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Land-use land-cover change (LULCC) has become an important topic of research for the central United States because of the extensive conversion of the natural prairie into agricultural land, especially in the northern Great Plains. As a result, shifts in the natural climate (minimum/maximum temperature, precipitation, etc.) across the north-central United States have been observed, as noted within the Fourth National Climate Assessment (NCA4) report. Thus, it is necessary to understand how further LULCC will affect the near-surface atmosphere, the lower troposphere, and the planetary boundary layer (PBL) atmosphere over this region. The goal of this work was to investigate the utility of a new future land-use land-cover (LULC) dataset within the Weather Research and Forecasting (WRF) modeling system. The present study utilizes a modeled future land-use dataset developed by the Forecasting Scenarios of Land-Use Change (FORE-SCE) model to investigate the influence of future (2050) land use on a simulated PBL development within the WRF Model. Three primary areas of LULCC were identified within the FORE-SCE future LULC dataset across Nebraska and South Dakota. Variations in LULC between the 2005 LULC control simulation and four FORE-SCE simulations affected near-surface temperature (0.5°–1°C) and specific humidity (0.3–0.5 g kg−1). The differences noted in the temperature and moisture fields affected the development of the simulated PBL, leading to variations in PBL height and convective available potential energy. Overall, utilizing the FORE-SCE dataset within WRF produced notable differences relative to the control simulation over areas of LULCC represented in the FORE-SCE dataset.

Drought is a familiar climatic phenomenon in the United States Southwest, with complex human-environment interactions that extend beyond just the physical drought events. Due to continued climate variability and change, droughts are expected to become more frequent and/or severe in the future. Decision-makers are charged with mitigating and adapting to these more extreme conditions and to do that they need to understand the specific impacts drought has on regional and local scales, and how these impacts compare to historical conditions. Tremendous progress in drought monitoring strategies has occurred over the past several decades, with more tools providing greater spatial and temporal resolutions for a variety of variables, including drought impacts. Many of these updated tools can be used to develop improved drought climatologies for decision-makers to use in their drought risk management actions. In support of a Food-Energy-Water (FEW) systems study for New Mexico, this article explores the use of updated drought monitoring tools to analyze data and develop a more holistic drought climatology applicable for New Mexico. Based upon the drought climatology, droughts appear to be occurring with greater frequency and magnitude over the last two decades. This improved drought climatology information, using New Mexico as the example, increases the understanding of the effects of drought on the FEW systems, allowing for better management of current and future drought events and associated impacts.

South Korea has experienced severe droughts and water scarcity problems that have influenced agriculture, food prices, and crop production in recent years. Traditionally, climate-based drought indices using point-based meteorological observations have been used to help quantify drought impacts on the vegetation in South Korea. However, these approaches have a limited spatial precision when mapping detailed vegetation stress caused by drought. For these reasons, the development of a drought index that provides detailed spatial-resolution information on drought-affected vegetation conditions is essential to improve the country’s drought monitoring capabilities, which are needed to help develop more effective adaptation and mitigation strategies. The objective of this study was to develop a satellite-based hybrid drought index called the vegetation drought response index for South Korea (VegDRI-SKorea) that could improve the spatial resolution of agricultural drought monitoring on a national scale. T...

Drought is the meteorological phenomenon with the greatest impact on agriculture. Accordingly, drought forecasting is vital in lessening its associated negative impacts. Utilizing remote exploration in the agricultural sector allows for the collection of large amounts of quantitative data across a wide range of areas. In this study, we confirmed the applicability of drought assessment using the evaporative stress index (ESI) in major East Asian countries. The ESI is an indicator of agricultural drought that describes anomalies in actual/reference evapotranspiration (ET) ratios that are retrieved using remotely sensed inputs of land surface temperature (LST) and leaf area index (LAI). The ESI is available through SERVIR Global, a joint venture between the National Aeronautics and Space Administration (NASA) and the United States Agency for International Development (USAID). This study evaluated the performance of ESI in assessing drought events in South Korea. The evaluation of ESI is possible because of the availability of good statistical data. Comparing drought trends identified by ESI data from this study to actual drought conditions showed similar trends. Additionally, ESI reacted to the drought more quickly and with greater sensitivity than other drought indices. Our results confirmed that the ESI is advantageous for short and medium-term drought assessment compared to vegetation indices alone.

