Dry Sub-humid Regions Research Articles

Balancing Water Yield and Water Use Efficiency Between Planted and Natural Forests: A Global Analysis.

Climate warming is projected to affect hydrological cycle in forest ecosystems and makes the forest-water relationship more controversial. Currently, planted forests are gaining more public attention due to their role in carbon sequestration and wood production relative to natural forests. However, little is known about how the global patterns and drivers of water yield and water-use efficiency (WUE) differ between planted and natural forests. Here, we conduct a global analysis to compare water yield and WUE in planted and natural forests using 946 observations from 112 published studies. The results showed that global average water yield coefficient was 0.29 for planted forests and 0.34 for natural forests. Planted forests exhibited lower water yield coefficient (p < 0.05) in three climatic regions (arid, dry subhumid, and humid regions), but higher (p < 0.01) WUE only in arid region, compared with natural forests. Both water yield coefficient and WUE in planted forests were significantly lower (p < 0.05) than that in natural forests for stand characteristic groups (stand density, average tree height, leaf area index [LAI], and basal area). Additionally, stand density within the ranging between 1000 to 2000 stem ha-1 can maximize the water yield and WUE in planted and natural forests. Water yield coefficient in planted forests was primarily controlled by the factors related to tree growth (i.e., tree height, DBH), while that of natural forest mainly affected by stand structure (i.e., LAI, stand density, DBH). WUE in planted forest was more sensitive to climate than in natural forests. This work highlights the critical role of natural forests in water supply and the importance of tree species selection and stand management (e.g., stand density adjustment) in plantations in future forest restoration policies and climate change mitigation.

The increasing rate of net carbon uptake in Eurasia has been declining since the early 2000s

The analysis of terrestrial ecosystem carbon dynamics, based on scarce carbon flux observations or carbon flux products simulated by reanalysis meteorological data, has great uncertainties. A more accurate understanding of carbon dynamics in Eurasia was achieved by using a carbon flux dataset (CFD) from meteorological stations with quasi-observational characteristics. The growth of net carbon uptake of ecosystems over Eurasia has been decreasing since the early 2000s. The net ecosystem productivity (NEP) increased significantly with the growth rate of 8.7 × 10−3 g C m−2d−1 yr−1 in spring, summer, and autumn (SSA) during 2003–2011 (p < 0.05), which was correlated with the enhanced vegetation index (EVI) and land surface water index (LSWI). This growth was mostly in dry subhumid and humid regions. However, the change in Eurasian NEP was not significant after 2011. Additionally, about 79 % of the stations in Eurasia were in net carbon uptake in SSA, and net carbon emission stations were mainly located in southwestern Eurasia. The intensity of net carbon uptake was highest in the forest, with a mean carbon uptake of 1.73 ± 0.76 g C m−2d−1 in SSA during 2003–2018, and almost all stations demonstrated carbon uptake. During 2011–2018, the number of stations experiencing reduced NEP exceeded those with increased NEP, and this ratio was higher compared to 2003–2011, mainly due to the decrease in EVI and LSWI. The rate of NEP decline at stations with reduced NEP was 5.2 × 10−3 g C m−2d−1 yr−1 faster during 2011–2018 than in the previous period (p < 0.01). Most of the decreases in NEP during 2011–2018 occurred in cropland, grassland and urban land. The spatio-temporal dynamic analysis of Eurasian NEP could provide references for effective carbon management.

Climatic Variations Influenced Distribution and Productivity of Different Agroforestry Systems in Rajasthan, India

