Dryness Index Research Articles

Influence of mulched drip irrigation on landscape scale evapotranspiration from farmland in an arid area

Water-saving irrigation measures in arid areas affect evapotranspiration (ET) processes while conserving water. Mulched drip irrigation is considered the most efficient irrigation method because it distributes water uniformly in the soil, restricts deep percolation, and minimizes unproductive evaporation from soil. The effect of mulched drip irrigation on farmland ET during the growing season at a landscape scale remains unclear, despite being vital for developing optimal water resource management strategies in arid areas. To compare the effects of mulched drip irrigation and mulched border irrigation on ET, based on Landsat satellite imagery (2007–2009 with mulched border irrigation, and 2014–2016 with mulched drip irrigation), an improved Surface Energy Balance Algorithm for Land (SEBAL) model was used to evaluate ET for the two periods of an oasis at Sangong River Basin in the arid region of Northwest China. The results show that daily ET rates from farmland managed under mulched drip irrigation were on average 0.2–1 mm.d−1 higher than under mulched border irrigation between June and August. Correspondingly, the net radiation flux (R) increased 73.32 W·m-2 on average, and R was found to be the main determinant of the ET differences. Meanwhile, the average land surface albedo decreased by 20%, and negatively correlated with R (P < 0.05), indicating that the land surface albedo was the main factor affecting R. Furthermore, the Normalized Difference Vegetation Index (NDVI) exhibited a significant correlation with land surface albedo. More importantly, the Temperature–Vegetation Dryness Index (TVDI) under mulched drip irrigation was found to be approximately 21% lower than that under mulched border irrigation, indicating that the soil moisture conditions of the farmland under mulched drip irrigation was significantly improved compared to mulched border irrigation. Namely, less water stress resulted in better developed canopy of the crops, which in turn captured more radiation and thus increased ET. In the end, the observed increases in landscape-scale ET under mulched drip irrigation in arid area resulted from enhanced productivity of the crops due to lessened drought stress.

Analysis of the Drought Mitigated Mechanism in Terraced Paddy Fields Using CWSI and TVDI Indices and Hydrological Monitoring

Food security is often threatened by droughts during rice production. Although most of the rice is produced in lowland or irrigated “wet” rice fields, terraced paddy fields are important in the rice production system in island or mountainous countries. With the intensifying frequency of El Niño periods in recent decades, there has been a risk of droughts in terraced paddy areas. To mitigate drought, remote sensing data analysis could be an efficient and reliable tool for obtaining scarce ground monitoring data. In this study, crop water stress index (CWSI) and temperature vegetation dryness index (TVDI) were applied to evaluate the drought intensity, and hydrological monitoring data was provided as a support for the evaluation. The results indicated that droughts normally occurred during the dry season, and intensified during El Niño periods. CWSI and TVDI were visible to predict drought occurrences in the watershed area. TVDI overestimated the drought inside Keduang watershed compared to CWSI because of the complex condition of the terraced paddy area, including the hydrology in this area. The complex topography, high groundwater table, and continuous plot-to-plot irrigation helped to maintain the water availability and mitigated the drought impact for rice production in the studied terraced paddy field.

Seasonal Evaluation of Soil Moisture Changes in Isfahan Province Using Temperature Vegetation Dryness Index (TVDI)

Seasonal Evaluation of Soil Moisture Changes in Isfahan Province Using Temperature Vegetation Dryness Index (TVDI)

The Index Optimization Method in Neural Network for Soil Moisture Inversion

Soil moisture, an important evaluation index in the field of environmental studies, plays a vital role in the exchange process between global surface and atmosphere. Although its content just takes a small percentage of freshwater resources, it is involved in the surface evapotranspiration process, moisture exchange process and many other cyclic processes. Temperature vegetation dryness index (TVDI) is a major mean that is based on optical and thermal infrared remote sensing to inverse soil moisture. However, its inversion accuracy is affected by soil background and the resolution of the thermal infrared data. Aiming at solving the problem of limited conditions of the data and complicated mathematical relations in modeling, ASTER NIR/TIR data is used in this study, and normalized differential vegetation index (NDVI) is replaced by the modified soil-adjusted vegetation index (MSAVI). Then, piecewise linear model is used to downscale the resolution of land surface temperature (LST), and modified temperature vegetation dryness index (MTVDI) is gained. Finally, back propagation (BP) neural network is established to calculate soil moisture. The result shows that the precision root mean square error (RMSE) of the two-parameter optimization model is higher than that of the none optimization model and single parameter optimization model. Improving the precision of soil moisture inversion by optimizing the input parameters of the neural network is feasible.

