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This study presents a robust approach to assess climate change impact variability on future extreme events (e.g., rainfall depth and river discharge) over Dehbar catchment in Iran. Climate change impact is assessed using five general circulation models (GCMs) including EC-EARTH, GFDL-CM3, HadGEM2-ES, MIROC5, and MPI-ESM-MR with several emission scenarios (e.g., RCP26, RCP45, and RCP85). Daily discharge data is simulated based on the distributed rainfall-runoff model called Soil and Water Assessment Tool (SWAT), while calibration and validation phases are performed using SWAT-CUP. Future annual extreme events (i.e., rainfall depth and river discharge) are computed by means of frequency analysis. Results show that future annual maximum values are increased significantly, where the most increase occurs in the future annual river discharge and rainfall depth according to the EC-EARTH-RCP85 as 142% and 81% with MPI-ESM-MR-RCP85 model. The highest future extreme river discharge and rainfall depth values through different return periods (50–1000 year) are obtained from EC-EARTH-RCP85 as 6.8~8.08 cms and 57.41~105.76 mm based on EC-EARTH-RCP45 model. Uncertainty analysis results indicate that climate models/scenarios have significant effect on the future extreme events variability, while the same for extreme river discharge is the least sensitive to different return periods.

A noticeable increase in drought frequency and severity has been observed across the globe due to climate change, which attracted scientists in development of drought prediction models for mitigation of impacts. Droughts are usually monitored using drought indices (DIs), most of which are probabilistic and therefore, highly stochastic and non-linear. The current research investigated the capability of different versions of relatively well-explored machine learning (ML) models including random forest (RF), minimum probability machine regression (MPMR), M5 Tree (M5tree), extreme learning machine (ELM) and online sequential-ELM (OSELM) in predicting the most widely used DI known as standardized precipitation index (SPI) at multiple month horizons (i.e., 1, 3, 6 and 12). Models were developed using monthly rainfall data for the period of 1949–2013 at four meteorological stations namely, Barisal, Bogra, Faridpur and Mymensingh, each representing a geographical region of Bangladesh which frequently experiences droughts. The model inputs were decided based on correlation statistics and the prediction capability was evaluated using several statistical metrics including mean square error ( MSE ), root mean square error ( RMSE ), mean absolute error ( MAE ), correlation coefficient ( R ), Willmott’s Index of agreement ( WI ), Nash Sutcliffe efficiency ( NSE ), and Legates and McCabe Index ( LM ). The results revealed that the proposed models are reliable and robust in predicting droughts in the region. Comparison of the models revealed ELM as the best model in forecasting droughts with minimal RMSE in the range of 0.07–0.85, 0.08–0.76, 0.062–0.80 and 0.042–0.605 for Barisal, Bogra, Faridpur and Mymensingh, respectively for all the SPI scales except one-month SPI for which the RF showed the best performance with minimal RMSE of 0.57, 0.45, 0.59 and 0.42, respectively.

Satellite precipitation products are important data sources in different spatial resolutions, time scales, and spatio-temporal coverage. In this study, the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) satellite precipitation product with a high spatial resolution (0.05°) is evaluated in the period of 1987 to 2017 over different climate regions of Iran. The accuracy of the satellite product is compared with the 68 ground-based meteorological stations over different time scales (i.e., daily, monthly, and annual) and precipitation classes. Results show that the performance of CHIRPS depends on the time scale, precipitation depth, and climate type. The best performance of the product (CC = 0.80, FRMSE = 0.57, NSE = 0.63) across the country is observed in the annual time scale, while the monthly product offers the best performance in the regional scale. The product provides inadequate performance (CC = 0.34, FRMSE = 5.72, NSE = − 0.2) in daily time scale across the country and most of the climatic regions. The product is found to be most accurate in the south and southwest of the country, while the lowest performance is observed over the Caspian coast. The CHIRPS satellite provides the best performance in detection of no/tiny precipitation (POD > 0.90) and the worst performance in light and low, moderate precipitation (POD < 0.10). It is expected that the findings of the current study can be used to manage the water resources and mitigate the disaster at the national level.

This study presents an innovative stochastic approach based on the failure modes and effects analysis to evaluate the failure risk of Amir Kabir dam in Iran. To compute the risk priority number, several risk factors including, occurrence probability, severity, and detectability are quantified using expert opinions. In this way, the participant experts are divided into four groups including; dam operation staff, water company staff, academic staff and, other experts. Moreover, the weights of the expert groups are also considered to compute the risk priority number. Hence, a new failure modes and effects analysis called modified stochastic failure modes and effects analysis is provided by considering weights of expert groups and risk factors. Furthermore, Monte Carlo simulation is used to consider the weights uncertainty and compute a stochastic-based risk priority number. Results show that the first priority failure modes of the upstream and downstream basins of Amir Kabir dam are the human-based failure modes while a nature-based failure mode is observed with most priority in dam body. In general, the proposed stochastic failure risk method provides reliable results with low uncertainty to evaluate the failure modes through a risk assessment process.

