An integrated study of earth resources in the State of California using remote sensing techniques

The author has identified the following significant results. The supply, demand, and impact relationships of California's water resources as exemplified by the Feather River project and other aspects of the California Water Plan are discussed

TempNet -- Temporal Super Resolution of Radar Rainfall Products with Residual CNNs

The temporal and spatial resolution of rainfall data is crucial for environmental modeling studies in which its variability in space and time is considered as a primary factor. Rainfall products from different remote sensing instruments (e.g., radar, satellite) have different space-time resolutions because of the differences in their sensing capabilities and post-processing methods. In this study, we developed a deep learning approach that augments rainfall data with increased time resolutions to complement relatively lower resolution products. We propose a neural network architecture based on Convolutional Neural Networks (CNNs) to improve the temporal resolution of radar-based rainfall products and compare the proposed model with an optical flow-based interpolation method and CNN-baseline model. The methodology presented in this study could be used for enhancing rainfall maps with better temporal resolution and imputation of missing frames in sequences of 2D rainfall maps to support hydrological and flood forecasting studies

Assessing Climate Change Impact on Water Resources in Water Demand Scenarios Using SWAT-MODFLOW-WEAP

In this article, we present the use of the coupled land surface model and groundwater flow model SWAT-MODFLOW with the decision support tool WEAP (Water Evaluation and Planning software) to predict future surface-water abstraction scenarios in a complex river basin under conditions of climate change. The modelling framework is applied to the Dee River catchment in Wales, United Kingdom. Regarding hydrology, the coupled model improves overall water balance and low-streamflow conditions compared with a stand-alone SWAT model. The calibrated SWAT-MODFLOW is employed with high-resolution climate model data from the UKCP18 project with the future scenario of RCP85 from 2020 to 2040. Then, water supply results from SWAT-MODFLOW are fed into WEAP as input for the river reach in the downstream region of the river basin. This system is utilized to create various future scenarios of the surface-water abstraction of public water supply in the downstream region—maximum licensed withdraw, 50% authorized abstractions, monthly time series with 1% increases in water use, and maximum water withdraw per year based on historical records repeated every year with 1% increases in water use—to estimate the unmet demands and streamflow requirement. This modelling approach can be used in other river basins to manage scenarios of supply and demand

Database Analysis to Support Nutrient Criteria Development (Phase I)

The intent of this publication of the Arkansas Water Resources Center is to provide a location whereby a final report on water research to a funding agency can be archived. The Texas Commission on Environmental Quality (TCEQ) contracted with University of Arkansas researchers for a multiple year project titled “Database Analysis to Support Nutrient Criteria Development”. This publication covers the first of three phases of that project and has maintained the original format of the report as submitted to TCEQ. This report can be cited either as an AWRC publication (see below) or directly as the final report to TCEQ

Detection and Predictability of Spatial and Temporal Patterns and Trends of Riverine Nutrient Loads in the Midwest

The deleterious effects of multiple stressors on global water resources have become more significant over the past few decades. Anthropogenic activities such as industrialization, urbanization, deforestation, and increased application of agricultural nutrients have led to a decline in overall quality of our aquatic environment. Additionally, these activities have increased greenhouse gas concentrations globally, warming the earth’s atmosphere and eventually having a detrimental effect on global water and energy balances. The global water cycle has been altered, leading to its overall intensification and an increase in frequency of extreme floods and droughts. Addressing increasing water demands coupled with declining water quality and a depletion of water resources requires new approaches in water management. In determining optimum management actions, it is critical to understand the spatial and temporal variability and trends in water quantity and quality. This research aims to improve our knowledge of anthropogenic and natural impacts on water resources by evaluating and refining the science of predicting pollutant (nutrient and sediment) loadings from medium- to large-scale watersheds. To enable these goals, this research is centered on large watersheds in the Midwestern United States, which have been some of the primary sources of nutrient and sediment loadings to downstream water bodies such as the Gulf of Mexico and Lake Erie. In total, 14 watersheds in Illinois, Indiana, Ohio, and Michigan, with extensive water quality datasets, are analyzed in different stages of this research. Most of these watersheds are predominantly agricultural with intensive row-cropped farmlands and have a network of sub-surface tile drainage systems. Pollutant loadings and associated hydrological processes have been simulated using four major modeling approaches: statistical modeling, empirical modeling, physically based modeling, and data mining methods. This report includes eight chapters. The first three chapters describe the problem and research objectives, study area, and data preparation and processing. Next, the impacts of available water quality data on concentration and load predictions and trend calculations are assessed based on traditional statistical methods and several new, improved, and modified approaches (Chapter 4). This segment emphasizes the difficulties in predicting nutrient load and concentration trends under changing climatic conditions, highlighting the importance of continuous nutrient monitoring. Next, two data mining techniques (the nearest-neighbor method and decision trees), scarcely used in hydrology, were applied to predict the missing Nitrate Nitrogen (NO3-N) concentrations for two extensively monitored watersheds in the Lake Erie basin. These predictions (Chapter 5) are important in load estimations and demonstrate the potential of data mining to produce results comparable with statistical and empirical methods presented in the previous chapter. In Chapter 6, statistical regression techniques are used to assess the role of large load events in predicting Total Suspended Solids (SS), Total Phosphorus (TP), and NO3-N annual loads. A novel constituent-specific baseflow separation technique based on mechanistic differences in nutrient and sediment loadings is proposed and applied. As a result, regression relationships between the largest annual loads and total annual loads were developed for all three constituents. An Analysis of Covariance (ANCOVA) indicated that these relationships are often statistically indistinguishable from each other when applied to watersheds with a similar land use. Then, in Chapter 7, the temporal patterns of pollutant loadings from large Midwestern watersheds are analyzed using circular statistics. Critical periods of high loadings, precipitation, and river flow were identified. While river flows and pollutant loadings are highest in late winter and early spring (e.g., March and April), rainfall totals are highest during late spring and early summer (e.g., May through August). Finally, Chapter 8 shows the results based on the physically based SWAT model. The model is calibrated for river discharge and water quality in the largest watershed in the Lake Erie basin, the Maumee River watershed. The calibrated model is used to gauge the impacts of future projected climate change from the mid-century and late-century time periods on the hydrology and water quality in the watershed. The results indicate that climate change could have a significant impact on sediment and nutrient loads, and that more detailed studies are needed to more accurately assess this impact and its confidence limits.published or submitted for publicationis peer reviewedOpe