Land management practices and disturbances (e.g. overgrazing, fire) have substantial effects on grassland forage production. When using satellite remote sensing to monitor climate impacts, such as drought stress on annual forage production, minimizing land management practices and disturbance effects sends a clear climate signal to the productivity data. This study investigates the effect of this climate signal by: (1) providing spatial estimates of expected biomass under specific climate conditions, (2) determining which drought indices explain the majority of interannual variability in this biomass, and (3) developing a predictive model that estimates the annual biomass early in the growing season. To address objective 1, this study uses an established methodology to determine Expected Ecosystem Performance (EEP) in the Nebraska Sandhills, US, representing annual forage levels after accounting for non-climatic influences. Moderate Resolution Imaging Spectroradiometer (MODIS)-based Normalized Difference Vegetation Index (NDVI) data were used to approximate actual ecosystem performance. Seventeen years (2000−2016) of annual EEP was calculated using piecewise regression tree models of site potential and climate data. Expected biomass (EB), EEP converted to biomass in kg*ha−1*yr−1, was then used to examine the predictive capacity of several drought indices and the onset date of the growing season. Subsets of these indices were used to monitor and predict annual expected grassland biomass. Independent field-based biomass production data available from two Sandhills locations were used for validation of the EEP model. The EB was related to field-based biomass production (R2 = 0.66 and 0.57) and regional rangeland productivity statistics of the Soil Survey Geographic Database (SSURGO) dataset. The Evaporative Stress Index (ESI), the 3- and 6-month Standardized Precipitation Index (SPI), and the U.S. Drought Monitor (USDM), which represented moisture conditions during May, June and July, explained the majority of the interannual biomass variability in this grassland system (three-month ESI explained roughly 72% of the interannual biomass variability). A new model was developed to use drought indices from early in the growing season to predict the total EB for the whole growing season. This unique approach considers only climate-related drought signal on productivity. The capability to estimate annual EB by the end of May will potentially enable land managers to make informed decisions about stocking rates, hay purchase needs, and other management issues early in the season, minimizing their potential drought losses.

Vigorous discussions and disagreements about the future changes in drought intensity in the U.S. Great Plains have been taking place recently within the literature. These discussions have involved widely varying estimates based on drought indices and model-based projections of the future. To investigate and understand the causes for such a disparity between these previous estimates, the authors analyzed the soil moisture at the near-surface soil layer and the entire soil column, as well as the Palmer drought severity index, the Palmer Z index, and the standardized precipitation and evaporation index using the output from the Community Climate System Model, version 4 (CCSM4), and 25 state-of-the-art climate models. These drought indices were computed using potential evapotranspiration estimated by the physically based Penman–Monteith method (PE_pm) and the empirically based Thornthwaite method (PE_th). The results showed that the short-term drought indices are similar to modeled surface soil moisture and show a small but consistent drying trend in the future. The long-term drought indices and the total column soil moisture, however, are consistent in projecting more intense future drought. When normalized, the drought indices with PE_th all show unprecedented future drying, while the drought indices with PE_pm show comparable dryness with the modeled soil moisture. Additionally, the drought indices with PE_pm are closely related to soil moisture during both the twentieth and twenty-first centuries. Overall, the drought indices with PE_pm, as well as the modeled total column soil moisture, suggest a widespread and very significant drying in the Great Plains toward the end of the century. The results suggest that the sharp contrasts about future drought risk in the Great Plains discussed in previous studies are caused by 1) comparing the projected changes in short-term droughts with that of the long-term droughts and/or 2) computing the atmospheric evaporative demand using an empirically based method (e.g., PE_th). The analysis here may be applied for drought projections in other regions across the globe.

Monitoring crop phenology provides essential information for crop management, as well as for understanding regional to global scale vegetation dynamics. In this study, a hybrid phenology detection method is presented that incorporates the “shape-model fitting” concept of the two-step filtering method and a simulation concept of the crop models to detect the critical vegetative stages and reproductive stages of corn (Zea mays L.) and soybeans (Glycine max L.) from MODIS 250-m Wide Dynamic Range Vegetation Index (WDRVI) time-series data and 1000-m Land Surface Temperature (LST) data. The method was first developed and tested at the field scale over a ten-year period (2003–2012) for three experimental study sites in eastern Nebraska of USA, where the estimated phenology dates were compared to the ground-based phenology observations for both corn and soybeans. The average root mean square error (RMSE) of phenology stage estimation of the individual development stages across all sites ranged from 1.9 to 4.3 days for corn and from 1.9 to 4.9 days for soybeans. The approach was then tested at a regional scale over eastern Nebraska and the state of Iowa to evaluate its ability to characterize the spatio-temporal variation of targeted corn and soybean phenology stage dates over a larger area. Quantitative regional assessments were conducted by comparing the estimated crop stage dates with crop developmental stage statistics reported by the USDA NASS Crop Progress Reports (NASS-CPR) for both eastern Nebraska and Iowa. The accuracy of the regional-scale phenology estimation in Iowa (RMSE ranged from 2.6 to 3.9 days for corn and from 3.2 to 3.9 days for soybeans) was slightly lower than in eastern Nebraska (RMSE ranged from 1.8 to 2.9 days for corn and from 1.7 to 2.9 days for soybeans), However, the estimation accuracy in Iowa was still reasonable with the estimated phenology dates being within 4 days or less of the observed dates for both corn and soybeans.

