Showing papers in "Theoretical and Applied Climatology in 2018"

Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: case study in North Iran

Abstract: An accurate computational approach for the prediction of pan evaporation over daily time horizons is a useful decisive tool in sustainable agriculture and hydrological applications, particularly in designing the rural water resource systems, water use allocations, utilization and demand assessments, and the management of irrigation systems. In this study, a hybrid predictive model (Multilayer Perceptron-Firefly Algorithm (MLP-FFA)) based on the FFA optimizer that is embedded within the MLP technique is developed and evaluated for its suitability for the prediction of daily pan evaporation. To develop the hybrid MLP-FFA model, the pan evaporation data measured between 2012 and 2014 for two major meteorological stations (Talesh and Manjil) located at Northern Iran are employed to train and test the predictive model. The ability of the hybrid MLP-FFA model is compared with the traditional MLP and support vector machine (SVM) models. The results are evaluated using five performance criteria metrics: root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NS), and the Willmott’s Index (WI). Taylor diagrams are also used to examine the similarity between the observed and predicted pan evaporation data in the test period. Results show that an optimal MLP-FFA model outperforms the MLP and SVM model for both tested stations. For Talesh, a value of WI = 0.926, NS = 0.791, and RMSE = 1.007 mm day−1 is obtained using MLP-FFA model, compared with 0.912, 0.713, and 1.181 mm day−1 (MLP) and 0.916, 0.726, and 1.153 mm day−1 (SVM), whereas for Manjil, a value of WI = 0.976, NS = 0.922, and 1.406 mm day−1 is attained that contrasts 0.972, 0.901, and 1.583 mm day−1 (MLP) and 0.971, 0.893, and 1.646 mm day−1 (SVM). The results demonstrate the importance of the Firefly Algorithm applied to improve the performance of the MLP-FFA model, as verified through its better predictive performance compared to the MLP and SVM model.

A comparison between ten advanced and soft computing models for groundwater qanat potential assessment in Iran using R and GIS

Abstract: Considering the unstable condition of water resources in Iran and many other countries in arid and semi-arid regions, groundwater studies are very important. Therefore, the aim of this study is to model groundwater potential by qanat locations as indicators and ten advanced and soft computing models applied to the Beheshtabad Watershed, Iran. Qanat is a man-made underground construction which gathers groundwater from higher altitudes and transmits it to low land areas where it can be used for different purposes. For this purpose, at first, the location of the qanats was detected using extensive field surveys. These qanats were classified into two datasets including training (70%) and validation (30%). Then, 14 influence factors depicting the region’s physical, morphological, lithological, and hydrological features were identified to model groundwater potential. Linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), flexible discriminant analysis (FDA), penalized discriminant analysis (PDA), boosted regression tree (BRT), random forest (RF), artificial neural network (ANN), K-nearest neighbor (KNN), multivariate adaptive regression splines (MARS), and support vector machine (SVM) models were applied in R scripts to produce groundwater potential maps. For evaluation of the performance accuracies of the developed models, ROC curve and kappa index were implemented. According to the results, RF had the best performance, followed by SVM and BRT models. Our results showed that qanat locations could be used as a good indicator for groundwater potential. Furthermore, altitude, slope, plan curvature, and profile curvature were found to be the most important influence factors. On the other hand, lithology, land use, and slope aspect were the least significant factors. The methodology in the current study could be used by land use and terrestrial planners and water resource managers to reduce the costs of groundwater resource discovery.

Spatio-temporal trends of rainfall across Indian river basins

Abstract: Daily gridded high-resolution rainfall data of India Meteorological Department at 0.25° spatial resolution (1901–2015) was analyzed to detect the trend in seasonal, annual, and maximum cumulative rainfall for 1, 2, 3, and 5 days. The present study was carried out for 85 river basins of India during 1901–2015 and pre- and post-urbanization era, i.e., 1901–1970 and 1971–2015, respectively. Mann–Kendall (α = 0.05) and Theil–Sen’s tests were employed for detecting the trend and percentage of change over the period of time, respectively. Daily extreme rainfall events, above 95 and 99 percentile threshold, were also analyzed to detect any trend in their magnitude and number of occurrences. The upward trend was found for the majority of the sub-basins for 1-, 2-, 3-, and 5-day maximum cumulative rainfall during the post-urbanization era. The magnitude of extreme threshold events is also found to be increasing in the majority of the river basins during the post-urbanization era. A 30-year moving window analysis further revealed a widespread upward trend in a number of extreme threshold rainfall events possibly due to urbanization and climatic factors. Overall trends studied against intra-basin trend across Ganga basin reveal the mixed pattern of trends due to inherent spatial heterogeneity of rainfall, therefore, highlighting the importance of scale for such studies.

Climate change scenarios of heat waves in Central Europe and their uncertainties

Abstract: The study examines climate change scenarios of Central European heat waves with a focus on related uncertainties in a large ensemble of regional climate model (RCM) simulations from the EURO-CORDEX and ENSEMBLES projects. Historical runs (1970–1999) driven by global climate models (GCMs) are evaluated against the E-OBS gridded data set in the first step. Although the RCMs are found to reproduce the frequency of heat waves quite well, those RCMs with the coarser grid (25 and 50 km) considerably overestimate the frequency of severe heat waves. This deficiency is improved in higher-resolution (12.5 km) EURO-CORDEX RCMs. In the near future (2020–2049), heat waves are projected to be nearly twice as frequent in comparison to the modelled historical period, and the increase is even larger for severe heat waves. Uncertainty originates mainly from the selection of RCMs and GCMs because the increase is similar for all concentration scenarios. For the late twenty-first century (2070–2099), a substantial increase in heat wave frequencies is projected, the magnitude of which depends mainly upon concentration scenario. Three to four heat waves per summer are projected in this period (compared to less than one in the recent climate), and severe heat waves are likely to become a regular phenomenon. This increment is primarily driven by a positive shift of temperature distribution, but changes in its scale and enhanced temporal autocorrelation of temperature also contribute to the projected increase in heat wave frequencies.

