Hydro-Meteorological Incident and Disaster Response in Sri Lanka. Case Study: 2016 May Rain Events

This paper investigates hydro-meteorological hazards faced by Sri Lanka, a lower-middle-income island country in Asia. It provides a case study of a major hydro-meteorological disaster incident that resulted in one of the largest landslides in the history of the country, the Post-Disaster Needs Assessment (PDNA) process, and the national disaster response. Rainfall and flood inundation data are provided for the whole country. The fact that data are held by several government agencies (namely Department of Meteorology, Department of Irrigation, and NBRO), somewhat coordinated by the Disaster Management Center (DMC) is shown. The need for more streamlined coordination of hydro-met data with online access of data for researchers is emphasized. The flood disaster situation and disaster declaration of the Western Province (which contributes nearly 40% of the GDP) is looked at, and evidence is presented to recommend a smaller governance unit for future disaster declarations, in order to bring aid to the places where it is needed and leaving other areas of the province to carry on with the normal economic activity. An example of the use of climate change scenarios in rainfall prediction is provided from a developed island nation (New Zealand). The need for Sri Lanka to increase its spending for hydro-met services (both infrastructure and skills) is highlighted (the global norm being 0.02 of GDP), as the return on such investment is tenfold.

The relevance of different heterogeneous ice formation processes for the precipitation budget

<p>Heterogeneous ice formation in mixed-phase precipitating clouds plays an important role in current weather and climate research. The complex interaction between aerosols, clouds and dynamics taking place within these clouds is still not understood. One major reason for that gap in knowledge is the fact that most of the relevant processes take place inside the complex turbulent environment inside of the cloud, making observations difficult. Also, the unknown impact of ice formation on cloud lifetime and precipitation evolution introduces large uncertainties into numeric weather prediction and climate projections.</p> <p>In the present study, we analyze datasets gathered at four different Cloudnet (Illingworth et al., 2007) sites in order to quantify and disentangle the impact of temperature and vertical air motions on precipitation formation. Basis for the investigation are combined measurements of lidar, cloud radar and ground-based disdrometer/rain sensor measurements processes with the Cloudnet algorithm. Fallstreak tracking methods are applied in order to connect rain events on the ground with their generating level/temperature at cloud top. We have evaluated combined remote sensing data gathered at different Cloudnet sites in order to contrast the relationship between cloud top temperature (CTT) and rain formation processes. The datasets at Leipzig (Germany), Limassol (Cyprus) and Punta Arenas (Chile) were collected with the Leipzig Aerosol and Cloud Remote Observations System (LACROS). The Barbados dataset was acquired with the Barbados Cloud Observatory (BCO) of Max-Planck Institute for Meteorology Hamburg.</p>

No Relief from Rain: Climate Change Fuels Compound Disasters

Climate change is increasing the risk of fire-rain events, raising mudslide concerns in fire-prone communities.

Salmonella enterica Serovar Diversity, Distribution, and Prevalence in Public Access Waters from a Central California Coastal Leafy Green Growing Region during 2011 - 2016

Prevalence and serovar diversity of Salmonella enterica was measured during a five-year survey of surface waters in a 500 mi^2 agricultural region of the Central California Coast. Rivers, streams, lakes, and ponds were sampled bimonthly resulting in 2,979 samples. Overall prevalence was 56.4% with higher levels detected in Spring than in Fall. Small, but significant, differences in prevalence were detected based on sample locations. Detection of Salmonella was correlated positively with both significant rain events and, in some environments, levels of generic Escherichia coli . Analysis of 1,936 isolates revealed significant serovar diversity, with 91 different serovars detected. The most common isolated serovars were S. enterica subsp. enterica serovars I 6,8:d:- (406 isolates, 21.0%, and potentially monophasic Salmonella Muenchen), Give (334 isolates, 17.3%), Muenchen (158 isolates, 8.2%), Typhimurium (227 isolates, 11.7%), Oranienburg (106 isolates, 5.5%), and Montevideo (78 isolates, 4%). Sixteen of the 24 most common serovars detected in the region are among the serovars reported to cause the most human salmonellosis in the United States. Some of the serovars were associated with location and seasonal bias. Analysis of Xba I Pulsed Field Gel Electrophoresis (PFGE) patterns of strains of serovars Typhimurium, Oranienburg, and Montevideo showed significant intra-serovar diversity. PFGE pulsotypes were identified in the region for multiple years of the survey, indicating persistence or regular re-introduction to the region. Importance Non-typhoidal Salmonella is the among the leading causes of bacterial foodborne illness and increasing numbers of outbreaks and recalls are due to contaminated produce. High prevalence and 91 different serovars were detected in this leafy green growing region. Seventeen serovars that cause most of the human salmonellosis in the United States were detected, with 16 of those serovars detected in multiple locations and multiple years of the 5-year survey. Understanding the widespread prevalence and diversity of Salmonella in the region will assist in promoting food safety practices and intervention methods for growers and regulators.

