Browsing by Author "Barros, Ana P"

This study examines whether and how land-atmosphere interactions can have an impact on the nocturnal convection over the Southern Great Plains (SGP) through numerical simulations of an intense nocturnal mesoscale convective system (MCS) on 19-20 June 2007 with the Weather Research and Forecasting (WRF V3.3) model. High-resolution nested simulations were conducted using realistic and idealized land-surfaces and two different planetary boundary layer parameterizations: Yonsei University (YSU) and Mellor-Yamada-Janjic (MYJ). All simulations show a persistent dry layer around 2 km during daytime and, despite ample instability in the boundary layer, the lack of a mesoscale lifting mechanism prevents precipitating convection in the daytime and in the evening ahead of the MCS passage after local midnight. Integral differences in timing and amount of MCS precipitation among observations and model results were examined in the light of daytime land-atmosphere interactions, nocturnal pre-storm environment, cold pool strength, squall line morphology and propagation speed, and storm rainfall. At the meso-gamma scale, differences in land-cover and soil type have as much of an effect on the simulated pre-storm environment as the choice of PBL parameterization: MYJ simulations exhibit strong sensitivity to changes in the land-surface in contrast to negligible impact in the case of YSU. A comparison of one-way and two-way nested MYJ results demonstrates that daytime land-atmosphere interactions modify the pre-storm environment remotely through advection of low-level thermodynamic features, which strongly impact the development phases of the MCS. At the end of the afternoon, as the boundary layer collapses, a more homogenous and deeper PBL (and stronger low level shear) is evident in the case of YSU as compared to MYJ when initial land-surface conditions are the same. For different land-surface conditions, propagation speed is generally faster, and organization (bow echo morphology) and cold pool strength enhanced when nocturnal PBL heights are higher and there is stronger low level shear in the pre-storm environment independently of the boundary layer parameterization. To elucidate the distinct roles of mesoscale transport and redistribution of low level instability (daytime remote feedbacks) and low level shear in the downwind pre-storm environment (nighttime local feedbacks), which is to separate the nonlinear land-atmosphere physical processes from PBL parameterization-specific effects on simulated storm dynamics, requires addressing the phase delay in storm development and propagation between the observed and the simulated MCS.

Another research objective was to examine the contribution of the land surface at short time scales. A second set of experiments was performed in which the land surface properties were homogenized every 5 minutes. The results show that surface effects are most pronounced during periods of insolation and, for the Yonsei University PBL parameterization, effects on the PBL height are most pronounced at the time of PBL collapse. Image processing techniques were found to be a useful measure of the spatial variation within fields. The results of this study show that, for this case, the integrated effect of the land surface can have a noticeable effect on convection, but such effects are not readily discernible at the 5-minute scale. While this study focused on the thermodynamic effects, further work should examine sensitivity to grid spacing and surface roughness.

In high elevated regions, ground-based precipitation measurements are scarce or non-existing. Remote sensing estimates are prone to underestimation due to a lack of sensitivity to light precipitation and ground-clutter contamination of radar signals by steep terrain. The latter creates a blind zone extending up a few kilometers above the ground for the vertical profiling radars onboard the Global Precipitation Measurement mission (GPM) core satellite that serves as a reference standard to unify precipitation measurements from research and operational satellites. Ground radars which are not affected by the blind zone, show an increase in effective reflectivity factor (ERF) from the melting layer below the isotherm of zero towards the surface. The blind zone is critical as rainfall characteristics at the ground results from the vertical evolution of the rain drop-size distributions (RDSDs). Explicit modeling of the rainfall microphysics from the top of the blind zone, if located below the zero-degree isotherm, towards the surface can improve rainfall estimation at the ground, as demonstrated in the Southern Appalachian Mountains. However, if the top of the blind zone is located above the zero-degree isotherm, ice and mix-phase hydrometeors coexist at the top boundary condition precluding explicit modeling because the microphysics of these hydrometeors is yet an active research topic. This work has three parts. The first part is devoted to characterizing precipitating systems and the blind zone in the Central Andes of Peru. Twenty years of TRMM and GPM radar measurements reveal that stratiform precipitation is the most frequent type in the Central Andes. Long-lasting precipitating systems (LDPSs) having a stratiform structure with embedded convection determine the interannual variability of the diurnal cycle of precipitation. Moisture availability at high elevations in the Central Andes is scarce. Moisture sources that sustain LDPSs are located in the adjacent eastern foothills of the Andes (Western Amazon basin) that result from enhanced moisture convergence and low and mid-levels due to the interplay of the South American low-level jet and cold air incursions. In the Central Andes of Peru, the top of the GPM blind zone is 46 % of the time above the zero-degree isotherm located at 5000 masl missing liquid precipitation, and a comparison with ground disdrometer data shows that GPM DPR does not capture the variability of the rain drop size distribution. These findings show that rainfall in the Central Andes depends on the dynamics of atmospheric circulation and microphysics at multiple spatial and temporal scales. These scales cannot be modeled explicitly by one single model nor validated with current observations. However, progress in that direction can be made by using different models to simulate different weather regimes to elucidate the role of different microphysics and dynamic processes. Observations needed to validate the models are lacking in the Andes but available in the Southern Appalachian Mountains (SAM). So, the modeling part of this work is focused on the SAM, and the knowledge gained by modeling the SAM region can be used to improve the representation of dynamical and microphysical processes in numerical models to be used in the Andes.Changes in the cloud drop size distribution ultimately capture the interplay of microphysics, dynamics, and thermodynamics in clouds. The chain of microphysical mechanisms from aerosol activation to cloud drop size distribution (CDSD) evolution during cloud development to the raindrop size distribution (RDSD) dynamics spans a scale range of four orders of magnitude from fractions of micrometers to millimeters. It can be modeled explicitly in cloud parcel and rain-shaft models (for liquid hydrometeors), and it is described using different parameterizations with varying degrees of complexity in Numerical Weather Prediction (NWP) models. However, in the Weather Research and Forecast (WRF) model, parameterized microphysics often fail to capture the diurnal cycle and spatial distribution of precipitation, rainfall intensity, and duration depending on the weather regime, regional topography, and regional aerosol characteristics. Besides, the representation of the dynamical processes affecting the aerosol activation and the CDSDs evolution during cloud formation and of the cloud-droplet-raindrop continuum in precipitating clouds is lacking in NWP models. In the second part of this work, a two-moment bulk microphysics scheme in WRF is modified to add an aerosol activation spectrum from in-situ measurement and compared to the default activation spectrum to characterize the impact of aerosol activation in the onset of precipitation in different weather conditions. WRF simulations show that using the in-situ aerosol activation spectrum yields higher cloud droplet number concentrations (CDNC) than the default WRF aerosol activation spectrum, with smaller cloud droplets and delayed onset of rainfall under weak synoptic forcing conditions. For large-scale systems with strong and sustained moisture convergence at low levels (frontal and tropical systems), mechanically forced rainfall efficiency is enhanced despite high CDNC, there is no delay in the onset of precipitation, and the impact of ACPI on the spatial and temporal variability of rainfall is negligible (significant) at onset (hours later) consistent with rainfall observations. The simulated cloud vertical structure from CDNC indicates that convective development is more intense in the inner SAM region than in the adjacent plains. In the inner region, valley-ridge circulations organize the spatial patterns of cloudiness under weak synoptic forcing conditions. The formation of early afternoon low-level clouds over the ridges in the summertime reflects the high sensitivity of cloud mixing ratios and cloud droplet concentrations to aerosol activation properties. In the third part of this work, the dynamical and microphysical processes not resolved by WRF are modeled using a large eddy simulation coupled with a spectrum bin microphysics that permits aerosol replenishment to characterize the heterogeneity of cloud microstructure associated with cloud circulations and entrainment in non-precipitating cumulus clouds. Furthermore, the sensitivity of cloud variables to different vertical profiles of aerosol loading is tested and validated with aircraft observations. The coupled model simulated a convective case under weak synoptic conditions, and the spatial variability of the LWC, CDNC, and CDSD was characterized. Simulations show large differences between the region around the updraft and regions of entrainment located at cloud edges and preferred locations. The interplay of environmental wind and cloud circulations explains these locations. More and larger droplets towards the location of the updraft and towards but before the cloud's top result in large LWC in the upper half of the cloud. The opposite in regions of entrainment, having small droplets in minor concentrations resulting in low LWC. Different initial vertical profiles of aerosol concentrations result in significantly different values of CDNC; as larger the aerosol loading, the larger the CDNC. The updraft transport aerosols and moisture up to the cloud altitude from near the ground elevation. In the updraft, a sharp CDSD with a small standard deviation has small values of relative dispersion. In regions of entrainment, the CDSD has smaller droplets in minor concentrations due to the evaporation of droplets. Its shape resembles the distribution of interstitial aerosols with a large standard deviation resulting in large values of relative dispersion and smaller values of LWC. Aircraft measurements show agreement with the simulated CDNC and relative dispersion for the simulation with an initial vertical profile of aerosol concentration that decays exponentially with height and has a scale height of 500m. The significant impact of aerosol loading in the CDNC affects the cloud optical thickness (COT). The COT in the cloud mature stage for a scale height of 2000m is approximately 1.75 times the COT for a scale height of 500m.

