Drought Analysis Based on the Information Diffusion and Fractal Technology: A Case Study of Winter Wheat in China

2020 ◽  
Vol 36 (6) ◽  
pp. 869-877
Author(s):  
Jian-bin Yao ◽  
Jian-hua Liu ◽  
Hui-jie Ma ◽  
Hong-wei Pan
Keyword(s):  
Winter Wheat ◽  
Information Diffusion ◽  
Fractal Theory ◽  
Meteorological Drought ◽  
Agricultural Drought ◽  
Data Series ◽  
List Type ◽  
Drought Forecasting ◽  
Study Region ◽  
Drought Prediction

HighlightsThere is no good correlation between meteorological drought and crop drought.The data series of meteorological drought and crop drought at the same time have fractal characteristics.Fractal theory can be used to predict the next drought year.Abstract.Drought is one of the natural disasters of global concern. Drought forecasting is an important tool for drought management. Uncertainty is a major challenge in drought forecasting. In order to provide a short-term effective drought prediction, this study provides a new point into drought prediction from the timing-prediction perspective. The key part of this essay lies in its fractal theoretical framework guided by the self-similarity principle, which fully considers the complexity, disorder and regularity of agricultural drought. At the same time, information diffusion theory is used to polish the raw data, especially some data about winter wheat in Zhengzhou in China. The results as follows: 1) the change trend of drought in the study region is consistent with the past; 2) the time of meteorological drought, summer maize does not necessarily lead to drought, but most timing prediction work is consistent, they have shared the similar cyclical changing-laws; and 3) the occurring time of the next drought calculated is consistent with the actual observation results. Therefore, the method established in this study is effective, and it can provide some reference for the prediction of agricultural drought outbreak time. Keywords: Crop drought, Fractal theory, Information diffusion, Meteorological drought, Winter wheat.

scholarly journals Response of Agricultural Drought to Meteorological Drought: A Case Study of the Winter Wheat above the Bengbu Sluice in the Huaihe River Basin, China

Water ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 2805
Author(s):  
Chao Gao ◽  
Cai Chen ◽  
Yi He ◽  
Tian Ruan ◽  
Gang Luo ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Time Lag ◽  
Growth Period ◽  
Meteorological Drought ◽  
Agricultural Drought ◽  
Underlying Surface ◽  
Huaihe River ◽  
Moisture Index ◽  
Geographical Factors ◽  
The Huaihe River

This study investigated the responses of winter wheat to drought for the above part of the Bengbu Sluice in the Huaihe River based on the daily scale dataset of 60 meteorological stations from 1961–2015. Crop water deficit index (CWDI) and relative moisture index (M) were used to examine the winter wheat drought and meteorological drought, respectively. We then analyzed the spatial-temporal evolution characteristics of these two kinds of drought to calculate the time lag of winter wheat drought to meteorological drought, and finally discuss the relationship between the time lag of winter wheat drought to meteorological drought and the underlying surface geographical factors, and drew the following conclusions. (1) In terms of time scale, for CWDI, except for the filling and mature period, the CWDI at other growth periods showed a slight downward trend; for M, there was no significant change in the interannual trend of each growth period. In terms of spatial scale, the proportion of above moderate drought level in each station of CWDI and M presented a decreasing feature from north to south. (2) The time lag of winter wheat drought to meteorological drought was the shortest (3.21 days) in the greening and heading period and the longest in the over-wintering period (84.35 days). (3) The correlation between the geographical factors and the time lag of winter wheat drought in each growth period was better than 0.5. The high-value points of the relation between the underlying surface geographical factors and the time lag of winter wheat drought were mostly distributed in the mountainous areas with poor soil field capacity and at a greater depth of shallow groundwater, high elevation and steep slope in the areas with aspects to the east and northeast, and the northern areas with less precipitation and lower temperature.

scholarly journals Controls on the development and persistence of soil moisture drought across Southwestern Germany