Applicability of Difference in Oxygen-18 and Deuterium of Water Sources and Isotopic Hydrograph Separation in a Bamboo Catchment during Different Rainfall Types

Typhoon storm and plum rain are two typical rainfall types in the lower regions of the Yangtze River Basin, which frequently cause flood disasters in China. New information in stable water isotopes offers the opportunity to advance understanding of runoff mechanisms and water source dynamics in response to these two typical rainfall types. We intensively monitored two representative rainfall events in a small bamboo forestry watershed in 2016. Results showed that precipitation isotopic variations during the event were generally larger than those of other monitored compartments (including throughfall, surface overland water, groundwater and river water) and also larger for the plum rain than for the typhoon event (δ18O varied in 5.2‰ and 3.7‰, respectively). Importantly, the differences of isotopic temporal variation between rainfall and throughfall showed significant impacts on the two-component hydrograph separation for both rainfall types (e.g., if not considered, the pre-event water fractions were 26.6% and 15.3% higher for the typhoon and plum rain events, respectively). Furthermore, we evaluated the role of soil water on the three-component isotopic hydrograph separation model; results revealed that soil water accounted for 10.9% and 28.3% of the total discharge in typhoon and plum rain events, respectively. This underpins the important role of soil water dynamics during the rainy season in this humid region.

Process-Based Evaluation of Stochastic Perturbed Microphysics Parameterization Tendencies on Ensemble Forecasts of Heavy Rainfall Events

Stochastic model error schemes, such as the stochastic perturbed parameterization tendencies (SPPT) and independent SPPT (iSPPT) schemes, have become an increasingly accepted method to represent model error associated with uncertain subgrid-scale processes in ensemble prediction systems (EPSs). While much of the current literature focuses on the effects of these schemes on forecast skill, this research examines the physical processes by which iSPPT perturbations to the microphysics parameterization scheme yield variability in ensemble rainfall forecasts. Members of three 120-member Weather Research and Forecasting (WRF) model ensemble case studies, including two distinct heavy rain events over Taiwan and one over the northeastern United States, are ranked according to an area-averaged accumulated rainfall metric in order to highlight differences between high- and low-precipitation forecasts. In each case, high-precipitation members are characterized by a damping of the microphysics water vapor and temperature tendencies over the region of heaviest rainfall, while the opposite is true for low-precipitation members. Physically, the perturbations to microphysics tendencies have the greatest impact at the cloud-level and act to modify precipitation efficiency. To this end, the damping of tendencies in high-precipitation forecasts suppresses both the loss of water vapor due to condensation and the corresponding latent heat release, leading to grid-scale supersaturation. Conversely, amplified tendencies in low-precipitation forecasts yield both drying and increased positive buoyancy within clouds.

Cascading Models of CNN and GRU with Autoencoder Loss for Precipitation Forecast in Thailand

It is a crucial task to accurately forecast precipitation, especially rainfall in Thailand, since it relates to flood prevention and agricultural planning. In our prior work, we have presented a model based on deep learning approach; however, its performance is still limited due to two main issues. First, there is an imbalance issue, where most rainfall is zero or no rain because Thailand has short rainy season. Second, predicted rainfall is still underestimated since moderate and heavy rainfall cases barely occurs. In this paper, we propose an enhanced deep learning model to forecast rainfall in Thailand. Our model is a cascading of CNN and GRU along with exogenous variables, i.e., temperature, pressure, and humidity. There are two stages in our model. First, CNN is specialized for classifying rain and non-rain events. In this stage, an imbalanced issue is alleviated by applying “focal loss”. Second, GRU is responsible for forecasting rainfall. Its predicted range is lifted using “autoencoder loss”. The experiment was conducted on hourly rainfall dataset between 2012 and 2018 obtained from a public government sector in Thailand. The results show that our enhanced model outperforms ARIMA and CNN-GRU in terms on RMSE of most regions in Thailand.

Rapid Assessment and Long-Term Monitoring of Green Stormwater Infrastructure with Citizen Scientists

Green stormwater infrastructure (GSI) has emerged as a promising decentralized management approach to urban stormwater challenges. A lack of data about GSI performance interferes with widespread adoption of GSI. A citizen science program that benefits researchers, lay scientists, and municipalities offers a way to provide these lacking data. We have developed an open-source, transferable green infrastructure rapid assessment (GIRA) protocol for studying the performance of GSI with citizen scientists. This protocol has been tested in six North American cities (New York City, Toronto, Vancouver, Chicago, San Francisco, and Buffalo). In this research we define the performance of GSI in varying geographic, climatic, and maintenance conditions with the intent to create technological, institutional, and management solutions to urban stormwater problems. The GIRA protocol was used by citizen scientists to assess the physical properties and capabilities of bioswales, while small, affordable Green Infrastructure Sensors Boxes (GIBoxes) were used to determine longer-term function across several rain events. Our results indicate that teams of citizen scientists can be effective for collecting and archiving widespread information on the post-installation function of GSI. The effort also showed that citizen scientists had changes in understanding of urban stormwater challenges and the role that GSI can play in solving these problems. We explore the multiple benefits to knowledge, participants, and municipal partners as a result of this research.

Freezing fraction in freezing rain

Freezing rain can cause significant tree damage with fallen trees and branches blocking roads and taking power distribution lines out of service. Power transmission lines are designed for ice loads from freezing rain, using models to estimate equivalent radial ice thicknesses from historical weather data. The conservative simple flux model assumes that all the freezing rain that impinges on a horizontal cylinder, representing vegetation or components of the built infrastructure, freezes. Here I present a simplified heat-balance formulation to calculate the fraction of the impinging precipitation that freezes, using parameters measured at ASOS weather stations and an estimate of solar heating. Radial ice thickness estimates from this approach are compared with the simple model and those generated from the ASOS icing sensor. These estimates can all be tested by comparing to measurements on cylinders at weather stations. A link to an Excel spreadsheet that calculates freezing fraction using user-input weather data is provided. In forecast freezing rain events, this tool could be used by utility crews and emergency response teams to estimate the likely range of equivalent radial ice thicknesses over the affected region and plan their response accordingly.