Wet Days Research Articles

Three methods of characterizing climate-induced changes in extreme rainfall: a comparison study

Abstract (1) Three practical and easily implementable methods are provided to estimate percent increases in extreme rainfall due to climate change for the period 2020–2090 using Global Climate Model (GCM) output. (2) Methods are designed to bracket the expected range of extreme rainfall intensification for 1–24-h events with return intervals of 1 year to 100 years. (3) One method is based on the 20 largest wet days produced by an ensemble of GCMs, and the other two use GCM projections of temperature and Clausius–Clapeyron assumptions. (4) The results of the case study for the Philadelphia area show that, by the end-of-century, extreme rain event volumes might increase from a low of 18% to a high of 61%, depending on the duration and return interval under consideration. (5) Methods have been benchmarked against existing, publicly available projected rainfall intensities to show the methods that provide an accurate range of extreme rainfall intensification due to climate change.

Trend Analysis for Temperature and Precipitation Extreme Events over the Headwater of Upper Blue Nile, Ethiopia

Purpose: Increased trends in extreme climate events such as prolonged hot or cold days and intensively rainy days or consecutive dry days have greater negative impacts on society. While studies are conducted on climate variability and impacts most of them focus on the mean climatologies. Extrem events of Tempertaure and preciptation however have profound effect on human health and the environment. The present study aims to examine the spatial and temporal characteristics of means and extremes of temperature and precipitation events in the Tana-Beles Sub-basin for the 1980-2015 periods. Methodology: Daily temperature and preciptaion time series was collected from the Ethiopian meteorological institute covering the 1980-2015. From the daily climate variables 22 daily extreme climate indices are calculated based on the Expert Team on Sector-specific Climate Indices (ET-SCI) definitions. Findings: The Monthly, seasonal and annual temperatures show mostly increasing trends. The mean annual maximum and minimum temperatures increased slightly, respectively, by 0.047 and 0.014 oC/yr. The frequency of cold days/nights has reduced by 33/38 days/nights annually; whereas, the frequency of warm days/nights has increased by 91/83 days/nights. The mean monthly, seasonal and annual precipitations also show mostly increasing trends.Among the selected precipitation indices the number of very heavy rain days (R20mm), Simple Daily Intensity Index (SDII), very wet days exceeding the 95th percentile (R95p), and annual total wet days precipitation (RCPTOT) shows increasing trend; whereas, only the maximum 1-day precipitation (Rx1day), show an insignificant decreasing trend. Precipitation during the study period shows high variability spatially and temporally. Unique Contribution to Theory, Policy and Practice: The study reveals increasing in number of hot days and vraible prciptation events, hence measures must be taken to reduce risks of extreme tempertures such as direct effect and indirect malaria distributions to new areas that have been cold before, and agricultural advised forming is also suggested to cope with the variabilities of prciptation. Keywords: Climate trends; extremes climate events; Temperature and rainfall; and Tana-Beles Sub basins.

Changes in the Seasonal Cycle of Heatwaves, Dry and Wet Spells over West Africa Using CORDEX Simulations

This study analyzes the potential response of the seasonal cycle of heatwaves (HWDI) and dry (CDD) and wet (CWD) spell indices over West Africa for the near- (2031–2060) and the far-future periods (2071–2100) under RCP4.5 and RCP8.5 scenarios using Coordinated Regional Climate Downscaling Experiment (CORDEX) simulations. Despite the fact that some relative biases (an underestimation of 30% for CDD, an overestimation of about 60% for CWD, and an overestimation of about 50% for HWDI) exist, during the historical period (1976–2005) in general, the CORDEX simulations and their ensemble mean outperform the seasonal variability in the above-mentioned indices over three defined subregions of West Africa (i.e., the Gulf of Guinea and Western and Eastern Sahel). They show high correlation coefficients (0.9 in general) and less RMSE. They project an increase (about 10 and 20 days) in heatwave days for both the near- and far-future periods over the whole West African region under both RCP scenarios. In addition, projections indicate that the Sahel regions will experience a decrease (about 5 days) in wet spell days from March to November, while in the Gulf of Guinea, a decrease (about 3 days) is projected throughout the year, except in the CCCLM simulation, which indicates an increase (about 5 days) during the retreat phase of the monsoon (October to December). Our results also highlight an increase (about 80%) in dry spells over the Sahel regions that are more pronounced during the March–November period, while over the Gulf of Guinea, an increase (about 40%) is projected over the entire year. Moreover, the months of increasing dry spells and decreasing wet spells coincide, suggesting that countries in these regions could be simultaneously exposed to dry seasons associated with a high risk of drought and heatwaves under future climate conditions.

