Application of CryoSat-2 altimetry data for river analysis and modelling

Availability of in situ river monitoring data, especially of data shared across boundaries, is decreasing, despite growing challenges for water resource management across the entire globe. This is especially valid for the case study of this work, the Brahmaputra Basin in South Asia. Commonly, satellite altimeters are used in various ways to provide information about such river basins. Most missions provide virtual station time series of water levels at locations where their repeat orbits cross rivers. CryoSat-2 is equipped with a new type of altimeter, providing estimates of the actual ground location seen in the reflected signal. It also uses a drifting orbit, challenging conventional ways of processing altimetry data to river water levels and their incorporation in hydrologic–hydrodynamic models. However, CryoSat-2 altimetry data provides an unprecedentedly high spatial resolution. This paper suggests a procedure to (i) filter CryoSat-2 observations over rivers to extract water-level profiles along the river, and (ii) use this information in combination with a hydrologic–hydrodynamic model to fit the simulated water levels with an accuracy that cannot be reached using information from globally available digital elevation models (DEMs) such as from the Shuttle Radar Topography Mission (SRTM) only. The filtering was done based on dynamic river masks extracted from Landsat imagery, providing spatial and temporal resolutions high enough to map the braided river channels and their dynamic morphology. This allowed extraction of river water levels over previously unmonitored narrow stretches of the river. In the Assam Valley section of the Brahmaputra River, CryoSat-2 data and Envisat virtual station data were combined to calibrate cross sections in a 1-D hydrodynamic model of the river. The hydrologic–hydrodynamic model setup and calibration are almost exclusively based on openly available remote sensing data and other global data sources, ensuring transferability of the developed methods. They provide an opportunity to achieve forecasts of both discharge and water levels in a poorly gauged river system

Discharge Estimation From Dense Arrays of Pressure Transducers

In situ river discharge estimation is a critical component of studying rivers. A dominant method for establishing discharge monitoring in situ is a temporary gauge, which uses a rating curve to relate stage to discharge. However, this approach is constrained by cost and the time to develop the stage-discharge rating curve, as rating curves rely on numerous flow measurements at high and low stages. Here, we offer a novel alternative approach to traditional temporary gauges: estimating Discharge via Arrays of Pressure Transducers (DAPT). DAPT uses a Bayesian discharge algorithm developed for the upcoming Surface Water Ocean Topography satellite (SWOT) to estimate in situ discharge from automated water surface elevation measurements. We conducted sensitivity tests over 4,954 model runs on five gauged rivers and conclude that the DAPT method can robustly reproduce discharge with an average Nash-Sutcliffe Efficiency (NSE) of 0.79 and Kling-Gupta Efficiency of 0.78. Further, we find that the DAPT method estimates discharge similarly to an idealized temporary gauge created from the same input data (NSE differences of less than 0.1), and that results improve significantly with accurate priors. Finally, we test the DAPT method in nine poorly gauged rivers in a realistic and complex field setting in the Peace-Athabasca Delta, and show that the DAPT method largely outperforms a temporary gauge in this time and budget constrained setting. We therefore recommend DAPT as an effective tool for in situ discharge estimation in cases where there is not enough time or resources to develop a temporary gauge

MONITORING CHANGE IN LAKE WATER STORAGE OVER TIME WITH SATELLITE IMAGERY AND CITIZEN SCIENCE

Despite lakes being key part of the global water cycle and a crucial water resource, there is limited understanding of how water storage in small lakes varies over time. Here, we study change in lake water storage over time in clusters of small natural lakes in North Carolina, Washington, Illinois, and Wisconsin, using lake level measurements gathered by citizen scientists and lake surface area measurements taken from optical satellite imagery. We compare the change in lake water storage between pairs of lakes within regional clusters. On average, water storage variations in lakes across all study regions are moderately positively correlated (rho = 0.49) with substantial spread in the degree of correlation. Overall, there is a weak, but statistically significant, negative relationship between distance and paired lake correlations. This study addresses a gap in understanding about small lakes and provides a baseline about regional patterns of change in lake water storage.Master of Scienc

Flood hazard hydrology: interdisciplinary geospatial preparedness and policy

Thesis (Ph.D.) University of Alaska Fairbanks, 2017Floods rank as the deadliest and most frequently occurring natural hazard worldwide, and in 2013 floods in the United States ranked second only to wind storms in accounting for loss of life and damage to property. While flood disasters remain difficult to accurately predict, more precise forecasts and better understanding of the frequency, magnitude and timing of floods can help reduce the loss of life and costs associated with the impact of flood events. There is a common perception that 1) local-to-national-level decision makers do not have accurate, reliable and actionable data and knowledge they need in order to make informed flood-related decisions, and 2) because of science--policy disconnects, critical flood and scientific analyses and insights are failing to influence policymakers in national water resource and flood-related decisions that have significant local impact. This dissertation explores these perceived information gaps and disconnects, and seeks to answer the question of whether flood data can be accurately generated, transformed into useful actionable knowledge for local flood event decision makers, and then effectively communicated to influence policy. Utilizing an interdisciplinary mixed-methods research design approach, this thesis develops a methodological framework and interpretative lens for each of three distinct stages of flood-related information interaction: 1) data generation—using machine learning to estimate streamflow flood data for forecasting and response; 2) knowledge development and sharing—creating a geoanalytic visualization decision support system for flood events; and 3) knowledge actualization—using heuristic toolsets for translating scientific knowledge into policy action. Each stage is elaborated on in three distinct research papers, incorporated as chapters in this dissertation, that focus on developing practical data and methodologies that are useful to scientists, local flood event decision makers, and policymakers. Data and analytical results of this research indicate that, if certain conditions are met, it is possible to provide local decision makers and policy makers with the useful actionable knowledge they need to make timely and informed decisions

