Drought Detection Research Articles

Trenutna perspektiva primjene daljinskih istraživanja u upravljanju poljoprivrednim usjevima

Knowledge of the spatial distribution of agricultural crops and crop rotation is necessary for the understanding of the farming practices concerning the long-term sustainability of agricultural production. Agricultural crops are increasingly subject to drought due to the effects of global climate change, and the same is true for Croatia due to the constant rise in mean annual temperatures and uneven rainfall distribution. Remote sensing methods have proven to be superior in the detection and monitoring of drought compared to conventional methods of observation from meteorological stations. Information on the condition of crops in the early stages of development indicates potential irregularities in the development of agricultural crops. The objective of this paper is to provide a perspective for the application of remote sensing in crop management using state-of-the-art methods. Analysis of the possible implementation of these methods in Croatia was performed on a macro- and micro-level. Spatial classification, cropland suitability multicriteria analysis, drought assessment, weed detection and crop density calculation were evaluated according to the necessary equipment and data processing segments. Remote sensing application in crop management offers a potential basis for better crop management both at the macro-level for land use planning and at the micro-level for family farms.

Detection of flash drought using evaporative stress index in South Korea

Detection of flash drought using evaporative stress index in South Korea

The Added-Value of Remotely-Sensed Soil Moisture Data for Agricultural Drought Detection in Argentina

In countries where the economy relies mostly on agricultural-livestock activities, such as Argentina, droughts cause significant economic losses. Currently, the most-used drought indices by the Argentinian National Meteorological and Hydrological Services are based on field precipitation data, such as the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI). In this article, we explored the performance of the satellite-based soil moisture agricultural drought index (SMADI) for agricultural drought detection in Argentina during 2010-2015, and compared it with the one from the standardized soil moisture anomalies (SSMA), SPI and SPEI (at one-month and three-month temporal scales), using the Agricultural Ministry's drought emergency database as a benchmark. The performances were analyzed in terms of the suitability of each index to be included in an early warning system for agricultural droughts, including true positive rate (TPR), and both false positive and false negative rates. In our experiments, SMADI showed the best overall performance, with the highest TPR and F1-score, and the second best false positive rate (FPR), positive predictive value, and overall accuracy. SMADI also showed the largest difference between TPR and FPR. SSMA showed the lowest FPR, but also the lowest TPR, making it not useful for an alert system. Furthermore, field precipitation-based indices, yet simple and widely used, showed not to be suitable indicators for detection of agricultural drought for Argentina, neither in the one-month nor in the three-month scale.

Drought Detection over Papua New Guinea Using Satellite-Derived Products

This study evaluates the World Meteorological Organization’s (WMO) Space-based Weather and Climate Extremes Monitoring (SWCEM) Demonstration Project precipitation products over Papua New Guinea (PNG). The products evaluated were based on remotely-sensed precipitation, vegetation health, soil moisture, and outgoing longwave radiation (OLR) data. The satellite precipitation estimates of the Climate Prediction Center/National Oceanic and Atmospheric Administration’s (CPC/NOAA) morphing technique (CMORPH) and Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP) were assessed on a monthly timescale over an 18-year period from 2001 to 2018. Station data along with the ERA5 reanalysis were used as the reference datasets for assessment purposes. In addition, a case study was performed to investigate how well the SWCEM precipitation products characterised drought in PNG associated with the 2015–2016 El Niño. Overall statistics from the validation study suggest that although there remains significant variability between satellite and ERA5 rainfall data in remote areas, this difference is much less at locations where rain gauges exist. The case study illustrated that the Vegetation Health Index (VHI), OLR anomaly and the Standardized Precipitation Index (SPI) were able to reliably capture the spatial and temporal aspects of the severe 2015–2016 El Niño-induced drought in PNG. Of the three, VHI appeared to be the most effective, in part due to its reduced incidence of false alarms. This study is novel as modern-day satellite-derived products have not been evaluated over PNG before. A focus on their value in monitoring drought can bring great value in mitigating the impact of future droughts. It is concluded that these satellite-derived precipitation products could be recommended for operational use for drought detection and monitoring in PNG, and that even a modest increase in ground-based observations will increase the accuracy of satellite-derived observations remotely.

