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A Non‑Stationary Based Approach to Understand the Propagation of Meteorological to Agricultural Droughts

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Subhadarsini Das at Central Michigan University
Subhadarsini Das
Jew Das at National Institute of Technology, Warangal
Jew Das
  • National Institute of Technology, Warangal
Umamahesh V. Nanduri at National Institute of Technology, Warangal
Umamahesh V. Nanduri
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Abstract and Figures

The agricultural drought significantly affects the socio-economic sectors in the agrarian country like India. Though there is a larger variability in the drought characteristics, the time to propagation from meteorological to agricultural drought is not investigated at regional scale in India. The Standardised Precipitation Evapotranspiration Index (SPEI), and Standardised Soil moisture Index (SSI) are computed incorporating large-scale climatic oscillations and regional hydro-meteorological variables. The time to propagation is calculated based on three different approaches. In addition, the internal characteristics of agricultural drought propagation is computed. The important findings from the study suggest that the time of propagation varies between 5 to 7 months for drought initiation, 9 to 15 months for drought peak, and 10 to 20 months for drought termination. The internal drought development and recover periods varies from 3.1 to 6 months. Over most of the area, the instantaneous drought development and recovery speed magnitude varies between 0.20 and 0.60. Lastly, it is observed that the exclusion of physical covariates leads to underestimation of agricultural drought propagation characteristics over India. The results of the current study can be used to guide future early warning and monitoring systems for agricultural drought as well as the study of agricultural drought at the regional level.
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Vol.:(0123456789)
Water Resources Management (2023) 37:2483–2504
https://doi.org/10.1007/s11269-022-03297-9
1 3
A Non‑Stationary Based Approach toUnderstand
thePropagation ofMeteorological toAgricultural Droughts
SubhadarsiniDas1· JewDas1 · N.V.Umamahesh1
Received: 26 April 2022 / Accepted: 9 August 2022 / Published online: 3 September 2022
© The Author(s), under exclusive licence to Springer Nature B.V. 2022
Abstract
The agricultural drought significantly affects the socio-economic sectors in the agrar-
ian country like India. Though there is a larger variability in the drought characteristics,
the time to propagation from meteorological to agricultural drought is not investigated at
regional scale in India. The Standardised Precipitation Evapotranspiration Index (SPEI),
and Standardised Soil moisture Index (SSI) are computed incorporating large-scale cli-
matic oscillations and regional hydro-meteorological variables. The time to propagation
is calculated based on three different approaches. In addition, the internal characteristics
of agricultural drought propagation is computed. The important findings from the study
suggest that the time of propagation varies between 5 to 7months for drought initiation, 9
to 15months for drought peak, and 10 to 20months for drought termination. The internal
drought development and recover periods varies from 3.1 to 6months. Over most of the
area, the instantaneous drought development and recovery speed magnitude varies between
0.20 and 0.60. Lastly, it is observed that the exclusion of physical covariates leads to under-
estimation of agricultural drought propagation characteristics over India. The results of the
current study can be used to guide future early warning and monitoring systems for agri-
cultural drought as well as the study of agricultural drought at the regional level.
Keywords Agricultural drought· Covariates· Drought propagation· Soil moisture· India
1 Introduction
Droughts are different from other natural disasters as the development is usually slow and
its impact on ecology, hydrology, agriculture, and economy is remarkable due to long-term
water shortage (Wang etal. 2019; Das etal. 2021b). In addition, the recovery period after
a drought event can be lengthy and affects the ecosystem resilience and stability (Liu etal.
2019). Under the background of climate change, the frequency and intensity of drought
events are expected to increase (Spinoni etal. 2020; Tigkas etal. 2020; Das etal. 2021a).
With increasing number of drought events, regions with long recovery time are likely to
suffer a new drought event before full recovery. Moreover, the industry and agricultural
* Jew Das
jewdas05@gmail.com
1 Department ofCivil Engineering, National Institute ofTechnology Warangal, Warangal506004, India
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... Recently, due to the impacts of global climate change and human activities, drought occurrence and related natural disaster loss have been aggravated (Das et al. 2022;Tsakiris and Loucks 2023), and it is investigated that the annual global socio-economic losses caused by drought disasters has reached to 6-8 billion dollars (Wu et al 2021). Meteorological drought is the source of entire variation chain of drought occurrence, propagation and resulting disaster losses (Raposo et al. 2023). ...