Evaluating the performance of ENVI-met model in diurnal cycles for different meteorological conditions

Abstract: Urban areas are known to modify meteorological variables producing important differences in small spatial scales (i.e. microscale). These affect human thermal comfort conditions and the dispersion of pollutants, especially those emitted inside the urban area, which finally influence quality of life and the use of public open spaces. In this study, the diurnal evolution of meteorological variables measured in four urban spaces is compared with the results provided by ENVI-met (v 4.0). Measurements were carried out during 3 days with different meteorological conditions in Bilbao in the north of the Iberian Peninsula. The evaluation of the model accuracy (i.e. the degree to which modelled values approach measured values) was carried out with several quantitative difference metrics. The results for air temperature and humidity show a good agreement of measured and modelled values independently of the regional meteorological conditions. However, in the case of mean radiant temperature and wind speed, relevant differences are encountered highlighting the limitation of the model to estimate these meteorological variables precisely during diurnal cycles, in the considered evaluation conditions (sites and weather).

Observed rainfall trends and precipitation uncertainty in the vicinity of the Mediterranean, Middle East and North Africa

Abstract: The present study investigates the century-long and more recent rainfall trends over the greater region of Middle East and North Africa (MENA). Five up-to-date gridded observational datasets are employed. Besides mean annual values, trends of six indices of drought and extreme precipitation are also considered in the analysis. Most important findings include the significant negative trends over the Maghreb, Levant, Arabian Peninsula, and Sahel regions that are evident since the beginning of the twentieth century and are more or less extended to today. On the other hand, for some Mediterranean regions such as the Balkans and the Anatolian Plateau, precipitation records during the most recent decades indicate a significant increasing trend and a recovering from the dry conditions that occurred during the mid-1970s and mid-1980s. The fact that over parts of the study region the selected datasets were found to have substantial differences in terms of mean climate, trends, and interannual variability, motivated the more thorough investigation of the precipitation observational uncertainty. Several aspects, such as annual and monthly mean climatologies and also discrepancies in the monthly time-series distribution, are discussed using common methods in the field of climatology but also more sophisticated, nonparametric approaches such as the Kruskal–Wallis and Dunn’s tests. Results indicate that in the best case, the data sources are found to have statistically significant differences in the distribution of monthly precipitation for about 50% of the study region extent. This percentage is increased up to 70% when particular datasets are compared. Indicatively, the range between the tested rainfall datasets is found to be more than 20% of their mean annual values for most of the extent of MENA, while locally, for the hyper-arid regions, this percentage is increased up to 100%. Precipitation observational uncertainty is also profound for parts of southern Europe. Outlier datasets over individual regions are identified in order to be more cautiously used in future regional climate studies.

Performance of CMORPH, TMPA, and PERSIANN rainfall datasets over plain, mountainous, and glacial regions of Pakistan

Abstract: The present study aims at the assessment of six satellite rainfall estimates (SREs) in Pakistan. For each assessed products, both real-time (RT) and post adjusted (Adj) versions are considered to highlight their potential benefits in the rainfall estimation at annual, monthly, and daily temporal scales. Three geomorphological climatic zones, i.e., plain, mountainous, and glacial are taken under considerations for the determination of relative potentials of these SREs over Pakistan at global and regional scales. All SREs, in general, have well captured the annual north-south rainfall decreasing patterns and rainfall amounts over the typical arid regions of the country. Regarding the zonal approach, the performance of all SREs has remained good over mountainous region comparative to arid regions. This poor performance in accurate rainfall estimation of all the six SREs over arid regions has made their use questionable in these regions. Over glacier region, all SREs have highly overestimated the rainfall. One possible cause of this overestimation may be due to the low surface temperature and radiation absorption over snow and ice cover, resulting in their misidentification with rainy clouds as daily false alarm ratio has increased from mountainous to glacial regions. Among RT products, CMORPH-RT is the most biased product. The Bias was almost removed on CMORPH-Adj thanks to the gauge adjustment. On a general way, all Adj versions outperformed their respective RT versions at all considered temporal scales and have confirmed the positive effects of gauge adjustment. CMORPH-Adj and TMPA-Adj have shown the best agreement with in situ data in terms of Bias, RMSE, and CC over the entire study area.

Modified physiologically equivalent temperature—basics and applications for western European climate

Abstract: A new thermal index, the modified physiologically equivalent temperature (mPET) has been developed for universal application in different climate zones The mPET has been improved against the weaknesses of the original physiologically equivalent temperature (PET) by enhancing evaluation of the humidity and clothing variability The principles of mPET and differences between original PET and mPET are introduced and discussed in this study Furthermore, this study has also evidenced the usability of mPET with climatic data in Freiburg, which is located in Western Europe Comparisons of PET, mPET, and Universal Thermal Climate Index (UTCI) have shown that mPET gives a more realistic estimation of human thermal sensation than the other two thermal indices (PET, UTCI) for the thermal conditions in Freiburg Additionally, a comparison of physiological parameters between mPET model and PET model (Munich Energy Balance Model for Individual, namely MEMI) is proposed The core temperatures and skin temperatures of PET model vary more violently to a low temperature during cold stress than the mPET model It can be regarded as that the mPET model gives a more realistic core temperature and mean skin temperature than the PET model Statistical regression analysis of mPET based on the air temperature, mean radiant temperature, vapor pressure, and wind speed has been carried out The R square (0995) has shown a well co-relationship between human biometeorological factors and mPET The regression coefficient of each factor represents the influence of the each factor on changing mPET (ie, ±1 °C of T a = ± 054 °C of mPET) The first-order regression has been considered predicting a more realistic estimation of mPET at Freiburg during 2003 than the other higher order regression model, because the predicted mPET from the first-order regression has less difference from mPET calculated from measurement data Statistic tests recognize that mPET can effectively evaluate the influences of all human biometeorological factors on thermal environments Moreover, a first-order regression function can also predict the thermal evaluations of the mPET by using human biometeorological factors in Freiburg