Ecological Zonation of Soils in the Lake Baikal Basin

Soil-ecological zonation is a new approach to the study of soils, making it possible to consider soil cover a holistic ecological system. On the basis of long-term research in Russia and Mongolia, the authors have ranked plots with a similar soil cover pattern and combination of soil-forming factors and quantified their areas. All the data are unified using the same methodological and classification approach for the two countries. Soil groups with relatively similar bioclimatic factors (the dryness index by M.M. Budyko, the sum of biologically active temperatures, and the type and productivity of vegetation), which play the leading role in soil formation, are combined into soil-ecological provinces (9). Soils with similar lithologic and geomorphologic features (rocks and topography) are grouped into districts (28) at the regional level. The presented soil-ecological map is a kind of integrated information source which reflects exogenous factors of soil formation: climate, relief, rocks, and vegetation.

Poor oral hygiene is associated with the detection of obligate anaerobes in pneumonia.

Oral cavity is a reservoir of various respiratory pathogens, and poor oral hygiene is associated with an increase in anaerobic bacteria in oral cavity. In addition, it positively relates higher risk of developing pneumonia and increased pneumonia-related mortality. However, the association between poor oral hygiene and increase in obligate anaerobes in the lungs of pneumonia patients is unclear. A total of 39 patients with pneumonia in whom bronchoscopic examination and oral hygiene evaluation were performed were prospectively enrolled. The microbiota of the bronchoalveolar lavage fluid (BALF) directly obtained from the pneumonia lesion was analysed by the clone library analysis. In addition, oral hygiene evaluations were performed using oral hygiene index (OHI), tongue coating score, oral dryness, and community periodontal index of treatment needs (CPITN). The association between the detection of oral streptococci and obligate anaerobes and oral hygiene status was evaluated. Using the clone library analysis of BALF, the phylotypes of oral streptococci and obligate anaerobes were detected in 31 (79.5%) and 26 (66.7%) patients, respectively. Increased oral dryness, OHI, and CPITN, but not the tongue coating score, significantly correlated with higher rate of detection of obligate anaerobes, although no significant associations between the detection of oral streptococci in the lungs and each oral hygiene evaluation were observed. Significantly higher number of obligate anaerobes were detected in the lungs in patients with total oral hygiene score of ≥ 5 (P=0.008). Poor oral hygiene is associated with increased obligate anaerobes in the lungs of patients with pneumonia.

The assessment of different vegetation indices for spatial disaggregating of thermal imagery over the humid agricultural region

ABSTRACT Land surface temperature (LST) plays a significant role in surface water circulation and energy balance at both global and regional scales. Thermal disaggregation technique, which relies on vegetation indices, has been widely used due to its advantage in producing relatively high resolution LST data. However, the spatial enhancement of satellite LST using soil moisture delineated vegetation indices has not gained enough attention. Here we compared the performances of temperature vegetation dryness index (TVDI), normalized difference vegetation index (NDVI), and fractional vegetation coverage (FVC), in disaggregating LST over the humid agriculture region. The random forest (RF) regression was used to depict the relationship between LST and vegetation indices in implementing thermal disaggregating. To improve the model performance, we used the thin plate spline (TPS) approach to calibrate the RF residual estimation. Results suggested that the models based on TVDI performed better than those based on NDVI and FVC, with a reduced average root mean square error and mean absolute error of 0.20 K and 0.16 K, respectively. Moreover, based on the surface energy balance model, we found the surface evapotranspiration (ET) derived with the TVDI disaggregated LST as inputs achieved higher accuracy than those derived with NDVI and FVC disaggregated LST. It is indicated that TVDI, a soil moisture delineated vegetation indices, can improve the performance of LST enhancement and ET estimation over the humid agriculture region, when combining random forest regression and TPS calibration. This work is valuable for terrestrial hydrology related research.

