Earth observation data is increasingly ubiquitous, easily accessible, freely available, and generally usable due to improvements in software, data standards, network infrastructure, and national policies. As a result, greater opportunities arise for using these data in a wider field of application including decision support for local and regional environmental and water resources management efforts. In parts of the world where in situ data are less readily available, global Earth observation data used in such decision support tools can be a boon to underfunded government and private water management agencies. The United Nations Group on Earth Observations Global Water Sustainability initiative (GEOGloWS) works to coordinate such solutions, bringing global water management capabilities to local decision makers. The recent development and deployment of a global hydrologic modelling system based on historical simulations and daily ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) using Earth observations and streamflow routing on every river of the world results in a highly informative and potentially transformative dataset for users at local scales. However, for this data to reach its full potential at the local level, it needs to be subsetted at a regional or local scale, presented in a local geographic context, and interpreted in terms of local water management challenges. Furthermore, this subsetting allows for customization to support the way information is used and the kinds of decisions that are made. This paper presents the design, development, and experimental testing of the GEOGloWS HydroViewer, which is an open source, web-based software that effectively localizes global ECMWF forecasts to meet the needs of water managers and decision makers through subsetting the mapping and modelling services and supporting other customization as needed. The unique Software-as-a-Service (SaaS) deployment method, developed and tested here, allows for individual water management agencies to automatically generate custom HydroViewer applications that can be managed and/or customized depending on need and capacity in-country without reliance on external software and capacity, removing typical interdependence relationships that often define technology transfer to developing countries.

Streamflow from two drainages of the Davis County Experimental Watershed, Utah, was evaluated with respect to changes in distribution and volume following trenching of one of the drainages in 1964. Fifteen percent of the Halfway Creek drainage was trenched according to established U.S. Forest Service methods. Twelve years of records before trenching and four years of records after trenching were analyzed. Analysis of the annual streamflow, the low streamflow period, and the spring streamflow period indicated no significant change in either volume or distribution of streamflow as a result of trenching. This conclusion was further substantiated by supplemental data of precipitation, soil moisture, snowpack water equivalent, and vegetation.

PURPOSE: The method for streamflow forecasting by using Fourier Series and Multiple Regression as a mathematical model have been suggested and proved with high accuracy for the streamflow forecasting on Logan River, Utah by Professor Cleve H. Milligan and Dr. Rex L. Hurst. In this thesis the method is extended to the forecasting for the Blacksmith Fork River, south of the Logan River. Because the climatological data are not available in the Blacksmith Fork watershed, this thesis also provides a technique for using the available data from adjacent watersheds. OBJECTIVES: 1. To forecast the streamflow on Blacksmith Fork River, Cache County, Utah by using Fourier Series and Multiple Regression as a mathematical model. 2. To test the consistency of the snow, temperature, precipitation, and streamflow data by statistical methods. 3. To test the significance of the variables considered in the mathematical model.

Reliability and accuracy of the forcing data plays a vital role in the Hydrological Streamflow Prediction. Reliability of the forcing data leads to accurate predictions and ultimately reduction of uncertainty. Currently, Numerical Weather Prediction (NWP) models are developing ensemble forecasts for various temporal and spatial scales. However, it is proven that the raw products of the NWP models may be biased at the basin scale; unlike model grid scale, depending on the size of the catchment. Due to the large space-time variability of precipitation, bias-correcting the ensemble forecasts has proven to be a challenging task. In recent years, Ensemble Pre-Processing (EPP), a statistical approach, has proven to be helpful in reduction of bias and generation of reliable forecast. The procedure is based on the bivariate probability distribution between observation and single-value precipitation forecasts. In the current work, we have applied and evaluated a Bayesian approach, based on the Copula density functions, to develop an ensemble precipitation forecasts from the conditional distribution of the single-value precipitation. Copula functions are the multivariate joint distribution of univariate marginal distributions and are capable of modeling the joint distribution of two variables with any level of correlation and dependency. The advantage of using Copulas, amongst others, includes its capability of modeling the joint distribution independent of the type of marginal distribution. In the present study, we have evaluated the capability of copula-based functions in EPP and comparison is made against an existing and commonly used procedure for same i.e. meta-Gaussian distribution. Monthly precipitation forecast from Climate Forecast System (CFS) and gridded observation from Parameter-elevation Relationships on Independent Slopes Model (PRISM) have been utilized to create ensemble pre-processed precipitation over three sub-basins in the western USA at 0.5-degree spatial resolution. The comparison has been made using both deterministic and probabilistic frameworks of evaluation. Across all the sub-basins and evaluation techniques, copula-based technique shows more reliability and robustness as compared to the meta-Gaussian approach.

