A document by Chaithanya B P. Click on the document to view its contents.

Evaporation decreases the mass and increases the isotope composition of falling drops. Combining and integrating the dependence of the evaporation on the drop diameter and on the drop-environment humidity difference, the square of drop diameter is found to decrease with the square of vertical distance below cloud base. Drops smaller than 0.5 mm evaporate completely before falling 700 m in typical subtropical marine boundary layer conditions. The effect on the isotope ratio of equilibration with the environment, evaporation, and kinetic molecular diffusion is modeled by molecular and eddy diffusive fluxes after Craig and Gordon (1965), with a size-dependent parameterization of diffusion that enriches small drops more strongly, and approaches the rough aerodynamic limit for large drops. Rain shortly approaches a steady state with the subcloud vapor by exchange with a length scale of 40 m. Kinetic molecular diffusion enriches drops up to as they evaporate by up to +5~\permil~for deuterated water (HDO) and +3.5~\permil~for H$_2$$^{18}$O. Rain evaporation enriches undiluted subcloud vapor by +12~\permil~per mm rain, explaining enrichment of vapor in evaporatively cooled downdrafts that contribute to cold pools. Microphysics enriches the vapor lost by the early and complete evaporation of smaller drops in the distribution. Vapor from hydrometeors is more enriched than it would be by Rayleigh distillation or by mixtures of liquid rain and vapor in equilibrium with rain.

Flooding has been a significant problem over the past century in the United States (US), causing growing threats to human lives and socioeconomic damage. In the state of Kansas, since 1996, more than 1,500 flood events were recorded, resulting in an economic loss of between US$2b and US$5b. Many factors influence flood-susceptibility at a local scale. It may be helpful and timely to improve community resilience to flood disasters in Kansas. Our initial step was to assess factors that trigger flooding using Machine Learning (ML). Six ML algorithms: 1) Logistic Regression (LR); 2) Random Forest (RF); 3) Support Vector Machine (SVM); 4) K-nearest neighbor (KNN); 5) Adaptive Boosting (Ada Boost); 6) Extreme Gradient Boosting (XG boost) were used to evaluate their ability to classify locations in terms of floodsusceptibility. The learning data for these ML algorithms comprised a geo-spatial database of twelve floodsusceptibility factors from 1,528 flood inventories since 1996. The susceptibility factors comprised: rainfall, elevation, slope, aspect, flow direction, flow accumulation, Topographic Wetness Index (TWI), distance from the nearest stream, evapotranspiration, land cover, land surface temperature, and hydrographic soil type. The ML algorithms were compared, and the best algorithm was selected to estimate floodsusceptibility for each location in the geodatabase resulting in a flood-susceptibility map. A sensitivity analysis of floodsusceptibility factors indicated that the intensity or magnitude of the rainfall, land cover and soil type were the most significant factors for Kansas during this period.

Monitoring the various water cycle components are instrumental in ecological preservation, disaster preparedness, and achieving sustainable water resource management. Remote sensing observations, along with Land Data Assimilation System-derived information, can aid in investigating individual components and processes within the water cycle to characterize spatiotemporal patterns in the change in water availability in large river basins. The Ganges- Brahmaputra, one of the world's largest and most densely populated river basins, covering parts of India, Bangladesh, Nepal, Bhutan, and China, yet poorly gauged for water monitoring, is the area of interest for this case study focusing on estimates of precipitation, evapotranspiration, change in terrestrial water storage, and storm surface runoff from satellite-based NASA GPM (IMERG), NASA MODIS, and NASA GRACE/GRACE FO observations, and GLDAS Catchment Land Surface Model simulations. Data on each water cycle component was analyzed to approximate the total water budget on a sub-basin level. Intra-annual (wet and dry seasons) and inter-annual variability were also quantified for the years 2004-2005, 2009-2010, 2014-2015, and 2019-2020 for the entire Ganges-Brahmaputra basin. Variation in the water budgets, as estimated in billions of cubic meters (BCM) over the analyzed time period, indicates the extent of water stress, drought severity, and flood occurrence in this study area where annual rainfall patterns are predominantly governed by the wet season (i.e., monsoon). The uncertainty of the estimates leading to the inability to close the water balance equation is possibly due to the limitations in satellite observations/model simulations and human activities (e.g., stream flow, irrigation, groundwater pumping, diversion).

