A Radar Object-Based Examination of Rain System Climatology and Including Climate Variability

We know the climate is warming and this is changing some aspects of storms, but we have little knowledge of storm characteristics beyond intensity, which limits our understanding of storms overall. In this study, we apply a cell-tracking algorithm to 20 years of radar data at a mid-latitude coastal-site (Sydney, Australia), to establish a regional storm climatology. The results show that extreme storms in terms of translation-speed, size and rainfall intensity usually occur in the warm season, and are slower and more intense over land between ~10am and ~8pm (AEST), peaking in the afternoon. Storms are more frequent in the cold season and often initiate over the ocean and move northward, leading to precipitation mostly over the ocean. Using clustering algorithms, we have found five storm types with distinct properties, occurring throughout the year but peaking in different seasons. While overall rainfall statistics don't show any link to climate modes, links do appear for some storm types using a multivariate approach. This climatology for a variety of storm characteristics will allow future study of any changes in these characteristics due to climate change.

Responses of Spatiotemporal Vegetative Land Cover To Meteorological Changes in Bangladesh

Quantifying the response of vegetation cover change (VCC) to climatic variables is a gap that is mandatory for the conservation and rehabilitation of natural landscape to ensure sustainability. This study aims to assess the response of VCC to temperature and rainfall change in Bangladesh. We used (i) Landsat images to analyze VCC using image classification method, Normalized Difference Vegetation Index (NDVI) (ii) temperature and rainfall statistics to investigate the spatiotemporal variations (SV) of meteorological factors, urban lands, VCC in all the 64 districts of Bangladesh during 1990-2018 and examined their correlation. To quantify the impact of urbanization on VCC, two regression models were built between growing-season NDVI (GNDVI) and urban land proportion (PLU). Results show that the SV of precipitation, temperature, GNDVI, and PUL varied greatly among the districts. GNDVI was found closely related to climatic variables and less sensitive to climatic factor changes. There has been found a significant correlation between the trend of GNDVI and GP while the negative correlation between GNDVI trend and GT, ΔPUL. Strong sensitivity of GNDVI change to GP was calculated in the range of precipitation 2200-3000mm and GNDVI to GT change in the range of temperature 300C-310C. Besides, urban expansion was found mostly responsible for VCC in the study area.

Extreme Rainfall Statistics May Shift as U.S. Climate Warms

Precipitation data and high-resolution modeling suggest that extreme rainfall events under a changing climate will be shorter, more intense, and more widely spread out.

A Typical Basalt Platform Landslide: Mechanism and Stability Prediction of Xiashan Landslide

The Xiashan landslide, which is classified as a typical basalt platform landslide, is the most massive landslide in Zhejiang Province, China. Once sliding occurs, it will pose a severe threat to the life and property of downstream residents and the nearby section of Hangzhou-Taizhou Expressway. On the basis of the geological conditions, present situation, and latest monitoring data of the landslide, this study finds that rainfall is the main influencing factor, and the creep mode is the main prediction mode of its subsequent deformation. The rainfall statistics of the landslide area in the past 30 years show that the rainfall and rainfall frequency in the landslide area display an increasing trend. The probability of heavy rain with rainfall intensity of 100–250 mm/day in the landslide area is very high. On this basis, combined with the numerical analysis method, a finite element model of the slope considering rainfall and groundwater conditions is constructed to analyze the causes and failure mechanism of this landslide comprehensively. Results indicate that the maximum tensile stress at the top of the trailing edge under the natural state is 5.10 MPa, which is very close to the saturated tensile strength of rock mass. Thus, tensile cracks are easily generated and developed, thereby causing the failure mode to be the hydraulic driving type. Also, with the increase in rainfall intensity, the slope plastic strain increases and the slope plastic zone develops and extends until it is completely penetrated. When the rainfall intensity is more than 200 mm/day, the slope safety factor is close to unity, and the slope approaches a failure condition. Therefore, the landslide should be controlled through water treatment and integrated with engineering measures.

