Matching Items (3)
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- All Subjects: hydrologic modeling
- Creators: Mascaro, Giuseppe
Description
The hydrologic cycle in drylands is complex with large spatiotemporal variationsacross scales and is particularly vulnerable to changes in climate and land cover. To
address the challenges posed by hydrologic changes, a synergistic approach that
combines numerical models, ground and remotely sensed observations, and data analysis
is crucial. This dissertation uses innovative detection and modeling techniques to assess
key hydrologic variables in drylands, including irrigated water use, streamflow, and
snowpack conditions, answering following research questions that also have broad
societal implications: (1) What are the individual and combined effects of future climate
and land use change on irrigation water use (IWU) in the Phoenix Metropolitan Area
(PMA)?; (2) How can temporal changes in streamflow and the impacts of flash flooding
be detected in dryland rivers?; and (3) What are the impacts of rainfall-snow partitioning
on future snowpack and streamflow in the Colorado River Basin (CRB)?
Firstly, I conducted a scenario modeling using the Variable Infiltration Capacity
(VIC) model under future climate and land use change scenarios. Results showed that
future IWU will change from -0.5% to +6.8% in the far future (2071-2100) relative to the
historical period (1981-2010). Secondly, I employed CubeSat imagery to map streamflow
presence in the Hassayampa River of Arizona, finding that the imaging capacity of
CubeSats enabled the detection of ephemeral flow events using the surface reflectance of
the near-infrared (NIR) band. Results showed that 12% of reaches were classified as
intermittent, with the remaining as ephemeral. Finally, I implemented a physically-based
rainfall-snow partitioning scheme in the VIC model that estimates snowfall fraction from
the wet-bulb temperature using a sigmoid function. The new scheme predicts more
significant declines in snowfall (-8 to -11%) and streamflow (-14 to -27%) by the end of
the 21st century over the CRB, relative to historical conditions.
Overall, this dissertation demonstrates how innovative technologies can enhance
the understanding of dryland hydrologic changes and inform decision-making of water
resources management. The findings offer important insights for policymakers, water
managers, and researchers who seek to ensure water resources sustainability under the
effects of climate and land use change.
ContributorsWang, Zhaocheng (Author) / Vivoni, Enrique R (Thesis advisor) / White, Dave D (Committee member) / Mascaro, Giuseppe (Committee member) / Huang, Huei-Ping (Committee member) / Wang, Zhihua (Committee member) / Arizona State University (Publisher)
Created2023
Description
Soil moisture (θ) is a fundamental variable controlling the exchange of water and energy at the land surface. As a result, the characterization of the statistical properties of θ across multiple scales is essential for many applications including flood prediction, drought monitoring, and weather forecasting. Empirical evidences have demonstrated the existence of emergent relationships and scale invariance properties in θ fields collected from the ground and airborne sensors during intensive field campaigns, mostly in natural landscapes. This dissertation advances the characterization of these relations and statistical properties of θ by (1) analyzing the role of irrigation, and (2) investigating how these properties change in time and across different landscape conditions through θ outputs of a distributed hydrologic model. First, θ observations from two field campaigns in Australia are used to explore how the presence of irrigated fields modifies the spatial distribution of θ and the associated scale invariance properties. Results reveal that the impact of irrigation is larger in drier regions or conditions, where irrigation creates a drastic contrast with the surrounding areas. Second, a physically-based distributed hydrologic model is applied in a regional basin in northern Mexico to generate hyperresolution θ fields, which are useful to conduct analyses in regions and times where θ has not been monitored. For this aim, strategies are proposed to address data, model validation, and computational challenges associated with hyperresolution hydrologic simulations. Third, analyses are carried out to investigate whether the hyperresolution simulated θ fields reproduce the statistical and scaling properties observed from the ground or remote sensors. Results confirm that (i) the relations between spatial mean and standard deviation of θ derived from the model outputs are very similar to those observed in other areas, and (ii) simulated θ fields exhibit the scale invariance properties that are consistent with those analyzed from aircraft-derived estimates. The simulated θ fields are then used to explore the influence of physical controls on the statistical properties, finding that soil properties significantly affect spatial variability and multifractality. The knowledge acquired through this dissertation provides insights on θ statistical properties in regions and landscape conditions that were never investigated before; supports the refinement of the calibration of multifractal downscaling models; and contributes to the improvement of hyperresolution hydrologic modeling.
ContributorsKo, Ara (Author) / Mascaro, Giuseppe (Thesis advisor) / Vivoni, Enrique R. (Thesis advisor) / Myint, Soe (Committee member) / Wang, Zhihua (Committee member) / Muenich, Rebecca (Committee member) / Arizona State University (Publisher)
Created2018
Description
The Phoenix Metropolitan region is subject to intense summer monsoon thunderstorms that cause highly localized flooding. Due to the challenges in predicting these meteorological phenomena and modeling rainfall-runoff transformations in urban areas, the ability of the current operational forecasting system to predict the exact occurrence in space and time of floods in the urban region is still very limited. This thesis contributes to addressing this limitation in two ways. First, the existing 4-km, 1-h Stage IV and the new 1-km, 2-min Multi-Radar Multi-Sensor (MRMS) radar products are compared using a network of 365 gages as reference. It is found that MRMS products consistently overestimate rainfall during both monsoonal and tropical storms compared to Stage IV and local rain gauge measurements, although once bias-corrected offer a reasonable estimate for true rainfall at a higher spatial and temporal resolution than rain gauges can offer. Second, a model that quantifies the uncertainty of the radar products is applied and used to assess the propagation of rainfall errors through a hydrologic-hydraulic model of a small urban catchment in Downtown Phoenix using a Monte Carlo simulation. The results of these simulations suggest that for this catchment, the magnitude of variability in the distribution of runoff values is proportional to that of the input rainfall values.
ContributorsHjelmstad, Annika (Author) / Mascaro, Giuseppe (Thesis advisor) / Garcia, Margaret (Thesis advisor) / Xu, Tianfang (Committee member) / Arizona State University (Publisher)
Created2020