This study investigated the climatic influence on the corn sowing date in the Midwestern United States by comparing the survey data of corn cultivation with meteorological records in nine states for the last 36 years (1979–2014). The results show that the year-to-year changes in the sowing date were significantly affected by springtime air temperature and precipitation in the nine states, although large state-to-state differences were found in the degree of sowing date–meteorology relationship. We determined that the 36-year climatological warm period (CWP) with daily mean temperatures ≥10 °C plays an important role in the state-to-state differences. For the states with longer CWPs, the influence of air temperature (precipitation) was generally weaker (stronger). This observed counteractive relationship should be considered for crop modelling for more effective assessment of the impact of climate change on agriculture.

Long-term monitoring of crop phenology is a critical process to understand changes in cropland vegetation dynamics related to climate and human management. During the last four decades, seasonal changes in vegetation have been measured by satellite instruments, the Advanced Very High Resolution Radiometers (AVHRRs). However, an acceptable detection of crop phenology has been prevented due to its coarse spatial resolution (~8 km) pixels in which croplands are frequently mixed with non-cropland. This study introduces a novel method for detecting the start of the growing season in cropland (SOSC) from the AVHRR Normalized Difference Vegetation Index (NDVI) to examine the long-term changes in SOSC at the 8-km pixel level across the core cropping regions of the Midwestern United States (the so-called ‘the Corn Belt’) for 1982–2015. The AVHRR-based SOSC was compared with the SOSC retrieved from the pixels covering mostly croplands of the 250-m Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI. These two satellite-based SOSC shows reasonable correspondence in spatial patterns so that the annual values of the root mean square difference ranges from 3.6 to 7.4 days during the overlapping period of 2008–2015. Also, an acceptable correspondence was found between the interannual variations in the AVHRR SOSCs and in the state-level survey of crop stages. For sowing stages, correlation coefficients ranged from 0.43 to 0.68; for flowering stages those ranged from 0.51 to 0.83. By analyzing long-term SOSC changes with climate factors, we found a strong tendency that warmer pre-season condition is accompanied with earlier SOSC. In addition, larger pre-season precipitation tends to postpone SOSC as strongly as the pre-season temperature affects SOSC in a considerable portion of the core cropland. This study suggests that the postponing of sowing due to larger precipitation, which has rarely been highlighted in previous studies, requires more attentions in order for better understanding of vegetation dynamics in agriculture-climate studies.

Drought has had large impacts on economies, societies and the environment, and could become even more disruptive given the context of climate change characterized by increasing temperature and more variable and extreme precipitation. Changes in the frequency, duration, and severity of droughts will have enormous impacts on the hydrological cycle, water management and agricultural production. Therefore, one major concern arising from climate change is its potential effects on water resources. Although South Korea has been experiencing serious drought and water scarcity issues in recent years, preparedness for potential changes in the frequency, severity and duration of drought disasters due to climate change effects has received only limited attention. It is important to detect changes in temporal trends of drought at the regional scale. This information will aid understanding the impacts of climate change and its subsequent effects on hydrology and agriculture. In this paper, we have addressed the question of how climate change might influence the impact of drought hazard by estimating the potential changes in temporal trends of drought in South Korea. We have assessed the temporal trends of future drought with drought indices (Standardized Precipitation Index [SPI], Standardized Precipitation Evapotranspiration Index [SPEI], and Self-Calibrating Palmer Drought Severity Index [SC-PDSI]) using past observed data (1981–2010) from 54 meteorological stations maintained by the Korea Meteorological Administration (KMA) and projected climate change scenarios (2011–2100) as depicted by the Representative Concentration Pathways models (RCPs). The drought hazard assessment was quantitatively evaluated by analyzing drought frequency, duration, severity and magnitude using the run theory method based on different timescales of the drought indices. The results demonstrated a significant increase of potential drought impacts in the future. Additionally, significant increases in the drought magnitude and severity were found at different time scales for each drought indicator. The results indicated that the temporal pattern of potential drought progression and recession across South Korea can be used for the development of proactive drought risk management and mitigation strategies.

Water laws and drought plans are used to prioritize and allocate scarce water resources. Both have historically been human-centric, failing to account for non-human water needs. In this paper, we examine the development of instream flow legislation and the evolution of drought planning to highlight the growing concern for the non-human impacts of water scarcity. Utilizing a new framework for ecological drought, we analyzed five watershed-scale drought plans in southwestern Montana, USA to understand if, and how, the ecological impacts of drought are currently being assessed. We found that while these plans do account for some ecological impacts, it is primarily through the narrow lens of impacts to fish as measured by water temperature and streamflow. The latter is typically based on the same ecological principles used to determine instream flow requirements. We also found that other resource plans in the same watersheds (e.g., Watershed Restoration Plans, Bureau of Land Management (BLM) Watershed Assessments or United States Forest Service (USFS) Forest Plans) identify a broader range of ecological drought risks. Given limited resources and the potential for mutual benefits and synergies, we suggest greater integration between various planning processes could result in a more holistic consideration of water needs and uses across the landscape.