Projected changes in rainfall amount and distribution in the Democratic Republic of Congo – Evidence from an ensemble of high-resolution climate simulations

Extreme precipitation has been threatening many sectors of human society and is likely to intensify with global warming. In the present study, we have analysed the impact of future climate change on daily mean rainfall and heavy rainfall on seasonal and annual scales in the Democratic Republic of Congo (DRC) by using ten long-term runs of high-resolution regional climate models. We initially assessed the performance of each run and the multi-model ensemble mean in simulating the daily mean rainfall (PRE), the maximum one-day rainfall (Rx1day), the simple rainfall intensity index (SDII), the count of days when rainfall is greater than or equal to 20 mm (RR20mm), the total rainfall when the daily rainfall exceeds the 95th percentile of the wet-day rainfall (R95p) and the maximum number of consecutive days with more than or equal to 1 mm (CWD). Next, we examined the future changes of these indices, focusing on the multi-model ensemble mean and spread under the high emission scenario RCP8.5. The time frames considered are the mid and end of the twenty-first century (2035–2065 and 2070–2100, respectively). The results indicate that the performance of REMO2015-MPI-ESM was close to the multi-model ensemble mean in representing the mean rainfall and most heavy rainfall indices, while CanRCM4-CamESM2 was identified as the worst performing model. The key finding of this study is that the multi-model ensemble mean project no significant change regarding the daily mean rainfall throughout the year and across all seasons by the middle of the 21st century, except of the western region of the country where a decrease is projected. Simultaneously, Rx1day, SDII, RR20mm, and R95p are projected to decrease almost everywhere during all seasons. Moreover, a consistent decrease in the number of wet days is projected. Focusing on the end of the century, the multi-model ensemble mean project an overall decrease in daily mean rainfall, especially in the western region of the DRC, with a more pronounced effect during MAM. The increase in Rx1day, SDII, RR20mm, and R95p, along with a decrease in CWD, is amplified in this period. These findings are useful for predicting the potential threats of precipitation-related diseases and natural hazards, as well as for designing climate-resilient infrastructure and socioeconomic activities in the DRC.

Temporal and Spatial Variations of Extreme Climate Events in Northwestern China from 1960 to 2020

In the context of global warming, the frequency and intensity of extreme weather and climate events have been increasing. Characterized by scarce water resources and fragile ecosystems, Northwest China has experienced a climate shift since the 1980s. In this study, spatial and temporal patterns of changes in the indices of climate extremes, based on daily maximum and minimum temperature and precipitation at 172 meteorological stations in Northwest China, were analyzed for the period 1960–2020. A total of 26 indices divided into two categories, 16 extreme temperature indices and 10 extreme precipitation indices, were used. Analysis of these indices revealed a general warming trend in the region, which consistent with global warming. The regional occurrence of summer days, tropical nights, growing season length, warm nights, warm days, and warm spell duration index increased by 0.22, 0.14, 0.29, 0.73, 0.46, and 0.11 days/decade, respectively. Over the same period, the occurrence of frost days, icing days, cool nights, cool days, and cold spell duration index decreased by −0.38, −0.21, −0.93, −0.44, and −0.13 days/decade, respectively. The decreasing trends in cold extremes were greater than the increasing trends in warm extremes. Additionally, many regions have experienced increasing trends in several precipitation indices. The annual total wet-day precipitation increased by 5.3 mm/decade. Increasing trends were also evident in simple daily intensity index, heavy precipitation days, very heavy precipitation days, very wet days, and extremely wet days. Consecutive dry days decreased by −1.5 days/decade, while no significant change was observed in consecutive wet days. In contrast to the remarkable spatial consistency of temperature extremes, precipitation extremes exhibited large and expected spatial variability. Most precipitation indices showed increasing trends in the western region of Northwest China and decreasing trends in the eastern part of Northwest China. These results indicate a transition from cold–dry to warm–wet in Northwestern China. Our findings suggest that Northwest China is experiencing more extreme climate events, which could consequently impact hydrological processes, ecological processes, and human health. These observations increase our understanding of the interactions between climate change and regional climate variability, which is conducive to improving disaster prevention.

