Deficit Index Research Articles

Pre- and post-drought conditions drive resilience of Pinus halepensis across its distribution range

Severe droughts limit tree growth and forest productivity worldwide, a phenomenon which is expected to aggravate over the next decades. However, how drought intensity and climatic conditions before and after drought events modulate tree growth resilience remains unclear, especially when considering the range-wide phenotypic variability of a tree species.We gathered 4632 Aleppo pine (Pinus halepensis Mill.) tree-ring width series from 281 sites located in 11 countries across the Mediterranean basin, representing the entire geographic and bioclimatic range of the species. For each site and year of the period 1950–2020, we quantified tree-growth resilience and its two components, resistance and recovery, to account for the impact of drought and the capacity to recover from it. Relative drought intensity of each year was assessed using SPEI (Standardized Precipitation Evapotranspiration Index), a climatic water deficit index. Generalized additive mixed models were used to explore the non-linear relationships between resilience and its two components and drought intensity, preceding and following years climatic conditions.We found that P. halepensis radial growth was highly dependent on the SPEI from September of the previous year to June of the current year. Trees growing under more arid bioclimates showed higher inter-annual growth variability and were more sensitive to drought, resulting in an increased response magnitude to pre-, during and post-drought conditions. In contrast to our expectations, drought intensity only slightly affected resilience, which was rather negatively affected by favorable preceding conditions and improved by favorable following conditions.Resilience and its components are highly dependent on preceding and following years climatic conditions, which should always be taken into account when studying growth response to drought. With the observed and predicted increase in drought frequency, duration and intensity, favorable conditions following drought episodes may become rare, thus threatening the future acclimation capacity of P. halepensis in its current distribution.

Quantifying effects of different types of droughts on gross primary production in China

AbstractThe increasing impact of drought stress on ecosystems with regard to climate change has received considerable attention, which has been demonstrated to have a time‐lag effect on ecosystems. However, the time‐lag effect on different types of droughts and gross primary production (GPP) at large regional scales remains unclear. Thus, using the GOSIF GPP product data of solar‐induced chlorophyll fluorescence, the meteorological drought index (standardized precipitation evapotranspiration index [SPEI]), and the agricultural drought index (soil moisture deficit index [SMDI]) based on the soil moisture data obtained from the Global Land Data Assimilation System, the time‐lag effect of regional drought and GPP and the contribution of drought to GPP variation were analysed in China from 2000 to 2018. Results showed that (1) China's annual GPP increased at an average rate of 9.26 g C m−2 y−1. With regard to the average rate for each climate zone, the largest increase of 30.44 g C m−2 y−1 was observed in the southern subtropical region, and the smallest increase (4.01 g C m−2 y−1) was observed in the Qinghai–Tibetan plateau, with about 53.11% of the regions showing a significant upward trend. In addition, the significantly declining regions were primarily concentrated in urban areas, accounting for about 2.26% of China. (2) SPEI and GPP had an average lag time of 6.28 months, whereas SMDI and GPP had an average lag time of 7.17 months. The comparison demonstrated that SMDI had a greater influence on GPP than SPEI. (3) The average contribution rate of meteorological drought and agricultural drought to the decrease of GPP was 79.37% and 87.06%, and their average contribution rate to the increase of GPP was 39.11% and 50.57%, respectively. This study will provide comprehensive understanding of the effects of drought on carbon and water cycles in terrestrial ecosystems with regard to climate change.

A new multivariate agricultural drought composite index based on random forest algorithm and remote sensing data developed for Sahelian agrosystems