This dissertation presents the first findings of aerosol indirect effect in the foothills of the Himalayas (Central Nepal), through a systematic research approach involving satellite data analysis, field campaign, growth factor estimation and numerical modeling studies. Satellite retrieved aerosol optical depth data over the region were first used to identify the dominant modes of spatial/temporal variability of aerosols in the region. Based on the observed dominant spatial mode of aerosol in the pre-monsoon season (Shrestha and Barros 2010, ACP), a field campaign was organized under the Joint Aerosol Monsoon Experiment (JAMEX09) at Dhulikhel and Besisahar to simultaneously measure dry and ambient aerosols size spectra using SMPS and chemical composition using filters (Shrestha et al. 2010, ACP). The diurnal cycle of aerosol number concentration exhibited a consistent peak in the morning and evening period, which was found to be associated with increase in local emission and the delay in ventilation of aerosol through upslope flows and mixing (inferred from an idealized numerical study over Besisahar). The aerosol size distribution was mostly unimodal at night and bimodal during the day, with a consistent larger mode around 100nm and a smaller mode located around 20nm. The chemical composition of PM2.5 was dominated by organic matter at both sites. Organic carbon (OC) comprised the major fraction (64~68%) of the aerosol concentration followed by ionic species (24~26%, mainly and ). Elemental Carbon (EC) compromised 7~10% of the total composition and 27% of OC was found to be water soluble at both sites. The aerosol number concentration increased and decreased in the presence of synoptic scale aerosol plumes and after rainfall events respectively.

A simple model based on Köhler theory was used to explain the observed growth factor using an assumption of (NH4)2SO4 aqueous solution including the presence of slightly soluble organic compounds (SSC) with an insoluble core as a function of molality and mass-fraction. The measured growth factors suggest that the aerosols are in metastable state due to the strong diurnal cycle of relative humidity (RH). The bulk hygroscopic parameter estimated from the DGF and chemical composition of aerosols suggests less hygroscopic aerosols at both locations as compared to previous studies. The dry aerosol size distribution and the bulk hygroscopic parameters were used to estimate the cloud condensation nuclei (CCN) spectrum, which was vertically scaled up to lifting condensation level (LCL) assuming that the shape and chemical properties of aerosol remains unchanged (Shrestha et al. 2011, submitted to JGR). Finally, these regional CCN spectra for polluted and clean conditions as well as standard continental and marine spectra used in numerical weather prediction models (Cohard et al. 1998) were used to probe CCN sensitivity for a pre-monsoon storm system in Central Nepal during JAMEX09. A significant shift in the maxima of the accumulated precipitation was observed between the continental aerosol spectra (Cohard et al. 1998) and the polluted spectra for Dhulikhel. This shift caused the displacement of rainfall maximum away from the Kulekhani water reserve catchment, which is key to hydropower in Nepal. Detailed analysis of the simulations suggests that simgnificant differences in the space-time variability and intensity of precipitation, if not areally integrated amounts, can be explained by differences in the timing and intensity of latent heat release and absorption due to freezing/melting of hydrometers and evaporative cooling of droplets, strengthening cold pool formation and associated circulations. This numerical study provides the first look on the aerosol indirect effect over Nepal for a single pre-monsoon rainfall event, and how aerosols can potentially affect the precipitation distribution (to be submitted to JGR). In addition, it shows the importance of using regionally consistent CCN spectra in model parameterizations of aerosol-cloud interactions. At local places, the differences in simulated precipitation between marine, JAMEX09 clean and polluted air spectra were smaller (up tp ± 50%) than the difference between those simulations and the standard continental aerosol spectra (±200%).

While Soil temperature and moisture information are urgently needed in meteorology, agriculture, and forestry, field measurements are not easy to be carried out due to geographical conditions, soil types, and other restrictions. Modeling of the physical processes in soil provides an alternative method to get access to the information. Soil heat and moisture are interrelated, and the relationship of the two-state variables makes a considerable difference in the modeling of soil physical processes. In order to study the importance of soil moisture and improve the energy simulation of soil heat transfer, a one-dimensional, finite difference, coupled soil heat and moisture transfer model is established. The difference between the uncoupled and coupled soil heat and moisture models shows different behavior according to the boundary conditions imposed, the resulting differences are up to $1\ ^oC$ in soil temperature profile and $0.04$ in soil moisture profile. The coupling model is tested with both idealized boundary conditions and realistic boundary conditions, by utilizing energy fluxes outputs from the land surface model. The effect of soil moisture on soil thermal properties and the temperature effect on water content distributions are addressed in the coupling model. Radiation and atmospheric forcings adapted to the coupled model gives agreeable results of the diurnal temperature change of the whole soil profile. This gap between the simulation results and the ERA5 data can be narrowed by increasing the model's initial soil moisture content according to the ERA5 data, which shows the adjustability of the coupled model. The coupled model displays a better defined diurnal cycle than ERA5, with more sensitivity to the variations in incoming shortwave and longwave, because of the higher spatial resolution.

This research aims to understand the development of the atmospheric energy spectrum and energy transfer mechanisms across scales. A clear understanding of energy spectrum development and transfer mechanisms is necessary for improving the representation of multiscale processes in the atmosphere, modeling turbulence in the boundary layer, and understanding the limits of atmospheric predictability. This work consists of three parts.

The first part investigates the Navier Stokes Equations (NSE) behavior under idealized conditions relevant to large-scale atmospheric turbulence. This study uses a series of direct numerical simulations (DNS) of two-dimensional (2D) with forcing applied at different scales to investigate energy transfer between the synoptic scale and the mesoscale. The DNS results, forced by a single kinetic energy source at large scales, show that the energy spectra slopes of the direct enstrophy cascade are steeper than the theoretically predicted -3 slope. Next, the presence of two inertial ranges in 2D turbulence at intermediate scales is investigated by introducing a second energy source in the meso-a-scale range. The energy spectra for the simulations with two kinetic energy sources exhibit flatter slopes closer to -5/3, consistent with the observed kinetic energy spectra of horizontal winds in the atmosphere at synoptic scales. Further, the results are independent of model resolution and scale separation between the two energy sources, with a robust transition region between the lower synoptic and the upper meso-a scales in agreement with classical observations in the upper troposphere. These results suggest the existence of mesoscale feedback on synoptic-scale predictability that emerges from the concurrence of the direct (downscale) enstrophy transfer in the synoptic scales and the inverse (upscale) kinetic energy transfer from the mesoscale to the synoptic-scale in the troposphere.

