Dry Spell Length Research Articles

A landscape hydrology approach to inform sustainable water resource management under a changing environment. A case study for the Kaleya River Catchment, Zambia

Study regionKaleya River Catchment in southern Zambia. Study focusThe ability of a landscape hydrology approach to detect controls on water availability in a fragmented landscape to inform interventions under a changing environment was investigated. Simple and measurable climatic and landscape pattern attributes were analysed using change detection, trend analysis and backward variable elimination with Partial Least Squares Regression (PLSR) to identify controls on seasonal river flows and how landscape components could be enhanced to augment natural river flows. New hydrological insights for the regionLandscape pattern showed increasing fragmentation, expansion of irrigated cropland and reservoirs and loss of forestland. Significant increasing trends (p < 0.05) were observed for reference evapotranspiration (ETo), one-day maximum rainfall, coefficient of variation (CV) of rainfall, maximum dry spell length, and start of rains but not annual rainfall. Increased CV of rainfall, rainfall intensity and ETo were the main climatic stressors on river flows. Increased Percentage of Landscape (PLAND) of irrigated cropland, PLAND of reservoirs, Patch Density (PD) and Largest Patch Index (LPI) of reservoirs were the main landscape pattern stressors. Only the LPI of forestland positively explained seasonal river flows. Water resource interventions in the region must adapt more to changing seasonal rainfall characteristics than to annual rainfall totals. Additionally, regeneration of larger forest patches could improve river flows. The approach can be applied in other regions.

Comparison of Trend Preserving Statistical Downscaling Algorithms Toward an Improved Precipitation Extremes Projection in the Headwaters of Blue Nile River in Ethiopia

Projected changes in precipitation extremes can greatly impact the natural environment. Hence, the precipitation extremes must be precisely estimated with an appropriate bias correction algorithm to provide reliable information for the formulation of climate change impact adaptation and mitigation strategies. However, there is a lack of studies that discuss the effect of bias correction algorithms on the reproduction of precipitation extremes in the Blue Nile River Basin. This study compared three commonly used bias correction algorithms: the quantile mapping (QM), detrended QM (DQM), and quantile delta mapping (QDM). The QDM and DQM algorithms outperformed the standard QM bias correction algorithm in preserving the raw climate models projected relative changes of precipitation extremes. The performance differences between the standard QM and other bias correction algorithms (DQM and QDM) were more pronounced in the projection of extreme daily precipitation. Conversely, the projection of dry and wet spells was less sensitive for the choice of the bias correction algorithm. In general, the climate change impact analysis with the QDM algorithm revealed the increase in the frequency and severity of precipitation extremes. Moreover, the results showed the increase (decrease) in the maximum length of dry (wet) spells; indicating the increase in the severity of the meteorological droughts in the future that could potentially reduce the rain-fed agricultural productivity of the region.

Daily gridded temperature and precipitation datasets over the Black Sea catchment: 1961–1990 and climate change scenarios for 2071–2100

This dataset improves on previous products in its spatial resolution, spatial extent and time period covering the Black Sea catchment (BSC). The spatial prediction of daily datasets was performed using the integrated nested Laplace approximation (INLA) methodology. The results show that for minimum and maximum temperature, the model with the elevation and distance to shorelines predictors is the best fitted model. The best fitted model for precipitation is obtained with the elevation predictor. The downscaling of climate change scenarios is based on HIRHAM regional climate model (RCM) from the European project PRUDENCE. The downscaling was made by means of a modified delta method. The modified delta method applied in this study explicitly considers the spatial differences of the climate scenarios and the monthly variability. For each grid point, the delta method is applied according to the rank order of values in the monthly distribution of the closest RCM grid point. The results show that the delta method gives satisfying results when considering the monthly variability. Impacts on minimum temperature, maximum temperature, precipitation, number of days without precipitation, dry spell length, number and length of days above maximum temperature above 30∘C, under conditions of climate change are also examined for this region.