Daily precipitation grids for Austria since 1961—development and evaluation of a spatial dataset for hydroclimatic monitoring and modelling

Abstract: Spatial precipitation datasets that are long-term consistent, highly resolved and extend over several decades are an increasingly popular basis for modelling and monitoring environmental processes and planning tasks in hydrology, agriculture, energy resources management, etc. Here, we present a grid dataset of daily precipitation for Austria meant to promote such applications. It has a grid spacing of 1 km, extends back till 1961 and is continuously updated. It is constructed with the classical two-tier analysis, involving separate interpolations for mean monthly precipitation and daily relative anomalies. The former was accomplished by kriging with topographic predictors as external drift utilising 1249 stations. The latter is based on angular distance weighting and uses 523 stations. The input station network was kept largely stationary over time to avoid artefacts on long-term consistency. Example cases suggest that the new analysis is at least as plausible as previously existing datasets. Cross-validation and comparison against experimental high-resolution observations (WegenerNet) suggest that the accuracy of the dataset depends on interpretation. Users interpreting grid point values as point estimates must expect systematic overestimates for light and underestimates for heavy precipitation as well as substantial random errors. Grid point estimates are typically within a factor of 1.5 from in situ observations. Interpreting grid point values as area mean values, conditional biases are reduced and the magnitude of random errors is considerably smaller. Together with a similar dataset of temperature, the new dataset (SPARTACUS) is an interesting basis for modelling environmental processes, studying climate change impacts and monitoring the climate of Austria.

Variability of extreme weather events over the equatorial East Africa, a case study of rainfall in Kenya and Uganda

Abstract: This study investigates the variability of extreme rainfall events over East Africa (EA), using indices from the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). The analysis was based on observed daily rainfall from 23 weather stations, with length varying within 1961 and 2010. The indices considered are: wet days (R ≥1 mm), annual total precipitation in wet days (PRCPTOT), simple daily intensity index (SDII), heavy precipitation days (R ≥ 10 mm), very heavy precipitation days (R ≥ 20 mm), and severe precipitation (R ≥ 50 mm). The non-parametric Mann-Kendall statistical analysis was carried out to identify trends in the data. Temporal precipitation distribution was different from station to station. Almost all indices considered are decreasing with time. The analysis shows that the PRCPTOT, very heavy precipitation, and severe precipitation are generally declining insignificantly at 5 % significant level. The PRCPTOT is evidently decreasing over Arid and Semi-Arid Land (ASAL) as compared to other parts of EA. The number of days that recorded heavy rainfall is generally decreasing but starts to rise in the last decade although the changes are insignificant. Both PRCPTOT and heavy precipitation show a recovery in trend starting in the 1990s. The SDII shows a reduction in most areas, especially the in ASAL. The changes give a possible indication of the ongoing climate variability and change which modify the rainfall regime of EA. The results form a basis for further research, utilizing longer datasets over the entire region to reduce the generalizations made herein. Continuous monitoring of extreme events in EA is critical, given that rainfall is projected to increase in the twenty-first century.

Evaluation of rainfall simulations over West Africa in dynamically downscaled CMIP5 global circulation models

Abstract: This study presents evaluation of the ability of Rossby Centre Regional Climate Model (RCA4) driven by nine global circulation models (GCMs), to skilfully reproduce the key features of rainfall climatology over West Africa for the period of 1980-2005. The seasonal climatology and annual cycle of the RCA4 simulations were assessed over three homogenous subregions of West Africa (Guinea coast, Savannah, and Sahel) and evaluated using observed precipitation data from the Global Precipitation Climatology Project (GPCP). Furthermore, the model output was evaluated using a wide range of statistical measures. The interseasonal and interannual variability of the RCA4 were further assessed over the subregions and the whole of the West Africa domain. Results indicate that the RCA4 captures the spatial and interseasonal rainfall pattern adequately but exhibits a weak performance over the Guinea coast. Findings from the interannual rainfall variability indicate that the model performance is better over the larger West Africa domain than the subregions. The largest difference across the RCA4 simulated annual rainfall was found in the Sahel. Result from the Mann–Kendall test showed no significant trend for the 1980–2005 period in annual rainfall either in GPCP observation data or in the model simulations over West Africa. In many aspects, the RCA4 simulation driven by the HadGEM2-ES perform best over the region. The use of the multimodel ensemble mean has resulted to the improved representation of rainfall characteristics over the study domain.