The second part of this research is devoted to the characterization of atmospheric energy spectra over mountainous terrain under various environmental conditions using the Weather and Research Forecasting (WRF) model. First, a comprehensive analysis of climatology and mesoscale structure of cold air intrusions (CAIs) over the Andes shows that the events are responsible for localized heavy rainfall events (200 mm, less than 6 hours) in the mid-elevations (~1,500) Peruvian Andes. The climatology analysis uses European Center Medium-Range Weather Forecasts (ECMWF) reanalysis, Tropical Rainfall Measurement Mission (TRMM) data products, and rain-gauge observations. This analysis emphasized characterizing year-round CAI frequency, CAI interactions with Andes topography, and their impact on orographic precipitation climatology. The results show a robust enhancement of the diurnal cycle of precipitation during CAI events in all seasons, particularly in the increase in surface rainfall rate during early morning at intermediate elevations (~ 1,500m), the eastern Andes orographic maximum. This link between CAI frequency and rainfall suggests that they play an essential role in maintaining the Andes to Amazon year-round terrestrial connectivity through runoff production and transport by the river networks. Second, the next step examines the transient behavior of horizontal scaling in the vertical during the evolution of extreme orographic precipitation storms. Furthermore, a mechanistic framework to examine the implications of ongoing deforestation in the western Amazon on orographic precipitation in the tropical Andes through land-atmosphere interactions is provided. Understanding the interplay between large-scale dynamics and land-atmosphere interactions is critical to developing an effective boundary layer processes parameterizations for future numerical weather prediction models. The study includes a case over the Appalachians as middle mountains in comparison to high mountains (Andes) highlighting terrain and weather contrasts. Previous work showed that atmospheric model simulations exhibit different scaling behavior of vertically averaged horizontal wind (u, v) and moisture (q) in the mesoscales for convective (spectral slopes β~−5/3) and non-convective (β~−11/5) conditions. Here, the results show that β exhibits a strong diurnal cycle switching between convective and non-convective behavior following the space‐time evolution of atmospheric stability in the lower troposphere (below 700 hPa) depending on latitude, topography, landform, and the synoptic environment. Anomalous flattening of the wind and moisture spectra (i.e., spectral saturation, ∣β ∣ < 5/3) at high wavenumbers and up to 200 hPa is tied to convective activity, where and when strong vertical motions develop, corresponding to an abrupt directional switch from horizontal energy transfer to vertical energy transfer including latent heating release and parameterized microphysical processes. In the small mesoscales (<50 km), β~ − 5/3 at all times up to 200 hPa with nighttime steepening (β~−11/5) below the orographic envelope where cold air pools form at low elevations and vertical motion weakens in the Appalachians. In the Andes, at a high elevation, the scaling behavior exhibits a stronger diurnal cycle at low levels (below 700 hPa) with significant shoaling between tropical and high latitudes. Furthermore, blocking and strong modification of regional circulations result in nighttime anisotropy at midlevels on the altitudinal profile along the North‐South topographic divide.

The third part focuses on modeling turbulent fluxes near the surface, which is essential for an accurate representation of the heterogeneous surface boundary layer. Second-moment turbulent models have been widely used in numerical weather prediction models for parameterizing the planetary boundary layer (PBL). The most commonly used schemes are based on the Mellor and Yamada (1982) model, which are currently implemented to only account for the contribution of the vertical divergences of the vertical turbulent fluxes in the WRF model. Horizontal tendencies are parameterized separately based on a Smagorinsky scheme. Although this approach may be successful in coarse grid models (e.g., dx~12-2 km), the influence of horizontal gradients becomes more important when the grid spacing is smaller (less than 1 km). Recently, a full 3D PBL scheme (3DPBL) has been implemented in WRF to reconcile the representation of the vertical and horizontal turbulent mixing. The model integrates 3DPBL parameterization with a diagnostic model of the three-dimensional second-order turbulent properties of the flow in the surface layer. A set of modifications introduced to the surface parameters provides a better diagnosis of the surface layer covering different flow regimes based on anisotropy and stability conditions. The near-surface diagnostic variables are analyzed and compared with the data from the Weather Forecast Improvement Project II (WFIPII).

Finally, the dissertation discusses and recommends potential directions for future research focusing on boundary layer processes.

Light rainfall is the baseline input to the annual water budget in mountainous landscapes through the tropics and at mid-latitudes. In the Southern Appalachians, the contribution from light rainfall ranges from 50-60% during wet years to 80-90% during dry years, with convective activity and tropical cyclone input providing most of the interannual variability. The Southern Appalachians is a region characterized by rich biodiversity that is vulnerable to land use/land cover changes due to its proximity to a rapidly growing population. Persistent near surface moisture and associated microclimates observed in this region has been well documented since the colonization of the area in terms of species health, fire frequency, and overall biodiversity. The overarching objective of this research is to elucidate the microphysics of light rainfall and the dynamics of low level moisture in the inner region of the Southern Appalachians during the warm season, with a focus on orographically mediated processes. The overarching research hypothesis is that physical processes leading to and governing the life cycle of orographic fog, low level clouds, and precipitation, and their interactions, are strongly tied to landform, land cover, and the diurnal cycles of flow patterns, radiative forcing, and surface fluxes at the ridge-valley scale. The following science questions will be addressed specifically: 1) How do orographic clouds and fog affect the hydrometeorological regime from event to annual scale and as a function of terrain characteristics and land cover?; 2) What are the source areas, governing processes, and relevant time-scales of near surface moisture convergence patterns in the region?; and 3) What are the four dimensional microphysical and dynamical characteristics, including variability and controlling factors and processes, of fog and light rainfall? The research was conducted with two major components: 1) ground-based high-quality observations using multi-sensor platforms and 2) interpretive numerical modeling guided by the analysis of the in situ data collection. Findings illuminate a high level of spatial – down to the ridge scale - and temporal – from event to annual scale - heterogeneity in observations, and a significant impact on the hydrological regime as a result of seeder-feeder interactions among fog, low level clouds, and stratiform rainfall that enhance coalescence efficiency and lead to significantly higher rainfall rates at the land surface. Specifically, results show that enhancement of an event up to one order of magnitude in short-term accumulation can occur as a result of concurrent fog presence. Results also show that events are modulated strongly by terrain characteristics including elevation, slope, geometry, and land cover. These factors produce interactions between highly localized flows and gradients of temperature and moisture with larger scale circulations. Resulting observations of DSD and rainfall patterns are stratified by region and altitude and exhibit clear diurnal and seasonal cycles.

The Andes Mountains provide a unique setting to study the interplay between climate and geomorphology. The mechanism proposed to describe the evolution of Andean topography is a feedback loop where precipitation erodes the surface, causing the earth's crust to thin and, through buoyancy, uplift the surface. The uplifted surface acts as a barrier which in turn increases precipitation and reinforces the feedback. Demonstrating this feedback is difficult due to the long temporal scales involved. To overcome this challenge, we consider current topographic constraints and climate regimes as a means to evaluate geomorphologic behavior. Initial data analysis leads to the identification of qualitative similarities in the distributions of outlets and precipitation events by elevation, which suggest a link between climatic and fluvial erosion and a strong interaction between orography and precipitation. To explore impacts of this link on regional geomorphology, we estimate uplift rates under a Bayesian hierarchical modeling framework based on the stream power erosion law (SPEL). We specify model parameters using slope and area data generated from a high-resolution, digital elevation map and mean annual precipitation (MAP) derived from 14 years of TRMM 3B42 v.7 precipitation rainfall rates, supplemented with rain gauge data from the Kospinata network in Peru. A key component of the analysis is the development of a natural spatial scale which captures the qualitative similarities observed in the region and provides a means to compare estimated uplift rates to the geomorphologic behavior of each basin. The estimated uplift values recovered from the analysis range from 0.81 to 11.59 mm/yr and thus fall within a physically-reasonable range for the central Andes region. These estimates also are in strong agreement with basin hypsometry. The analysis further reveals a pattern of spatially dependent uplift, which is consistent with the differential tectonic forcing imposed on the basins by the subducting Nazca plate. The adaptation of the physical-statistical model represents a novel method for quantifying the relationship between climate and orography and estimating key parameters of SPEL.