Assessment of Hydrological Response to Climatic Variables over the Hindu Kush Mountains, South Asia

The impact of climate extremes, such as heat waves and extreme rainfall, can cause harvest failures, flooding, and droughts that ultimately threaten global food security, harming the region’s economy. Fluctuations in streamflow indicate the sensitivity of streamflow responding to extreme precipitation events and other climatic variables (temperature extremes) that play a significant role in its generation. Pakistan is also considered one of the climate change hotspot regions in the world. The devastating impacts have often occurred in recent decades due to an excess or shortage of streamflow, majorly generated from the Upper Indus Basin (UIB). To better understand climate extremes’ impact on streamflow, this study examined climate extremes and streamflow (Q) changes for three decades: 1990–1999, 2000–2009, and 2010–2019. Observed streamflow and meteorological data from nine sub-catchments across all climatic zones of the UIB were analyzed using RGui (R language coding program) and partial least squares regression (PLSR). Climatic variables were estimated, including precipitation extremes, temperature extremes, and potential evapotranspiration. The Mann–Kendal test was applied to the climatic indices, revealing that precipitation increased during the last 30 years, while maximum and minimum temperatures during the summer months decreased in the Karakoram region from 1990 to 2019. The spatiotemporal trend of consecutive dry days (CDD) indicated a more increasing tendency from 1990 to 2019, compared to the consecutive wet days (CWD), which showed a decreasing trend. PLSR was applied to assess the relation between climatic variables (extreme P, T indices, and evapotranspiration). It was found that the dominant climatic variables controlling annual streamflow include the r95p (very wet days) and R25mm (heavy precipitation days), maximum precipitation event amount, CWD, PRCPTOT (annual total precipitation), and RX5 (maximum five-day precipitation). The TXn (Min Tmax) and Tmax mean (average maximum temperature) dominate streamflow variables. Moreover, the impact of evapotranspiration (ET) on variations in streamflow is more pronounced in arid catchments. Precipitation is the predominant factor influencing streamflow generation in the UIB, followed by temperature. From streamflow quantification, it was found that climate-driven annual streamflow decreased during 1999–2019 in comparison to 1990–1999, with an increase in a few catchments like Kalam, which increased by about 3.94% from 2000 to 2010 and 10.30% from 2010 to 2019, and Shigar, which increased by 0.48% from 2000 to 2009 and 37.37% from 2010 to 2019 concerning 1990–1999. These variations were due to changes in these climatic parameters. The PLSR approach enables the identification of linkages between climatic variables and streamflow variability and the prediction of climate-driven floods. This study contributes to an enhanced identification and hydroclimatological trends and projections.

Significant Reduction in Precipitation Seasonality and the Association with Extreme Precipitation in the Hai River Basin of China from 1960 to 2018

The Hai River Basin (HRB) serves as a vital center for the population, economy and politics in northern China. Natural hazards, particularly floods, pose significant risks to the region, often attributed to extreme precipitation events. Changes in precipitation seasonalitycc play a pivotal role in influencing precipitation extreme events. Therefore, this study presents a comprehensive analysis of precipitation seasonality and its impact on precipitation extremes in HRB. By implementing a novel relative entropy method, we calculated the precipitation seasonality indicators using daily precipitation observations from 1960 to 2018 in HRB. We found a significant decreasing trend in precipitation seasonality (−0.03 decade−1, p = 0.04), accompanied by an earlier onset date (4.0 days decade−1, p = 0.01) and longer duration (4.3 days decade−1, p = 0.03) of the wet season. Notably, these trends are notably concentrated in the Beijing-Tianjin administrative regions. Additionally, a lower precipitation seasonality value indicated a more evenly distributed precipitation throughout the year, resulting in reduced occurrences of precipitation extremes. Consistently, we observed two precipitation extremes, extreme wet day precipitation R99T and maximum 1-day precipitation RX1Day, which exhibited significant decreasing trends at the rate of −0.5 mm decade−1 (p = 0.02) and −1.4 mm decade−1 (p = 0.05), respectively. Furthermore, we detected significant positive correlations of 0.31 (p = 0.02) and 0.35 (p = 0.01) between precipitation seasonality and precipitation extremes (R95T and R99T), suggesting that a more evenly distributed precipitation across seasons corresponds to fewer precipitation extremes over the past sixty years. Metropolitan areas, in particular, experienced a noteworthy reduction in precipitation seasonality and a decreased frequency of precipitation extreme events. The findings of this study shed new light on the intricate relationship between precipitation seasonality and extreme events, further helping policy making develop effective risk regulations for agriculture, floods, and urban waterlogging, ensuring sustainable development within the HRB.