The water vapor transport in the extratropics is mainly organized in narrow elongated filaments. These filaments are referred to with a variety of names depending on the contexts. When making landfall on a coastline, they are generally referred to as atmospheric rivers; when occurring at high latitudes, many authors regard them as warm moist intrusions; when occurring ahead of a cold front towards the core on an extratropical cyclone, the most commonly used term is warm conveyor belt. Here, we propose an algorithm that detects these various lines of moisture transport in instantaneous maps of the vertically integrated water vapor transport. The detection algorithm extracts well-defined maxima in the water vapor transport and connects them to lines that we refer to as moisture transport axes. By only requiring a well-defined maximum in the vapor transport, we avoid imposing a threshold in the absolute magnitude of this transport (or the total column water vapor). Consequently, the algorithm is able to pick up moisture transport axes at all latitudes without requiring region-specific tuning or normalization. We demonstrate that the algorithm can detect both atmospheric rivers and warm moist intrusions, but also prominent monsoon air streams. Atmospheric rivers sometimes consist of several distinct moisture transport axes, indicating the merging of several moisture filaments into one atmospheric river. We showcase the synoptic situations and precipitation patterns associated with the occurrence of the identified moisture transport axes in example regions in the low, mid, and high latitudes.

Using Unmanned Aerial Systems (UAS) equipped with optical RGB cameras and Doppler radar, surface velocity can be efficiently measured at high spatial resolution. UAS-borne Doppler radar is particularly attractive because it is suitable for real-time velocity determination, because the measurement is contactless, and because it has fewer limitations than image velocimetry techniques. In this paper, five cross-sections (XSs) were surveyed within a 10 km stretch of Rönne Å in Sweden. Ground-truth surface velocity observations were retrieved with an electromagnetic velocity sensor (OTT MF Pro) along the XS at 1 m spacing. Videos from a UAS RGB camera were analyzed using both Particle Image Velocimetry (PIV) and Space-Time Image Velocimetry (STIV) techniques. Furthermore, we recorded full waveform signal data using a Doppler radar at multiple waypoints across the river. An algorithm fits two alternative models to the average amplitude curve to derive the correct river surface velocity: a Gaussian one peak model, or a Gaussian two peak model. Results indicate that river flow velocity and propwash velocity caused by the drone can be found in XS where the flow velocity is low, while the drone-induced propwash velocity can be neglected in fast and highly turbulent flows. To verify the river flow velocity derived from Doppler radar, a mean PIV value within the footprint of the Doppler radar at each waypoint was calculated. Finally, quantitative comparisons of OTT MF Pro data with STIV, mean PIV and Doppler radar revealed that UAS-borne Doppler radar could reliably measure the river surface velocity.

Understanding the physical mechanisms which contribute towards the rapid intensification of flash droughts is crucial for improving their forecasts. These mechanisms are difficult to elucidate using statistical techniques due to the complex interactions between land surface and atmospheric processes. In order to overcome this limitation, we use a slab model to model the coupled energy and water balance of the land and atmosphere. We develop an analytical framework to disentangle the influence of external forcings and system response driven by the state variables using the energy and water balance equations of the model. We apply the model to six locations selected from different climate regions of India to identify the physical mechanisms of flash droughts. We find that most flash droughts in India happen during the monsoon season, with higher frequency in humid regions of Northeast India and Southern Peninsular India. We find that all flash droughts occur during periods of deficient rainfall and the drying is predominantly driven by net shortwave radiation. However, the flash droughts differ in terms of contribution of winds towards drying, based on which we classify the flash drought mechanisms into three types: (a) flash droughts with wind-driven intensification due to land-atmospheric feedback (b) flash droughts with minimal contribution of winds towards drying and (c) flash droughts with wind-driven intensification due to advected heat. We also show that although the enhanced vapor pressure deficit is a frequently recurring feature of flash droughts, it is not necessarily the most relevant contributor in their development.