Farmers of Rajasthan conserve and promote scattered trees and shrubs growing on farmlands to dealwith climatic adversities. These woody perennials sustain livelihoods by providing economic, social, and environmental benefits. Tree-based farming systems were surveyed in all 10 agroclimatic zones (ACZs) of Rajasthan for screening best models and suggests for further improvement. Trees were assessed for density, and growth parameters in 0.5 ha plots, whereas agricultural crops yields were estimated in1 m2 plots laid at 1 m from tree trunk, canopy edge and 5 m away from trees canopy (control). There were 107 tree-crop combinations involving 14 trees and 21 crop species. P. cineraria-based systems were dominant in the arid western, whereas A. nilotica dominated in semi-arid to dry sub-humidregions. Average tree density was 11.7 trees ha-1 ranging from 3.3 tree ha-1 in ACZ-IIIb to 23.6 tree ha-1 in ACZ-IIa. Senegalia senegal showed highest (27.6 tree ha−1) density. Height and canopy diameter were 11.73 m and 10.35 m respectively, in irrigated ACZ- IIIb and decreased to 6.53 m and 5.02 m in ACZ-IIb, respectively. Both height and density decreased with the decrease in rainfall from south-east to north-west. D. sissoo and T. grandis were taller, S. oleoides was thicker and Z. mauritiana was shorter and thinner than other species. Grain yields varied from 0.66 in ACZ-Ia to 1.85 Mg ha-1 in ACZ-IIIa, but exhibited low yield as compared to control. Crop yield reduced by 22.9-47.3% between ACZs, 20.3-56.5% between agroforestry systems, and 15.9-79.8% between intercrops highlighting the interactive effects of these components. S. senegal, T. undulata, P. cineraria, Z. mauritiana, S. oleoides, V. tortilis and P. juliflora based systems showed 29.1-54.9% low, whereas V. nilotica, D. sissoo, A. indica, T. grandis and V. leucophloea based systems showed 4.5-51.7% high yield over the state average. The yield of groundnut (IIIa), cotton (IVa), barley (IIIa), jowar (IVa), maize (IVa), paddy (V), pearl millet (IIIa) and wheat (V) with A. nilotica was above state average

Local hydroclimate drives differential warming rates between regular summer days and extreme hot days in the Northern Hemisphere

In this work, we compare the rate of warming of summertime extreme temperatures (summer maximum value of daily maximum temperature; TXx) relative to the local mean (summer mean daily maximum temperature; TXm) over the Northern Hemisphere in observations and one set of large ensemble (LE) simulations. During the 1979–2021 historical period, observations and simulations show robust warming trends in both TXm and TXx almost everywhere in the Northern Hemisphere, except over the eastern U.S. where observations show a slight cooling trend in TXx, which may be a manifestation of internal variability. We find that the observed warming rate in TXx is significantly smaller than in TXm in North Africa, western North America, Siberia, and Eastern Asia, whereas the warming rate in TXx is significantly larger over the Eastern U.S., the U.K., and Northwestern Europe. This observed geographical pattern is successfully reproduced by the vast majority of the LE members over the historical period, and is persistent (although less intense) in future climate projections over the 2051–2100 period. We also find that these relative warming patterns are mostly driven by the local hydroclimate conditions. TXx warms slower than TXm in the hyper-arid, arid, semi-arid and moist regions, where trends in the partitioning of the turbulent surface fluxes between the latent and sensible heat flux are similar during regular and extreme hot days. In contrast, TXx warms faster than TXm in dry-subhumid regions where trends in the partitioning of the surface fluxes are significantly different between regular and extreme hot days, with a larger role of sensible heat flux during the extreme hot days.

Patterns and environmental drivers of C, N, and P stoichiometry in the leaf-litter-soil system associated with Mongolian pine forests.

