応用水文 = Applied hydrology

This article reviews recent literature on drought of the last millennium, followed by an update on global aridity changes from 1950 to 2008. Projected future aridity is presented based on recent studies and our analysis of model simulations. Dry periods lasting for years to decades have occurred many times during the last millennium over, for example, North America, West Africa, and East Asia. These droughts were likely triggered by anomalous tropical sea surface temperatures (SSTs), with La Ni˜ na-like SST anomalies leading to drought in North America, and El-Ni˜ no-like SSTs causing drought in East China. Over Africa, the southward shift of the warmest SSTs in the Atlantic and warming in the Indian Ocean are responsible for the recent Sahel droughts. Local feedbacks may enhance and prolong drought. Global aridity has increased substantially since the 1970s due to recent drying over Africa, southern Europe, East and South Asia, and eastern Australia. Although El Ni˜ no-Southern Oscillation (ENSO), tropical Atlantic SSTs, and Asian monsoons have played a large role in the recent drying, recent warming has increased atmospheric moisture demand and likely altered atmospheric circulation patterns, both contributing to the drying. Climate models project increased aridity in the 21 st century over most of Africa, southern Europe and the Middle East, most of the Americas, Australia, and Southeast Asia. Regions like the United States have avoided prolonged droughts during the last 50 years due to natural climate variations, but might see persistent droughts in the next 20–50 years. Future efforts to predict drought will depend on models’ ability to predict tropical SSTs. 2010 JohnWiley &Sons,Ltd.WIREs Clim Change2010 DOI:10.1002/wcc.81

Drought under Global Warming: A Review

. This study examines the uncertainties in simulating mass balance and radiative forcing of mineral dust due to biases in the dust size parameterization. Simulations are conducted quasi-globally (180° W–180° E and 60° S–70° N) using the WRF-Chem model with three different approaches to represent dust size distribution (8-bin, 4-bin, and 3-mode). The biases in the 3-mode or 4-bin approaches against a relatively more accurate 8-bin approach in simulating dust mass balance and radiative forcing are identified. Compared to the 8-bin approach, the 4-bin approach simulates similar but coarser size distributions of dust particles in the atmosphere, while the 3-mode approach retains more fine dust particles but fewer coarse dust particles due to its prescribed σ g of each mode. Although the 3-mode approach yields up to 10 days of longer dust mass lifetime over the remote oceanic regions than the 8-bin approach, the three size approaches produce a similar dust mass lifetime (3.2 days to 3.5 days) on quasi-global average, reflecting that the global dust mass lifetime is mainly determined by the dust mass lifetime near the dust source regions. With the same global dust emission (~4600 Tg yr −1 ), the 8-bin approach produces a dust mass loading of 39 Tg, while the 4-bin and 3-mode approaches produce 3% (40.2 Tg) and 25% (49.1 Tg) higher dust mass loading, respectively. The difference in dust mass loading between the 8-bin approach and the 4-bin or 3-mode approaches has large spatial variations, with generally smaller relative difference ( −2 ) and atmospheric warming (0.39~0.96 W m −2 ) and in a tremendous difference of a factor of ~10 in dust TOA (top of atmosphere) cooling (−0.24~−2.20 W m −2 ). The impact of different size representations on dust radiative forcing efficiency is smaller. An uncertainty of a factor of 2 is quantified in dust emission estimation due to the different size parameterizations. This study also highlights the uncertainties in modeling dust mass and number loading, deposition fluxes, and radiative forcing resulting from different size parameterizations, and motivates further investigation of the impact of size parameterizations on modeling dust impacts on air quality, climate, and ecosystems.

Uncertainty in modeling dust mass balance and radiative forcing from size parameterization

Dust storms in Iran - Distribution, causes, frequencies and impacts

Numerical simulations of dust storms originated from dried lakes in central and southwest Asia: The case of Aral Sea and Sistan Basin

Meteorological drought analysis using copula theory and drought indicators under climate change scenarios (RCP)

The accurate modeling of landslide risk is essential pre-requisite for the development of reliable landslide control and mitigation strategies. However, landslide risk depends on the poorly known environmental and socio-economic factors for regional patterns of landslide occurrence probability and vulnerability, which constitute still a matter of research. Here, a hybrid model is described that couples data mining and multi-criteria decision-making methods for hazard and vulnerability mapping and presents its application to landslide risk assessment in Golestan Province, Northeastern Iran. To this end, landslide probability is mapped using three state-of-the-art machine learning (ML) algorithms—Maximum Entropy, Support Vector Machine and Genetic Algorithm for Rule Set Production—and combine the results with Fuzzy Analytical Hierarchy Process computations of vulnerability to obtain the landslide risk map. Based on obtained results, a discussion is presented on landslide probability as a function of the main relevant human-environmental conditioning factors in Golestan Province. In particular, from the response curves of the machine learning algorithms, it can be found that the probability p of landslide occurrence decreases nearly exponentially with the distance x to the next road, fault, or river. Specifically, the results indicated that $$p \approx \exp \left( { - \lambda x} \right)$$
 where the length scale λ is about $$0.0797$$
 km−1 for road, $$0.108$$
 km−1 for fault, and $$0.734$$
 km−1 0.734 km−1 for river. Furthermore, according to the results, p follows, approximately, a lognormal function of elevation, while the equation $$p = p_{0} - K\left( {\theta - \theta_{0} } \right)^{2}$$
 fits well the dependence of landslide modeling on the slope-angle θ, with $$p_{0} \approx 0.64,\;\theta_{0} \approx 25.6^{ \circ } \;{\text{and}}\;\left| K \right| \approx 6.6 \times 10^{ - 4}$$
 . However, the highest predicted landslide risk levels in Golestan Province are located in the south and southwest areas surrounding Gorgan City, owing to the combined effect of dense local human occupation and strongly landslide-prone environmental conditions. Obtained results provide insights for quantitative modeling of landslide risk, as well as for priority planning in landslide risk management.