Uso de sensores remotos en el seguimiento de la vegetación de dehesa y su influencia en el balance hidrológico a escala de cuenca

The Mediterranean region is characterized by hot summers with long dry periods, a situation that may be exacerbated by the progressive global warming. In these water-limited environments where productivity of the ecosystems depends mainly on water availability, the reduction of freshwater resources can have severe consequences. An increase in aridity may lead to low productivity, land degradation and unwanted changes in land use. To reduce the vulnerability of Mediterranean landscapes it is important to improve our knowledge of the hydrological processes conditioning the water exchanges, with evapotranspiration (ET) being a key indicator of the state of ecosystems and playing a crucial role in the basin's water and energy balances. The goal of this dissertation is to improve our understanding of the evapotranspiration dynamics over Mediterranean heterogeneous and complex vegetation covers, with a focus on the dehesa ecosystem. The final aim is to contribute to the conservation of the water resources in these regions in the medium to long term, supporting the decision-making processes with quantitative, distributed, and high-quality information. To reach this goal, in this research the evaluation of remote sensing-based soil water balance (SWB) and surface energy balance (SEB) models was proposed to monitor the water consumption and water stress of typical Mediterranean vegetation at different spatial and temporal scales. In particular, the VI-ETo methodology (SWB) and the ALEXI/DisALEXI approach (SEB) have been adapted and applied. ET modeling using the VI-ETo scheme has been improved through the assessment of the vegetation layers' effective parameters. A data fusion algorithm was applied to the ET maps produced by the SEB model over the dehesa ecosystem, and we analyzed the opportunities that this high-resolution ET product in time and space can provide for water and vegetation resource management. The results have demonstrated the feasibility of both approaches (SWB and SEB models) to accurately monitor ET dynamics over the dehesa landscape, adequately reproducing the annual bimodal behavior and the response of the vegetation in periods of water deficit. The error obtained using the SWB approach (the VI-ETo method) was RMSE = 0.47 mm day-1 over the whole dehesa system (grass + trees) and over an open grassland. The monitoring of water stress for both systems with different canopy structure, using as a proxy the ET/ETo ratio, and the stress coefficient (Ks), was successful. Improvements on the specific spectral properties of oak trees and layer-specific parameters were included into the modeling. We also analyzed the influence of the spectral properties of oak trees and another typical Mediterranean tree canopy, the olive orchard, in the VI-ETo model. We found that the use of appropriate values of the parameter SAVImax (0.51 for oak trees and 0.57 for olive trees) had notable implications in the computation of ET and water stress, in contrast to using a generic value for Mediterranean crops (SAVImax= 0.75). The accuracy of this water balance-based approach was also evaluated over two heterogeneous Mediterranean basins, with a mosaic of holm oaks and grasslands, shrubs, coniferous plantations, and irrigated horticultural crops. The annual discharge flows of both watersheds, which were determined from the modeled ET data and using a simple surface water balance, were very similar to those obtained with the HBV hydrological model, and to the values measured at the outlet of one of the basins, corroborating the usefulness of the VI-ETo methodology on these vegetation types. On the other hand, the resulting ET series (30 m, daily) derived with the SEB approach (ALEXI/DisALEXI method) and the STARFM fusion algorithm provided an RMSE value of 0.67 mm day-1, which was considered an acceptable error for management purposes. This error was slightly lower compared to using simpler interpolation methods, probably due to the high temporal frequency and better spatial representation of the flux tower footprint of the fused time series. The analysis of ET patterns over small heterogeneous vegetated patches that form the dehesa structure revealed the importance of having fine resolution information at field scale to distinguish the water consumed by the different vegetation components, which influences the provision of many ecosystem services. For example, it was key for identifying phenology dates of grasslands, or understanding the hydrological functioning of riverside dense evergreen vegetation with high ET rates during the whole year, in contrast with the herbaceous areas. Accurately modeling these different behaviors of dehesa microclimates is useful to support farmers‘ management and provide recommendations tailored for each structural component and requirements.La región mediterránea se caracteriza por veranos calurosos con largos períodos sin precipitaciones, situación que puede agravarse con el progresivo calentamiento