Our water resources have changed over the last century through a combination of water management evolutions and climate change. Understanding and decomposing these drivers of discharge changes is essential to preparing and planning adaptive strategies. We propose a methodology combining a physical-based model to reproduce the natural behavior of river catchments and a parsimonious model to serve as a framework of interpretation, comparing the physical-based model outputs to observations of discharge trends. We show that over Europe, especially in the South, the dominant explanations for discharge trends are non-climatic factors. Still, in some catchments of Northern Europe, climate change seems to be the dominating driver of change. We hypothesize that the dominating non-climatic factors are irrigation development, groundwater pumping and other human water usage, which need to be taken into account in physical-based models to understand the main drivers of discharge and project future changes.

Accurately predicting the extent of compound flooding events, including storm surge, pluvial, and fluvial flooding, is vital for protecting coastal communities. However, high computational demands associated with detailed probabilistic models highlight the need for simplified models to enable rapid forecasting. The objective of this study was to assess the accuracy and efficiency of a reduced-complexity, hydrodynamic solver – the Super-Fast INundation of CoastS (SFINCS) model – in a probabilistic ensemble simulation setting, using Hurricane Ike (2008) in the Texas Gulf Coast as a case study. Results show that the SFINCS-based framework can provide probabilistic outputs under reasonable simulation times (e.g., less than 4 hours for a 100-member ensemble on a single CPU). The model agrees well with observed data from NOAA tidal stations and USGS gage height stations. The ensemble approach significantly reduced errors (average 16%) across all stations compared to a deterministic case. The ensemble improved overall performance and revealed wider flood extents and lower depths. Sensitivity studies performed on ensemble sizes (1,000, 189, 81) and lead times (1 to 3 days before landfall) further demonstrate the reliability of flood extent predictions over varying lead times. In particular, Counties adjacent to the Trinity River Basin had ≥ 80% probability in exceeding the critical 3-m flood threshold during Hurricane Ike. Our study highlights the effectiveness of the SFINCS-based framework in providing probabilistic flood extent/depth forecasts over long lead times in a timely manner. Thus, the framework constitutes a valuable tool for effective flood preparedness and response planning during compound flooding.

Using Unmanned Aerial Systems (UAS) equipped with optical RGB cameras and Doppler radar, surface velocity can be efficiently measured at high spatial resolution. UAS-borne Doppler radar is particularly attractive because it is suitable for real-time velocity determination, because the measurement is contactless, and because it has fewer limitations than image velocimetry techniques. In this paper, five cross-sections (XSs) were surveyed within a 10 km stretch of Rönne Å in Sweden. Ground-truth surface velocity observations were retrieved with an electromagnetic velocity sensor (OTT MF Pro) along the XS at 1 m spacing. Videos from a UAS RGB camera were analyzed using both Particle Image Velocimetry (PIV) and Space-Time Image Velocimetry (STIV) techniques. Furthermore, we recorded full waveform signal data using a Doppler radar at multiple waypoints across the river. An algorithm fits two alternative models to the average amplitude curve to derive the correct river surface velocity: a Gaussian one peak model, or a Gaussian two peak model. Results indicate that river flow velocity and propwash velocity caused by the drone can be found in XS where the flow velocity is low, while the drone-induced propwash velocity can be neglected in fast and highly turbulent flows. To verify the river flow velocity derived from Doppler radar, a mean PIV value within the footprint of the Doppler radar at each waypoint was calculated. Finally, quantitative comparisons of OTT MF Pro data with STIV, mean PIV and Doppler radar revealed that UAS-borne Doppler radar could reliably measure the river surface velocity.