Central Europe is generally not considered a drought-prone region compared with, for example, the Mediterranean. However, Central Europe, including the Czech Republic, recently experienced a series of drought events with substantial impacts, especially on crop production. Because agriculture systems, and vegetation in general, have adapted to evenly distributed precipitation, the region is susceptible to even short-term droughts. The recent drought events may be the result of multi-decadal climate variability or a more general trend, with some studies showing a link to a more frequent occurrence of atmospheric circulation patterns that are conducive to drought. This study introduces an innovation to the standard methodological approaches in evaluating drought climatology by analysing soil moisture conditions over more than 50 years. This approach relies on state-of-the-art observed weather data and state-of-the-art soil moisture model data, and focuses on the dynamic simulation of soil moisture content with high temporal (daily) and spatial (500 m) resolution in a diverse landscape. Statistically significant trends of decreasing soil moisture content were found, notably during May and June between 1961 and 2012. In contrast, trends towards higher soil moisture content were noted during the October-March time period. When the periods of 2001-2012 and 1961-1980 were compared, the probability of drought between April and June was found to increase by 50%. This indicates a loading of the climate dice' towards drier conditions. The probability of extreme drought events has also been found to increase. These results support concerns about the potentially increased severity of drought events in Central Europe under projected climate change.

The vegetation drought response index (VegDRI), which combines traditional climate- and satellite-based approaches for assessing vegetation conditions, offers new insights into assessing the impacts of drought from local to regional scales. In 2011, the U.S. southern Great Plains, which includes Texas, Oklahoma, and New Mexico, was plagued by moderate to extreme drought that was intensified by an extended period of record-breaking heat. The 2011 drought presented an ideal case study to evaluate the performance of VegDRI in characterizing developing drought conditions. Assessment of the spatiotemporal drought patterns represented in the VegDRI maps showed that the severity and patterns of the drought across the region corresponded well to the record warm temperatures and much-below-normal precipitation reported by the National Climatic Data Center and the sectoral drought impacts documented by the Drought Impact Reporter (DIR). VegDRI values and maps also showed the evolution of the drought signal before the Las Conchas Fire (the largest fire in New Mexico’s history). Reports in the DIR indicated that the 2011 drought had major adverse impacts on most rangeland and pastures in Texas and Oklahoma, resulting in total direct losses of more than $12 billion associated with crop, livestock, and timber production. These severe impacts on vegetation were depicted by the VegDRI at subcounty, state, and regional levels. This study indicates that the VegDRI maps can be used with traditional drought indicators and other in situ measures to help producers and government officials with various management decisions, such as justifying disaster assistance, assessing fire risk, and identifying locations to move livestock for grazing.

Air temperature (Ta) is a key input in a wide range of agroclimatic applications. Moderate Resolution Imaging Spectroradiometer (MODIS) Ts (Land Surface Temperature (LST)) products are widely used to estimate daily Ta. However, only daytime LST (Ts-day) or nighttime LST (Ts-night) data have been used to estimate Tmax/Tmin (daily maximum or minimum air temperature), respectively. The relationship between Tmax and Ts-night, and the one between Tmin and Ts-day has not been studied. In this study, both the ability of Ts-night data to estimate Tmax and the ability of Ts-day data to estimate Tmin were tested and studied in the Corn Belt during the growing season (May–September) from 2008 to 2012, using MODIS daily LST products from both Terra and Aqua. The results show that using Ts-night for estimating Tmax could result in a higher accuracy than using Ts-day for a similar estimate. Combining Ts-day and Ts-night, the estimation of Tmax was improved by 0.19–1.85, 0.37–1.12 and 0.26–0.93 °C for crops, deciduous forest and developed areas, respectively, when compared with using only Ts-day or Ts-night data. The main factors influencing the Ta estimation errors spatially and temporally were analyzed and discussed, such as satellite overpassing time, air masses, irrigation, etc.

Under global climate change, drought has become one of the most serious natural hazards, affecting the ecological environment and human life. Drought can be categorized as meteorological, agricultural, hydrological or socio-economic drought. Among the different categories of drought, hydrological drought, especially streamflow drought, has been given more attention by local governments, researchers and the public in recent years. Identifying the occurrence of streamflow drought and issuing early warning can provide timely information for effective water resources management. In this study, streamflow drought is detected by using the Standardized Runoff Index, whereas meteorological drought is detected by the Standardized Precipitation Index. Comparative analyses of frequency, magnitude, onset and duration are conducted to identify the impact of meteorological drought on streamflow drought. This study focuses on the Jinghe River Basin in Northwest China, mainly providing the following findings. 1) Eleven meteorological droughts and six streamflow droughts were indicated during 1970 and 1990 after pooling using Inter-event time and volume Criterion method. 2) Streamflow drought in the Jinghe River Basin lagged meteorological drought for about 127 days. 3) The frequency of streamflow drought in Jinghe River Basin was less than meteorological drought. However, the average duration of streamflow drought is longer. 4) The magnitude of streamflow drought is greater than meteorological drought. These results not only play an important theoretical role in understanding relationships between different drought categories, but also have practical implications for streamflow drought mitigation and regional water resources management.

