Drought Variability and Land Degradation in Semiarid Regions: Assessment Using Remote Sensing Data and Drought Indices (1982–2011)

The impact of extreme precipitation on water use efficiency along vertical vegetation belts in Hengduan Mountain during 2001 and 2020

In the context of climate change, extreme precipitation events are continuously increasing and impact the water‑carbon coupling of ecosystems. The vertical vegetation zonation, as a characteristic of mountain ecosystems, reflects the differences in vegetation response to climate change at different elevations. In this study, we used the water use efficiency (WUE) as an indicator to evaluate the water‑carbon relationship. By using MODIS data, we analyzed the spatiotemporal patterns of gross primary productivity (GPP), evapotranspiration (ET), and WUE from 2001 to 2020, as well as the responses of WUE to extreme wetness factor Number of precipitation days (R0.1), extreme dryness factor Consecutive dry days (CDD), and meteorological factors under the vertical vegetation zonation. Our results showed that annual GPP and ET displayed a significant increasing trend between 2001 and 2020, whereas WUE showed a weak decreasing trend. Spatially, GPP and WUE decreased with increasing elevation. Analyzing the WUE of mountainous ecosystems as a unified whole may not precisely capture the reactions of vegetation to severe rainfall occurrences. In fact, across different vegetation belts in mountainous areas, there exists a negative correlation between WUE and R0.1, and a positive correlation with CDD. In terms of meteorological factors, the temporal variation of GPP was primarily associated with vapor pressure deficit (VPD) and temperature (Ta), while those of ET was mainly related to soil water content (SWC). WUE was affected by a combination of meteorological factors and had a certain degree of variation between different altitude intervals. These findings contribute to a better understanding and prediction of the relationship between extreme rainfall climate and water‑carbon coupling in mountainous areas.

Water budget-salt balance model for calculating net water saving considering different non-conventional water resources in agricultural process

Iraq is facing a dire water crisis due to the decrease in water quantities flow in Tigris and Euphrates Rivers. Due to population growth, several studies estimated the water shortage in 2035 to be 44 Billion Cubic Meter (BCM). Thus, Water Budget-Salt Balance Model (WBSBM) has been developed, applied and examined for the Euphrates River basin to compute the net water saving from Non-Conventional Water Resources (NCWRs). WBSBM includes 4-stages; the first is to identify the required data correspond to the conventional water resources in the study-area. The second stage is demonstrating the water-users activities. Thirdly, develop model through the proposed NCWR projects that reflect the required data. The final stage involves net water saving computation while applying all the NCWR projects simultaneously. The results obtained the optimal potential net water saving amount, which are 6.823 and 6.626 BCM/year in 2025 and 2035, respectively. In conclusion, the proposed WBSBM model has comprehensively examined different scenarios of utilizing NCWRs and has determined the optimal potential the net water saving amounts.

Multifaceted responses of vegetation to average and extreme climate change over global drylands

Average climatic events describe the occurrence of weather or climate at an average value, whereas extreme events are defined as events that exceed the upper or lower threshold value of statistical or observational average climatic events. This study investigated the impacts of both average climate change (ACC) (i.e., average precipitation, temperature, and potential evapotranspiration [PET]) and extreme climate change (ECC) (i.e., five precipitation and five temperature extremes) on dryland vegetation based on the Normalized Difference Vegetation Index (NDVI). The spatial divergences of ACC and ECC in affecting changes in NDVI over drylands were determined using the geographical detector model. In this study, the growth of vegetation in 40.29 % of global drylands was driven by average precipitation and this dominant effect also occurred in all the plant species, particularly shrubs. However, the sensitivity of grassland to average precipitation exceeded that of most of the woody vegetation. The average temperature and PET controlled 28.64 % and 31.07 % of the changes in NDVI, respectively. Precipitation extremes (except for consecutive dry days and consecutive wet days) and warm temperature extremes (WTE) had positive influences on dryland vegetation, and the effect of WTE on NDVI exceeded that of the remaining temperature extremes. Temperature extremes exerted more significant effects than precipitation extremes for changes in the grassland NDVI. In contrast, the variations in shrub NDVI were primarily dominated by precipitation extremes. We also found that the impacts of parts of average and extreme climatic factors on vegetation had changed over time. Furthermore, temperature extremes had far exceeded the average temperature in affecting vegetation growth at the spatial scale, and this action gradually intensified from 1982 to 2015. The influences of all precipitation extremes were weaker than those of the average precipitation. Those can offer scientific references for ecosystem protection in drylands.