Assessments of Climate Indices on the Characterization of Extreme Rainfall in Senegal

In recent years, Senegal has experienced a considerable increase in extreme events. The context of climate change, we conducted this study to characterize the extreme rainfall events in Senegal through nine (9) indices of extreme precipitation calculated, using R software (Rclimdex) for the period 1950 to 2010 on several stations. These analyses show that the annual precipitation totals describe a decreasing trend. However, a return of these accumulations have been noted in 1999 in almost all stations of rainfall in Senegal. The extreme rainfall indices show also a linear decreasing trend over the period 1950 to 2010, with the exception of consecutive dry days (CDD). However, non-linear trend analysis (Loess curve) shows increasing trends in rainfall indices over the last decades (from 1991), except for consecutive dry days (CDD) and extremely wet days (R99p) which show a decreasing trend over the last decade.

Observed changes in wet days and dry spells over the IGAD region of eastern Africa

Changes in wet and dry patterns have an impact on rain-fed agriculture, crop productivity, and food security in Eastern Africa. The purpose of this research is to look into the changes in wet days and dry periods within the Intergovernmental Authority on Development (IGAD) region. Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS) and Multi Models Ensembles (MME) of 10 historical simulations and projections from Coupled Model Intercomparison Project (CMIP6) models were employed as the data source. Several statistical approaches, as well as wet and dry spell thresholds, were used to calculate patterns of change in wet and dry spells on a decadal (10-year), 20, 30, and 41-year time scale. The results show the region exhibits decrease a decrease in the number of wet days and protracted dry spells in the 1980s, followed by an extraordinary (exceptional) increase in wet days in the subsequent decades (2011–2020) during March–May (MAM), June–September (JJAS), and October-December (OND). In Kenya, Somalia, southeastern Ethiopia, Eritrea, and Djibouti, the probability of surpassing 7, 14, 21, 28 days (1, 2, 3, 4 spells) was less than 5%. Furthermore, floods in 1997, 2018, 2019, and 2020, as well as droughts in 1983, 1984, 1985, and 2021, were triggered by an increase or decrease in the number of wet days and dry spells over most of the region. The number of wet days is projected to decrease by 10–20% during the MAM season across Sudan, South Sudan, and central and northern Ethiopia, JJAS is projected to increase by 30–50% across central and northern Sudan. However, during the OND season, increases are projected over Uganda, Ethiopia, and Kenya under three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5) scenarios. These findings contributed to the advancement of scientific knowledge in the IGAD region, as well as decision-making, food security, and the development of adaptation and mitigation strategies. We encourage rain-fed agriculture, crop variety planning, and irrigation supplement.

Optimal selection of daily satellite precipitation product based on structural similarity index at 1 km resolution for the Pra catchment, Ghana

Thirteen satellite precipitation products (SPPs), re-gridded to 1 km resolution, were evaluated in terms of the structural similarity index (SSI) over the Pra catchment in Ghana. Three SPP scenarios were considered: Scenario one (S1) was the original SPPs; Scenario two (S2) was bias-corrected SPPs; and Scenario three (S3) was the better of S1 and S2 for each wet day. For each scenario, the best SPP was selected to constitute the 14th SPP referred to as the BEST SPP. Each SPP was evaluated in terms of SSI against the rain gauge rainfield for each wet day. For S1, the top three SPPs were TMPA, GSMAP and CMORPH; for S2, CMORPH, PERCCS and MSWEP were the top three; and for S3, CMORPH, PERCCS and TMPA came out on top in order of decreasing performance. Bias correction led to improvement in the overall SSI measure (SSIM) for 73% of wet days. The BEST SPP increased the SSIM of the best individual SPP by over 50% for S1, and over 30% for both S2 and S3. Comparing the BEST SPP of the three scenarios, S2 increased the SSIM statistic by 20% over that for S1, and SSIM was further improved by 4% for S3. It is highly recommended to use BEST SPP (S3) to generate the required 1 km × 1 km rainfields for the Pra, or other catchments around the world with a sparse rain gauge network, through conditional merging with rain gauge data as demonstrated.