Ecological stoichiometry is an important approach to understand plant nutrient cycling and balance in the forest ecosystem. However, understanding of stoichiometric patterns through the leaf-litter-soil system of Mongolian pine among different stand origins is still scarce. Therefore, to reveal the variations in Mongolian pine carbon (C), nitrogen (N), and phosphorus (P) stoichiometry and stoichiometric homeostasis among different stand origins, we measured C, N, and P concentrations of leaves, litter, and soil, and analyzed the nutrient resorption efficiencies of leaves in differently aged plantations and natural forests from semi-arid and dry sub-humid regions. The results showed that (1) the stand origin had a significant effect on the C-N-P stoichiometry, and also significantly affected leaf N and P reabsorption efficiencies. Leaf N/P ratios indicated that Mongolian pine was co-limited by N and P in the NF, HB and HQ, and was mainly limited by P in MU. (2) With increasing stand age, C concentrations in the leaf-litter-soil system initially increased and then decreased, the N and P concentrations and reabsorption efficiencies in the leaf-litter-soil system were gradually increased. Overall, stand age had a significant effect on N concentrations, C/N and C/P ratios in the leaf-litter-soil system. (3) The C and N elements between the leaf-litter-soil system had a strong coupling relationship, and the P element between litter-soil had a strong coupling relationship. In addition, plantations exhibited greater N/P homeostasis than natural forests, and N/P exhibited greater homeostasis than N and P alone, which may be a nutrient utilization strategy for forests to alleviate N or P limitation. (4) Environmental factors have a significant influence on C-N-P stoichiometry in the leaf-litter-soil system, the most important soil properties and meteorological factors being soil water content and precipitation, respectively. These results will be essential to provide guidance for plantation restoration and management in desert regions.

Global Soil Salinity Estimation at 10 m Using Multi-Source Remote Sensing

Salinization is a threat to global agricultural and soil resource allocation. Current investigations of global soil salinity are limited to coarse spatial resolution of the available datasets (&gt;250 m) and semiqualitative classification rules (five ranks). Based on these two limitations, we proposed a framework to quantitatively estimate global soil salt content in five climate regions at 10 m by integrating Sentinel-1/2 remotely sensed images, climate, parent material, terrain data, and machine learning. In hyper-arid and arid region, models established using Sentinel-2 and other geospatial data showed the highest accuracy with R 2 of 0.85 and 0.62, respectively. In semi-arid, dry sub-humid, and humid regions, models performed best using Sentinel-1, Sentinel-2, and other geospatial data with R 2 of 0.87, 0.80, and 0.87, respectively. The accuracy of the global models is considerable with field validation in Iran and Xinjiang, and compared with digitized salinity maps in California, Brazil, Turkey, South Africa, and Shandong. The proportion of extremely saline soils in Europe is 10.21%, followed by South America (5.91%), Oceania (5.80%), North America (4.05%), Asia (1.19%), and Africa (1.11%). Climatic conditions, groundwater, and salinity index are key covariates in global soil salinity estimation. Use of radar data improves estimation accuracy in wet regions. The map of global soil salinity at 10 m provides a detailed, high-precision basis for soil property investigation and resource management.

Impacts of climate change on winter wheat net primary production: the regulatory role of crop management.

The Huang-Huai-Hai Plain (3HP) is the main agricultural area in China. Although climate change (CC) and crop management (CM) are considered factors affecting the winter wheat net primary production (NPP) in this region, their effects remain unclear. In the present study, we evaluated the relative contributions of CC and CM to winter wheat aboveground NPP (ANPP) in the 3HP and the relationships between climatic factors and ANPP using the first-order difference method from 2000 to 2020. CM had a greater influence on the ANPP of winter wheat than did CC. However, the relative contribution of CM to ANPP gradually decreased in humid and dry sub-humid regions with the development of winter wheat. Furthermore, in areas characterized by low temperatures and limited precipitation, CC became the dominant factor contributing to ANPP, indicating that varieties resilient to drought and cold should be selected in these regions. Minimum and average temperatures were the dominant factors driving spatiotemporal variations in ANPP during the early stage of winter wheat growth, whereas maximum temperature constrained growth throughout the winter wheat growth cycle. When winter wheat entered the vigorous growth stage, precipitation and solar radiation replaced temperature as the driving factors influencing winter wheat growth. The results of the present study provide guidance for optimizing winter wheat crop management in the 3HP. © 2023 Society of Chemical Industry.

