Improving the Physical Processes and Model Integration Functionality of an Energy Balance Model for Snow and Glacier Melt

The Hindu-Kush Himalayan region possesses a large resource of snow and ice, which acts as a freshwater reservoir for irrigation, domestic water consumption or hydroelectric power for billions of people in South Asia. Monitoring hydrologic resources in this region is challenging because of the difficulty of installing and maintaining a climate and hydrologic monitoring network, limited transportation and communication infrastructure and difficult access to glaciers. As a result of the high, rugged topographic relief, ground observations in the region are extremely sparse. Reanalysis data offer the potential to compensate for the data scarcity, which is a barrier in hydrological modeling and analysis for improving water resources management. Reanalysis weather data products integrate observations with atmospheric model physics to produce a spatially and temporally complete weather record in the post-satellite era. This dissertation creates an integrated hydrologic modeling system that tests whether streamflow prediction can be improved by taking advantage of the National Aeronautics and Space Administration (NASA) remote sensing and reanalysis weather data products in physically based energy balance snow melt and hydrologic models. This study also enhances the energy balance snowmelt model by adding capability to quantify glacier melt. The novelty of this integrated modeling tool resides in allowing the user to isolate various components of surface water inputs (rainfall, snow and glacier ice melt) in a cost-free, open source graphical-user interface-based system that can be used for government and institutional decision-making. Direct, physically based validation of this system is challenging due to the data scarcity in this region, but, to the extent possible, the model was validated through comparison to observed streamflow and to point measurements at locations in the United States having available dat

Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management

The main aim of this book is to present various implementations of ML methods and metaheuristic algorithms to improve modelling and prediction hydrological and water resources phenomena having vital importance in water resource management

Oceanus.

v. 42, no. 1 (2000

Quantifying supraglacial debris thickness at local to regional scales

Supraglacial debris thickness is a key control on the surface energy balance of debris-covered glaciers, which are common in temperate mountain ranges around the world. As such, it is an important input variable to the sorts of models that are used to understand and predict glacier change, which are essential for determining future water supply in glacierised regions and glacier contributions to sea-level rise. However, to quantify supraglacial debris thickness is difficult: making direct measurements is laborious and existing remote sensing approaches have not been thoroughly validated, so there is a general paucity of supraglacial debris thickness data. This thesis investigates methods of quantifying supraglacial debris thickness at local to regional scales. First, it makes in-situ field measurements of debris thickness at the local scale on glaciers in the Himalaya and the European Alps by manual excavation and by ground-penetrating radar (GPR). Second, it uses some of these field measurements to test and develop thermal remote sensing approaches to quantifying supraglacial debris thickness at the glacier scale. Third, it uses a dynamic energy-balance model in an inverse approach to quantify debris thickness on the glaciers of three watersheds in High Mountain Asia from thermal satellite imagery and high-resolution meteorological reanalysis data. At the local scale, GPR is found to be useful for measuring supraglacial debris thickness accurately and precisely, at least in the range 0.16-4.9 m. Debris thickness is highly variable over horizontal distances of < 10 m on individual glaciers due to gravitational reworking, which necessarily implies higher sub-debris ice melt rates than if debris thickness was spatially invariable. At the glacier scale, thermal remote sensing approaches can reproduce field measurements, and remote sensing estimates of supraglacial debris thickness can be used successfully to model sub-debris melting. If well-distributed field measurements are available, supraglacial debris thickness should be extrapolated using remote sensing-derived pseudo daily mean surface temperatures. Otherwise, it should be determined iteratively by minimising the mismatch between remotely sensed surface temperatures, preferably from night-time thermal images, and surface temperatures determined using a dynamic energy-balance model. At the regional scale, thermal satellite imagery and high-resolution meteorological reanalysis data can be used to provide reasonable estimates of supraglacial debris thickness. However, modelled uncertainties are not always able to explain ground-truth measurements, and there is a tendency towards underestimation due to problems associated with supraglacial ponds and ice cliffs and the spatial resolution of input data. The findings of this thesis will lead to improvements in the quantification of supraglacial debris thickness at a range of scales and, therefore, in the understanding and prediction of glacier change in temperate mountain ranges.Funded by NERC DTP grant number NE/L002507/1. CASE sponsorship provided by Reynolds International Ltd

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

There are no author-identified significant results in this report

Data Assimilation in high resolution Numerical Weather Prediction models to improve forecast skill of extreme hydrometeorological events.