Global Warming Impacts on Severe Drought Characteristics in Asia Monsoon Region

Climate change influences the changes in drought features. This study assesses the changes in severe drought characteristics over the Asian monsoon region responding to 1.5 and 2.0 °C of global average temperature increases above preindustrial levels. Based on the selected 5 global climate models, the drought characteristics are analyzed according to different regional climate zones using the standardized precipitation index. Under global warming, the severity and frequency of severe drought (i.e., SPI &lt;−1.5) are modulated by the changes in seasonal and regional precipitation features regardless of the region. Due to the different regional change trends, global warming is likely to aggravate (or alleviate) severe drought in warm (or dry/cold) climate zones. For seasonal analysis, the ranges of changes in drought severity (and frequency) are −11.5%~6.1% (and −57.1%~23.2%) under 1.5 and 2.0 °C of warming compared to reference condition. The significant decreases in drought frequency are indicated in all climate zones due to the increasing precipitation tendency. In general, drought features under global warming closely tend to be affected by the changes in the amount of precipitation as well as the changes in dry spell length. As the warming enhanced, the spatial variation of drought severity will be increased across climate zones, which can lead to increased water stress over Asia. This study demonstrates that precipitation characteristic changes can explicitly modulate severe regional drought features.

Rainfall and dry spell occurrence in Ghana: trends and seasonal predictions with a dynamical and a statistical model

Improved information on the distribution of seasonal rainfall is important for crop production in Ghana. The predictability of key agro-meteorological indices, namely, seasonal rainfall, maximum dry spell length (MDSL) and dry spell frequency (DSF) was investigated across Ghana (with an interest on the coastal savannah agro-ecological zone). These three variables are relevant for local agricultural water management. A dynamical model (i.e. European Centre for Medium-Range Weather Forecasts (ECMWF) System 4 seasonal forecasts) and a statistical model (i.e. response to sea surface temperatures (SSTs)) were used and analysed using correlation and other discrimination skill metrics. ECMWF-System 4 was bias-corrected and verified with 14 local stations’ observations. Results show that differences in variability and skills of the agro-meteorological indices are small between agro-ecological zones as compared to the differences between stations. The dynamic model System 4 explains up to 31% of the variability of the MDSL and seasonal rainfall indices. Coastal savannah exhibits the highest level of discrimination skills. However, these skills are generally higher for the below and above normal MDSL and seasonal rainfall categories at lead time 0. Similarity in skills for the agro-meteorological indices over the same zones and stations is found both for the dynamical and statistical models. Although System 4 performs slightly better than the statistical model, especially, for dry spell length and seasonal rainfall. For dry spell frequency and longer lead time dry spell length, the statistical model tends to perform better. These results suggest that the agro-meteorological indices derived from System 4′ updated versions, corrected with local observations, together with the response to SST information, can potentially support decision-making of local smallholder farmers in Ghana.

Intensification of the hydrological cycle expected in West Africa over the 21st century

Abstract. This study uses the high-resolution outputs of the recent CORDEX-Africa climate projections to investigate the future changes in different aspects of the hydrological cycle over West Africa. Over the twenty-first century, temperatures in West Africa are expected to increase at a faster rate (+0.5 ∘C per decade) than the global average (+0.3 ∘C per decade), and mean precipitation is expected to increase over the Guinea Coast (+0.03 mm d−1 per decade) but decrease over the Sahel (−0.005 mm d−1 per decade). In addition, precipitation is expected to become more intense (+0.2 mm d−1 per decade) and less frequent (−1.5 d per decade) over all of West Africa as a result of increasing regional temperature (precipitation intensity increases on average by +0.35 mm d−1 ∘C−1 and precipitation frequency decreases on average by −2.2 d ∘C−1). Over the Sahel, the average length of dry spells is also expected to increase with temperature (+4 % d ∘C−1), which increases the likelihood for droughts with warming in this subregion. Hence, the hydrological cycle is expected to increase throughout the twenty-first century over all of West Africa, on average by +11 % ∘C−1 over the Sahel as a result of increasing precipitation intensity and lengthening of dry spells, and on average by +3 % ∘C−1 over the Guinea Coast as a result of increasing precipitation intensity only.