Large areas of the world depend on snowmelt as a freshwater resource and for food production. Space-based remote sensing of seasonal snowpacks provides the only realistic means to monitor and quantify water availability (storage during the accumulation season, and release during the melt season) at global scales. The overarching goal of this study is to elucidate how meteorologic, topographic, and vegetation impact microwave remote-sensing measurements of high-elevation seasonal snowpacks. The working hypothesis is that changes in snowpack microwave behavior can be unambiguously attributed to snow physical processes modulated by meteorology, topography, and vegetation type. The research approach relies on the application of coupled snow hydrology and radiative transfer models to characterize the space-time evolution of snowpacks, and to support the interpretation of satellite-based microwave measurements toward enabling physically-guided estimation of Snow Water Equivalent (SWE). In the first part of this research, ensemble predictions of the seasonal snowpack over Grand Mesa, CO (~ 300 km2) for the hydrologic year 2016-2017 were conducted using a multilayer snow hydrology model. Snowpack ensembles were driven by gridded atmospheric reanalysis and evaluated against SnowEx’17 measurements. The multi-frequency microwave brightness temperatures and backscattering behavior of the snowpack (separate from soil and vegetation contributions) show that at sub-daily time scales, the ensemble standard deviation (i.e., weather variability at 3 × 3 km2) is < 3 dB for dry snow, and increases to 8-10 dB at mid-day when there is surficial melt that also explains the wide ensemble range (~20 dB). The linear relationship of SWE with the mean ensemble backscatter (R2 > 0.95) depends on weather conditions (e.g., 5-6 cm/dB in January; 2-2.5 cm/dB in late February as melt-refreeze cycles modify the microphysics in the top 50 cm of the snowpack). The nonlinear evolution of ensemble snowpack physics translates into seasonal hysteresis in the mesoscale microwave behavior. The backscatter hysteretic offsets between accumulation and melt regimes are robust in the L- and C-bands and collapse for wet, shallow snowpacks at Ku-band. The emissions behave as limit-cycles with weak sensitivity in the accumulation regime, and hysteretic behavior, with offsets increasing with frequency, is different for deep snowpacks at winter-spring transition and shallow ones at spring-summer transition. These findings suggest potential for multi-frequency active-passive remote sensing of high-elevation SWE depending on snowpack regime, particularly suited for data-assimilation via coupled snow hydrology-radiative transfer models extended to include the snow-soil and snow-vegetation interactions. To investigate snowpack microwave behavior in complex topography, an uncalibrated distributed multiphysics snow model driven by downscaled weather forecasts (30-m, 15-min) was implemented as a Radar Observing System Simulator (ROSS) in Senator Beck Basin (SBB), Colorado to elucidate topographic controls on C-, X- and Ku-bands active microwave sensing of mountain snowpacks. Phase-space maps of time-evolving grid-scale ROSS volume backscatter show the accumulation branch of the backscatter-snow water equivalent (σ-SWE) hysteresis seasonal loop that is the physical basis for radar retrieval (direct inference) of SWE and snowpack physical properties. There is good agreement in the accumulation season (R2 ~ 0.7) between Sentinel-1 and ROSS predictions corrected using average Sentinel-1 measurements under snow free conditions to estimate snow-ground backscatter, capturing well spatial patterns tied to elevation, slope, and aspect. Root Mean Square Deviations (RMSDs) do not exceed ±3.2 dB for ripening snowpacks in early spring and ±2.4 dB for dry snowpacks in the accumulation season when the mean absolute bias is < 1 dB for all land-cover types with topographic slopes ≤ 30°. Grid-point RMSDs are attributed to the underestimation of snowfall on upwind slopes compounded with forecast errors for the weather near the ground. Like Sentinel-1, ROSS backscatter fields exhibit frequency-independent single-scaling behavior in the 60-150 m scale range for dry snowpacks in the accumulation season, while frequency-dependent scaling behavior emerges in the ablation season. This study demonstrates skillful physical modeling capabilities to emulate Sentinel-1 observations in complex terrain. Conversely, it suggests high readiness to retrieve snow mass and snowpack properties in mountainous regions from radar measurements at high-spatial resolutions enabled by SAR technology. To estimate vegetation impacts on the snowpack microwave behavior, a coupled snow physics-radiative transfer forward-inversion modeling system was applied over snow-covered terrain in Grand Mesa to estimate vegetation contributions to the total backscatter from the ground-snow-vegetation system via referring to dual-frequency SnowSAR measurements. A simplified but comprehensive first-order microwave emission model (MEMLS-V) was iteratively inverted by a global optimizer – simulated annealing to retrieve unknown parameters and backscatter components from double-bounce, snowpack volume, and snow-ground interface. The retrieved parameters offered the simulations 100% correlation with the observed SnowSAR signal dynamics tied to vegetation and snowpack heterogeneities, which highlights that the forward-inversion system accounting for complex multiple scattering within the ground-snow-vegetation system reliably regulated compensation effects of vegetation and snow-ground interface. To the best of our knowledge, this is the first time that the system, with reduced computational requirements and ancillary data demands, has been successfully operated for SnowSAR data analysis, while maintaining robustness and interpretability. The findings have practical potentials of retrieving large-scale SWE in the northern hemisphere through Earth Observation radars and satellites.

Periods of drought and low streamflow can have profound impacts on both human and natural systems. People depend on a reliable source of water for numerous reasons including potable water supply and to produce economic value through agriculture or energy production. Aquatic ecosystems depend on water in addition to the economic benefits they provide to society through ecosystem services. Given that periods of low streamflow may become more extreme and frequent in the future, it is important to study the factors that control water availability during these times. In the absence of precipitation the slower hydrological response of groundwater systems will play an amplified role in water supply. Understanding the variability of the fraction of streamflow contribution from baseflow or groundwater during periods of drought provides insight into what future water availability may look like and how it can best be managed. The Mills River Basin in North Carolina is chosen as a case-study to test this understanding. First, obtaining a physically meaningful estimation of baseflow from USGS streamflow data via computerized hydrograph analysis techniques is carried out. Then applying a method of time series analysis including wavelet analysis can highlight signals of non-stationarity and evaluate the changes in variance required to better understand the natural variability of baseflow and low flows. In addition to natural variability, human influence must be taken into account in order to accurately assess how the combined system reacts to periods of low flow. Defining a combined demand that consists of both natural and human demand allows us to be more rigorous in assessing the level of sustainable use of a shared resource, in this case water. The analysis of baseflow variability can differ based on regional location and local hydrogeology, but it was found that baseflow varies from multiyear scales such as those associated with ENSO (3.5, 7 years) up to multi decadal time scales, but with most of the contributing variance coming from decadal or multiyear scales. It was also found that the behavior of baseflow and subsequently water availability depends a great deal on overall precipitation, the tracks of hurricanes or tropical storms and associated climate indices, as well as physiography and hydrogeology. Evaluating and utilizing the Duke Combined Hydrology Model (DCHM), reasonably accurate estimates of streamflow during periods of low flow were obtained in part due to the model’s ability to capture subsurface processes. Being able to accurately simulate streamflow levels and subsurface interactions during periods of drought can be very valuable to water suppliers, decision makers, and ultimately impact citizens. Knowledge of future droughts and periods of low flow in addition to tracking customer demand will allow for better management practices on the part of water suppliers such as knowing when they should withdraw more water during a surplus so that the level of stress on the system is minimized when there is not ample water supply.

Tropical Cyclones (TCs) intensity and frequency are expected to be impacted by climate change. Despite their destructive potential, these phenomena, which can produce heavy precipitation, are also an important source of freshwater. Therefore any change in frequency, seasonal timing and intensity of TCs is expected to strongly impact the regional water cycle and consequently the freshwater availability and distribution. This is critical, due to the fact that freshwater resources in the US are under stress due to the population growth and economic development that increasingly create more demands from agricultural, municipal and industrial uses, resulting in frequent over-allocation of water resources.

In this study we concentrate on monitoring the impact of hurricanes and tropical storms on vegetation activity along their terrestrial tracks and investigate the underlying physical processes. To characterize and monitor the spatial organization and time of recovery of vegetation disturbance in the aftermath of major hurricanes over the entire southeastern US, a remote sensed framework based on MODIS enhanced vegetation index (EVI) was developed. At the SE scale, this framework was complemented by a water balance approach to estimate the variability in hurricane groundwater recharge capacity spatially and between events. Then we investigate the contribution of TCs (season totals and event by event) to the SE US annual precipitation totals from 2002 to 2011. A water budget approach applied at the drainage basins scale is used to investigate the partitioning of TCs' precipitation into surface runoff and groundwater system in the direct aftermath of major TCs. This framework allows exploring the contribution of TCs to annual precipitation totals and the consequent recharge of groundwater reservoirs across different physiographic regions (mountains, coastal and alluvial plains) versus the fraction that is quickly evacuated through the river network and surface runoff.

