion algorithm, because of parameter crosstalk and sensitivity problems when the data contain large noise. In particular, estimation is difficult for zones with relatively low permeability due to the low sensitivity to the strain changes. The estimated permeability/compressibility structures for the field test are largely consistent with other geological information from well logs. Our study suggests that DSS data can be quite useful in aquifer characterization and fluid flow profiling in addition to geomechanical monitoring. The obtained hydraulic information is beneficial for the optimized reservoir management of water and oil/gas storage.

High-resolution space-based spectral imaging of the Earth’s surface delivers critical information for monitoring changes in the Earth system as well as resource management and utilization. Orbiting spectrometers are built according to multiple design parameters, including ground sampling distance (GSD), spectral resolution, temporal resolution, and signal-to-noise. The different applications drive divergent instrument designs, so optimization for wide-reaching missions is complex. The Surface Biology and Geology component of NASA’s Earth System Observatory addresses science questions and meets applications needs across diverse fields, including terrestrial and aquatic ecosystems, natural disasters, and the cryosphere. The algorithms required to generate the geophysical variables from the observed spectral imagery each have their own inherent dependencies and sensitivities, and weighting these objectively is challenging. Here, we introduce intrinsic dimensionality (ID), a measure of information content, as an applications-agnostic, data-driven metric to quantify performance sensitivity to various design parameters. ID is computed through the analysis of the eigenvalues of the image covariance matrix, and can be thought of as the number of significant principal components. This metric is extremely powerful for quantifying the information content in high-dimensional data, such as spectrally resolved radiances and their changes over space and time. We find that the intrinsic dimensionality decreases for coarser GSD, decreased spectral resolution and range, less frequent acquisitions, and lower signal-to-noise levels. This decrease in information content has implications for all derived products. Intrinsic dimensionality is simple to compute, providing a single quantitative standard to evaluate combinations of design parameters, irrespective of higher-level algorithms, products, applications, or disciplines.

Coastal areas are highly vulnerable to flooding, due to hydrological extreme events such heavy rainfalls and/or storm surges which are supposed to be increasing in the next future due to the emission in atmosphere of anthropogenic greenhouse gases. In this study, in order to assess the future hydraulic risk in coastal regions, as well as, to identify optimal defense/adaptation policies, a risk analysis model is developed to calculate the present day and future flood risk, accounting for climate change uncertainties and mitigation measures. Such model juxtaposes a number of coupled/nested models as: a) a stacking daily rainfall downscaling model which combines simulations from multiple predictive models, as Random Forest, extreme gradient boosting and Non-homogeneous Hidden Markov Model (NHMM) (Cioffi et al. 2018); b) a Bivariate Point Process model (BPPM) (Zheng et al., 2014) that calculates Joint probability density function between extreme daily rainfall amount and daily extreme storm tide depth; c) a simulation-optimization model - in which multi-objective GA optimization model (Deb et al., 2002) and 2D hydraulic model are combined (Cioffi et al. 2018) - calculates sets of Pareto optimal solutions which are obtained by defining two optimality criteria consisting in: minimizing both the cost of the flood defense infrastructure system and the flooding hydraulic risk. ; d) a mathematical decision model which is aimed to identify the best policies of mitigation of hydraulic risk and the timing, taking into account the uncertainties in hydrological extreme event predictions. The risk analysis model is applied to the study case of Mazzocchio area which is the most depressed area (about 10000 ha) within the Pontinia Plain, a large reclamation region in the south of Lazio (Italy), particularly vulnerable to extreme events - as extreme rainfall amount and sea level rise due to storm surge at the sea outfall of the river- which in the past caused the crisis of hydraulic network system with flooding of large areas and collapse of levees. XXI Century projections of daily rainfall amount and sea level for the RCP 8.5-IPCC scenarios were performed using ensemble of 35 GCM simulations (CESM1 CAM5 BGC 20C + RCP8.5 Large Ensemble) (Kay et al., 2015).

Groundwater fluoride is the major cause of the endemic fluorosis. Global fluorosis data indicate that that granitic aquifer which fractures controlled hydrology is highly susceptible to contaminate groundwater with high fluoride. Till date there has not been any sincere effort to understand the type of granitic aquifer based on the different type of the granites and their fluoride content. The present paper assesses the different types of granites and their fluoride content. Dissolution of fluoride from these rock types are the major source of high fluoride contamination in the groundwater. The granitic aquifers are also dominated by fracture control hydrology which enhances the chances of rock water interaction and dissolution of fluoride. The mineralogy of the rocks is also favorable due to the presence of biotite and muscovite which are found to have high affinity to donate fluoride during rock water interaction as shown by the following equation. KAl2[AlSi3O10]F2+2OH- = KAl2[AlSi3O10]OH-+2F- (Muscovite) KMg3[AlSi3O10] F2+2 OH- = KMg3[AlSi3O10]OH-+2F- (Biotite) Delineation of the type of aquifer and the geochemistry of the granitic rocks needs to assess to understand the geogenic causes of fluoride in the granitic aquifer along with the water chemistry (pH>8) which enhances the rate of dissolution of fluoride during rock water interaction. Further estimation can be made by the application pf isotopic data particularly 18O, 2H, 3H, 34S isotope which can quantitatively estimate the sources of fluoride and contribution from different sources as well as rock water interaction time.