Dry Days Research Articles

Physical Activity Dependence on Relative Temperature and Humidity Characteristics in a Young, Insufficiently Active Population: A Weather Typing Analysis.

Physical activity (PA) is an important contributor to one's physical and mental health both acutely and across the lifespan. Much research has done on the ambient environment's impact on PA; however, these studies have used absolute values of atmospheric measures such as temperature and humidity, which vary spatiotemporally and make comparisons between studies which differ in location or time of year difficult to square with one another. Here, we employ the Global Weather Type Classification, Version 2, to determine the combined impact of temperature and humidity on PA in a sample of insufficiently active young adults. We conducted secondary analyses of data from a single-group behavioral intervention trial that varied the number of digital messages sent daily. Young adults (n = 81) wore Fitbit Versa smartwatches for a 6-month period sometime between April 2019 and July 2020, and location was tracked using a custom smartphone application. Mixed linear models indicated that, across 8179 person-days, PA was significantly lower on days with humid conditions and significantly higher on warm dry days, though the latter relationship was no longer significant when controlling for timing in relation to the COVID-19 pandemic declaration. Demographic factors did not affect the relationship between weather and PA. Results are a first step in providing additional guidance for encouraging PA in insufficiently active individuals given forecasted daily weather conditions. Future work should examine seasonal variability in the weather type-PA relationship without the influence of a world-altering event influencing results.

Ecosystem Water‐Saving Timescale Varies Spatially With Typical Drydown Length

AbstractStomatal optimization theory is a commonly used framework for modeling how plants regulate transpiration in response to the environment. Most stomatal optimization models assume that plants instantaneously optimize a reward function such as carbon gain. However, plants are expected to optimize over longer timescales given the rapid environmental variability they encounter. There are currently no observational constraints on these timescales. Here, a new stomatal model is developed and is used to analyze the timescales over which stomatal closure is optimized. The proposed model assumes plants maximize carbon gain subject to the constraint that they cannot draw down soil moisture below a critical value. The reward is integrated over time, after being weighted by a discount factor that represents the timescale (τ) that a plant considers when optimizing stomatal conductance to save water. The model is simple enough to be analytically solvable, which allows the value of τ to be inferred from observations of stomatal behavior under known environmental conditions. The model is fitted to eddy covariance data in a range of ecosystems, finding the value of τ that best predicts the dynamics of evapotranspiration at each site. Across 82 sites, the climate metrics with the strongest correlation to τ are measures of the average number of dry days between rainfall events. Values of τ are similar in magnitude to the longest such dry period encountered in an average year. The results here shed light on which climate characteristics shape spatial variations in ecosystem‐level water use strategy.

Quantifying causal teleconnections to drought and fire risks in Indonesian Borneo

AbstractFires occurring over the peatlands in Indonesian Borneo accompanied by droughts have posed devastating impacts on human health, livelihoods, economy and the natural environment, and their prevention requires comprehensive understanding of climate‐associated risk. Although it is widely known that the droughts are associated with El Niño events, the onset process of El Niño and thus the drought precursors and their possible changes under the future climate are not clearly understood. Here, we use a causal network approach to quantify the strength of teleconnections to droughts at a seasonal timescale shown in observations and climate models. We portray two drivers of June‐July‐August (JJA) droughts identified through literature review and causal analysis, namely Niño 3.4 sea surface temperature (SST) in JJA (El Niño Southern Oscillation [abbreviated as ENSO]) and SST anomaly over the eastern North Pacific to the east of the Hawaiian Islands (abbreviated as Pacific SST) in March‐April‐May (MAM) period. We argue that the droughts are strongly linked to ENSO variability, with drier years corresponding to El Niño conditions. The droughts can be predicted with a lead time of 3 months based on their associations with Pacific SST, with higher SST preceding drier conditions. We find that under the SSP585 scenario, the Coupled Model Intercomparison Phase 6 (CMIP6) multi‐model ensembles show significant increase in both the maximum number of consecutive dry days in the Indonesian Borneo region in JJA (p = 0.006) and its linear association with Pacific SST in MAM (p = 0.001) from year 2061 to 2100 compared with the historical baseline. Some models are showing unrealistic amounts of JJA rainfall and underestimate drought risks in Indonesian Borneo and their teleconnections, owing to the underestimation of ENSO amplitude and overestimation of local convections. Our study strengthens the possibility of early warning triggers of fires and stresses the need for taking enhanced climate risk into consideration when formulating long‐term policies to eliminate fires.

