An autoencoder‑based snow drought index

In several regions across the globe, snow has a significant impact on hydrology. The amounts of water that infiltrate the ground and flow as runoff are driven by the melting of snow. Therefore, it is crucial to study the magnitude and effect of snowmelt. Snow droughts, resulting from reduced snow storage, can drastically impact the water supplies in basins where snow predominates, such as in the western United States. Hence, it is important to detect the time and severity of snow droughts efficiently. We propose the Snow Drought Response Index or SnoDRI, a novel indicator that could be used to identify and quantify snow drought occurrences. Our index is calculated using cutting-edge ML algorithms from various snow-related variables. The self-supervised learning of an autoencoder is combined with mutual information in the model. In this study, we use Random Forests for feature extraction for SnoDRI and assess the importance of each variable. We use reanalysis data (NLDAS-2) from 1981 to 2021 for the Pacific United States to study the efficacy of the new snow drought index. We evaluate the index by confirming the coincidence of its interpretation and the actual snow drought incidents

Evaluation of spatio-temporal rainfall variability and performance of a stochastic rainfall model in Bangladesh

Rainfall in Bangladesh exhibits persistent wet and dry anomalies associated with occurrence of floods and droughts. Assessing inter‐annual variability of rainfall is vital to account these hydrological extremes in the design and operations of water systems. However, the inter‐annual variability obtained from short record rainfall data might be misleading as it does not contain whole climate variability which signifies the utmost importance of stochastic rainfall models. Since the inter‐annual variability and stochastic models have not been explored adequately for rainfall in Bangladesh, this study evaluated (a) the spatio‐temporal variability of rainfall focusing on inter‐annual variability, and (b) applicability of a stochastic daily rainfall model, referred as the Decadal and Hierarchical Markov Chain (DHMC) model. Daily rainfall data of 1973–2012 for 18 stations across Bangladesh were used to investigate the probability distributions and autocorrelations of rainfall, and the model performances. Results show a higher magnitude of inter‐annual variabilities of rainfall depth (standard deviation 80–250 mm) and wet spells (standard deviation 4–6 days) in wetter months (June to September) across rainfall stations in the east region of the country. In contrast, higher rates of inter‐annual variabilities (i.e., coefficients of variations) were observed in drier months across the west region. Spatially, the dry spells were observed consistent across the country. Monthly rainfall showed decreasing trend over the region from west to the middle part of the country, whereas monthly number of wet days showed increasing trend over the eastern part. The DHMC was found to preserve the observed variabilities of rainfall at daily to multiyear resolutions at all stations, except a tendency to underestimate the autocorrelation of monthly rainfall depth. Despite this limitation, DHMC can be considered as a suitable stochastic rainfall simulator for a tropical monsoon climate like Bangladesh

Regional frequency analysis for consecutive hour rainfall using L-moments approach in Jeju Island, Korea

Abstract Background Extreme rainfall events are enormously frequent and abrupt in tropical areas like the Jeju Island of South Korea, impacting the hydrological functions as well as the social and economic situation. Rainfall magnitude and frequency distribution related information are essential for water system design, water resources management and hydro-meteorological emergencies. This study therefore has investigated the use of L-moments approach for hourly regional rainfall frequency estimation so as to ensure better accuracy and efficiency of the estimation process from the usually limited data sets. Results The Hancheon catchment was considered as the primary study domain and several best fitted statistical tools were used to analyze consecutive hour rainfall data from five hydro-meteorological stations (Jeju, Ara, Eorimok, Witsaeorum and Jindallaebat) adjacent to the area. The cluster analysis and discordancy measure categorized the Hancheon catchment in three regions (1, 2 and 3). Based on the L-moments heterogeneity and goodness-of-fit measure, Gumbel and generalized extreme value (GEV) distribution were identified as robust distributions for the study area. The RMSE ratios for the catchment were found as 0.014 to 0.237 for Gumbel and 0.115 to 0.301 for GEV distribution. The linear regression analysis of the different rainfall quantiles inferred r-square values from 0.842 to 0.974. Conclusions The L-moments and other statistical information derived from the study can be useful for important hydrological design considerations in connection with flood risk management, mitigation and safety; whereas the methodological framework of the study may be suitable for other small scaled catchment areas with high slope

An autoencoder‑based snow drought index

In several regions across the globe, snow has a significant impact on hydrology. The amounts of water that infiltrate the ground and flow as runoff are driven by the melting of snow. Therefore, it is crucial to study the magnitude and effect of snowmelt. Snow droughts, resulting from reduced snow storage, can drastically impact the water supplies in basins where snow predominates, such as in the western United States. Hence, it is important to detect the time and severity of snow droughts efficiently. We propose the Snow Drought Response Index or SnoDRI, a novel indicator that could be used to identify and quantify snow drought occurrences. Our index is calculated using cutting-edge ML algorithms from various snow-related variables. The self-supervised learning of an autoencoder is combined with mutual information in the model. In this study, we use Random Forests for feature extraction for SnoDRI and assess the importance of each variable. We use reanalysis data (NLDAS-2) from 1981 to 2021 for the Pacific United States to study the efficacy of the new snow drought index. We evaluate the index by confirming the coincidence of its interpretation and the actual snow drought incidents