付記する学位プログラム名: グローバル生存学大学院連携プログラム / 京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第21058号 / 工博第4422号 / 新制||工||1687(附属図書館) / 京都大学大学院工学研究科社会基盤工学専攻 / (主査)教授 寶 馨, 教授 堀 智晴, 准教授 佐山 敬洋 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

The Mississippi Alluvial Plain, a robust agricultural region in the South-Central United States, provides commodities across the United States and around the world. Water for irrigation, which is necessary due to irregular rainfall patterns during the growing season, is withdrawn largely from the Mississippi River Valley Alluvial aquifer, one of the most intensely used aquifers in the United States. The groundwater-dependent region has observed recent declines in groundwater and streamflow levels, raising concerns about the availability and use of fresh-water resources. Declining water levels have prompted investigation into the current understanding of groundwater and surface-water interaction. Previous research does not adequately quantify the unobservable exchange of water between surface-water bodies and the underlying aquifer. This research was designed to advance the current understanding of the interaction between groundwater and surface water through the quantification of spatial and temporal trends in streamflow and groundwater level changes and the use of high-resolution spatial estimates of streambed hydraulic conductivity. Changes in streamflow and groundwater levels were quantified with the use of hydrograph-separation techniques and trend analyses. High-resolution estimates of streambed hydraulic conductivity were found through the correlation of waterborne continuous resistivity profiling data to hydraulic conductivity and streambed hydraulic conductivity estimates were incorporated into the existing Mississippi Embayment Regional Aquifer Study (MERAS) groundwaterlow model. Site-specific empirical relationships between resistivity and hydraulic conductivity were developed with near-stream borehole geophysical logs to improve model estimates of streambed hydraulic conductivity. Results of the quantification of changes in streamflow and groundwater levels suggested agricultural groundwater withdrawals for irrigation to be the primary source of groundwater-level declines. Results from the incorporation of high-resolution estimates of streambed hydraulic conductivity showed that the existing groundwaterlow model is sensitive to changes in streambed hydraulic conductivity, which may impact model accuracy. The incorporation of streambed hydraulic conductivity estimates derived from site-specific empirical relationships impacted MERAS model water-budget estimates. Information gained from this research will be used to improve the existing groundwaterlow model, which acts as a decision-support tool for water-resource managers at state and local levels to make informed water-use decisions for the conservation of fresh-water resources for sustainable agricultural irrigation practices.