Delineating the Intrinsic, Long-Term Path of Land Degradation: A Spatially Explicit Transition Matrix for Italy, 1960-2010

Vulnerability to land degradation in southern Europe has increased substantially in the last decades because of climate and land-use change, soil deterioration, and rising human pressure. The present work focuses on a quantitative evaluation of changes over time in the level of vulnerability to land degradation of a Mediterranean country (Italy) using a composite indicator, the environmentally sensitive area index (ESAI), which is the final outcome of a complex model conceived to assess land vulnerability on the basis of climate, soil, vegetation, and human pressure. Considering four different levels of vulnerability to land degradation (not affected, potentially affected, fragile, and critical), the main trajectories of this index were highlighted in a long-time perspective (1960-2010), discriminating dynamics over two sub-periods (1960-1990 and 1990-2010). The empirical results at a very detailed spatial scale (1 km² grid) reflect spatial consolidation of degradation hot-spots over time. However, aggregated trajectories of change indicate an overall improvement in the environmental conditions between 1990 and 2010 compared with what is observed during the first period (1960-1990). Worse environmental conditions concerned southern Italian regions with a dry climate and poor soil conditions in the first time interval, large parts of northern Italy, traditionally recognized as a wet and affluent agricultural region, experienced increasing levels of land vulnerability in the second time interval. Being classified as an unaffected region according with the Italian national action plan (NAP), the expansion of (originally sparse) degradation hot-spots in northern Italy, reflective of an overall increase in critical areas, suggests a substantial re-thinking of the Italian NAP. This may lead to a redesign of individual regional action plans (RAPs) implementing place-specific approaches and comprehensive measures to be adopted to mitigate land degradation.

A Drought Dataset Based on a Composite Index for the Sahelian Climate Zone of Niger

Agricultural drought monitoring in Niger is relevant for the implementation of effective early warning systems and for improving climate change adaptation strategies. However, the scarcity of in situ data hampers an efficient analysis of drought in the country. The present dataset was created for agricultural drought characterization in the Sahelian climate zone of Niger. The dataset comprises the three-month scale and monthly time series of a composite drought index (CDI) and their corresponding drought classes at a spatial resolution of 1 km2 for the period 2000–2020. The CDI was generated from remote sensing data, namely CHIRPS (Climate Hazards Group InfraRed Precipitation with Stations), normalized difference vegetation index (NDVI) and land surface temperature (LST) from MODIS (Moderate Resolution Imaging Spectroradiometer). A weighing technique combining entropy and Euclidian distance was applied in the CDI derivation. From the present dataset, the extraction of the CDI time series can be performed for any location of the study area using its geographic coordinates. Therefore, seasonal drought characteristics, such as onset, end, duration, severity and frequency can be computed from the CDI time series using the theory of runs. The availability of the present dataset is relevant for the socio-economic assessment of drought impacts at small spatial scales, such as district and household level. This dataset is also important for the assessment of drought characteristics in remote areas or areas inaccessible due to civil insecurity in the country as it was entirely generated from remote sensing data. Finally, by including temperature data, the dataset enables drought modelling under global warming.

Bacterial bioinoculants adapted for sustainable plant health and soil fertility enhancement in Namibia

The increase in dryland agriculture elicits the need to develop sustainable practices that improve crop yield and protect soil fertility. The use of biofertilisers adapted to nutrient deficient soils and arid climates would help achieve this. In this review, the use of plant growth-promoting bacteria is explored as a possible solution to the current state of dryland agriculture and climate change threats to agriculture. Plant microbe interactions form the basis of this review as evidence has shown that these interactions often exist to improve the health of plants. This is achieved by the production of important biochemicals and enzymes like indole acetic acid and amino cyclopropane-1-carboxylate deaminase while also actively protecting plants from pathogens including fungal pathogens. Research, therefore, has shown that these plant-growth promoting bacteria may be exploited and developed into biofertilisers. These biofertilisers are both economically and environmentally sustainable while improving soil quality and crop yield. The literature presented in this review is in context of the Namibian climate and soil profiles.

Multispectral Analysis of Small Plots Based on Field and Remote Sensing Surveys—A Comparative Evaluation

Remote sensing is an efficient method of monitoring experiments rapidly and by enabling the collection of significantly more detailed data, than using only field measurements, ensuring new possibilities in scientific research. A small plot field experiment was conducted in a randomized block design with winter oat (Avena sativa L.) varieties in Debrecen, Hungary in the 2020/2021 cropping year. Multiple field measurements and aerial surveys were carried out examining the response of oat on Silicon and Sulfur foliar fertilization treatments thereby monitoring their effects on the physiology, production and stress tolerance. Parallel application of in situ (elevation, soil pH, NDVI, SPAD, chlorophyll content) and aerial (NDVI, NDRE) surveys including unmanned aerial vehicles (UAVs) provided a diverse source of data for evaluation. Both the oat varieties (88.9%) and the foliar fertilization treatments (87.5%) were correctly classified and clearly separated with the discriminant analysis based on measured data. The Pearson correlation analysis showed a very strong positive connection (r = 0.895–1.00) between the NDVI values measured using a hand-held system and UAV-installed camera, except the third measurement time, where the correlation was weaker (r = 0.70). Our results indicate that field experiments can be effectively supported by UAVs.

