Consecutive Dry Days Research Articles

Evaluation of IMERG for GPM satellite-based precipitation products for extreme precipitation indices over Turkiye

As hydrological disasters become more common, the need for monitoring extreme precipitation events is growing globally. Remote sensing technologies developed in recent years help to meet this demand with satellite-based precipitation products, particularly for ungauged basins. However, due to limitations and uncertainties, these products must be validated before being used in hydrometeorological/climatological applications. Validation studies also help improve retrieval algorithms by identifying errors and biases in the data that can be corrected or adjusted. One of NASA's Global Precipitation Measurement products, called Integrated Multi-satellitE Retrievals for GPM (IMERG), has been extensively validated for its ability to detect extreme precipitation events in drastically different climate regions across the world. However, such studies remain limited for Turkiye. In this study, we examined the ability of IMERG Early (E), Late (L), and Final (F) products to detect extreme precipitation in Turkiye using rain gauge data over an eleven-year period (2010−2020). We used extreme precipitation indices recommended by the Expert Team on Climate Change Detection Indices (ETCCDI) to evaluate the ability of IMERG products to detect extreme precipitation. For the stations selected across Turkiye, the Pearson correlation coefficients (R) between daily ground-based precipitation measurements and the three IMERG precipitation products (IMERG-E, IMERG-L, and IMERG-F) were found to be 0.54, 0.55, and 0.64, respectively. IMERG products performed better in detecting the precipitation extreme based on the 85% percentile compared to the 95% and 99% percentiles. IMERG underestimates consecutive dry days by about 25% while overestimating consecutive wet days at the same rate. The performance of IMERG Early, Late, and Final products is inversely proportional to the daily precipitation totals. The annual intensity of precipitation outperformed in the Final run compared to other products across the country. Light and moderate rainfall was detected well, while the detection capability of IMERG decreased for heavy and heavy torrential precipitation. Geographically, the Aegean, Mediterranean, and Southeastern Anatolia regions generally performed better, while Eastern Anatolia and interior parts of the Black Sea region exhibited poorer performance. The low performance in Eastern Anatolia may be attributed to the region's mountainous terrain, while the low performance in the Black Sea region may be due to orographic forcing and land-sea interaction. Overall, the findings of this study showed that the detection capability of IMERG products performed better with increasing latency.

Comparison of multimodel ensembles of global and regional climate models projections for extreme precipitation over four major river basins in southern Africa— assessment of the historical simulations

This study assesses the performance of large ensembles of global (CMIP5, CMIP6) and regional (CORDEX, CORE) climate models in simulating extreme precipitation over four major river basins (Limpopo, Okavango, Orange, and Zambezi) in southern Africa during the period 1983–2005. The ability of the model ensembles to simulate seasonal extreme precipitation indices is assessed using three high-resolution satellite-based datasets. The results show that all ensembles overestimate the annual cycle of mean precipitation over all basins, although the intermodel spread is large, with CORDEX being the closest to the observed values. Generally, all ensembles overestimate the mean and interannual variability of rainy days (RR1), maximum consecutive wet days (CWD), and heavy and very heavy precipitation days (R10mm and R20mm, respectively) over all basins during all three seasons. Simple daily rainfall intensity (SDII) and the number of consecutive dry days (CDD) are generally underestimated. The lowest Taylor skill scores (TSS) and spatial correlation coefficients (SCC) are depicted for CDD over Limpopo compared with the other indices and basins, respectively. Additionally, the ensembles exhibit the highest normalized standard deviations (NSD) for CWD compared to other indices. The intermodel spread and performance of the RCM ensembles are lower and better, respectively, than those of GCM ensembles (except for the interannual variability of CDD). In particular, CORDEX performs better than CORE in simulating extreme precipitation over all basins. Although the ensemble biases are often within the range of observations, the statistically significant wet biases shown by all ensembles underline the need for bias correction when using these ensembles in impact assessments.