2020 ◽  
Author(s):  
Erik Tijdeman ◽  
Lucas Menzel
Keyword(s):  
Soil Moisture ◽  
Drought Stress ◽  
Development Time ◽  
Storage Capacity ◽  
Root Zone ◽  
Meteorological Drought ◽  
Agricultural Drought ◽  
Spatial And Temporal Variability ◽  
Grid Cells ◽  
Study Region

Abstract. The drought of 2018 in Central and Northern Europe showed once more the large impact this natural hazard can have on the environment and society. Such droughts are often seen as slowly developing phenomena. However, root zone soil moisture deficits can rapidly develop during periods of lacking precipitation and meteorological conditions that favour high evapotranspiration rates. These periods of soil moisture drought stress can persist for as long as the meteorological drought conditions last, thereby negatively affecting vegetation and crop health. In this study, we aim to characterize past soil moisture drought stress events over the cropland of South-Western Germany as well as to relate the characteristics of these past events to different soil and climate properties. We first simulated daily soil moisture over the period 1989–2018 on a 1-km resolution grid using the physical based hydrological model TRAIN. We then derived various soil moisture drought stress characteristics; likelihood, development time and persistence, from the simulated time series of all agricultural grid cells (n ≈ 15 000). Logistic regression and correlation were then applied to relate the derived characteristics to the storage capacity of the root zone as well as to the climatological setting. Results reveal that the majority of the agricultural grid cells across the study region reached soil moisture drought stress during prominent drought years. The development time of these soil moisture drought stress events varied substantially, from as little as 10 days to up to 4 months. The persistence of soil moisture drought stress varied as well and was especially high for the drought of 2018. The dominant control on the likelihood and development time of soil moisture drought stress was found to be the storage capacity of the root zone, whereas the persistence was not strongly linearly related to any of the considered controls. Overall, results give insights in the large spatial and temporal variability of soil moisture drought stress characteristics and highlight the importance of considering differences in root zone soil storage for agricultural drought assessments.

Crop specific assessment of droughts in the growing season considering the spatiotemporal variability in phenology

2021 ◽  
Author(s):  
Erik Tijdeman ◽  
Lucas Menzel
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Winter Wheat ◽  
Time Window ◽  
Grid Cell ◽  
Growing Season ◽  
Agricultural Drought ◽  
Spatiotemporal Variability ◽  
Study Region ◽  
Phenological Phases

<p>In the context of climate change, it is important to understand whether drought conditions over the growing season of agricultural crops have changed over the past decades. Common drought metrics used for such assessments compare hydrometeorological anomalies using a static time window. However, the growing season varies among crops as well as in space; driven by climatic differences, and time; driven by e.g. changes in climate or crop-genotypes. Focusing on Southwestern Germany, we aim to investigate how the ranking of drought years varies between crops as well as among static and spatiotemporally varying growing season scenarios. First, we derived annual information on the timing of different phenological phases of two crops, winter wheat and maize, resp. early and late covering, from observations available from the German Weather Services. We then interpolated the timing of these phenological phases to 1 km resolution grids covering all agricultural areas in the study region, using static and spatiotemporally varying interpolation scenarios. Following, we extracted climatological timeseries for all agricultural grid cells and used those to simulate the climatic water balance as well as soil moisture for each grid cell with the hydrological model TRAIN. Finally, we derived for each year different drought metrics, i.e. anomalies in precipitation, temperature, climatic water balance and minimum soil moisture, and correlated those with crop yield anomalies. Results revealed distinct differences in the start and end of the growing season among considered crops. Further, the timing of different phenological phases varied by over a month in both space and time. During the most prominent drought years (2003, 2015, 2018), the growing season of both crops was particularly dry, independent on whether a fixed or variable growing season was considered. On the other hand, there were also some crop specific drought years, e.g., 1991 for maize or 2008 for winter wheat. The difference in hydrometeorological anomalies derived for static and variable growing seasons mainly relates to differences in temperature, but also affected the ranking of some drought years according to other hydrometeorological variables. More apparent were differences between drought metrics, e.g. between the climatic water balance and minimum soil moisture. From these metrics, especially minimum soil moisture correlated well with maize yields, whereas correlations with winter wheat were generally weak for all metrics. To conclude, crop specific agricultural drought assessments could benefit from a crop-relevant growing season specific definition of drought.</p>

scholarly journals Model Comparisons Between Canonical Vine Copulas and Meta-Gaussian for Agricultural Drought Forecasting over China