/pdf/a-hybrid-model-using-data-mining-and-multi-criteria-decision-5a6pmj91dn.pdf

A hybrid model using data mining and multi-criteria decision-making methods for landslide risk mapping at Golestan Province, Iran

Dust storms are a major natural hazard to human health. Severe erosive storms in parts of the Central Plateau of Iran have made the situation very difficult for the inhabitants, to the extent that some areas have become depopulated. To better understand this phenomenon, dust day counts at 37 synoptic stations from 1999 to 2018 were analyzed. Dust days were most common in June, with 45% of the total number occurring in summer (June–August) and 34% in spring (March–May), and were more frequent since 2008, as compared to 1999-2007. While the spatial pattern of dust days was complex, the highest number tended to be in the southeast of the region. The stations with the most dust days, Zabol, Zahedan, and Arak, averaged 126 days, 74 days, and 73 days of dust per year, respectively. The statistical distributions that most often best fitted the time series of number of dust days (NDD) per year were Johnson SB, Log-Logistic 3-Parameter, and Burr. These fitted probability distributions were used to estimate different return period values for annual number of dust days. For example, Zabol and Sirjan stations had, respectively, the highest and lowest 2-year return period NDD values, 125 and 2 days, respectively. Overall, the spatial pattern of the NDD at different return periods indicated that southeastern Iran, as well as some northwestern and eastern portions of the study region, had particularly high values of NDD at longer return periods, while much of the northern and southwestern margins of the region have low NDD at all return periods. These results may be useful for informing the regional management of dust storms.

Climatology of dust days in the Central Plateau of Iran

Dust and sand storms are among the major threats to central Iran. These phenomena pose irreparable risks to natural ecosystems and human societies, including effects on health. In this study, the spatial and temporal pattern of vertical dust flux (VDF) was used to identify dust sources as well as areas with high potential for dust generation. To simulate VDF, two intense dust storms, from 21 February 2015 and 14 February 2018, were selected using synoptic data and Moderate Resolution Imaging Spectroradiometer (MODIS) images. These dust storms were identified as responsible for a reduction of horizontal visibility to less than 1000 m, using remote sensing tools and Ackerman Dust Index. MODIS images show that these two storms covered most of Central Plateau of Iran. The Weather Research and Forecasting model with chemistry (WRF-Chem) was used to simulate the storms, with either the Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) or Air Force Weather Agency (AFWA) scheme to calculate VDF. Modeled vertical dust fluxes in both events indicate that the Arabian deserts in Saudi Arabia and in southwestern Iran can be identified as main sources of the dust in the central Iranian plateau. The other source of dust is the Hirmand Basin, located in the country of Afghanistan and in the southeast of Iran. The results of VDF simulations indicate that central southeast Iran could be the main dust source of internal origin. Additionally, over seasonal wetlands in Iran, the amount of VDF was simulated to be sometimes over 4000 μg/(m2s), an indication that these areas are sensitive to wind erosion in dry conditions and can be a source of dust. The WRF-Chem results were compared with the horizontal visibility measured in synoptic stations in the area. The results showed that the coefficients of determination of GOCART results with the measured horizontal visibility on 21 February 2015 and 14 February 2018 were 0.72 and 0.76, respectively, while the coefficient values from the simulations with AFWA scheme on 21 February 2015 and 14 February 2018 with the measured horizontal visibility were lower, 0.44 and 0.50, respectively. Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA2) re-analysis data also showed timing of peak dust levels consistent with the GOCART scheme.

Spatial-Temporal Variation Characteristics of Vertical Dust Flux Simulated by WRF-Chem Model with GOCART and AFWA Dust Emission Schemes (Case Study: Central Plateau of Iran)

https://onlinelibrary.wiley.com/doi/epdf/10.1111/nrm.12254

Observed and projected trends of extreme precipitation and maximum temperature during 1992–2100 in Isfahan province, Iran using REMO model and copula theory

Farshad Soleimani Sardoo

Papers

Meteorological drought analysis using copula theory and drought indicators under climate change scenarios (RCP)

A hybrid model using data mining and multi-criteria decision-making methods for landslide risk mapping at Golestan Province, Iran

Climatology of dust days in the Central Plateau of Iran

Spatial-Temporal Variation Characteristics of Vertical Dust Flux Simulated by WRF-Chem Model with GOCART and AFWA Dust Emission Schemes (Case Study: Central Plateau of Iran)

Observed and projected trends of extreme precipitation and maximum temperature during 1992–2100 in Isfahan province, Iran using REMO model and copula theory