Then a Land surface Eco-Hydrological Model (LEHM), combining water and energy budgets with photosynthesis activity, is used to estimate Gross Primary Production (GPP) over the SE US The obtained data is compared to AmeriFlux and MODIS GPP data over the SE United States in order to establish the model's ability to capture vegetation dynamics for the different biomes of the SE US. Then, a suite of numerical experiments is conducted to evaluate the impact of Tropical Cyclones (TCs) precipitation over the SE US. The numerical experiments consist of with and without TC precipitation simulations by replacing the signature of TC forcing by NARR-derived climatology of atmospheric forcing ahead of landfall during the TC terrestrial path. The comparison of these GPP estimates with those obtained with the normal forcing result in areas of discrepancies where the GPP was significantly modulated by TC activity. These areas show up to 10% variability over the last decade.

Light rainfall (< 3 mm/hr) amounts to 30–70% of the annual water budget in the Southern Appalachian Mountains (SAM), a mid-latitude mid-mountain system in the SE CONUS. Topographic complexity favors the diurnal development of regional-scale convergence patterns that provide the moisture source for low-level clouds and fog (LLCF). Low-level moisture and cloud condensation nuclei (CCN) are distributed by ridge-valley circulations favoring LLCF formation that modulate the diurnal cycle of rainfall especially the mid-day peak. The overarching objective of this dissertation is to advance the quantitative understanding of the indirect effect of aerosols on the diurnal cycle of LLCF and warm-season precipitation in mountainous regions generally, and in the SAM in particular, for the purpose of improving the representation of orographic precipitation processes in remote sensing retrievals and physically-based models.

The research approach consists of integrating analysis of in situ observations from long-term observation networks and an intensive field campaign, multi-sensor satellite data, and modeling studies. In the first part of this dissertation, long-term satellite observations are analyzed to characterize the spatial and temporal variability of LLCF and to elucidate the physical basis of the space-time error structure in precipitation retrievals. Significantly underestimated precipitation errors are attributed to variations in low-level rainfall microstructure undetected by satellites. Column model simulations including observed LLCF microphysics demonstrate that seeder-feeder interactions (SFI) among upper-level precipitation and LLCF contribute to an three-fold increase in observed rainfall accumulation and can enhance surface rainfall by up to ten-fold. The second part of this dissertation examines the indirect effect of aerosols on cloud formation and warm-season daytime precipitation in the SAM. A new entraining spectral cloud parcel model was developed and applied to provide the first assessment of aerosol-cloud interactions in the early development of mid-day cumulus congestus over the inner SAM. Leveraging comprehensive measurements from the Integrated Precipitation and Hydrology Experiment (IPHEx) in 2014, model results indicate that simulated spectra with a low value of condensation coefficient (0.01) are in good agreement with IPHEx aircraft observations. Further, to explore sensitivity of warm-season precipitation processes to CCN characteristics, detailed intercomparisons of Weather Research and Forecasting (WRF) model simulations using IPHEx and standard continental CCN spectra were conducted. The simulated CDNC using the local spectrum show better agreement with IPHEx airborne observations and better replicate the widespread low-level cloudiness around mid-day over the inner region. The local spectrum simulation also indicate suppressed early precipitation, enhanced ice processes tied to more vigorous vertical development of individual storm cells. The studied processes here are representative of dominant moist atmospheric processes in complex terrain and cloud forests in the humid tropics and extra-tropics, thus findings from this research in the SAM are transferable to mountainous areas elsewhere.

The overall objective of this thesis is to characterize the space-time variability of snowpack physical properties at high spatial and temporal resolution for downscaling of remote-sensing products of snow cover, snow depth and snow water equivalent. The hypothesis is that the temporal evolution of the sub grid-scale statistical structure of relative permittivity fields and other snow properties can be related to the temporal evolution of the areal averages obtained from remote-sensing, thus enabling downscaling of snow water equivalent and snow depth even in the absence of ground-based measurements. For this purpose, research was conducted on ground-based measurements of subgrid-scale properties, and on the development and evaluation of a microwave simulation system consisting of coupled snow hydrology and radiative transfer models.

First, an L-band TX-RX wireless sensor to monitor snow accumulation and snow wetness was designed, fabricated, and tested under laboratory conditions. The sensor was designed to operate at 39 discrete frequencies (39 channels) in the 1.00-1.76-GHz frequency range (0.02-GHz increments). Full-system testing of the first-generation system was conducted using commercial attenuators up to 20.0 dB to test the prototypes against design specifications. It was determined that performance was nearly optimal in the 1-1.2-GHz range. Next, snow layers of varying snow wetness were physically modeled under controlled laboratory conditions. This was achieved by adding varying amounts of water to a layer of fixed porosity foam inside a rectangular tank placed above the transmitter. The attenuation and relative phase shift of the RF signal propagating through the experimental "snowpack" and through the laboratory "atmosphere" were subsequently analyzed as a function of volumetric water content equivalent to snow wetness. Under the space and geometry limitations of the laboratory setup, the data show that the single-frequency measurements exhibit high sensitivity for wetness values up to 24%, whereas multifrequency retrieval is necessary for higher liquid water contents. Measurements from a field deployment during snowfall in January 2009 are also presented. The results suggest that there is potential for using the RF sensor to measure cumulative snowfall for short-duration events.

Second, a land-surface hydrology model (LSHM) [Devonec 2002] with one-layer snowpack physics was coupled to a microwave emission model (MEMLS, [Wiesmann 1999], [Matzler 1999]) including and atmospheric attenuation correction algorithm. The objective is to develop a parsimonious and autonomous framework for monitoring snow water equivalent in remote regions where ancillary data and ground-based observations for model calibration and, or data assimilation are lacking. Two case-studies were conducted to evaluate the coupled hydrology-emission model in forward mode: 1) the intercomparison of a multi-year simulation of snowpack radio-brightness behavior at Valdai, Russia, against Scanning Multi-channel Microwave Radiometer (SMMR) observations at three frequencies (18, 21, and 37 GHz, V and H polarizations) for a six year period, 1978-1983; and 2) an intercomparison against Special Sensor Microwave Imager (SSM/I) and Advanced Microwave Scanning Radiometer-EOS (AMSR-E) during February 2003 in Colorado as part of CLPX (Cold Land Processes Field Experiment). The results show that the model captures well the radiative behavior of the snowpack, especially for vertical polarization in the winter accumulation season January-March for all years of simulation and all cases. Large biases for the Valdai case study were identified for intermittent snowpack conditions at the beginning of the cold season (e.g. fall) which can be explained by uncertainty in fractional snow cover and spatial variability of skin liquid water content at the large spatial scale of SMMR products. Nevertheless, the modeling system uncertainty range remains below the known biases of SMMR products as compared to SSM/I [Derksen 2003]. Using meteorological data from NASA CLPX [Cline 2003], the simulated brightness temperatures agree well with SSM/I and AMSR-E during February, 2003. Overall, the coupled LSHM-MEMLS forward model also performs well at smaller scales and where more ancillary data are available, and its performance is consistent with that of more complex snow models. This research suggests there is therefore potential of this modeling framework model that does not require calibration for useful physically-based estimation of snow water equivalent from remote sensing observations over large areas at multiple spatial resolutions.