2021 ◽  
Author(s):  
Haijiang Wu ◽  
Xiaoling Su ◽  
Vijay P. Singh ◽  
Te Zhang ◽  
Jixia Qi
Keyword(s):  
Performance Metrics ◽  
Reference Model ◽  
Predictive Ability ◽  
Meteorological Drought ◽  
Three Dimensions ◽  
Agricultural Drought ◽  
Drought Forecasting ◽  
Drought Mitigation ◽  
Drought Early Warning ◽  
Vine Copulas

Abstract. Agricultural drought is caused by reduced soil moisture and precipitation and affects the growth of crops and vegetation, and in turn agricultural production and food security. For developing measures for drought mitigation, reliable agricultural drought forecasting is essential. In this study, we developed an agricultural drought forecasting model based on canonical vine copulas under three-dimensions (3C-vine model), in which the antecedent meteorological drought and agricultural drought persistence were utilized as predictors. Besides, the meta-Gaussian (MG) model was selected as a reference model to evaluate the forecast skill. The agricultural drought in August of 2018 was selected as a case study, and the spatial patterns of 1–3-month lead forecasts of agricultural drought utilizing the 3C-vine model resembled the corresponding observations, indicating the predictive ability of the model. The performance metrics (NSE, R2, and RMSE) showed that the 3C-vine model outperformed the MG model for August under diverse lead times. Also, the 3C-vine model exhibited excellent forecast skills in capturing the extreme agricultural drought over different selected typical regions. This study may help with drought early warning, drought mitigation, and water resources scheduling.

Satellite Data Based Soil Water Index as a Tool to Monitor Droughts and Agricultural Crisis -An Example of Yavatmal District, Maharashtra, India

2020 ◽  
Vol 4 (1-2) ◽  
pp. 12-18
Author(s):  
Vijendra Boken
Keyword(s):  
Soil Water ◽  
Satellite Data ◽  
Suicide Rate ◽  
Crop Yields ◽  
Meteorological Drought ◽  
Agricultural Drought ◽  
Coefficient Of Correlation ◽  
Water Index ◽  
Farmer Suicides ◽  
June July

Yavatmal is one of the drought prone districts in Maharashtra state of India and has witnessed an agricultural crisis to the extent that hundreds of its farmers have committed suicides in recent years. Satellite data based products have previously been used globally for monitoring and predicting of drought, but not for monitoring their extreme impacts that may include farmer-suicides. In this study, the performance of the Soil Water Index (SWI) derived from the surface soil moisture estimated by the European Space Agency’s Advanced Scatterometer (ASCAT) is assessed. Using the 2007-2015 data, it was found that the relationship of the SWI anomaly was bit stronger (coefficient. of correlation = 0.59) with the meteorological drought or precipitation than with the agricultural drought or crop yields of major crops (coefficient. of correlation = 0.50).  The farmer-suicide rate was better correlated with the SWI anomaly averaged annually than with the SWI anomaly averaged only for the monsoon months (June, July, August, and September). The correlation between the SWI averaged annually increased to 0.89 when the averages were taken for three years, with the highest correlation occurring between the suicide rate and the SWI anomaly averaged for three years. However, a positive relationship between SWI and the suicide rate indicated that drought was not a major factor responsible for suicide occurrence and other possible factors responsible for suicide occurrence need to examine in detail.