TempNet – Temporal Super-resolution Of Radar Rainfall Products With Residual CNNs

The temporal and spatial resolution of rainfall data is crucial for environmental modeling studies in which its variability in space and time is considered as a primary factor. Rainfall products from different remote sensing instruments (e.g., radar, satellite) have different space-time resolutions because of the differences in their sensing capabilities and post-processing methods. In this study, we developed a deep-learning approach that augments rainfall data with increased time resolutions to complement relatively lower-resolution products. We propose a neural network architecture based on Convolutional Neural Networks (CNNs), namely TempNet, to improve the temporal resolution of radar-based rainfall products and compare the proposed model with an optical flow-based interpolation method and CNN-baseline model. While TempNet achieves a mean absolute error of 0.332 mm/h, comparison methods achieve 0.35 and 0.341, respectively. The methodology presented in this study could be used for enhancing rainfall maps with better temporal resolution and imputation of missing frames in sequences of 2D rainfall maps to support hydrological and flood forecasting studies

Detecting the effects of environmental change on Alaska's small mammal fauna using machine-learning-based geographic and isotopic niche modeling

Thesis (Ph.D.) University of Alaska Fairbanks, 2015As anthropogenic climate change continues to alter biomes, ecosystems, and wildlife communities, determining how the niche spaces of species will respond is vital for determining appropriate conservation policy that promotes biodiversity and species persistence. In Alaska, quantifications of dietary patterns and geographic distributions of small mammals (rodents and shrews) are incomplete. As a result, wildlife managers are often ill-equipped to adequately account for these ecologically important taxa. I used stable isotopes, open-access occurrence records, and machine learning methods to model the dietary and geographic niche spaces of 17 species of small mammals in mainland Alaska. I also calculated the degree of niche overlap among species to estimate potential competition among conspecifics for both food and space. Using `bio-blitz' sampling along two statewide megatransects, I documented small mammal species richness and collected stable isotope samples at 20 locations across Alaska. Stable isotope (δ¹⁵N and δ¹³C) mixing models were used to define proportions of fungi, herbaceous plants, woody plants, lichens, and mosses in the diets of each species and to outline their fundamental and realized foraging niches. I created spatial distribution models for each species for the years 2010 and 2100 by applying machine learning methods to 4,408 unique occurrence records attributed with 27 and 33 environmental predictor variables, respectively. Spatial relationships between co-occurring species helped to determine the dominant structure of small mammal community assemblages for both time periods. Land change analyses identified regions of species loss, persistence, or gain over time. Stable isotopes (δ¹⁵N and δ¹³C) of shrews, rodents, fungi, and herbaceous plants were also modeled spatially to create continuous baseline isoscape predictions for Alaska. Dietary niche models showed a high degree of fundamental niche overlap among species at the statewide scale, whereas realized niches were more segregated at the study area scale. This suggests that species may be plastic in their use of shared resources in order to avoid competition. Isoscape models highlighted mid-elevations in the Yukon-Tanana Uplands, Brooks Range foothills, and the Yukon-Kuskokwim Delta as isotopic `hot-spots.' Isotope values were considerably higher than trophic baselines in these regions, indicating where small mammals may have been consuming more fungi than herbaceous plants. On average, 2010 distribution models accurately predicted the occurrence of species in the field 75% of the time, and a composite species richness model highlighted biodiversity hotspots (11-13 species) across the Yukon-Tanana Uplands and western Brooks Range. Community assemblage analysis for 2010 parsed species into 5 main community groups: northern, cold-climate, interior, continental, and southern, but membership to these communities was predicted to remain largely unchanged by 2100. Individual distributions, however, were predicted to change dramatically by 2100 as members of the northern, cold-climate, and interior communities shifted northward, inland, and upward in elevation following moving climate envelopes. Regions such as southwest Alaska and the Seward Peninsula experienced projected declines in species richness, while the number of species inhabiting the western Brooks Range and Alaska Range were predicted to increase. Results indicated that while species assemblages were robust in their organization over time, evidence of dietary niche plasticity suggests that communities may remain amenable to the addition of new species as shifting distributions overlap in new and unexpected patterns. Mid-elevations in topographically diverse regions such as the Brooks Range, Alaska Range, and the Yukon-Tanana Uplands will likely be centers for increased species richness and contact zones for novel species interactions in the future. These models, intended for public use, describe baseline conditions and future projections of small mammal niche ecology, with far-reaching implications for terrestrial trophic systems. I recommend that wildlife conservation and management decisions consider these models as we seek to describe and conserve biodiversity and the persistence of small mammal species across Alaska in a future altered by climate change

