Drought Monitoring Information Research Articles

Generating surface soil moisture at the 30 m resolution in grape-growing areas based on stacked ensemble learning

ABSTRACT Accurate and timely monitoring of drought conditions in grape-producing regions is crucial for achieving healthy growth of grapes. Current soil moisture (SM) products are primarily available at coarse resolutions (e.g. several to tens of kilometres), constraining its applications at fine scales. Here, we trained a weighted stacking ensemble model including three tree-based models (categorical boosting, random forest, and gradient boosting decision tree), using seven forcing parameters related to spectral reflectance (SR), land surface temperature (LST), and evapotranspiration (ET), in conjunction with the digital elevation model (DEM) feature. The weighted stacking ensemble model exhibited an average R2 of 0.86 and an average RMSE of 0.021 m3/m3 in simulating SM in the vegetive stage and the mid-ripening stage of grape. Then we generated high spatiotemporal downscaled SM (HSM) data at a grape growing area at high spatiotemporal resolutions (30 m, 8-day) from 2009 to 2018. Our HSM dataset demonstrated strong spatial, seasonal and interannual dynamics that align with 500 m SM dataset derived from single MODIS data, and the HSM dataset shows more details in SM distribution. Additionally, the SM time series in the HSM is consistently correlated with drought events, offering intricate spatiotemporal information for drought monitoring. The application of downscaled SM results identified a concentration of drought events in the eastern foothills of the Helan Mountains, particularly severe drought conditions were observed in the Hongsipu production area. Drought occurrences in the Hongsipu production area ranged from 90% to 91% during May and June, decreasing to 73% and 41% in July and August, respectively. These findings significantly contribute to enhancing high spatiotemporal SM monitoring capabilities, offering valuable guidance for timely water management in grape-growing regions.

Spatio-temporal assessment of agricultural drought using remote sensing and ground-based data indices in the Northern Ethiopian Highland

Study regionNorthern Ethiopian highland, North Wollo, Ethiopia Study focusDrought is one of the most serious environmental and socioeconomic problems of Sub-Sahara Africa's countries. The study aims to assess and evaluate the agricultural drought occurrence and its severity by using remote sensing-based multivariate indices to provide real-time spatial-temporal information for drought monitoring. Satellite datasets from Landsat images with a spatial resolution of 30 m were processed and rescaled to compute vegetation and temperature indices and derive drought status indices. The temporal and spatial variability of drought were detected and mapped by using satellite-based drought indices and validated by available in-situ data. New hydrological insights for the regionThe study revealed that the area was exposed to extreme drought in 1984, 2009, and 2014. On the contrary, no longer extreme drought dominated in 1989, 1994, 1999, and 2004. Moreover, the relationships between various indices have been examined by using a linear regression model. It has been detected that there is a significant strong relationship between NDVI and LST (R2 /p = 0.93/0.00), VCI and TCI (R2 /p = 0.71/0.00), VHI and SPI (R2/p = 0.85/0.00). Therefore, the spatial extent and temporal assessment of drought-related information are important for decision-making in the field of agriculture.

Spatiotemporal responses of net primary productivity of alpine ecosystems to flash drought: The Qilian Mountains

Understanding the impact of high-altitude flash droughts on vegetation net primary productivity (NPP) is crucially important for ecosystem management. Here, we investigated the response of NPP to flash drought with a case of the Qilian Mountains (QLMs). Our results indicated that the intensity of flash drought in the QLMs increased significantly during 1982–2018, and NPP was highly sensitive to flash drought, with 64.3 % (124,245 km2) of the region showing highly negative responses. The partial correlation analysis between NPP and meteorological factors in terms of different drought intensities showed that intensified flash drought enhanced the coupling between NPP and temperature/VPD. In the short-duration flash drought events with positive temperature anomalies, temperature increase will promote the accumulation of NPP; however, the carbon uptake of plants was significantly compromised by VPD in the flash drought events with negative temperature anomalies. Our study may provide valuable information for drought monitoring and comprehensive ecosystem management.