Applications of Remote Sensing as Climate Resilience Technique: A Bibliometric Research Trends

Regional variances in climate, soil, and topography make agricultural production systems particularly fragile. Animal health are negatively impacted by variations in air temperature, precipitation, frequency, and intensity of extreme weather events. For its assessment and administration, cutting-edge methods like remote sensing (RS), global positioning systems, and geographic information systems (GIS) might be very beneficial. The RM &amp; GIS are essential tools with numerous applications for tackling these problems. The impact of climatic and human-induced changes on the environment is receiving more attention as a result of climate change. Due to climate change and human activity, "desertification" describes the degradation of land in arid, semiarid, and dry sub-humid regions. Natural resource sustainability in changing climates can be obtained with the application of RS and GIS. In this review article, the issues towards wildlife were demonstrated and the application of remote sensing was discussed to reduce the impact of climate change to save the wildlife and its preservation. Further, the bibliometric analysis was conducted via R-studio Bibliometric tool, which entailed that developed countries (USA, Canada, Germany) are more forward to applying remote sensing tool to mitigate climate risks.

Loss of Net primary production of seasonal winter wheat due to multiple drought types in the main Grain-Producing area of China

Droughts have a severe impact on ecosystems and food security. Net primary production (NPP), a critical component of the carbon cycle of an ecosystem, is strongly associated with crop yield and serves as an indicator of drought impact. In the present study, the loss of NPP in seasonal winter wheat was evaluated through an analysis of the changes in frequency and probability of exceeding NPP loss. This study also analyzed the drought thresholds for various types of drought, namely meteorological drought (MD), natural agricultural drought (NAD), and actual agricultural drought (AAD), in the North China Plain (NCP). The standardized precipitation evapotranspiration index, Palmer Drought Severity Index, and Vegetation Health Index were used to define these drought categories. The study findings reveal that although the NCP has experienced increased dryness, the severity of AAD has decreased. Within the study period, the frequency of winter wheat NPP loss due to drought in the area was within the range of 30% to 40%, with higher sensitivity to drought occurring during the middle growth period of winter wheat and in the NCP’s dry subhumid region. The highest probability of NPP loss was observed in March in the humid and dry subhumid regions of the NCP and in April in the semiarid region. Farmers in the NCP can effectively cope with extreme loss caused by drought. However, they have less experience with handling mild and moderate losses, which were the primary forms of NPP loss during the study period, particularly in the semiarid region. To ensure NPP can be sustained at a normal level or increased, MD and AAD must be prevented during the growing period of winter wheat, and NAD in the semiarid region must be managed to ensure its effects remain mild. Furthermore, farmers’ management of AAD may lead winter wheat to have a higher tolerance to MD and NAD. Our results deepen the understanding of seasonal NPP loss in winter wheat due to different types of drought, providing practical assistance to farmers in the region.

Long-term effect of manuring-fertilization on nutrients availability and yield under rice (Oryza sativa)-lentil (Lens culinaris) system

Soil health degradation is a major threat in agricultural sustainability specially under rainfed agriculture. The purpose of this study was to assess the impact of long-term fertilization and manuring on soil organic carbon, nutrient status, uptake and yield trends under a rainfed rice (Oryza sativa L.)-lentil (Lens culinaris L.) cropping system situated at Agriculture Research Farm of Banaras Hindu University, Varanasi, Uttar Pradesh. Soil samples were collected in 2019 and 2021 after harvesting of rainy (kharif) season rice from 34 years old long-term experiment with rice-lentil cropping system of dry sub-humid region of India and analysed for various pools of SOC, nutrients availability, uptake and yield attributes. Results showed that 50% NPK+FYM treatment significantly influenced soil organic carbon pools and improved macronutrients (Nitrogen, Phosphorus, Potassium), microbial biomass carbon and dehydrogenase activity in soil as compared to the control. In addition, the integrated application of farmyard manure and inorganic fertilizers significantly enhanced the nutrient uptake, productivity of rice and lentil, and maintained the nutrient balance in the soil. Therefore, the balanced use of inorganic fertilizer along with organic manure may be recommended for maintaining soil health and crop productivity under subtropical India.