... To date, much work has been conducted focusing on the exploration of drought propagation laws, response mechanism and driving factors recognition analysis. Specifically, (1) in terms of drought evolution characteristics analysis, it has been testified that the variation of hydrological variable presents obvious inconsistent features in the long run as a result of the changes of global climate and underlying surface conditions instead of satisfying stationary hypothesis (Song and Singh 2010;Das et al. 2022;Alehu and Bitana 2023). Therefore, Xiong and Xiong (2016) extended the widely-applied Generalized Additive Models for Location, Scale and Shape (GAMLSS) model of inconsistent flood frequency analysis in drought frequency analysis. ...
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Drought propagation analysis is of great significance to develop reliable drought-resistant schemes. In this study, based on the construction of time-variant meteorological and agricultural drought indicators SPIt and SSMIt through Generalized Additive Models for Location, Scale and Shape (GAMLSS) method, the static and dynamic drought propagation time were defined and determined by Five-element Subtraction Set Pair Potential (FSSPP) and Copula function methods. Then, the driving factors resulted in meteorological to agricultural drought propagation were recognized through Pearson Correlation Coefficient (PCC) indicator. And finally, the application results of the proposed approach in Anhui province, China indicated that, (1) the static propagation time of meteorological to agricultural drought process varied within 1 to 4 months, and differed slightly in different seasons. And drought propagation time in spring and summer was noticeably longer than that of autumn and winter. (2) the dynamic drought propagation time presented decreasing trend in winter, spring and summer but displayed slight increasing trend in autumn in Anhui province. On the whole, the research findings are beneficial for theoretical and practical research of drought propagation system.
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... Many researchers carried out a probability-based stochastic approach in estimating the drought hazard propagation threshold from meteorological to the hydrological domain (Sattar et al. 2019;Gu et al. 2020). However, there can be non-stationarity in the drought index time series intending non-stationarity in the drought propagation (Das et al. 2023;Jehanzaib et al. 2023). Cross Wavelet Transform (XWT) and Wavelet Coherence (WTC) are applied to understand drought propagation mostly by identifying high spectral densities corresponding to historical times (Huang et al. 2017;Wang et al. 2020;Li et al. 2020). ...
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The advent of climate change has induced frequent occurrence of droughts in the past few decades. Identification of hydrological droughts require computation of drought indices by probabilistic standardization procedures. The existing hydrological drought indices could not answer the zero monthly streamflow condition for the ephemeral streams. This issue was resolved by developing a modified Standardized Streamflow Index to characterize the hydrological drought of Upper Kangsabati River Basin, West Bengal, India. 45 hydrological droughts were extracted for the basin and the most severe drought occurred in the year 2015–2016. The basin experienced the most severe drought of 10.67 severity and longest drought duration of 13 months. A bivariate analysis of drought characteristics was carried out using copula technique to determine different design drought events. The bivariate distribution which showed the basin experienced most severe drought of 16 years and longest duration drought of 15 years ‘OR’ return period. Propagation time of the drought hazard from the meteorological to the hydrological drought is extensively studied in this research using both correlation and Cross Wavelet Transform (XWT) methods. XWT was mostly used for qualitative comparison of hydrological and meteorological signal in drought propagation studies in the past. In this research, a novel quantitative approach of using XWT and the phase angles obtained between the hydrological and meteorological signals is proposed to determine the drought propagation times. It was concluded that the basin had in general a drought propagation time of 2 months. However, there were seasonal variability in the drought propagation times showing prompt response in the summer season which increased to 2 months for monsoons and stretching far to 5 months for the late winter.
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... In India, about 68% of the country is at risk of some level of drought, 35% of the areas that get between 750 and 1125 mm of rain are considered droughtprone, and 33 percent of the areas that get less than 750 mm of rain are considered chronically drought-prone (Rawat et al. 2022). India has faced significant droughts from 1870 to 2016, causing the famine resulting in the death of millions of people with heavy socio-economic agricultural loss (Amrit et al. 2018;Mishra et al. 2019;Das et al. 2023). The paucity of water over a long period is termed drought, possibly due to below-average precipitation in that region (Mishra and Desai 2005). ...