Strategic Integration of a Vegetative Component on a Metal Roof Base: An Evaluation of Its Impacts on Thermal and Acoustic Performance in the Tropics

This paper attempts to ascertain the thermal and acoustic impacts of introducing a vegetative roof layer on insulated and uninsulated metal roofs for tropical climates, through field measurements in Skudai, Johor, Malaysia, that were conducted for both dry and wet days. Four small-scale roof modules were tested, namely an uninsulated metal roof (uiMDR), an insulated metal roof (iMDR), and two identical corresponding modules with an additional vegetative component (uiGR and iGR, respectively). Outdoor ambient temperature (Tamb) was the most influential correlated variable affecting the roof outer surface temperature (RTOS) in 50% of the assessed scenarios. On the selected dry day, the inter-quartile ranges (IQR) of iGR, iMDR, uiGR, and uiMDR were 6.21 °C, 8.32 °C, 6.69 °C, and 1.66 °C, respectively; the IQRs were 1.6 °C, 4.11 °C, 2.59 °C, and 1.78 °C, respectively, on the selected wet day. Based on design U-value calculations, iGR was better than iMDR and uiMDR for both dry and wet days. The U-value of uiGR was also better than iMDR under dry-day conditions. The Wilcoxon signed-rank test also indicated a statistically significant difference in the roof inner surface temperature (RTIS) measurements (p-value = 0.00) during Malaysian daylight hours, between 8.00 a.m. and 6.00 p.m., regardless of the weather. In terms of sound level reduction under dry-day conditions, the Kruskal–Wallis and Wilcoxon signed-rank tests showed statistically significant differences in sound level reductions, with iGR and uiGR performing better than iMDR and uiMDR (p-values = 0.00). The sound level reductions for iGR, iMDR, and uiGR were 51%, 32%, and 31%, respectively, while uiMDR experienced sound level amplifications by 6%, possibly due to the acoustic resonance effect. This proof of concept may encourage a broader application of extensive GRs in Malaysia using metal roofs, beyond the conventional RC base construction method.

Spatiotemporal variations of extreme weather events and climate drivers in the Three Gorges Reservoir Area and its surrounding regions from 1960 to 2020

The Three Gorges Reservoir Area and its Surrounding Region (TGRASR) play a crucial role in the Yangtze River Basin. Over the past years, there has been growing concern about the effects of extreme weather events in this area, mainly attributed to climate change. To effectively address the implications of climate change, a thorough understanding of the temporal and spatial variability of these events is necessary. However, some of the previous studies have been limited to analyzing trend changes in events and have not sufficiently considered the connection with atmospheric circulation indices. In this study, we analyzed the spatio-temporal variations of extreme weather events using the Mann-Kendall and Sen's slope trend methods. Then, the correlation between extreme weather events and climate factors is explored using the Mantel test, Geo-Detector method (GDM), and Partial wavelet coherency (PWC) in terms of linear, hierarchical heterogeneity, and nonlinear, respectively. Finally, we investigated the potential climate factors that affect the temporal and spatial variability of extreme weather occurrences. The results showed that(1) among the 12 extreme indices analyzed in the TGRASR region from 1960 to 2020, there was a decreasing trend observed in Consecutive dry days (CDD), Consecutive wet days (CWD), and Annual count of days when precipitation is ≥10 mm (R10), while the remaining indices exhibited an increasing trend. Subsequent analysis employing the M-K mutation revealed that the mutation point for CWD occurred around 1975, resulting in a significant decrease in its trend. Additionally, the Simple daily intensity index (SDII), Annual count of days when the daily maximum temperature (TX) is >25 °C (SU25), and R10 displayed a decreasing trend. Mutations in the trends SU25, Annual count of days when the daily minimum temperature (TN) is >20 °C (TR20), and Annual count of days with at least 6 consecutive days when TX > 90th percentile (WSDI) were observed in 1997, 1999, 2003, and 2005, respectively. Following these mutations, all four indices displayed a substantial increase. (2) CDD, CWD, and R10 exhibit a decreasing trend across most stations, whereas the remaining indices demonstrate an increasing trend. Regarding spatial distribution, indices characterizing precipitation frequency and intensity exhibit higher mean values in the southeastern part of the TGRASR. (3) Mantel test: North Pacific pattern (NP), North Tropical Atlantic (NTA), and Global Mean Land/Ocean Temperature (GMLOT) have significant linear correlations with extreme precipitation and extreme temperature events; GDM: GMLOT and NTA are the main contributors to extreme precipitation and extreme temperature; PWC: 12 extreme indices have some nonlinear associations with all five climate factors. The study's findings can assist decision-makers in formulating sustainable water resource management strategies and measures for mitigating drought and flood risks. Additionally, the study contributes to uncovering climatic factors that influence changes in extreme weather events.

