United States Drought Monitor Research Articles

Soil Moisture Information Improves Drought Risk Protection Provided by the USDA Livestock Forage Disaster Program

Abstract The USDA Livestock Forage Disaster Program (LFP) offers financial assistance to farmers and ranchers with grazed forage losses caused by fire or drought. Payments for drought losses are based on the U.S. Drought Monitor (USDM), which is designed to integrate meteorological, agricultural, hydrological, ecological, and socioeconomic droughts. Because soil moisture deficit is a more specific measure of agricultural drought, we hypothesized that basing LFP payments on soil moisture observations could better reduce producers’ risk. Therefore, our objectives were to 1) quantify relationships of forage yield with USDM-based LFP payment multipliers and with in situ soil moisture, 2) develop an alternative LFP payment multiplier structure based on in situ soil moisture, and 3) quantify risk reduction using the current and alternative payment structures. We focused on Oklahoma, United States, which has led the nation in LFP payments received and has >25 years of in situ soil moisture observations statewide. Using non-alfalfa hay yield as a surrogate for forage production, we found that LFP payment multiplier values and soil moisture anomaly were each related to yield, and soil moisture anomaly explained 54% of yield variability. However, the USDM-based LFP payment structure sometimes resulted in payments for above-average yield, and higher payments did not always correspond with greater yield losses. We developed an alternative soil-moisture-based payment structure that reduced financial risk by >20% compared with the current USDM-based structure. Our study identifies an improved LFP payment structure for Oklahoma that can be evaluated and refined in other states or nationwide using other soil moisture data sources.

Emergent Trends Complicate the Interpretation of the United States Drought Monitor (USDM)

AbstractEffective drought management must be informed by an understanding of whether and how current drought monitoring and assessment practices represent underlying nonstationary climate conditions, either naturally occurring or forced by climate change. Here we investigate the emerging climatology and associated trends in drought classes defined by the United States Drought Monitor (USDM), a weekly product that, since 2000, has been used to inform drought management in the United States. The USDM classifies drought intensity based in part on threshold percentiles in key hydroclimate quantities. Here we assess how those USDM‐defined drought threshold percentiles have changed over the last 23 years, examining precipitation, runoff, soil moisture (SM), terrestrial water storage (TWS), vapor pressure deficit (VPD), and near‐surface air temperature. We also assess underlying trends in the frequency of drought classifications across the U.S. Our analysis suggests that the frequency of drought class occurrence is exceeding the threshold percentiles defined by the USDM in a number of regions in the United States, particularly in the American West, where the last 23 years have emerged as a prolonged dry period. These trends are also reflected in percentile‐based thresholds in precipitation, runoff, SM, TWS, VPD, and temperature. Our results emphasize that while the USDM appears to be accurately reflecting observed nonstationarity in the physical climate, such trends raise critical questions about whether and how drought diagnosis, classification, and monitoring should address long‐term intervals of wet and dry periods or trends.

Quantifying the Importance of Selected Drought Indicators for the United States Drought Monitor

Abstract Using information theory, our study quantifies the importance of selected indicators for the U.S. Drought Monitor (USDM) maps. We use the technique of mutual information (MI) to measure the importance of any indicator to the USDM, and because MI is derived solely from the data, our findings are independent of any model structure (conceptual, physically based, or empirical). We also compare these MIs against the drought representation effectiveness ratings in the North America Drought Indices and Indicators Assessment (NADIIA) survey for Köppen climate zones. This reveals 1) agreement between some ratings and our MI values [high for example indicators like standardized precipitation evapotranspiration index (SPEI)]; 2) some divergences (e.g., soil moisture has high ratings but near-zero MIs for ESA Climate Change Initiative (CCI) soil moisture in the Western United States, indicating the need of another remotely sensed soil moisture source); and 3) new insights into the importance of variables such as snow water equivalent (SWE) that are not included in sources like NADIIA. Further analysis of the MI results yields findings related to 1) hydrological mechanisms (summertime SWE domination during individual drought events through snowmelt into the water-scarce soil); 2) hydroclimatic types (the top pair of inputs in the Western and non-Western regions are SPEIs and soil moistures, respectively); and 3) predictability (high for the California 2012–17 event, with longer-time scale indicators dominating). Finally, the high MIs between multiple indicators jointly and the USDM indicate potentially high drought forecasting accuracies achievable using only model-based inputs, and the potential for global drought monitoring using only remotely sensed inputs, especially for locations having insufficient in situ observations. Significance Statement Drought maps from the U.S. Drought Monitor and the Objective Short- and Long-Term Drought Indicator Blends and Blend Equivalents are integrated information sources of the different types of drought. Multiple indicators go into creation of these maps, yet it is usually not clear to both public and private stakeholders like local agencies and insurance companies about the importance of any indicator in any region and season to the drought maps. Our study provides such objective information to enable understanding the mechanism and type of drought occurring at a location, season, and possibly event of interest, as well as to potentially aid in better drought monitoring and forecasting using smaller custom sets of indicators.