Soil moisture dynamics and their temporal trends in the Czech Republic are forced by various drivers. The methodology of applying remotely sensed data with both high temporal and spatial resolutions provides detailed insight and objective quantification of the causes of changes in soil moisture patterns. Our analysis of temporal trends indicates that shifts in drought severity between 1961 and 2012 (especially in the April, May, and June period, which displayed a 50% increase in drought probability between 1961-1980 and 2001-2012) are alarming. We found that increased global radiation and air temperature together with decreased relative humidity (all statistically significant at the 0.05 level) led to increases in the reference evapotranspiration in all months of the growing season; this trend was particularly evident in April, May, and August, when more than 80% of the territory displayed an increased demand for soil water. This finding was shown to be consistent with the measured pan evaporation (1968-2012) that was characterized by increasing trends, particularly during the April-June period. These changes, in combination with the earlier end of snow cover and the earlier start of growing season (up to 20days in some regions), led to an increased actual evapotranspiration at the start of growing season that tends to deplete the soil moisture earlier, leaving the soil more exposed to the impacts of rainfall variability. These results support concerns related to the potentially increased severity of drought events in Central Europe. The reported trend patterns are of particular importance with respect to the expected climate change, given the robustness and consistency of the trends shown and the fact that they can be aligned with the existing climate model projections.

Drought, as an extreme climate event, affects the ecological environment for vegetation and agricultural production. Studies of the vegetative response to drought are paramount to providing scientific information for drought risk mitigation. In this paper, the spatial-temporal pattern of drought and the response lag of vegetation in Nebraska were analyzed from 2000 to 2015. Based on the long-term Daymet data set, the standard precipitation index (SPI) was computed to identify precipitation anomalies, and the Gaussian function was applied to obtain temperature anomalies. Vegetation anomaly was identified by dynamic time warping technique using a remote sensing Normalized Difference Vegetation Index (NDVI) time series. Finally, multilayer correlation analysis was applied to obtain the response lag of different vegetation types. The results show that Nebraska suffered severe drought events in 2002 and 2012. The response lag of vegetation to drought typically ranged from 30 to 45 days varying for different vegetation types and human activities (water use and management). Grasslands had the shortest response lag (~35 days), while forests had the longest lag period (~48 days). For specific crop types, the response lag of winter wheat varied among different regions of Nebraska (35–45 days), while soybeans, corn and alfalfa had similar response lag times of approximately 40 days.

Future climate conditions for the Mediterranean region based on an ensemble of 16 Global Climate Models are expressed and mapped using three approaches, giving special attention to the intermodel uncertainty. (1) The scenarios of mean seasonal temperature and precipitation agree with the projections published previously by other authors. The results show an increase in temperature in all seasons and for all parts of the Mediterranean with good intermodel agreement. Precipitation is projected to decrease in all parts and all seasons (most significantly in summer) except for the northernmost parts in winter. The intermodel agreement for the precipitation changes is lower than for temperature. (2) Changes in drought conditions are represented using the Palmer Drought Severity Index and its intermediate Z-index product. The results indicate a significant decrease in soil moisture in all seasons, with the most significant decrease occurring in summer. The displayed changes exhibit high intermodel agreement. (3) The climate change scenarios are defined in terms of the changes in parameters of the stochastic daily weather generator calibrated with the modeled daily data; the emphasis is put on the parameters, which affect the diurnal and interdiurnal variability in weather series. These scenarios indicate a trend toward more extreme weather in the Mediterranean. Temperature maxima will increase not only because of an overall rise in temperature means, but partly (in some areas) because of increases in temperature variability and daily temperature range. Increased mean daily precipitation sums on wet days occurring in some seasons, and some parts of the Mediterranean may imply higher daily precipitation extremes, and decreased probability of wet day occurrence will imply longer drought spells all across the Mediterranean.