Understanding the impacts of climate change and human activities on streamflow: a case study of the Soan River basin, Pakistan

Abstract: Climate change and land use change are the two main factors that can alter the catchment hydrological process. The objective of this study is to evaluate the relative contribution of climate change and land use change to runoff change of the Soan River basin. The Mann-Kendal and the Pettit tests are used to find out the trends and change point in hydroclimatic variables during the period 1983–2012. Two different approaches including the abcd hydrological model and the Budyko framework are then used to quantify the impact of climate change and land use change on streamflow. The results from both methods are consistent and show that annual runoff has significantly decreased with a change point around 1997. The decrease in precipitation and increases in potential evapotranspiration contribute 68% of the detected change while the rest of the detected change is due to land use change. The land use change acquired from Landsat shows that during post-change period, the agriculture has increased in the Soan basin, which is in line with the positive contribution of land use change to runoff decrease. This study concludes that aforementioned methods performed well in quantifying the relative contribution of land use change and climate change to runoff change.

Compound summer temperature and precipitation extremes over central Europe

Abstract: Reliable knowledge of the near-future climate change signal of extremes is important for adaptation and mitigation strategies. Especially compound extremes, like heat and drought occurring simultaneously, may have a greater impact on society than their univariate counterparts and have recently become an active field of study. In this paper, we use a 12-member ensemble of high-resolution (7 km) regional climate simulations with the regional climate model COSMO-CLM over central Europe to analyze the climate change signal and its uncertainty for compound heat and drought extremes in summer by two different measures: one describing absolute (i.e., number of exceedances of absolute thresholds like hot days), the other relative (i.e., number of exceedances of time series intrinsic thresholds) compound extreme events. Changes are assessed between a reference period (1971–2000) and a projection period (2021–2050). Our findings show an increase in the number of absolute compound events for the whole investigation area. The change signal of relative extremes is more region-dependent, but there is a strong signal change in the southern and eastern parts of Germany and the neighboring countries. Especially the Czech Republic shows strong change in absolute and relative extreme events.

Response of vegetation NDVI to climatic extremes in the arid region of Central Asia: a case study in Xinjiang, China

Abstract: Observed data showed the climatic transition from warm-dry to warm-wet in Xinjiang during the past 30 years and will probably affect vegetation dynamics. Here, we analyze the interannual change of vegetation index based on the satellite-derived normalized difference vegetation index (NDVI) with temperature and precipitation extreme over the Xinjiang, using the 8-km NDVI third-generation (NDVI3g) from the Global Inventory Modelling and Mapping Studies (GIMMS) from 1982 to 2010. Few previous studies analyzed the link between climate extremes and vegetation response. From the satellite-based results, annual NDVI significantly increased in the first two decades (1981–1998) and then decreased after 1998. We show that the NDVI decrease over the past decade may conjointly be triggered by the increases of temperature and precipitation extremes. The correlation analyses demonstrated that the trends of NDVI was close to the trend of extreme precipitation; that is, consecutive dry days (CDD) and torrential rainfall days (R24) positively correlated with NDVI during 1998–2010. For the temperature extreme, while the decreases of NDVI correlate positively with warmer mean minimum temperature (Tnav), it correlates negatively with the number of warmest night days (Rwn). The results suggest that the climatic extremes have possible negative effects on the ecosystem.

The microscale cooling effects of water sensitive urban design and irrigation in a suburban environment

Abstract: Prolonged drought has threatened traditional potable urban water supplies in Australian cities, reducing capability to adapt to climate change and mitigate against extreme. Integrated urban water management (IUWM) approaches, such as water sensitive urban design (WSUD), reduce the reliance on centralised potable water supply systems and provide a means for retaining water in the urban environment through stormwater harvesting and reuse. This study examines the potential for WSUD to provide cooling benefits and reduce human exposure and heat stress and thermal discomfort. A high-resolution observational field campaign, measuring surface level microclimate variables and remotely sensed land surface characteristics, was conducted in a mixed residential suburb containing WSUD in Adelaide, South Australia. Clear evidence was found that WSUD features and irrigation can reduce surface temperature (T s) and air temperature (T a) and improve human thermal comfort (HTC) in urban environments. The average 3 pm T a near water bodies was found to be up to 1.8 °C cooler than the domain maximum. Cooling was broadly observed in the area 50 m downwind of lakes and wetlands. Design and placement of water bodies were found to affect their cooling effectiveness. HTC was improved by proximity to WSUD features, but shading and ventilation were also effective at improving thermal comfort. This study demonstrates that WSUD can be used to cool urban microclimates, while simultaneously achieving other environmental benefits, such as improved stream ecology and flood mitigation.

Regional climate assessment of precipitation and temperature in Southern Punjab (Pakistan) using SimCLIM climate model for different temporal scales

Abstract: Unbalanced climate during the last decades has created spatially alarming and destructive situations in the world. Anomalies in temperature and precipitation enhance the risks for crop production in large agricultural region (especially the Southern Punjab) of Pakistan. Detailed analysis of historic weather data (1980–2011) record helped in creating baseline data to compare with model projection (SimCLIM) for regional level. Ensemble of 40 GCMs used for climatic projections with greenhouse gas (GHG) representative concentration pathways (RCP-4.5, 6.0, 8.5) was selected on the baseline comparison and used for 2025 and 2050 climate projection. Precipitation projected by ensemble and regional weather observatory at baseline showed highly unpredictable nature while both temperature extremes showed 95 % confidence level on a monthly projection. Percentage change in precipitation projected by model with RCP-4.5, RCP-6.0, and RCP-8.5 showed uncertainty 3.3 to 5.6 %, 2.9 to 5.2 %, and 3.6 to 7.9 % for 2025 and 2050, respectively. Percentage change of minimum temperature from base temperature showed that 5.1, 4.7, and 5.8 % for 2025 and 9.0, 8.1, and 12.0 % increase for projection year 2050 with RCP-4.5, 6.0, and 8.5 and maximum temperature 2.7, 2.5, and 3.0 % for 2025 and 4.7, 4.4, and 6.4 % for 2050 will be increased with RCP-4.5, 6.0, and 8.5, respectively. Uneven increase in precipitation and asymmetric increase in temperature extremes in future would also increase the risk associated with management of climatic uncertainties. Future climate projection will enable us for better risk management decisions.