Rainfall extremes in Northern Vietnam: a comprehensive analysis of patterns and trends

This study examines the characteristics and trends of extreme rainfall in Northern Vietnam from 1961 to 2018, using daily rainfall data collected from 37 meteorological stations. The study reveals that the average annual rainfall varies significantly across stations, ranging from 1140 mm to 4758 mm. The rainy season accounts for 73% to 92% of the total annual rainfall. Most stations show a declining trend in the annual total rainfall during wet days (PRCPTOT) and the number of wet days (WDAY), while rainfall intensity (SDII) has increased in most stations, particularly during the dry season. This can be attributed to an increase in PRCPTOT and a decrease in WDAY in the dry season. The study also finds a general decreasing trend in the annual maximum 1-day precipitation (RX1day) and consecutive 5-day precipitation (RX5day), as well as for the number of moderate (R16mm) and heavy (R50mm) rainfall days. However, most stations in Northern Vietnam demonstrate no trend in the annual maximum number of consecutive dry days (CDD) and the annual maximum number of consecutive wet days (CWD). Furthermore, the frequency of extreme rainfall events in Northern Vietnam exceeding the 5-year and 10-year return values of 1961-2018 has decreased in recent decades at many stations. Overall, the findings of this study provide insights into the changing patterns of extreme rainfall in Northern Vietnam, with significant implications for climate change adaptation and disaster risk reduction efforts in the region.

Historical assessment and future projection of extreme precipitation in CMIP6 models: Global and continental

AbstractIn this study, the new gridded daily precipitation dataset REGEN (Rainfall Estimates on a Gridded Network) was used to verify the performance of 23 global climate models participating in the 6th phase of the Coupled Model Intercomparison Project (CMIP6) in terms of extreme precipitation over global and continents during 1950–2014. Projected changes were also evaluated using CMIP6 multi‐model ensemble (MME) under different Shared Socioeconomic Pathway (SSP) by the end of the 21st century. Although positive biases exist in the climatology of most extreme precipitation indices simulated by the CMIP6 MME compared with observations, most of the CMIP6 models can reproduce the spatial distribution characteristics and temporal evolution trends of global extreme precipitation. The simulation is improved by using the CMIP6 MME, relative to the results from individual models. The projected results showed that in most regions of the world, extreme precipitation will significantly increase in the future, with more significant increase under higher SSPs. By the end of the 21st century, total precipitation of wet days (PRCPTOT), simple daily intensity (SDII), the number of very heavy rain days (R20mm), maximum 1‐day precipitation (Rx1day) and maximum consecutive 5‐day precipitation (Rx5day) over the global will increase by up to 50%, 20%, 70%, 50% and 50%, respectively, under the SSP5‐8.5 scenario. Nevertheless, marked uncertainties exist in the projection of extreme precipitation in South America and in the global for consecutive dry days (CDD) index. Noteworthy, average PRCPTOT, Rx1day and Rx5day over Asia, Europe and North America, as well as of the whole world, have significantly increased where the CDD index is also higher, shown by projected results. In summary, global extreme precipitation will increase in the future, and precipitation will become more uneven in space and time. Moreover, as the SSP scenario becomes higher, the uneven distribution of precipitation will become more pronounced.

Evaluation of Changes in Dry and Wet Precipitation Extremes in Warmer Climates Using a Passive Water Vapor Modeling Approach

Abstract Hydroclimatic extremes, such as heavy daily rainfall and dry spells, are expected to intensify under anthropogenic warming. Often, these changes are diagnostically related to thermodynamic increases in humidity with warming. Here, we develop a framework that uses an online calculation of the thermodynamically induced changes of the full precipitation distribution with warming in an idealized moist atmospheric general circulation model. Two water vapor variables, the standard active one and an additional passive one (i.e., no latent heat release when condensation occurs), are advected by the resolved circulation. The passive water vapor is thermodynamically perturbed by modifying the saturation specific humidity used in the calculation of its condensation tendency and surface evaporation. The difference between the precipitation of the perturbed passive water vapor relative to the control one corresponds to the thermodynamic component of precipitation change, which can be evaluated for the entire distribution. Here, we evaluate wet and dry extremes. Our simulations have tropical increases and higher-latitude decreases of dry spell’s length (defined as the maximum consecutive dry days), as found in the zonal mean of comprehensive models. This simulated thermodynamically induced intensification of dry spells in the tropics arises from the decreased contrast between sea surface temperature and surface air temperature with warming. There is a simulated increase in heavy daily rainfall (e.g., the 99.9th percentile of the daily precipitation distribution) at all latitudes that differ modestly from a previous theory that assumes moist-adiabatic stratification. Consistent with this theory, increased warming aloft slightly dampens the simulated increase.