In the present study, the spatiotemporal evaluation of the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) satellite precipitation product is performed in capturing meteorological drought over different climatic regions of Iran. The performance of the product as a high spatial resolution dataset in monitoring drought is evaluated against the 68 meteorological stations from short to long scale (i.e., SPI1, SPI3, SPI6, SPI9, and SPI12) in the period of 1987 to 2017. Besides, the capability of the CHIRPS in detecting drought events is assessed in different drought classes. The results suggest that the climate type, the time scale, and the drought class affect the quality of the CHIRPS performance. The CHIRPS offers the best performance in the detection of all drought events with SPI <  − 1 over the SPI1 (0.69 < POD < 0.85). However, the product provides the worst performance for SPI12 (0.50 < POD < 0.70). At the country level, the highest agreement between the CHIRPS- and observation data-based SPI is found over the SPI6 (CC = 0.56), while the lowest is observed over the SPI12 (CC = 0.47). Based on the temporal evaluation, the G6 (0.18 < CC < 0.44, 1.06 < RMSE < 1.28) and G8 (0.17 < CC < 0.43, 1.06 < RMSE < 1.29) regions located in the southern coast of the Caspian Sea have an inadequate performance. However, the southern parts (G4 region) (0.38 < CC < 0.65, 0.83 < RMSE < 1.27) and the northwestern area (G3 region) (0.53 < CC < 0.62, 0.87 < RMSE < 0.97) of the country offer the best performance. The spatial evaluation describes the high accuracy (CC > 0.7, RMSE < 0.5) in some regions, including the western parts of G1, the northern area of G3, and the southern parts of G4. The research findings provided an important opportunity to advance the understanding of drought monitoring over the different climatic regions based on the high-resolution satellite precipitation products.

PurposeThe purpose of this study is to identify future changes in weather variables (precipitation and temperature) due to climate change using different general circulation models (GCMs) for different emission scenarios, so as to assess the impact of climate change on river discharge and sediment yield in the Dehbar river basin in Iran.Materials and methodsThe magnitude and uncertainty of the impact of climate change on river discharge and sediment yield in the Dehbar river basin in Iran is quantified using a calibrated and validated SWAT model with future weather inputs generated using the LARS-WG6 program to downscale the output of five large-scale GCMs for three possible emission scenarios (RCP26, RCP45, and RCP85) and the period 2021–2040.Results and discussionAnnual maximum and minimum temperatures are projected to increase by 22–28% and 65–84%, respectively, with a 1-month shift in temperature peak. The future rainfall amounts show both increasing (fall and winter) and decreasing (spring and summer) trends. Future temperature and rainfall patterns are predicted to cause the largest flows to occur a month earlier (February instead of March), an increase in discharge in the “wet” months of fall and winter (up to 137%) and a decrease in the “dry” months of spring and summer (down to − 100%). Sediment yield, which is caused by runoff (also controlling river discharge), has a similar projected trend, with a general decrease in spring and summer (down to − 95%) and an increase in fall and winter (up to 340%). The coefficient of variation of the future monthly, seasonal and annual river discharges and sediment yields are relatively low, revealing a general agreement in projections among the different GCM and RCP scenarios considered.ConclusionsThis study highlights the significant negative impact of climate change on the Dehbar river basin, with amplification of river flows and sediment concentrations in the wet season and increased water scarcity in the dry season. Both effects may adversely impact the region’s livelihood (cultivation, fish farming) and land resources.

This study evaluates the future climate fluctuations in Iran’s eight major climate regions (G1–G8). Synoptic data for the period 1995–2014 was used as the reference for downscaling and estimation of possible alternation of precipitation, maximum and minimum temperature in three future periods, near future (2020–2040), middle future (2040–2060), and far future (2060–2080) for two shared socioeconomic pathways (SSP) scenarios, SSP119 and SSP245. The Gradient Boosting Regression Tree (GBRT) ensemble algorithm has been utilized to implement the downscaling model. Pearson’s correlation coefficient (CC) was used to assess the ability of CMIP6 global climate models (GCMs) in replicating observed precipitation and temperature in different climate zones for the based period (1995–2014) to select the most suitable GCM for Iran. The suitability of 21 meteorological variables was evaluated to select the best combination of inputs to develop the GBRT downscaling model. The results revealed GFDL-ESM4 as the most suitable GCM for replicating the synoptic climate of Iran for the base period. Two variables, namely sea surface temperature (ts) and air temperature (tas), are the most suitable variable for developing a downscaling model for precipitation, while ts, tas, and geopotential height (zg) for maximum temperature, and tas, zg, and sea level pressure (psl) for minimum temperature. The GBRT showed significant improvement in downscaling GCM simulation compared to support vector regression, previously found as most suitable for the downscaling climate in Iran. The projected precipitation revealed the highest increase in arid and semi-arid regions (G1) by an average of 144%, while a declination in the margins of the Caspian Sea (G8) by −74%. The projected maximum temperature showed an increase up to +8°C in highland climate regions. The minimum temperature revealed an increase up to +4°C in the Zagros mountains and decreased by −4°C in different climate zones. The results indicate the potential of the GBRT ensemble machine learning model for reliable downscaling of CMIP6 GCMs for better projections of climate.

