Availability of precipitation data is very important in every aspect related to hydrology. Readings from the ground stations are reliable and are used in hydrological models to do various analysis. However, the predictions are always associated with uncertainties due to the limited number of ground stations, which requires interpolation of the data. Meanwhile, groundbreaking approach in capturing precipitation events from vantage point through satellites in space has created a platform to not only merge ground data with satellite estimates to produce more accurate result, but also to find the data where ground stations are not available or scarcely available. Nevertheless, the data obtained through these satellite missions needs to be verified on its temporal and spatial resolution as well as the uncertainties associated before we make any decisions on its basis. This study focuses on finding and evaluating data obtained from two multi-satellite precipitation measurements missions: i) Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) ii) Global Precipitation Measurement (GPM) mission. GPM is the latest mission launched on Feb 28, 2014 after the successful completion of TRMM mission which collected valuable data for 17 years since its launch in November 1997. Both near real time and final version precipitation products for TMPA and GPM are considered for this study. Two study areas representing eastern and western parts of the United States of America (USA) are considered: i) Charlotte (CLT) in North Carolina ii) San Francisco (SF) in California. Evaluation is carried out for daily accumulated rainfall estimates and single rainfall events. Statistical analysis and error categorization of daily accumulated rainfall estimates were analyzed in two parts: i) Ten yeas data available for TMPA products were considered for historical analysis ii) Both TMPA and GPM data available for a ten-month common period was considered for GPM Era analysis. To study how well the satellite estimates with their finest temporal and spatial resolution capture single rainfall event and to explore their engineering application potential, an existing model of SF watershed prepared in Infoworks Integrated Catchment Model (ICM) was considered for hydrological simulation. Infoworks ICM is developed and maintained by Wallingford Software in the UK and SF watershed model is owned by San Francisco Public Works (SFPW). The historical analysis of TMPA products suggested overestimation of rainfall in CLT region while underestimation in SF region. This underestimation was largely associated with missed-rainfall events and negative hit events in SF. This inconsistency in estimation was evident in GPM products as well. However, in the study of single rainfall events with higher magnitude of rainfall depth in SF, the total rainfall volume and runoff volume generated in the watershed were over-estimated. Hence, satellite estimates in general tends to miss rainfall events of lower magnitude and over-estimate rainfall events of higher magnitude. From statistical analysis of GPM Era data, it was evident that GPM has been able to correct this inconsistency to some extent where it minimized overestimation in CLT region and minimized negative error due to underestimation in SF. GPM products fairly captured the hydrograph shape of outflow in SF watershed in comparison to TMPA. From this study, it can be concluded that even though GPM precipitation estimates could not quiet completely replace ground rain gage measurements as of now, with the perpetual updating of algorithms to correct its associated error, it holds realistic engineering application potential in the near future.

abstract: Cities are, at once, a habitat for humans, a center of economic production, a direct consumer of natural resources in the local environment, and an indirect consumer of natural resources at regional, national, and global scales. These processes do not take place in isolation: rather they are nested within complex coupled natural-human (CNH) systems that have nearby and distant teleconnections. Infrastructure systems—roads, electrical grids, pipelines, damns, and aqueducts, to name a few—have been built to convey and store these resources from their point of origin to their point of consumption. Traditional hard infrastructure systems are complemented by soft infrastructure, such as governance, legal, economic, and social systems, which rely upon the conveyance of information and currency rather than a physical commodity, creating teleconnections that link multiple CNH systems. The underlying structure of these systems allows for the creation of novel network methodologies to study the interdependencies, feedbacks, and timescales between direct and indirect resource consumers and producers; to identify potential vulnerabilities within the system; and to model the configuration of ideal system states. Direct and indirect water consumption provides an ideal indicator for such study because water risk is highly location-based in terms of geography, climate, economics, and cultural norms and is manifest at multiple geographic scales. Taken together, the CNH formed by economic trade and indirect water exchange networks create hydro-economic networks. Given the importance of hydro-economic networks for human well-being and economic production, this dissertation answers the overarching research question: What information do we gain from analyzing virtual water trade at the systems level rather than the component city level? Three studies are presented with case studies pertaining to the State of Arizona. The first derives a robust methodology to disaggregate indirect water flows to subcounty geographies. The second creates city-level metrics of hydro-economic vulnerability and functional diversity. The third analyzes the physical, legal, and economic allocation of a shared river basin to identify vulnerable nodes in river basin hydro-economic networks. This dissertation contributes to the literature through the creation of novel metrics to measure hydro-economic network properties and to generate insight into potential US hydro-economic shocks. / Dissertation/Thesis / Doctoral Dissertation Civil and Environmental Engineering 2016