Evaluation of Three Long-Term Remotely Sensed Precipitation Estimates for Meteorological Drought Monitoring over China

Remotely sensed precipitation estimates (RSPEs) play an essential role in monitoring drought, especially in ungauged or sparsely gauged areas. In this study, we evaluated the ability of three popular long-term RSPEs (PERSIANN, CHIRPS, and MSWEP) in capturing the meteorological drought variations over the 10 first-level water resource basins of China, based on the standardized precipitation index (SPI). Drought events were identified by run theory, and the drought characteristics (i.e., duration, severity, and intensity) were also evaluated and compared with a gridded in situ observational precipitation dataset (CMA). The results showed that the three RSPEs could generally capture the spatial patterns and trends of the CMA and showed better performance in the wetter basins. MSWEP had the best performance for the categorical skill of POD, followed by CHIRPS and PERSIANN for the four timescales. SPI6 was the optimal timescale for identifying meteorological drought events. There were large skill divergences in the 10 first-level basins for capturing the drought characteristics. CHIRPS can efficiently reproduce the spatial distribution of drought characteristics, with similar metrics of MDS, MDI, and MDP, followed by MSWEP and PERSIANN. Overall, no single product always outperformed the other products in capturing drought characteristics, underscoring the necessity of multiproduct ensemble applications. Our study’s findings may provide useful information for drought monitoring in areas with complex terrain and sparse rain-gauge networks.

Agricultural drought monitoring in Sri Lanka using multisource satellite data

Drought is a complex phenomenon affecting agricultural, environmental, water resources, and socio-economic systems in developing regions. Climate change is going to increase the frequency and intensity of drought events and the associated socio-economic impact, including that on the food security among marginal smallholder farmers. For timely early action, it is important to have robust drought monitoring and warning in place to determine the timely drought situation to assist end-users for the decision-making process. Spatial-temporal remote sensing data provides crucial information on near real-time drought monitoring and early warning. The present study developed a composite index, i.e., Integrated Drought Severity Index (IDSI) that combines inputs from rainfall, vegetation, and temperature to determine agricultural drought progression, intensity, and frequency for entire Sri Lanka between 2001 and 2019. The study has successfully identified 10 drought events of which 2001, 2012, 2017, and 2019 reported severe droughts across the two rainy seasons, namely Yala (May–August) and Maha (October–March). We analyzed various indices meteorological drought Standardized Precipitation Index (SPI) and field-based rice Crop Yield Anomaly Index (CYA). It is evident from the study that the Yala season reported more drought events compared to the Maha season due to changes in monsoon onset and duration and its seasonal variability. The correlation coefficient for SPI with IDSI is 0.70 and IDSI with CYA is 0.68, which explains the reliability of drought monitoring information across Sri Lanka. In terms of sub-national drought events, the North, North Central, North Eastern, Eastern, and South Eastern Provinces which cover the majority of the dry zone of Sri Lanka and districts such as Anuradhapura, Monaragala, Polonnaruwa, Hambantota, Trincomalee, and Ampara are highly prone to agricultural drought impacting agricultural production and the vulnerable rural population. From the basin analysis, both Yan Oya and Malwathu Oya (Aruri Aru) are reported to have severe drought events, which highlights the need for timely action using satellite-derived agricultural drought monitoring to mitigate drought risks and reduce food insecurity.

Evaluation of Remotely Sensed Precipitation Estimates from the NASA POWER Project for Drought Detection Over Jordan

Droughts can cause devastating impacts on water and land resources and therefore monitoring these events forms an integral part of planning. The most common approach for detecting drought events and assessing their intensity is use of the Standardized Precipitation Index (SPI), which requires abundant precipitation records at good spatial distribution. This may restrict SPI usage in many regions around the world, particularly in areas with limited numbers of ground meteorological stations. Therefore, the use of remotely sensed derived data of precipitation can contribute to drought monitoring. In this study, remotely sensed precipitation estimates from the POWER/Agroclimatology archive of NASA and their derived SPI for different time intervals were evaluated against gauged observations of precipitation from 13 different stations in arid and semiarid locations in Jordan. Results showed significant correlations between remotely sensed and ground data with relatively high R values (0.67–0.91), particularly where seasonal precipitation exceeded 50 mm/year. For evaluation of remotely sensed data in SPI calculation, several objective functions were used; the results showed that SPI based on satellite estimates (SAT-SPI) showed good performance in detecting extreme droughts and indicating wet/dry conditions. However, SAT-SPI showed high tendency to overestimate drought intensity. Based on these findings, remotely sensed precipitation from the POWER/Agroclimatology archive showed good potential for use in detecting extreme meteorological drought with the provision of careful interpretation of the data. These types of studies are essential for evaluating the applicability of new drought monitoring information and tools to support decision-making at relevant scales.