Third, the snow hydrology model was modified to include a multi-layer transient representation of the evolution of snowpack properties with time. The coupled multi-layer snow hydrology and emission model was implemented and tested independently for two very different climatic and physiographic regions (Valdai and CLPX2002-2003) for both wet and dry snow regimes over multiple years with good results both in terms of capturing the evolution of snowpack physical properties, and the radiometric signature consistent with SMMR, SSM/I and AMSR-E observations at 18-19, 22-23, and 36-37 GHz V and H polarizations. These applications show transferability of the modeling system, and its potential utility in large-scale retrieval over large areas with limited if any ground-based observations to constrain the model or for data-assimilation. Despite overall good skill as demonstrated by relatively low errors, one weakness was identified with respect to the simulation of the emission behavior of the snowpack, especially for horizontal polarization induced by liquid water in the snowpack, when ice layers (ice lenses) form due to freezing of liquid water either due to daytime melting, or due to rain-on-snow events. Furthermore, it was established that a more accurate estimation of snow density especially in the case of wet snow regimes would be important to improve skill for vertical polarization. Consequently, a multi-layer snow hydrology model (MLSHM) that can capture the events and snowpack gradients in water content and structure through accumulation, ripening and melting phases was developed and coupled to MEMLS. Significant differences between the simulations using the single and multilayer model formulations were found in the ripening and melting phases when wet snow regimes are more frequent. These differences result from differences in snow density, with the single-layer formulation exhibiting higher density (shallower snow depths) and faster melting rates. Whereas there are no significant changes in the microwave brightness temperatures in the vertical polarization from single to multilayer simulations, there is dramatic improvement in the results for horizontal polarization in Valdai, but not in the case of the more complex snow regimes in CLPX. Further work is required to improve parameterizations of snow density and snow structure including evolution of grain size distribution.

Overall error statistics and detailed analysis of physical behavior show that the coupled MLSHM-MEMLS is apt to be used in data-assimilation in snow retrieval.

Extreme weather events including droughts, heat waves, cold snaps, fires and large storms have the potential to interrupt normal plant development and inhibit healthy plant function during the growing season. Long-term changes in the intensity and inter-arrival times of extreme events (i.e. drought, tropical cyclones, and wildfires) have the ability to alter the regional hydroclimate of areas where incoming rainfall follows prescribed patterns. In the Southeast U.S., much of the warm season precipitation is delivered by landfalling tropical cyclones which have high inter-annual variability in number, intensity, and rainfall amounts. Conversely, in Southern Africa rainfall distributions are controlled by the interactions among large climate boundaries that dictate the timing and location of wet season rainfall. However, the impacts of growing season disturbances on carbon uptake rates by vegetation extend to drought and fire conditions that limit plant growth and leaf development; thus, restricting the maximum carbon uptake potential under favorable atmospheric and soil conditions. As such, changes in the plant life cycle, or phenology, as a result of meteorological disturbances must also be considered in a thorough investigation of water limitations on carbon assimilation rates. The overarching objective of the proposed research is to investigate the inter-cycle sensitivity of carbon and water fluxes between the land-surface and atmosphere in two distinct ecosystems by modelling how changes in the water cycle impact spatial and temporal variability in carbon assimilation rates, soil moisture, evapotranspiration, and plant phenology, specifically in the context of variability in the spatial and temporal delivery of precipitation. The research objectives are: 1) Investigate how spatial and temporal changes in precipitation alter carbon uptake by vegetation and plant phenology; and 2) Elucidate ecosystem recovery dynamics in terms of adapting energy and water budgets after extreme disturbance events (i.e. drought, tropical cyclones, fires) in the Southeast U.S. and Southern Africa, representative respectively of humid extratropical mid-latitudes and semi-arid tropical climate regions in the Atlantic basin; and 3) Quantify the impacts of large disturbances of regional and local carbon and water budgets. The Duke Coupled Hydrology Model with Vegetation (DCHM-V) is used to investigate feedbacks and inter-cycle interactions between the carbon, energy, and water cycles in the Southeast U.S. and Southern Africa. A predictive Dynamic Canopy Biophysical Properties (DCBP) model is developed and coupled to the DCHM (i.e. DCHM-PV) to dynamically estimate changes in canopy structure and development under water-limiting conditions.

Findings from the Southeast U.S. show that precipitation provided by tropical cyclones can increase plant carbon uptake by 4-8% in the Southeast U.S. over the course of a drought year. Further, soil hydraulic properties alone explain most of the variability in warm season water stress in the Southeast U.S and can explain differences in carbon uptake rates when compared against available satellite data. Sensitivity tests of the DCBP model for specific locations within the Southeast U.S. reveal that selecting an inference period for the data assimilation step in the predictive phenology model amounts to imposing a plant water use strategy as a result of the non-stationarity of wet and dry periods in the assimilated data. In a study where the DCHM-PV is applied to the entire Southeast U.S. to dynamically estimate changes in canopy structure in tandem with photosynthesis rates, we find that extreme hurricane and wildfire events significantly reduce vegetation canopies with losses in potential carbon uptake rates as high as 400 g C/m2.

Finally, in Southern Africa persistent wetlands correspond to less efficient water use for photosynthesis during the wet season, but higher overall photosynthesis rates because wetland vegetation has unlimited access to water in soils. Further, the persistence of these wetland well into the dry season depends on localized convective storms during the months transitioning from wet to dry seasons.

Overall, this thesis contributes a quantitative understanding of the impacts of local and regional disturbances on the coupled water and carbon cycles and provides a general roadmap to evaluate ecosystem health and sustainability in light of phenologic and photosynthetic demands for water resources that can be adapted globally.

Quantitative precipitation estimation (QPE) in mountainous regions remains a challenging task owing to its high spatial and temporal variability. Satellite-based radar observations at high resolution have the best potential to capture the spatial patterns of precipitation, but there is high uncertainty in the interpretation of low-level measurements due to ground clutter effects, observing geometry, and sub-grid scale vertical and horizontal heterogeneity of precipitation systems that result from interactions among orographic clouds and propagating storm systems. In the high elevation tropics and in middle mountains everywhere, the landscape is often immersed in multi-layered cloud systems that modify precipitation significantly at low levels in a complex manner depending on time of day and location that is very different from the classical understanding of orographic precipitation enhancement with elevation, and are not easily parameterized or corrected for in QPE algorithms. The overall objective of this proposal is to characterize and elucidate the physical basis of uncertainty in Global Precipitation Measurement Mission (GPM) Dual-frequency Precipitation Radar (DPR) QPE in mountainous regions and develop an improved retrieval framework for orographic precipitation. The following science objectives will be addressed specifically: i) to characterize the dependencies among the spatial and temporal variability of errors in orographic QPE and associated hydrometeorological regimes; ii) to characterize the vertical structure of radar reflectivity associated with QPE retrieval error and establish a physics-based retrieval model; and iii) to develop an operational framework to integrate DPR observations and Numerical Weather Prediction (NWP) model toward improving the retrieval of orographic QPE. The research hypothesis are two folds: a) satellite QPE errors (false alarms, missed detections, underestimations and overestimations) exhibit a robust spatial and temporal organization that is explained by the spatial and temporal variability in the vertical microstructure of precipitation; and b) current satellite-based QPE algorithms fail because the vertical structure of precipitation cannot be detected. If the vertical structure of precipitation systems can be predicted, then the key microphysical processes can be modelled to improve QPE. The study will be conducted using observations from the Southern Appalachian Mountains (SAM) region including a high-density rain gauge network, Micro Rain Radars (MRR), and Parsivel disdrometers since the launch of GPM as well as Integrated Precipitation and Hydrology Experiment (IPHEx) 2014 data.

This research approach consists of integrating ground-based point measurements from long-term observation networks, fields campaign (IPHEx), multi-satellite data, and modeling studies to develop a physically-based retrieval framework for orographic precipitation. To characterize the dependencies among the spatial and temporal variability of errors in orographic QPE, GPM estimations were evaluated using the ground-based precipitation observations and investigated for the robust organization of uncertainty. The physics-based retrieval of near-surface rain-rates was demonstrated through explicit modeling of rain shaft microphysical processes constrained by GPM-DPR observations. Finally, a general data-driven operational framework was developed to improve the detection and to predict the vertical structure of precipitation systems by integrating GPM observations with NWP model simulations.