<p> If given the correct remotely sensed information, machine learning can accurately describe soil moisture conditions in a heterogeneous region at the large scale based on soil moisture readings at the small scale through rule transference across scale. This paper reviews an approach to increase soil moisture resolution over a sample region over Australia using the Soil Moisture Active Passive (SMAP) sensor and Landsat 8 only and a validation experiment using Sentinal-2 and the Advanced Microwave Scanning Radiometer (AMSR-E) over Nevada. This approach uses an inductive localized approach, replacing the need to obtain a deterministic model in favor of a learning model. This model is adaptable to heterogeneous conditions within a single scene unlike traditional polynomial fitting models and has fixed variables unlike most leaning models. For the purposes of this analysis, the SMAP 36 km soil moisture product is considered fully valid and accurate. Landsat bands coinciding in collection date with a SMAP capture are down sampled to match the resolution of the SMAP product. A series of indices describing the Soil-Vegetation-Atmosphere Triangle (SVAT) relationship are then produced, including two novel variables, using the down sampled Landsat bands. These indices are then related to the local coincident SMAP values to identify a series of rules or trees to identify the local rules defining the relationship between soil moisture and the indices. The defined rules are then applied to the Landsat image in the native Landsat resolution to determine local soil moisture. Ground truth comparison is done via a series of grids using point soil moisture samples and air-borne L-band Multibeam Radiometer (PLMR) observations done under the SMAPEx-5 campaign (Panciera 2013). This paper uses a random forest due to its highly accurate learning against local ground truth data yet easily understandable rules. The predictive power of the inferred learning soil moisture algorithm did well with a mean absolute error of 0.054 over an airborne L-band retrieved surface over the same region. The validation experiment also demonstrated a strong linkage to the soil moisture, but the algorithm suffered from a lack of training data over such a small site. However, soil moisture estimation still exhibited a mean average error (MAE) of 0.028, compared to a 0.129 MAE of a deterministic model built upon the Air Force Weather Model.</p><p>

Changes in watershed vegetative cover from natural and anthropogenic causes including, climatic fluctuations, wildfires and land management practices, can result in increased surface water runoff and erosion. Hydrologic models play an important role in the decision support process for managing these landscape alterations. However, model parameterization requires quantified measures of watershed biophysical condition to generate accurate results. These inputs are often obtained from nationally available land cover data sets that are static in terms of vegetation condition and phenology. Obtaining vegetative data for model input of sufficient spatiotemporal resolution for long-term, watershed-scale change analysis has been a challenge. The purpose of this research was to assess the implications of parameterizing the event-based, Rangeland Hydrology and Erosion Model (RHEM) with dynamic, remotely sensed foliar cover data. The study was conducted on a small, instrumented, grassland watershed within the Walnut Gulch Experimental Watershed surrounding Tombstone, Arizona. A time series of foliar cover rasters was produced by calibrating Landsat-based Soil Adjusted Total Vegetation Index (SATVI) scenes with field measurements. Estimates of basal and litter cover were calculated using allometric relationships derived from ground-based transect data. The model was parameterized using these remotely sensed inputs for all recorded runoff events from 1996-2014. Model performance was improved using the remotely sensed foliar cover compared to using an a priori value based on static national land cover classes. Significant (p<0.05) correlation was shown for the linear relationships between foliar cover and SATVI, foliar cover and basal cover, and foliar cover and litter cover. The integration of Landsat-based vegetative data into RHEM shows potential for modelling on a broadened spatiotemporal scale, allowing for improved landscape characterization and the ability to track watershed response to long-term vegetation changes.

<p>Quantifying groundwater recharge is of crucial importance for sustainable groundwater management. While many recharge quantification techniques have been devised, few provide spatially and temporally distributed estimates for regional-scale water resource assessments. In this study, a GIS-based and USGS-developed recharge quantification tool ? the Soil Water Balance (SWB) model ? was applied to produce fine-tuned recharge constraints and document spatial and temporal dynamics of recharge. SWB has, as of yet, been tested solely in coastal and continental temperate-humid climate zones. This study expands testing of SWB to a Mediterranean climate zone, focusing on Catalina Island, California. Catalina has experienced significant water supply issues due to a prolonged drought. Using available climate, land use/land cover and hydrology data, the SWB model yields annual recharge values for the time period 2008-2014 of 0.05 mm/year to over 82 mm/year. Results of this thesis provide information on spatial and temporal patterns of groundwater recharge on Catalina Island.

<p> Despite a rising interest in intermittent river systems, landscape influences on long-term wetting and drying patterns of streamflow are not well understood. There has been a significant increase in the presence of intermittent rivers worldwide due to climate change and subsequent increases in groundwater abstraction, and these effects are intensified in already arid regions such as the American Southwest. Consequently, the spatial extent of wet and dry reaches of Arizona&rsquo;s Agua Fria River, Cienega Creek, and San Pedro River has been documented by citizen scientists for several years. Citizen science involves the use of trained members of the public for data collection, and the analysis of datasets produced from citizen science projects have become a huge asset to the scientific community. Here, we synthesize the most current data (1999&ndash;2016) to determine what stream and valley characteristics act as drivers for patterns of surface water flow. Geologic, geomorphic, and land cover characteristics of these rivers were analyzed via aerial imagery and Digital Elevation Models within ArcGIS 10.3 in conjunction with the Soil and Water Assessment Tool model. Principal Component Analysis was used in order to assess trends across sites. A set of landscape intermittency metrics was produced and then further analyzed using Multiple Linear Regression. We found that land cover had a significant (p-value &lt; 0.01) positive correlation with reach average (i.e., the proportion of channel wet). Physical watershed and channel characteristics each had a negative correlation with both intermittency metrics (i.e., wet/dry status and reach average). However, their results were not significant to the 0.05 level. This study begins to shed light on the drivers of landscape intermittency patterns of desert streams and demonstrates the utility of citizen science in regard to the study of intermittent river systems.</p><p>

No description available.