Developing Impacts-Based Drought Thresholds for Ohio

Abstract Drought monitoring is critical for managing agriculture and water resources and for triggering state emergency response plans and hazard mitigation activities. Fixed thresholds serve as guidelines for the U.S. Drought Monitor (USDM). However, fixed drought thresholds (i.e., using the same threshold in all seasons and climate regions) may not properly reflect local conditions and impacts. Therefore, this study develops impacts-based drought thresholds that are appropriate for drought monitoring in Ohio. We examined four drought indices that are currently used by the state of Ohio: standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), Palmer’s Z index, and Palmer hydrological drought index (PHDI). Streamflow and corn yield are used as indicators of hydrological and agricultural drought impacts, respectively. Our results show that fixed thresholds tend to indicate milder drought conditions in Ohio, while the proposed impacts-based drought thresholds are more sensitive to exceptional drought (D4) conditions. The area percentage of D4 based on impacts-based drought thresholds is more strongly correlated with corn yield and streamflow. This study provides a methodology for developing local impacts-based drought thresholds that can be applied to other regions where long-term drought impact records exist to provide regionally representative depictions of conditions and improve drought monitoring.

Evaluating Drought Indices for Alaska

Abstract Drought is a recurrent natural phenomenon, but there is concern that climate change may increase the frequency or severity of drought in Alaska. Because most common drought indices were designed for lower latitudes, it is unclear how effectively they characterize drought in Alaska’s diverse, high-latitude climates. Here, we compare three commonly used meteorological drought indices [the standardized precipitation index (SPI), the standardized precipitation evapotranspiration index (SPEI), and the self-calibrating Palmer drought severity index (scPDSI)] with each other and with streamflow across Alaska’s 13 climate divisions. All of the drought indices identify major droughts, but the severity of the drought varies depending on the index used. The SPI and the SPEI are more flexible and often better correlated with streamflow than the scPDSI, and we recommend using them. Although SPI and SPEI are very similar in energy-limited climates, the drought metrics do diverge in drier locations in recent years, and consideration of the impact of temperature on drought may grow more important in the coming decades. Hargreaves potential evapotranspiration (PET) estimates appeared more physically realistic than the more commonly used Thornthwaite equation and are equally easy to calculate, so we suggest using the Hargreaves equation when PET is estimated from temperature. This study, one of the first to evaluate drought indices for high-latitude regions, has the potential to improve drought monitoring and representation within the U.S. Drought Monitor, leading to more informed decision-making during drought in Alaska, and it improves our ability to track changes in drought driven by rising temperatures. Significance Statement Tracking drought at high latitudes is challenging because we have not adequately studied drought impacts in cold climates, and the primary meteorological drought indices were designed for lower latitudes and may not accurately estimate evaporative demand and the influence of snow. We investigate three common drought indices and recommend using the standardized precipitation index (SPI) or the standardized precipitation evapotranspiration index (SPEI) because they can track short and long droughts. The SPEI may be more useful because comparisons between the SPI and SPEI show that, in recent decades, temperature has made noticeable contributions to drought in drier parts of Alaska. If using the SPEI, we suggest the Hargreaves potential evapotranspiration rather than the Thornthwaite because it is more physically realistic.