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The complex topography of the Himalayan region makes it difcult to analyze its climatic variables over the region. The study has been carried out to identify the trends in climate variables and drought analysis over the Beas River basin in the western Himalayas. To understand the impact of changing climate on the Beas River basin, fve downscaled global circulation models (GCMs) were used, namely BNU-ESM, Can-ESM2, CNRM CM5, MPI-ESM MR, and MPI-ESM LR. These GCMs were obtained for two representative concentration pathway (RCP) scenarios: 4.5, which represents the normal scenario, and 8.5, which represents the most extreme scenario for anticipated concentrations of carbon and greenhouse gases. The multi-model ensemble (MME) of these 5 GCMs were used to project rainfall and temperature. Further Innovative Trends Analysis (ITA) and modifed Mann–Kendell (mMK) trend tests have been used for trend analysis at a 5% signifcance level. The drought pattern in the future timescale of the ensembled model is calculated using the Standardised Precipitation Index (SPI) for both RCPs. The ITA, Mann–Kendell, and Sen’s slope trends showed decreased precipitation under RCP 4.5 in the Manali region and showed an increasing trend for the remaining locations under both scenarios. Furthermore, SPI values showed frequent droughts under both RCPs. The study outcomes will serve as a scientifc foundation for the sustainability of water resources and agricultural output in arid inland regions vulnerable to changing climate.
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... Although the PCC effectively measures linear relationships, it falls short in capturing the complex, nonlinear interactions often found in drought dynamics, a limitation significant in meteorological to agricultural drought propagation where relationships are not strictly linear . Recent studies by Xu et al. (2023) and Das et al. (2023) have investigated the propagation times of agricultural droughts from meteorological droughts, considering their nonlinear linkage. Fang et al. (2020) utilized a comprehensive approach to identify drought propagation, considering both linear and nonlinear dependencies, and Li et al. (2022) employed copula techniques to explore this aspect further. ...
Application of ESACCI SM product-assimilated to a statistical model to assess the drought propagation for different Agro-Climatic zones of India using copula
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Meteorological drought precedes the agricultural drought and studying the propagation time from meteorological to agricultural drought can substantially reduce agricultural losses. To find this propagation time between the meteorological and agricultural drought, this study analyzed the copula-based conditional probability between the Standardized Precipitation Index at 12 timescales (SPI-1 to12, meteorological drought) and Standardized Soil Moisture Index at 1 month timescale (SSI-1, agricultural drought), over the fifteen Agro-Climatic Zones (ACZs) of India. SSI is computed using a total column Soil Moisture (SM) derived from ESACCI SM using the Statistical Soil Moisture Profile (SSMP) model. The SSMP-based ESACCI SM is positively correlated (Correlation Coefficient of 0.871) with ERA5 Land SM. To compute the conditional probability, three copulas namely Frank, Clayton, and Gumbel copulas are fitted between SPI-1 to 12 and SSI-1. The Goodness of Fit analysis showed the dominance of the Gumbel copula among the Clayton, Frank, and Gumbel copulas. Gumbel copula completely dominated the SPI timescales of 1-3 and 11-12 whereas Clayton copula was relatively favored for SPI timescales of 4-10. The propagation time results revealed that the monsoon season (JAS) consistently exhibits the highest propagation time, averaging around 10 months followed by summer (AMJ, 7 months), spring (JFM, 4 months), and winter (OND, 2-3 months) seasons. The Mann-Kendall trend test revealed that the same season can have different trends in different ACZs and vice versa, underlining the localized and season-specific strategies for drought management, considering each ACZ's unique environmental, agricultural, and socioeconomic conditions in each zone. Overall, this study showcases the capability of remote sensing and copula-based statistical models, in understanding meteorological to agricultural drought propagation. It also highlights the efficiency of statistical models in deriving total column SM from surface SM.
View
... The frequency and spatial extent of droughts and heatwaves in India have increased significantly in past decades, primarily driven by a mean summer monsoon rainfall decline and rise in temperatures Nepal et al. 2021;Pal and Ojha 2021;Saha and Sateesh 2022;Goyal et al. 2023). Recent years have seen an emphasis on flash droughts, transition between different types of droughts, and drought indices (Bhardwaj et al. 2020;Shah and Mishra 2020;Prajapati et al. 2021;Rehana and Naidu 2021;Das et al. 2022Das et al. , 2023Kanthavel et al. 2023;Mahto and Mishra 2023). Bhardwaj et al. (2020) found that the Indus, Sabarmati, and Godavari River basins have higher propagation times for meteorological to hydrological droughts. ...