Impact of land cover data on the simulation of urban heat island for Berlin using WRF coupled with bulk approach of Noah-LSM

Abstract: Urban-rural difference of land cover is the key determinant of urban heat island (UHI). In order to evaluate the impact of land cover data on the simulation of UHI, a comparative study between up-to-date CORINE land cover (CLC) and Urban Atlas (UA) with fine resolution (100 and 10 m) and old US Geological Survey (USGS) data with coarse resolution (30 s) was conducted using the Weather Research and Forecasting model (WRF) coupled with bulk approach of Noah-LSM for Berlin. The comparison between old data and new data partly reveals the effect of urbanization on UHI and the historical evolution of UHI, while the comparison between different resolution data reveals the impact of resolution of land cover on the simulation of UHI. Given the high heterogeneity of urban surface and the fine-resolution land cover data, the mosaic approach was implemented in this study to calculate the sub-grid variability in land cover compositions. Results showed that the simulations using UA and CLC data perform better than that using USGS data for both air and land surface temperatures. USGS-based simulation underestimates the temperature, especially in rural areas. The longitudinal variations of both temperature and land surface temperature show good agreement with urban fraction for all the three simulations. To better study the comprehensive characteristic of UHI over Berlin, the UHI curves (UHIC) are developed for all the three simulations based on the relationship between temperature and urban fraction. CLC- and UA-based simulations show smoother UHICs than USGS-based simulation. The simulation with old USGS data obviously underestimates the extent of UHI, while the up-to-date CLC and UA data better reflect the real urbanization and simulate the spatial distribution of UHI more accurately. However, the intensity of UHI simulated by CLC and UA data is not higher than that simulated by USGS data. The simulated air temperature is not dominated by the land cover as much as the land surface temperature, as air temperature is also affected by air advection.

Spatiotemporal changes of normalized difference vegetation index (NDVI) and response to climate extremes and ecological restoration in the Loess Plateau, China

Abstract: Extreme drought, precipitation, and other extreme climatic events often have impacts on vegetation. Based on meteorological data from 52 stations in the Loess Plateau (LP) and a satellite-derived normalized difference vegetation index (NDVI) from the third-generation Global Inventory Modeling and Mapping Studies (GIMMS3g) dataset, this study investigated the relationship between vegetation change and climatic extremes from 1982 to 2013. Our results showed that the vegetation coverage increased significantly, with a linear rate of 0.025/10a (P < 0.001) from 1982 to 2013. As for the spatial distribution, NDVI revealed an increasing trend from the northwest to the southeast, with about 61.79% of the LP exhibiting a significant increasing trend (P < 0.05). Some temperature extreme indices, including TMAXmean, TMINmean, TN90p, TNx, TX90p, and TXx, increased significantly at rates of 0.77 mm/10a, 0.52 °C/10a, 0.62 °C/10a, 0.80 °C/10a, 5.16 days/10a, and 0.65 °C/10a, respectively. On the other hand, other extreme temperature indices including TX10p and TN10p decreased significantly at rates of −2.77 days/10a and 4.57 days/10a (P < 0.01), respectively. Correlation analysis showed that only TMINmean had a significant relationship with NDVI at the yearly time scale (P < 0.05). At the monthly time scale, vegetation coverage and different vegetation types responded significantly positively to precipitation and temperature extremes (TMAXmean, TMINmean, TNx, TNn, TXn, and TXx) (P < 0.01). All of the precipitation extremes and temperature extremes exhibited significant positive relationships with NDVI during the spring and autumn (P < 0.01). However, the relationship between NDVI and RX1day, TMAXmean, TXn, and TXx was insignificant in summer. Vegetation exhibited a significant negative relationship with precipitation extremes in winter (P < 0.05). In terms of human activity, our results indicate a strong correlation between the cumulative afforestation area and NDVI in Yan’an and Yulin during 1998–2013, r = 0.859 and 0.85, n = 16, P < 0.001.

Spatio-temporal variability and trends of precipitation and extreme rainfall events in Ethiopia in 1980-2010

Abstract: This article summarizes the results from an analysis conducted to investigate the spatio-temporal variability and trends in the rainfall over Ethiopia over a period of 31 years from 1980 to 2010. The data is mostly observed station data supplemented by bias-corrected AgMERRA climate data. Changes in annual and Belg (March–May) and Kiremt (June to September) season rainfalls and rainy days have been analysed over the entire Ethiopia. Rainfall is characterized by high temporal variability with coefficient of variation (CV, %) varying from 9 to 30% in the annual, 9 to 69% during the Kiremt season and 15–55% during the Belg season rainfall amounts. Rainfall variability increased disproportionately as the amount of rainfall declined from 700 to 100 mm or less. No significant trend was observed in the annual rainfall amounts over the country, but increasing and decreasing trends were observed in the seasonal rainfall amounts in some areas. A declining trend is also observed in the number of rainy days especially in Oromia, Benishangul-Gumuz and Gambella regions. Trends in seasonal rainfall indicated a general decline in the Belg season and an increase in the Kiremt season rainfall amounts. The increase in rainfall during the main Kiremt season along with the decrease in the number of rainy days leads to an increase in extreme rainfall events over Ethiopia. The trends in the 95th-percentile rainfall events illustrate that the annual extreme rainfall events are increasing over the eastern and south-western parts of Ethiopia covering Oromia and Benishangul-Gumuz regions. During the Belg season, extreme rainfall events are mostly observed over central Ethiopia extending towards the southern part of the country while during the Kiremt season, they are observed over parts of Oromia, (covering Borena, Guji, Bali, west Harerge and east Harerge), Somali, Gambella, southern Tigray and Afar regions. Changes in the intensity of extreme rainfall events are mostly observed over south-eastern parts of Ethiopia extending to the south-west covering Somali and Oromia regions. Similar trends are also observed in the greatest 3-, 5- and 10-day rainfall amounts. Changes in the consecutive dry and wet days showed that consecutive wet days during Belg and Kiremt seasons decreased significantly in many areas in Ethiopia while consecutive dry days increased. The consistency in the trends over large spatial areas confirms the robustness of the trends and serves as a basis for understanding the projected changes in the climate. These results were discussed in relation to their significance to agriculture.