Projected changes in extreme precipitation intensity and dry spell length in Côte d’Ivoire under future climates

This study analyzes projected changes in seasonal extreme precipitation intensity and dry spell length in the investigation area (Cote d’Ivoire) under RCP4.5 and RCP8.5 forcing scenarios. To this end, a multi-model ensemble of fourteen CORDEX-Africa regional climate model simulations is used during the three stages of the West African Monsoon (WAM) season (April–June (AMJ), July–September (JAS), and October–December (OND)). The results indicate that Cote d’Ivoire is subject to a robust increase of cumulative intensity of precipitation associated with an amplification of extreme precipitation events during the WAM. In particular during JAS, a substantial increase in extreme precipitation reaching up to 50–60% compared to the reference mean value prevails in the western and coastal areas in the far future and under the RCP8.5 scenario. In addition, AMJ season is dominated by an increase in dry spell length of about 12% and 17% in the near future and 20% and 30% in the far future in the entire country under RCP4.5 and RCP8.5 scenarios, respectively, albeit considerable uncertainties. OND considered as the post-monsoon season is mostly characterized by a robust decrease in dry spell length more marked in the southwest in the RCP8.5 scenario during the far future. These results suggest that agricultural production and particularly cocoa plantations in the southwestern regions could be at the risk of flooding events and that water stress remains a threat for cocoa, coffee, and other cash crop plantations in the eastern regions.

A Markov Chain-Based Bias Correction Method for Simulating the Temporal Sequence of Daily Precipitation

Bias correction methods are routinely used to correct climate model outputs for hydrological and agricultural impact studies. Even though superior bias correction methods can correct the distribution of daily precipitation amounts, as well as the wet-day frequency, they usually fail to correct the temporal sequence or structure of precipitation occurrence. To solve this problem, we presented a hybrid bias correction method for simulating the temporal sequence of daily precipitation occurrence. We did this by combining a first-order two-state Markov chain with a quantile-mapping (QM) based bias correction method. Specifically, a QM-based method was used to correct the distributional attributes of daily precipitation amounts and the wet-day frequency simulated by climate models. Then, the sequence of precipitation occurrence was simulated using the first-order two-state Markov chain with its parameters adjusted based on linear relationships between QM-corrected mean monthly precipitation and the transition probabilities of precipitation occurrence. The proposed Markov chain-based bias correction (MCBC) method was compared with the QM-based method with respect to reproducing the temporal structure of precipitation occurrence over 10 meteorological stations across China. The results showed that the QM-based method was unable to correct the temporal sequence, with the cumulative frequency of wet- and dry-spell length being considerably underestimated for most stations. The MCBC method can could reproduce the temporal sequence of precipitation occurrence, with the generated cumulative frequency of wet- and dry-spell lengths fitting that of the observation well. The proposed method also performed reasonably well with respect to reproducing the mean, standard deviation, and the longest length of observed wet- and dry-spells. Overall, the MCBC method can simulate the temporal sequence of precipitation occurrence, along with correcting the distributional attributes of precipitation amounts. This method can be used with crop and hydrological models in climate change impact studies at the field and small watershed scales.

Toward understanding 1.5°C global warming influences on temperature and precipitation extremes at different timings: before and after overshooting

To better understand the long-term influences of the 1.5°C target, we provide an assessment of the changes in climate extremes under same warming level but at different timings, based on the magnitude of change, the internal variability, and the model agreement derived from CMIP5. Our findings imply that the changes in climate extremes and their impacts under 1.5°C warming level are projected to be more modest at the stage after overshoot. Specifically, less shares of land area will experience robust increase in extreme hot event and warm spell duration. Potential less cold events after overshooting are also projected. The land areas and population directly experiencing increase in heavy precipitation intensity are projected to slightly increase, whereas more land areas will benefit from the robust decrease in dry spell length. Nevertheless, most of climate extremes analysed in our study will change beyond the upper limit of present-day natural variability.

Analysis of Rainfall and Temperature Trends and Variability in Semi-arid North-eastern Ethiopia