Peatlands are fundamental deposits of organic carbon. Thus, their protection is of crucial importance to avoid emissions from their degradation. Peat is a mixture of organic soil that originates from the accumulation of wetland plants under continuous or cyclical anaerobic conditions for long periods. Hence, a precise quantification of peat deposits is extremely important; for that, remote- and proximal-sensing techniques are excellent candidates. Unfortunately, remote-sensing can provide information only on the few shallowest centimeters, whereas peatlands often extend to several meters in depth. In addition, peatlands are usually characterized by difficult (flooded) terrains. So, frequency-domain electromagnetic instruments, as they are compact and contactless, seem to be the ideal solution for the quantitative assessment of the extension and geometry of peatlands. Generally, electromagnetic methods are used to infer the electrical resistivity of the subsurface. In turn, the resistivity distribution can, in principle, be interpreted to infer the morphology of the peatland. Here, to some extent, we show how to shortcut the process and include the expectation and uncertainty regarding the peat resistivity directly into a probabilistic inversion workflow. The present approach allows for retrieving what really matters: the spatial distribution of the probability of peat occurrence, rather than the mere electrical resistivity. To evaluate the efficiency and effectiveness of the proposed probabilistic approach, we compare the outcomes against the more traditional deterministic fully nonlinear (Occam's) inversion and against some boreholes available in the investigated area.  

A document by Freya Muir. Click on the document to view its contents.

River networks around the world exhibit statistical scaling laws, including the distribution of independent basin sizes in landscapes. The widespread occurrence of these patterns in various landscapes suggests that there are fundamental, but not yet fully understood, processes responsible for these power law distributions. This study investigates the distribution of independent basin areas across 25 islands worldwide, revealing a clear adherence to a power law pattern. The research suggests that the power law exponent is influenced by landscape boundary characteristics, such as the compactness coefficient and fractal dimension, with the exponent value increasing with these factors. Furthermore, the study demonstrates the development of power law patterns in basin areas using a probabilistic network growth model. This model, based on a preferential headward growth mechanism, underscores the significant roles of boundary conditions and headward growth dynamics in the self-organization of power law patterns in fluvial landscapes.

Topography is a key control on runoff generation, as topographic slope affects hydraulic gradients and curvature affects water flow paths. At the same time, runoff generation shapes topography through erosion, which affects landscape morphology over long timescales. Previous modeling efforts suggest that subsurface hydrological properties, relative to climate, are key mediators of this relationship. Specifically, when subsurface transmissivity and water storage capacity are low, (1) saturated areas and storm runoff should be larger and more variable, and (2) hillslopes shorter and with less relief, assuming other geomorphic factors are held constant. While these patterns appear in simulations, it remains uncertain whether subsurface properties can exert such a strong control on emergent properties in the field. We compared emergent hydrological function and topography in two watersheds that have very similar climatic and geologic history, but very different subsurface properties due to contrasting bedrock lithology. We found that hillslopes were systematically shorter and saturated areas more dynamic at the site with lower transmissivity. To confirm that these differences were due to subsurface hydrology rather than differences in geomorphic process rates, we estimated all parameters of a coupled groundwater-landscape evolution model without calibration. We showed that the difference in subsurface properties has a profound effect on topography and hydrological function that cannot be explained by differences in geomorphic process rates alone. The comparison to field data also exposed model limitations, which we discuss in the context of future efforts to understand the role of hydrology in the long-term evolution of Earth’s critical zone.

This study explores the impact of deep ( >5 m) critical zone (CZ) architecture on vegetation distribution in a semi-arid snow-dominated climate. Utilizing seismic refraction surveys, we identified a significant correlation between saprolite thickness and LiDAR-derived canopy heights (R²=0.47). We argue that CZ structure, specifically shallow fractured bedrock under valley bottoms, redirects groundwater to locations where trees are established—suggesting they are located in specific locations with access to nutrients and water. This work provides a unique spatially exhaustive perspective and adds to growing evidence that in addition to other factors such as slope, aspect, and climate, deep CZ structure plays a vital role in ecosystem development and resilience.