Analysis of the critical components of flash drought using the standardized evaporative stress ratio

Flash droughts develop rapidly (∼1 month timescale) and produce significant ecological, agricultural, and socioeconomical impacts. Recent advances in our understanding of flash droughts have resulted in methods to identify and quantify flash drought events. However, few studies have been done to isolate the individual rapid intensification and drought components of flash drought, which could further determine their causes, evolution, and predictability. This study utilized the standardized evaporative stress ratio (SESR) to quantify individual components of flash drought from 1979 – 2019, using evapotranspiration (ET) and potential evapotranspiration (PET) data from the North American Regional Reanalysis (NARR) dataset. The temporal change in SESR was utilized to quantify the rapid intensification component of flash drought. The drought component was also determined using SESR and compared to the United States Drought Monitor. The results showed that SESR was able to represent the spatial coverage of drought well for regions east of the Rocky Mountains. Furthermore, the rapid intensification component agreed well with previous flash drought studies, with the overall climatology of rapid intensification events showing similar hotspots to the flash drought climatology east of the Rocky Mountains. The rapid intensification climatology suggested areas west of the Rocky Mountains experience rapid drying more often than east of the Rocky Mountains.

Evaluating the impact of different normalization strategies on the construction of drought condition indices

Drought condition indices (CIs), including single condition (SCIs) and combined condition indices (CCIs), can reveal the characteristics of drought evolution and have been widely used in drought monitoring tasks. However, in practical applications, CIs are constructed in different normalization methods rather than based on a uniform standard. The diversely constructed CIs not only mislead the scientific community but also confuse farmers and stockholders to prevent them from taking timely measures when drought events occur. Thus, this study aims to comprehensively evaluate the differently constructed CIs and quantify the impact of different normalization strategies. To achieve this, four SCIs, including the Vegetation Condition Index (VCI), the Temperature Condition Index (TCI), the Soil Moisture Condition Index (SMCI), and the Precipitation Condition Index (PCI), together with three CCIs, including the Vegetation Health Index (VHI), the Scaled Drought Condition Index (SDCI), and the Microwave Integrated Drought Index (MIDI) were chosen as typical CI representatives. Each original CI was compared in the Continental United States (CONUS) with its variants by calculating the correlation coefficients with six in-situ indices (PDSI, Zindex, SPI-1, SPI-3, SPI-6, and SPI-9). Furthermore, the different CIs were qualitatively evaluated with the United States Drought Monitor in the extreme drought event in 2011. The results indicated that CI variants built through temporal normalization reduce the accuracy of drought severity estimation, and the CI variants built through spatial normalization lost the ability to track the spatiotemporal evolution of drought. The impacts of different normalization methods on single indices are transmissible and cumulative when integrating them into combined indices. These findings highlight the risks of diverse normalization methods in CIs establishment and underscore the necessity of considering seasonality and spatiality in drought applications.

Flash Drought Mechanism and Characteristics in South Korea

A flash drought is a drought that occurs rapidly within a short period of time, owing to changes in weather and environmental factors that occur rapidly due to extreme weather caused by climate change. Drought damage occurs within a relatively short period of time, and extreme vegetation stress can damage crops due to reduced water supply. Thus, different monitoring methods are required for different drought types. In this study, ESI (Evaporative Stress Index) and EDDI (Evaporative Demand Drought Index) were calculated to evaluate the short-term drought and determine the mechanism that causes flash droughts. The ESI and EDDI both indicates faster responses to the 2017 spring drought event than the SPI (Standardized Precipitation Index), confirming their sensitive drought assessment abilities. A flash drought detection standard was established based on the drought category used in USDM (United States Drought Monitor), and seven flash drought events were detected in South Korea over 5 years (2014-2018). A correlation analysis indicates that, among the hydrometeorological factors, the correlation coefficients between soil moisture/maximum temperature and flash drought occurrence exhibited strong positive (negative) correlations (0.9 and -0.9, respectively). The carbon change pattern obtained using GPP (Gross Primary Productivity) differed, exhibiting a -60% average GPP reduction rate compared to three other regions (JB, CN, and CB) where flash droughts occurred in 2018. We focus our discussion on opportunities to extend flash drought detection and mechanism determination using two drought indices that must be addressed to improve flash drought monitoring in South Korea.