Identification of water-stressed area based on the interrelationship of soil moisture and seasonal rice cultivation

The present study identifies water-stressed areas based on linkages between NOAH model-based soil moisture and seasonal paddy rice cultivation in the Dwarakeswar–Gandheswari river basin. Soil moisture provides information about crop water stress distribution. A new analytic hierarchy process (AHP)-based index is proposed for water stress evaluation. The spatially distributed paddy rice cultivation is essential from the water resource management point of view. Temperature vegetation dryness index (TVDI), regional water stress index, standardized water level index, and vegetation health index (VHI) are considered for detecting water-stressed area. Water stress indices, soil moisture, and paddy rice cultivation are considered on a seasonal basis (Boro, Aus, and Aman) for the period 2011–2016. The spatial patterns of TVDI, VHI, and rice cultivation are derived from Landsat 7 Enhanced Thematic Mapper Plus and Landsat 8 Operational Land Imager satellite images. Kendall’s tau is considered for correlation analysis. Sensitivity analysis is performed for the generation of AHP-based new water-stressed zone map. The results show that the downstream portion of the river basin has low water stress compared to the upstream part. Ultimately, the results are validated using 20 field sample points. This analysis will be helpful for the irrigation water management framework.

Bioclimatic conditions of the Portuguese wine denominations of origin under changing climates

AbstractWine production and quality are highly sensitive to local weather variability and climatic conditions. To assess these characteristics, this research examines high‐resolution bioclimatic zoning over 50 protected denominations of origin (DOs)/sub‐regions in mainland Portugal through the analysis of two selected bioclimatic indices (dryness and Huglin indices). The analysis is based on a new very high‐resolution dataset over mainland Portugal and for a baseline period (1981–2015). Climate change projections are also assessed for two scenarios (RCP4.5 and RCP8.5) and using a 5‐member climate model ensemble over the future periods of 2041–2070 and 2071–2100. A principal component analysis was applied to the time mean spatial patterns of the two selected bioclimatic indices, for the baseline period (1981–2015) and only over the planted vineyard cover areas in each region, isolating a new optimized combined index which was used for subsequent analysis. The results for the present conditions highlight the spatial variability of Portuguese DO/sub‐regions. This study also shows that for the future periods, and regardless of the scenario, the wine sector in Portugal will likely see important bioclimatic changes across most DOs. Increases in the growing‐season mean temperatures in all the Portuguese winemaking DO/sub‐regions, accompanied by increasing severe dryness, are projected in future climates, mainly in south‐eastern Portugal and along the upper Douro Valley (Douro Superior) in north‐eastern Portugal. These DO/sub‐regions are projected to become much drier than currently so that irrigation or the introduction of new varieties are likely adaptation measures to maintain the viability and sustainability of regional viticulture in future decades.

The Spread Of Dry Area Based On TVDI Index (Temperature Vegetation Dryness Index)

This research explains the use of Remote Sensing to know which areas are dry that are connected by the morphological characteristics in the Sukaresmi district. The purpose of this research is to see the spread of the dry areas in Sukaresmi district which are vulnerable towards drought when the dry season comes using the Remote Sensing application. The Landsat image 8 OLI was used throughout July-September 2013 and between June-August 2017. One of the methods that used in Remote Sensing is triangulation. This method is known as TVDI (Temperature Vegetation Dryness Index) which consists of NDVI (Normalized Difference Vegetation Index), LST (Land Surface Temperature) with the algorithm formula of (LST–LSTmin)/(a+b*NDVI–LSTmin) that used in determining the dry areas in Sukaresmi district. The relation between the spatial pattern of the dry areas and the morphological characteristics of an area analyzed statistically. This research concludes that the spatial pattern of the dry areas in Sukaresmi district based on the TVDI method reached the highest rate of drought in September 2013 with 163.26 ha, and the drought in June 2017 with a whopping 336.43 ha. The drought areas usually concentrated on the morphological characteristics of the soil of the inhabitants, rice fields, and inclination of the slopes.