<p> Agricultural drought is a creeping disaster that overshadows the vegetative cover in general and cropland specifically in Iraq, a country that was well known for its agricultural production and fertile soil. In the recent years, the arable lands in Iraq experienced increasing land degradation that led to desertification, economic losses, food insecurity, and deteriorating environment. Remote sensing is employed in this study and four different indices are utilized, each of which is derived from MODIS satellite mission products. Agricultural drought maps are produced from 2003 to 2015 after masking the vegetation cover. Year 2008 was found the most severe drought year during the study period, where drought covered 37% of the vegetated land. This part of the study demonstrated the capability of remote sensing in fulfilling the need of an early warning system for agricultural drought over such a data-scarce region.</p><p> This study also aims to monitor hydrological drought. The Gravity Recovery and Climate Experiment (GRACE) satellite-derived monthly Terrestrial Water Storage (TWS) is the hydrological drought indicator, that is used to calculate the deficit. Severity of drought events are calculated by integrating monthly water deficit over the drought period. In addition, drought recovery time is assessed depending on the estimated deficit. Major drought events are classified into several levels of severity by applying a drought monograph approach. The results demonstrated that GRACE TWS is a reliable indicator for drought assessment over Iraq, and provides useful information for decision makers which can be utilized in developing drought adaptation and mitigation strategies. </p><p>

<p> The Interior Alaska boreal forest ecosystem is one of the largest ecosystems on earth and lies between the warmer southerly temperate and colder Arctic regions. The ecosystem is underlain by discontinuous permafrost. The presence or absence of permafrost primarily controls water pathways and ecosystem composition. As a result, the region hosts two distinct ecotypes that transition over a very short spatial scale&mdash;often on the order of meters. Accurate mesoscale hydrological modeling of the region is critical as the region is experiencing unprecedented ecological and hydrological changes that have regional and global implications. However, accurate representation of the landscape heterogeneity and mesoscale hydrological processes has remained a big challenge. This study addressed this challenge by developing a simple landscape model from the hill-slope studies and in situ measurements over the past several decades. The new approach improves the mesoscale prediction of several hydrological processes including streamflow and evapotranspiration (ET). </p><p> The impact of climate induced landscape change under a changing climate is also investigated. In the projected climate scenario, Interior Alaska is projected to undergo a major landscape shift including transitioning from a coniferous-dominated to deciduous-dominated ecosystem and from discontinuous permafrost to either a sporadic or isolated permafrost region. This major landscape shift is predicted to have a larger and complex impact in the predicted runoff, evapotranspiration, and moisture deficit (precipitation minus evapotranspiration). Overall, a large increase in runoff, evapotranspiration, and moisture deficit is predicted under future climate. Most hydrological climate change impact studies do not usually include the projected change in landscape into the model. In this study, we found that ignoring the projected ecosystem change could lead to an inaccurate conclusion. Hence climate induced vegetation and permafrost changes must be considered in order to fully account the changes in hydrology.</p><p>

<p> Recent research and technological advances in the field of hydrology and water resources call for parallel educational reforms at the undergraduate level. This thesis describes the design, development, and evaluation of a series of undergraduate learning modules that engage students in investigative and inquiry-based learning experiences and introduces data analysis and numerical modeling skills. The modules are situated in the coastal hydrologic basins of Louisiana, USA. Centered on the current crisis of coastal land loss in the region, the modules immerse students in a suite of active-learning experiences in which they prepare and analyze data, reproduce model simulations, interpret results, and balance the beneficial and detrimental impacts of several real-world coastal restoration projects. The modules were developed using a web-based design that includes geospatial visualization via a built-in map-interface, textual instructions, video tutorials, and immediate feedback mechanisms. Following pilot implementations, an improvement-focused evaluation was conducted to examine the effectiveness of the modules and their potential for advancing students&rsquo; experiences with modeling-based analysis in hydrology and water resources. Both qualitative and quantitative data was collected including Likert-scale surveys, student performance grades, informal interviews, and text-response surveys. Students&rsquo; perceptions indicated that data and modeling-driven pedagogy using local real-world projects contributed to their learning and served as an effective supplement to instruction. The evaluation results also pointed out some key aspects on how to design effective and conducive undergraduate learning experiences that adopt technology-enhanced, data and modeling-based strategies, and how to pedagogically strike a balance between sufficient module complexity, ensurance of students&rsquo; continuous engagement, and flexibility to fit within existing curricula limitations. Additionally, to investigate how such learning modules can achieve large scale adoption, a total of 100 interviews were conducted with academic instructors and practicing professionals in the field of hydrology and water resources engineering. Key perspectives indicate that future efforts should appease hindering factors such as steep learning curves, lack of assessment data, refurbishment requirements, rigidness of material, time limitations.</p><p>