Surfactants and Microbial Aerosol of Urban Particulate Matter (PM10) in Kuala Lumpur City Centre

Surfactants in the atmosphere potentially influence the global climate and may affect human health. This study aims to determine the compositions of surfactants extracted from urban particulate matter (PM10) and motor vehicle exhaust soot. In addition, microbial aerosol was also determined. A high-volume air sampler (HVAS) has been used to collect PM10 samples in Kuala Lumpur urban area. Meanwhile, the vehicle exhaust soot was collected randomly from the exhaust pipes of various types of diesel and petrol vehicles, using a soft brush during dry days. The concentration of anionic surfactants as Methylene Blue Active Substances (MBAS) was determined by the colorimetric method using a UV-Vis Spectrophotometer. The presence of microorganisms such as bacteria and fungi were investigated by colony count. In addition, the correlation between surfactants and traffic-related air pollutants has also been investigated. Results revealed that the average concentration of MBAS in particulate matter is 107.68 ± 7.91 µgm-3. Diesel vehicles demonstrated higher surfactant concentration in exhaust particulate matter with an average value of 0.227 ± 0.006 µmolg-1 compared to petrol vehicles (0.146 ± 0.003 µmolg-1). Based on this study, various mitigation procedures can be implemented to reduce the impacts of PM and exhaust soot for a better future.

Evaluation of Pre-Analytical Variables for Human Papillomavirus Primary Screening from Self-Collected Vaginal Swabs

HPV primary screening is an effective approach to assessing cervical cancer risk. Self-collected vaginal swabs have shown promise to expand testing access, but there are limited data defining analytical performance criteria necessary for adoption of self-collected specimens, especially for those occurring outside the clinic where the swab remains dry during transport. Here, we evaluated the performance of self-collected vaginal swabs for HPV detection using the Roche Cobas 6800. There was insignificant variability between swabs self-collected by the same individual (n=15 participants collecting 5 swabs/participant), measured by amplification of HPV and human β-globin control DNA. Comparison of self-collected vaginal swab and provider-collected cervical samples (n=144 paired collections) proved highly concordant for HPV detection (total agreement=90.3%;PPA=84.2%). There was no relationship between the number of dry storage days and amplification of HPV (n=68, range 4-41days). Exposure of self-collected dry swabs to extreme summer and winter temperatures did not affect testing outcomes. We assessed a second internal control (RNAseP) in a subset and demonstrate that lack of amplification for β-globin from self-collected specimens was consistent with poor, but not absent, cellularity. These data suggest that self-collected vaginal samples enable accurate clinical HPV testing, and that extended ambient dry storage or exposure to extreme temperatures do not influence HPV detection. Further, lack of β-globin amplification in HPV negative samples accurately identified participants that required recollection.