Modern streamflow measuring equipment, water quality sampling techniques and a better understanding of pollutant washoff are continuously being developed as today's society is in critical need of improving water management, minimizing developmental impacts and preventing environmental hazards. In particular, the study of the spatial, temporal and volumetric characteristics of annual pollutant loading caused by variations in precipitation, land use and other anthropogenic factors is of great significance due to their relation to future global water demands. The research presented here falls in three parts. In the first part of the dissertation, an acoustical Doppler velocity profiler installed in a submerged concrete channel is proposed to continually measure the annual fluctuation in streamflow levels down to dry channel conditions. The tailwater influenced, intermittent streamflow conditions for the City of Kissimmee, Florida were selected for the evaluation of this approach under a 3-year study from 2006 to 2008. The performance of these concrete channels were systematically evaluated by comparisons with established field measurement techniques over various stream configurations and flow conditions. The second part of this research investigates the dynamics of flood wave detection with respect to enabling an automatic water quality sampler to start collecting samples. The main focus was on the accurate detection of flood waves in the absence of rainfall and the presence of fluctuating baseflows and stream stages. In the 3-year study, it was shown that a dual parameter trigger, utilizing independent measuring equipment, resulted in accurate flood wave detection with minimal false triggering of the autosampler. In addition, an incremental or percent deviation from a moving average of stage or flow proved to be a more consistent indicator for the presence of a flood wave. In the third part of this work, the frequency of water quality sampling and the associated level of detail for sampling of rainfall events were investigated with respect to accurately depicting annual pollutant loads. It was found that the seasonal variations in baseflow pollutant loads are not accurately represented by current 4-quarter grab sampling. Also, significant pollutant loading within rainfall events may not be captured by only performing grab sampling during baseflow conditions. In addition, although increased pollutant concentrations were observed within the initial 30 minutes of the flood wave, their actual loadings did not represent a significant impact on the annual pollutant loads. A biweekly grab sampling frequency was found to be adequate in many cases to depict the annual pollutant loads, but depending upon the targeted constituent and particular streamflow condition, rainfall event sampling might also be necessary. The results of this research complemented with other studies will promote better understanding of intermittent streamflows, accurate flood wave detection, and assessment of annual pollutant loads to our nation's waterbodies.