Evaluating a new temperature-vegetation-shortwave infrared reflectance dryness index (TVSDI) in the continental United States

Accurate dryness monitoring is important for formulating reasonable response measures to reduce social and economic losses caused by drought. The land surface temperature (LST), shortwave infrared (SWIR) reflectance, and vegetation index (VI) are popular remote sensing (RS) indices that can be individually used to characterize surface dryness. Given the interactions of these factors, limitations are inevitably associated with using a single factor. Integrated dryness indices that combine LST or SWIR reflectance with the VI have thus been successively proposed and applied for dryness monitoring and soil moisture (SM) retrieval work. However, the advantages of these three indicators have not yet been combined to construct a more comprehensive dryness index. In this study, we integrated the LST, enhanced vegetation index (EVI), and SWIR reflectance and developed an integrated satellite-based dryness index with simple calculations, called the temperature vegetation shortwave infrared reflectance dryness index (TVSDI). The proposed TVSDI was thoroughly assessed in the continental United States (CONUS) using the following data: the soil moisture active passive (SMAP) SM; six commonly used dryness indices (i.e., temperature vegetation soil moisture dryness index (TVMDI), temperature vegetation dryness index (TVDI), modified perpendicular dryness index (MPDI), perpendicular dryness index (PDI), standardized precipitation evapotranspiration index (SPEI), and standardized precipitation index (SPI)); in situ SM data collected from 24 Cosmic-ray neutron probe (CRNP) sites covering different climates, soil types, and land cover types; and the United States Drought Monitor (USDM) maps. The results demonstrated that the TVSDI was significantly correlated with SMAP SM (R = − 0.75, p < 0.01) and exhibited better performance than the use of LST, EVI, and SWIR reflectance individually. Moreover, the TVSDI and the other six commonly used dryness indices exhibited good spatiotemporal consistency, all with consistency areas > 60%. The evaluation based on in situ SM from 24 CRNP sites indicated that the TVSDI exhibited more stability and accuracy than other satellite-based agricultural dryness indices (TVMDI, MPDI, PDI, and TVDI). Moreover, the spatial patterns of TVSDI maps were not only well-matched with SMAP SM maps but also provided more detailed spatial information. TVSDI maps could capture more dryness and drought variations in natural ecosystems and areas with less intensive human activities than USDM maps. Furthermore, the application of the TVSDI for dryness monitoring in the CONUS revealed that the dryness distributions differed greatly across different geographic regions at monthly and annual scales. In conclusion, the TVSDI was found to be a reliable and accurate satellite-based dryness index.

Flash drought identification from satellite-based land surface water index

Flash droughts can lead to significant agricultural and ecosystem impacts via rapid land surface desiccation. While gridded weather and climate datasets, land surface models, or widely spaced in situ observations are typically used to quantify flash drought development, coarse spatial data limits the ability to determine fine-scale spatial evolution of flash drought at landscape and ecosystem scales. In this study, a novel approach is introduced to objectively identify flash drought using the land surface water index (LSWI) derived from satellite observations. LSWI is a water-related vegetation index that represents the total water content in vegetation by using the near-infrared and shortwave infrared bands. LSWI was derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra surface reflectance (MOD09A1) with a 500 m spatial resolution and an 8-day temporal resolution. When applied to two well-established case studies, LSWI anomalies were able to capture the temporal and spatial evolution of flash drought over Oklahoma during the years of 2011 and 2012. In addition, rapid changes of LSWI during flash drought were comparable across space and time to the reanalysis-based Standardized Evaporative Stress Ratio (SESR), while negative anomalies of LSWI following flash drought corresponded with drought impacts via the United States Drought Monitor. It was found that LSWI was able to identify flash drought with a finer spatial resolution (500 m) and revealed spatial propagation of flash drought events that would not otherwise be seen with coarser meteorological data (e.g., ∼32 km at the lowest latitude for the North American Regional Reanalysis data). Furthermore, LSWI greatly enhanced the ability to detect rapid changes in surface conditions driven by flash drought and was able to provide early warning for drought development when compared with the USDM across Oklahoma. As such, the temporal and spatial evolution of flash drought depicted by LSWI and the presented methodology improves our ability to identify flash drought at high spatial resolution using satellite remote sensing and detect rapid changes in surface conditions. In light of these results, the novel LSWI approach demonstrates that satellite remote sensing applications using an objective technique are advantageous for flash drought detection in near real-time and at fine spatial scales.