A data-driven SVR model for long-term runoff prediction and uncertainty analysis based on the Bayesian framework

Abstract: Accurate and reliable long-term forecasting plays an important role in water resources management and utilization. In this paper, a hybrid model called SVR-HUP is presented to predict long-term runoff and quantify the prediction uncertainty. The model is created based on three steps. First, appropriate predictors are selected according to the correlations between meteorological factors and runoff. Second, a support vector regression (SVR) model is structured and optimized based on the LibSVM toolbox and a genetic algorithm. Finally, using forecasted and observed runoff, a hydrologic uncertainty processor (HUP) based on a Bayesian framework is used to estimate the posterior probability distribution of the simulated values, and the associated uncertainty of prediction was quantitatively analyzed. Six precision evaluation indexes, including the correlation coefficient (CC), relative root mean square error (RRMSE), relative error (RE), mean absolute percentage error (MAPE), Nash-Sutcliffe efficiency (NSE), and qualification rate (QR), are used to measure the prediction accuracy. As a case study, the proposed approach is applied in the Han River basin, South Central China. Three types of SVR models are established to forecast the monthly, flood season and annual runoff volumes. The results indicate that SVR yields satisfactory accuracy and reliability at all three scales. In addition, the results suggest that the HUP cannot only quantify the uncertainty of prediction based on a confidence interval but also provide a more accurate single value prediction than the initial SVR forecasting result. Thus, the SVR-HUP model provides an alternative method for long-term runoff forecasting.

Climate change over Leh (Ladakh), India

Abstract: Mountains over the world are considered as the indicators of climate change. The Himalayas are comprised of five ranges, viz., Pir Panjal, Great Himalayas, Zanskar, Ladhak, and Karakorum. The Ladakh region lies in the northernmost state of India, Jammu and Kashmir, in the Ladhak range. It has a unique cold-arid climate and lies immediately south of the Karakorum range. With scarce water resources, such regions show high sensitivity and vulnerability to the change in climate and need urgent attention. The objective of this study is to understand the climate of the Ladakh region and to characterize its changing climate. Using different temperature and precipitation datasets over Leh and surrounding regions, we statistically analyze the current trends of climatic patterns over the region. The study shows that the climate over Leh shows a warming trend with reduced precipitation in the current decade. The reduced average seasonal precipitation might also be associated with some indications of reducing number of days with higher precipitation amounts over the region.

Statistical evaluation of rainfall time series in concurrence with agriculture and water resources of Ken River basin, Central India (1901–2010)

Abstract: Trend analysis of long-term rainfall records can be used to facilitate better agriculture water management decision and climate risk studies. The main objective of this study was to identify the existing trends in the long-term rainfall time series over the period 1901–2010 utilizing 12 hydrological stations located at the Ken River basin (KRB) in Madhya Pradesh, India. To investigate the different trends, the rainfall time series data were divided into annual and seasonal (i.e., pre-monsoon, monsoon, post-monsoon, and winter season) sub-sets, and a statistical analysis of data using the non-parametric Mann–Kendall (MK) test and the Sen’s slope approach was applied to identify the nature of the existing trends in rainfall series for the Ken River basin. The obtained results were further interpolated with the aid of the Quantum Geographic Information System (GIS) approach employing the inverse distance weighted approach. The results showed that the monsoon and the winter season exhibited a negative trend in rainfall changes over the period of study, and this was true for all stations, although the changes during the pre- and the post-monsoon seasons were less significant. The outcomes of this research study also suggest significant decreases in the seasonal and annual trends of rainfall amounts in the study period. These findings showing a clear signature of climate change impacts on KRB region potentially have implications in terms of climate risk management strategies to be developed during major growing and harvesting seasons and also to aid in the appropriate water resource management strategies that must be implemented in decision-making process.

Using eddy geopotential height to measure the western North Pacific subtropical high in a warming climate

Abstract: The western North Pacific subtropical high (WNPSH) is crucial to the East Asian summer climate, and geopotential height (H) is widely used to measure the WPNSH. However, a rapidly rising trend of H in the future is projected by the models from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Diagnoses based on the hypsometric equation suggest that more than 80% of the rise in H are attributable to zonal uniform warming. Because circulation is determined by the gradient of H rather than its absolute magnitude, the spatially uniform rising trend of H gives rise to difficulties when measuring the WNPSH with H. These difficulties include an invalid western boundary of WNPSH in the future and spurious information regarding long-term trends and interannual variability of WNPSH. Using CMIP5 model simulations and reanalysis data, the applicability of a metric based on eddy geopotential height (H e ) to the warming climate is investigated. The results show that the H e metric outperforms the H metric under warming climate conditions. First, the mean state rainfall-H e relationship is more robust than the rainfall-H relationship. Second, the area, intensity, and western boundary indices of WNPSH can be effectively defined by the H e = 0-m contour in future warming climate scenarios without spurious trends. Third, the interannual variability of East Asian summer rainfall is more closely related to the H e -based WNPSH indices. We recommend that the H e metric be adopted as an operational metric on the WNPSH under the current warming climate.