Northeast Ethiopia is a semi-arid region that exhibits high rainfall and temperature variability. The impact of climate change has received a great deal of attention worldwide. This study focused on detecting trends in in rainfall (1983-2013) and temperature (1981-2010) at annual, seasonal and monthly time scales for six weather stations in semi-arid north eastern Ethiopia. The study also aimed to determine dry spell length, number of rainy days, onset and cessation dates, and length of growing period. The non-parametric tests such as Mann-Kendall and Sen’s Slope were used to determine climatic trends. The results indicated that both the annual maximum and minimum temperature in the study region showed an increasing trend, but only the trend for maximum temperature was significant. In the same manner, both the Belg and Kiremt seasons maximum and minimum temperature also showed an increasing trend but only trend of maximum temperature during Belg season and trend of minimum and maximum temperature during Kiremt seasons was significant. The annual and Belg season rainfall in the region showed a decreasing trend and its trend was significant. The coefficient of rainfall variability for annual and Belg season was very high which could affect agricultural production in the region. In the contrary, the trend for Kiremt season rainfall was increasing without significant trend. On the other hand, the average length of dry spell during the Kiremt season in the region was generally long that ranged from 27 days to 39 days and showed both decreasing and increasing but with no significant trend in most stations. The number of annual rainy days also showed both increasing and decreasing trends but its trend was not significant. The length of growing period in the region was relatively stable at all stations during the last 30 years period without non-significant trend. In general, from the analysis of annual, seasonal and monthly rainfall and temperature data series it can be concluded that rainfall and temperature characteristics of the study area is changing, even though some of the trends on both parameters were not statistically significant.

Evaluation of CORDEX Regional Climate Models in Simulating Extreme Dry Spells in Southwest China

In recent decades, Southwest China (SWC) has experienced a series of severe and extensive droughts resulting in tremendous socioeconomic losses. The annual maximum dry spell length (AMDSL), which refers to the number of consecutive days without rainfall, or days with rainfall below a threshold, plays an important role in triggering drought. The main objective of this study is to provide a comparison of the capabilities of current regional climate models (RCMs) in simulating extreme dry spell characteristics in mountainous SWC. Five available RCM simulations utilized in the Coordinated Regional Climate Downscaling Experiment (CORDEX)-East Asia project over 1981 to 2005 were employed in this study; the RCMs being NIMR-HadGEM3, SNU-MM5, SNU-WRF, KNU-RegCM4 and YSU-RSM. Firstly, it was found that all RCMs reasonably simulate the main seasonality features of rainfall and dry days in SWC. Furthermore, besides YSU-RSM, the other four RCMs can accurately capture the spatial pattern of dry-day occurrence based on Taylor diagram diagnosis. Secondly, we assessed the performance of five RCMs to detect and reproduce the climatology and variability of the AMDSL. In general, RCMs simulate the spatial pattern of long-term mean and interannual variability of the AMDSL in SWC well. Based on Taylor diagram evaluation, NIMR-HadGEM3 was the best among 5 RCMs in simulating the AMDSL characteristics. Thirdly, the generalized extreme value (GEV) distribution is considered the most suitable model fitting the AMDSL in both observation and RCM experimental data in comparison to other three-parameter probabilistic models. Higher value centers of the scale parameter and the location parameter indicate a wider and amplified distribution of the AMDSL over the low-latitude highlands (LLH) region against other areas in Southwest China, which is consistent with spatial patterns of climatological AMDSL. In addition, sensitivity analysis of different thresholds for dry days show that the 1 mm threshold is suitable for this study and different threshold choices have little effect on simulation ability.

Dry spells and probability of rainfall occurrence for Lake Kyoga Basin in Uganda, East Africa

The economy of Uganda depends heavily on rainfed agriculture. In this study, daily observed rainfall datasets from 9 weather stations with length varying within 1955 and 2017 were used to generate the probability of rainfall and dry spells occurrence using a Markov chain approach. The length of the maximum dry spell was obtained using the direct method based on the definition of a dry day that rainfall is less than 0.85 mm (R < 0.85 mm) and the length of a dry spell is the sum of the number of dry days in a sequence. Mann–Kendall’s statistics (MK) was used to assess the trends in the length of maximum dry spells and Sen’s slope test to estimate the magnitude of change (Q2) in days/per month. MK test results show increasing trends in the length of the maximum dry spells in March at 5 stations, while an insignificant decrease in the length of maximum dry spells is revealed for remaining stations. For the month of April and May, the length of a maximum dry spell is observed to be decreasing across most stations although not statistically significant at the 5% significance level during their respective study periods. The probability of 8 days dry spell is high across all the stations (38–69%) in March, April, and August. This could strongly be related to the changing climate in the region. Negative impacts due to increased length of dry spells could be mitigated through well-timed planting of crops, use of irrigation, and growing of heat-/drought-tolerant crop varieties to match the changing weather and climate patterns.