Seasonal variation and controlling factors of carbon balance over dry semi-humid cropland in Guanzhong Plain

A theoretical basis for optimizing mitigation and adaptation to future climate change can be obtained by determining the carbon balance and their control mechanism in agroecosystems. To better understand summer maize (Zea mays L.)/winter wheat (Triticum aestivum L.) agroecosystems in dry semi-humid carbon balance mechanism, we conducted an in-depth analysis of three-year (2019.6–2022.6) seasonal dynamics of carbon fluxes and their driving factors in summer maize/winter wheat cropland in the Guanzhong Plain combined with path analysis. The results showed that net radiation (Rn) was the main direct controlling factor of the net ecosystem exchange (NEE) in the summer maize growing season, followed by leaf area index (LAI). However, LAI was the main factor controlling the seasonal variation of NEE in the winter wheat growing season, followed by Rn. Furthermore, LAI was the dominant direct driving factor for the gross primary productivity (GPP) in the summer maize and winter wheat growing seasons. In addition, LAI was the dominant driving factor for the total ecosystem respiration (TER) in the summer maize season, while Rn (indirect effect) and LAI (direct effect) were the main controlling factors of TER in the winter wheat season. The NEE values of the summer maize system, winter wheat system, and summer maize/winter wheat system in the study area were from 119 to 221, − 601 to − 595, and − 433 to − 291 g C m−2, respectively (the net biological production (NBP) were − 709 to − 437, 220–303, and − 496 and − 260 g C m−2, respectively). Notably, the average carbon emission rate of the summer maize/winter wheat agroecosystem in the Guanzhong Plain, considering the carbon output at harvest is 386.2 g C m−2year−1. Therefore, this study may guide assessment of carbon balance of agroecosystems in dry sub-humid regions, and provide a strong reference for achieving carbon neutrality in local agroecosystems.

Temporal remote sensing based soil salinity mapping in Indo-Gangetic plain employing machine-learning techniques

Soil salinization is one of the most active land degradation processes, affecting predominantly arid, semi-arid, and dry sub-humid regions and leading to decreased agricultural yields. The Indo-Gangetic plain, which includes the irrigated command areas with arid and semi-arid climatic conditions are severely affected by secondary soil salinization. Assessing the spatial and temporal extent as well as the severity of salinization is an important step for adoption of proper reclamation measures to boost agricultural productivity in the salt affected areas. The study was conducted with this background to evaluate the extent and severity of soil salinization in alluvial plains of Mathura district of Uttar Pradesh, India. In this study, the satellite data of 6 months from January 2019 to June 2019 were pre-processed and various spectral indices were generated in Google Earth Engine. Remote sensing techniques provides an ideal platform for addressing this problem at larger scales and thus we employed Sentinel-2 derived vegetation and salinity spectral indices for distinguishing temporal change in severity of soil salinization and map the salinity as a function of these indices for the entire study area. The time series salinity analysis showed that among the various spectral indices Ratio Vegetation Index (RVI), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Soil Index (NDSI) had a clear differentiation between slight, moderate and severe salinity class in the first three months of the study period and the Salinity Index II (SI_II) could differentiate for the first four months. Further, two machine learning algorithms namely Random Forest (RF) and Support Vector Machine (SVM), were used to create soil salinity prediction models making use of the soil Electrical Conductivity (EC) values of 115 ground-sampling sites as the predictand variable and the optimal spectral indices as the predictor variables. Further, we evaluated the prediction ability of different models using 12 and 24 variables combination using R2 and RMSE values. The prediction accuracy of the RF model was found to be slightly higher than that of the SVM model, and the spatial distribution pattern of soil salinity predicted by the two models were comparable. We concluded that spectral indices combined with machine learning techniques have the potential for low cost reliable spatial and temporal soil salinity distribution mapping for planning and implementation of salinity reclamation measures.