The contribution of surface evapotranspiration (ET) to moist convection, cloudiness and precipitation along the eastern flanks of the Andes (EADS) was investigated using the Weather Research and Forecasting (ARW-WRF3.4.1) model with nested simulations of selected weather conditions down to 1.2 km grid spacing. To isolate the role of surface ET, numerical experiments were conducted using a quasi-idealized approach whereby at every time step the surface sensible heat effects are exactly the same as in the reference simulations, whereas the surface latent heat fluxes are prevented from entering the atmosphere.

Energy balance analysis indicates that local surface ET along the EADS influences moist convection primarily through its impact on conditional instability, because it acts as an important source of moist entropy in this region. The energy available for convection decreases by up to ~60% when the ET contribution is withdrawn. In contrast, when convective motion is not thermally driven, or under conditionally stable conditions, latent heating from the land surface becomes secondary. At the scale of the Andes proper, removal of surface ET weakens upslope flows by increasing static stability of the lower troposphere, as the vertical gradient of water vapor mixing ratio tends to be less negative. Consequently, moisture convergence is reduced over the EADS. In the absence of local surface ET, this process operates in concert with damped convective energy, suppressing cloudiness, and decreasing daily precipitation by up to ~50% in the simulations presented here.

When the surface ET is eliminated over the Amazon lowlands (AMZL), the results show that, without surface ET, daily precipitation within the AMZL drops by up to ~75%, but nearly doubles over the surrounded mountainous regions. This dramatic influence is attributed to a dipole structure of convergence-divergence anomalies over the AMZL, primarily due to the considerable cooling of the troposphere associated with suppressed convection. Further examination of moist static energy evolution indicates that the net decrease in CAPE (Convective Available Potential Energy) over the AMZL is due to the removal of surface ET that is only partially compensated by related regional circulation changes. Because of the concave shape of the Andean mountain range, the enhanced low-level divergence promotes air mass accumulation to the east of the central EADS. This perturbation becomes sufficiently strong around nightfall and produces significant eastward low-level pressure gradient force, rendering wind currents more away from the Andes. Moisture convergence and convection over the EADS vary accordingly, strengthened in the day but attenuated at night. Nocturnal convective motion, however, is more widespread. Analytical solutions of simplified diagnostic equations of convective fraction suggest that reduction of lower troposphere evaporation is the driving mechanism. Additional exploratory experiments mimicking various levels of thinning and densification of AMZL forests via changes in surface ET magnitude demonstrate that the connection between the AMZL ET and EADS precipitation is robust.

Quantitative Precipitation Estimation (QPE) is crucial in hydrology and water resources research and applications. QPE remains the most pressing challenge due to the lack of high-resolution precipitation measurements and, or inconsistencies among measurements across a wide range of hydrometeor sizes (e.g.; six order of magnitude from haze to raindrops) and large measurement uncertainty that is technology dependent (e.g., disdrometers, rain gauges, radars, satellite versus ground-based, etc) and often precipitation-regime dependent as well. This is a more significant issue in complex terrain because rain gauge networks are not adequate and radar measurements suffer from retrieval algorithm uncertainties and observing geometry artifacts that result from operations to avoid ground clutter effects. In models, QPF (Quantitative Precipitation Forecasts) result from incomplete model physics and physical parameterizations, coarse resolution that cannot capture storm dynamics and orographic flows, as well as uncertainty in boundary conditions. Therefore, QPE products are often associated with very large errors in mountainous regions. This is known from water budget analysis of hydrological prediction at watershed scales that show large discrepancies between simulated and observed hydrographs.The overarching goal of this study is to investigate the spatiotemporal structure of QPE error in observational data sets and develop a physics-based methodology to correct QPE with the goal of minimizing water budget closure errors in headwater basins from event-scales to the annual cycle. Traditionally, water budget studies estimate streamflow as an integrated residual, and the estimated streamflow is compared against streamflow observations to quantify the closure error. Statistical QPE error models on the other hand rely on statistical assumptions about the underlying statistics and in the case of data-driven models lack the physical underpinnings that are needed for predictive studies. In this work, QPE error is estimated as a dynamic residual using a distributed hydrology model. The underlying hypothesis is that the discrepancies between simulated and observed hydrographs result from the convolution of hydrologic processes with rainfall forcing and therefore it is necessary to deconvolve QPE errors in space and time to develop robust quantitative error models for correcting QPE. The principal research objective to construct a general framework for predictive QPE error modeling in complex terrain. This research leverages IPHEx (Integrated Hydrology and Precipitation Experiment observations) in the Southern Appalachian Mountains. Understanding scale-dependence and missing physics in the hydrologic model used for deconvolution is necessary toward improving QPE error estimates at multiple scales for both hydrologic operations and hydroclimatic studies, and this is the third objective of this research. Presently work has been completed for operational prediction of flood events in headwater basins in the SAM. Efforts currently are directed to error modeling regionalization and extension to seasonal and annual time-scales. In the first part of this work, Stage IV (STIV), a commonly used combined radar-raingauge NOAA product, is utilized to derive a reference precipitation product for the SAM by merging IPHEx raingauge observations. The merging process consists fractal downscaling for STIV data, bias correction, and geostatistical mapping techniques. In addition, hydroclimatic corrections to capture the diurnal and seasonal cycles of observed rainfall, and in particular the contribution of light rainfall (Liao and Barros, 2019). Hydrological simulations are conducted to assess the hydrological performance of the optimally combined QPE data show significant discrepancy between simulated streamflow and United States Geological Survey (USGS) streamflow observations. To resolve this problem and close the water budget, a Lagrangian-based backtracking algorithm was developed to deconvolve the signature of hydrologic processes and estimate the space-time dynamics of precipitation error that contributes to the streamflow error. This inverse process is hereafter referred to as Inverse Rainfall Correction or IRC. Hydrologic simulations using IRC corrected QPE exhibit significant improvements in Nash-Sutcliffe Efficiency (NSE) scores at hourly timescales that are dramatically increased from less than -0.5 to above 0.6 on average. Error attribution suggests that these hydrologic errors in QPE data are conditional on precipitation regime, and specifically separating cold and warm season processes. In the second part of this work, it is demonstrated that a predictive QPE error model can be derived from the climatology of derived IRC errors. Specifically, Multi-Layer Perceptron (MLP) network models were developed and applied to the 57 largest floods in the Cataloochee Creek, a USGS benchmark watershed, from 2008 to 2017. The results demonstrate a significant improvement in hydrologic response as the average NSE is improved from -0.4 to 0.5 consistent with the physically-constrained IRC results. Presently, the focus of ongoing efforts is on the regionalization of this approach with emphasis on expressing deconvolution kernels in terms of geomorphic parameters, precipitation regime, and eliminate systematic biases associated to measurement system operations. Simulated hydrographs show that the rising limb is too early in almost every event independently of storm regime and initial conditions. This behavior remains when the spatial resolution of the model increases to 85m. This error is embedded in the travel time distributions used in the IRC, which imposes space-time errors in the rainfall corrections resulting in excessive corrections along the stream network and overall shift of rainfall toward the beginning of the event. Further analysis suggest that unrealistically saturated river channel pixels cause rapid rising limbs and a Hillslope-Streamway Connectivity Parameterization (HSCP) is used to separate river channels from the pixels. Resulting hydrographs are characterized with an improved rising limb by reducing timing error by 2 hours, suggesting a need to investigating model structure errors proving to have great impacts on hydrological simulations. Model structure error comes from a wide variety of sources, including but not limited to model resolution, and small-scale physics parameterization. In the third part of this work, the effects of model spatial scale and sub-grid physics parameterization on hydrological simulation are analyzed. In hydrological modeling, higher spatial resolution does not necessarily produce better hydrological simulations against streamflow observations due to the limitation of understanding and parameterizing small-scale physics. It is true that sub-grid physics are usually parameterized or not represented entirely at coarse resolution in hydrological modeling and thus its effects on simulated hydrological processes are not fully understood. This is especially the case in the streamway and along the riverbanks where hillslope processes interact with river processes. After extensive analysis, it is hypothesized that the rapid rising limb are at least partially contributed by inadequate representation of riverbank and floodplain storage, and errors that result from poorly constrained bank-full conditions, hereafter referred to as dynamic River-Bank-Storage (RBS). A new RBS parameterization is developed and preliminary results from this study suggest that it can effectively delay streamflow travel time for approximately 2 hours, which is significant as flash floods usually occur within a few hours after extensive precipitation in mountainous regions. The final component of the research plan is to assess and improve model physics in the hydrologic model, and to characterize the impact of improved hydrograph simulation skill on QPE error modeling. In the future work, the structure of QPE error models derived from the DCHM incorporated with HSCP and RBS parameterization will be analyzed, and QPE error models will be used for operational forecasting in the Great Smoky National Park located in the SAM.