Development and evaluation of a watershed-scale hybrid hydrologic model

A watershed-scale hybrid hydrologic model (Distributed-Clark), which is a lumped conceptual and distributed feature model, was developed to predict spatially distributed short- and long-term rainfall runoff generation and routing using relatively simple methodologies and state-of-the-art spatial data in a GIS environment. In Distributed-Clark, spatially distributed excess rainfall estimated with the SCS curve number method and a GIS-based set of separated unit hydrographs (spatially distributed unit hydrograph) are utilized to calculate a direct runoff flow hydrograph, and time-varied SCS CN values and conditional unit hydrograph approach for different runoff depth-based flow convolution are also used to compute long-term rainfall-runoff flow hydrographs. Spatial data processing and model execution can be performed by Python script tools that were developed in a GIS platform. Model case studies of short- and long-term hydrologic application for four river watersheds to evaluate performance using spatially distributed (Thiessen polygon and NEXRAD radar-based) precipitation data demonstrate relatively good fit against observed streamflow as well as improved fit in comparison with the outputs of spatially averaged rainfall data simulations as follows: (1) application with 24 single storm events using Thiessen polygon distributed rainfall provided overall statistical results in ENS of 0.84 and R2 of 0.86 (improved ENS by 1.8% and R2 by 2.1% relative to averaged data inputs) for direct runoff, (2) simulation of direct runoff flow for the same storm events using NEXRAD data provided ENS of 0.85 and R2 of 0.89 (increase of ENS by 3.0% and R 2 by 6.0%), and (3) 6-year long-term daily NEXRAD data provided total simulated streamflow statistics of ENS 0.71 and R2 0.72 (increased ENS of 42.0% and R2 of 33.3%). These results also indicate that NEXRAD radar-based data are more appropriate for rainfall-runoff flow predictions than rain gauge observations by capturing spatially distributed rainfall amounts and having fewer missing or erroneous records. The Distributed-Clark model presented in this research is, therefore, potentially significant to improved implementation of hydrologic simulation, particularly for spatially distributed rainfall-runoff routing using gridded types of quantitative precipitation estimation (QPE) data in a GIS environment, as a relatively simple (few parameter) hydrologic model

GCIP water and energy budget synthesis (WEBS)

As part of the World Climate Research Program\u27s (WCRPs) Global Energy and Water-Cycle Experiment (GEWEX) Continental-scale International Project (GCIP), a preliminary water and energy budget synthesis (WEBS) was developed for the period 1996–1999 from the “best available” observations and models. Besides this summary paper, a companion CD-ROM with more extensive discussion, figures, tables, and raw data is available to the interested researcher from the GEWEX project office, the GAPP project office, or the first author. An updated online version of the CD-ROM is also available at http://ecpc.ucsd.edu/gcip/webs.htm/. Observations cannot adequately characterize or “close” budgets since too many fundamental processes are missing. Models that properly represent the many complicated atmospheric and near-surface interactions are also required. This preliminary synthesis therefore included a representative global general circulation model, regional climate model, and a macroscale hydrologic model as well as a global reanalysis and a regional analysis. By the qualitative agreement among the models and available observations, it did appear that we now qualitatively understand water and energy budgets of the Mississippi River Basin. However, there is still much quantitative uncertainty. In that regard, there did appear to be a clear advantage to using a regional analysis over a global analysis or a regional simulation over a global simulation to describe the Mississippi River Basin water and energy budgets. There also appeared to be some advantage to using a macroscale hydrologic model for at least the surface water budgets