This study aims to evaluate soil climate quantitatively under present and projected climatic conditions across Central Europe (12.1°–18.9° E and 46.8°–51.1° N) and the U.S. Central Plains (90°–104° W and 37°–49° N), with a special focus on soil temperature, hydric regime, drought risk and potential productivity (assessed as a period suitable for crop growth). The analysis was completed for the baselines (1961–1990 for Europe and 1985–2005 for the U.S.) and time horizons of 2025, 2050 and 2100 based on the outputs of three global circulation models using two levels of climate sensitivity. The results indicate that the soil climate (soil temperature and hydric soil regimes) will change dramatically in both regions, with significant consequences for soil genesis. However, the predicted changes of the pathways are very uncertain because of the range of future climate systems predicted by climate models. Nevertheless, our findings suggest that the risk of unfavourable dry years will increase, resulting in greater risk of soil erosion and lower productivity. The projected increase in the variability of dry and wet events combined with the uncertainty (particularly in the U.S.) poses a challenge for selecting the most appropriate adaptation strategies and for setting adequate policies. The results also suggest that the soil resources are likely be under increased pressure from changes in climate.

The newly developed SoilClim model is introduced as a tool for estimates of reference ( ETo ) and actual ( ETa ) evapotranspiration, presence of snow cover, soil temperature at 0.5 m depth and the soil moisture course within two defined layers. It enables one to determine the soil moisture and temperature regimes according to the United States Department of Agriculture (USDA) soil taxonomy. SoilClim works with daily time steps and requires maximum and minimum air temperature, global solar radiation, precipitation, vapor pressure and wind speed as meteorological inputs as well as basic information about the soil properties and vegetation cover. The behavior of SoilClim was assessed using observations at 5 stations in central Europe and 15 stations in the central U.S. The modeled ETo was compared with atmometers so that the coefficient of determination ( R 2 ) was 0.91 and root mean square error (RMSE) was 0.53 mm. The estimated ETa was compared against eddy-covariance and Bowen ratio measurements ( R 2 varied from 0.74 to 0.80; RMSE varied from 0.49 to 0.58 mm). The soil temperature (at 0.5 m depth) was estimated with good accuracy ( R 2 varied from 0.94 to 0.97; RMSE varied from 1.23 °C to 2.95 °C). The ability of the SoilClim model to mimic the observed soil water dynamics was carefully investigated (relative root mean square error rRMSE varied from 2.8% to 34.0%). The analysis conducted showed that SoilClim gives reasonable estimates of evaluated parameters at a majority of the included stations. Finally, a spatial analysis of soil moisture and temperature regimes (according to USDA) within the region of the Czech Republic and the northern part of Austria under present conditions was conducted and diagnosed the appearance of Perudic, Subhumid Udic, Dry Tempudic (the highest frequency), Wet Tempustic and Typic Tempustic. The simulated mean soil temperature (0.5 m depth) varied from less than 7.0 °C to 11.0 °C throughout this region. Based on these results, the SoilClim model is a useful and suitable tool for water balance and soil climate assessment on local and regional scales.

No Abstract available.

The common versions (referred to as self-calibrated here) of the Standardized Precipitation Index (SPI) and the Palmer Drought Severity Index (PDSI) are calibrated and then applied to the same weather series. Therefore, the distribution of the index values is about the same for any weather series. We introduce here the relative SPI and PDSI, abbreviated as rSPI and rPDSI. These are calibrated using a reference weather series as a first step, which is then applied to the tested series. The reference series may result from either a different station to allow for the inter-station comparison or from a different period to allow for climate-change impact assessments. The PDSI and 1–24 month aggregations of the SPI are used here. In the first part, the relationships between the self-calibrated and relative indices are studied. The relative drought indices are then used to assess drought conditions for 45 Czech stations under present (1961–2000) and future (2060–2099) climates. In the present climate experiment, the drought indices are calibrated by using the reference station weather series. Of all drought indices, the PDSI exhibits the widest spectrum of drought conditions across Czechia, in part because it depends not only on precipitation (as does the SPI) but also on temperature. In our climate-change impact experiments, the future climate is represented by modifying the observed series according to scenarios based on five Global Climate Models (GCMs). Changes in the SPI-based drought risk closely follow the modeled changes in precipitation, which is predicted to decrease in summer and increase in both winter and spring. Changes in the PDSI indicate an increased drought risk at all stations under all climate-change scenarios, which relates to temperature increases predicted by all of the GCMs throughout the whole year. As drought depends on both precipitation and temperature, we conclude that the PDSI is more appropriate (when compared to the SPI) for use in assessing the potential impact of climate change on future droughts.

The development of new tools that provide timely, detailed-spatial-resolution drought information is essential for improving drought preparedness and response. This paper presents a new method for monitoring drought-induced vegetation stress called the Vegetation Drought Response Index (VegDRI). VegDRI integrates traditional climate-based drought indicators and satellite-derived vegetation index metrics with other biophysical information to produce a 1 km map of drought conditions that can be produced in near-real time. The initial VegDRI map results for a 2002 case study conducted across seven states in the north-central United States illustrates the utility of VegDRI for improved large-area drought monitoring.