Earth ecosystems and environment have been changing rapidly due to the advanced technologies and developments of humans. Impacts caused by human activities and developments are difficult to acquire for evaluations due to the rapid changes. Remote sensing (RS) technology has been implemented for environmental managements. A new and promising trend in remote sensing for environment is widely used to measure and monitor the earth environment and its changes. RS allows large-scaled measurements over a large region within a very short period of time. Continuous and repeatable measurements are the very indispensable features of RS. Soil moisture is a critical element in the hydrological cycle especially in a semiarid or arid region. Point measurement to comprehend the soil moisture distribution contiguously in a vast watershed is difficult because the soil moisture patterns might greatly vary temporally and spatially. Space-borne radar imaging satellites have been popular because they have the capability to exhibit all weather observations. Yet the estimation methods of soil moisture based on the active or passive satellite imageries remain uncertain. This study aims at presenting a systematic soil moisture estimation method for the Choke Canyon Reservoir Watershed (CCRW), a semiarid watershed with an area of over 14,200 km2 in south Texas. With the aid of five corner reflectors, the RADARSAT-1 Synthetic Aperture Radar (SAR) imageries of the study area acquired in April and September 2004 were processed by both radiometric and geometric calibrations at first. New soil moisture estimation models derived by genetic programming (GP) technique were then developed and applied to support the soil moisture distribution analysis. The GP-based nonlinear function derived in the evolutionary process uniquely links a series of crucial topographic and geographic features. Included in this process are slope, aspect, vegetation cover, and soil permeability to compliment the well-calibrated SAR data. Research indicates that the novel application of GP proved useful for generating a highly nonlinear structure in regression regime, which exhibits very strong correlations statistically between the model estimates and the ground truth measurements (volumetric water content) on the basis of the unseen data sets. In an effort to produce the soil moisture distributions over seasons, it eventually leads to characterizing local- to regional-scale soil moisture variability and performing the possible estimation of water storages of the terrestrial hydrosphere. A new evolutionary computational, supervised classification scheme (Riparian Classification Algorithm, RICAL) was developed and used to identify the change of riparian zones in a semi-arid watershed temporally and spatially. The case study uniquely demonstrates an effort to incorporating both vegetation index and soil moisture estimates based on Landsat 5 TM and RADARSAT-1 imageries while trying to improve the riparian classification in the Choke Canyon Reservoir Watershed (CCRW), South Texas. The CCRW was selected as the study area contributing to the reservoir, which is mostly agricultural and range land in a semi-arid coastal environment. This makes the change detection of riparian buffers significant due to their interception capability of non-point source impacts within the riparian buffer zones and the maintenance of ecosystem integrity region wide. The estimation of soil moisture based on RADARSAT-1 Synthetic Aperture Radar (SAR) satellite imagery as previously developed was used. Eight commonly used vegetation indices were calculated from the reflectance obtained from Landsat 5 TM satellite images. The vegetation indices were individually used to classify vegetation cover in association with genetic programming algorithm. The soil moisture and vegetation indices were integrated into Landsat TM images based on a pre-pixel channel approach for riparian classification. Two different classification algorithms were used including genetic programming, and a combination of ISODATA and maximum likelihood supervised classification. The white box feature of genetic programming revealed the comparative advantage of all input parameters. The GP algorithm yielded more than 90% accuracy, based on unseen ground data, using vegetation index and Landsat reflectance band 1, 2, 3, and 4. The detection of changes in the buffer zone was proved to be technically feasible with high accuracy. Overall, the development of the RICAL algorithm may lead to the formulation of more effective management strategies for the handling of non-point source pollution control, bird habitat monitoring, and grazing and live stock management in the future. Soil properties, landscapes, channels, fault lines, erosion/deposition patches, and bedload transport history show geologic and geomorphologic features in a variety of watersheds. In response to these unique watershed characteristics, the hydrology of large-scale watersheds is often very complex. Precipitation, infiltration and percolation, stream flow, plant transpiration, soil moisture changes, and groundwater recharge are intimately related with each other to form water balance dynamics on the surface of these watersheds. Within this chapter, depicted is an optimal site selection technology using a grey integer programming (GIP) model to assimilate remote sensing-based geo-environmental patterns in an uncertain environment with respect to some technical and resources constraints. It enables us to retrieve the hydrological trends and pinpoint the most critical locations for the deployment of monitoring stations in a vast watershed. Geo-environmental information amassed in this study includes soil permeability, surface temperature, soil moisture, precipitation, leaf area index (LAI) and normalized difference vegetation index (NDVI). With the aid of a remote sensing-based GIP analysis, only five locations out of more than 800 candidate sites were selected by the spatial analysis, and then confirmed by a field investigation. The methodology developed in this remote sensing-based GIP analysis will significantly advance the state-of-the-art technology in optimum arrangement/distribution of water sensor platforms for maximum sensing coverage and information-extraction capacity. Effective water resources management is a critically important priority across the globe. While water scarcity limits the uses of water in many ways, floods also have caused so many damages and lives. To more efficiently use the limited amount of water or to resourcefully provide adequate time for flood warning, the results have led us to seek advanced techniques for improving streamflow forecasting. The objective of this section of research is to incorporate sea surface temperature (SST), Next Generation Radar (NEXRAD) and meteorological characteristics with historical stream data to forecast the actual streamflow using genetic programming. This study case concerns the forecasting of stream discharge of a complex-terrain, semi-arid watershed. This study elicits microclimatological factors and the resultant stream flow rate in river system given the influence of dynamic basin features such as soil moisture, soil temperature, ambient relative humidity, air temperature, sea surface temperature, and precipitation. Evaluations of the forecasting results are expressed in terms of the percentage error (PE), the root-mean-square error (RMSE), and the square of the Pearson product moment correlation coefficient (r-squared value). The developed models can predict streamflow with very good accuracy with an r-square of 0.84 and PE of 1% for a 30-day prediction.