Influence of vegetation cover and rainfall intensity on soil attributes in an area undergoing desertification in Brazil

Desertification is a major environmental problem in arid, semiarid, and dry sub-humid regions throughout the world, which causes considerable reductions of ecosystem services, increased food insecurity, and poverty. The influence of vegetation cover and rainfall intensity on the dynamics of water infiltration, surface runoff, and soil loss was measured in situ in a desertification zone in the semi-arid region of Brazil. Twenty-four plots were evaluated in two areas, each with the same type of soil that is predominant in the region: 1) Luvisol with dense vegetation cover (LD) and 2) Luvisol with sparse vegetation cover (LS). A rainfall simulator was used to simulate either 60 or 100 mm h−1 of rainfall. Soil hydraulic conductivity, soil water retention curves, and soil carbon and nitrogen stocks were also determined. Water infiltration was greater in the LD (53 mm h−1) than in the LS (15 mm h−1), while the runoff rates were 27 mm h−1 for the LD and 61 mm h−1 for the LS. Rainfall intensity significantly influenced the time when runoff started and the runoff rate accelerated soil loss. Vegetation cover significantly influenced the total soil carbon and nitrogen stocks, as well as the saturated hydraulic conductivity of the soil. This is the first study in a semi-arid desertification zone of Brazil that used simulated rainfall to determine effects rain intensity on soil properties. The results of this study can be used as a reference for calibrating erosion models in areas undergoing a desertification process.

Soil coarsening alleviates precipitation constraint on vegetation growth in global drylands

Drought is an important and complicated topic, and the specific variables that are considered to represent climate drought stress and plant water stress often generate highly contradictory conclusions. As the junction of the atmosphere and the biosphere, soil may play an important role in separating climatic drought stress from vegetation water constraint. Here, we conduct a comprehensive evaluation of water constraint on vegetation growth in global drylands by separating precipitation constraint and soil moisture constraint. Although global drylands are characterized by low precipitation supply capacity, there are indeed a large number of grids showing decoupled water availability for plants from variability of precipitation, with ratios of 47%, 64%, and 61% for arid, semiarid, and subhumid regions, respectively. Soil properties, instead of climate and root length regimes, can explain the water constraint divergence between precipitation and soil moisture. Sand content emerges as the most significant soil property to weaken the precipitation constraint on vegetation growth, with a 1% increase in sand content of global arid, semiarid, and dry subhumid regions increasing an average of 0.31, 0.45, and 0.04 gC m−2 yr−1 gross primary productivity (GPP) deviation from the theoretical GPP determined by precipitation, respectively. This study provides new insight into how soil texture interacts with precipitation constraints to influence plant-available water in global drylands, which contributes to assessing ecological drought in global drylands.

Regrowth Response and Nutritional Composition of Moringa oleifera to Cutting Back in Three Agro-Ecological Zones in South Africa

Moringa (Moringa oleifera Lam.) is a fast-growing tree that can reach a height of 3 m when left to grow naturally. Cutting-back management can enhance lateral branching, water use efficiency, regrowth, and leaf biomass production. Although M. oleifera can thrive in diverse ecological environments, including areas with high temperatures and drought where most crops cannot survive, the prevailing agro-ecological conditions can influence the tree’s response to cut-back and nutritional composition. An observation trial on the re-sprouting of M. oleifera after cutting back was conducted in three agro-ecological zones, including arid, semi-arid, and dry sub-humid regions. The soil analysis from the three agro-ecological zones showed variations, with the soil collected from the arid area recording the highest clay content (24.4%), nitrogen (8.14%), and phosphorus (168.2 mg/kg). The nutritional composition of the M. oleifera leaves in response to the cutting back was assessed. The trees planted in the arid region responded well to the cutting back by producing considerable new vegetative growth in the spring (13.4 sprouts averaging 21.50 cm in length) compared to semi-arid and dry sub-humid regions. The region’s climatic conditions favoured M. oleifera re-sprouting, and the stems did not die after cutting back. During the winter, the stems die back, sprout from the root collar area during more favourable weather conditions, and take longer to reach the harvestable stage. The leaves of M. oleifera harvested from the arid and dry sub-humid regions exhibited increased total fat, magnesium, sodium, phosphorus, potassium, and zinc content when compared to the semi-arid agro-ecological zone. In contrast, the calcium content of the leaves was higher in the semi-arid region than in the other agro-ecological zones. The influence of cutting back M. oleifera trees on biomass production and quality in varying agro-ecological zones requires further investigation to ensure that smallholder farmers use appropriate crop management practices in those regions for long-term, economically viable tree production.