Exploring the nexus of climate variability, population dynamics, and maize production in Togo: Implications for global warming and food security

Maize, which serves as the primary staple crop in Togo, plays a crucial role in supporting rural communities and ensuring food security. Comprehending the complex relationship between Togo's maize production, population dynamics, and climate variability is crucial for mitigating the impacts of global warming and guaranteeing long-term food security. The present study examines the climate patterns and their impact on maize cultivation in Togo for the period spanning from 1990 to 2020. During this temporal interval, significant variations were seen in important climate indices. The levels of rainfall and the frequency of wet days exhibited notable fluctuations, whereas the temperature and concentrations of air pollutants, specifically carbon dioxide (CO2) and nitrous oxide (N2O), had a persistent rising trajectory. The recorded temperature exhibited an increase from 27 ​°C in 1990 to 29 ​°C in 2020. The levels of CO2 emissions had a substantial increase from 1000 ​kt in 1990 to an estimated 2500 ​kt in 2020. Similarly, the emissions of N2O witnessed a notable rise of more than 800 ​kt over the same time frame. Using time series analysis, we examined the temporal characteristics and employed ARIMA predictive models to project maize production over the upcoming decade, considering the gradual decrease in rural population density. The studies highlight the significant implications of global warming on a worldwide scale, exerting a profound impact on the Earth. The agriculture sector, specifically maize production, had notable variations in yield and overall output. Notwithstanding the inherent fluctuations in output, maize continues to maintain its position as the most widely consumed and preferred crop in Togo, even in the face of the country's rapidly expanding population. As a result, Togo has increased its imports of maize to satisfy the growing demand. The present study sheds light on the intricate relationship between production parameters, including yield and quantity, and a range of environmental variables such as the frequency of precipitation, temperature, and levels of N2O, in addition to rural and urban population density. The statement underscores the pressing necessity to confront the issue of global warming and its subsequent impacts on agricultural methodologies. Furthermore, the results underscore the significance of considering population dynamics and climate variables in the prediction of forthcoming agricultural outcomes.

Extreme weather events and its impacts on rice production in coastal Odisha region of India

The study examines extreme daily precipitation and temperature trends in coastal Odisha, India by calculating 18 weather indices (8 temperature indices and 10 rainfall indices) using the RClimDex software package for the period 1980-2010. Statistical significance of the indices was determined through trend analysis using linear regression and non-parametric Mann-Kendall test. Results indicated, a strong and significant trend in temperature indices while the weak and non-significant trend in precipitation indices. The positive trend in Tmax mean, Tmin mean, TN90p (warm nights), TX90p (warm days), diurnal temperature range, warm spell duration indicator, consecutive dry days indicates increasing the frequency of warming events in coastal Odisha. Similarly, positive trend in highest maximum 1-day precipitation, highest maximum 2-consecutive day precipitation, highest maximum 3 consecutive day precipitation, highest maximum 5-consecutive day precipitation, number of heavy precipitation days (64.5mm), number of very heavy precipitation days (124.5mm) and negative trend in the number of rainy days (R2.5mm), consecutive wet days indicate changes toward the more intense and poor distribution of precipitation in coastal Odisha. Extreme precipitation and temperature events negatively impacted rice yield, with a sharp decline observed in all coastal districts. The study highlights the need for new technology/management practices to minimize these impacts.

Warmer and wetter: Outlining climate services for snow-dependent tourism in Norway – The case of Lofoten

Human-induced climate change potentially impacts nature-based activities in Lofoten and may limit the attractiveness of the destination for tourists seeking recreation and adventure in the mountains. As a climate service, we calculated climate indicators relevant to the tourism sector based on the representative concentration pathways RCP4.5 and RCP8.5 until 2060. We used high-resolution gridded climate data and projections to calculate indicators such as changes in the frequency and intensity of consecutive wet days, changes in precipitation type (snow, sleet, rain), changes in the number of skiing days on ungroomed, natural snow, and changes of the monthly 0 °C-isoline. We found a minor, but non-robust increase in the number of consecutive wet days with a precipitation intensity > 8 mm/day, and a clear change in the precipitation regime depending on altitude that leads to more precipitation falling as rain instead of snow. Also, a strong decrease in the number of skiing days is projected by the climate models as the monthly near-surface 0 °C-isoline increases. These are important findings for long-term planning and investments in the tourism sector in Lofoten, especially as tourism growth is considered an important tool for regional economic development. The analytical methods used in this study are transferable to analyses on a regional to national scale. National maps and data material for 11 regions were recently published on https://klimaservicesenter.no/kss/framskr/sno-sludd-regn.