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India has a growing water crisis fueled by global warming and a rising population. There is an urgent need for accurate water availability assessments and sustainable water management strategies for urban and rural areas. This can be achieved by developing novel decision-making tools for effective water resource management by improving the hydrological models and our understanding of hydrological processes. The changing climate adds complexity to hydrological processes, necessitating accurate modelling and impact assessments to build climate change-resilient water resource systems. This review examines the advancements in hydrological process understanding and surface hydrological modelling in India from 2019 to 2023. Recent years have witnessed substantial contributions from the Indian hydrology community, which include quantifying climate change impacts on water and carbon cycle at a basin scale, improvements in hydrological modelling and forecasting extremes, the introduction of novel physics-based data-driven approaches, urban flood modelling and the development of first-ever state-of-the-art flood early warning system among other notable climate services. In addition, the idea of studying natural systems as coupled human-natural systems has gained prominence in India. This review aims to provide insights into recent developments in surface water hydrology in India and highlight the potential future avenues of research that can uplift water resources management in India.
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... As irrigation requirements are highly dependant on the prevailing meteorological conditions, a comprehensive assessment of the interannual variability of requirements should be based on a relatively long time series (Döll and Siebert, 2002). To characterise the relationship between meteorological drought and agricultural drought, various studies have compared the standardised precipitation-evapotranspiration index (SPEI; Vicente-Serrano et al., 2010;Beguería et al., 2014) with information related to agricultural droughts (e.g., Bachmair et al., 2018;Parsons et al., 2019;Vicente-Serrano et al., 2019;Das et al., 2022;Qin et al., 2023). As meteorological data are readily available, the linking of irrigation requirements to meteorological drought conditions can serve an important need for water resource planning for both researchers and practitioners. ...
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As climate change brings about hotter and often drier summers, an improved understanding of how irrigation requirements vary according to climatic conditions is of increasing importance. Within Germany, temperate conditions have historically enabled most agriculture to be supplied solely by green water, but recent crop yield reductions and crop failures have demonstrated its increased vulnerability to climatic conditions. The raster-based mGROWA hydrological water balance model was implemented over all agricultural areas in Germany for the period 1961–2020 at a high spatial (200 m) and temporal (daily) resolution. Grid-cells were each assigned one of 10 major crop classes, which account for 86.7 % of all agricultural areas in Germany, and effectively all irrigated areas. Using crop-specific irrigation rules that reflect actual practices, irrigation requirements were simulated for all crop areas. To investigate the relationship between climatic water balance over the crop growing season and irrigation requirements, the simulated annual irrigation requirements were compared with the standardised precipitation-evapotranspiration index (SPEI-6), calculated at the end of September. Through this comparison, irrigation requirements could be characterised for near-normal and dry conditions, and results were aggregated to the district level. Additionally, using district-level data on the areas with irrigation infrastructure, the actual water used for irrigation was estimated. The results highlight marked increases in irrigation requirements in dry conditions compared to near-normal conditions (median increase of 72 %), which are more pronounced over crops in silty soils than in sandy soils. The results also demonstrate how the increased irrigation requirements in dry years are in many cases higher than what is suggested by guidelines for irrigation management in Germany. This study provides important information for actors related to the agricultural sector and water management and is based on a robust and transferable framework to quantify how irrigation requirements vary according to climatic variability and local soil conditions.