Stressed about Drought Stress

Climate change is causing widespread forest mortality due to intensified drought conditions. In light of a dynamically changing planet, understanding when forest die-off will occur is vital in predicting forest response to future climate trends. The Environmental Ecology Lab studies plant physiological response to drought stress to determine the lethal level of drought for pinyon pine. This drought research inspired this high school biology lesson, which addresses the NGSS Performance Expectation HS-LS4-6. Students engage in a climate change discussion regarding the devastation of California wildfires. Ongoing research in the lab is then introduced, leading students to design their own drought experiment using radish plants. Students determine an effective drought detector as a solution to mitigate human-induced climate change. Experimental data are statistically tested using R, to determine the effectiveness of drought detectors. To place their observations in a global context, students research the NASA Global Climate Change website to provide evidence to support their claim of human-induced climate change and relate this to a reduction in biodiversity. In a final presentation, groups share their most effective physiological measurement and propose potential applications of drought detection in mitigating adverse impacts of climate change.

Impact of drought associated with high temperatures on Coffea canephora plantations: a case study in Esp\xedrito Santo State, Brazil

Droughts are major natural disasters that affect many parts of the world all years and recently affected one of the major conilon coffee-producing regions of the world in state of Espírito Santo, which caused a huge crisis in the sector. Therefore, the objective of this study was to conduct an analysis with technical-scientific basis of the real impact of drought associated with high temperatures and irradiances on the conilon coffee (Coffea canephora Pierre ex Froehner) plantations located in the north, northwest, and northeast regions of the state of Espírito Santo, Brazil. Data from 2010 to 2016 of rainfall, air temperature, production, yield, planted area and surface remote sensing were obtained from different sources, statistically analyzed, and correlated. The 2015/2016 season was the most affected by the drought and high temperatures (mean annual above 26 °C) because, in addition to the adverse weather conditions, coffee plants were already damaged by the climatic conditions of the previous season. The increase in air temperature has higher impact (negative) on production than the decrease in annual precipitation. The average annual air temperatures in the two harvest seasons that stood out for the lowest yields (i.e. 2012/2013 and 2015/2016) were approximately 1 °C higher than in the previous seasons. In addition, in the 2015/2016 season, the average annual air temperature was the highest in the entire series. The spatial and temporal distribution of Enhanced Vegetation Index values enabled the detection and perception of droughts in the conilon coffee-producing regions of Espírito Santo. The rainfall volume accumulated in the periods from September to December and from April to August are the ones that most affect coffee yield. The conilon coffee plantations in these regions are susceptible to new climate extremes, as they continue to be managed under irrigation and full sun. The adoption of agroforestry systems and construction of small reservoirs can be useful to alleviate these climate effects, reducing the risk of coffee production losses and contributing to the sustainability of crops in Espírito Santo.

EVALUATION OF THE SOIL MOISTURE AGRICULTURAL DROUGHT INDEX (SMADI) AND PRECIPITATION-BASED DROUGHT INDICES IN ARGENTINA

Abstract. Agricultural drought is one of the most critical hazards with regard to intensity, severity, frequency, spatial extension and impact on livelihoods. This is especially true for Argentina, where agricultural exports can represent up to 10% of gross domestic product (GDP), and where drought events for 2018 led to a decrease of nearly 0.5% of GDP. In this work, we investigate the applicability of the Soil Moisture Agricultural Drought Index (SMADI) for detection of droughts in Argentina, and compare its performance with the use of two well-known precipitation-based indices: the Standardized Precipitation Index (SPI) and the Standardized Precipitation- Evaporation Index (SPEI). SMADI includes satellite-based information of soil moisture, surface temperature and vegetation greenness, and was designed to capture the hydric stress on the soil-vegetation ensemble. Results indicate that SMADI has greater capabilities for agricultural drought detection than SPI and SPEI: it was able to recognize more than 83% of the registered emergencies, correctly classifying 75% of them as extreme droughts, and outperforming SPI and SPEI in all the analyzed metrics.