Hydro-meteorological drought risk assessment using linear and nonlinear multivariate methods

Drought is multivariate, yet several univariate and bivariate indices have been proposed for the evaluation of drought characteristics. However, limited attention has been paid to the mechanisms and performance of these drought indices. This study considered canonical correlation analysis (CCA), principal component analysis (PCA), and copula-based method to construct three composite hydro-meteorological indices, namely, JDHMI-CCA, JDHMI-PCA, and JDHMI-Copula. The main elements of these indices are rainfall and runoff for which historical data for the period of 1986–2016 at a 12-month time scale were collected for Kol-Mehran and Bandar-Sedij basins, Iran. Results demonstrated that the pattern of composite indices reflected the comprehensive moisture status well and was not affected by a single element. Analysis indicated that although the composite, univariate, and multivariate indices followed similar patterns in various parts but the behavior of runoff was the main source of inconsistency in the study region. While PCA and CCA ignored or assigned smaller weights to runoff, the copula method used the information of runoff in constructing the copula-based index. This study investigated the mechanisms of linear and non-linear methods in assessing the drought condition. The methods had a significant effect on the construction of composite indices which can provide useful information for drought monitoring.

Analysis of Precipitation and Streamflow Data for Drought Assessment in an Unregulated Watershed

Predicting drought occurrence accurately still remains a challenging task. To fill research gaps, this study identified and analysed meteorological and hydrological droughts using the Standardized Precipitation Index (SPI) and Streamflow Drought Index (SDI), respectively, in the upper Lam Pao watershed in Thailand. The study also focused on investigating the relationships between both droughts. The SPI and SDI were computed based on observed long-term precipitation and streamflow data during the period of 1988-2017. The drought analysis was carried out by using the R packages. The location, period and severity level of drought events were graphically presented. On the basis of trend analysis, the SPI series showed slightly increasing trends, whereas no trend was found for the SDI series. This implied that the hydrological drought was influenced by not only precipitation but also other factors. The key findings indicated that there was a positive relationship between meteorological and hydrological droughts. In addition, there was a specific lag time, which may depend on physical characteristics of a basin, in drought propagating from meteorological drought to hydrological drought. Overall, the drought indices can help to predict hydrological drought events, which could be valuable information for drought monitoring and early warning systems.

Water quality risk assessment for drought conditions: A case study of the Korean Nakdong River basin

As the frequency of drought from climate change increases and the severity of drought become worse, it is imperative to prepare appropriate countermeasures against severe droughts. Despite the severe impacts of droughts on coupled humans and environmental systems, we do not fully understand the consequences of severe droughts affecting the environment. Therefore, the present study proposed a quantitative assessment index for water quality risk for extreme droughts to improve the safety of water environment management by providing flows to areas vulnerable to the drought. The application of this study is the Nakdong River basin in Korea, which has assessed the vulnerability of water quality to severe droughts as water quality deterioration problems continue to be experienced event after the river restoration project was completed. The results of this study are expected to provide scientific environmental drought monitoring information in a changing climate.

Monitoring Soil Moisture Drought over Northern High Latitudes from Space

Mapping drought from space using, e.g., surface soil moisture (SSM), has become viable in the last decade. However, state of the art SSM retrieval products suffer from very poor coverage over northern latitudes. In this study, we propose an innovative drought indicator with a wider spatial and temporal coverage than that obtained from satellite SSM retrievals. We evaluate passive microwave brightness temperature observations from the Soil Moisture and Ocean Salinity (SMOS) satellite as a surrogate drought metric, and introduce a Standardized Brightness Temperature Index (STBI). We compute the STBI by fitting a Gaussian distribution using monthly brightness temperature data from SMOS; the normal assumption is tested using the Shapior-Wilk test. Our results indicate that the assumption of normally distributed brightness temperature data is valid at the 0.05 significance level. The STBI is validated against drought indices from a land surface data assimilation system (LDAS-Monde), two satellite derived SSM indices, one from SMOS and one from the ESA CCI soil moisture project and a standardized precipitation index based on in situ data from the European Climate Assessment & Dataset (ECA&D) project. When comparing the temporal dynamics of the STBI to the LDAS-Monde drought index we find that it has equal correlation skill to that of the ESA CCI soil moisture product ( 0.71 ). However, in addition the STBI provides improved spatial coverage because no masking has been applied over regions with dense boreal forest. Finally, we evaluate the STBI in a case study of the 2018 Nordic drought. The STBI is found to provide improved spatial and temporal coverage when compared to the drought index created from satellite derived SSM over the Nordic region. Our results indicate that when compared to drought indices from precipitation data and a land data assimilation system, the STBI is qualitatively able to capture the 2018 drought onset, severity and spatial extent. We did see that the STBI was unable to detect the 2018 drought recovery for some areas in the Nordic countries. This false drought detection is likely linked to the recovery of vegetation after the drought, which causes an increase in the passive microwave brightness temperature, hence the STBI shows a dry anomaly instead of normal conditions, as seen for the other drought indices. We argue that the STBI could provide additional information for drought monitoring in regions where the SSM retrieval problem is not well defined. However, it then needs to be accompanied by a vegetation index to account for the recovery of the vegetation which could cause false drought detection.