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Dynamic variation of meteorological drought and its relationships with agricultural drought across China
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  • Sep 2021
  • AGR WATER MANAGE
Drought propagation describes the changes in a drought signal as it moves from one major type of drought to another. It is important to investigate the propagation among meteorological, agricultural and hydrological drought, as well as their major impacting factors, to improve understanding of the drought propagation relationship, monitor agricultural drought and reduce crop losses. This study presents the first exploration of the interplay between multiple droughts among different climate zones and seasons in China. The standardized precipitation evapotranspiration index (SPEI), standardized runoff index (SRI) and self-calibrating Palmer drought severity index (scPDSI) were used to represent meteorological, agricultural and hydrological drought, respectively. The Pearson correlation coefficient was used to analyze the propagation relationships among different droughts and identify the most sensitive season for drought propagation. The Lindeman–Merenda–Gold (LMG) method was used to quantify the relative importance of PRE (precipitation), PET (potential evapotranspiration) and SM (soil moisture) to hydrological and agricultural drought. The propagation from meteorological to agricultural drought was prominent in different seasons at the annual scale over China. In general, the propagation relationship from agricultural to hydrological drought was weaker than that from meteorological to agricultural drought. In Northern China (arid and semi-arid areas), there was a stronger propagation relationship from agricultural to hydrological drought in summer and autumn than in spring. There was also stronger propagation from agricultural to hydrological drought in eastern China than in western China. Different climate regions had different major factors driving hydrological drought because of the different climate characteristics. However, SM was generally the most important driving factor for agricultural drought in all climate regions. Mulching plastic film might be an effective and feasible method to reduce PET from soil evaporation in sub-regions that apply high irrigation levels. These findings may also be applied to strengthen the study of artificial regulation of water resources, which could be an approach to reducing crop losses from drought.
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Drought Monitoring using High Spatial Resolution Soil Moisture Data over Australia in 2015-2019
Article
  • Jan 2021
  • J HYDROL
Drought is one of the major hazards that could have a significant impact on agriculture. In this study, two drought indices at high spatial resolution: Soil Water Deficit Index (SWDI) and Soil Moisture Deficit Index (SMDI) were derived by 1 km downscaled Soil Moisture Active Passive (SMAP) soil moisture (SM), Global Land Data Assimilation System (GLDAS) long-term SM and soil attribute products, and used to analyze the drought conditions in Australia in 2015-2019. The SWDI was calculated from SMAP SM estimates and SM at field capacity/wilting point derived from soil attribute data, while the SMDI was calculated by integrating GLDAS and SMAP SM using a temporally incremental based method. We found that in the eastern and western coastal regions, the drought condition occurred during spring and summer and were relieved in fall and winter. The temporal change pattern of drought conditions for the northern coastal regions was opposite of the eastern/western coasts. On the other hand, the inland regions always had more severe drought conditions. Additionally, the validation results for the 1 km SMAP SM using International Soil Moisture Network (ISMN) in situ data showed reliable accuracy and the Root Mean Square Deviation (RMSD) ranged from 0.02–0.09 m³/m³. Both SWDI and SMDI showed clear seasonal and interannual variability, and the drought conditions worsened in 2017-2019. From the 1 km SWDI/SMDI maps in Murray-Darling River Basin, terrain and streamflow were found to be two deterministic factors for the drought conditions.
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The use of combined soil moisture data to characterize agricultural drought conditions and the relationship among different drought types in China
Article
  • Jan 2021
  • AGR WATER MANAGE
Drought monitoring and assessment are of great importance due to the costly damage caused by drought. Datasets, drought indexes and drought relationships are three critical areas of drought research. Satellite-retrieved soil moisture (SM) products derived from the European Space Agency Climate Change Initiative (ESA CCI) show application potential in drought monitoring. However, the products are missing certain data in some areas. The model-assimilated SM product derived from the Global Land Data Assimilation System (GLDAS) was used to supplement these missing data. The main goals of this paper are to characterize agricultural drought after the utility and applicability of the combined SM product and the monthly scaled soil water deficit index (SWDI) have been evaluated and to investigate the relationships among meteorological, agricultural and vegetation droughts. First, we provided a long series of highly accurate SM products through simple calculations. The drought index, SWDI, was extended to a monthly scale for long-term drought analysis by using the combined SM product. The probability of detection (POD) between the SWDI and in situ drought records performed fairly well. Half of the 566 stations had PODs higher than 0.9, and one-third of these stations had POD values equal to 1. Through correlation analysis and grey incidence analysis (GIA) between the standardized precipitation index (SPI) and SWDI, we found that the propagation time from meteorological drought to agricultural drought was shorter under drier conditions than wetter conditions, and at the regional scale, the response time ranged from 1 month to 2.5 months. Correlation analysis between the SWDI and vegetation condition index (VCI) indicated that there was no delay effect from agricultural to vegetation drought on a monthly scale in most parts of China except in several provinces distributed in the South; additionally, there was a significant time lag in forests, while grassland and agriculture were more inclined to have no time lag or the response time was less than 1 month.
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