Assessment of Seasonal Drought Impact on Potato in the Northern Single Cropping Area of China

Drought is one of the key limiting factors for potato yield in the northern single cropping area (NSCA) in China. To analyze the impact of drought on potato yield in the NSCA, this study first analyzed the variation of dry/wet conditions in the plantable areas on a seasonal scale using the standardized precipitation evapotranspiration index (SPEI). Secondly, the changes in yield structure in the last 36 years were systematically analyzed and divided the total yield change into planting area contribution and climate yield contribution. Finally, a regression model of the seasonal drought index and contributing factors of total yield change in different administrative regions was constructed. The results showed that the main factors affecting the total potato yield of the NSCA began to change from yield to planting area in the 1990s, while the barycenter of the output structure and population moved to the southwest, with grassland being the main source; dry/wet conditions (year i) had varying degrees of effect on contributing factors (year i, year i + 1) of total yield change in different administrative regions that were not limited to the growing season; the non-overlap of high-yield area, high-adaptability area and planting area was the urgent problem to be solved for the NSCA. The results of this study can provide a scientific basis for NSCA crop management and communication with farmers, providing new ideas for sustainable production in other agricultural regions in the world.

Mapping South America’s Drylands through Remote Sensing—A Review of the Methodological Trends and Current Challenges

The scientific grasp of the distribution and dynamics of land use and land cover (LULC) changes in South America is still limited. This is especially true for the continent’s hyperarid, arid, semiarid, and dry subhumid zones, collectively known as drylands, which are under-represented ecosystems that are highly threatened by climate change and human activity. Maps of LULC in drylands are, thus, essential in order to investigate their vulnerability to both natural and anthropogenic impacts. This paper comprehensively reviewed existing mapping initiatives of South America’s drylands to discuss the main knowledge gaps, as well as central methodological trends and challenges, for advancing our understanding of LULC dynamics in these fragile ecosystems. Our review centered on five essential aspects of remote-sensing-based LULC mapping: scale, datasets, classification techniques, number of classes (legends), and validation protocols. The results indicated that the Landsat sensor dataset was the most frequently used, followed by AVHRR and MODIS, and no studies used recently available high-resolution satellite sensors. Machine learning algorithms emerged as a broadly employed methodology for land cover classification in South America. Still, such advancement in classification methods did not yet reflect in the upsurge of detailed mapping of dryland vegetation types and functional groups. Among the 23 mapping initiatives, the number of LULC classes in their respective legends varied from 6 to 39, with 1 to 14 classes representing drylands. Validation protocols included fieldwork and automatic processes with sampling strategies ranging from solely random to stratified approaches. Finally, we discussed the opportunities and challenges for advancing research on desertification, climate change, fire mapping, and the resilience of dryland populations. By and large, multi-level studies for dryland vegetation mapping are still lacking.

Satellite Based Fraction of Absorbed Photosynthetically Active Radiation Is Congruent with Plant Diversity in India

A dynamic habitat index (DHI) based on satellite derived biophysical proxy (fraction of absorbed photosynthetically active radiation, FAPAR) was used to evaluate the vegetation greenness pattern across deserts to alpine ecosystems in India that account to different biodiversity. The cumulative (DHI-cum), minimum (DHI-min), and seasonal (DHI-sea) DHI were generated using Moderate Resolution Imaging Spectroradiometer (MODIS)-based FAPAR. The higher DHI-cum and DHI-min represented the biodiversity hotspots of India, whereas the DHI-sea was higher in the semi-arid, the Gangetic plain, and the Deccan peninsula. The arid and the trans-Himalaya are dominated with grassland or barren land exhibit very high DHI-sea. The inter-year correlation demonstrated an increase in vegetation greenness in the semi-arid region, and continuous reduction in greenness in the Northeastern region. The DHI components validated using field-measured plant richness data from four biogeographic regions (semi-arid, eastern Ghats, the Western Ghats, and Northeast) demonstrated good congruence. DHI-cum that represents the annual greenness strongly correlated with the plant richness (R2 = 0.90, p-value < 0.001), thereby emerging as a suitable indicator for assessing plant richness in large-scale biogeographic studies. Overall, the FAPAR-based DHI components across Indian biogeographic regions provided understanding of natural variability of the greenness pattern and its congruence with plant diversity.