Increasing impacts from extreme precipitation on population over China with global warming

Precipitation-related extremes are among the most impact-relevant consequences of a warmer climate, particularly for China, a region vulnerable to global warming and with a large population. Understanding the impacts and risks induced by future extreme precipitation changes is critical for mitigation and adaptation planning. Here, extreme precipitation changes under different levels of global warming and their associated impacts on populations in China are investigated using multimodel climate projections from the Coupled Model Intercomparison Project Phase 5 and population projections under Shared Socio-economic Pathways. Heavy precipitation would intensify with warming across China at a rate of 6.52% (5.22%–8.57%) per degree of global warming. The longest dry spell length would increase (decrease) south (north) of ~34°N. The low warming target of the Paris Agreement could substantially reduce the extreme precipitation related impacts compared to higher warming levels. For the area weighted average changes, the intensification in wet extremes could be reduced by 3.22%, 9.42% and 16.70% over China, and the lengthening of dry spells could be reduced by 0.72%, 4.75% and 5.31% in southeastern China, respectively, if global warming is limited to 1.5 °C as compared to 2, 3 and 4 °C. The Southeastern China is the hotspot of enhanced impacts due to the dense population. The impacts on populations induced by extreme precipitation changes are dominated by climate change, while future population redistribution plays a minor role.

Projected local rain events due to climate change and the impacts on waterborne diseases in Vancouver, British Columbia, Canada

BackgroundClimate change is increasing the number and intensity of extreme weather events in many parts of the world. Precipitation extremes have been linked to both outbreaks and sporadic cases of waterborne illness. We have previously shown a link between heavy rain and turbidity to population-level risk of sporadic cryptosporidiosis and giardiasis in a major Canadian urban population. The risk increased with 30 or more dry days in the 60 days preceding the week of extreme rain. The goal of this study was to investigate the change in cryptosporidiosis and giardiasis risk due to climate change, primarily change in extreme precipitation.MethodsCases of cryptosporidiosis and giardiasis were extracted from a reportable disease system (1997–2009). We used distributed lag non-linear Poisson regression models and projections of the exposure-outcome relationship to estimate future illness (2020–2099). The climate projections are derived from twelve statistically downscaled regional climate models. Relative Concentration Pathway 8.5 was used to project precipitation derived from daily gridded weather observation data (~ 6 × 10 km resolution) covering the central of three adjacent watersheds serving metropolitan Vancouver for the 2020s, 2040s, 2060s and 2080s.ResultsPrecipitation is predicted to steadily increase in these watersheds during the wet season (Oct. -Mar.) and decrease in other parts of the year up through the 2080s. More weeks with extreme rain (>90th percentile) are expected. These weeks are predicted to increase the annual rates of cryptosporidiosis and giardiasis by approximately 16% by the 2080s corresponding to an increase of 55–136 additional cases per year depending upon the climate model used. The predicted increase in the number of waterborne illness cases are during the wet months. The range in future projections compared to historical monthly case counts typically differed by 10–20% across climate models but the direction of change was consistent for all models.DiscussionIf new water filtration measures had not been implemented in our study area in 2010–2015, the risk of cryptosporidiosis and giardiasis would have been expected to increase with climate change, particularly precipitation changes. In addition to the predicted increase in the frequency and intensity of extreme precipitation events, the frequency and length of wet and dry spells could also affect the risk of waterborne diseases as we observed in the historical period. These findings add to the growing evidence regarding the need to prepare water systems to manage and become resilient to climate change-related health risks.

Evaluation of Daily Gridded Meteorological Datasets over the Niger Delta Region of Nigeria and Implication to Water Resources Management

Hydro-climatological study is difficult in most of the developing countries due to the paucity of monitoring stations. Gridded climatological data provides an opportunity to extrapolate climate to areas without monitoring stations based on their ability to replicate the Spatio-temporal distribution and variability of observed datasets. Simple correlation and error analyses are not enough to predict the variability and distribution of precipitation and temperature. In this study, the coefficient of correlation (R2), Root mean square error (RMSE), mean bias error (MBE) and mean wet and dry spell lengths were used to evaluate the performance of three widely used daily gridded precipitation, maximum and minimum temperature datasets from the Climatic Research Unit (CRU), Princeton University Global Meteorological Forcing (PGF) and Climate Forecast System Reanalysis (CFSR) datasets available over the Niger Delta part of Nigeria. The Standardised Precipitation Index was used to assess the confidence of using gridded precipitation products on water resource management. Results of correlation, error, and spell length analysis revealed that the CRU and PGF datasets performed much better than the CFSR datasets. SPI values also indicate a good association between station and CRU precipitation products. The CFSR datasets in comparison with the other data products in many years overestimated and underestimated the SPI. This indicates weak accuracy in predictability, hence not reliable for water resource management in the study area. However, CRU data products were found to perform much better in most of the statistical assessments conducted. This makes the methods used in this study to be useful for the assessment of various gridded datasets in various hydrological and climatic applications.

