Informing the operations of water reservoirs over multiple temporal scales by direct use of hydro-meteorological data

open4siWater reservoir systems may become more adaptive and reliable to external changes by enlarging the information sets used in their operations. Models and forecasts of future hydro-climatic and socio-economic conditions are traditionally used for this purpose. Nevertheless, the identification of skillful forecasts and models might be highly critical when the system comprises several processes with inconsistent dynamics (fast and slow) and disparate levels of predictability. In these contexts, the direct use of observational data, describing the current conditions of the water system, may represent a practicable and zero-cost alternative. This paper contrasts the relative contribution of state observations and perfect forecasts of future water availability in improving multipurpose water reservoirs operation over short- and long-term temporal scales. The approach is demonstrated on the snow-dominated Lake Como system, operated for flood control and water supply. The Information Selection Assessment (ISA) framework is adopted to retrieve the most relevant information to be used for conditioning the operations. By explicitly distinguishing between observational dataset and future forecasts, we quantify the relative contribution of current water system state estimates and perfect streamflow forecasts in improving the lake regulation with respect to both flood control and water supply. Results show that using the available observational data capturing slow dynamic processes, particularly the snow melting process, produces a 10% improvement in the system performance. This latter represents the lower bound of the potential improvement, which may increase to the upper limit of 40% in case skillful (perfect) long-term streamflow forecasts are used.embargoed_20180525Denaro, S; Anghileri, D.; Giuliani, M; Castelletti, A.Denaro, Simona; Anghileri, D.; Giuliani, Matteo; Castelletti, ANDREA FRANCESC

Microwave Indices from Active and Passive Sensors for Remote Sensing Applications

Past research has comprehensively assessed the capabilities of satellite sensors operating at microwave frequencies, both active (SAR, scatterometers) and passive (radiometers), for the remote sensing of Earth’s surface. Besides brightness temperature and backscattering coefficient, microwave indices, defined as a combination of data collected at different frequencies and polarizations, revealed a good sensitivity to hydrological cycle parameters such as surface soil moisture, vegetation water content, and snow depth and its water equivalent. The differences between microwave backscattering and emission at more frequencies and polarizations have been well established in relation to these parameters, enabling operational retrieval algorithms based on microwave indices to be developed. This Special Issue aims at providing an overview of microwave signal capabilities in estimating the main land parameters of the hydrological cycle, e.g., soil moisture, vegetation water content, and snow water equivalent, on both local and global scales, with a particular focus on the applications of microwave indices

Realising Global Water Futures: a Summary of Progress in Delivering Solutions to Water Threats in an Era of Global Change

Canada First Research Excellence FundNon-Peer ReviewedOver the past six years the Global Water Futures program has produced a wide range of scientific findings and engagements with multiple types of potential users of the research. This briefing book provides a snapshot of some of the science advancements and user engagement that have taken place to date. Annual reports to the funding agency are the most up to date source of information: this compilation has been created from reports submitted by projects in 2022, representing both completed and current project work. The briefing book aims to provide quick access to information about GWF projects in a single place for GWF’s User Advisory Panel: we hope that knowing more about the research being produced will spark conversations about how to make the best use of the new knowledge in both policy and practice

Landscapes of Danger: A Geospatial Analysis of Perceived and Realistic Risk in Bryce Canyon National Park

The quantification of risk has inspired a wide breath of literature from the physical sciences, social sciences, and interdisciplinary disciplines like geography. Many attempts to estimate risk via natural hazards either focus on quantifying realistic risk or perceived risk of lay persons, with very little overlap between these paradigms. Due to this, a considerable knowledge gap exists within perceived risk and natural hazards research. This study aims to provide a comprehensive, risk estimation and assessment strategy through a multi-hazard risk assessment of Bryce Canyon National Park (BRCA). This case study analyzed knowledge of risk among visitors with perception surveys and Likert-based scales, in addition to identifying high risk areas of the park through Geographic Information Systems (gis). With a sample size of 254, a systematic stratified sampling method was implemented at specific sites in the park chosen for their distinctive viewsheds, accessibility, and popularity. To identify risky areas, two fuzzy logic models were built: one to identify areas susceptible to rockfall and another to identify areas susceptible to landslides/slumps. Overall, respondents reported feeling largely unconcerned when ranking their perception of various risks within the park (µ = 2.1, σ = .78), however, perception gaps and demographic influences were revealed on individual event types. When asked to identify dangerous areas of the park, participants tended to select locations in the main amphitheater – the most highly trafficked area of the park – even though the fuzzy logic models showed a wider range of locations were susceptible to mass wasting events

Guideline and indicators for Target 6.6 of the SDGs: “Change in the extent of waterrelated ecosystems over time”

This guideline provides supporting information to assist with implementation of monitoring procedures for Target 6.6 indicator, which focuses on protecting and maintaining water-related ecosystems

Earth Observation Science and Applications for Risk Reduction and Enhanced Resilience in Hindu Kush Himalaya Region

This open access book is a consolidation of lessons learnt and experiences gathered from our efforts to utilise Earth observation (EO) science and applications to address environmental challenges in the Hindu Kush Himalayan region. It includes a complete package of knowledge on service life cycles including multi-disciplinary topics and practically tested applications for the HKH. It comprises 19 chapters drawing from a decade’s worth of experience gleaned over the course of our implementation of SERVIR-HKH – a joint initiative of NASA, USAID, and ICIMOD – to build capacity on using EO and geospatial technology for effective decision making in the region. The book highlights SERVIR’s approaches to the design and delivery of information services – in agriculture and food security; land cover and land use change, and ecosystems; water resources and hydro-climatic disasters; and weather and climate services. It also touches upon multidisciplinary topics such as service planning; gender integration; user engagement; capacity building; communication; and monitoring, evaluation, and learning. We hope that this book will be a good reference document for professionals and practitioners working in remote sensing, geographic information systems, regional and spatial sciences, climate change, ecosystems, and environmental analysis. Furthermore, we are hopeful that policymakers, academics, and other informed audiences working in sustainable development and evaluation – beyond the wider SERVIR network and well as within it – will greatly benefit from what we share here on our applications, case studies, and documentation across cross-cutting topics

Land Degradation Assessment with Earth Observation

This Special Issue (SI) on “Land Degradation Assessment with Earth Observation” comprises 17 original research papers with a focus on land degradation in arid, semiarid and dry-subhumid areas (i.e., desertification) in addition to temperate rangelands, grasslands, woodlands and the humid tropics. The studies cover different spatial, spectral and temporal scales and employ a wealth of different optical and radar sensors. Some studies incorporate time-series analysis techniques that assess the general trend of vegetation or the timing and duration of the reduction in biological productivity caused by land degradation. As anticipated from the latest trend in Earth Observation (EO) literature, some studies utilize the cloud-computing infrastructure of Google Earth Engine to cope with the unprecedented volume of data involved in current methodological approaches. This SI clearly demonstrates the ever-increasing relevance of EO technologies when it comes to assessing and monitoring land degradation. With the recently published IPCC Reports informing us of the severe impacts and risks to terrestrial and freshwater ecosystems and the ecosystem services they provide, the EO scientific community has a clear obligation to increase its efforts to address any remaining gaps—some of which have been identified in this SI—and produce highly accurate and relevant land-degradation assessment and monitoring tools