Modeling large-scale biometeorological indices to monitor agricultural-growing areas: applications in the fruit circuit region, São Paulo, Brazil

This paper aimed to support the rational crop expansion in agricultural-growing regions. MODIS satellite images are used together with gridded weather data to model biometeorological parameters at the Fruit Circuit region, state of São Paulo, Southeast Brazil. This region has experienced some cases of drought, while arising rainfall water excess in some periods, demanding large-scale water and energy balance studies to subsidize water resource policies. The SAFER (Simple Algorithm for Evapotranspiration Retrieving) algorithm was applied together with the radiation-use efficiency (RUE) model for biometeorological index assessments. The highest latent heat fluxes (λE), above 8.0 MJ m^-2 d^-1, at the end of the year, coincide with the progressive increases on both rainfall and global solar radiation (R_G) levels, and drop to below 5.0 MJ m^-2 d^-1 in the middle of the year, during the driest conditions. The same tendencies along the year are verified for sensible heat fluxes (H), for which mean pixel values are above 3.5 MJ m^-2 d^-1 at the end of the year. On the one hand, the highest values for water productivity (WP), which is considered the ratio of actual evapotranspiration (ET) to biomass production (BIO), above 4.0 kg m^-3, are verified in April, period under increasing BIO and low ET rates. On the other hand, the lowest WP values (below 2.0 kg m^-3) occur between August and October, when BIO is low, and ET is high. Although the area featuring good WP levels under high precipitation (P), with rainfalls generally above ET, supplementary irrigation may benefit agriculture in some periods of the year. The results of the large-scale modeling showed applicability of the models for monitoring water and vegetation dynamics over 16-day timescale and at a 250-m spatial resolution in areas experiencing climate and land-use changes by combining climate data and MODIS images. Application of these tools enables to indicate the best options for expanding the agriculture activities, being of great potential for rational natural resources management, in regions under environmental vulnerability conditions.

Drought Impacts on Vegetation in Southeastern Europe

We evaluated the response of vegetation’s photosynthetic activity to drought conditions from 1998 to 2014 over Romania and the Republic of Moldova. The connection between vegetation stress and drought events was assessed by means of a correlation analysis between the monthly Standardized Precipitation Evaporation Index (SPEI), at several time scales, and the Normalized Difference Vegetation Index (NDVI), as well as an assessment of the simultaneous occurrence of extremes in both indices. The analysis of the relationship between drought and vegetation was made for the growing season (from April to October of the entire period), and special attention was devoted to the severe drought event of 2000/2001, considered as the driest since 1961 for the study area. More than three quarters (77%) of the agricultural land exhibits a positive correlation between the two indices. The sensitivity of crop areas to drought is strong, as the impacts were detected from May to October, with a peak in July. On the other hand, forests were found to be less sensitive to drought, as the impacts were limited mostly to July and August. Moreover, vegetation of all land cover classes showed a dependence between the sign of the correlation and the elevation gradient. Roughly 60% (20%) of the study domain shows a concordance of anomalously low vegetation activity with dry conditions of at least 50% (80%) in August. By contrast, a lower value of concordance was observed over the Carpathian Mountains. During the severe drought event of 2000/2001, a decrease in vegetation activity was detected for most of the study area, showing a decrease lasting at least 4 months, between April and October, for more than two thirds (71%) of the study domain.

On the Performances of Trend and Change-Point Detection Methods for Remote Sensing Data

Detecting change-points and trends are common tasks in the analysis of remote sensing data. Over the years, many different methods have been proposed for those purposes, including (modified) Mann–Kendall and Cox–Stuart tests for detecting trends; and Pettitt, Buishand range, Buishand U, standard normal homogeneity (Snh), Meanvar, structure change (Strucchange), breaks for additive season and trend (BFAST), and hierarchical divisive (E.divisive) for detecting change-points. In this paper, we describe a simulation study based on including different artificial, abrupt changes at different time-periods of image time series to assess the performances of such methods. The power of the test, type I error probability, and mean absolute error (MAE) were used as performance criteria, although MAE was only calculated for change-point detection methods. The study reveals that if the magnitude of change (or trend slope) is high, and/or the change does not occur in the first or last time-periods, the methods generally have a high power and a low MAE. However, in the presence of temporal autocorrelation, MAE raises, and the probability of introducing false positives increases noticeably. The modified versions of the Mann–Kendall method for autocorrelated data reduce/moderate its type I error probability, but this reduction comes with an important power diminution. In conclusion, taking a trade-off between the power of the test and type I error probability, we conclude that the original Mann–Kendall test is generally the preferable choice. Although Mann–Kendall is not able to identify the time-period of abrupt changes, it is more reliable than other methods when detecting the existence of such changes. Finally, we look for trend/change-points in land surface temperature (LST), day and night, via monthly MODIS images in Navarre, Spain, from January 2001 to December 2018.

