Exceptional heavy rainfall over Ajoy, Mayurakshi and Kansabati catchments and QPF verification during flood season of September 2009

Ajoy, Mayurakshi, Kansabati are three important river catchments of West Bengal and Jharkhand state, received very heavy rainfall during two consecutive days of flood season in the month of September 2009. The contribution of heavy rainfall & combined discharges from Damodar Valley Corporation (DVC) reservoirs during the period of heavy rainspells over these catchments enhanced flood situation in some districts of West Bengal. The synoptic features based on weather charts, cloud imageries of satellite and radar pictures have been taken to analyse. The realized areal average precipitation (AAP) as per rainfall recorded at 0300 UTC of next day have also been taken to verify the quantitative precipitation forecast (QPF) of 6&7 September 2009.

Large-scale environments of successive atmospheric river events leading to compound precipitation extremes in California

Successive atmospheric river (AR) events—known as AR families—can result in prolonged and elevated hydrological impacts relative to single ARs due to the lack of recovery time between periods of precipitation. Despite the outsized societal impacts that often stem from AR families, the large-scale environments and mechanisms associated with these compound events remain poorly understood. In this work, a new reanalysis-based 39-year catalog of 248 AR family events affecting California between 1981 and 2019 is introduced. Nearly all (94%) of the inter-annual variability in AR frequency is driven by AR family versus single events. Using K-means clustering on the 500-hPa geopotential height field, six distinct clusters of large-scale patterns associated with AR families are identified. Two clusters are of particular interest due to their strong relationship with phases of the El Niño/Southern Oscillation (ENSO). One of these clusters is characterized by a strong ridge in the Bering Sea and Rossby wave propagation, most frequently occurs during La Niña and neutral ENSO years and is associated with the highest cluster-average precipitation across California. The other cluster, characterized by a zonal elongation of lower geopotential heights across the Pacific basin and an extended North Pacific Jet, most frequently occurs during El Niño years and is associated with lower cluster-average precipitation across California but a longer duration. In contrast, single AR events do not show obvious clustering of spatial patterns. This difference suggests that the potential predictability of AR families may be enhanced relative to single AR events, especially on sub-seasonal to seasonal timescales.

Vanishing weekly hydropeaking cycles in American and Canadian rivers

Sub-daily and weekly flow cycles termed ‘hydropeaking’ are common features in regulated rivers worldwide. Weekly flow periodicity arises from fluctuating electricity demand and production tied to socioeconomic activity, typically with higher consumption during weekdays followed by reductions on weekends. Here, we propose a weekly hydropeaking index to quantify the 1920–2019 intensity and prevalence of weekly hydropeaking cycles at 500 sites across the United States of America and Canada. A robust weekly hydropeaking signal exists at 1.8% of sites starting in 1920, peaking at 18.9% in 1963, and diminishing to 3.1% in 2019, marking a 21st century decline in weekly hydropeaking intensity. We propose this decline may be tied to recent, above-average precipitation, socioeconomic shifts, alternative energy production, and legislative and policy changes impacting water management in regulated systems. Vanishing weekly hydropeaking cycles may offset some of the prior deleterious ecohydrological impacts from hydropeaking in highly regulated rivers.

Evaluation of Areal Monthly Average Precipitation Estimates from MERRA2 and ERA5 Reanalysis in a Colombian Caribbean Basin

Global reanalysis dataset estimations of climate variables constitute an alternative for overcoming data scarcity associated with sparsely and unevenly distributed hydrometeorological networks often found in developing countries. However, reanalysis datasets require detailed validation to determine their accuracy and reliability. This paper evaluates the performance of MERRA2 and ERA5 regarding their monthly rainfall products, comparing their areal precipitation averages with estimates based on ground measurement records from 49 rain gauges managed by the Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM) and the Thiessen polygons method in the Sinu River basin, Colombia. The performance metrics employed in this research are the correlation coefficient, the bias, the normalized root mean square error (NRMSE), and the Nash–Sutcliffe efficiency (NSE). The results show that ERA5 generally outperforms MERRA2 in the study area. However, both reanalyses consistently overestimate the monthly averages calculated from IDEAM records at all time and spatial scales. The negative NSE values indicate that historical monthly averages from IDEAM records are better predictors than both MERRA2 and ERA5 rainfall products.