Quantitative vulnerability assessment of water quality to extreme drought in a changing climate

Abstract Projected changes in the global climate indicate warmer temperatures and a highly variable hydrologic cycle, portending significant societal effects, particularly those stemming from drought. Whereas the extent of drought-related effects ranges from humans to environmental systems, the impacts on water quality, in particular, require careful investigation. Such investigation should include the interlinkages across climate and stream quality variables and how risk translates for various watersheds, given their particularities with respect to land use, watershed characteristics, and infrastructure. To this end, this study investigated drought assessment and the water quality vulnerability to severe drought, with the goal of informing planning and mitigation as a means to enhance drought resilience. This study was conducted in the Nakdong River basin in South Korea, which is vulnerable to water quality degradation during severe drought. We propose a method to evaluate spatial-temporal droughts based on the water quality risk and to monitor environmental droughts using the probability of exceeding the target water quality to facilitate a resilient proactive response. The results of this study can be used to provide scientific drought monitoring information for assessing real-time water quality monitoring from meteorological drought information. In addition, we expect to categorize vulnerable drought areas and to suggest essential management measures and tailored countermeasures at a local scale.

Identification of hydroclimate subregions for seasonal drought monitoring in the U.S. Great Plains

Identification of subregions that share similar historical drought variability provides useful information for drought monitoring, mitigation planning, and resource allocation. This study examined space-time historical drought variability for the Great Plains spanning from 1901 to 2015 by using rotated Empirical Orthogonal Functions (rEOFs). The Standardized Precipitation-Evapotranspiration Index (SPEI) on a three-month timescale was utilized to examine spatial and temporal changes in agricultural drought. We propose a new procedure for identifying the number of rEOFs to be selected for reconstructing subregions. Drought event intensities of moderate, severe, and extreme categories increased in recent years although the number of drought events decreased. Seasonal rEOFs demonstrated that 9–12 subregions were adequate to explain a significant proportion of the original variability in the Great Plains. The time series for each subregion was highly correlated to the original SPEI data and reflected the seasonal meteorological processes that drive drought variability. Several significant wetting trends were found, and there was statistical evidence that drought and wetting event severities had increased for a few subregions. Summer drought has become more variable across space and time, indicating that a more diverse set of resources and strategies might be needed to mitigate impacts of spatially-variable drought and wetting events in coming decades. Winter season drought has become less variable, indicating that perhaps resources could be consolidated when dealing with impacts on a larger scale; however, less variability implies that drought and wetting events may occur across larger regions of the Great Plains during a given season.

A Method for Objectively Integrating Soil Moisture Satellite Observations and Model Simulations Toward a Blended Drought Index

AbstractWith satellite soil moisture (SM) retrievals becoming widely and continuously available, we aim to develop a method to objectively integrate the drought indices into one that is more accurate and consistently reliable. The data sets used in this paper include the Noah land surface model‐based SM estimations, Atmosphere‐Land‐Exchange‐Inverse model‐based Evaporative Stress Index, and the satellite SM products from the Advanced Scatterometer, WindSat, Soil Moisture and Ocean Salinity, and Soil Moisture Operational Product System. Using the Triple Collocation Error Model (TCEM) to quantify the uncertainties of these data, we developed an optically blended drought index (BDI_b) that objectively integrates drought estimations with the lowest TCEM‐derived root‐mean‐square‐errors in this paper. With respect to the reported drought records and the drought monitoring benchmarks including the U.S. Drought Monitor, the Palmer Drought Severity Index and the standardized precipitation evapotranspiration index products, the BDI_b was compared with the sample average blending drought index (BDI_s) and the RMSE‐weighted average blending drought indices (BDI_w). Relative to the BDI_s and the BDI_w, the BDI_b performs more consistently with the drought monitoring benchmarks. With respect to the official drought records, the developed BDI_b shows the best performance on tracking drought development in terms of time evolution and spatial patterns of 2010‐Russia, 2011‐USA, 2013‐New Zealand droughts and other reported agricultural drought occurrences over the 2009–2014 period. These results suggest that model simulations and remotely sensed observations of SM can be objectively translated into useful information for drought monitoring and early warning, in turn can reduce drought risk and impacts.