Climate Change Affected Vegetation Dynamics in the Northern Xinjiang of China: Evaluation by SPEI and NDVI

Drought and vegetation dynamics in the northern Xinjiang Uygur Autonomous Region of China (NXC), the centre of Asia with arid climate, were assessed using the standardized precipitation evapotranspiration index (SPEI) and the normalized difference vegetation index (NDVI). Analyses were performed through the use of Sen’s method and Spearman’s correlation to investigate variations in the NDVI and the impacts of drought on vegetation from 1998 to 2015. The severity of droughts in the NXC was assessed by the SPEI, which was revealed to increase over the last 60 years at a rate of 0.017 per decade. This indicates that an alleviating tendency of drought intensity occurred in the NXC. Specifically, the spatial pattern of drought intensity increased gradually from the north-western to south-eastern regions. The average yearly NDVI was 0.28 and increased slightly by 0.001 yr−1 (r = 0.94, p = 3.64) between 1998 and 2015. Additionally, the NDVI showed an obviously spatial heterogeneity, with greater values in the west and small values in the east. Significantly, positive correlations between SPEI and NDVI were observed, while drought exerted a five-year lag effect on vegetation.

The Future of Semi-Arid Regions: A Weak Fabric Unravels

The regions of the world where average precipitation is between one fifth and half of the potential plant water demand are termed ‘semi-arid’. They make up 15.2% of the global land surface, and the approximately 1.1 billion people who live there are among the world’s poorest. The inter-annual variability of rainfall in semi-arid regions is exceptionally high, due to intrinsic features of the global atmospheric circulation. The observed and projected climate trends for most semi-arid regions indicate warming at rates above the global mean rate over land, increasing evaporative demand, and reduced and more variable rainfall. Historically, the ecosystems and people coped with the challenges of semi-arid climates using a range of strategies that are now less viable. Semi-arid ecosystems are by definition water limited, generally only suitable for extensive pastoralism and opportunistic cropping, unless irrigation supplementation is available. The characteristics of dryland plant production in semi-arid ecosystems, as they interact with climate change and human systems, provide a conceptual framework for why land degradation is so conspicuous in semi-arid regions. The coupled social-ecological failures are contagious, both within the landscape and at regional and global scales. Thus, semi-arid lands are a likely flashpoint for Earth system changes in the 21st century.

Characteristic Analysis of Droughts and Waterlogging Events for Maize Based on a New Comprehensive Index through Coupling of Multisource Data in Midwestern Jilin Province, China

Frequent droughts and waterlogging events are a threat to food security. An understanding of the spatial and temporal variations that occur during such events is essential when conducting a regional and/or global drought and waterlogging assessment. The goal of this study is to define a comprehensive index that considers the continuum system of atmosphere, crops, and soil moisture impacts on droughts and waterlogging events, and to analyze the temporal and spatial distribution of such events in the development of maize. The results show that the proposed comprehensive drought and waterlogging index (CDWI) can bring together the advantages of a single drought and waterlogging index and reasonably describe its range. During the study period, the annual trends of the CDWI decreased at different growth stages from 1982 to 2015, whereas the CDWI did not show significant spatial heterogeneity during any particular stage. Increasing trends of CDWI over 0.019/year were found in the northern part of Midwestern Jilin Province from the emergence to tasseling stages. In addition, decreasing trends were observed in the study area from the tasseling to maturation stages. Slight drought and waterlogging events occurred more frequently than moderate and serious drought and waterlogging events.

Effect of Climate Change on Maize Yield in the Growing Season: A Case Study of the Songliao Plain Maize Belt

Based on the 1965–2017 climate data of 18 meteorological stations in the Songliao Plain maize belt, the Coupled Model Intercomparision Project (CMIP5) data, and the 1998–2017 maize yield data, the drought change characteristics in the study area were analyzed by using the standardized precipitation evapotranspiration index (SPEI) and the Mann–Kendall mutation test; furthermore, the relationship between meteorological factors, drought index, and maize climate yield was determined. Finally, the maize climate yields under 1.5 °C and 2.0 °C global warming scenarios were predicted. The results revealed that: (1) from 1965 to 2017, the study area experienced increasing temperature, decreasing precipitation, and intensifying drought trends; (2) the yield of the study area showed a downward trend from 1998 to 2017. Furthermore, the climate yield was negatively correlated with temperature, positively correlated with precipitation, and positively correlated with SPEI-1 and SPEI-3; and (3) under the 1.5 °C and the 2.0 °C global warming scenarios, the temperature and the precipitation increased in the maize growing season. Furthermore, under the studied global warming scenarios, the yield changes predicted by multiple regression were −7.7% and −15.9%, respectively, and the yield changes predicted by one-variable regression were −12.2% and −21.8%, respectively.