Predicting suspended sediment load (SSL) in water resource management requires efficient and reliable predicted models. This study considers the support vector regression (SVR) method to predict daily suspended sediment load. Since the SVR has unknown parameters, the observer-teacher-learner-based Optimization (OTLBO) method is integrated with the SVR model to provide a novel hybrid predictive model. The SVR combined with the genetic algorithm (SVR-GA) is used as an alternative model. To explore the performance and application of the proposed models, five input combinations of rainfall and discharge data of Cham Siah River catchment are provided. The predictive models are assessed using various numerical and visual indicators. The results indicate that the SVR-OTLBO model offers a higher prediction performance than other models employed in the current study. Specifically, SVR-OTLBO model offers highest Pearson correlation coefficient (R = 0.9768), Willmott’s Index (WI = 0.9812), ratio of performance to IQ (RPIQ = 0.9201), and modified index of agreement (md = 0.7411) and the lowest relative root mean square error (RRMSE = 0.5371) in comparison with SVR-GA (R = 0.9704, WI = 0.9794, RPIQ = 0.8521, and md = 0.7323, 0.5617) and SVR (R = 0.9501, WI = 0.9734, RPIQ = 0.3229, md = 0.4338, and RRMSE = 1.0829) models, respectively.

Soil moisture (SM) governs the exchange of energy and water between the atmosphere and land surface. In situ measurements of SM are uneven in Iran. This knowledge gap can be filled using satellite- and model-based products. This study assessed the performance of SM products, including Soil Moisture Active Passive (SMAP), Advanced Microwave Scanning Radiometer (AMSR2), and Global Land Data Assimilation System (GLDAS) Catchment Land Surface Model (CLSM) against in situ observations considering the influence of soil texture, climate, and land cover over Lake Urmia Basin, which is the largest salt lake in Iran and the Middle East. In situ SM was measured over Lake Urmia Basin in the morning and afternoon using the time domain reflectometry (TDR) and oven drying and weighing techniques. Five statistical indicators, including correlation (R), absolute correlation (R(abs)), bias, root mean square error (RMSE), and unbiased root mean square error (ubRMSE), were applied. C-band AMSR2 products showed the best performance in grassland and croplands with the highest absolute correlation (0.63) and lowest average bias (−0.01). Among soil textures, SM products performed better in clay soils with the highest absolute correlation between C-band AMSR2 products and in situ observations (0.64) and low average bias and RMSE. Analyzing data based on climate, AMSR2 C1, and GLDAS products with the lowest average RMSE (0.08 m3m−3) and bias (0.01) and AMSR2 C2 with the absolute correlation of 0.6 showed the best performance in both temperate (Csa) and cold (Dsa) climate classes. For all classifications (land cover, soil texture, climate divisions), SMAP products reported the lowest average value of ubRMSE (0.03 m3m−3). The major contribution of the paper is finding the best SM products that can fill the gap in SM measurements data in Lake Urmia. In this analysis, the impacts of land cover, climate, and soil texture on the performance of products were considered.

We assessed the changes in meteorological drought severity and drought return periods during cropping seasons in Afghanistan for the period of 1901 to 2010. The droughts in the country were analyzed using the standardized precipitation evapotranspiration index (SPEI). Global Precipitation Climatology Center rainfall and Climate Research Unit temperature data both at 0.5° resolutions were used for this purpose. Seasonal drought return periods were estimated using the values of the SPEI fitted with the best distribution function. Trends in climatic variables and SPEI were assessed using modified Mann–Kendal trend test, which has the ability to remove the influence of long-term persistence on trend significance. The study revealed increases in drought severity and frequency in Afghanistan over the study period. Temperature, which increased up to 0.14 °C/decade, was the major factor influencing the decreasing trend in the SPEI values in the northwest and southwest of the country during rice- and corn-growing seasons, whereas increasing temperature and decreasing rainfall were the cause of a decrease in SPEI during wheat-growing season. We concluded that temperature plays a more significant role in decreasing the SPEI values and, therefore, more severe droughts in the future are expected due to global warming.