A formula for downscaling extreme sub-daily rainfall intensities

<p>Global warming is associated with an increased rate of evaporation due to higher surface temperatures which also implies a higher hydrological cycle turn-around in a steady-state atmosphere with respect to the water budget. The latter is accompanied with increased atmospheric overturning and more convective activity. In addition, there have been indications of a decreasing area of 24-hr rainfall on a global scale over the last decades, suggesting that rainfall is becoming concentrated over smaller regions. There have also been indications of higher cloud tops. In sum, a consequence of an increased greenhouse effect and modified hydrological cycle is an increased probability for heavy rainfall on local scales and a greater risk of flooding. Changes in risks connected to meteorological and hydrological challenges make it necessary to adapt to new weather statistics. For instance, there is a need to estimate the frequency of heavy downpour and their return levels, both for 24-hr amounts and sub-daily timescales. It is common to account for extreme rainfall by designing infrastructure with the help of intensity-duration-frequency (IDF) curves. One problem is that the IDF curves are based on long records of hourly rainfall measurements that are not widely available. Traditional IDF curves have also been fitted assuming stationary statistics, while climate change implies non-stationary weather statistics. We propose a formula for downscaling sub-daily rainfall intensity based on 24-hr rainfall statistics that is not as limited by data availability nor assumes stationarity. This formula provides a crude and approximate and rule-of-thumb for sites with 24-hr rain gauge data and can be used in connection with downscaling of climate model results. It also represents a way of downscaling rainfall statistics in terms of the time dimension.</p>

Comparison of Local, Regional, and Scaling Models for Rainfall Intensity–Duration–Frequency Analysis

Intensity–duration–frequency (IDF) analyses of rainfall extremes provide critical information to mitigate, manage, and adapt to urban flooding. The accuracy and uncertainty of IDF analyses depend on the availability of historical rainfall records, which are more accessible at daily resolution and, quite often, are very sparse in developing countries. In this work, we quantify performances of different IDF models as a function of the number of available high-resolution (Nτ) and daily (N24h) rain gauges. For this aim, we apply a cross-validation framework that is based on Monte Carlo bootstrapping experiments on records of 223 high-resolution gauges in central Arizona. We test five IDF models based on (two) local, (one) regional, and (two) scaling frequency analyses of annual rainfall maxima from 30-min to 24-h durations with the generalized extreme value (GEV) distribution. All models exhibit similar performances in simulating observed quantiles associated with return periods up to 30 years. When Nτ > 10, local and regional models have the best accuracy; bias correcting the GEV shape parameter for record length is recommended to estimate quantiles for large return periods. The uncertainty of all models, evaluated via Monte Carlo experiments, is very large when Nτ ≤ 5; however, if N24h ≥ 10 additional daily gauges are available, the uncertainty is greatly reduced and accuracy is increased by applying simple scaling models, which infer estimates on subdaily rainfall statistics from information at daily scale. For all models, performances depend on the ability to capture the elevation control on their parameters. Although our work is site specific, its results provide insights to conduct future IDF analyses, especially in regions with sparse data.

Refinements to Data Acquired by 2-Dimensional Video Disdrometers

The 2-Dimensional Video Disdrometer (2DVD) is a commonly used tool for exploring rain microphysics and for validating remotely sensed rain retrievals. Recent work has revealed a persistent anomaly in 2DVD data. Early investigations of this anomaly concluded that the resulting errors in rain measurement were modest, but the methods used to flag anomalous data were not optimized, and related considerations associated with the sample sensing area were not fully investigated. Here, we (i) refine the anomaly-detecting algorithm for increased sensitivity and reliability and (ii) develop a related algorithm for refining the estimate of sample sensing area for all detected drops, including those not directly impacted by the anomaly. Using these algorithms, we explore the corrected data to measure any resulting changes to estimates of bulk rainfall statistics from two separate 2DVDs deployed in South Carolina combining for approximately 10 total years of instrumental uptime. Analysis of this data set consisting of over 200 million drops shows that the error induced in estimated total rain accumulations using the manufacturer-reported area is larger than the error due to considerations related to the anomaly. The algorithms presented here imply that approximately 4.2% of detected drops are spurious and the mean reported effective sample area for drops believed to be correctly detected is overestimated by ~8.5%. Simultaneously accounting for all of these effects suggests that the total accumulated rainfall in the data record is approximately 1.1% larger than the raw data record suggests.