Drought risk evaluation model with interval number ranking and its application

In the context of more and more extreme weather events around the world, it is of great practical significance to accurately monitor drought and evaluate its drought risk for the sustainable development of regional agriculture. This study aims at establishing a regional drought risk evaluation method based on remote sensing drought monitoring and uncertainty method. In this paper, multi-model optimization method is adopted. 5 models were used to invert soil moisture content. After analysis and verification, the most suitable drought monitoring model of Temperature and vegetation polynomial model (TVPM) was obtained. The uncertainty method is introduced in this paper using Statistical-based interval weight determination of evaluation index method and Interval number sorting method based on two-dimensional information to establish drought risk evaluation model. On this basis, it was applied in Heilongjiang province of China to evaluate and rank the risk of drought in 8 regions in April 2018. The ranking result was: Nenjiang > Boli > Linkou > Wudalianchi > Ning'an > Suifenhe > Hailin > Yanshou. The results show that the evaluation method based on interval number can better deal with the uncertainty in reality.

An Investigation into the Spatial and Temporal Variability of the Meteorological Drought in Jordan

Following the impact of droughts witnessed during the last decade there is an urgent need to develop a drought management strategy, policy framework, and action plan for Jordan. This study aims to provide a historical baseline using the standardized precipitation index (SPI) and meteorological drought maps, and to investigate the spatial and temporal trends using long-term historical precipitation records. Specifically, this study is based on the statistical analysis of 38 years of monthly rainfall data, gathered from all 29 meteorological stations that cover Jordan. The Mann–Kendall test and linear regression analysis were used to uncover evidence of long-term trends in precipitation. Drought indices were used for calculating the meteorological SPI on an annual (SPI12), 6-months (SPI6), and 3-months basis (SPI3). At each level, every drought event was characterized according to its duration, interval, and intensity. Then, drought maps were generated using interpolation kriging to investigate the spatial extent of drought events, while drought patterns were temporally characterized using multilinear regression and spatial grouped using the hierarchical clustering technique. Both annual and monthly trend analyses and the Mann–Kendall test indicated significant reduction of precipitation in time for all weather stations except for Madaba. The rate of decrease is estimated at approximately 1.8 mm/year for the whole country. The spatial SPI krig maps that were generated suggest the presence of two drought types in the spatial dimension: Local and national. Local droughts reveal no actual observed trends or repeatable patterns of occurrence. However, looking at meteorological droughts across all time scales indicated that Jordan is facing an increasing number of local droughts. With a probability of occurrence of once every two years to three years. On the other hand, extreme national droughts occur once every 15 to 20 years and last for two or more consecutive years. Linear trends indicated significant increase in drought magnitude by time with a rate of 0.02 (p < 0.0001). Regression analysis indicated that draught in Jordan is time dependent (p < 0.001) rather than being spatially dependent (p > 0.99). Hierarchical clustering was able to group national draughts into three zones, namely the northern zone, the eastern zone, and the southern zone. This study highlights the urgent need for a monitoring program to investigate local and national drought impacts on all sectors, as well as the development of a set of proactive risk management measures and preparedness plans for various physiographic regions.

Water resources and nitrate discharges in relation to agricultural land uses in an intensively irrigated watershed

Application of integrated hydrological models to manage water resources and non-point agricultural pollutants are increasingly used in decision-making processes. In this study SWAT (Soil and Water Assessment Tool) was used to simulate the water balance and nitrate pollution in an intensively irrigated agricultural catchment (Flumen River in Monegros, Aragon, NE Spain). Rainfall comprised only 45% of the inputs of water in the Flumen watershed and the rest is contributed through irrigation canals from two other rivers outside the Flumen watershed. Green water storage and green water flow are the dominant components of the water balance in the watershed, which is related to the important contribution of water for irrigation. In general, green water storage and green water flow are quite similar in the subwatersheds dominated by irrigation agriculture that are located in the central part of the watershed. A similar pattern was observed for blue water, with high amounts in the central irrigated subwatersheds compared to the non-irrigated subwatersheds. Consequently, nitrate infiltration in the aquifer was higher in the inner irrigated subwatersheds (100-250 kg N ha^-1 year^-1) but much lower than the lateral flow rates estimated in the non-irrigated subwatersheds (1400-2000 kg N ha^-1 year^-1). Two scenarios simulating the effects of expected climate change factors in this zone were performed. A reduction in the availability of water for irrigation will transform the area from irrigated crops to cereal. In this case the water flow of River Flumen at the outlet of the watershed is reduced by 15%. If a reduction of 40% nitrate fertilization is applied, the nitrate exported to Flumen River would decreased by 28%. These results suggest that dosing irrigation water and fertilizers in accordance with crop requirements would contribute to buffer peaks of water and nitrate discharges and to a more efficient agricultural use of the resources.