夏玉米叶片气体交换参数对干旱过程的响应

PDF HTML阅读 XML下载 导出引用 引用提醒 夏玉米叶片气体交换参数对干旱过程的响应 DOI: 10.5846/stxb201703260520 作者: 作者单位: 中国气象科学研究院,中国气象科学研究院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金重点项目（41330531）；国家重点研发计划项目（2016YFD0300106）；公益性行业（气象）科研专项（重大专项）（GYHY201506001-3） Effect of drought on leaf gas exchange in Summer Maize Author: Affiliation: Chinese Academy of Meteorological Sciences,Chinese Academy of Meteorological Sciences Fund Project: National Natural Science Foundation of China (41330531) and China Special Fund for Meteorological Research in the Public Interest (Major projects) (GYHY201506001-3) 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:目前已经开展了大量的干旱对作物叶片气体交换参数影响的研究，但关于作物叶片气体交换参数对干旱过程的响应及其关键阈值的研究仍较少。基于夏玉米七叶期开始的5个初始水分梯度的长时间持续干旱模拟实验资料，分析了不同强度持续干旱过程中夏玉米叶片气体交换参数（净光合速率Pn，气孔导度Gs，蒸腾速率Tr，胞间CO2浓度Ci和气孔限制值Ls）的变化规律及其关键阈值。结果表明，玉米的净光合速率（Pn），蒸腾速率（Tr）和气孔导度（Gs）在干旱发生初期呈大幅度下降，但随着干旱持续会出现一定的适应性。利用统计容忍限方法确定了夏玉米拔节期Pn，Tr和Gs响应干旱的临界土壤相对湿度（0-30cm）分别为53%，51%和48%，对应的临界叶含水率分别为81.8%，81.3%和81.2%。夏玉米光合作用由气孔限制向非气孔限制转换的0-30cm土壤相对湿度均为44%±2%，对应的叶含水率均为77.6%±0.3%。研究结果可为夏玉米干旱发生发展过程的监测预警提供依据。 Abstract:A large number of studies have been carried out to investigate how crop photosynthesis responds to drought, but few have investigated the response of crop leaf gas exchange to drought processes, and their response thresholds. Based on a prolonged drought experiment in summer maize conducted in 2013, which included five watering treatments, and began from the 7-leaf stage, we investigated how drought developed with different initial amounts of irrigation, and how leaf gas exchange parameters changed as the drought progressed. Then, we determined the thresholds of soil and leaf moisture content when leaf gas exchange parameters began to respond to drought. The results showed that treatments with different initial amounts of irrigation induced different drought processes. Drought occurred earlier, persisted longer, and was more severe with treatments receiving less irrigation. The net photosynthetic rate (Pn), transpiration rate (Tr), and stomatal conductance (Gs) of summer maize under different watering treatments decreased sharply at the initial stage of drought; however, these parameters declined slower as drought prolonged, and tended to be almost identical to those growing under normal environment conditions. This indicated the acclimation of maize photosynthesis in response to prolonged drought. The onset of agricultural drought was typically marked by a decline in soil moisture below a critical point, which significantly impact crops. As drought progressed, the plant constituents and physiological processes could be altered sequentially, while lower-level responses would lead to changes in higher-level responses, and eventually changes at an individual plant level. Plant leaf gas exchange was found to be more directly affected by leaf water status than soil moisture content. Therefore, we identified the tipping points when maize leaf gas exchange parameters started to be affected by drought based on the tolerance limits of normal distribution; these were then quantified by soil moisture and leaf moisture content, respectively. The results showed that, Pn, Tr, and Gs decreased sharply when the relative soil moisture of 0-30cm depth was lower than 53%, 51%, and 48%, respectively, and leaf moisture content was lower than 81.8%, 81.3%, and 81.2%, respectively. At the initial stage of drought, the intercellular CO2 concentration (Ci) decreased, and stomatal limitation value (Ls) increased, indicating that stomatal closure accounted for the major decline in maize photosynthesis. As drought progressed, Ci increased while Ls decreased, indicating that non-stomatal limitation, other than stomatal closure, contributes to the major decreases in maize photosynthesis. The point at which the dominant limiting factor of maize photosynthesis converted from stomatal to non-stomatal varied under different treatments. The conversion time was earlier in maize that received lower initial amounts of irrigation due to longer persistence and greater severity of drought, and later in maize that received relatively higher initial amounts of irrigation. However, the thresholds of relative soil moisture and leaf moisture content when the conversion occurred were almost identical under all five treatments. The critical relative soil moisture of 0-30cm depth was about 44%±2% and the corresponding leaf moisture content was about 77.6%±0.3%. The results could provide reference information for drought monitoring and assessment of summer maize. 参考文献 相似文献 引证文献