Characterizing streamflow trends is important for water resources management. Streamflow conditions, and trends thereof, are critical drivers of all aspects of stream geomorphology, sediment and nutrient transport, and ecological processes. Using the non-parametric modified Mann-Kendall test, we analyzed streamflow trends from 1996 to 2022 for the Southern Appalachian (SA) region of the U.S. The forested uplands of the SA receive high amounts of rain and act as a "water tower" for the surrounding lowland area, both of which have experienced higher than average population growth and urban development. For the total of 127 USGS gages with continuous streamflow measurements, we also evaluated precipitation and land change rates and patterns within the upstream contributing areas. Statistical methods (i.e., generalized linear models) were then used to assess any linkages between land cover change (LCC) and streamflow trends. Our results show that 42 drainage areas are experiencing increasing trends in their precipitation, and 1 is experiencing a negative trend. A total of 71 drainage areas are experiencing increasing trends in either their annual streamflow minimums, maximums, medians, or variability, with some experiencing changes in multiple. From our models, it is suggested that agricultural expansion is associated with increasing minimum streamflow trends, but increasing precipitation is also positively linked. With this information, water managers would be aware of which areas are experiencing changes in streamflow amounts from LCC or precipitation and could then apply this in planning and predictions. / Master of Science / Water availability is important for resources management. Streamflow is a measure of available surface water and is an important component in the hydrological cycle. Using the non-parametric modified Mann-Kendall test, we analyzed streamflow trends from 1996 to 2022 for the Southern Appalachian (SA) region of the U.S. The forested uplands of the SA receive high amounts of rain and act as a "water tower" for the surrounding lowland area, both of which have experienced higher than average population growth and city expansion. For the total of 127 USGS gages with continuous streamflow measurements, we also evaluated precipitation and land cover change rates within the area upstream of the gage (or drainage/contributing area). Statistical methods (i.e., generalized linear models) were then used to assess any linkages between land cover change (LCC) and streamflow trends. Our results show that 42 drainage areas are experiencing increasing trends in their precipitation, and 1 is experiencing a negative trend. A total of 71 drainage areas are experiencing increasing trends in either their annual streamflow minimums, maximums, medians, or variability, with some experiencing changes in multiple. From our models, it is suggested that agricultural expansion is associated with increasing minimum streamflow trends, but increasing precipitation is also positively linked. With this information, water managers would be aware of which areas are experiencing changes in streamflow amounts from LCC or precipitation and could then apply this in planning and predictions.

Land use/cover has shown significant changes during the past three decades in Ethiopia especially in the highlands of the country. That resulted in changes in streamflows and other hydrological processes. The existing land and water resources system of the area is adversely affected due the rapid growth of population, deforestation, surface erosion and sediment transport. The main objective of this study is to evaluate the impact of land use/cover changes in the hydrology of Beles Basin, Ethiopia. The physically based hydrologic model, SWAT, was developed for the Beles basin, Ethiopia by combining geospatial and climatic data. ArcGIS has been used to process geospatial data which includes the Digital Elevation Model (DEM) which has a resolution of 90 m, land use/cover and soil maps. A simple Interpolation technique has been used to fill in the missing precipitation data. The GIS interface version of SWAT (ArcSWAT) has the capability to utilize ArcGIS to facilitate input data preparation and output data generation. Idrisi Andes in cooperation with ArcGIS 9.2 used to generate landuse/cover maps from Landsat data of three different years. Three SWAT models were set up using the three generated land use/cover maps and used to evaluate the land use/cover change and its impacts on the streamflow of study basin. The primary hydrological model was evaluated through sensitivity analysis, model calibration, and model validation for realistic prediction of the different hydrological components in the basin. Out of twenty six flow parameters sixteen parameters were found to be sensitive. But the most sensitive ten parameters were selected and used for model calibration. The model calibration was carried out using observed streamflow data from 01 January 2001 to 31 December 2002 and a validation period from 01 January 2003 to 31 December 2004. The coefficient of determinations (R2) was 0.74 and the Nash-Sutcliffe simulation efficiency (NSE) was 0.62which indicated that the model was able to predict streamflow with reasonable accuracy. However, the hydrograph of the cumulative hydrographs of the calibration and validation periods showed significant discrepancies between the observed and the simulated data of each period.  The average yearly flow volume of the observed streamflow on the cumulative hydrograph of the calibration period has exceeded the simulated streamflow. On the other hand on the cumulative hydrograph of the validation period the average yearly flow volume of the simulated streamflow was higher than the observed streamflow. The simulated result of the streamflow data from different land use/cover maps revealed that the change in the land use/cover classes of the basin throughout the study periods.