An Improved Spatio-Temporal Adaptive Data Fusion Algorithm for Evapotranspiration Mapping

Continuous high spatio-temporal resolution monitoring of evapotranspiration (ET) is critical for water resource management and the quantification of irrigation water efficiency at both global and local scales. However, available remote sensing satellites cannot generally provide ET data at both high spatial and temporal resolutions. Data fusion methods have been widely applied to estimate ET at a high spatio-temporal resolution. Nevertheless, most fusion methods applied to ET are initially used to integrate land surface reflectance, the spectral index and land surface temperature, and few studies completely consider the influencing factor of ET. To overcome this limitation, this paper presents an improved ET fusion method, namely, the spatio-temporal adaptive data fusion algorithm for evapotranspiration mapping (SADFAET), by introducing critical surface temperature (the corresponding temperature to decide soil moisture), importing the weights of surface ET-indicative similarity (the influencing factor of ET, which is estimated from remote sensing data) and modifying the spectral similarity (the differences in spectral characteristics of different spatial resolution images) for the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). We fused daily Moderate Resolution Imaging Spectroradiometer (MODIS) and periodic Landsat 8 ET data in the SADFAET for the experimental area downstream of the Heihe River basin from April to October 2015. The validation results, based on ground-based ET measurements, indicated that the SADFAET could successfully fuse MODIS and Landsat 8 ET data (mean percent error: −5%), with a root mean square error of 45.7 W/m2, whereas the ESTARFM performed slightly worse, with a root mean square error of 50.6 W/m2. The more physically explainable SADFAET could be a better alternative to the ESTARFM for producing ET at a high spatio-temporal resolution.

Changes of Grassland Rain Use Efficiency and NDVI in Northwestern China from 1982 to 2013 and Its Response to Climate Change

The grasslands in arid and semi-arid regions rely heavily on the use of rain, thus, improving rain use efficiency (RUE) is essential for securing sustainable development of grassland ecosystems in these areas with limited rainfall. In this study, the spatial and temporal variabilities of RUE for grassland ecosystems over Northwestern China during 1982–2013 were analyzed using the normalized difference vegetation index (NDVI) and precipitation data. Results showed that: (1) Although grassland area has decreased gradually over the past 30 years, the NDVI in most areas showed that the vegetation was gradually restored; (2) The trends of RUE increased in the east of Northwestern China and decreased in the west of Northwestern China. However, the trends of RUE for the high-coverage grasslands (vs. low-coverage grassland) increased (decreased) significantly over the past 30 years. (3) The RUE for the grasslands was positively correlated with air temperature, while it was negatively correlated with the change of annual mean precipitation in northwestern China. Moreover, the obvious RUE increasing trends were found in the vegetation restoration areas, while the RUE decreasing trends appeared in the vegetation degradation areas. This study will be helpful for understanding the impacts of climate change on securing the sustainable development of grassland ecosystems in arid and semi-arid regions.

Drought Sensitiveness on Forest Growth in Peninsular Spain and the Balearic Islands

Drought is one of the key natural hazards impacting net primary production and tree growth in forest ecosystems. Nonetheless, tree species show different responses to drought events, which make it difficult to adopt fixed tools for monitoring drought impacts under contrasting environmental and climatic conditions. In this study, we assess the response of forest growth and a satellite proxy of the net primary production (NPP) to drought in peninsular Spain and the Balearic Islands, a region characterized by complex climatological, topographical, and environmental characteristics. Herein, we employed three different indicators based on in situ measurements and satellite image-derived vegetation information (i.e., tree-ring width, maximum annual greenness, and an indicator of NPP). We used seven different climate drought indices to assess drought impacts on the tree variables analyzed. The selected drought indices include four versions of the Palmer Drought Severity Index (PDSI, Palmer Hydrological Drought Index (PHDI), Z-index, and Palmer Modified Drought Index (PMDI)) and three multi-scalar indices (Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Precipitation Index (SPI), and Standardized Precipitation Drought Index (SPDI)). Our results suggest that—irrespective of drought index and tree species—tree-ring width shows a stronger response to interannual variability of drought, compared to the greenness and the NPP. In comparison to other drought indices (e.g., PDSI), and our results demonstrate that multi-scalar drought indices (e.g., SPI, SPEI) are more advantageous in monitoring drought impacts on tree-ring growth, maximum greenness, and NPP. This finding suggests that multi-scalar indices are more appropriate for monitoring and modelling forest drought in peninsular Spain and the Balearic Islands.