An increasing trend in daily monsoon precipitation extreme indices over Pakistan and its relationship with atmospheric circulations

This study assessed spatiotemporal trends in daily monsoon precipitation extremes at seasonal and sub-seasonal scales (June, July, August, and September) and their links with atmospheric circulations over Pakistan. The study used observed precipitation data from fifty in-situ stations and reanalysis products from the European Centre for Medium-Range Weather Forecasts (ECMWF) and National Centers for Environmental Prediction/the National Center for Atmospheric Research (NCEP/NCAR) during 1981–2018. A suite of seven extreme precipitation indices and non-parametric statistical techniques were used to infer trends in the frequency and intensity of extreme precipitation indices. An increase in frequency and intensity of overall extreme indices was evident, with a maximum tendency in the country’s northwestern (z-score=>2.5), central, and eastern (z-score > 4) monsoon-dominant parts. The northern and southwestern parts of the country exhibited a slight decrease (z-score <–2) in frequency and intensity. The Sen’s Slope estimator (SSE) shows an increase in western parts (0.20 days) indicating a shift in the maxima of the monsoon precipitation. The regional precipitation shows an increase in wet days (R1 mm) with higher values of mMK (3.71) and SSE (0.3) in region 2 Similar results of moderate regional increase are evident for extreme indices except regions 1 and 3. The extreme 1-day maximum precipitation increased in region 3 (mMK: 1.39, SSE: 2.32). The extremely wet days (R99p TOT) precipitation has a moderate increase in all regions with a decrease in region 1. The temporal mutations showed dynamic changes, clearly reflecting the country’s historical extreme events. The frequency and intensity of precipitation extremes negatively correlated with the altitude (R = −0.00039). The probability density function (PDF) showed a significant increase in the density during June and September with a probabilistic positive shift during July and August. The intensified mid-latitude westerlies and subtropical zonal easterlies teleconnections, strengthening of the monsoon trough, and land-ocean thermal contrast are the potential drivers of the increasing trend in precipitation extremes. The current study could serve as a benchmark for future researchers and policymakers to devise effective mitigation strategies for sustainable development.

Lesser Prairie-chicken incubation behavior and nest success most influenced by nest vegetation structure.

Incubation breaks are necessary for any nesting bird but can increase the mortality risk of the nest or attending parent. How intrinsic and extrinsic variables affect nest attentiveness-the proportion of time a female is on nest during incubation- and subsequent survival of the nest remains unclear for uniparental species. We related female nest attentiveness to nest survival and tested the effects of intrinsic and extrinsic variables on nest attentiveness by female Lesser Prairie-chickens (Tympanuchus pallidicinctus) using GPS locations of 87 females at 109 nest sites in 3 study areas in Kansas during 2013-2015. Daily nest survival increased by 39% when nest attentiveness increased from 21% to 98%. Female Lesser Prairie-chickens were 18% less attentive as body mass increased from 600 to 920 g. Daily precipitation and temperature, controlled for days into the incubation period, had interactive effects on nest attentiveness with nest attentiveness lowest on cool, wet days and increasing as temperature increased, regardless of precipitation (41% attentiveness at 16°C and 79 mm of precipitation to 90% attentiveness at 37°C and 41 mm of precipitation). Nest attentiveness increased by 11% as the quantity of grass at the nest site increased from 5% to 78% when visual obstruction was at 1 and 2 decimeters (dm) and increased 9% as the quantity of grass at the nest site increased from 5% to 83% when visual obstruction was at its maximum (3 dm). Our findings reveal the critical importance of nest attentiveness and incubation behavior, not only in relation to demography, but within the context of changing environmental conditions. As warmer temperatures and extreme precipitation events become more common and change the growth rates of vegetation, species like the Lesser Prairie-chicken that are ground-nesting, rely on vegetation cover, and exhibit uniparental care could experience negative demographic consequences.