Analysis of Rainfall Variability for Mekelle Meteorological Station, Northern Ethiopia (1960-2009)

Nowadays rainfall variability is threatening the world. Despite this, studies related to the determination of variations, trends and fluctuations of rainfall have little local and regional specificity. Hence, this study aimed to analyze the trend and variability of the annual and seasonal rainfall occurrence at Mekelle Airport meteorological station, Northern Ethiopia. Historical rainfall data for the period 1960-2009 of the rain gauge station were obtained from National Meteorological Service Agency. Annual, seasonal and monthly rainfall trends; onset and cessation date of kiremt rainfall; growing period length and the number of rainy days and dry spell length were considered. Standardize anomaly index (SAI) and coefficient of variance was employed to analyze the rainfall data variability using INSTAT plus, SPSS version 20 and Excel. Analysis of the historical data showed extremely severe drought happened in the area in 1984 which resulted in the death of one million people, 8 million people highly affected, and 2.3 million people food in secured. 1961 was an extremely wet year. Generally, 70%, 18% and 12% years were normal, dry and wet respectively for the last 50 years. High annual, seasonal and monthly rainfall variability was observed. Though the station showed both increasing (1984-1988) and decreasing trends (1961-1968) of annual rainfall totals a declining trend was more pronounced. Besides, one to two weeks variability on the onset and cessation date was observed. The minimum and maximum kiremt season rainy days recorded in 1987 (27days) and 1986 (86 days) respectively while the maximum dry spell days recorded in the 50 years was in 1969. Thus, devising agronomic practices that retain moisture at the plant root zone and reduce crop failure due to variability of rainfall including extended dry spell could be important. Keywords Rainfall variability, trends, standard anomaly index, Mekelle DOI : 10.7176/CER/11-9-02 Publication date :October 31 st 2019

A Stochastic Approach for the Analysis of Long Dry Spells with Different Threshold Values in Southern Italy

A non-homogeneous Poisson model was proposed to analyze the sequences of dry spells below prefixed thresholds as an upgrade of a stochastic procedure previously used to describe long periods of no rainfall. Its application concerned the daily precipitation series in a 60-year time span at four rain gauges (Calabria, southern Italy), aiming at testing the different behaviors of the dry spells below prefixed thresholds in two paired periods (1951–1980 and 1981–2010). A simulation analysis performed through a Monte Carlo approach assessed the statistical significance of the variation of the mean values of dry spells observed at an annual scale in the two 30-year periods. The results evidenced that the dry spells durations increased passing from the first 30-year period to the second one for all the thresholds analyzed. For instance, for the Cassano station, an increase of about 10% of the maximum dry spell duration was detected for a threshold of 5 mm. Moreover, the return periods evaluated for fixed long dry spells through the synthetic data of the period 1981–2010 were lower than the corresponding ones evaluated with the data generated for the previous 30-year period. Specifically, the difference between the two 30-year periods in terms of the return period of long dry spells occurrence increased with the growing thresholds. As an example, for the Cosenza rain gauge with a threshold of 1 mm, the return period for a dry spell length of 70 days decreased from 20 years (in 1951–1980) to about 10 years (in 1981–2010), while for a threshold of 5 mm, the return period for the dry spell lengths of 120 days decreases from 70 years to about 20 years. These results show a higher probability of the occurrence of long dry spells in the more recent period than in the past.