DroughtCast: A Machine Learning Forecast of the United States Drought Monitor.

Drought is one of the most ecologically and economically devastating natural phenomena affecting the United States, causing the U.S. economy billions of dollars in damage, and driving widespread degradation of ecosystem health. Many drought indices are implemented to monitor the current extent and status of drought so stakeholders such as farmers and local governments can appropriately respond. Methods to forecast drought conditions weeks to months in advance are less common but would provide a more effective early warning system to enhance drought response, mitigation, and adaptation planning. To resolve this issue, we introduce DroughtCast, a machine learning framework for forecasting the United States Drought Monitor (USDM). DroughtCast operates on the knowledge that recent anomalies in hydrology and meteorology drive future changes in drought conditions. We use simulated meteorology and satellite observed soil moisture as inputs into a recurrent neural network to accurately forecast the USDM between 1 and 12 weeks into the future. Our analysis shows that precipitation, soil moisture, and temperature are the most important input variables when forecasting future drought conditions. Additionally, a case study of the 2017 Northern Plains Flash Drought shows that DroughtCast was able to forecast a very extreme drought event up to 12 weeks before its onset. Given the favorable forecasting skill of the model, DroughtCast may provide a promising tool for land managers and local governments in preparing for and mitigating the effects of drought.

Beef Cattle Price and Production Patterns in Relation to Drought in New Mexico

Understanding the fluctuations in monthly and annual cattle prices plays a key role in supporting the sustainability of New Mexico’s (NM’s), United States (US), beef cattle industry under variable environmental conditions. The goal of this study was to provide an improved understanding of NM’s beef cattle production systems in terms of prices and production patterns and related drought impacts. The main objectives were to evaluate monthly and annual prices patterns for heifers and steers (cattle) and calves, the relationships between annual cattle prices and inventory and drought, and the effects of drought on ranch net return. Drought events were assessed using the Self-Calibrated Palmer Drought Severity Index (SC-PDSI). The generalized autoregressive conditional heteroscedasticity models and their exponential version were used to investigate the effects of drought and cattle supply on cattle prices, and the effects of drought on ranch net return. Spectral analysis and timeseries decomposition were used to identify the cycles of the annual price and numbers of cattle and calf. Coherence analysis was used to examine the relationships between inventory of cattle classes and drought. The results indicated that prices of cattle and calf usually drop in October through January and peak in April. The inventory of replacement heifers and steers were negatively related to cattle prices, while the inventory of calves was positively related to calf prices. Cattle and calf prices showed negative linear relationships with droughts. Annual cattle and calf prices showed 6- and 10-year cycles, while their inventory showed 6- and 8- year cycles, respectively. Our finding suggested that a rancher can still earn some net return when drought falls within the “Abnormally Dry” category of the US Drought Monitor. However, a rancher with a large herd or ranch size can endure drought more than a rancher with a medium herd or ranch size and reach the breakeven point. Specifically, the net return ($/head) is expected to increase (or decrease) by $62.29, $60.51, and $64.07 per head if the SC-PDSI increase (or decrease) by one unit in all large and medium ranch sizes, respectively. The effects of drought on ranch net return that we identified need further improvements using additional data. Due to NM’s location and the diversity of its rangeland, understanding the response of cattle prices to drought and beef cattle supply based on these findings can be used to help NM’s ranchers and those in other similar regions make informed ranch management decisions. These findings can also support the development of improved understanding of beef cattle production systems regionally.