Characterization of road-deposited sediment wash-off and accurate splitting of initial runoff pollution in heterogeneous urban spaces

Particulate materials arising from road-deposited sediments (RDS) are an essential target for the control and management of surface runoff pollution. However, the heterogeneity of urban spaces hinders the identification and quantification of particulate pollution, which is challenging when formulating precise control measures. To elucidate the factors that drive particulate pollution in heterogeneous urban spaces, the accumulation of RDS on dry days and the total suspended solids during six natural rainfall events were investigated across three urban–rural spatial units (central urban, central suburban, and remote suburban). The underlying surface type (asphalt or cement roads) and particle size composition jointly determined the spatial heterogeneity in the static accumulation and dynamic output loads of RDS during rainfall. These two factors explained 59.6% and 18.9% of the spatial heterogeneity, respectively, according to principal component analysis. A novel CPSI exponential wash-off equation that incorporates particle size composition and underlying surface type was applied. It precisely described the spatial heterogeneity of RDS wash-off loads, the estimated values exhibiting event mean concentration errors of 10.8–18.2%. When coupled with the M(V) curve, this CPSI exponential wash-off equation more precisely split the initial volume of runoff: a lower total volume (17.6–38.0%) was shown to carry a higher proportion of the load (70.0–93.7%) compared to the traditional coupled exponential wash-off equation (volume: 31.6–49.0%, load: 37–90%). This study provides a new approach to characterizing RDS wash-off processes and splitting initial runoff in heterogeneous spaces.

Space–Time Characterization of Extreme Precipitation Indices for the Semiarid Region of Brazil

Various indices of climate variability and extremes are extensively employed to characterize potential effects of climate change. Particularly, the semiarid region of Brazil is influenced by adverse effects of these changes, especially in terms of precipitation. In this context, the main objective of the present study was to characterize the regional trends of extreme precipitation indices in the semiarid region of Brazil (SAB), using daily precipitation data from the IMERG V06 product, spanning the period from 1 January 2001 to 31 December 2020. Twelve extreme precipitation indices were considered, which were estimated annually, and their spatial and temporal trends were subsequently analyzed using the nonparametric Mann–Kendall test and Sen’s slope. The analysis revealed that the peripheral areas of the SAB, especially in the northwest and extreme south regions, exhibited higher intensity and frequency of extreme precipitation events compared to the central portion of the area. However, a negative trend in event intensity was noted in the north, while positive trends were identified in the south. The frequency of extreme events showed a predominance of negative trends across most of the region, with an increase in consecutive dry days particularly throughout the western SAB. The average total precipitation index was above 1000 mm in the north of the SAB, whereas in the central region, the precipitation averages were predominantly below 600 mm, with rainfall intensity values ranging between 6 and 10 mm/day. Over the span of 20 years, the region underwent an average of 40 consecutive dry days in certain localities. A negative trend was observed in most of the indices, indicating a reduction in precipitation intensity in future decades, with variations in some indices. The dry years observed towards the end of the analyzed period likely contributed to the observed negative trends in the majority of extreme precipitation indices. Such trends directly impact the intensity and frequency of extreme weather events in the SAB. The study is important for highlighting and considering the impacts of changes in precipitation extremes in the semiarid region of Brazil. Based on the obtained results, we advocate the implementation of public policies to address future challenges, such as incorporating adaptations in water resource management, sustainable agricultural practices, and planning for urban and rural areas.

Dynamic Changes in Vegetation Ecological Quality in the Tarim Basin and Its Response to Extreme Climate during 2000–2022

The Tarim Basin is located in an arid inland area; the ecological environment is fragile, and it is extremely sensitive to climate change. For the purpose of studying dynamic changes in the vegetation response of vegetation in the Tarim Basin to extreme climate, this study used the Vegetation Ecological Quality Index (EQI) as a vegetation indicator and calculated 12 extreme climate indices using Rclimdex. Pearson correlation analysis was used to explore the relationship between EQI values and various extreme climate indices at both inter-annual and intra-annual scales. Additionally, geographic detector analysis was employed to examine the single and interactive effects of extreme climate on the EQI for different vegetation types. The following was found: (1) During 2000–2022, the EQI showed an upward trend in the Tarim Basin, and the increase in agricultural vegetation was the fastest. (2) Since 2000, the extreme warm temperature indices have risen, whereas the extreme cold temperature indices have declined. The warming rate of nighttime temperatures exceeds that of daytime, and the extreme precipitation rises intensively. Simultaneously, continuous dry days have also increased. (3) On an inter-annual scale, the EQI is primarily negatively correlated with the most extreme warm temperature indices, while it is positively correlated with extreme cold temperatures and extreme precipitation indices. On an intra-annual scale, there is an obvious regional concentration in the correlation between the EQI and extreme climate indices. The diurnal temperature range (DTR) and cold daytimes (TX10P) have inhibitory and promoting effects on areas with high and low EQI, respectively. The extremum indices, temperature warm indices, and precipitation intensity indices have a promoting effect on areas with a high EQI and an inhibiting effect on areas with a low EQI. The interaction between extreme climate indices has a greater impact on the EQI than the effect of a single extreme climate index, especially with a significant impact on forests and shrubs. This study provides a reference for the early warning of meteorological disasters, ecosystem protection, and sustainable management in the Tarim Basin.