The Standardized Precipitation Index (SPI) is now widely used throughout the world in both a research and an operational mode. For arid climates, or those with a distinct dry season where zero values are common, the SPI at short time scales is lower bounded, referring to non-normally distributed in this study. In these cases, the SPI is always greater than a certain value and fails to indicate a drought occurrence. The nationwide statistics based on our study suggest that the non-normality rates are closely related to local precipitation climates. In the eastern United States, SPI values at short time scales can be used in drought/flood monitoring and research in any season, while in the western United States, because of its distinct seasonal precipitation distribution, the appropriate usage and interpretation of this index becomes complicated. This would also be the case for all arid climates. From a mathematical point of view, the non-normally distributed SPI is caused by a high probability of no-rain cases represented in the mixed distribution that is employed in the SPI construction. From a statistical point of view, the 2-parameter gamma model used to estimate the precipitation probability density function and the limited sample size in dry areas and times would also reduce the confidence of the SPI values. On the basis of the results identified within this study, we recommend that the SPI user be cautious when applying short-time-scale SPIs in arid climatic regimes, and interpret the SPI values appropriately. In dry climates, the user should focus on the duration of the drought rather than on just its severity. It is also worth noting that the SPI results from a statistical product of the input data. This character makes it difficult to link the SPI data to the physical functioning of the Earth system. Copyright © 2006 Royal Meteorological Society.

Droughts are normal climate episodes, yet they are among the most expensive natural disasters in the world. Knowledge about the timing, severity, and pattern of droughts on the landscape can be incorporated into effective planning and decision-making. In this study, we present a data mining approach to modeling vegetation stress due to drought and mapping its spatial extent during the growing season. Rule-based regression tree models were generated that identify relationships between satellite-derived vegetation conditions, climatic drought indices, and biophysical data, including land-cover type, available soil water capacity, percent of irrigated farm land, and ecological type. The data mining method builds numerical rule-based models that find relationships among the input variables. Because the models can be applied iteratively with input data from previous time periods, the method enables to provide predictions of vegetation conditions farther into the growing season based on earlier conditions. Visualizing the model outputs as mapped information (called VegPredict) provides a means to evaluate the model. We present prototype maps for the 2002 drought year for Nebraska and South Dakota and discuss potential uses for these maps.

Drought is a complex natural hazard that is best characterized by multiple climatological and hydrological parameters. Improving our understanding of the relationships between these parameters is necessary to reduce the impacts of drought. Data mining is a recently developed technique that can be used to interact with large databases and assist in the discovery of associations between drought and oceanic data by extracting information from massive and multiple data archives. In this study, a new data-mining algorithm [i.e., Minimal Occurrences With Constraints and Time Lags (MOWCATL)] has been used to identify the relationships between oceanic parameters and drought indices. Rather than using traditional global statistical associations, the algorithm identifies drought episodes separate from normal and wet conditions and then uses drought episodes to find time-lagged relationships with oceanic parameters. As with all association-based data-mining algorithms, MOWCATL is used to find existing relationships in the data, and is not by itself a prediction tool. Using the MOWCATL algorithm, the analyses of the rules generated for selected stations and state-averaged data for Nebraska from 1950 to 1999 indicate that most occurrences of drought are preceded by positive values of the Southern Oscillation index (SOI), negative values of the multivariate ENSO index (MEI), negative values of the Pacific–North American (PNA) index, negative values of the Pacific decadal oscillation (PDO), and negative values of the North Atlantic Oscillation (NAO). The frequency and confidence of the time-lagged relationships between oceanic indices and droughts at the selected stations in Nebraska indicate that oceanic parameters can be used as indicators of drought in Nebraska.

Drought has an impact on many aspects of society. To help decision makers reduce the impacts of drought, it is important to improve our understanding of the characteristics and relationships of atmospheric and oceanic parameters that cause drought. In this study, the use of data mining techniques is introduced to find associations between drought and several oceanic and climatic indices that could help users in making knowledgeable decisions about drought responses before the drought actually occurs. Data mining techniques enable users to search for hidden patterns and find association rules for target data sets such as drought episodes. These techniques have been used for commercial applications, medical research, and telecommunications, but not for drought. In this study, two time-series data mining algorithms are used in Nebraska to illustrate the identification of the relationships between oceanic parameters and drought indices. The algorithms provide flexibility in time-series analyses and identify drought episodes separate from normal and wet conditions, and find relationships between drought and oceanic indices in a manner different from the traditional statistical associations. The drought episodes were determined based on the Standardized Precipitation Index (SPI) and Palmer Drought Severity Index (PDSI). Associations were observed between drought episodes and oceanic and atmospheric indices that include the Southern Oscillation Index (SOI), the Multivariate ENSO Index (MEI), the Pacific/North American (PNA) index, the North Atlantic Oscillation (NAO) Index, and the Pacific Decadal Oscillation (PDO) Index. The experimental results showed that among these indices, the SOI, MEI, and PDO have relatively stronger relationships with drought episodes over selected stations in Nebraska. Moreover, the study suggests that data mining techniques can help us to monitor drought using oceanic indices as a precursor of drought.

