The negative drought impacts on crop yield are well recognized in the literature, but are evaluated mainly in a deterministic manner. Considering the randomness feature of droughts and the ...compounding effects of other factors, we hypothesize that droughts effects on yields are probabilistic especially for assessment in large geographical regions. Taking US maize yield as an example, we found that a moderate, severe, extreme and exceptional drought event (based on the standardized precipitation evapotranspiration index) would lead to a yield loss risk (i.e. the probability of yield reduction lower than expected value) of 64.3%, 69.9%, 73.6%, and 78.1%, respectively, with hotspots identified in Central and Southeastern US. Irrigation has reduced yield loss risk by 10%-27%, with the benefit magnitude depending on the drought intensity. Evaluations of eight process crop models indicate that they can well reproduce observed drought risks for the country as a whole, but show difficult in capturing the spatial distribution patterns. The results highlight the diverse risk pattern in response to a drought event of specific intensity, and emphasize the need for better representation of drought effects in process models at local scales. The analysis framework developed in this study is novel in that it allows for an event-based assessment of drought effects in a risk manner in both observations and process crop models. Such information is valuable not only for robust decision-makings but also for the insurance sector, which typically require the risk information rather than a single value of outcome especially given the uncertainty of drought effects.
Understanding the potential drought impacts on agricultural production is critical for ensuring global food security. Instead of providing a deterministic estimate, this study investigates the ...likelihood of yield loss of wheat, maize, rice and soybeans in response to droughts of various intensities in the 10 largest producing countries. We use crop-country specific standardized precipitation index (SPI) and census yield data for 1961–2016 to build a probabilistic modeling framework for estimating yield loss risk under a moderate (−1.2 < SPI < −0.8), severe (−1.5 < SPI < −1.3), extreme (−1.9 < SPI < −1.6) and exceptional (SPI < −2.0) drought. Results show that there is >80% probability that wheat production will fall below its long-term average when experiencing an exceptional drought, especially in USA and Canada. As for maize, India shows the highest risk of yield reduction under droughts, while rice is the crop that is most vulnerable to droughts in Vietnam and Thailand. Risk of drought-driven soybean yield loss is the highest in USA, Russian and India. Yield loss risk tends to grow faster when experiencing a shift in drought severity from moderate to severe than that from extreme to the exceptional category, demonstrating the non-linear response of yield to the increase in drought severity. Sensitivity analysis shows that temperature plays an important role in determining drought impacts, through reducing or amplifying drought-driven yield loss risk. Compared to present conditions, an ensemble of 11 crop models simulated an increase in yield loss risk by 9%–12%, 5.6%–6.3%, 18.1%–19.4% and 15.1%–16.1 for wheat, maize, rice and soybeans by the end of 21st century, respectively, without considering the benefits of CO2 fertilization and adaptations. This study highlights the non-linear response of yield loss risk to the increase in drought severity. This implies that adaptations should be more targeted, considering not only the crop type and region but also the specific drought severity of interest.
Crop yield loss risk (%) in the past and future when experiencing a moderate, extreme, severe and exceptional drought. Each dot represents the multi-model ensemble mean, with the grey error lines denoting the uncertainty range. Display omitted
•The probability of yield loss under droughts is estimated for four major crops.•Yield loss risk grows non-linearly with increase in drought severity.•Yield loss risk is projected to increase in the future by an ensemble of models.
Understanding the drivers behind urban floods is critical for reducing its devastating impacts to human and society. This study investigates the impacts of recent urban development on hydrological ...runoff and urban flood volumes in a major city located in northern China, and compares the urbanization impacts with the effects induced by climate change under two representative concentration pathways (RCPs 2.6 and 8.5). We then quantify the role of urban drainage system in mitigating flood volumes to inform future adaptation strategies. A geo-spatial database on landuse types, surface imperviousness and drainage systems is developed and used as inputs into the SWMM urban drainage model to estimate the flood volumes and related risks under various urbanization and climate change scenarios. It is found that urbanization has led to an increase in annual surface runoff by 208 to 413%, but the changes in urban flood volumes can vary greatly depending on performance of drainage system along the development. Specifically, changes caused by urbanization in expected annual flood volumes are within a range of 194 to 942%, which are much higher than the effects induced by climate change under the RCP 2.6 scenario (64 to 200%). Through comparing the impacts of urbanization and climate change on urban runoff and flood volumes, this study highlights the importance for re-assessment of current and future urban drainage in coping with the changing urban floods induced by local and large-scale changes.