Effective Deep Learning Seasonal Prediction Model for Summer Drought Over China

AbstractDrought is an important meteorological event in China and can cause severe damage to both livelihoods and socio‐ecological systems, but current seasonal prediction skill for drought is far from successful. This study used convolutional neural network (CNN) to build an effective seasonal forecast model for the summer consecutive dry days (CDD) over China. The principal components (PC) of the six leading empirical orthogonal function modes of CDD anomaly were predicted by CNN, using the previous winter precipitation, 2‐m temperature and 500 hPa geopotential height as predictors. These predicted PCs were then projected onto the observed spatial patterns to obtain the final predicted summer CDD anomaly over China. In the independent hindcast period of 2007–2018, the interannual variabilities of first six PCs were significantly predicted by CNN. The spatial patterns of CDD were then skillfully predicted with anomaly correlation coefficient skills generally higher than 0.2. The interannual variability of summer CDD over the middle and lower Yangtze River valley, northwestern China and northern China were significant predicted by our CNN model three months in advance. CNN identified that the previous winter precipitation was the important predictor for PC1–PC3, whereas the previous winter 2‐m temperature and 500 hPa geopotential height were important for the prediction of PC4–PC6. This research provides a new and effective method for the seasonal prediction of summer drought.

Performance of CHIRPS for estimating precipitation extremes in the Wala Basin, Jordan

Abstract The evaluation of open-source precipitation data is crucial to enable the selection of the most appropriate product for a specific research. This study aims to evaluate the capability of the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) with a spatial resolution of 0.05° for estimating monthly and annual precipitation in the Wala basin, Jordan, from 1987 to 2017 using a point-to-pixel comparison approach. Eleven precipitation extreme indices, recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI), were used in this study. The findings show that CHIRPS correlated moderately with stations in monthly precipitation estimation, with the Pearson correlation coefficient values ranging from 0.50 to 0.73. However, CHIRPS had low correlations with stations in most of the extreme indices, except PRCPTOT, R10mm, and R20mm. The CHIRPS, particularly in the extreme years, overestimated low precipitation amounts and underestimated high ones. Moreover, CHIRPS underestimated the calculation of consecutive dry days, consecutive wet days, R10mm, R20mm, and R30mm, while an overestimation was found for the R95p, R99p, and Rx1day. The trend analysis and Wilcox text showed a lack of resemblance between the CHIRPS and gauges, showing a bias correction is needed before applying an extreme analysis in this region.

Profile of Rainfall Patterns and Extreme Air Temperatures 1981-2020 in the Mosunal Region of Indonesia (Case Study: South Sumatra)

Increasing concentrations of greenhouse gases have led to changes in extreme climate events. This study aims to describe the picture of climate change in the South Sumatra Region during the last 40 years, namely from 1981-2020. Rainfall and extreme temperature data were obtained from BMKG data for South Sumatra Province at the Palembang City Climatology Station (SK Palembang City) and the Sultan Mahmud Badaruddin II Meteorological Station (SM SMB II). Maximum temperature data, minimum temperature and rainfall were obtained based on parameter profiles obtained from BMKG data for South Sumatra Province based on the ETCCDMI provisions. Maximum temperature data, minimum temperature and rainfall are processed with RClimDex software so that temperature and extreme rain index data are obtained. These indices are extreme climate indices consisting of TN90p, TX90p, TNn, TNx,TXx, TXx, TMAXmean, TMINmean, RX1day, RX5day, SDII, CDD and CWD. The results of this study indicate that the trend of air temperature will increase significantly in South Sumatra Province, especially the minimum air temperature, then the precipitation pattern will also increase, especially the accumulation of rainfall for 5 consecutive days. There is an increase in the number of dry days and a decrease in the number of wet days, as well as the higher accumulation of daily rainfall but the wet days are decreasing day by day. This is also increasingly having an impact on climate change in South Sumatra Province which is getting hotter day by day and the smoke disaster due to land and peat fires in South Sumatra Province is increasingly worrying.