Monitoring Desertification Using a Small Set of Biophysical Indicators in the Brazilian Semiarid Region

Desertification is defined as land degradation in arid, semiarid, and dry sub-humid regions, and it is caused primarily by human activities and climate change. The semiarid region of Northeast (NE) Brazil is a very large, populous region, and thus, it is hard to monitor the dynamics of its desertified areas; therefore, the present study aimed to develop a minimum set of biophysical indicators to qualify and monitor desertification in this region. This approach included sampling a pair of preserved forested areas and nearby degraded, non-forested areas which had no vegetation growth in the previous years. The study was developed in four stages: (a) pilot site selection; (b) quantification and analysis of soil and vegetation biophysical indicators; (c) biophysical indicator selection; and (d) elaboration of the minimum set of biophysical indicators and desertification levels. Of the analyzed 24 biophysical indicators, 11 were retained and subjected to factor analysis by its principal components. This yielded the minimum set of indicators used to estimate the desertification levels of the pilot sites, which consisted of four soil characteristics: Total Organic Carbon (TOC), cation exchange capacity, clay content, and magnesium content. Regressions were conducted using the SQI, and these indicators showed that the TOC had the highest coefficient of determination. In an exploratory analysis, high SQI (low desertification) showed a positive correlation with the normalized difference vegetation index (R = 0.70) and Aridity Index (R = 0.97). This methodological approach could form the basis of a dynamic monitoring system that is capable of supplying objective, quantitative, and easy to obtain information to decision-makers in NE Brazil and other dry ecosystems around the globe.

Sinkhole Flooding and Aquifer Recharge in Arid to Dry Sub-Humid Regions: A Systematic Review in the Perspective of Climate Change

On the one hand, Sinkhole Flooding (SF) is an essential hydrological process to recharge karst aquifer in arid to dry sub-humid regions. On the other hand, the increase of rain extremes is one of the major consequences of global warming, together with the expansion of drylands. Thus, appropriate runoff regulation in endorheic karst basins in order to reduce the risk of flooding and improve the quantity and quality of the water drained by sinkholes will be more and more crucial. With these premises, a systematic review of SF cases study was performed by using Web of Science (WoS) engine to infer the hydrological properties for modeling the water management in regions actually or potentially affected by water scarcity. Hydrological models are essential to manage the consequences of climate change on karst water resource, however the review shows that providing the tools necessary for reliable modeling is still challenging. Finally, due to the intrinsic vulnerability of the karst aquifers, pollution reduction and wastewater recycling policy will play a key role in the next decades.

Quantifying Drought Resistance of Drylands in Northern China from 1982 to 2015: Regional Disparity in Drought Resistance

Drylands are expected to be affected by greater global drought variability in the future; consequently, how dryland ecosystems respond to drought events needs urgent attention. In this study, the Normalized Vegetation Index (NDVI) and Standardized Precipitation and Evaporation Index (SPEI) were employed to quantify the resistance of ecosystem productivity to drought events in drylands of northern China between 1982 and 2015. The relationships and temporal trends of resistance and drought characteristics, which included length, severity, and interval, were examined. The temporal trends of resistance responded greatest to those of drought length, and drought length was the most sensitive and had the strongest negative effect with respect to resistance. Resistance decreased with increasing drought length and did not recover with decreasing drought length in hyper-arid regions after 2004, but did recover in arid and semi-arid regions from 2004 and in dry sub-humid regions from 1997. We reason that the regional differences in resistance may result from the seed bank and compensatory effects of plant species under drought events. In particular, this study implies that the ecosystem productivity of hyper-arid regions is the most vulnerable to drought events, and the drought–resistance and drought–recovery interactions are likely to respond abnormally or even shift under ongoing drought change.