Spatial and temporal variations of extreme climate index in the Songhua River Basin during 1961-2020.

Understanding climate change and extreme climate is of great significance for ensuring food security and socio-economic development of the Songhua River Basin. Based on the daily precipitation, maximum temperature and minimum temperature data during 1961-2020 from 69 meteorological stations in and around the Songhua River Basin, we analyzed the temporal and spatial variations of extreme temperature and precipitation in the Songhua River Basin using 27 extreme climate indices recommended by the World Meteorological Organization, and linear trend method, Mann-Kendall trend test and ordinary Kriging interpolation methodology. The results showed that, from 1961 to 2020, except for cold speel duration, the extreme cold index in the study area showed a downward trend, while the extreme warm index, extreme value index and other temperature indices showed an upward trend. The increasing trend of the minimum temperature was greater than that of the maximum temperature. Icing days, cold speel duration and warm speel duration showed an increasing trend from south to north, while the minimum value of maximum temperature and that of minimum temperature showed opposite spatial characteristics. The high value areas of summer days and tropical nights were mainly distributed in the southwestern region, while there was no obvious spatial variations of cool days, warm nights, and warm days. Overall, except for cold speel duration, other extreme cold indices had a rapid decreasing trend in the north and west of the Songhua River Basin. In the warm index, summer days, warm nights, warm days, and warm speel duration had a rapid upward trend in the north and west, and tropical nights had the fastest rise in the southwest. In the extreme value index, the maximum of temperature rose fastest in the northwest, while the minimum rose fastest in the northeast. Except for consecutive dry days, the rest of precipitation indices showed an increasing trend, and the fastest rising areas were mainly in the north-central part of the Nenjiang River Basin, while some areas in the south of the Nenjiang River Basin became dry. Heavy precipitation days, very heavy precipitation days, heaviest precipitation days, consecutive wet days, very wet day precipitation, extremely wet day precipitation, and annual precipitation showed a gradual decreasing pattern from southeast to northwest. Overall, the Songhua River Basin was warming and wetting, but there were some differences among different regions, especially the northern and southern parts of the Nenjiang River Basin.

Spatiotemporal variability characteristics of extreme climate events in Xinjiang during 1960–2019

Under the global warming, it is particularly important to explore the response of extreme climate to global climate change over the arid regions. Based on daily temperature (maximum, minimum, and average) and precipitation data from meteorological stations in Xinjiang, China, we analyzed the spatiotemporal characteristics of extreme temperature and extreme precipitation events via combining thin plate smoothing spline function interpolation, Sen's slope, and Mann-Kendall test. Our results showed that during 1960-2019, the extreme low temperature index of frost days (FD), icing days (ID), cold days (TX10p), cold nights (TN10p), and cold speel duration index (CSDI) all showed the downward trend to varying degrees, and the extreme high temperature index of summer days (SD25), warm days (TX90p), warm night (TN90p), and warm speel duration index (WSDI) all showed an upward trend to varying degrees, and the extreme low temperature index of high altitude mountains decreases more than that of the basin and plains. In addition, all the extreme temperature indices are closely related to the annual average temperature in Xinjiang (R > 0.6). Among the extreme precipitation indices, except for the consecutive dry days (CDD), the other extreme precipitation indices showed increasing trends to different degrees, but the changes in extreme precipitation in Xinjiang were mainly manifested by the increase of heavy precipitation in a short period (the increase of heavy precipitation and extreme heavy precipitation was the largest, 44.8mm/10a and 17.6mm/10a, respectively) and spatially concentrated in the Ili River and Altai Mountains in northern Xinjiang. Meanwhile, annual precipitation was positively correlated with the extreme precipitation index (R > 0.4), except for the CDD. This study provides theoretical support for the prevention and control of natural disasters in the dry zone.