The study is set in a major city located in Northern China with a focus on comparing the impacts of recent urbanization and future climate change on urban runoff and flood volumes, in particular taking into account the role of urban drainage. Display omitted
•Investigate changes in landuse and drainage systems to assess urbanization impacts•Projections of climate change based on 5 bias-corrected GCMs under RCP 2.6 and 8.5•Evaluate hydrological runoff and total flood volume using SWMM drainage modeling•Compare effects of climate change and urbanization on urban flood volume and risk•Assess the role of urban drainage in mitigating urban flood volume
The linkage between crop yield and climate variability has been confirmed in numerous studies using statistical approaches. A crucial assumption in these studies is that crop spatial distribution ...pattern is constant over time. Here, we explore how changes in county-level corn spatial distribution pattern modulate the response of its yields to climate change at the state level over the Contiguous United States. Our results show that corn yield response to climate change varies with crop spatial distribution pattern, with distinct impacts on the magnitude and even the direction at the state level. Corn yield is predicted to decrease by 20~40% by 2050 s when considering crop spatial distribution pattern changes, which is 6~12% less than the estimates with fixed cropping pattern. The beneficial effects are mainly achieved by reducing the negative impacts of daily maximum temperature and strengthening the positive impacts of precipitation. Our results indicate that previous empirical studies could be biased in assessing climate change impacts by ignoring the changes in crop spatial distribution pattern. This has great implications for understanding the increasing debates on whether climate change will be a net gain or loss for regional agriculture.
•The propagation time from meteorological to hydrological drought was examined.•The propagation time shows obvious seasonal characteristics.•ENSO and AO are strongly correlated with the propagation ...time on long timescales.•The parameter w values of the Fu’s equation exhibit positive linkages with the propagation time.
It is important to investigate the propagation from meteorological to hydrological drought and its potential influence factors, which helps to reveal drought propagation process, thereby being helpful for drought mitigation. In this study, Standardized Precipitation Index (SPI) and Standardized Streamflow Index (SSI) were adopted to characterize meteorological and hydrological droughts, respectively. The propagation time from meteorological to hydrological drought was investigated. The cross wavelet analysis was utilized to examine the correlations between hydrological and meteorological droughts in the Wei River Basin (WRB), a typical arid and semi-arid region in China. Moreover, the potential influence factors on the propagation were explored from the perspectives of large-scale atmospheric circulation anomaly and underlying surface characteristics. Results indicated: (1) the propagation time from meteorological to hydrological drought has noticeably seasonal characteristics, that in spring and summer is short, whilst that in autumn and winter is long; (2) hydrological and meteorological droughts are primarily characterized by statistically positive linkages on both long and short time scales; (3) El Niño Southern Oscillation (ENSO) and Arctic Oscillation (AO) are strongly correlated with actual evaporation, thus strongly impacting the propagation time from meteorological to hydrological drought. Additionally, the propagation time has roughly positive associations with the parameter w of the Fu’s equation from the Budyko framework.
Pervious assessments of crop yield response to climate change are mainly aided with either process-based models or statistical models, with a focus on predicting the changes in average yields, whilst ...there is growing interest in yield variability and extremes. In this study, we simulate US maize yield using process-based models, traditional regression model and a machine-learning algorithm, and importantly, identify the weakness and strength of each method in simulating the average, variability and extremes of maize yield across the country. We show that both regression and machine learning models can well reproduce the observed pattern of yield averages, while large bias is found for process-based crop models even fed with harmonized parameters. As for the probability distribution of yields, machine learning shows the best skill, followed by regression model and process-based models. For the country as a whole, machine learning can explain 93% of observed yield variability, followed by regression model (51%) and process-based models (42%). Based on the improved capability of the machine learning algorithm, we estimate that US maize yield is projected to decrease by 13.5% under the 2 °C global warming scenario (by ∼2050 s). Yields less than or equal to the 10th percentile in the yield distribution for the baseline period are predicted to occur in 19% and 25% of years in 1.5 °C (by ∼2040 s) and 2 °C global warming scenarios, with potentially significant implications for food supply, prices and trade. The machine learning and regression methods are computationally much more efficient than process-based models, making it feasible to do probabilistic risk analysis of climate impacts on crop production for a wide range of future scenarios.
•An integrated drought indicator based on variable fuzzy theory was proposed.•This integrated indicator is sensitive to capture drought onset and persistence.•Detected change points were closely ...associated with human activities and ENSO events.