Characteristics of vegetation response to drought in the CONUS based on long-term remote sensing and meteorological data

Drought is one of the billion-dollar natural disasters and hard to trace and measure. In recent years drought monitoring becomes much easier with remote sensing. However, it is still difficult to pin vegetation variances on drought because of the delay of the caused vegetation stress. To assess vegetative drought, it is important to first understand the relationship between meteorological condition and vegetation condition, and measure the vegetation responses to meteorological drought. It would be very helpful for effective early warning about agricultural drought. This study uses the CONUS as the study area, and utilizes remote sensing products such as NDVI/VCI (normalized difference vegetation index/vegetation condition index) and SPI/SPEI (standardized precipitation index/standardized precipitation evapotranspiration index) to give a comparative evaluation to the vegetation’s drought response. The used vegetation products and meteorological data are ensured to be consistent. The scale and lag of vegetation response to drought for various vegetation types and aridity levels were thoroughly investigated. The results show that: The AVHRR and MODIS NDVI series pairs and the meteorological drought index series pairs (SPEI and SPI) have fairly good consistencies. Among them, 69.5% and 84% have rho (correlation coefficient) values greater than 0.8 respectively. For the NDVI series pairs, the maximum rho value is 0.98, the minimum rho value is −0.47, and the mean rho value is 0.79, which are 0.97, 0.68, and 0.87 respectively for the meteorological index series pairs. Compared to rho values of the meteorological index series pairs, the rho values of the NDVI series pairs have more outliers indicating instability. The correlation between SPEI and VCI significantly relies on time lags and has high spatial inhomogeneity. 1- and 2-month lags of SPEI have more significant positive correlation with VCI in Arid, Semi-Arid and Dry sub-humid areas of central west CONUS with less precipitation and lower temperature. For various time scales (time scale is SPEI reference range), the most significant positive correlation between SPEI and VCI happens in the time scales of 6- to 12-month in summer, the time scales of 3- and 6-month in spring and autumn, and the time scales of 2- and 3-month in winter regardless of time lags. Despite of the different vegetation types and aridity levels, the maximum correlations between SPEI and VCI are observed in Hyper arid regions in January, Arid regions in April, Semi-arid regions in July, and Dry sub-humid regions in October. Shrub has prominent responses in January and April, grass responses appear in July and October, and Evergreen forest shows least responses in all seasons. The results add more insights of the connection between vegetation and climate, and guide the development of new technology leveraging remote sensing data for vegetation drought monitoring and early-warning. The results are also helpful to provide important references for studying large-scale physiological and phenological properties of the vegetation under different climate conditions.

Aridity Changes and Related Climatic Drivers in the Drylands of China during 1960–2019

AbstractDrylands cover about one-half of the land surface in China and are highly sensitive to climate change. Understanding climate change and its impact drivers on dryland is essential for supporting dryland planning and sustainable development. Using meteorological observations for 1960–2019, the aridity changes in drylands of China were evaluated using aridity index (AI), and the impact of various climatic factors [i.e., precipitation P; sunshine duration (SSD); relative humidity (RH); maximum temperature (Tmax); minimum temperature (Tmin); wind speed (WS)] on the aridity changes was decomposed and quantified. Results of trend analysis based on Sen’s slope estimator and Mann–Kendall test indicated that the aridity trends were very weak when averaged over the whole drylands in China during 1960–2019 but exhibited a significant wetting trend in hyperarid and arid regions of drylands. The AI was most sensitive to changes in water factors (i.e., P and RH), followed by SSD, Tmax, and WS, but the sensitivity of AI to Tmin was very small and negligible. Interestingly, the dominant climatic driver to AI change varied in the four dryland subtypes. The significantly increased P dominated the increase in AI in the hyperarid and arid regions. The significantly reduced WS and the significantly increased Tmax contributed more to AI changes than the P in the semiarid and dry subhumid regions of drylands. Previous studies emphasized the impact of precipitation and temperature on the global or regional dry–wet changes; however, the findings of this study suggest that, beyond precipitation and temperature, the impact of wind speed on aridity changes of drylands in China should be given equal attention.