Summer extreme consecutive dry days over Northeast China in the changing climate: Observed features and projected future changes based on CESM-LE

Northeast China (NEC) is a major crop base in East Asia, and summer drought is one of the climate extremes that significantly influences NEC agricultural production. Therefore, understanding the response of NEC summer drought to global warming is of significance. In this study, based on observation and large-ensemble simulations of the Community Earth System Model (CESM-LE), the variabilities in summer extreme consecutive dry days (CDDs) over NEC are investigated in the present and future climate. In the observation, the NEC summer extreme CDDs showed an increasing trend during the past half century and experienced a significant interdecadal change around the middle 1990s, which is mainly due to the change in the anticyclone over Lake Baikal-Northeast Asia. The anticyclone-related anomalous downward motion and moisture divergence provided favorable conditions for increased summer CDDs over NEC. The CESM-LE multimember ensemble (MME) simulation could reproduce the change in NEC summer extreme CDDs and its related atmospheric circulations, indicating that the observed change in NEC summer extreme CDDs could be largely contributed by anthropogenic forcing. In the future warmer climate, the NEC summer extreme CDDs are projected to show interdecadal variability, which increase by approximately 6.7% in the early 21st century (2020–2030), then decrease by approximately 0.3% in the middle to late 21st century (2040–2080), and further increase by approximately 2.1% in the late 21st century (2085–2100). In addition, the projected changes in the anticyclone over Lake Baikal-Northeast Asia show a similar feature to that of the NEC summer extreme CDDs, which might further provide some confidence in the projection of the NEC summer extreme CDDs due to the physical connection between CDDs and anticyclone in the future.

Variability in Projected North American Mean and Extreme Temperature and Precipitation Trends for the 21st Century: Model‐To‐Model Differences Versus Internal Variability

AbstractWhile climate change is expected to alter mean and extreme temperature and precipitation over the 21st century, regional changes in these fields remain difficult to project. For a given future emissions scenario, the uncertainty in future projections can be attributed to either model‐to‐model differences, which can presumably be reduced if models are improved, or internal variability, which cannot be constrained to a single outcome but can be used to quantify a range of plausible outcomes. This study examines the range of possible trends in North American mean and extreme temperature and precipitation across phase 6 of the Coupled Model Intercomparison Project models under the SSP3‐7.0 emissions scenario. While model‐to‐model differences primarily govern the spread in mean and extreme temperature trends across these model simulations, the spread in mean and extreme precipitation trends is dominated by internal variability in many regions. A storylines approach—a type of regression‐based analysis—is used to identify key dynamical drivers that explain the variance in future trends across model simulations. The dynamical drivers considered include trends in global mean surface temperature (GMST) and four common modes of climate variability. Combinations of these drivers can reinforce each other to create high‐impact storylines. For example, model simulations with large positive GMST and El Niño‐Southern Oscillation trends are associated with a large reduction in the annual maximum number of consecutive dry days in the American Southwest. However, limiting these storylines to only models with climate sensitivities consistent with observational constraints reduces the likelihood of a large reduction of consecutive dry days in this region.

The main factors that drive plant dieback under extreme drought differ among Mediterranean shrubland plant biotypes

AbstractQuestionsKnowledge of how extreme drought events induce plant dieback and, eventually, plant mortality, may improve our forecasting of ecosystem change according to future climate projections, especially in Mediterranean drylands. In them, shrublands are the main vegetation communities in transition areas from a subhumid to semi‐arid climate. This study analyzed differences in plant dieback after an unusual drought in 2014 and identified their main underlying factors in relation to three groups of explanatory variables: water availability, soil properties and vegetation structure attributes.LocationFour Mediterranean shrublands along a climatic gradient in SE Spain.MethodsAt each experimental field site, we sampled a pool of vegetation structure characteristics, soil depth and soil surface properties, and we also determined water availability by continuously monitoring soil moisture and the microclimate conditions.ResultsThe climatic analysis showed that there was an extreme drought event in 2014, which was below the first percentile of the driest years. Under such conditions, vegetation dieback occurred at all the study sites. However, plant dieback differed between sites and plant biotypes. Subshrubs were the main affected biotype, with diebacks close to 60% at the driest sites, and up to 40% dieback for shrubs depending on their vertical development. Relative extractable water and bare soil surface cover were the best explanatory variables of plant community dieback but changed between plant biotypes. Vegetation structure variables related to plant vertical development (leaf area index [LAI], plant height, phytovolume) were significant explanatory variables of plant dieback in shrubs, subshrubs and grasses. Consecutive dry days fitted the best model to explain subshrub dieback.ConclusionsWe found that rainfall pattern rather than total annual rainfall was the climatic factor that best determined water availability for plants in Mediterranean drylands. These results also pointed out the relevance of plant structure and soil properties for explaining ecosystem responses to extreme drought.