Assessing Agricultural Vulnerability to Drought in a Heterogeneous Environment: A Remote Sensing-Based Approach

Agriculture is one of the fundamental economic activities in most countries; however, this sector suffers from various natural hazards including flood and drought. The determination of drought-prone areas is essential to select drought-tolerant crops in climate sensitive vulnerable areas. This study aims to enhance the detection of agricultural areas with vulnerability to drought conditions in a heterogeneous environment, taking Bangladesh as a case study. The normalized difference vegetation index (NDVI) and land cover products from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images have been incorporated to compute the vegetation index. In this study, a modified vegetation condition index (mVCI) is proposed to enhance the estimation of agricultural drought. The NDVI values ranging between 0.44 to 0.66 for croplands are utilized for the mVCI. The outcomes of the mVCI are compared with the traditional vegetation condition index (VCI). Precipitation and crop yield data are used for the evaluation. The mVCI maps from multiple years (2006–2018) have been produced to compute the drought hazard index (DHI) using a weighted sum overlay method. The results show that the proposed mVCI enhances the detection of agricultural drought compared to the traditional VCI in a heterogeneous environment. The “Aus” rice-growing season (sown in mid-March to mid-April and harvested in mid-July to early August) receives the highest average precipitation (>400 mm), and thereby this season is less vulnerable to drought. A comparison of crop yields reveals the lowest productivity in the drought year (2006) compared to the non-drought year (2018), and the DHI map presents that the north-west region of Bangladesh is highly vulnerable to agricultural drought. This study has undertaken a large-scale analysis that is important to prioritize agricultural zones and initiate development projects based on the associated level of vulnerability.

Evaluation of Drought Stress in Cereal through Probabilistic Modelling of Soil Moisture Dynamics

The early and accurate detection of drought episodes is crucial for managing agricultural yield losses and planning adequate policy responses. This study aimed to evaluate the potential of two novel indices, static and dynamic plant water stress, for drought detection and yield prediction. The study was conducted in SW Spain (Córdoba province), covering a 13-year period (2001–2014). The calculation of static and dynamic drought indices was derived from previous ecohydrological work but using a probabilistic simulation of soil moisture content, based on a bucket-type soil water balance, and measured climate data. The results show that both indices satisfactorily detected drought periods occurring in 2005, 2006 and 2012. Both their frequency and length correlated well with annual precipitation, declining exponentially and increasing linearly, respectively. Static and dynamic drought stresses were shown to be highly sensitive to soil depth and annual precipitation, with a complex response, as stress can either increase or decrease as a function of soil depth, depending on the annual precipitation. Finally, the results show that both static and dynamic drought stresses outperform traditional indicators such as the Standardized Precipitation Index (SPI)-3 as predictors of crop yield, and the R2 values are around 0.70, compared to 0.40 for the latter. The results from this study highlight the potential of these new indicators for agricultural drought monitoring and management (e.g., as early warning systems, insurance schemes or water management tools).

Assessment of meteorological drought and trend detection in Khyber Pakhtunkhwa, Pakistan

The intensity and frequency of drought events are increased in the last five decades in South Asia, particularly Pakistan, Nepal, Sri Lanka, India, and Bangladesh. The severe drought of 2000 affected central and southern Pakistan and India. The recent severe droughts of 1997–2002 in Khyber Pakhtunkhwa caused scarcity in water and led to negative agriculture growth. In this paper, assessment of meteorological drought and trend detection was carried out using 43-year (1975–2017) monthly rainfall data for all 14 meteorological stations. The standardized precipitation index (SPI) developed by McKee et al. (1993) is used for drought assessment and trend detection. The results of SPI are carried out in all four regions of the province, i.e., northern part, eastern part, central part, and southern part, in 3, 6, 9, 12, and 24 months returned period (MRP). The study has revealed negative trend of SPI values in eastern part, while central and southern parts of the province have positive trend. The highest frequency of drought is analyzed in 6 MPR, 9 MRP, 12 MRP, and 24 MRP in northern parts. The analysis also revealed the topmost frequency of extreme droughts in central and northern parts, i.e., in 3 MRP at Drosh met station and in 6 MPR, 9 MRP, 12 MRP, and 24 MRP at Cherat met station. By the application of this methodology, the province can develop strategies for timely response, adaptation, and mitigation of the existence of different drought characteristics.