AGRICULTURAL DROUGHT AND SOIL MOISTURE ANALYSIS USING SATELLITE IMAGE BASED INDICES

Abstract. The deficiency in rainfall leads to meteorological droughts. Its manifestations are visible both in the vegetation cover and soil moisture. The present study assessed the characteristics of agricultural drought following meteorological droughts. The study also assessed the severity of meteorological droughts and their manifestation on the agriculture and soil moisture in a semi-arid area. The study has been carried out for the Malaprabha sub-basin which partly covers three districts of North Interior Karnataka, India. India Meteorological Department’s (IMD) criteria have been used to identify the drought years, and its severity has been assessed through the Standard Precipitation Index (SPI). The IMD’s monthly rainfall data were used to identify the drought years and periods for the region. Among the drought years, the mild, moderate, and severe drought along with deficit and excess rainfall years were considered to assess and characterize the soil moisture conditions and the agricultural drought. The satellite image based indices for these selected years were constructed to determine the soil moisture conditions and the agricultural drought severity. The Temperature-Vegetation Dryness Index (TVDI) was used to determine the soil moisture conditions. The indices employed to determine the agriculture drought are NDVI, Thermal Condition Index (TCI), Vegetation Condition Index (VCI), and Vegetation Health Index (VHI). These satellite-based indices were calculated using the Landsat images of the selected drought and non-drought years. The results showed that the seasonal and annual drought are frequent in the study area. There are spatial and temporal variations in the drought years and their severity. The satellite-based indices clearly indicate the spatial variation in the agriculture droughts and its intensity. It has been found that the impact of drought on agriculture has significantly reduced due to the development of well-irrigation in the sub-basin. VHI is more appropriate in determining the agricultural drought and its characteristics.

Spatio-temporal variation in grassland degradation and its main drivers, based on biomass: Case study in the Altay Prefecture, China

Analyzing the spatio-temporal characteristics and identifying of main factors that drive grassland degradation are essential for formulating ecological restoration measures for grassland. In this study, we addressed two main scientific questions: (1) how to accurately characterize the spatial and temporal variability in grassland degradation; and (2) how to identify the dominant factors that affect grassland degradation. Based on the data obtained from 270 field sampling points (912 quadrats) and MODIS, we selected the temperature vegetation dryness index (TVDI), vegetation coverage (VC), biomass and ecosystem service values (ESVs) to study the degradation of grassland in Altay Prefecture, China. From 2000 to 2016, the area with decrease in TVDI accounted for 93.4% of the total grassland. The VC values in the warm steppe and mountain meadow areas decreased by 5.3% and 1.6%, respectively, and the biomass decreased by 80% and 76.4% in these areas. Compared with 2000–2005, the mean total ESVs decreased by 5.1% in 2011–2016, where the reductions in the ESVs for warm steppe and mountain meadow accounted for 54.3% and 38% of the total reduction, respectively. The results showed that the degradation trends were more significant in the warm steppe and mountain meadow than the other land types. These findings improve our understanding of the spatial heterogeneity of grassland degradation. Human disturbance was the main factor responsible for grassland degradation in Altay Prefecture, according to the framework developed in this study based on biomass and TVDI metrics. Our results provide a reference for characterizing the spatio-temporal variability of grassland degradation by combining statistical and spatial analyses, as well as a simple framework for application in similar regions to analyze the effects of climate change and human disturbance on grassland degradation.

Stand carbon density drivers and changes under future climate scenarios across global forests

Climate and soil factors drive forest carbon (C) density. However, their impacts on the aboveground carbon density (ACD) and belowground carbon density (BCD) of global forest types have not been quantified in previous studies. In this study, we compiled global forest biomass data that included 8800 plots to better quantify the changes in total carbon density (TCD) of five forest types under current climate and future climate change scenarios (e.g., RCP2.6, RCP4.5 and RCP8.5) using the boosted regression tree method and TCD-stand age-climate models. Tropical forests have the largest amount of C density (130.0 ± 3.2 Mg C ha−1; 173.60 ± 17.24 Mg C ha−1), while boreal forests have the lowest TCDs (59.4 ± 0.9 Mg C ha−1; 62.34 ± 15.10 Mg C ha−1) in the current and future. For the current forest scenarios, the dryness index (DI) for tropical forests exhibited a negative mean change rate for the ACD (4.5 Mg C ha−1/0.1 DI) and BCD (0.9 Mg C ha−1/0.1 DI). The mean annual precipitation (MAP) had a positive mean change rate for the ACD (7.1 Mg C ha−1/100 mm) and BCD (1.3 Mg C ha−1/100 mm) when the MAP was ≤1100 mm in the temperate forests. The MAP was negative for the ACD (1.9 Mg C ha−1/100 mm) and BCD (0.4 Mg C ha−1/100 mm) when the MAP was >1100 mm. The mean annual temperature (MAT) in boreal forests had a positive mean change rate for the ACD (1.78 Mg C ha−1/1°C) and BCD (0.4 Mg C ha−1/1°C). We found that in contrast to 2050, there were positive impacts of the DI on the TCD change in tropical forests in 2070, and the DI was the most important driver for tropical forests. There were strong positive MAP impacts on the TCD change in temperate forests in both 2050 and 2070, where the MAP was the most important driver for temperate forests. There were negative MAT impacts on the TCD change in boreal forests under RCP4.5 (−0.22 ± 10.53 Mg C ha−1) and RCP8.5 (−4.75 ± 10.80 Mg C ha−1) in 2070, and the MAT was the most important driver in boreal forests. Our results could be used to improve the accuracy of C density estimates and assess climate change impacts on the C budgets of global forests.