Projected changes in extreme rainfall and temperature events and possible implications for Cameroon's socio‐economic sectors

AbstractExtreme events like flooding, droughts and heatwave are among the factors causing huge socio‐economic losses to Cameroonians. Investigating the potential response of rainfall and temperature extremes to global warming is therefore critically needed for tailoring and adjusting the country's policies. Recent datasets have been developed for this purpose within the Coordinated Output for Regional Evaluations (CORDEX‐CORE) initiative, at ~25 km grid spacing. These regional climate models were used to dynamically downscaled four global climate models participating in the Coupled Model Intercomparison Project phase 5 (CMIP5), under the optimistic and pessimistic representative concentration pathways (RCPs) 2.6 and 8.5, respectively. These models were employed in this study for characterizing the response of Cameroon's extreme precipitation and temperature events to global warming, using seven indices defined by the Expert Team on Climate Change Detection and Indices. Under global warming, the maximum number of consecutive dry (wet) days' is expected to increase (decrease). However, the annual total rainfall amount is expected to increase, mainly due to the intensification of very wet days and daily rainfall intensity. Furthermore, the temperature‐based indices reveal an increase (decrease) in the total annual hot (cold) days, and overall, changes intensify with increased radiative forcing. The high‐mitigated low‐emission pathway RCP2.6 features attenuated changes, and even sometimes adapts to reverse the sign of changes. Designing reliable policies to limit the risks associated with the above changes is required, as their socio‐economic consequences are likely to include food insecurity, heat‐related illness, population impoverishment, price rises and market instability.

Trends in Rainfall and Temperature Extremes in Ethiopia: Station and Agro-Ecological Zone Levels of Analysis

Climate extreme events have been observed more frequently since the 1970s throughout Ethiopia, which adversely affects the socio-economic development of the country, as its economy depends on agriculture, which, in turn, relies heavily on annual and seasonal rainfall. Climate extremes studies conducted in Ethiopia are mainly limited to a specific location or watershed, making it difficult to have insights at the national level. The present study thus aims to examine the observed climate extreme events in Ethiopia at both station and agro-ecological zone (AEZ) levels. Daily rainfall and temperature data for 47 and 37 stations, respectively (1986 up to 2020), were obtained from the National Meteorology Agency (NMA). The Modified Mann–Kendall (MMK) trend test and the Theil–Sen slope estimator were employed to estimate the trends in rainfall and temperature extremes. This study examines trends of 13 temperature and 10 rainfall extreme indices using RClimDex in R software. The results revealed that most of the extreme rainfall indices showed a positive trend in the majority of the climate stations. For example, an increase in consecutive dry days (CDD), very heavy rainfall days (R20), number of heavy rainfall days (R10) and consecutive wet days (CWD) were exhibited in most climate stations. In relation to AEZs, the greater number of extreme rainfall indices illustrated an upward trend in cool and sub-humid, cool and humid, and cool and moist AEZs, a declining trend in hot arid AEZ, and equal proportions of increasing and decreasing trends in warm semi-arid AEZs. Concerning extreme temperature indices, the result indicated an increasing trend of warm temperature extreme indices and a downward trend of cold temperature extreme indices in most of the climate stations, indicating the overall warming and dryness trends in the country. With reference to AEZs, an overall warming was exhibited in all AEZs, except in the hot arid AEZ. The observed trends in the rainfall and temperature extremes will have tremendous direct and indirect impacts on agriculture, water resources, health, and other sectors in the country. Therefore, the findings suggest the need for identifying and developing climate change adaptation strategies to minimize the ill effects of these extreme climate events on the social, economic, and developmental sectors.