The complex orography typical of the Mediterranean area supports the formation, mainly during the fall season, of the so-called back-building Mesoscale Convective Systems (MCS) producing torrential rainfall often resulting into flash floods. These events are hardly predictable from a hydrometeorological standpoint and may cause significant amount of fatalities and socio-economic damages. Liguria region is characterized by small catchments with very short hydrological response time, and it has been proven to be very exposed to back-building MCSs occurrence. Indeed this region between 2011 and 2014 has been hit by three intense back-building MCSs causing a total death toll of 20 people and several hundred million of euros of damages. Building on the existing relationship between significant lightning activity and deep convection and precipitation, the first part of this work assesses the performance of the Lightning Potential Index, as a measure of the potential for charge generation and separation that leads to lightning occurrence in clouds, for the back-building Mesoscale Convective System which hit Genoa city (Italy) in 2014. An ensemble of Weather Research and Forecasting simulations at cloud-permitting grid spacing (1 km) with different microphysical parameterizations is performed and compared to the available observational radar and lightning data. The results allow gaining a deeper understanding of the role of lightning phenomena in the predictability of back-building Mesoscale Convective Systems often producing flash flood over western Mediterranean complex topography areas. Despite these positive and promising outcomes for the understanding highly-impacting MCS, the main forecasting issue, namely the uncertainty in the correct reproduction of the convective field (location, timing, and intensity) for this kind of events still remains open. Thus, the second part of the work assesses the predictive capability, for a set of back-building Liguria MCS episodes (including Genoa 2014), of a hydro-meteorological forecasting chain composed by a km-scale cloud resolving WRF model, including a 6 hour cycling 3DVAR assimilation of radar reflectivity and conventional ground sensors data, by the Rainfall Filtered Autoregressive Model (RainFARM) and the fully distributed hydrological model Continuum. A rich portfolio of WRF 3DVAR direct and indirect reflectivity operators, has been explored to drive the meteorological component of the proposed forecasting chain. The results confirm the importance of rapidly refreshing and data intensive 3DVAR for improving first quantitative precipitation forecast, and, subsequently flash-floods occurrence prediction in case of back-building MCSs events. The third part of this work devoted the improvement of severe hydrometeorological events prediction has been undertaken in the framework of the European Space Agency (ESA) STEAM (SaTellite Earth observation for Atmospheric Modelling) project aiming at investigating, new areas of synergy between high-resolution numerical atmosphere models and data from spaceborne remote sensing sensors, with focus on Copernicus Sentinels 1, 2 and 3 satellites and Global Positioning System stations. In this context, the Copernicus Sentinel satellites represent an important source of data, because they provide a set of high-resolution observations of physical variables (e.g. soil moisture, land/sea surface temperature, wind speed, columnar water vapor) to be used in NWP models runs operated at cloud resolving grid spacing . For this project two different use cases are analyzed: the Livorno flash flood of 9 Sept 2017, with a death tool of 9 people, and the Silvi Marina flood of 15 November 2017. Overall the results show an improvement of the forecast accuracy by assimilating the Sentinel-1 derived wind and soil moisture products as well as the Zenith Total Delay assimilation both from GPS stations and SAR Interferometry technique applied to Sentinel-1 data

ANALYZING ECOHYDROLOGY OF SUBIRRIGATED MEADOW, DRY VALLEY AND UPLAND DUNE ECOSYSTEMS USING REMOTE SENSING AND IN-SITU ESTIMATIONS IN THE SEMIARID SAND HILLS REGION OF NEBRASKA, USA

Nebraska’s dependence upon the High Plains (Ogallala) Aquifer for agricultural production is vital to the state’s economy, ecology and hydrology. The Sand Hills region (58,000 km2) of Nebraska is a unique system of lakes, (~5%) wetlands, (~10%) subirrigated meadows, (~20%) dry valleys and (~65%) upland sand dune ecosystems. Understanding how each of these land cover types reacts to climate conditions of different water limitations is vital to regional water resource management. This research explores the ecohydrological behavior of different land cover types at the Gudmundsen Sand Hills Research Laboratory (GSRL) near Whitman, Nebraska in the heart of the Sand Hills region of Nebraska by using remote sensing and in-situ estimations of energy partitioning. By employing satellite technology and micrometeorological instrumentation this research establishes a better understanding how energy partitioning, and resulting evapotranspiration (ET), differs between different vegetative communities. We present findings of diurnal and seasonal estimates of energy partitioning as well as daily estimations of ET from both satellite image processing and in-situ observations by Bowen ratio energy balance systems (BREBS). This research also employed different techniques to estimate energy partitioning via remote sensing by adjusting radiation, wind speed, and stability parameters to better represent areas with high topographic relief. The last focal point of this research was to analyze how energy partitioning and ET varied both spatially and temporally under different climate conditions between 2004 (normal year), 2006 (dry year), and 2009 (wet year). Adviser: John D. Lenter

Glacier-climate interactions: a synoptic approach

The reliance on freshwater released by mountain glaciers and ice caps demands that the effects of climate change on these thermally-sensitive systems are evaluated thoroughly. Coupling climate variability to processes of mass and energy exchange at the glacier scale is challenged, however, by a lack of climate data at an appropriately fine spatial resolution. The thesis addresses this challenge through attempting to reconcile this scale mismatch: glacier boundary-layer observations of meteorology and ablation at Vestari Hagafellsjökull, Iceland, and Storglaciären, Sweden, are related to synoptic-scale meteorological variability recorded in gridded, reanalysis data. Specific attention is directed toward synoptic controls on: i) near-surface air temperature lapse rates; ii) stationarity of temperature-index melt model parameters; and iii) glacier-surface ablation. A synoptic weather-typing procedure, which groups days of similar reanalysis meteorology into weather categories , forms the basis of the analytical approach adopted to achieve these aims. Lapse rates at Vestari Hagafellsjökull were found to be shallowest during weather categories characterised by warm, cloud-free weather that encouraged katabatic drainage; steep lapse rates were encountered in weather categories associated with strong synoptic winds. Quantitatively, 26% to 38% of the daily lapse-rate variability could be explained by weather-category and regression-based models utilizing the reanalysis data: a level of skill sufficient to effect appreciable improvements in the accuracy of air temperatures extrapolated vertically over Vestari Hagafellsjökull. Weather categories also highlighted the dynamic nature of the temperature-ablation relationship. Notably, the sensitivity of ablation to changes in air temperature was observed to be non-stationary between weather categories, highlighting vulnerabilities of temperature-index models. An innovative solution to this limitation is suggested: the relationship between temperature and ablation can be varied as a function of weather-category membership. This flexibility leads to an overall improvement in the simulation of daily ablation compared to traditional temperature-index formulations (up to a 14% improvement in the amount of variance explained), without the need for additional meteorological data recorded in-situ. It is concluded that weather categories are highly appropriate for evaluating synoptic controls on glacier meteorology and surface energetics; significant improvements in the parameterization of boundary-layer meteorology and ablation rates are realised through their application