In modelling evapotranspiration, the need for land surface variables including ground heat fluxes (G), surface temperature (Ts), surface relative humidity (RHs) and surface resistance often present a challenge due to land heterogeneity and limited measurements. This study introduces a simple formulation rooted in the shared physical basis of the maximum entropy model (MaxEnt), the Relative Humidity at Equilibrium (ETRHEQ) method, and the Surface Flux Equilibrium (SFE) method, and it estimates sensible (H) and latent fluxes (LE) in wetlands without requiring land surface variables or site-specific calibration, except for an assumed vegetation height. Further, it effectively estimates LE from half-hourly to monthly scales in FLUXNET and AmeriFlux wetland sites. While its performance in estimating H is less satisfactory due to loosely constrained boundary conditions, it shows promising potential for simultaneously and precisely estimating LE, H, G, Ts, and RHs from weather data in various ecosystems.

A document by Amina Khatun. Click on the document to view its contents.

A document by Amina Khatun. Click on the document to view its contents.

A document by Orlando Viloria. Click on the document to view its contents.

Soil moisture (SM) analyses and assessments hold significance for numerous applications in the fields of hydrometeorology and agriculture. Throughout history, flux tower sites have been a primary source of data for observationally-based SM examinations and evaluations of landatmosphere interaction. However, these monitoring stations are not evenly distributed worldwide. One of the ways in which the comprehensive understanding of how land and atmosphere interact can be improved is by incorporating remotely sensed SM observations. The Soil Moisture Active Passive (SMAP) satellite is one of the satellite resources which closely aligns with in-site observations. However, the remote sensing nature of SMAP data means that it is prone to unpredictable random distortions. Since variations in SM tend to follow a fundamental Markov process, they typically display a specific "red noise" pattern of variability. On the other hand, satellite data that incorporates random fluctuations exhibits a more uniform "white noise" pattern at higher frequencies, which contrasts with the anticipated red noise pattern. Furthermore, gaps in SMAP data are not randomly distributed; due to its orbital characteristics, the satellite experiences regular instances of missing data during its 8-day orbital cycle, differing depending on the orbital pass. This introduces additional anomalies in the power spectrum, performed through examining correlations in the time series data, leading to recurring spikes at intervals of 8, 4 (half of 8), 2 and 2/3 (one-third of 8), and 2 days (one-fourth of 8). These spectral spikes become broader due to small variations in the satellite's orbit. To make the satellite data most effective for assessing land-atmosphere interactions, which tend to rely on estimates of covariability of SM with other environmental variables, it is crucial to minimize the impact of random distortions and systematic missing data. A technique for adjusting the power spectrum, and thus the time series, of SM has been developed to minimize the influence of orbital harmonic spikes in the gridded Level 3 (L3) SMAP dataset. This is achieved by fitting a catenary function to the power spectrum between the harmonic spikes and then removing their influence. The adjusted spectrum is then aligned with soil moisture data from the surface layer, collected from sites within the AmeriFlux network (in-situ flux tower data). These sites demonstrate relatively minimal distortion and exhibit SM power spectra that closely resemble those generated by offline land surface models (LSMs), which are free of random noise by nature. Using validated spectral data from gridded LSM-based datasets, an improved global L3 SMAP dataset is being generated that accounts for noise and harmonic effects. This presentation will showcase the outcomes of this technique in enhancing SMAP data and its temporal correspondence with observational data.