Multimodel comparison and assessment of short to medium range precipitation and temperature forecasts over India: Implications towards forecasting of meteorological indices in India

AbstractA comprehensive assessment of the forecast skill of various meteorological indices over the Indian region from different numerical weather prediction (NWP) models is lacking in the literature. In this study the performance of four NWP models, namely Global Ensemble Forecast System (GEFSv12), European Center for Medium Range Forecasting (ECMWF), Climate Forecast System (CFSv2) and Indian Institute of Tropical Meteorology (IITM) towards forecasting of precipitation, temperature and associated meteorological indices, is evaluated at short to medium timescales across the Indian region. Further, the effect of ocean atmospheric (OA) oscillations on the precipitation/temperature forecast skill from the different NWP models is also assessed. Results show that the NWP models are better in predicting the meteorological indices than the quantitative forecasts. The ECMWF model was found to be the best for PCP forecasting with CFSv2 performing poorly. For temperature the GEFSv12 model performance was the lowest, compared to rest of the models. The models show poor skill in forecasting monsoon season precipitation compared to non‐monsoon and the temperature forecasts from the NWP models are particularly poor for the northern basins. Skilful temperature forecasts are observed in the Northwestern, Indo‐Gangetic and Central basins for the CFSv2 and ECMWF models. The forecast skill of precipitation indices are higher in the northwestern, central and Indo‐Gangetic basins compared to the rest. The skill of precipitation indices, namely rainy days, extreme rainy days and consecutive wet days, is higher during the monsoon seasons while the prediction skill of consecutive dry days is higher during the non‐monsoon season. OA analysis revealed that the ENSO phases have dominant effect on the forecast skill of the precipitation only. The temperature and meteorological indices forecasts are not affected significantly by the OA phases. The outcomes of this study have implications towards irrigation scheduling and water resources management decision making in India.

Exposure of African ape sites to climate change impacts

Large gaps remain in our understanding of the vulnerability of specific animal taxa and regions to climate change, especially regarding extreme climate impact events. Here, we assess African apes, flagship and highly important umbrella species for sympatric biodiversity. We estimated past (1981–2010) and future exposure to climate change impacts across 363 sites in Africa for RCP2.6 and RCP6.0 for near term (2021–2050) and long term (2071–2099). We used fully harmonized climate data and data on extreme climate impact events from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). Historic data show that 171 sites had positive temperature anomalies for at least nine of the past ten years with the strongest anomalies (up to 0.56°C) estimated for eastern chimpanzees. Climate projections suggest that temperatures will increase across all sites, while precipitation changes are more heterogeneous. We estimated a future increase in heavy precipitation events for 288 sites, and an increase in the number of consecutive dry days by up to 20 days per year (maximum increase estimated for eastern gorillas). All sites will be frequently exposed to wildfires and crop failures in the future, and the latter could impact apes indirectly through increased deforestation. 84% of sites are projected to be exposed to heatwaves and 78% of sites to river floods. Tropical cyclones and droughts were only projected for individual sites in western and central Africa. We further compiled available evidence on how climate change impacts could affect apes, for example, through heat stress and dehydration, a reduction in water sources and fruit trees, and reduced physiological performance, body condition, fertility, and survival. To support necessary research on the sensitivity and adaptability of African apes to climate change impacts, and the planning and implementation of conservation measures, we provide detailed results for each ape site on the open-access platform A.P.E.S. Wiki.