Towerpy: An open-source toolbox for processing polarimetric weather radar data

Weather radars produce high spatial and temporal resolution observations of precipitation events. Over the years, weather radar operators have updated their radar networks to exploit the latest technological advancements. One of the most significant improvements in this matter was providing the radars with polarimetric capabilities, as this allows the radars to gather more detailed information about the precipitation targets’ shape, size, phase, and orientation. The radar research community has been working along with these advancements to develop robust algorithms that boost the radar data applications, such as radar quantitative precipitation estimation (QPE), rain microphysics analysis, nowcasting of precipitation or numerical weather prediction based on radar measurements. Some of these algorithms have been implemented in open-source toolboxes that aim to facilitate the processing and quality control of radar data produced by different radar systems. However, these open-source projects have not yet included a chain process tailored to the UK radar research context. This paper presents a TOolbox to process WEather Radar data in PYthon (Towerpy). Towerpy can read, process and display polarimetric radar data from different radar systems, but it is specially customised for data produced by the UK Met Office radar network. Towerpy is built upon robust algorithms that cover various aspects of the radar data quality control (e.g., calibration of radar measurements, identification of non-meteorological echoes, attenuation correction, among others) and the computation of radar rainfall rates. Additionally, a radar processing chain was devised using the Towerpy modules to produce radar rainfall estimates. Raw polarimetric radar measurements collected by the UK Met Office radar network throughout the UK’s wettest day on record were used as input for this processing chain. The results confirm that Towerpy is a powerful radar research tool and demonstrate its ability to generate functional radar QPE that can be used to improve operational radar rainfall products.

Changes in Extreme Rainfall in New Capital of Indonesia (IKN) Based on 20 Years of GPM-IMERG Data

The Indonesian government has decided to relocate the nation’s capital city from Jakarta on Java to the Nusantara Capital (IKN) on Kalimantan for political, socioeconomic, and infrastructure reasons, among others, including the threat of flood, earthquake, and land subsidence in Jakarta. However, IKN remains susceptible to hydro-meteorological disasters due to its equatorial location and high yearly rainfall amounts. In this article, we investigated changes in extreme rainfall in the IKN area over the last 20 years (2001- 2020) based on Integrated Multi-Satellite Retrievals for GPM (IMERG) version 6 data. Overall, extreme rain in the IKN area, especially in the Sepaku and Semboja sub-districts, has slightly declined in the last 2 decades, as shown by negative and positive Sen’s slopes of precipitation amount-based indices and the consecutive dry days (CDD), respectively. A decrease was also observed in RX1 day (annual maximum 1-day precipitation) and SDII (annual total rainfall divided by the number of wet days in the year) indices. This indicates that the IKN area is getting drier and could impact the availability of clean water in the future by increasing the intensity and duration of the drought. While the total amount of precipitation has decreased over the past 2 decades, there has been an increase in the intensity of more extreme precipitation events. This is evident from the rise in precipitation frequency-based indices (annual count of days with rainfall exceeding certain thresholds), such as R20mm and R50mm. The RX5day index (maximum 5-day precipitation) has also shown an increase. These changes increase the risk of flooding, so it is crucial to implement appropriate measures to mitigate the risk of floods in the IKN area. HIGHLIGHTS Extreme rain in the IKN area, especially in Sepaku and Semboja sub-districts, has slightly declined in the last 20 years. Increases were observed in the consecutive dry days (CDD) and some precipitation frequency-based indices. It is important to take appropriate measures to reduce the risk of floods and drought in the IKN areas. GRAPHICAL ABSTRACT

Variability of Extreme Events in Coastal and Inland Areas of South Korea during 1961–2020