An autoencoder-based snow drought index

Abstract In several regions across the globe, snow has a significant impact on hydrology. The amounts of water that infiltrate the ground and flow as runoff are driven by the melting of snow. Therefore, it is crucial to study the magnitude and effect of snowmelt. Snow droughts, resulting from reduced snow storage, can drastically impact the water supplies in basins where snow predominates, such as in the western United States. Hence, it is important to detect the time and severity of snow droughts efficiently. We propose the Snow Drought Response Index or SnoDRI, a novel indicator that could be used to identify and quantify snow drought occurrences. Our index is calculated using cutting-edge ML algorithms from various snow-related variables. The self-supervised learning of an autoencoder is combined with mutual information in the model. In this study, we use Random Forests for feature extraction for SnoDRI and assess the importance of each variable. We use reanalysis data (NLDAS-2) from 1981 to 2021 for the Pacific United States to study the efficacy of the new snow drought index. We evaluate the index by confirming the coincidence of its interpretation and the actual snow drought incidents

Integrated Water Resources Management (IWRM) Impacts in South West Coastal Zone of Bangladesh and Fact-Finding on Tidal River Management (TRM)

Copyright © 2013 Fahad Khan Khadim et al. This is an open access article distributed under the Creative Commons Attribution Li-cense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The south west coastal zone of Bangladesh has been affected by rampant water logging due to vulnerable climate, silted rivers and stumpy terrain; and introduction of IWRM and TRM at some places of the zone has substantially safe-guarded the circumstance. This study aims to assess the benefits achieved due to implementation of IWRM in parts of Khulna and Jessore districts, and investigate some technical aspects evolving TRM. Analyses have been carried out using satellite images, RS and GIS technology, Digital Elevation Model (DEM) and field investigations. A mathemati-cal formulation has been made to assess rate of tidal sedimentation due to TRM and selection strategies of tidal basins. The study comes up with evidences of considerable advancements in regional livelihood i.e. flood resistance, cultivated lands, cultivable area, cropping intensities and food security due to IWRM. Moreover, the technical facts established on TRM would help planners to have vivid perception regarding the process

Evaluating ranitidine, pantoprazole and placebo on gastric pH in elective surgery

Background and Aim: Concern about the grim nature of postoperative acid aspiration syndrome grew among the anesthesiologist over the years warranting the need for pre-emptive intervention. The aim of the study is to compare the effects of preoperative oral ranitidine versus pantoprazole given in regulating gastric pH in elective surgery. Methods: This prospective, parallel group, controlled, randomized, single-blind study was conducted at a tertiary care postgraduate teaching institute at Kolkata, involving 120 participants of either sex, aged 18-60 years of American Society of Anesthesiologists physical status I and II, who were scheduled for elective surgery under general anesthesia lasting for more than 2 h. The participants were divided into three groups. In group A (n=40) participants received placebo tablet, in group B (n=40) participants received ranitidine tablet while in group C (n=40), participants received pantoprazole tablet and their gastric pH estimated serially. Results: The participants in the three groups were comparable in terms of age, sex, body weight, duration of surgery and type of surgery distribution. In regard to changes in gastric pH trends, there was no statistically significant difference between serial pH values in group A (Friedman test; P>0.05) and group C participants. (P>0.05). However, the mean preoperative gastric pH values (7.140±.7652) were significantly lower than mean pH values (7.253±.7514) after 2 h postoperatively in group B participants (P<0.05). Conclusion: From the observations and analyses of the present study, it can be inferred that ranitidine is more effective than pantoprazole to raise the gastric pH for prevention of aspiration pneumonitis

The history of rainfall data time-resolution in a wide variety of geographical areas

Collected rainfall records by gauges lead to key forcings in most hydrological studies. Depending on sensor type and recording systems, such data are characterized by different time-resolutions (or temporal aggregations), ta. We present an historical analysis of the time-evolution of ta based on a large database of rain gauge networks operative in many study areas. Globally, ta data were collected for 25,423 rain gauge stations across 32 geographic areas, with larger contributions from Australia, USA, Italy and Spain. For very old networks early recordings were manual with coarse time-resolution, typically daily or sometimes monthly. With a few exceptions, mechanical recordings on paper rolls began in the first half of the 20th century, typically with ta of 1 h or 30 min. Digital registrations started only during the last three decades of the 20th century. This short period limits investigations that require long time-series of sub-daily rainfall data, e.g, analyses of the effects of climate change on short-duration (sub-hourly) heavy rainfall. In addition, in the areas with rainfall data characterized for many years by coarse time-resolutions, annual maximum rainfall depths of short duration can be potentially underestimated and their use would produce errors in the results of successive applications. Currently, only 50% of the stations provide useful data at any time-resolution, that practically means ta = 1 min. However, a significant reduction of these issues can be obtained through the information content of the present database. Finally, we suggest an integration of the database by including additional rain gauge networks to enhance its usefulness particularly in a comparative analysis of the effects of climate change on extreme rainfalls of short duration available in different locations