Assessing the response of vegetation photosynthesis to meteorological drought across northern China

AbstractSatellite‐based solar‐induced chlorophyll fluorescence (SIF) has the potential to offer early detection and accurate impact assessment of meteorological drought on vegetation photosynthesis. However, how the response of satellite SIF to meteorological drought varies under different climatic conditions and biome types remains poorly understood. In this study, we determined the drought time‐scale at which the vegetation photosynthesis response was highest based on the standardized precipitation evapotranspiration index (SPEI) and satellite SIF and examined how the sensitivity of SIF signals from different ecosystems to drought varied along an aridity gradient in northern China. The results showed that spatial variability of the annual maximum SIF was constrained by wetness conditions and biome types. Annual maximum SIF was positively correlated with SPEI in 57.9% of vegetated lands (p < .05). About 34.8% of humid ecosystems were characterized by a significant SIF‐SPEI correlation (p < .05). This percentage reached 44, 71.4, and 86.2% for arid, subhumid, and semiarid ecosystems, respectively. The variation of SIF‐SPEI correlations was a Gaussian function of the aridity index (AI), with the highest SIF‐SPEI correlation appearing in the AI bin of 0.4 (0.37–0.46). The drivers for this pattern were vegetation composition and water availability. The variation of SIF time scales in response to SPEI was a linear function of the AI, but the slope varied among biomes. To summarize, with increasing aridity drought‐induced declines in vegetation photosynthesis will be quicker and more significant.

Regional-scale drought monitor using synthesized index based on remote sensing in northeast China

Abstract Drought has a significant impact on agricultural, ecological, and socioeconomic spheres. Although many drought indices have been proposed until now, the detection of droughts at regional scales still needs to be further studied. The Standardized Vegetation Index (SVI) that represents vegetation growing condition, the Standardized Water Index (SWI) that represents canopy water content, and the Evaporative Stress Index (ESI) that quantifies anomalies in the ratio of actual to potential evapotranspiration were calculated based on the Moderate-resolution Imaging Spectroradiometer (MODIS) data. A new remote sensing-based Vegetation Drought Monitor Synthesized Index (VDSI) was proposed by integrating the SVI, SWI, and ESI in the northeast China. When tested against the in situ Standardized Precipitation Evapotranspiration Index (SPEI), VDSI with proper weights of three variables outperformed individual remote sensing drought indices. The county-level yields of the main crops in the study area from 2001 to 2010 were also used to validate the VDSI. The correlation analysis between the yield data and the VDSI data during the crop growing season was performed, and its results showed that VDSI during the crop reproductive growth period was strongly correlated with the variation of crop yield. It was proved that this index is a potential indicator for assessment of the spatial pattern of drought severity in northeast China.

Evaluation of Different Rainfall-based Drought Indices Detection of Meteorological Drought Events in Imo State, Nigeria

Drought Indices are extensively adopted as a drought detection and monitoring tool in all climatic regions. This study assesses and compares the performance of four rainfall based drought indices, Deciles index (DI), Percent of Normal Index (PNI), Standardized Precipitation Index (SPI) and Z-score Index (ZSI) in detection and classification of drought events in Imo state , a humid environment of Nigeria from (1982 to 2016). The resultshows that droughts do occur in humid climatic region of Nigeria. The various indices differ in their detection and classification of drought events in the study area. However, they classified 2007 and 2008 as the year with the most severe drought event in the state. There is high correlation (r > 0.70) among the four indices, with ZSI and PNI showing the highest relationship to SPI (r= 0.97). The differences observed among the drought indices in detecting drought events in the study area can be reduced by using multiple drought indices in addition to the use of SPI in drought assessments especially in Nigeria since technological advancement in drought monitoring software’s has made it easier. Also it is worthwhile to research on why drought detection varies with indices.
 Keywords: Drought assessment, Rainfall-based drought indices, humid environment, Imo state, Nigeria.