scholarly journals Prediction of Multi-Scalar Standardized Precipitation Index by Using Artificial Intelligence and Regression Models

Climate ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 28
Author(s):  
Anurag Malik ◽  
Anil Kumar ◽  
Priya Rai ◽  
Alban Kuriqi
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Inference ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Vital Role ◽  
Precipitation Index ◽  
Visual Interpretation ◽  
Inference System ◽  
Drought Prediction ◽  
Optimal Functioning

Accurate monitoring and forecasting of drought are crucial. They play a vital role in the optimal functioning of irrigation systems, risk management, drought readiness, and alleviation. In this work, Artificial Intelligence (AI) models, comprising Multi-layer Perceptron Neural Network (MLPNN) and Co-Active Neuro-Fuzzy Inference System (CANFIS), and regression, model including Multiple Linear Regression (MLR), were investigated for multi-scalar Standardized Precipitation Index (SPI) prediction in the Garhwal region of Uttarakhand State, India. The SPI was computed on six different scales, i.e., 1-, 3-, 6-, 9-, 12-, and 24-month, by deploying monthly rainfall information of available years. The significant lags as inputs for the MLPNN, CANFIS, and MLR models were obtained by utilizing Partial Autocorrelation Function (PACF) with a significant level equal to 5% for SPI-1, SPI-3, SPI-6, SPI-9, SPI-12, and SPI-24. The predicted multi-scalar SPI values utilizing the MLPNN, CANFIS, and MLR models were compared with calculated SPI of multi-time scales through different performance evaluation indicators and visual interpretation. The appraisals of results indicated that CANFIS performance was more reliable for drought prediction at Dehradun (3-, 6-, 9-, and 12-month scales), Chamoli and Tehri Garhwal (1-, 3-, 6-, 9-, and 12-month scales), Haridwar and Pauri Garhwal (1-, 3-, 6-, and 9-month scales), Rudraprayag (1-, 3-, and 6-month scales), and Uttarkashi (3-month scale) stations. The MLPNN model was best at Dehradun (1- and 24- month scales), Tehri Garhwal and Chamoli (24-month scale), Haridwar (12- and 24-month scales), Pauri Garhwal (12-month scale), Rudraprayag (9-, 12-, and 24-month), and Uttarkashi (1- and 6-month scales) stations, while the MLR model was found to be optimal at Pauri Garhwal (24-month scale) and Uttarkashi (9-, 12-, and 24-month scales) stations. Furthermore, the modeling approach can foster a straightforward and trustworthy expert intelligent mechanism for projecting multi-scalar SPI and decision making for remedial arrangements to tackle meteorological drought at the stations under study.

scholarly journals Different Drought Legacies of Rain-Fed and Irrigated Croplands in a Typical Russian Agricultural Region

2020 ◽  
Vol 12 (11) ◽  
pp. 1700
Author(s):  
Yuanhuizi He ◽  
Fang Chen ◽  
Huicong Jia ◽  
Lei Wang ◽  
Valery G. Bondur
Keyword(s):  
Surface Temperature ◽  
Drought Index ◽  
Growth Period ◽  
Meteorological Drought ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Growth Stages ◽  
Drought Conditions ◽  
Irrigated Lands ◽  
Meteorological Droughts