Finding homogeneous regions of extreme rainfall in reanalysis data

<p>In this study a methodology is developed and tested to delineate homogeneous regions of extreme rainfall around a city of interest using meteorological indices from reanalysis data.</p><p>Scenarios of future climate change established with numerical climate models are well-established tools to help inform climate adaptation policy. The latest generation of regional climate models is now employed at a grid resolution of 2 to 3 kilometers. This enables the simulation of convection; whereby intensive convective rainfall is better represented (Kendon et al., 2017). However, the associated large computational burden limits the simulation length, which poses a challenge for estimating future rainfall statistics.</p><p>Rainfall return periods are a commonly used indicator in the planning, design and evaluation of urban water systems and urban water management. In order to estimate potential future rainfall for return periods larger than the length of the simulation length, regional frequency analysis (RFA) can be applied (Li et al., 2017).  For applying RFA, time series from nearby locations are pooled, the locations considered should fall within the same hydroclimatic climate. This is a region which can be assumed statistically homogeneous for extreme rainfall (Hosking & Wallis, 2009).</p><p>Traditionally, these homogeneous regions are defined on geographical region characteristics and rain gauge statistics (Hosking & Wallis, 2009).  To make the methodology less dependent on rain gauge record availability, Gabriele & Chiaravalloti (2013) used meteorological indices derived from reanalysis data to delineate the homogeneous regions.</p><p>Here we evaluate the methodology to delineate homogeneous regions around cities. Meteorological indices are calculated from the ERA-5 reanalysis dataset (Hersbach et al., 2018) for days with extreme rainfall. The variation herein is used as a measure of homogeneity. The derived homogeneous regions will in future work be used for data pooling of convection-permitting regional climate model simulations datasets to enable the derivation of future extreme rainfall statistics.</p><p>This study is embedded in the EU H2020 project EUCP (EUropean Climate Prediction system) (https://www.eucp-project.eu/), which aims to develop a regional climate prediction and projection system based on high-resolution climate models for Europe, to support climate adaptation and mitigation decisions for the coming decades.</p><p>References:</p><p>Gabriele, S., & Chiaravalloti, F. (2013). “Searching regional rainfall homogeneity using atmospheric fields”. Advances in Water Resources, 53, 163–174. https://doi.org/https://doi.org/10.1016/j.advwatres.2012.11.002</p><p>Hersbach, H., de Rosnay, P., Bell, B., Schepers, D., Simmons, A., Soci, C., …, Zuo, H. (2018). “Operational global reanalysis: progress, future directions and synergies with NWP”, ECMWF.</p><p>Hosking, J. R. M., & Wallis, J. R. (2009). “Regional Frequency Analysis: An Approach Based on L-Moments”. The Edinburgh Building, Cambridge CB2 2RU, UK: Cambridge University Press. ISBN: 9780511529443.</p><p>Kendon, E. J., Ban, N., Roberts, N. M., Fowler, H. J., Roberts, M. J., Chan, S. C., … Wilkinson, J. M. (2017). “Do Convection-Permitting Regional Climate Models Improve Projections of Future Precipitation Change?” BAMS, 98(1), 79–93. https://doi.org/10.1175/BAMS-D-15-0004.1</p><p> Li, J., Evans, J., Johnson, F., & Sharma, A. (2017). “A comparison of methods for estimating climate change impact on design rainfall using a high-resolution RCM.” Journal of Hydrology, 547(Supplement C), 413–427. https://doi.org/https://doi.org/10.1016/j.jhydrol.2017.02.019</p>

Modelling respiration pulses at rewetting as a stochastic process

<p>Respiration pulses at rewetting are prominent features of soil responses to soil moisture fluctuations. These pulses are much larger compared to respiration rates under constant soil moisture, pointing to variations in water availability as drivers of the enhanced CO<sub>2</sub> production. Moreover, the respiration pulses tend to be larger when soil moisture before rewetting is lower. Thus, both the pre-rainfall soil moisture and the variation in soil moisture control the size of the respiration pulse. While these patterns are known from empirical studies, models have struggled to capture the relations between rainfall statistical properties (frequency of occurrence and rain event depths) and the occurrence and size of respiration pulses, framing the scope of this contribution. Specifically, we ask – how are the statistical properties of respiration pulses related to rainfall statistics?</p><p>Because rainfall can be regarded as a stochastic process generating variations in soil moisture, also respiration pulses at rewetting can be modelled through a probabilistic model. Here we develop such a model based on the premises that rainfall can be described as a marked Poisson process, and that respiration pulses increase with increasing variations of soil moisture (i.e., larger pulses after larger rain events) and decreasing pre-rain soil moisture (i.e., larger pulses after a long dry period). This model provides analytical relations between the statistical properties of soil respiration (e.g., long-term mean and standard deviation) and those of rainfall, allowing to study in a probabilistic framework how respiration varies along existing climatic gradients or in response to climatic changes that affect rainfall statistics.</p><p>Results show that the long-term mean CO<sub>2</sub> production during respiration pulses increases with increasing frequency and depth of rainfall events. However, the relative contribution of respiration pulses to the total microbial respiration decreases with rainfall frequency and depth. Similarly, also the variability of the size of respiration pulses, as measured by their standard deviation, decreases with increasing rainfall frequency and depth. As a consequence, climatic changes exacerbating rainfall intermittency – longer dry periods and more intense rain events – are predicted to increase both the relative contribution of respiration pulses to total microbial respiration and the variability of the pulse sizes.</p>