Drought analysis framework based on copula and Poisson process with nonstationarity

Droughts have been occurring with increased frequency and bringing with them considerable losses. Due to its nature, the best approach to monitoring and assessing droughts is in terms of stochastic theories. As a consequence, this study applied SPI in the hydrological drought detection, and examine/interpret drought-related phenomena. The 3-month SPI is used to decide the drought development and terminate phases based on the coincidence of hydrological drought and anomalies in precipitation.Four primary characteristics were determined to clarify the descriptions of drought magnitude and occurrence: duration, deficit, occurrence time and recurrence year. A stochastic process of drought is established to take account these characteristics using the copula function and a Poisson process as well as non-stationarity. Case study on the Shihmen and Zengwen reservoir watersheds in Taiwan revealed that hydrological drought events often coincide with meteorological drought, but some inconsistencies due to unusual hydrological hysteresis in rainfall and runoff. Our analysis raised several issues, including the choice of drought identification parameters and SPI time scale/ threshold, which should take into account anthropogenic activity and hydrological characteristics. A positive correlation between drought duration and drought deficit and a negative correlation between them and occurrence time were identified. It was also observed that subsequent drought event may be more severe due to the partial recovery of hydrological condition. In terms of non-stationarity, our results do not show strong evidence of long-term trend for drought characteristics. Some limitations could be due to the percentile-to-percentile basis of copula. This issue may need to be examined using non-stationary copula analysis or time-variant correlation-covariance models. In the end, we demonstrated that this framework can apply in forecasting drought conditions and generating synthetic droughts for use in the formulation of water resources management strategies and the development of drought preparedness plans.

Drought Stress Detection Using Low-Cost Computer Vision Systems and Machine Learning Techniques

The real-time detection of drought stress has major implications for preventing cash crop yield loss due to variable weather conditions and ongoing climate change. The most widely used indicator of drought sensitivity/tolerance in corn and soybean is the presence or absence of leaf wilting during periods of water stress. We develop a low-cost automated drought detection system using computer vision coupled with machine learning (ML) algorithms that document the drought response in corn and soybeans field crops. Using ML, we predict the drought status of crop plants with more than 80% accuracy relative to expert-derived visual drought ratings.

Sensitivity and uncertainty quantification for the ECOSTRESS evapotranspiration algorithm – DisALEXI

Evapotranspiration (ET) is a measure of plant water use that is utilized regionally for drought detection and monitoring, and locally for agricultural water resource management. Understanding the uncertainty associated with this measurement is vital for science predictions and analysis and for water resource management decision making. In this manuscript, the uncertainty in disaggregated Atmosphere-Land Exchange (disALEXI) is quantified; disALEXI is an ET algorithm that utilizes land surface temperature (LST) derived from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), as well ancillary inputs for landcover, elevation, vegetation parameters, and meteorological inputs. Since each of these inputs has an associated, and potentially unknown, uncertainty, in this study a Monte Carlo simulation based on a spatial statistical model is used to determine the algorithm's sensitivity to each of its inputs, and to quantify the probability distribution of algorithm outputs. Analysis shows that algorithm is most sensitive to LST (the input derived from ECOSTRESS). Significantly, the output uncertainty distribution is non-Gaussian, due to the non-linear nature of the algorithm. This means that ET uncertainty cannot be prescribed by accuracy and precision alone. Here, uncertainty was represented using five quantiles of the output distribution. The distribution was consistent across five different datasets (mean offset is 0.01 mm/day, and 95% of the data is contained within 0.3 mm/day). An additional two datasets with low ET, showed higher uncertainty (95% of the data is within 1 mm/day), and a positive bias (i.e., ET was overestimated by an average of 0.12 mm/day when ET was low).

A framework for deriving drought indicators from the Gravity Recovery and Climate Experiment (GRACE)

Abstract. Identifying and quantifying drought in retrospective is a necessity for better understanding drought conditions and the propagation of drought through the hydrological cycle and eventually for developing forecast systems. Hydrological droughts refer to water deficits in surface and subsurface storage, and since these are difficult to monitor at larger scales, several studies have suggested exploiting total water storage data from the GRACE (Gravity Recovery and Climate Experiment) satellite gravity mission to analyze them. This has led to the development of GRACE-based drought indicators. However, it is unclear how the ubiquitous presence of climate-related or anthropogenic water storage trends found within GRACE analyses masks drought signals. Thus, this study aims to better understand how drought signals propagate through GRACE drought indicators in the presence of linear trends, constant accelerations, and GRACE-specific spatial noise. Synthetic data are constructed and existing indicators are modified to possibly improve drought detection. Our results indicate that while the choice of the indicator should be application-dependent, large differences in robustness can be observed. We found a modified, temporally accumulated version of the Zhao et al. (2017) indicator particularly robust under realistic simulations. We show that linear trends and constant accelerations seen in GRACE data tend to mask drought signals in indicators and that different spatial averaging methods required to suppress the spatially correlated GRACE noise affect the outcome. Finally, we identify and analyze two droughts in South Africa using real GRACE data and the modified indicators.