Responses of annual streamflow variability to annual precipitation, extreme climate events and large-scale climate phenomena in the Qinghai-Tibet Plateau

Solar activity and large-scale climate phenomena have significant effects on extreme climate events and streamflow variability. As the roof of the world and the water tower of Asia, the Qinghai-Tibet Plateau (QTP) is highly sensitive to climate change. Therefore, it is of great significance to study the relationship between extreme hydrometeorological events of the QTP and climate change for global hydroclimate research. In this study, we analyzed the spatiotemporal variation and significant oscillation period of several hydrometeorological variables such as extreme precipitation indices (EPIs), extreme temperature indices (ETIs) and annual streamflow variability based on the observation data of hydrometeorological stations in the QTP during 1962–2019 using Sen’s slope estimator, Mann-Kendall test and continuous wavelet analysis (CWT). And the teleconnection patterns and the lead-lag relationship between annual precipitation, extreme climate events, multiple nonstationary climate mode and annual streamflow were evaluated using wavelet coherence (WTC). The results show that the climate of the QTP has been wetter and warmer in the past 58 years. The annual streamflow of several natural rivers in the QTP mainly show an increasing trend over the last 60 years. The annual streamflow of natural river (except Yellow River) increases significantly in three-rivers headwater region, and the rivers in the northeast and northwest also show a significant increase trend, but there is no significant change in the natural river streamflow in the south of QTP. In addition, the annual streamflow of natural rivers had significant periodicities in the bands of 2–4 years, 4–7 years and 7–11 years. The significant coherence of WTC spectra between annual precipitation and streamflow indicate that precipitation is the main reason for annual streamflow variation in the QTP. The variation of extreme climate events significantly regulates the regional annual streamflow. All EPIs except consecutive dry days show an in-phase correlation with annual streamflow of most headwater. Annual streamflow of Naijin River is positively and negatively correlated with extreme warm and extreme cold, respectively, and has a clearer lead-lag relationship than that with EPIs. Annual streamflow of headwater has higher correlations with EI Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) than with sunspot number (SSN). Solar activity may first affect ENSO and PDO, and then this effect is transferred sequentially to the hydrological cycle of the QTP by SSN-modulated PDO and PDO-SSN co-modulated ENSO. It is of great significance to investigate the response of streamflow variation to annual precipitation, extreme climate events and large-scale climate phenomena for water resources management, flood control, ecosystem restoration, and the well-being of surrounding residents and sustainable development in the QTP.

Extreme weather events and Spatio-temporal characterization of climate change variables in Bangladesh during 1975–2019

Bangladesh is susceptible to climate change, thus a detailed study, including the analyses of trends, sub-trends, extreme events and indices was conducted to obtain a complete picture of the climate change pattern in Bangladesh utilizing daily rainfall, maximum, minimum and average temperature data of 26 stations from 1975 to 2019 using R 4.0.2 software. For the trend analysis Mann Kendal (MK), modified Mann Kendall (mMK), Innovative Trend Analysis (ITA) and Sen’s slope methods were used. Sub-trend analysis was conducted using ITA. Standard Anomaly Index (SAI) has been used to identify the frequency and severity of extreme events. ClimPACT2 software was used to check the homogeneity and calculate the extremes of temperature and rainfall data. Our analysis showed that during the last four decades, climate variables changed their patterns and trend heterogeneously over Bangladesh. Most stations showed decremental rainfall trend when central part of the country showed a substantial decrease. The northern and central parts of the country showed significant growth of trend for annual average temperature. The temperature in the monsoon season increased, whereas those in dry season decreased. The rainfall and maximum temperature were inversely related during monsoon whereas during dry season both of them decrease. The pre-monsoon and post-monsoonal rainfall also showed decreasing trends, indicating prevailing drought conditions especially in northern and central parts of the country. The SAI analysis showed alternating drought and wet years in almost all the stations. In the past 20 years, the country’s western region experienced more drought years than before whereas the coastal region experienced more wet years. The analysis of climate extreme indices suggests that, Consecutive Dry Days (CDD), Hot Days (TX90P) and Hot Nights (TN90P) show significant increasing trend throughout the country. The agricultural productivity, water resource management and food security are anticipated to benefit from this study.