Identifying changing precipitation extremes in Sub-Saharan Africa with gauge and satellite products

Sparse gauge networks in Sub-Saharan Africa (SSA) limit our ability to identify changing precipitation extremes with in situ observations. Given the potential for satellite and satellite-gauge precipitation products to help, we investigate how daily gridded gauge and satellite products compare for seven core climate change precipitation indices. According to a new gauge-only product, the Rainfall Estimates on a Gridded Network (REGEN), there were notable changes in SSA precipitation characteristics between 1950 and 2013 in well-gauged areas. We examine these trends and how these vary for wet, intermediate, and dry areas. For a 31 year period of overlap, we compare REGEN data, other gridded products and three satellite products. Then for 1998–2013, we compare a set of 12 satellite products. Finally, we compare spatial patterns of 1983–2013 trends across all of SSA. Robust 1950–2013 trends indicate that in well-gauged areas extreme events became wetter, particularly in wet areas. Annual totals decreased due to fewer rain days. Between 1983 and 2013 there were positive trends in average precipitation intensity and annual maximum 1 d totals. These trends only represent 15% of SSA, however, and only one tenth of the main wet areas. Unfortunately, gauge and satellite products do not provide consensus for wet area trends. A promising result for identifying regional changes is that numerous satellite products do well at interannual variations in precipitation totals and number of rain days, even as well as some gauge-only products. Products are less accurate for dry spell length and average intensity and least accurate for annual maximum 1 d totals. Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (3B42-V7) and Climate Hazards center Infrared Precipitation with Stations (CHIRPS v2.0) ranked highest for multiple indices. Several products have seemingly unrealistic trends outside of the well-gauged areas that may be due to influence of non-stationary systematic biases. Social media abstract. Sparse data show increasing Africa rainfall extremes and satellite products fill some missing pieces.

Warm Season Hydroclimatic Variability and Change in the Appalachian Region of the Southeastern U.S. from 1950 to 2018

The hydroclimatology of the southeastern U.S. is changing, with increased precipitation, intensified summer-to-fall rainfall, and prolonged dry periods. However, research has yet to determine whether these trends are present in the southern Appalachian Mountains, which contain the most topographic and hydroclimatic variability across the region and serve as a valuable resource for growing population centers. This study examines warm season hydroclimatic variability and changes from 1950 to 2018 using the Global Historical Climatological Network (GHCN) daily data record. Daily rainfall is classified according to different intensities, ranging from light to heavy, and a Mann-Kendall test is used to determine the trend at each station. Additionally, a Spearman’s rank correlation test is performed to test for significant linkages between precipitation class frequencies and large-scale modes of atmospheric variability (Atlantic Multidecadal Oscillation, Caribbean SST Index, Pacific/North American Pattern). The results suggest that dry days became less common and light precipitation became more common across the southern Appalachian region. Similarly, the length of dry spells became shorter at most elevations. Teleconnection patterns are linked to the variability of precipitation class frequencies, particularly with dry days and light precipitation. In conclusion, this research reveals the unique character of hydroclimatic variability and change across the southern Appalachian region in the context of the broader southeastern U.S.

An intercomparison of multiple statistical downscaling methods for daily precipitation and temperature over China: present climate evaluations

In this study, we use four statistical downscaling methods to statistically downscale seven Coupled Model Intercomparison Project (CMIP5) Global Climate Models (GCMs) and project the changes in precipitation and temperature over China under RCP4.5 and RCP8.5 emission scenarios. The four statistical downscaling methods are bias-correction and spatial downscaling (BCSD), bias-correction and climate imprint (BCCI), bias correction constructed analogues with quantile mapping reordering (BCCAQ), and cumulative distribution function transform (CDF-t). Though large inter-model variability exists in the distribution and magnitude of changes in projected precipitation, particularly for wet spell length (CWD), all downscaling methods generally project a consistent enhancement of precipitation in both summer and winter over most parts of China. For the arid and semiarid Northwest China, the shortened dry spell length (CDD) is accompanied by the pronouncedly intensified very wet days (R95p), as well as the increase in maximum 5-day precipitation amount (Rx5day). In contrast, southeastern regions may experience more consecutive dry days and more severe wet precipitation extremes. The projected changes from different downscaling techniques are fairly similar for temperature, apart from the diurnal temperature range for BCSD. Warming is projected across the whole domain with larger magnitude over the north and in winter under RCP8.5. More summer days and fewer frost days would appear in the future. The bias correction components of downscaling methods cause a higher degree of agreement among models, and the downscaled results generally retain the main climate change signal of the driving models.