Groundwater overdraft in western U.S. states has prompted water managers to start the development of groundwater management plans that include mandatory reporting of groundwater pumping (GP) to track water use. Most irrigation systems in the western U.S. are not equipped with irrigation water flow meters to record GP. Of those that do, performing quality assurance and quality control (QAQC) of the metered GP data is difficult due to the lack of reliable secondary GP estimates. We hypothesize that satellite (Landsat)-based actual evapotranspiration (ET) estimates from OpenET can be used to predict GP and aid in QAQC of the metered GP data. For this purpose, the objectives of this study are: 1) to pair OpenET estimates of consumptive use (Net ET, i.e., actual ET less effective precipitation) and metered annual GP data from Diamond Valley (DV), Nevada, and Harney Basin (HB), Oregon; 2) to evaluate linear regression and ensemble machine learning (ML) models (e.g., Random Forests) to establish the GP vs Net ET relationship; and 3) to compare GP estimates at the field- and basin-scales. Results from using a bootstrapping technique showed that the mean absolute errors (MAEs) for field-scale GP depth are 12% and 11% for DV and HB, respectively, and the corresponding root mean square errors (RMSEs) are 15% and 14%. Moreover, the regression models explained 50%-60% variance in GP depth and ~90% variance in GP volumes. The estimated average irrigation efficiency of 88% (92% and 83% for DV and HB, respectively) aligns with known center pivot system efficiencies. Additionally, OpenET proves to be useful for identifying discrepancies in the metered GP data, which are subsequently removed prior to model fitting. Results from this study illustrate the usefulness of satellite-based ET estimates for estimating GP, QAQC metered GP data and have the potential to help estimate historical GP.

A document by Indronil Sarkar. Click on the document to view its contents.

Atmospheric cold fronts are frequently occurring perturbations to the northern Gulf of Mexico coastal region. Given the low-lying elevations in this region and the connectivity between distributary channels and deltaic wetlands, the nonlinear interplay between the cold frontinduced storm surge and tidal oscillations are likely important hydrodynamic processes regulating circulation and sediment dynamics. This study uses the Delft3D Flexible Mesh numerical modeling suite to assess the water level fluctuations resulting from the non-linear interaction between cold front induced storm surge and tidal oscillations in Wax Lake Delta (WLD) between December 2022 and January 2023. The WLD is a small, dynamic sub-delta located in Louisiana, USA, known for its ongoing progradation. The primary focus of this study lies on analyzing two cold fronts that approach from the northwest direction. The results illustrate that within the shallow, vegetated wetland interiors with spatially-variable inundation, the water level fluctuations resulting from non-linear interaction can exceed four times the water depth, while the variations relative to the water depth in the relatively deep primary channels are insignificant. Analysis for two cold fronts from the northwest and further numerical experiments revealed that the water level variation response to the non-linear interaction between the cold front storm surge and tides is predominantly influenced by the intensity of the cold front and the magnitude of the tidal range. This study emphasizes the notable impact of the non-linear interaction between cold front and tide on water level variation, which, in turn, influences inundation extent, sediment transport, and ecological factors in the WLD.  AGU 2023, H51N-1280, San FranciscoLink: https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1293693

While yearly budgets of CO2 and evapotranspiration (ET) above forests can be readily obtained from eddy-covariance measurements, the quantification of their respective soil (respiration and evaporation) and canopy (photosynthesis and transpiration) components remains an elusive yet critical research objective. To this end, methods capable of reliably partitioning the measured ET and F_c fluxes into their respective soil and plant sources and sinks are highly valuable. In this work, we investigate four partitioning methods (two new, and two existing) that are based on analysis of conventional high frequency eddy-covariance (EC) data. The physical validity of the assumptions of all four methods, as well as their performance under different scenarios, are tested with the aid of large eddy simulations, which are used to replicate eddy-covariance field experiments. Our results indicate that canopies with large, exposed soil patches increase the mixing and correlation of scalars; this negatively impacts the performance of the partitioning methods, all of which require some degree of uncorrelatedness between CO2 and water vapor. In addition, best performance for all partitioning methods were found when all four flux components are non-negligible, and measurements are collected close to the canopy top. Methods relying on the water-use efficiency (W) perform better when W is known a priori, but are shown to be very sensitive to uncertainties in this input variable especially when canopy fluxes dominate. We conclude by showing how the correlation coefficient between CO2 and water vapor can be used to infer the reliability of different W parameterizations.