Evolution of the common indices of detection and monitoring of meteorological droughts

Las sequias son un fenomeno natural recurrente caracterizado por una disminucion de la precipitacion. Esta condicion puede ser exacerbada por las demandas de agua potable, riego y otros usos. Las sequias meteorologicas (SM) implican una reduccion en cantidad e intensidad de la lluvia, asi como incrementos en la temperatura y evaporacion, ya que aumenta la radicacion solar al no haber nubosidad. Los indices de sequias son procedimientos operativos que emplean las variables climaticas citadas, los cuales buscan cuantificar ciertas caracteristicas de las SM para asignar una severidad y asi poder establecer su inicio, desarrollo y terminacion, permitiendo con ello su comparacion en climas diferentes. En este trabajo se describe cronologicamente como han evolucionado los indices mas comunes de deteccion y seguimiento de las SM, para cuantificarlas cada ano y en lapsos de varios meses. Lo anterior es util para comenzar y avanzar en el estudio y caracterizacion de las SM de una localidad o de una region geografica. Se recomienda progresar en el estudio y la evaluacion cuantitativa de las SM, segun como se describen los indices, pues su evolucion historica y complejidad computacional coinciden.

Comprehensive Drought Analysis Using Statistical and Meteorological Indices Approach: A Case Study of Badin, Sindh

Drought is a global phenomenon that can occur in any ecological zone and render significant damages to both the natural environment and human lives. However, hydro-climatic stresses are growing distinctly in the arid zones across the globe. Literature suggests that the analysis of a long-term data-set could help in strengthening of mitigation planes and rationalization of disaster management policies. Thus, the present study is aimed to analyze the evidence-based historical drought events happened in arid-zone Badin, Pakistan and predict its occurrence and severity for the next 82 years (2018-2099). Drought indices viz standardized precipitation index and reconnaissance drought index have been used to detect the severity of the drought events. Thirty years (1988 to 2017) past data of precipitation and temperature were used to categorize the drought severity and validated against the local data. Climate projections based on RCP 4.5 and 8.5 made at 25x25 km resolution used for future drought analysis. The results demonstrate that the region faced severe to extreme drought in 1990-91 and 2001-04. While, in future 2020-21, 2036-37, 2038-39 would be the extreme driest years under RCP 4.5 and 2029-30, 2089-90 under RCP 8.5. Further insight revealed that the average annual temperature has increased and precipitation has decreased w.r.t the base year 1988. It is concluded that drought detection with SPI and RDI is suitable and drought prediction with the RCP 4.5 and 8.5 could be a better option.

Drought interval simulation using functional data analysis

Drought detection is an essential process for drought risk management and watershed sustainability. Describing a reliable predictive model for drought events has always been the motivation of the meteorology scientists. The current research proposes a functional time series analysis for the construction of a reliable predictive strategy of drought interval occurrences. The proposed model is validated with respect to its performance based on the Palmer Drought Severity Index (PDSI). The applied methodology is conducted to produce point and interval forecasts for the drought time series pattern. The modeling procedure is inspected on the Konya watershed, Central Anatolia, Turkey. The study is incorporated a second phase of modeling where entertained for heteroscedastic and seasonal hydro-climatic data (streamflow). Several statistical metrics and graphical presentation are computed for modeling evaluation. Two benchmark models are developed for modeling evaluation including linear exponential smoothing (LES) and autoregressive integrated moving average (ARIMA). In quantitative terms; for instance, the proposed functional principal component analysis (FPCA) model exhibited a remarkable drought interval prediction enhancement over the LES and ARIMA models by approximately 29.5% and 28.3% using the root mean square error (RMSE) metric at Aksaray station. In accordance with the attained results, the proposed methodology demonstrated a reliable predictive model for capturing future PDSI intervals and streamflow magnitudes.