Adapting rainfall bias-corrections to improve hydrological simulations generated from climate model forcings

Global circulation models (GCMs) provide important insights into future climate change. Bias-correction of downscaled GCM output is integral to any hydrological investigations of climate change due to discrepancies between the statistics of downscaled GCM simulations and observations. Many bias-correction techniques have been developed to support hydrological applications. However, there have been few comparisons of the sensitivity of hydrological simulations to different bias-correction assumptions. This paper investigates the importance of two common assumptions: (i) simultaneously correcting bias at multiple time scales, and (ii) explicitly handling rainfall autocorrelation; in quantile mapping of downscaled GCM rainfall data for hydrological simulation. Four quantile mapping methods are applied to correct bias in dynamically downscaled reanalysis and historical GCM simulations of rainfall for 11 catchments in south-eastern Australia and the performance of bias-corrected rainfall and streamflow simulations is evaluated.All quantile mapping methods investigated can effectively eliminate bias in monthly and annual rainfall totals. Quantile mapping methods that consider differences in the temporal dependence (autocorrelation) structure of downscaled GCM and observed rainfall are most effective in reducing bias in rainfall sequencing statistics, such as probability of consecutive wet or dry days. Streamflow simulations of mean and high streamflow percentiles are underestimated when generated using rainfall that is corrected using quantile mapping methods that do not consider differences in the temporal dependence structure of downscaled GCM and observed rainfall. Future hydrological investigations of climate change should therefore adopt methods that explicitly consider the temporal dependence structures of downscaled GCM and observed rainfall.

Projected Changes in Extreme Wet and Dry Conditions in Greece

Earth’s changing climate may have different effects around the planet. Regional changes in temperature and precipitation extremes are associated with damaging natural hazards. Decreases in precipitation are expected to occur in some places at mid-latitudes, for instance the Mediterranean, which has been classified as a climate change hotspot. Droughts are among the most damaging natural hazards with severe consequences in the socio-economic sectors, the environment, and living beings. In contrast, extreme heavy precipitation events may become more frequent. This study aims to project changes in precipitation extremes and assess drought variability and change across Greece. A better knowledge of the potential changes in drought variability under climate change is vital for managing potential risks and impacts associated with dry conditions. The spatiotemporal characteristics of heavy precipitation and drought events in Greece are investigated using extreme precipitation indices such as consecutive wet/dry days, total wet-day precipitation, fraction of total wet-day rainfall, maximum daily precipitation, and heavy precipitation days. The standardized precipitation index and the standardized precipitation and evapotranspiration index are also calculated to assess seasonal dryness variability. The analysis is performed using a sub-set of high-resolution simulations from EURO-CORDEX, under two different representative concentration pathway scenarios. The results show that the region is subject to future dry conditions. Total annual precipitation is found to decrease in most of the country, with western and southern parts tending to be the most vulnerable areas. The annual precipitation is estimated to decrease by 5–20% and 5–25% (RCP4.5 and RCP8.5 respectively) toward the period 2041–2070 and by 10–25% and 15–40% (RCP4.5 and RCP8.5) toward 2071–2100. Drought-related indices reveal positive trends, particularly under the high greenhouse-gas emission scenario, with the number of consecutive dry days increasing by 20–50% and 40–80% (during 2041–2070 and 2071–2100, respectively). On the contrary, extreme precipitation events tend to decrease in the future.

A Preliminary Assessment of the GSMaP Version 08 Products over Indonesian Maritime Continent against Gauge Data

This study is a preliminary assessment of the latest version of the Global Satellite Measurement of Precipitation (GSMaP version 08) data, which were released in December 2021, for the Indonesian Maritime Continent (IMC), using rain gauge (RG) observations from December 2021 to June 2022. Assessments were carried out with 586 rain gauge (RG) stations using a point-to-pixel approach through continuous statistical and contingency table metrics. It was found that the coefficient correlation (CC) of GSMaP version 08 products against RG observations varied between low (CC = 0.14–0.29), moderate (CC = 0.33–0.45), and good correlation (CC = 0.72–0.75), for the hourly, daily, and monthly scales with a tendency to overestimate, indicated by a positive relative bias (RB). Even though the correlation of hourly data is still low, GSMaP can still capture diurnal patterns in the IMC, as indicated by the compatibility of the estimated peak times for the precipitation amount and frequency. GSMaP data also manage to observe heavy rainfall, as indicated by the good of detection (POD) values for daily data ranging from probability 0.71 to 0.81. Such a good POD value of daily data is followed by a relatively low false alarm ratio (FAR) (FAR < 0.5). However, the GSMaP overestimates light rainfall (R < 1 mm/day); as a consequence, it overestimates the consecutive wet days (CWD) and number of days with rainfall ≥ 1 mm (R1mm) indices, and underestimates the consecutive dry days (CDD) extreme rain index. GSMaP daily data accuracy depends on IMC’s topographic conditions, especially for GSMaP real-time data. Of all GSMaP version 08 products evaluated, outperformed post-real-time non-gauge-calibrated (GSMaP_MVK), and followed by post-real-time gauge-calibrated (GSMaP_Gauge), near-real-time gauge-calibrated (GSMaP_NRT_G), near-real-time non-gauge-calibrated (GSMaP_NRT), real-time gauge-calibrated (GSMaP_Now_G), and real-time non-gauge-calibrated (GSMaP_Now). Thus, GSMaP near-real-time data have the potential for observing rainfall in IMC with faster latency.