The increased concentrations of greenhouse gases have led to global warming and an increased frequency and intensity of extreme weather events. Such changes in weather patterns may have unexpected implications for everyday life and water resource management in coastal and inland areas; thus, it is critical to understand the pattern of the changes. This study investigated how extreme weather events have changed in inland and coastal South Korea in the past 60 years (1961–2020) at different temporal scales, from monthly to yearly. This study quantified extreme weather events using multiple meteorological indices such as consecutive dry days (CDD), consecutive wet days (CWD), tropical nights, and icy and frosty days. The trends in the extreme weather indices were statistically tested using a non-parametric test. The results showed increases in the minimum and maximum air temperature and the frequency of warm and cold nights and days. The number of CDD and maximum five-day precipitation (RX5day) at the coastal and inland stations increased in the extreme precipitation-related index. The number of warm days and warm nights increased significantly at the majority of weather stations over the 60 year study period. The number of CWD increased during the selected period, but this was not statistically significant. In addition, we found that the temporal variations in the indices became greater over time, which implies the frequency and severity of extreme events such as drought and storm events may increase in the future. This study could help researchers determine the climatic areas at the selected stations that are critical for optimal water resource management planning and/or modeling.

Projected climate extremes over agro-climatic zones of Ganga River Basin under 1.5, 2, and 3° global warming levels.

Recurring floods, droughts, heatwaves, and other hydro-meteorological extreme events are likely to be increased under the climate change scenarios. The increased risk of these extreme events might have more exposure to the population; thus, it is important to discuss such extreme events and their projected behavior under a changing climate scenario. In the present study, we have computed the extreme precipitation and temperature indices over the 10 agro-climatic zones falling under the Ganga River Basin (GRB)utilizing a high-resolution daily gridded temperature and precipitation multi-model ensembled CMIP6 dataset (0.25° × 0.25°) under global warming levels of 1.5°C, 2°C, and 3°C. We found that the annual daily minimum temperature (TNN) showed a higher rise of about 67% than the maximum temperature (TXX) of 48% in GRB. The basin also experiences a greater increase in the frequency of warm nights (TN90P) of about 67.71% compared to warm days (TX90P) of 29.1% for the 3°C global warming level. Along with extreme indices, the population exposed due to the impact of the extreme maximum temperature has also been analyzed for progressive warming levels. Population exposure to extreme temperature event (TXX) has been analyzed with 20-year return period using GEV distribution method. The study concludes that the exposed population to extreme temperature event experienced an increase from 46.99 to 52.16% for the whole Ganga Basin. Consecutive dry days (CDD) and consecutive wet days (CWD) both show a significant increasing trend, but CWD has a significant increase in the majority of the zones, while CDD shows a significant decreasing trend for some of the zones for three warming levels periods. Extreme climate indices help to understand the frequency and intensity of extreme weather events such as heavy rainfall, droughts, and heatwaves to develop early warning systems and adaptation strategies to mitigate such events.

Understanding the effect of long term and short term hydrological components on landscape ecosystem

Modifications in river morphology are witnessed due to the hydrological fluctuations leading to braided rivers and sandbar developments. Understanding the hydrological alterations could offer crucial insight into the ecosystem's landscape pattern. To comprehend flood and drought in an agricultural ecosystem, we investigated the impact of short-term water level changes from 1999 to 2015. Numerical simulations were carried out on the fluctuant streamflow at different seasons in a braided river using a two-dimensional hydrodynamic model. The number of consecutive wet days (CWD), average wet days (AWD), consecutive dry days (CDD), and average dry days (ADD) were computed. The Standardized Streamflow Index (SSFI-4) was determined at a seasonal time scale, which is used to quantify the streamflow-based hydrological droughts. The calibration and validation of the hydrodynamic model was done with the measured velocity. To understand the planform geometry of the river, satellite images were derived for the historical period from 1976 to 80 till 2016–17. The Planform Index (PI) and Sinuosity Index (SI) were used to assess and classify the river into straight, braided and meander categories. Speedy modifications were observed across the longitudinal profile, with PFI varying between 3.72 and 42.8. Sandbars being an imperative asset for floodplain communities and dwellers, the effect of these fluctuations was studied in different landscape classes. We analyzed the changes in the spatial and temporal characteristics of dynamic landscape patterns between 2004 and 2014 at quinquennial and decadal scales. We found that a substantial-high water level from a 2010 flood led to a drastic change in the landscape pattern of the ecosystem. In addition, consecutive dry days witnessed over the sandbar regions affect the agriculture cropping patterns. Post flooding, the expansion of land under agriculture could be inferred from the fact that sediment was fertile and deposited after the flood recession; agriculture was dominantly practiced in the region. Overall, the study demonstrated that various hydrological components typically impact the spatial distribution characteristics of landscape patterns.