Contribution of Meteorological Downscaling to Skill and Precision of Seasonal Drought Forecasts

AbstractResearch in meteorological prediction on sub-seasonal to seasonal (S2S) timescales has seen growth in recent years. Concurrent with this, demand for seasonal drought forecasting has risen. While there is obvious synergy between these fields, S2S meteorological forecasting has typically focused on low resolution global models, while the development of drought can be sensitive to the local expression of weather anomalies and their interaction with local surface properties and processes. This suggests that downscaling might play an important role in the application of meteorological S2S forecasts to skillful forecasting of drought. Here, we apply the Generalized Analog Regression Downscaling (GARD) algorithm to downscale meteorological hindcasts from the NASA Goddard Earth Observing System (GEOS) global S2S forecast system. Downscaled meteorological fields are then applied to drive offline simulations with the Catchment Land Surface Model (CLSM) to forecast United States Drought Monitor (USDM) style drought indicators derived from simulated surface hydrology variables. We compare the representation of drought in these downscaled hindcasts to hindcasts that are not downscaled, using the North American Land Data Assimilation System Phase 2 (NLDAS-2) dataset as an observational reference. We find that downscaling using GARD improves hindcasts of temperature and temperature anomalies, but the results for precipitation are mixed and generally small. Overall, GARD downscaling led to improved hindcast skill for total drought across the Contiguous United States (CONUS), and improvements were greatest for extreme (D3) and exceptional (D4) drought categories.

A new drought monitoring approach: Vector Projection Analysis (VPA)

In this research, a new drought monitoring approach with an adaptive index—Vector Projection Analysis (VPA) and Vector Projection Index of Drought (VPID)— was developed that considers multiple drought indicators in various climate zones across the Contiguous United States (CONUS) and East Asia. A major advantage of VPA is that it uses multiple dependent variables (i.e., surface-based drought indices) and multiple independent variables (i.e., satellite-derived drought factors) to capture varied climate and environmental characteristics. Therefore, the VPA-based indices can be adopted for different drought types (i.e., meteorological, agricultural and hydrological droughts) depending on the user's selection of drought factors and indices. In VPA, the weights of each drought factor are generated through correlations (r) between each dependent variable and a satellite-derived drought factor that is obtained from the new generation sensor systems, Visible Infrared Imaging Radiometer Suite (VIIRS) and Global Precipitation Mission (GPM). Three schemes of VPID with different combinations of variables focused on integrated-, short-, and long-term drought (VPIDinte, VPIDshort, and VPIDlong, respectively) were evaluated over the CONUS and East Asia. All three schemes showed good agreement with surface-based drought indices resulting in averaged r (from 0.42 to 0.68) across both study areas. While VPIDshort or VPIDlong were more correlated with short (1–3 months) or long (9–12 months) term surface-based drought indices, VPIDinte provided a more generalized (i.e., integrated from 1 to 12 months) index that is suitable for varied drought conditions. The spatial distributions of drought from VPIDs agreed with United States Drought Monitor (USDM) data and Emergency Events Database across East Asia for both extreme drought and normal conditions. In particular, the drought areas of VPIDinte corresponded with the USDM results with r = 0.83 and root mean square error = 9.4%. Crop yield trends were also consistent with the VPIDs results in most years for both study areas. The proposed approach, VPA and VPID, can be adopted for any region with different combinations of surface-based drought indices and satellite-derived drought factors.

Continental drought monitoring using satellite soil moisture, data assimilation and an integrated drought index

Satellite remote sensing provides unprecedented information on near-surface soil moisture at a global scale, enabling a wide range of studies such as drought monitoring and forecasting. Data Assimilation (DA) has been recognized as an effective means to incorporate such observations into hydrologic models to better predict and forecast hydroclimatic variables. In this study, we use a recently developed Evolutionary Particle Filter with Markov Chain Monte Carlo (EPFM) approach to assimilate Soil Moisture Active Passive (SMAP) soil moisture data into Variable Infiltration Capacity (VIC) hydrologic model to provide more reliable topsoil layer moisture (0~–5 cm) over the entire Continental United States (CONUS). The EPFM outperformed an Ensemble Kalman filter (EnKF) in terms of correlations and the unbiased root mean square error (ubRMSE) with in situ measurements from the Soil Climate Analysis Network (SCAN) and the United States Climate Reference Network (USCRN). Also, we used a multivariate probability distribution based on a Copula function to integrate the posterior soil moisture, precipitation (from the North American Land Data Assimilation System (NLDAS)) and evapotranspiration (from the Moderate Resolution Imaging Spectroradiometer (MODIS)) information to develop a new integrated drought index, i.e. SPESMI. To validate the usefulness of the developed integrated drought index, we compared the drought events detected by this index with those reported by the United States Drought Monitor (USDM). The results indicated a strong temporal consistency of the drought areas detected by our approach and the USDM over the entire period of study (April 2015 to June 2018). In addition to such promising results, we noticed that our approach could capture the flash drought in 2017 in the U.S. Northern Plains earlier than the USDM, and could identify some severe to extreme drought events that had been underestimated by the USDM. Moreover, the SPESMI has a high correlation with the yield loss of spring and winter wheat in the United States. This novel drought monitoring framework can serve as an independent and potentially complementary drought monitoring system.

