Most climate change detection and attribution studies have focused on mean or extreme temperature or precipitation, neglecting to explore long-term changes in drought characteristics. Here we provide ...evidence that anthropogenic forcing has impacted interrelated meteorological drought characteristics. Using SPI and SPEI indices generated from an ensemble of 9 CMIP6 models (using 3 realizations per model), we show that the presence of anthropogenic forcing has increased the drought frequency, maximum drought duration, and maximum drought intensity experienced in large parts of the Americas, Africa, and Asia. Using individual greenhouse gas and anthropogenic aerosol forcings, we also highlight that regional balances between the two major forcings have contributed to the drying patterns detected in our results. Overall, we provide a comprehensive characterization of the influence of anthropogenic forcing on drought characteristics, providing important perspectives on the role of forcings in driving changes in drought events.
Using publicly-available average monthly groundwater level data in 478 sub-basins and 30 basins in Iran, we quantify country-wide groundwater depletion in Iran. Natural and anthropogenic elements ...affecting the dynamics of groundwater storage are taken into account and quantified during the period of 2002-2015. We estimate that the total groundwater depletion in Iran to be ~ 74 km
during this period with highly localized and variable rates of change at basin and sub-basin scales. The impact of depletion in Iran's groundwater reserves is already manifested by extreme overdrafts in ~ 77% of Iran's land area, a growing soil salinity across the entire country, and increasing frequency and extent of land subsidence in Iran's planes. While meteorological/hydrological droughts act as triggers and intensify the rate of depletion in country-wide groundwater storage, basin-scale groundwater depletions in Iran are mainly caused by extensive human water withdrawals. We warn that continuation of unsustainable groundwater management in Iran can lead to potentially irreversible impacts on land and environment, threatening country's water, food, socio-economic security.
A combination of climate events (e.g., low precipitation and high temperatures) may cause a significant impact on the ecosystem and society, although individual events involved may not be severe ...extremes themselves. Analyzing historical changes in concurrent climate extremes is critical to preparing for and mitigating the negative effects of climatic change and variability. This study focuses on the changes in concurrences of heatwaves and meteorological droughts from 1960 to 2010. Despite an apparent hiatus in rising temperature and no significant trend in droughts, we show a substantial increase in concurrent droughts and heatwaves across most parts of the United States, and a statistically significant shift in the distribution of concurrent extremes. Although commonly used trend analysis methods do not show any trend in concurrent droughts and heatwaves, a unique statistical approach discussed in this study exhibits a statistically significant change in the distribution of the data.
Extreme climatic events are growing more severe and frequent, calling into question how prepared our infrastructure is to deal with these changes. Current infrastructure design is primarily based on ...precipitation Intensity-Duration-Frequency (IDF) curves with the so-called stationary assumption, meaning extremes will not vary significantly over time. However, climate change is expected to alter climatic extremes, a concept termed nonstationarity. Here we show that given nonstationarity, current IDF curves can substantially underestimate precipitation extremes and thus, they may not be suitable for infrastructure design in a changing climate. We show that a stationary climate assumption may lead to underestimation of extreme precipitation by as much as 60%, which increases the flood risk and failure risk in infrastructure systems. We then present a generalized framework for estimating nonstationary IDF curves and their uncertainties using Bayesian inference. The methodology can potentially be integrated in future design concepts.
Accurate and reliable drought monitoring is essential to drought mitigation efforts and reduction of social vulnerability. A variety of indices, such as the standardized precipitation index (SPI), ...are used for drought monitoring based on different indicator variables. Because of the complexity of drought phenomena in their causation and impact, drought monitoring based on a single variable may be insufficient for detecting drought conditions in a prompt and reliable manner. This study outlines a multivariate, multi-index drought monitoring framework, namely, the multivariate standardized drought index (MSDI), for describing droughts based on the states of precipitation and soil moisture. In this study, the MSDI is evaluated against U.S. Drought Monitor (USDM) data as well as the commonly used standardized indices for drought monitoring, including detecting drought onset, persistence, and spatial extent across the continental United States. The results indicate that MSDI includes attractive properties, such as higher probability of drought detection, compared to individual precipitation and soil moisture–based drought indices. This study shows that the MSDI leads to drought information generally consistent with the USDM and provides additional information and insights into drought monitoring.
Precipitation process is generally considered to be poorly represented in numerical weather/climate models. Statistical downscaling (SD) methods, which relate precipitation with model resolved ...dynamics, often provide more accurate precipitation estimates compared to model's raw precipitation products. We introduce the convolutional neural network model to foster this aspect of SD for daily precipitation prediction. Specifically, we restrict the predictors to the variables that are directly resolved by discretizing the atmospheric dynamics equations. In this sense, our model works as an alternative to the existing precipitation‐related parameterization schemes for numerical precipitation estimation. We train the model to learn precipitation‐related dynamical features from the surrounding dynamical fields by optimizing a hierarchical set of spatial convolution kernels. We test the model at 14 geogrid points across the contiguous United States. Results show that provided with enough data, precipitation estimates from the convolutional neural network model outperform the reanalysis precipitation products, as well as SD products using linear regression, nearest neighbor, random forest, or fully connected deep neural network. Evaluation for the test set suggests that the improvements can be seamlessly transferred to numerical weather modeling for improving precipitation prediction. Based on the default network, we examine the impact of the network architectures on model performance. Also, we offer simple visualization and analyzing approaches to interpret the models and their results. Our study contributes to the following two aspects: First, we offer a novel approach to enhance numerical precipitation estimation; second, the proposed model provides important implications for improving precipitation‐related parameterization schemes using a data‐driven approach.