Water balance components and climate extremes over Brazil under 1.5°C and 2.0°C of global warming scenarios.

The online version contains supplementary material available at 10.1007/s10113-023-02042-1.

Evaluation of Precipitation Estimates from Remote Sensing and Artificial Neural Network Based Products (PERSIANN) Family in an Arid Region

Accurate and continuous rainfall monitoring is essential for effective water resources management, especially in arid and semi-arid regions such as the United Arab Emirates (UAE). Significant spatio-temporal precipitation variation in the UAE necessitates the use of the latest techniques to measure rainfall intensity accurately. This study investigates the consistency and applicability of four satellite precipitation products, namely PERSIANN, PERSIANN-CCS, PERSIANN-CDR, and PDIR-Now, over the UAE. Daily time series data from 2011 to 2020 were analyzed using various statistical measures and climate indices to develop the belief in the products and for their inter-comparison. The analysis revealed that the average probability of detection (POD) for PDIR and CDR was the highest, with values ranging from 0.7–0.9 and 0.6–0.9, respectively. Similarly, CDR has a better Heidke Skill Score (HSS) with an average value of 0.26. CDR outperformed its counterparts with an average correlation coefficient value of 0.70 vs. 0.65, 0.40, and 0.34 for PDIR, CCS, and PERSIANN, respectively. Precipitation indices analysis revealed that all the products overestimated the number of consecutive wet days by 15–20%, while underestimating consecutive dry days by 5–10%. The quantitative estimations indicate that all the products were matching with the gauge values during the wet months (January–April), while they showed significant overestimation during the dry months. CDR and PDIR were in close agreement with the gauge data in terms of maximum daily rainfall with an error of less than 10% for both products. As compared to others, PERSIANN-CDR provided better estimates, particularly in terms of capturing extreme rainfall events and spatial distribution of rainfall. This study provides the first comprehensive evaluation of four PERSIANN family products based on recent daily rainfall data of UAE. The findings can provide future insights into the applicability and improvement of PERSIANN products in arid and semi-arid regions.

Is climate really changing? Insights from analysis of 30-year daily CHIRPS and station rainfall data in Zimbabwe

Climate change debate has been ongoing for at least two decades world over. However, an understanding of how much change has occurred in developing countries remains limited. Using a combination of daily Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and gauge rainfall data, this study analysed whether rainfall characteristics over 30 years (1989 to 2018) in Zimbabwe exhibited any significant trends. Firstly, Kappa statistic and a confusion matrix were used to test the appropriateness of CHIRPS satellite derived rainfall estimates for carrying out trend analysis. Thereafter, the Mann Kendall test, Sen's slope and kriging were used, to detect trends, ascertain magnitude of change and map the spatial variation in rainfall characteristics, respectively. The rainfall characteristics included: Total Number of Wet Days (TNWD), Maximum Number of Consecutive Wet Days (MNCWD), Maximum Number of Consecutive Dry Days (MNCDD) and Maximum Rainfall per Day (MRD). Validation results showed that overall accuracy ranged from 0.862 to 0.897 while kappa statistic ranged from 0.739 to 0.81, suggesting a substantial agreement between satellite derived- and gauge rainfall. No statistically significant trends were detected for the four rainfall characteristics analysed at 95% confidence interval. Although no significant trends were detected at 95% confidence interval to signal climate change it was observed that MNCDD, MRD and TNWD were increasing while MNCWD was generally decreasing. This key finding opens new opportunities for geographically targeted and integrated intervention strategies for agricultural and water management in Zimbabwe. This is particularly important considering that Zimbabwe mainly depends on rain fed agriculture and understanding of the key rainfall characteristics can enable proper planning and decision making in terms of adaptation that which can ensure water availability and food security. Food security is fundamental in achieving national security. In addition, this study opens opportunities for further studies on the factors that influence the trends through using sub-daily and daily satellite data for a relatively longer period i.e., >30 years. It is therefore our contention that fine spatial and temporal scales adopted in this study yield better characterisation of changes in rainfall regimes, hence brings out better insights on understanding intra-seasonal fluctuations for climate change detection studies.