Droughts are one of the primary natural disasters that affect agricultural economies, as well as the fire hazards of territories. Monitoring and researching droughts is of great importance for agricultural disaster prevention and reduction. The research significance of investigating the hysteresis of agricultural to meteorological droughts is to provide an important reference for agricultural drought monitoring and early warnings. Remote sensing drought monitoring indices can be employed for rapid and accurate drought monitoring at regional scales. In this paper, the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices and the surface temperature product are used as the data sources. Calculating the temperature vegetation drought index (TVDI) and constructing a comprehensive drought disaster index (CDDI) based on the crop growth period allowed drought conditions and spatiotemporal evolution patterns in the Volgograd region in 2010 and 2012 to be effectively monitored. The causes of the drought were then analyzed based on the sensitivity of a drought to meteorological factors in rain-fed and irrigated lands. Finally, the lag time of agricultural to meteorological droughts and the hysteresis in different growth periods were analyzed using statistical analyses. The research shows that (1) the main drought patterns in 2010 were spring droughts from April to May and summer droughts from June to August, and the primary drought patterns in 2012 were spring droughts from April to June, with an affected area that reached 3.33% during the growth period; (2) local drought conditions are dominated by the average surface temperature factor. Rain-fed lands are sensitive to the temperature and are therefore prone to summer droughts. Irrigated lands are more sensitive to water shortages in the spring and less sensitive to extremely high temperature conditions; (3) there is a certain lag between meteorological and agricultural droughts during the different growth stages. The strongest lag relationship was found in the planting stage and the weakest one was found in the dormancy stage. Therefore, the meteorological drought index in the growth period has a better predictive ability for agricultural droughts during the appropriately selected growth stages.

Agricultural Drought Prediction Based on Conditional Distributions of Vine Copulas

2021 ◽  
Author(s):  
Haijiang Wu ◽  
Xiaoling Su ◽  
Vijay P. Singh ◽  
Kai Feng ◽  
Jiping Niu
Keyword(s):  
Agricultural Drought ◽  
Conditional Distributions ◽  
Drought Prediction ◽  
Vine Copulas

scholarly journals Seasonal drought prediction for semiarid northeastern Brazil: verification of six hydro-meteorological forecast products

2018 ◽  
Vol 22 (9) ◽  
pp. 5041-5056 ◽  
Author(s):  
José Miguel Delgado ◽  
Sebastian Voss ◽  
Gerd Bürger ◽  
Klaus Vormoor ◽  
Aline Murawski ◽  
...  
Keyword(s):  
Rainy Season ◽  
Meteorological Drought ◽  
Skill Score ◽  
Hydrological Drought ◽  
Northeastern Brazil ◽  
Drought Indices ◽  
Monthly Precipitation ◽  
Seasonal Forecasts ◽  
Seasonal Drought ◽  
Drought Prediction

Abstract. A set of seasonal drought forecast models was assessed and verified for the Jaguaribe River in semiarid northeastern Brazil. Meteorological seasonal forecasts were provided by the operational forecasting system used at FUNCEME (Ceará's research foundation for meteorology) and by the European Centre for Medium-Range Weather Forecasts (ECMWF). Three downscaling approaches (empirical quantile mapping, extended downscaling and weather pattern classification) were tested and combined with the models in hindcast mode for the period 1981 to 2014. The forecast issue time was January and the forecast period was January to June. Hydrological drought indices were obtained by fitting a multivariate linear regression to observations. In short, it was possible to obtain forecasts for (a) monthly precipitation, (b) meteorological drought indices, and (c) hydrological drought indices. The skill of the forecasting systems was evaluated with regard to root mean square error (RMSE), the Brier skill score (BSS) and the relative operating characteristic skill score (ROCSS). The tested forecasting products showed similar performance in the analyzed metrics. Forecasts of monthly precipitation had little or no skill considering RMSE and mostly no skill with BSS. A similar picture was seen when forecasting meteorological drought indices: low skill regarding RMSE and BSS and significant skill when discriminating hit rate and false alarm rate given by the ROCSS (forecasting drought events of, e.g., SPEI1 showed a ROCSS of around 0.5). Regarding the temporal variation of the forecast skill of the meteorological indices, it was greatest for April, when compared to the remaining months of the rainy season, while the skill of reservoir volume forecasts decreased with lead time. This work showed that a multi-model ensemble can forecast drought events of timescales relevant to water managers in northeastern Brazil with skill. But no or little skill could be found in the forecasts of monthly precipitation or drought indices of lower scales, like SPI1. Both this work and those here revisited showed that major steps forward are needed in forecasting the rainy season in northeastern Brazil.

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