Plain Language Summary
The precipitation process is not well simulated in numerical weather models, since it takes place at the scales beyond the resolution of current models. We develop a statistical model using deep learning technique to improve the estimation of precipitation in numerical weather models.
Key Points
We offer a novel approach to enhance numerical precipitation estimation using deep convolutional neural network (CNN)
The model provides important implications for improving precipitation‐related parameterization schemes using a data‐driven approach
The CNN model outperforms existing precipitation statistical downscaling approaches by learning dominant spatial dynamics features
This paper analyzes changes in areas under droughts over the past three decades and alters our understanding of how amplitude and frequency of droughts differ in the Southern Hemisphere (SH) and ...Northern Hemisphere (NH). Unlike most previous global-scale studies that have been based on climate models, this study is based on satellite gauge-adjusted precipitation observations. Here, we show that droughts in terms of both amplitude and frequency are more variable over land in the SH than in the NH. The results reveal no significant trend in the areas under drought over land in the past three decades. However, after investigating land in the NH and the SH separately, the results exhibit a significant positive trend in the area under drought over land in the SH, while no significant trend is observed over land in the NH. We investigate the spatial patterns of the wetness and dryness over the past three decades, and we show that several regions, such as the southwestern United States, Texas, parts of the Amazon, the Horn of Africa, northern India, and parts of the Mediterranean region, exhibit a significant drying trend. The global trend maps indicate that central Africa, parts of southwest Asia (e.g., Thailand, Taiwan), Central America, northern Australia, and parts of eastern Europe show a wetting trend during the same time span. The results of this satellite-based study disagree with several model-based studies which indicate that droughts have been increasing over land. On the other hand, our findings concur with some of the observation-based studies.
Socioeconomic drought broadly refers to conditions whereby the water supply cannot satisfy the demand. Most previous studies describe droughts based on large‐scale meteorological/hydrologic ...conditions, ignoring the demand and local resilience to cope with climate variability. Reservoirs provide resilience against climatic extremes and play a key role in water supply and demand management. Here we outline a unique multivariate approach as a measure of socioeconomic drought, termed Multivariate Standardized Reliability and Resilience Index (MSRRI). The model combines information on the inflow and reservoir storage relative to the demand. MSRRI combines (I) a “top‐down” approach that focuses on processes/phenomena that cannot be simply controlled or altered by decision makers, such as climate change and variability, and (II) a “bottom‐up” methodology that represents the local resilience and societal capacity to respond or adapt to droughts. MSRRI is based on a nonparametric multivariate distribution function that links inflow‐demand reliability indicator to water storage resilience indicator. These indicators are used to assess socioeconomic drought during the Australian Millennium drought (1998–2010) and the 2011–2014 California drought. The results show that MSRRI is superior to univariate indices because it captures both early onset and persistence of water stress over time. The suggested framework can be applied to both individual reservoirs and a group of reservoirs in a region, and it is consistent with the currently available standardized drought indicators. MSRRI provides complementary information on socioeconomic drought development and recovery based on reservoir storage and demand that cannot be achieved from the commonly used drought indicators.
Key Points
Defines socioeconomic drought by linking climate variability, local resilience, and demand
Presents a novel approach combining climatic variability and local resilience
Improved description of water stress onset and persistence
Compounding effects of sea level rise and fluvial flooding Moftakhari, Hamed R.; Salvadori, Gianfausto; AghaKouchak, Amir ...
Proceedings of the National Academy of Sciences - PNAS,
09/2017, Letnik:
114, Številka:
37
Journal Article
Recenzirano
Odprti dostop
Sea level rise (SLR), a well-documented and urgent aspect of anthropogenic global warming, threatens population and assets located in low-lying coastal regions all around the world. Common flood ...hazard assessment practices typically account for one driver at a time (e.g., either fluvial flooding only or ocean flooding only), whereas coastal cities vulnerable to SLR are at risk for flooding from multiple drivers (e.g., extreme coastal high tide, storm surge, and river flow). Here, we propose a bivariate flood hazard assessment approach that accounts for compound flooding from river flow and coastal water level, and we show that a univariate approach may not appropriately characterize the flood hazard if there are compounding effects. Using copulas and bivariate dependence analysis, we also quantify the increases in failure probabilities for 2030 and 2050 caused by SLR under representative concentration pathways 4.5 and 8.5. Additionally, the increase in failure probability is shown to be strongly affected by compounding effects. The proposed failure probability method offers an innovative tool for assessing compounding flood hazards in a warming climate.
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