The Palmer Drought Severity Index (PDSI) has been used for more than 30 years to quantify the long-term drought conditions for a given location and time. However, a common critique of the PDSI is that the behavior of the index at various locations is inconsistent, making spatial comparisons of PDSI values difficult, if not meaningless. A self-calibrating Palmer Drought Severity Index (SC-PDSI) is presented and evaluated. The SC-PDSI automatically calibrates the behavior of the index at any location by replacing empirical constants in the index computation with dynamically calculated values. An evaluation of the SC-PDSI at 761 sites within Nebraska, Kansas, Colorado, Wyoming, Montana, North Dakota, and South Dakota, as well as at all 344 climate divisions shows that it is more spatially comparable than the PDSI, and reports extreme wet and dry conditions with frequencies that would be expected for rare conditions.

Flood risk and the critical time of occurrence are difficult to monitor and detect. The availability and application of adapted indices may allow not only continuous monitoring of hydrological conditions in flood-prone areas, but also the potential threat of possible flood events in order to promote preventive actions to mitigate the impacts of the phenomenon. The standardized precipitation index (SPI) was developed for drought detection and monitoring. However, because of its characteristics, the SPI can also be used as a tool to monitor wetter-than-normal conditions. The aim of this paper, based on the recurrent floods affecting the southern Cordoba Province in Argentina, is to analyse the potential of the SPI as a tool for monitoring flood risk in that region. Results indicate that the SPI satisfactorily explains the development of conditions leading up to the three main flood events to occur in the region during the past 25 years. This fact supports the proposal for applying the SPI as an effective component of a regional system for climate risk monitoring. Copyright © 2002 Royal Meteorological Society.

The Standardized Precipitation Index (SPI) was developed to detect drought and wet periods at different time scales, an important characteristic that is not accomplished with typical drought indices. More and more users employ the SPI to monitor droughts. Although calculation of the SPI is easier than other drought indices, such as the Palmer Drought Index, it is still relatively complex. In China, an index called the China-Z Index (CZI) has been used since 1995 by the National Climate Centre of China to monitor moisture conditions across the country. The calculation of this index is easier than the SPI. A third index, the statistical Z-Score, can also be used to monitor droughts. This paper evaluates the SPI, CZI and Z-Score on 1-, 3-, 6-, 9- and 12-month time scales using monthly precipitation totals for four locations in China from January 1951 to December 1998 representing humid and arid climates, and cases of drought and flood. Advantages and disadvantages for the application of each index are compared. Study results indicate that the CZI and Z-Score can provide results similar to the SPI for all time scales, and that the calculations of the CZI and Z-Score are relatively easy compared with the SPI, possibly offering better tools to monitor moisture conditions. Copyright © 2001 Royal Meteorological Society

Droughts are difficult to detect and monitor. Drought indices, most commonly the Palmer Drought Severity Index (PDSI), have been used with limited success as operational drought monitoring tools and triggers for policy responses. Recently, a new index, the Standardized Precipitation Index (SPI), was developed to improve drought detection and monitoring capabilities. The SPI has several characteristics that are an improvement over previous indices, including its simplicity and temporal flexibility, that allow its application for water resources on all timescales. In this article, the 1996 drought in the southern plains and southwestern United States is examined using the SPI. A series of maps are used to illustrate how the SPI would have assisted in being able to detect the onset of the drought and monitor its progression. A case study investigating the drought in greater detail for Texas is also given. The SPI demonstrated that it is a tool that should be used operationally as part of a state, re...

Large-scale assessments of crop conditions prior to harvest are critical for providing early estimates of production. Satellite and weather information provide the opportunity for near real-time crop monitoring. The objective of this research was to develop an operational assessment system for crop production utilizing data from these sources. Maize (Zea mays) production was assessed in 42 Crop Reporting Districts (CRDs) across the United States Corn Belt, which produce 60% of all maize grown in the United States. Satellite, climatolocal, and agricultural data were collected for 8 years, 1985–1992, and aggregated into CRDs. A model predicting the normalized maize yields for each CRD was developed that included as independent variables a satellite data variable, the Vegetation Condition Index, and a climatological variable, the Crop Moisture Index. This model explained approximately three-quarters (R 2=0.73) of the variation observed in the normalized yields, and was examined both for its accuracy and its timeliness in providing production estimates. Predicted seasonal yields were summed to provide a maize production estimate for the entire Corn Belt study region. Production estimates deviated from the final USDA statistics, which become available several months after harvest, by less than 10% for all eight growing seasons. In addition, the production estimates were available approximately 2 months prior to the completion of the maize harvest. This system has the potential for providing timely in-formation to organizations monitoring regional or global agricultural production for humanitarian or economic benefits.