Joint atmospheric-terrestrial water balances for East Africa: a WRF-Hydro case study for the upper Tana River basin

Abstract: For an improved understanding of the hydrometeorological conditions of the Tana River basin of Kenya, East Africa, its joint atmospheric-terrestrial water balances are investigated. This is achieved through the application of the Weather Research and Forecasting (WRF) and the fully coupled WRF-Hydro modeling system over the Mathioya-Sagana subcatchment (3279 km2) and its surroundings in the upper Tana River basin for 4 years (2011–2014). The model setup consists of an outer domain at 25 km (East Africa) and an inner one at 5-km (Mathioya-Sagana subcatchment) horizontal resolution. The WRF-Hydro inner domain is enhanced with hydrological routing at 500-m horizontal resolution. The results from the fully coupled modeling system are compared to those of the WRF-only model. The coupled WRF-Hydro slightly reduces precipitation, evapotranspiration, and the soil water storage but increases runoff. The total precipitation from March to May and October to December for WRF-only (974 mm/year) and coupled WRF-Hydro (940 mm/year) is closer to that derived from the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data (989 mm/year) than from the TRMM (795 mm/year) precipitation product. The coupled WRF-Hydro-accumulated discharge (323 mm/year) is close to that observed (333 mm/year). However, the coupled WRF-Hydro underestimates the observed peak flows registering low but acceptable NSE (0.02) and RSR (0.99) at daily time step. The precipitation recycling and efficiency measures between WRF-only and coupled WRF-Hydro are very close and small. This suggests that most of precipitation in the region comes from moisture advection from the outside of the analysis domain, indicating a minor impact of potential land-precipitation feedback mechanisms in this case. The coupled WRF-Hydro nonetheless serves as a tool in quantifying the atmospheric-terrestrial water balance in this region.

Spatial analysis of precipitation time series over the Upper Indus Basin

Abstract: The upper Indus basin (UIB) holds one of the most substantial river systems in the world, contributing roughly half of the available surface water in Pakistan. This water provides necessary support for agriculture, domestic consumption, and hydropower generation; all critical for a stable economy in Pakistan. This study has identified trends, analyzed variability, and assessed changes in both annual and seasonal precipitation during four time series, identified herein as: (first) 1961–2013, (second) 1971–2013, (third) 1981–2013, and (fourth) 1991–2013, over the UIB. This study investigated spatial characteristics of the precipitation time series over 15 weather stations and provides strong evidence of annual precipitation by determining significant trends at 6 stations (Astore, Chilas, Dir, Drosh, Gupis, and Kakul) out of the 15 studied stations, revealing a significant negative trend during the fourth time series. Our study also showed significantly increased precipitation at Bunji, Chitral, and Skardu, whereas such trends at the rest of the stations appear insignificant. Moreover, our study found that seasonal precipitation decreased at some locations (at a high level of significance), as well as periods of scarce precipitation during all four seasons. The observed decreases in precipitation appear stronger and more significant in autumn; having 10 stations exhibiting decreasing precipitation during the fourth time series, with respect to time and space. Furthermore, the observed decreases in precipitation appear robust and more significant for regions at high elevation (>1300 m). This analysis concludes that decreasing precipitation dominated the UIB, both temporally and spatially including in the higher areas.

Statistical downscaling of precipitation using long short-term memory recurrent neural networks

Abstract: Hydrological impacts of global climate change on regional scale are generally assessed by downscaling large-scale climatic variables, simulated by General Circulation Models (GCMs), to regional, small-scale hydrometeorological variables like precipitation, temperature, etc. In this study, we propose a new statistical downscaling model based on Recurrent Neural Network with Long Short-Term Memory which captures the spatio-temporal dependencies in local rainfall. The previous studies have used several other methods such as linear regression, quantile regression, kernel regression, beta regression, and artificial neural networks. Deep neural networks and recurrent neural networks have been shown to be highly promising in modeling complex and highly non-linear relationships between input and output variables in different domains and hence we investigated their performance in the task of statistical downscaling. We have tested this model on two datasets—one on precipitation in Mahanadi basin in India and the second on precipitation in Campbell River basin in Canada. Our autoencoder coupled long short-term memory recurrent neural network model performs the best compared to other existing methods on both the datasets with respect to temporal cross-correlation, mean squared error, and capturing the extremes.

A precipitation regionalization and regime for Iran based on multivariate analysis

Abstract: Monthly precipitation time series of 155 synoptic stations distributed over Iran, covering 1990–2014 time period, were used to identify areas with different precipitation time variability and regimes utilizing S-mode principal component analysis (PCA) and cluster analysis (CA) preceded by T-mode PCA, respectively. Taking into account the maximum loading values of the rotated components, the first approach revealed five sub-regions characterized by different precipitation time variability, while the second method delineated eight sub-regions featured with different precipitation regimes. The sub-regions identified by the two used methods, although partly overlapping, are different considering their areal extent and complement each other as they are useful for different purposes and applications. Northwestern Iran and the Caspian Sea area were found as the two most distinctive Iranian precipitation sub-regions considering both time variability and precipitation regime since they were well captured with relatively identical areas by the two used approaches. However, the areal extents of the other three sub-regions identified by the first approach were not coincident with the coverage of their counterpart sub-regions defined by the second approach. Results suggest that the precipitation sub-region identified by the two methods would not be necessarily the same, as the first method which accounts for the variance of the data grouped stations with similar temporal variability while the second one which considers a fixed climatology defined by the average over the period 1990–2014 clusters stations having a similar march of monthly precipitation.