Comparative Analysis or Precipitation Variation Characteristics between Subtropical Monsoon Climate and Temperate Monsoon Climate-Take Changsha and Chengde for Example

Using the methods of literature review, regression analysis and moving average, this paper selects the daily precipitation of Changsha and Chengde from 1951 to 1986 as samples, and analyzes the average precipitation, precipitation frequency, precipitation intensity, extreme precipitation time and other indicators of Changsha and Chengde from the perspective of interannual and seasonal changes Trends. The researches show that: the average precipitation of Changsha in the 36 years is 1151.2mm, spring is the wet season, autumn and winter are the dry seasons, and the maximum average precipitation is in spring; the average annual precipitation, precipitation frequency in spring, summer and winter, annual precipitation frequency, annual precipitation intensity and extreme precipitation events show a decreasing trend. The average annual precipitation of Chengde city is 454.1 mm, wet season in summer and dry season in spring, autumn and winter; the average annual precipitation, precipitation in four seasons, annual precipitation frequency, precipitation frequency in spring, autumn and winter, annual precipitation intensity and extreme precipitation events show a decreasing trend, while the precipitation frequency in summer shows an increasing trend. The study of regional climate change based on the time series data of this stage is of great significance to comprehensively understand the law of regional climate change and predict the future trend of climate change.

Eliminating the “hook” in Precipitation-Temperature Scaling

Observational studies of extreme daily and subdaily precipitation-temperature sensitivities (apparent scaling) aim to provide evidence and improved understanding of how extreme precipitation will respond to a warming climate. However, interpretation of apparent scaling results is hindered by large variations in derived scaling rates and divergence from theoretical and modelled projections of systematic increases in extreme precipitation intensities (climate scaling). In warmer climatic regions, rainfall intensity has been reported to increase with temperature to a maximum before decreasing, creating a second order discontinuity or “hook” like structure. Here we investigate spatial and temporal discrepancies in apparent scaling results by isolating rainfall events and conditioning event precipitation on duration. We find that previously reported negative apparent scaling at higher temperatures which creates the hook structure, is the result of a decrease in the duration of the precipitation event, and not to the decrease in precipitation rate. We introduce standardized pooling using long records of Australian station data across climate zones, to show average precipitation intensities and 1-h peak precipitation intensities increase with temperature across all event durations and locations investigated. For shorter duration events (< 6-h), average precipitation intensity scaling is in line with the expected Clausius- Clapeyron (CC) relation at ~7 %/°C, and this decreases with increasing duration, down to 2 %/°C at 24-h duration. Consistent with climate scaling derived from model projections, 1-h peak precipitation intensities are found to increase with temperature at elevated rates compared to average precipitation intensities, with super-CC scaling (10 – 14 %/°C) found for short-duration events in tropical climates.

Influence of Recovery Treatments on Dicamba-injured Soybean

Non-dicamba resistant soybean yield loss resulting from dicamba off-target injury has become an increasing concern for soybean growers in recent years. After off-target dicamba movement occurs onto sensitive soybean, little information is available on tactics that could be used to mitigate the cosmetic or yield losses that may occur. Therefore, a field experiment was conducted in 2017, 2018, and 2019 to determine if certain recovery treatments of fungicide, plant growth hormone, macro- and micronutrient fertilizer combinations, or weekly irrigation could reduce dicamba injury and/or result in similar yield to soybean that was not injured with dicamba. Simulated drift events of dicamba (5.6 g ae ha−1) were applied to non-dicamba resistant soybean once they reached the V3 or R2 stages of growth. Recovery treatments were applied approximately 14 d after the simulated drift event. Weekly irrigation was the only recovery treatment that provided appreciable levels of injury reduction or increases in soybean height or yield compared to the dicamba-injured plants. Weekly irrigation following the R2 dicamba injury event resulted in an 1% to 14% increase in soybean yield compared to the dicamba-injured control. All other recovery treatments resulted in soybean yields similar to the dicamba-injured control, and similar to or lower than the non-treated control. Results from this study indicate that if soybean have become injured with dicamba, weekly irrigation will help soybean recover some of the yield loss and reduce injury symptoms that resulted from off-target dicamba movement, especially in a year with below average precipitation. However, yield loss will likely not be restored to that of non-injured soybean.