Spatiotemporal Characteristics of Groundwater Drought and Its Response to Meteorological Drought in Jiangsu Province, China

In this study, the temporal and spatial variations of groundwater drought using a Standardized Groundwater Level Index (SGI) were analyzed based on 40 monthly groundwater level observation wells from 1989 to 2012 in Jiangsu Province, China. Meteorological drought, calculated by the Standardized Precipitation Index (SPI), was also included to reveal its propagation and impact on the groundwater drought process. Results showed that the southern region of Jiangsu faced more frequent groundwater droughts and lower intensity, while the northern region faced less frequent groundwater drought with higher intensity. Furthermore, the cross-correlation between the spatial average of SGI and SPI for SPI accumulation periods of q = 1 to 12 was computed. The relationship between SGI and SPI varied in different regions. Detailed analysis of the characteristics of groundwater and meteorological drought for each region showed that meteorological droughts happened more frequently than groundwater drought in Jiangsu Province during the study period, while the mean duration and mean magnitude of groundwater droughts were longer and larger than those of meteorological droughts. It is expected that this study will provide useful information for drought monitoring and mitigation in Jiangsu and similar areas.

Monitoring winter wheat drought threat in Northern China using multiple climate-based drought indices and soil moisture during 2000–2013

Increasing drought poses a big threat to food security over recent decades, highlighting the need for effective tools and adequate information for drought monitoring and mitigation. This study analyzed the performance of five climate-based drought indices and soil moisture measurements for monitoring winter wheat drought threat in China. We employed the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), the Palmer Drought Severity Index (PDSI), the Palmer Z index and the self-calibrated Palmer Drought Severity Index (scPDSI). On average, soil moisture at 50-cm depth correlated better with winter wheat yield during October-December of the previous year of harvest compared to soil moisture at 10-cm and 20-cm depths. Moreover, the 3-layer soil moisture and reference evapotranspiration (ETo) correlated weakly (Pearson’s r<0.3) and even negatively with winter wheat yield. The SPI and SPEI at shorter (1–5 months) timescales during September-December in the previous year of harvest showed higher correlations with winter wheat yield. The SPEI trend in March-June has a significant positive influence on trend in winter wheat yield (r>0.40, p<0.05). The climate-based drought indices can facilitate crop drought monitoring in water-limited regions due to the wide-availability of climatic data, particularly in the light of uncertainties arising from the crop model. Among the investigated indices, results revealed that the SPEI is advantageous for drought monitoring over the study area due to its multiscalarity and effective characterization of agricultural droughts.

Development and evaluation of a physically based multiscalar drought index: The Standardized Moisture Anomaly Index

AbstractIn this study, a new physically based multiscalar drought index, the Standardized Moisture Anomaly Index (SZI), was developed and evaluated, which combines the advantages of the Palmer Drought Severity Index (PDSI) and the Standardized Precipitation Evapotranspiration Index (SPEI). The SZI is based on the water budget simulations produced with a sophisticated hydrological model, and it also includes a multiscalar feature to quantify drought events at different temporal scales taken from SPEI. The Chinese Loess Plateau was selected to evaluate the performance of the SZI. Our evaluation indicates that the SZI accurately captures the onset, duration, and ending of a multiyear drought event through its multiscalar feature, while the PDSI, which lacks this feature, is often unable to describe the evolution of a multiyear drought event. In addition, the variability of the SZI is more consistent with observed streamflow and the satellite normalized difference vegetation index than that of the Standardized Precipitation Index and the SPEI. Although the SPEI includes potential evapotranspiration (PE) as water demand, water demand is often unrealistically estimated based solely on PE, especially over arid and semiarid regions. The improved drought quantification with the SZI is the result of a more reasonable estimation of water demand by including evapotranspiration, runoff, and any change in soil moisture storage. In general, our newly developed SZI is physically based and includes a multiscalar feature, which enables it to provide better information for drought monitoring and identification at different temporal scales.