Development of heat and drought related extreme weather events and their effect on winter wheat yields in Germany

Abstract: Climate change constitutes a major challenge for high productivity in wheat, the most widely grown crop in Germany. Extreme weather events including dry spells and heat waves, which negatively affect wheat yields, are expected to aggravate in the future. It is crucial to improve the understanding of the spatiotemporal development of such extreme weather events and the respective crop-climate relationships in Germany. Thus, the present study is a first attempt to evaluate the historic development of relevant drought and heat-related extreme weather events from 1901 to 2010 on county level (NUTS-3) in Germany. Three simple drought indices and two simple heat stress indices were used in the analysis. A continuous increase in dry spells over time was observed over the investigated periods from 1901–1930, 1931–1960, 1961–1990 to 2001–2010. Short and medium dry spells, i.e., precipitation-free periods longer than 5 and 8 days, respectively, increased more strongly compared to longer dry spells (longer than 11 days). The heat-related stress indices with maximum temperatures above 25 and 28 °C during critical wheat growth phases showed no significant increase over the first three periods but an especially sharp increase in the final 1991–2010 period with the increases being particularly pronounced in parts of Southwestern Germany. Trend analysis over the entire 110-year period using Mann-Kendall test revealed a significant positive trend for all investigated indices except for heat stress above 25 °C during flowering period. The analysis of county-level yield data from 1981 to 2010 revealed declining spatial yield variability and rather constant temporal yield variability over the three investigated (1981–1990, 1991–2000, and 2001–2010) decades. A clear spatial gradient manifested over time with variability in the West being much smaller than in the east of Germany. Correlating yield variability with the previously analyzed extreme weather indices revealed strong spatiotemporal fluctuations in explanatory power of the different indices over all German counties and the three time periods. Over the 30 years, yield deviations were increasingly well correlated with heat and drought-related indices, with the number of days with maximum temperature above 25 °C during anthesis showing a sharp increase in explanatory power over entire Germany in the final 2001–2010 period.

Urban heat island research from 1991 to 2015: a bibliometric analysis

Abstract: A bibliometric analysis based on the Science Citation Index-Expanded (SCI-Expanded) database from the Web of Science was performed to review urban heat island (UHI) research from 1991 to 2015 and statistically assess its developments, trends, and directions. In total, 1822 papers published in 352 journals over the past 25 years were analyzed for scientific output; citations; subject categories; major journals; outstanding keywords; and leading countries, institutions, authors, and research collaborations. The number of UHI-related publications has continuously increased since 1991. Meteorology atmospheric sciences, environmental sciences, and construction building technology were the three most frequent subject categories. Building and Environment, International Journal of Climatology, and Theoretical and Applied Climatology were the three most popular publishing journals. The USA and China were the two leading countries in UHI research, contributing 49.56% of the total articles. Chinese Academy of Science, Arizona State University, and China Meteorological Administration published the most UHI articles. Weng QH and Santamouris M were the two most prolific authors. Author keywords were classified into four major groups: (1) research methods and indicators, e.g., remote sensing, field measurement, and models; (2) generation factors, e.g., impervious urban surfaces, urban geometry, waste heat, vegetation, and pollutants; (3) environmental effects, e.g., urban climate, heat wave, ecology, and pollution; and (4) mitigation and adaption strategies, e.g., roof technology cooling, reflective cooling, vegetation cooling, and urban geometry cooling. A comparative analysis of popular issues revealed that UHI determination (intensity, heat source, supporting techniques) remains the central topic, whereas UHI impacts and mitigation strategies are becoming the popular issues that will receive increasing scientific attention in the future. Modeling will continue to be the leading research method, and remote sensing will be used more widely. Additionally, a combination of remote sensing and field measurements with models is expected.

Regionalization of precipitation characteristics in Iran’s Lake Urmia basin

Abstract: Lake Urmia in northwest Iran, once one of the largest hypersaline lakes in the world, has shrunk by almost 90% in area and 80% in volume during the last four decades. To improve the understanding of regional differences in water availability throughout the region and to refine the existing information on precipitation variability, this study investigated the spatial pattern of precipitation for the Lake Urmia basin. Daily rainfall time series from 122 precipitation stations with different record lengths were used to extract 15 statistical descriptors comprising 25th percentile, 75th percentile, and coefficient of variation for annual and seasonal total precipitation. Principal component analysis in association with cluster analysis identified three main homogeneous precipitation groups in the lake basin. The first sub-region (group 1) includes stations located in the center and southeast; the second sub-region (group 2) covers mostly northern and northeastern part of the basin, and the third sub-region (group 3) covers the western and southern edges of the basin. Results of principal component (PC) and clustering analyses showed that seasonal precipitation variation is the most important feature controlling the spatial pattern of precipitation in the lake basin. The 25th and 75th percentiles of winter and autumn are the most important variables controlling the spatial pattern of the first rotated principal component explaining about 32% of the total variance. Summer and spring precipitation variations are the most important variables in the second and third rotated principal components, respectively. Seasonal variation in precipitation amount and seasonality are explained by topography and influenced by the lake and westerly winds that are related to the strength of the North Atlantic Oscillation. Despite using incomplete time series with different lengths, the identified sub-regions are physically meaningful.