The impact of drought on vegetation conditions within the Damqu River Basin, Yangtze River Source Region, China

Drought and vegetation conditions within the Damqu River Basin, part of the Yangtze River Source Region (YRSR), are assessed here using the standardized precipitation index (SPI), the standardized precipitation evapotranspiration index (SPEI), the normalized difference vegetation index (NDVI), and the leaf area index (LAI). We utilized Sen’s method, least squares regression method, linear regression and Pearson’s correlation analysis to study variations in drought and vegetation indices and the drought effect on vegetation between 1988 and 2015. Results reveal that droughts occurred at a 25% frequency over this period; SPI and SPEI analyses show that 1994, 1999, 2005, and 2010 were change points and that the basin was characterized by varying drought and humidity trends. Subsequent to 2010, both SPI and SPEI decreased within the basin, while 1995, 2000, 2004, and 2010 were change points for NDVI and LAI while the watershed exhibited variable trends in vegetation reduction and increase. The NDVI-annual values of 63.36% regions and the LAI-summer values of 68.39% areas within the basin were decreased during 1988–2015 and 2000–2015, respectively. Subsequent to 2010, both NDVI and LAI decreased within the basin and significant positive correlations at inter-annual and inter-summer time scales were seen in both drought and vegetation indices; drought has exerted a lag effect on vegetation as shown by significant positive correlations between annual SPI/SPEI values and following year NDVI/LAI values.

Contribution of Land Surface Temperature (TCI) to Vegetation Health Index: A Comparative Study Using Clear Sky and All-Weather Climate Data Records

The Vegetation Health Index (VHI) is widely used for monitoring drought using satellite data. VHI depends on vegetation state and thermal stress, respectively assessed via (i) the Vegetation Condition Index (VCI) that usually relies on information from the visible and near infra-red parts of the spectrum (in the form of Normalized Difference Vegetation Index, NDVI); and (ii) the Thermal Condition Index (TCI), based on top of atmosphere thermal infrared (TIR) brightness temperature or on TIR-derived Land Surface Temperature (LST). VHI is then estimated as a weighted average of VCI and TCI. However, the optimum weights of the two components are usually not known and VHI is usually estimated attributing a weight of 0.5 to both. Using a previously developed methodology for the Euro-Mediterranean region, we show that the multi-scalar drought index (SPEI) may be used to obtain optimal weights for VCI and TCI over the area covered by Meteosat satellites that includes Africa, Europe, and part of South America. The procedure is applied using clear-sky Meteosat Climate Data Records (CDRs) and all-sky LST derived by combining satellite and reanalysis data. Results obtained present a coherent spatial distribution of VCI and TCI weights when estimated using clear- and all-sky LST. This study paves the way for the development of a future VHI near-real time operational product for drought monitoring based on information from Meteosat satellites.

Spatiotemporal analysis of the effect of climate change on vegetation health in the Drakensberg Mountain Region of South Africa

The impact of climate change on mountain ecosystems has been in the spotlight for the past three decades. Climate change is generally considered to be a threat to ecosystem health in mountain regions. Vegetation indices can be used to detect shifts in ecosystem phenology and climate change in mountain regions while satellite imagery can play an important role in this process. However, what has remained problematic is determining the extent to which ecosystem phenology is affected by climate change under increasingly warming conditions. In this paper, we use climate and vegetation indices that were derived from satellite data to investigate the link between ecosystem phenology and climate change in the Namahadi Catchment Area of the Drakensberg Mountain Region of South Africa. The time series for climate indices as well as those for gridded precipitation and temperature data were analyzed in order to determine climate shifts, and concomitant changes in vegetation health were assessed in the resultant epochs using vegetation indices. The results indicate that vegetation indices should only be used to assess trends in climate change under relatively pristine conditions, where human influence is limited. This knowledge is important for designing climate change monitoring strategies that are based on ecosystem phenology and vegetation health.

Targeting carbon for crop yield and drought resilience

Current methods of crop improvement are not keeping pace with projected increases in population growth. Breeding, focused around key traits of stem height and disease resistance, delivered the step-change yield improvements of the green revolution of the 1960s. However, subsequently, yield increases through conventional breeding have been below the projected requirement of 2.4% per year required by 2050. Genetic modification (GM) mainly for herbicide tolerance and insect resistance has been transformational, akin to a second green revolution, although GM has yet to make major inroads into intrinsic yield processes themselves. Drought imposes the major restriction on crop yields globally but, as yet, has not benefited substantially from genetic improvement and still presents a major challenge to agriculture. Much still has to be learnt about the complex process of how drought limits yield and what should be targeted. Mechanisms of drought adaptation from the natural environment cannot be taken into crops without significant modification for the agricultural environment because mechanisms of drought tolerance are often in contrast with mechanisms of high productivity required in agriculture. However, through convergence of fundamental and translational science, it would appear that a mechanism of sucrose allocation in crops can be modified for both productivity and resilience to drought and other stresses. Recent publications show how this mechanism can be targeted by GM, natural variation and a new chemical approach. Here, with an emphasis on drought, we highlight how understanding fundamental science about how crops grow, develop and what limits their growth and yield can be combined with targeted genetic selection and pioneering chemical intervention technology for transformational yield improvements. © 2017 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.