Assessment of Crash Occurrence Using Historical Crash Data and a Random Effect Negative Binomial Model: A Case Study for a Rural State

This work identifies factors that influence crash occurrence within a traffic analysis zone (TAZ) by accounting for location-specific effects and serial correlation in longitudinal crash data. This is accomplished by applying a random effect negative binomial (RENB) model. Unlike commonly used count models such as Poisson and negative binomial (NB), RENB accounts for heterogeneity and serial correlation in crash occurrence. An RENB was applied to 15 years of crash data in Arkansas with 1,817 TAZs. Four models were developed for total crashes and by severity (property damage only (PDO), injury, and fatal). RENB-estimated impacts were measured using the incidence rate ratio (IRR). The significant causal factors found to increase in observed crashes include: (i) average precipitation (a one-unit increase in average precipitation results in a 134% increase in total monthly crashes for a TAZ); (ii) average wind speed (16%); (iii) urban designation (7%); (iv) traffic volume (2%); and (v) total roadway mileage (1% for each functional class). Snow depth and days of sunshine were found to decrease the number of accidents by 15% and 2%, respectively. Employment and total population had no impact on crash occurrence. Goodness-of-fit comparisons show that RENB provides the best fit among Poisson and NB formulations. All four model diagnostics confirm the presence of over-dispersion and serial correlation indicating the necessity of RENB model estimation. The main contribution of this work is the identification of crash causal factors at the TAZ level for longitudinal data, which supports data-driven performance measurement requirements of recent federal legislation.

Precipitation Variability and its Teleconnection with the Global SST and ENSO Indices in the Food Insecure Rural Areas of Tigray

The impact of precipitation variability on food production is very significant. For food insecure rural areas, understanding the nature of precipitation variability and its teleconnection has paramount importance in guiding regional and local level decisions. In this study, we analyzed the monthly, seasonal and annual precipitation variability and the strength of its teleconnection with the global sea-surface temperature (SST) and El Niño Southern Oscillation (ENSO) indices in the food insecure rural areas of Tigray region, Ethiopia. The precipitation, SST, and ENSO indices data for the study were used from 1979 to 2019. A Summary of descriptive statistics and Mann Kendall test methods were applied to detect existence of trends; and Sen’s Slope and coefficient of variation are used to analyze the magnitude of the trend, and degree of variation in the trend of precipitation. Further, Pearson’s correlation is used to determine the effect of ENSO, and SST variations on the precipitation using the Canonical Correlation Analysis (CCA). The results revealed that the precipitation over the study areas is characterized by a distinctive bi-modal pattern with limited rains in March – May preceding the main rainy season June – September. The limited amount of precipitation, exacerbated by higher degree of variability, makes the food production in the study areas more uncertain. Besides, there was a very significant decline in the trend of March – May average precipitation and a significant decline in the trend of the annual average precipitation of Hintalo area. The SSTs of the central and eastern equatorial Pacific Ocean, and northeast and northwest equatorial Atlantic Ocean was strongly correlated with April’s average precipitation of the study areas. Further, the SST of south, west and southwest of equatorial Indian Ocean, and west equatorial Pacific Ocean were associated with July – September average precipitation with greater variation in strength among of the study areas. Moreover, July’s average precipitation of all the study areas, April’s average precipitation of Atsbi and Eirop, and May’s precipitation of Hintalo are found significantly associated with the ENSO indices of JFM, FMA, MJJ and MAM. Therefore, the task of achieving food security in the study areas should incorporate the design of informed food production strategies that can adapt the limited and variable precipitation based on these SST and ENSO indices.

Estimation of the Average Retention Time of Precipitation at the Surface of a Catchment Area for Lake Biwa

In a lake catchment system, we analyzed the lake water-level responses to precipitation. Moreover, we identified the average precipitation retention time—due to subsurface flows—from the delay time calculated using the response function with data of water level and catchment precipitation (both rainfall and snowfall) collected over 30 years of continuous observations of Lake Biwa, Japan. We focused on the snow reserves and the water-level response delay due to the snowmelt of Lake Biwa catchment. We concluded that the average precipitation retention time of the catchment subsurface flow (i.e., above the impermeable layer) in Lake Biwa was approximately 45 days. Additionally, the precipitation retention time during snowmelt was shorter than that during the dry season. Overall, the shape of the response function reflects the lake system. This knowledge improves the understanding of lake systems and can be helpful for lake resource managers. Furthermore, finding the delay time from the response function may be useful for determining the contribution of rainfall to increasing the water levels of other lakes. Therefore, our results can contribute to the development of management strategies to address inland aquatic ecosystems and conservation.