The role of drought as a determinant of hemorrhagic disease in the eastern United States.

Bluetongue virus and epizootic hemorrhagic disease (HD) virus are globally distributed, vector-borne viruses that infect and cause disease in domestic and wild ruminant species. The forces driving increases in resulting HD may be linked to weather conditions and increasing severity has been noted in northerly latitudes. We evaluated the role of drought severity in both space and time on changes in HD reports across the eastern United States for a recent 15year period. The objectives of this study were to: (a) develop a spatiotemporal model to evaluate if drought severity explains changing patterns of HD presence; and (b) determine whether this potential risk factor varies in importance over the present range of HD in the eastern United States. Historic data (2000-2014) from an annual HD presence-absence survey conducted by the Southeastern Cooperative Wildlife Disease Study and from the United States Drought Monitor were used for this analysis. For every county in 23 states and for each of 15years, data were based on reported drought status for August, wetland cover, the physiographic region, and the status of HD in the previous year. We used a generalized linear mixed model to explain HD presence and evaluated spatiotemporal predictors across the region. We found that drought severity was a significant predictor of HD presence and the significance of this relationship was dependent on latitude. In more northerly latitudes, where immunological naivety is most likely, we demonstrated the increasing strength of drought severity as a determinant of reported HD and established the importance of variation in drought severity as a risk factor over the present range of HD in the eastern United States. Our research provides spatially explicit evidence for the link between climate forces and emerging disease patterns across latitude for a globally distributed disease.

A Spatially Transferable Drought Hazard and Drought Risk Modeling Approach Based on Remote Sensing Data

Drought adversely affects vegetation conditions and agricultural production and consequently the food security and livelihood situation of the often most vulnerable communities. In spite of recent advances in modeling drought risk and impact, coherent and explicit information on drought hazard, vulnerability and risk is still lacking over wider areas. In this study, a spatially explicit drought hazard, vulnerability, and risk modeling framework was investigated for agricultural land, grassland and shrubland areas. The developed drought hazard model operates on a higher spatial resolution than most available drought models while also being scalable to other regions. Initially, a logistic regression model was developed to predict drought hazard for rangelands and croplands in the USA. The drought hazard model was cross-verified for the USA using the United States Drought Monitor (USDM). The comparison of the model with the USDM showed a good spatiotemporal agreement, using visual interpretation. Subsequently, the explicit and accurate USA model was transferred and calibrated for South Africa and Zimbabwe, where drought vulnerability and drought risk were assessed in combination with drought hazard. The drought hazard model used time series crop yields data from the Food and Agriculture Organization Corporate Statistical Database (FAOSTAT) and biophysical predictors from satellite remote sensing (SPI, NDVI, NDII, LST, albedo). A McFadden’s Pseudo R² value of 0.17 for the South African model indicated a good model fit. The plausibility of the drought hazard model results in southern Africa was evaluated by using regional climate patterns, published drought reports and a visual comparison to a global drought risk model and food security classification data. Drought risk and vulnerability were assessed for southern Africa and could also be spatially explicit mapped showing, for example, lower drought vulnerability and risk over irrigated areas. The innovative aspect of the presented drought hazard model is that it can be applied to other countries at a global scale, since it only uses globally available data sets and therefore can be easily modified to account for country-specific characteristics. At the same time, it can capture regional drought conditions through a higher resolution than other existing global drought hazard models. This model addressed the gap between global drought models, that cannot spatially and temporally explicitly capture regional drought effects, and sub-regional drought models that may be spatially explicit but not spatially transferable. Since we used globally available and spatially consistent data sets (both as predictors and response variables), the approach of this study can potentially be used globally to enhance existing modelling routines, drought intervention strategies and preparedness measures.