The Energy Side of Budyko: Surface‐Energy Partitioning From Hydrological Observations

AbstractLand‐surface partitioning of net radiation into sensible and latent heat fluxes is critical for hydroclimatic processes but remains highly uncertain due to limited observations. We show that a suitable extension of the Budyko's curve, a well‐known framework in hydrology for water balance estimation, can be utilized effectively to partition the surface energy fluxes by expressing the long‐term evaporative fraction (EF) as a function of the dryness index only. The combination of this energy partitioning method with hydrological observations allows us to estimate the surface energy components at watershed and continental scales. Using this new framework, we show that North American Regional Reanalysis data overestimate surface evaporation, likely influencing the modeling of atmospheric convection. The obtained hydrologic constrains on energy partitioning can be used to provide more accurate estimations of surface energy fluxes for hydroclimatic predictions.

Spatial assessment of drought severity in Cape Town area, South Africa

In recent decades, drought has been identified as part of the several regular climate-related hazards happening in many African countries including South Africa, often with devastating implications on food security. Studies have shown that the earth temperature has increased over the recent years which can trigger drought occurrences and other climate-related hazards. Drought occurrence is principally a climate-related event that cannot be totally effaced though it can be managed. This study is aimed at appraising drought severity in Cape Town area, South Africa using Geographic Information System (GIS) and remotely sensed data obtained from United States Geological Survey (USGS) database between the years 2014 and 2018. The study revealed that the land use dynamics witnessed drastic changes where vegetation, water body and bare surface decreased from 2095 to 141 km2, 616 to 167 km2 and 2337 to 1381km2 respectively while built up and sparse vegetation increased from 5301 to 8191 km2 and 7382–7854 km2 during the period. Vegetation health and drought severity of the study area was assessed using vegetation indices and Normalized Drought Dryness Index (NDDI). The result reveals that Normalized Difference Water Index (NDWI) and other vegetation indices decreased considerably more in recent years (2017 and 2018) which might have triggered drought events during the period compared to the other years (2014–2016). Furthermore, the spatial trend of land surface temperature (LST) and NDDI increased in recent years with NDDI values ranging between moderate drought and severe drought threshold. Consequently, if the increment persists, it can lead to adverse impacts such as food insecurity, land degradation and environmental health deterioration. Evidently, this study reveals the current state of vegetation health regarding drought severity in the area using remotely sensed data.

A Model Analysis of Observed and Predicted Climatic Changes in Groundwater Recharge in a Small River Basin

The relationship between groundwater recharge in a small river basin to the current and expected climatic changes in the European area of Russia has been analyzed using the catchment basin of the Zhizdra River (Kaluga Region) as an example. The analysis is based on modeling of moisture transformation processes on the earth’s surface and moisture transfer in the unsaturated zone. The global climate predictions of five CMI5 Coupled Atmosphere–Ocean General Circulation Models (GCM) have been applied to forecast the groundwater recharge in the second half of the 21st century using the LARSWG5 forecast weather conditions generator. Despite the fact that all used GCMs predict a warming by 2–6°C in the studied region, the difference in the predicted recharge values is still considerable due to the difference in the predicted dryness index.