Extreme precipitation indices over India using CMIP6: a special emphasis on the SSP585 scenario.

In this study, a comparison of 17 Coupled Model Intercomparison Project Phase 6 (CMIP6) data sets with Indian Meteorological Department (IMD) data in terms of finding out extreme precipitation indices obtained by the Expert Team on Climate Change Detection and Indices (ETCCDI) is done. The extreme indices considered were the consecutive dry days (CDD), consecutive wet days (CWD), maximum 1-day precipitation (RX1DAY), maximum 5-day precipitation (RX5DAY), precipitation > 2.5mm (RR2.5), heavy precipitation > 10mm (R10MM), very heavy precipitation > 20mm (R20MM), and simple daily intensity (SDII) that have been calculated for 17 CMIP6 datasets from 1950 to 2014 with IMD-gridded precipitation datasets over India. Rankings were assigned using the TOPSIS method with evaluation metrics as CC and RMSE as conditions. Almost all datasets performed well for indices, i.e., CWD, R10MM, R20MM, and RR2.5. The top 5 performing models are EC-Earth3, EC-Earth3-Veg, MRI-ESM2-0, GFDL-ESM4, and MIROC6 which were ensembled and projected for future periods in the near (2015-2040), middle (2041-2070), and far future (2070-2100), and extreme indices were calculated under Shared Socioeconomic Pathways 126 (SSP126), SSP245, SSP370, and SSP585 scenarios. The ensemble mean shows that RX1DAY, RX5DAY, R10MM, R20MM, and CWD are observed to increase in western ghats and northeastern regions of India. Central India exhibits a dynamic influence on precipitation indices in different climate change scenarios. The temporal variation for the SSP585 scenario predicts significant increases of about 45.41%, 149.40%, 52.26%, and 45.92% in R10MM, R20MM, RX1DAY, and RX5DAY over the current climate. Future extreme precipitation indices help flood modelers and hydrologists with watershed management.

Bivariate Analysis of Extreme Precipitation Using Copula Functions in Arid and Semi-Arid Regions

This study analyzed extreme precipitation events, using daily rainfall data for 1966–2015. A Mann–Kendall trend test was used to evaluate trends in extreme precipitation, copula functions were applied to compute the joint return periods of extreme events, and univariate and bivariate distributions were used to determine risk. The results showed that the decrease in consecutive wet days (CWD) was significant in the west and the northwest of Iran, while the consecutive dry days (CDD) index was increasing therein. The precipitation on more than the 90th percentile (P90) very wet days and annual number of days with precipitation less than the 90th percentile threshold (D90) indices followed similar patterns, with no significant trend in most parts of Iran, but at several stations in the north, west, and northwest, their decline was extreme. Furthermore, the increase of D10 (annual number of days with precipitation less than the 90th percentile threshold) and P10 (total precipitation of D10 of a year) was extreme in the wet regions of Iran, including the north, west, and northwest areas, and also part of the center. More than 50 percent of Iran experienced a low risk level, with a return period of extreme events (CWD, CDD) of more than 27.5 years, and the joint return periods of (D10, D90), (P10, P90), and (D10, P10) pairs were less than 100 years in most regions of Iran. Due to the increasing number of dry days in the north, west, and northwest of Iran, the drought risk increased. Based on the changes in extreme precipitation indices in recent years, the findings of this study will be useful for copula-based frequency analysis under a changing environment at regional and global scales.