Mapping the agricultural drought based on the long-term AVHRR NDVI and North American Regional Reanalysis (NARR) in the United States, 1981–2013

To provide a long-term perspective of drought variability from 1981 to present, we develop a new monthly agriculturally-based drought index called the Integrated Scaled Drought Index (ISDI). This index integrates Normalized Difference Vegetation Index (NDVI) from Advanced Very High Resolution Radiometer (AVHRR) data (available from 1981 to present), land surface temperature (LST), precipitation (PCP), and soil moisture (SM) data from North American Regional Reanalysis (NARR) project (available from 1979 to present). This new agriculturally-based drought index incorporates important components controlling agricultural drought, particularly soil moisture, for which there are limited in-situ observations through time and across space. The optimum weights for each component of the ISDI are determined by correlation analysis with commonly used in-situ drought indices, such as the Palmer Drought Severity Index (PDSI), the Palmer Modified Drought Index (PMDI), the Palmer's Z-index, and the Standardized Precipitation Index (SPI) at different time scales. Resulting ISDI maps are also visually compared with United States Drought Monitor (USDM) and Vegetation Drought Response Index (VegDRI) maps for empirical validation. ISDI shows strong agreement with these two national-wide drought monitoring systems. ISDI also shows strong linear correlations with corn yield anomalies in July and with soybean yield anomalies in August and strong spatial correspondence with county-level corn/soybean yield anomalies during major drought events. These results illustrate the robustness and usefulness of ISDI. This agriculturally-based drought index integrates the benefits of numerical model simulation and remote sensing technology to account for interannual variability of drought for the longest possible time-frame in the satellite era. This long-term monthly drought index provides a longer historical perspective of drought impacts since 1981. It can be generalized to incorporate other satellite data or in-situ observation and has the potential for operational drought monitoring and assessment.

SMAP Soil Moisture Change as an Indicator of Drought Conditions

Soil moisture is considered a key variable in drought analysis. The soil moisture dynamics given by the change in soil moisture between two time periods can provide information on the intensification or improvement of drought conditions. The aim of this work is to analyze how the soil moisture dynamics respond to changes in drought conditions over multiple time intervals. The change in soil moisture estimated from the Soil Moisture Active Passive (SMAP) satellite observations was compared with the United States Drought Monitor (USDM) and the Standardized Precipitation Index (SPI) over the contiguous United States (CONUS). The results indicated that the soil moisture change over 13-week and 26-week intervals is able to capture the changes in drought intensity levels in the USDM, and the change over a four-week interval correlated well with the one-month SPI values. This suggested that a short-term negative soil moisture change may indicate a lack of precipitation, whereas a persistent long-term negative soil moisture change may indicate severe drought conditions. The results further indicate that the inclusion of soil moisture change will add more value to the existing drought-monitoring products.

Gauging the Severity of the 2012 Midwestern U.S. Drought for Agriculture

Different drought indices often provide different diagnoses of drought severity, making it difficult to determine the best way to evaluate these different drought monitoring results. Additionally, the ability of a newly proposed drought index, the Process-based Accumulated Drought Index (PADI) has not yet been tested in United States. In this study, we quantified the severity of 2012 drought which affected the agricultural output for much of the Midwestern US. We used several popular drought indices, including the Standardized Precipitation Index and Standardized Precipitation Evapotranspiration Index with multiple time scales, Palmer Drought Severity Index, Palmer Z-index, VegDRI, and PADI by comparing the spatial distribution, temporal evolution, and crop impacts produced by each of these indices with the United States Drought Monitor. Results suggested this drought incubated around June 2011 and ended in May 2013. While different drought indices depicted drought severity variously. SPI outperformed SPEI and has decent correlation with yield loss especially at a 6 months scale and in the middle growth season, while VegDRI and PADI demonstrated the highest correlation especially in late growth season, indicating they are complementary and should be used together. These results are valuable for comparing and understanding the different performances of drought indices in the Midwestern US.