In this dissertation, a Hydrologic Data Assimilation System (HDAS) relying on the Duke Coupled surface-groundwater Hydrology Model (DCHM) and various data assimilation techniques including EnKF (Ensemble Kalman Filter), the fixed-lag EnKS (Ensemble Kalman Smoother) and the Asynchronous EnKF (AEnKF) was developed to 1) investigate the hydrological predictability of precipitation-induced natural hazards (i.e. floods and landslides) in the Southern Appalachians in North Carolina, USA, and 2) to characterize the seasonal (wet/dry) and inter-annual variability of surface-groundwater interactions with implications for water resource management in the Upper Zambezi River Basin (UZRB) in southern Africa. The overarching research objective is to improve hydrologic predictability of precipitation-induced natural hazards and water resources in regions of complex terrain. The underlying research hypothesis is that hydrologic response in mountainous regions is governed by surface-subsurface interaction mechanisms, specifically interflow in soil-mantled slopes, surface-groundwater interactions in recharge areas, and wetland dynamics in alluvial floodplains at low elevations. The research approach is to investigate the modes of uncertainty propagation from atmospheric forcing and hydrologic states on processes at multiple scales using a parsimonious uncalibrated hydrologic model (i.e. the DCHM), and Monte Carlo and Data-Assimilation methods. In order to investigate the coupled surface-groundwater system and assess the predictability of precipitation-induced natural hazards (i.e. floods and landslides) in headwater basins, including the propagation of uncertainty in QPE/QPF (Quantitative Precipitation Estimates/Forecasts) to QFE/QFF (Quantitative Flood Estimates/Forecasts), the DCHM model was implemented first at high spatial resolution (250m) in the Southern Appalachian Mountains (SAM) in North Carolina, USA. The DCHM modeling system was implemented subsequently at coarse resolution (5 km) in the Upper Zambezi River Basin (UZRB) in southern Africa for decadal-scale simulations (i.e. water years from 2002 to 2012).

The research in the SAM showed that joint QPE-QFF distributions for flood response at the headwater catchment scale are highly non-linear with respect to the space-time structure of rainfall, exhibiting strong dependence on basin physiography, initial soil moisture conditions (transient basin storage capacity), the space-time organization of runoff generation and conveyance mechanisms, and in particular interflow dynamics. The errors associated with QPEs and QPFs were characterized using rainfall observations from a dense raingauge network in the Pigeon River Basin, resulting in a simple linear regression model for adjusting/improving QPEs. Deterministic QFEs simulated by the DCHM agree well with observations, with Nash–Sutcliffe (NS) coefficients of 0.8~0.9. Limitations with state-of-the-science operational QPF and the impact of even limited improvements in rainfall forcing was demonstrated through an experiment consisting of nudging satellite-like observations (i.e. Adjusted QPEs) into operational QPE/QPF that showed significant improvement in QFF performance, especially when the timing of satellite overpass is such that it captures transient episodes of heavy rainfall during the event. The research further showed that the dynamics of subsurface hydrologic processes play an important role as a trigger mechanism of shallow landslides through soil moisture redistribution by interflow. Specifically, transient mass fluxes associated with the temporal-spatial dynamics of interflow govern the timing of shallow landslide initiation, and subsequent debris flow mobilization, independently of storm characteristics such as precipitation intensity and duration. Interflow response was shown to be dominant at high elevations in the presence of deep soils as well as in basins with large alluvial fans or unconsolidated debris flow deposits. In recharge areas and where subsurface flow is an important contribution to streamflow, subsurface-groundwater interactions determine initial hydrologic conditions (e.g. soil moisture states and water table position), which in turn govern the timing and magnitude of flood response at the event scale. More generally, surface-groundwater interactions are essential to capture low flows in the summer season, and generally during persistent dry weather and drought conditions. Future advances in QFF and landslide monitoring remain principally constrained by progress in QPE and QPF at the spatial resolution necessary to resolve rainfall-interflow dynamics in mountainous regions.

The predictability of QFE/QFF was further scrutinized in a complete operational environment during the Intense Observing Period (IOP) of the Integrated Precipitation and Hydrology Experiment (IPHEx-IOP), in order to investigate the predictability of floods (and flashfloods) in headwater catchments in the Southern Appalachians with various drainage sizes. With the DCHM, a variety of operational QPEs were used to produce hydrological hindcasts for the previous day, from which the final states were used as initial conditions in the hydrological forecast for the current day. Although the IPHEx operational testbed results were promising in terms of not having missed any of the flash flood events during the IOP with large lead times of up to 6 hours, significant errors of overprediction or underprediction were identified that could be traced back to the QPFs and subgrid-scale variability of radar QPEs. Furthermore, the added value of improving QFE/QFF through assimilating discharge observations into the DCHM was investigated for advancing flood forecasting skills in the operational mode. Both the flood hindcast/forecast results were significantly improved by assimilating the discharge observations into the DCHM using the EnKF (Ensemble Kalman Filter), the fixed-lag EnKS (Ensemble Kalman Smoother) and Asynchronous EnKF (AEnKF). The results not only demonstrate the utility of discharge assimilation in operational forecasts, but also reveal the importance of initial water storage in the basin for issuing flood forecasts. Specifically, hindcast NSEs as high as 0.98, 0.71 and 0.99 at 15-min time-scales were attained for three headwater catchments in the inner mountain region, demonstrating that assimilation of discharge observations at the basin’s outlet can reduce the errors and uncertainties in soil moisture. Success in operational flood forecasting at lead times of 6, 9, 12 and 15hrs was also achieved through discharge assimilation, with NSEs of 0.87, 0.78, 0.72 and 0.51, respectively. The discharge assimilation experiments indicate that the optimal assimilating time window not only depends on basin properties but also on the storm-specific space-time-structure of rainfall within the basin, and therefore adaptive, context-aware configurations of the data assimilation system should prove useful to address the challenges of flood prediction in headwater basins.

A physical parameterization of wetland hydrology was incorporated in the DCHM for water resource assessment studies in the UZRB. The spatial distribution of wetlands was introduced in the model using probability occurrence maps generated by logistic regression models using MODIS reflectance-based indices as predictor variables. Continuous model simulations for the 2002-2012 period show that the DCHM with wetland parameterization was able to reproduce wetland hydrology processes adequately, including surface-groundwater interactions. The modelled regional terrestrial water storage anomaly (TWSA) captured very well the inter- and intra-annual variability of the system water storage changes in good agreement with the NASA’s GRACE (Gravity Recovery and Climate Experiment) TWSA observations. Specifically, the positive trend of TWSA documented by GRACE was simulated independently by the DCHM. Furthermore, it was determined that the TSWA positive trend results from cumulative water storage in the sandy soils of the Cuando-Luana sub-basin when shifts in storm tracks move rainfall to the western sector of the Angolan High Plateau.

Overall, the dissertation study demonstrates the capability of the DCHM in predicting specific characteristics of hydrological response to extreme events and also the inter- and intra-annual variability of surface-groundwater interactions at a decadal scale. The DCHM, coupled with slope stability module and wetland module featuring surface-groundwater interaction mechanism, not only is of great potential in the context of developing a regional warning system for natural hazards (i.e. flashfloods and landslides), but also is promising in investigating regional water budgets at decadal scale. In addition, the DCHM-HDAS demonstrated the ability to reduce forecasting uncertainty and errors associated with forcing data and the model proper, thus significantly improving the predictability of natural hazards. The HDAS could also be used to investigate the regional water resource assessment especially in poorly-gauged regions (e.g. southern Africa), taking advantage of satellite observations.