Evaluation of the Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) for assessment of large-scale meteorological drought

This study analyzes the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) precipitation products for assessment of meteorological drought. Two versions of the TMPA research datasets (3B42V6 and 3B42V7) and one real-time dataset (3B42RTV7) are considered. The TMPA datasets are evaluated against a merged precipitation product which is estimated by merging four non-TMPA global satellite-gauge based datasets (non-TMPA merged). Comparisons are made over global land areas between 50° S and 50° N at monthly and 0.25° spatial resolution from 2000 to 2009 (ten years). All the TMPA precipitation datasets show similar spatial patterns; however quantitatively they disagree considerably, especially over tropical regions. 3B42V7 and 3B42RTV7 show the lowest and highest differences with the non-TMPA merged product, respectively. The Standardized Precipitation Index (SPI) at various time scales (1month to 12months) is calculated for each dataset for detecting drought events, with drought defined as when monthly SPI<−1.0 and severe drought when monthly SPI<−1.5. The SPI results complement the spatial patterns found in the precipitation statistics. The non-TMPA merged and the 3B42V7 precipitation datasets simultaneously identify months under drought more frequently than any other pair (i.e., non-TMPA merged — 3B42V6 and non-TMPA merged — 3B42RTV7) of precipitation datasets. We consider four severe drought events: (a) 2007 southeastern US drought, (b) 2003 western European heat wave and drought, (c) 2005 Amazon drought and (d) 2006 Kenyan drought as case studies. All precipitation products are able to identify the drought events in time and space except a few cases. The spatial correlation of drought area is the highest (>0.8) for the 2007 southeastern US drought and the lowest (<0.62) for the 2006 Kenyan drought. For severe drought (SPI<−1.5), all three TMPA products and the non-TMPA merged product show more than 50% area under severe drought for the four drought events with few exceptions.Our results show that major differences among datasets are found over many sparse gauge density regions which suggests that the skill of the datasets primarily depends on the differential performance of the respective processing algorithms in different geographic and climatic regions, density of the underlying rain-gauge station networks and the quality of the input data used from non-gauge data sources. Even though the 3B42V7 product performs the best, the 3B42V6 product also performs reasonably well during our study period and domain. The 3B42RTV7 real-time data perform the worst and are not comparable with the two TMPA research products, due to lack of corrections from gauge observations. Therefore, caution should be applied when using this product for real-time monitoring of the drought conditions. Our evaluation of the TMPA research products indicates that they can provide useful information for drought monitoring and as input to hydrological modeling applications for assessment of land surface conditions.

Drought Prediction System for Improved Climate Change Mitigation

Due to climate changes and the uncertainties in future weather conditions, research on drought monitoring information received more attention from politicians and scientists. The objective of this paper is to develop a new intelligent system concept for drought information extraction and predictions from satellite images. For the modeling experiment, this study used 24 years of data sets on selected attributes. By using these data sets, ten models were developed for predicting DroughtObjects with a one- to four-month time lag for the growing season from June to October with an accuracy rate ranging from 0.71 to 0.95. The process of the system that uses the new concept was also demonstrated on an easy-to-use graphical user interface. The output of this new concept can be developed to a full system and is helpful for extracting the freely available satellite images for drought monitoring and climate change mitigation applications at different levels of decision making.

How drought looks from space

Abstract A large portion of the most productive world agricultural areas, are susceptible to drought. Mitigation drought consequences requires efficient drought monitoring. Satellite data proved to be a good source of information for drought monitoring. The National Oceanic and Atmospheric Administration, which manages operational meteorological satellites, has recently designed a new AVHRR‐based Vegetation Condition Index (VCI) which has been shown to be a good tool for drought detection and tracking. This paper present the developed algorithm and some results of drought monitoring and validation.