Model analysis of observed and predicted climate changes of groundwater recharge in the basin of a small river

The analysis of the connection of groundwater recharge in the basin of a small river with the current and expected climatic changes in the European territory of Russia is carried out using the catchment basin of Zhizdra river Kaluga region as an example. The analysis was based on the modeling of the processes of transformation of moisture on the earth surface and moisture transfer in the aeration zone. The results of global climate predictions for five models of the general circulation of the atmosphere and ocean (GCM) from the CMI5 family were applied for the forecast in the second half of the 21st century using the LARSWG5 forecast weather conditions generator. The simulation results show that despite the fact that all the GCM used predict a warming in the region at 2–6 ᵒC, the difference in the predicted recharge is still significantly large, which is associated with the difference in the predicted dryness index.

Drought‐Triggered Magmatic Inflation, Crustal Strain, and Seismicity Near the Long Valley Caldera, Central Walker Lane

AbstractWe use GPS data to show synchronization between the 2011 and 2016 drought cycle in California, accelerated uplift of the Sierra Nevada Mountains, and enhanced magmatic inflation of the Long Valley Caldera (LVC) magmatic system. The drought period coincided with faster uplift rate, changes in gravity seen in the Gravity Recovery and Climate Experiment (GRACE), and changes in standardized relative climate dryness index. These observations together suggest that the Sierra Nevada elevation is sensitive to changes in hydrological loading conditions, which subsequently influences the LVC magmatic system. We use robust imaging of horizontal GPS velocities to derive time‐variable shear and dilatational strain rates in a region with highly variable station distribution. The results show that the highest strain rates are near the eastern margin of the Sierra Nevada and western edge of the Central Walker Lane (CWL) passing directly through LVC. The drought period saw geographic shifts in the distribution in active shear strain in the CWL more than 60 km from the LVC, delineating the minimum extent over which the active magmatic system affects the CWL tectonic environment. We analyze declustered seismicity data to show that locations with higher seismicity rates tend to be (1) areas with higher strain rates and (2) areas in which strain rates increased during drought‐enhanced inflation. We hypothesize that drought conditions reduce vertical surface mass loading, which decreases pressure at depth in the LVC system, in turn enhances magmatic inflation, and drives horizontal elastic stress changes that redistribute active CWL strain and modulate seismicity.

An Agricultural Drought Index for Assessing Droughts Using a Water Balance Method: A Case Study in Jilin Province, Northeast China

Drought, which causes the economic, social, and environmental losses, also threatens food security worldwide. In this study, we developed a vegetation-soil water deficit (VSWD) method to better assess agricultural droughts. The VSWD method considers precipitation, potential evapotranspiration (PET) and soil moisture. The soil moisture from different soil layers was compared with the in situ drought indices to select the appropriate depths for calculating soil moisture during growing seasons. The VSWD method and other indices for assessing the agricultural droughts, i.e., Scaled Drought Condition Index (SDCI), Vegetation Health Index (VHI) and Temperature Vegetation Dryness Index (TVDI), were compared with the in situ and multi-scales of Standardized Precipitation Evapotranspiration Index (SPEIs). The results show that the VSWD method has better performance than SDCI, VHI, and TVDI. Based on the drought events collected from field sampling, it is found that the VSWD method can better distinguish the severities of agricultural droughts than other indices mentioned here. Moreover, the performances of VSWD, SPEIs, SDCI and VHI in the major historical drought events recorded in the study area show that VSWD has generated the most sensible results than others. However, the limitation of the VSWD method is also discussed.

Estimating growing-season root zone soil moisture from vegetation index-based evapotranspiration fraction and soil properties in the Northwest Mountain region, USA

ABSTRACTA soil moisture retrieval method is proposed, in the absence of ground-based auxiliary measurements, by deriving the soil moisture content relationship from the satellite vegetation index-based evapotranspiration fraction and soil moisture physical properties of a soil type. A temperature–vegetation dryness index threshold value is also proposed to identify water bodies and underlying saturated areas. Verification of the retrieved growing season soil moisture was performed by comparative analysis of soil moisture obtained by observed conventional in situ point measurements at the 239-km2 Reynolds Creek Experimental Watershed, Idaho, USA (2006–2009), and at the US Climate Reference Network (USCRN) soil moisture measurement sites in Sundance, Wyoming (2012–2015), and Lewistown, Montana (2014–2015). The proposed method best represented the effective root zone soil moisture condition, at a depth between 50 and 100 cm, with an overall average R2 value of 0.72 and average root mean square error (RMSE) of 0.042.