global. En estos ambientes donde la productividad de los ecosistemas depende principalmente de la disponibilidad de agua, la reducción de los recursos hídricos puede tener graves consecuencias. Un aumento de la aridez puede conducir a una baja productividad, degradación de la tierra y cambios no deseados en el uso del suelo. Para reducir la vulnerabilidad de las zonas mediterráneas es importante profundizar en el estudio de los procesos hidrológicos que condicionan los intercambios de agua, siendo la evapotranspiración (ET) un indicador clave del estado de los ecosistemas y jugando un papel crucial en los balances hídricos y energéticos de la cuenca. El objetivo de esta tesis es mejorar nuestro conocimiento sobre la dinámica de la evapotranspiración en cubiertas mediterráneas heterogéneas y complejas, con el foco en el ecosistema de dehesa. El objetivo final es contribuir a la conservación de los recursos hídricos de estas regiones en el medio-largo plazo, apoyando en los procesos de toma de decisiones con información cuantitativa, distribuida y de calidad. Para alcanzar este objetivo, en esta investigación se propuso evaluar modelos de balance de agua en el suelo (SWB) y balance de energía en superficie (SEB) basados en el uso de sensores remotos, para el seguimiento del consumo de agua y el estrés hídrico de la vegetación mediterránea a diferentes escalas espaciales y temporales. En particular, se ha adaptado y aplicado la metodología VI-ETo (SWB) y el enfoque ALEXI/DisALEXI (SEB). Se ha mejorado el modelado de ET utilizando el esquema VI-ETo mediante la evaluación de los parámetros efectivos de las capas de vegetación. Se aplicó un algoritmo de fusión de datos remotos a los mapas de ET generados por el modelo SEB sobre el ecosistema de dehesa, y estudiamos las oportunidades que este producto de ET con alta resolución espacial y temporal puede aportar en la gestión de los recursos hídricos y de los ecosistemas. Los resultados han demostrado la viabilidad de ambos enfoques (modelos SWB y SEB) para monitorear con precisión la dinámica de la ET sobre el ecosistema de dehesa, reproduciendo adecuadamente el comportamiento bimodal anual y la respuesta de la vegetación en períodos de déficit hídrico. El error obtenido usando el enfoque SWB (el método VI-ETo) fue RMSE = 0.47 mm día-1, tanto para el sistema dehesa (pasto + árboles) como para una zona de pastizal. El seguimiento del estrés hídrico para ambos sistemas con diferente estructura de vegetación, utilizando la relación ET/ETo y el coeficiente de estrés (Ks), fue satisfactorio. Se incluyeron en el modelado mejoras sobre las propiedades espectrales específicas de las encinas y los parámetros específicos de los diferentes estratos de vegetación. También analizamos la influencia de las propiedades espectrales de las encinas y otra cubierta mediterránea, el olivar, en el modelo VI-ETo. Encontramos que el uso de valores apropiados del parámetro SAVImax (0,51 para robles y 0,57 para olivos) tuvo un efecto significativo en la determinación del consumo de agua y estrés hídrico, en comparación con usar un valor genérico para cultivos mediterráneos (SAVImax = 0,75). La precisión de este enfoque basado en el balance hídrico también se evaluó en dos cuencas mediterráneas heterogéneas, con un mosaico de encinas y pastizales, arbustos, plantaciones de coníferas y cultivos hortícolas de regadío. Los caudales de descarga anual de ambas cuencas, determinados a partir de los datos de ET modelados y utilizando un balance hídrico superficial muy simple, fueron muy similares a los obtenidos con el modelo hidrológico HBV, y a los valores medidos en la salida de una de las cuencas, corroborando la utilidad de la metodología VI-ETo sobre estas formaciones vegetales. Por otra parte, la serie final de ET (30 m, diaria) derivada del enfoque SEB (método ALEXI/DisALEXI) y del algoritmo de fusión STARFM proporcionó un valor de RMSE de 0,67 mm día-1, considerado un error aceptable para fines de manejo. Este error fue ligeramente inferior a los obtenidos usando métodos de interpolación más simples, debido probablemente a la alta frecuencia temporal y una mejor representación espacial del footprint de la torre de medida de flujos en la serie temporal fusionada. El análisis de los patrones de la ET sobre pequeñas manchas de vegetación heterogéneas, que forman la estructura de la dehesa, reveló la importancia de tener información con alta resolución a escala de campo para distinguir el agua consumida por los diferentes componentes de la vegetación, que tienen influencia en el aprovisionamiento de muchos servicios ecosistémicos. Por ejemplo, fue clave para identificar ciertas fechas fenológicas de los pastizales, o entender el funcionamiento hidrológico de la vegetación densa de hoja perenne en zonas de ribera con altas tasas de ET durante todo el año, en comparación con zonas de especies herbáceas. Modelar con precisión estos comportamientos diferentes de los microclimas de la dehesa es útil para apoyar la gestión de los agricultores y ofrecer recomendaciones adaptadas a cada componente y necesidades estructurales