Assessment of mean precipitation and precipitation extremes in Iran as simulated by dynamically downscaled RegCM4

This paper aims to assess the mean precipitation and precipitation extremes over Iran as simulated by the Regional Climate Model (RegCM4). A simulation spanning 20 years (1991–2010) at a horizontal resolution of 20 Km is driven by the NCEP / NCAR reanalysis. We evaluated the model by comparing simulated precipitation with observations using Bias, Root-Mean-Square Error, and Index of Agreement metrics. We examined the extreme precipitations based on a set of extreme indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) in three categories of intensity (Rx1day and SDII), duration (CDD and CWD), and frequency (R10mm and R20mm). The linear trends are calculated using the Theil–Sen estimator method, and the statistical significance (95% confidence level) is determined by using a modified Mann-Kendall (MMK) trend test. The RegCM4 model satisfactory captured the spatial distribution of precipitation and precipitation extremes, although high bias remained in small parts of Iran, including the northwest and southeast. The northwest bias is due to spring convectional precipitation and the southeast bias could be caused by precipitation from the Asian summer monsoon system, which both of them may not be well simulated by the applied Grell convective scheme. Results indicate that the model reasonably captures Rx1day, SDII, CWD, R10mm, and R20mm Indices over Iran. In good agreement with precipitation observations, the southern coast of the Caspian Sea represents the second-highest extreme precipitation, except for SDII, which is probably due to the high frequency of rainy days in this region. The highest CDD of more than 200 days is found in the arid and semi-arid regions of the southeast. In general, precipitation decreased in most regions of Iran, especially the western, southern, and interior regions. In addition, the results reveal that heavy (R10mm) and very heavy (R20mm) precipitation events have also decreased in the same regions. Results also emphasize an increase in consecutive dry days (CDD) in most parts, especially in the southeast, which deserves more attention in future research. The decreasing trend of precipitation and the increasing trend of CDD show that Iran has become drier in the 2000 s compared to the 1990 s

Screening of representative rainfall event series for long-term hydrological performance evaluation of grassed swales.

Evaluation of the hydrological performance of grassed swales usually needs long-term monitoring data. At present, suitable techniques for simulating the hydrological performance using limited monitoring data are not available. Therefore, current study aims to investigate the relationship between saturated hydraulic conductivity (Ks) fitting results and rainfall characteristics of various events series length. Data from a full-scale grassed swale (Enschede, the Netherlands) were utilized as long-term rainfall event series length (95 rainfall events) on the fitting outcomes. Short-term rainfall event series were extracted from these long-term series and used as input in fitting into a multivariate nonlinear model between Ks and its influencing rainfall indicators (antecedent dry days, temperature, rainfall, rainfall duration, total rainfall, and seasonal factor (spring, summer, autumn, and winter, herein refer as 1, 2, 3, and 4). Comparison of short-term and long-term rainfall event series fitting results allowed to obtain a representative short-term series that leads to similar results with those using long-term series. A cluster analysis was conducted based on the fitting results of the representative rainfall event series with their rainfall event characteristics using average values of influencing rainfall indicators. The seasonal index (average value of seasonal factors) was found to be the most representative short rainfall event series indicator. Furthermore, a Bayesian network was proposed in the current study to predict if a given short-term rainfall event series is representative. It was validated by a data series (58 rainfall events) from another full-scale grassed swale located in Utrecht, the Netherlands. Results revealed that it is quite promising and useful to evaluate the representativeness of short-term rainfall event series used for long-term hydrological performance evaluation of grassed swales.

Wastewater generation model to predict impacts of urine separation on wastewater treatment plants.

Wastewater treatment plants (WWTPs) are under increasing pressure to enhance resource efficiency and reduce emissions into water bodies. The separation of urine within the catchment area may be an alternative to mitigate the need for costly expansions of central WWTPs. While previous investigations assumed a spatially uniform implementation of urine separation across the catchment area, the present study focuses on an adapted stochastic wastewater generation model, which allows the simulation of various wastewater streams (e.g., urine) on a household level. This enables the non-uniform separation of urine across a catchment area. The model is part of a holistic modelling framework to determine the influence of targeted urine separation in catchments on the operation and emissions of central WWTPs, which will be briefly introduced. The wastewater generation model is validated through an extensive sampling and measurement series. Results based on observed and simulated wastewater quantity and quality for a catchment area of 366 residents for two dry weather days indicate the suitability of the model for wastewater generation and transport modelling. Based on this, four scenarios for urine separation were defined. The results indicate a potential influence of spatial distribution on the peaks of total nitrogen and total phosphorus.