It is of great importance to construct an integrated drought indicator, which is of great importance to drought risk assessment and decision-making. Given the fuzzy nature of drought, the variable fuzzy set theory was applied to develop an Integrated Drought Index (IDI) combining meteorological, hydrological, and agricultural factors across the Yellow River basin in North China. The runoff and soil moisture were derived by driving the calibrated Variable Infiltration Capacity (VIC) model with observed atmospheric forcing. Furthermore, the law of mutual change of quality and quantity was adopted to identify qualitative change points of annual IDI series in the Yellow River basin. The results indicate that: (1) the Integrated Drought Index (IDI) has a better performance compared with Standardized Precipitation Index (SPI) and Standardized streamflow Index (SSFI), and it is more sensitive and effective to capture drought onset and persistence, largely owing to its combination with the information of different drought-related variables; (2) spatially, the middle reaches has a higher drought risk than the rest portions of the Yellow River basin; seasonally, drought risk in spring and winter is larger than other seasons; overall, the IDI of the basin is dominated by an insignificantly downward trend; (3) some qualitative change points of drought were identified in the Yellow River basin, and those are primarily induced by ENSO events and the construction of dams and reservoirs. This study proposed an alternative drought indicator coupled with multivariate drought-related variables by objectively determining their weights based on the entropy weight method, which has a great value in characterizing drought.
The increase in extreme climate events such as flooding and droughts predicted by the general circulation models (GCMs) is expected to significantly affect hydrological processes, erosive dynamics, ...and their associated nonpoint source (NPS) pollution, resulting in a major challenge to water availability for human life and ecosystems. Using the Hydrological Simulation Program-Fortran model, we evaluated the synergistic effects of droughts and rainfall events on hydrology and water quality in an upstream catchment of the Miyun Reservoir based on the outputs of five GCMs. It showed substantial increases in air temperature, precipitation intensity, frequency of heavy rains and rainstorms, and drought duration, as well as sediment and nutrient loads in the RCP 8.5 scenario. Sustained droughts followed by intense precipitation could cause complex interactions and mobilize accumulated sediment, nutrients and other pollutants into surface water that pose substantial risks to the drinking water security, with the comprehensive effects of soil water content, antecedent drought duration, precipitation amount and intensity, and other climate characteristics, although the effects varied greatly under different rainfall patterns. The Methods and findings of this study evidence the synergistic impacts of droughts and heavy rainfall on watershed system and the significant effects of initial soil moisture conditions on water quantity and quality, and help to guide a robust adaptive management system for future drinking water supply.
Abstract
Projecting future changes in crop yield usually relies on process-based crop models, but the associated uncertainties (i.e. the range between models) are often high. In this study, a Machine ...Learning (i.e. Random Forest, RF) based observational constraining approach is proposed for reducing the uncertainties of future maize yield projections by seven process-based crop models. Based on the observationally constrained crop models, future changes in yield average and yield variability for the period 2080–2099 are investigated for the globe and top ten producing countries. Results show that the uncertainties of crop models for projecting future changes in yield average and yield variability can be largely reduced by 62% and 52% by the RF-based constraint, respectively, while only 4% and 16% of uncertainty reduction is achieved by traditional linear regression-based constraint. Compared to the raw simulations of future change in yield average (−5.13 ± 18.19%) and yield variability (−0.24 ± 1.47%), the constrained crop models project a much higher yield loss (−34.58 ± 6.93%) and an increase in yield variability (3.15 ± 0.71%) for the globe. Regionally, the constrained models show the largest increase in yield loss magnitude in Brazil, India and Indonesia. Our results suggest more agricultural risks under climate change than previously expected after observationally constraining crop models. The results obtained in this study point to the importance for observationally constraining process crop models for robust yield projections, and highlight the added value of using Machine Learning for reducing the associated uncertainties.
Understanding historical changes in flood damage and the underlying mechanisms is critical for predicting future changes for better adaptations. In this study, a detailed assessment of flood damage ...for 1950-1999 is conducted at the state level in the conterminous United States (CONUS). Geospatial datasets on possible influencing factors are then developed by synthesizing natural hazards, population, wealth, cropland and urban area to explore the relations with flood damage. A considerable increase in flood damage in CONUS is recorded for the study period which is well correlated with hazards. Comparably, runoff indexed hazards simulated by the Variable Infiltration Capacity (VIC) model can explain a larger portion of flood damage variations than precipitation in 84% of the states. Cropland is identified as an important factor contributing to increased flood damage in central US while urbanland exhibits positive and negative relations with total flood damage and damage per unit wealth in 20 and 16 states, respectively. Overall, flood damage in 34 out of 48 investigated states can be predicted at the 90% confidence level. In extreme cases, ~76% of flood damage variations can be explained in some states, highlighting the potential of future flood damage prediction based on climate change and socioeconomic scenarios.
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