This manuscript aims to develop a new multivariate composite index for monitoring agricultural drought. To achieve this, the AVHRR, VIIRS, CHIRPS data series over a period of 40 years, rainfall and crop yield data as references were used. Variables include parameters for vegetative stress (SVCI, PV, SMN), water stress (PCI, RDI, NRDI), and heat stress (SMT, TCI, STCI), and a new variable related to environmental conditions was calculated through a normalized rainfall efficiency index. Then, random forest algorithm was used to determine the weights of each component of the model by considering interannual fluctuations in cereal yields as an impact variable. The multivariate composite model was compared to the VHI, NVSWI and SPI-12 indices for validation. The results show a large spatiotemporal concordance between the MDCI and the validation indices with a maximum correlation of 0.95 with the VHI and a highly significant p value (< 2.2e-16). Validation of the MDCI model by SPI-12 shows a significantly higher statistically significant relationship than that observed between SPI and VHI and NVSWI. P value range from 3.531e-05 to 6.137e-06 with correlations that vary between 0.6 and 0.64 depending on the station. It is also highly correlated with the Palmer drought severity index (PDSI) and climatic water deficit index (CWDI), with R = 0.85 and p value < 5.8e-10 and R = 0.72 and p value < 1.9e-6, respectively. Finally, the study provides a new direction for multivariate modeling of agricultural drought that should be further explored under various agroclimatic conditions.

Tracing Time-Varying Characteristics of Meteorological Drought through Nonstationary Joint Deficit Index

Abstract Standardized precipitation index (SPI) is one of the frequently used meteorological drought indices. However, the time-varying characteristics observed in the historical precipitation data questions the reliability of SPI and motivated the development of nonstationary SPI. To overcome some of the limitations in the existing nonstationary drought indices, a new framework for drought index is proposed, incorporating the temporal dynamics in the precipitation. The proposed drought index is developed by coupling the joint deficit index with the extended time sliding window–based nonstationary modeling (TSW-NSM). The proposed nonstationary joint deficit index (NJDI) detects the signature of nonstationarity in the distribution parameter and models both long-term (i.e., trend) and short-term (i.e., step-change) temporal dynamics of distribution parameters. The efficacy of NJDI is demonstrated by employing it to identify the meteorological drought-prone areas over India. The changes observed in the distribution parameter of rainfall series reveal an increasing number of dry days in recent decades all over India, except the northeast. Comparison of NJDI and stationary joint deficit index (JDI) reveals that JDI overestimates drought when frequent severe dry events are clustered and underestimates when these events are scattered, which indicates that the traditional index is biased toward the lowest magnitude of precipitation while classifying the drought. Moreover, NJDI could closely capture historical droughts and their spatial variations, thereby reflecting the temporal dynamics of rainfall series and the changes in the pattern of dry events over India. NJDI proves to be a potentially reliable index for drought monitoring in a nonstationary climate. Significance Statement Drought is one of the most severe natural disasters and is expected to intensify under a warming climate. There has been progress on developing newer methodologies to characterize drought severity under the changing climate. However, some limitations remain in capturing the temporal changes of the precipitation, especially the nonstationarity in variance (rapid increases in extreme precipitation versus the average precipitation). We propose an extension to the time sliding window approach to capture the nonstationarity in variance and mean while incorporating short-term (step-change) and long-term (trend) temporal dynamics. We apply the methodology to a subcontinent-sized heterogeneous country of India with gridded rainfall dataset (0.25° × 0.25°, 1901–2013) from the India Meteorological Department. We find that the majority of grids exhibit negative and positive trends in shape and scale parameters, respectively, which ultimately leads to an increase in the number of drier events, whereas a contradictory pattern is exhibited in the northwest Indian region (decrease in the number of dry days). The proposed index gives a potential framework for drought monitoring under the changing climate and can be extended to develop a multivariate nonstationary joint deficit index by incorporating other hydrological variables (e.g., soil moisture, diurnal temperature range, streamflow).

Dynamic vulnerability assessment of maize under low temperature and drought concurrent stress in Songliao Plain

With global climate change, most parts of the world face more than one natural disaster. Field crops must cope with a combination of two or more stresses. System vulnerability has changed, and quantitative vulnerability analysis is imminent and challenging. Based on the crop water surplus deficit index (CWSDI) and low-temperature index (LTC), this study used the copula method to establish a dynamic assessment model of maize vulnerability under different low temperature and drought concurrent stress intensities, finally showed it in two forms of three-dimensional surface and spatial distribution, which was more vivid and comprehensive. The main conclusions are as follows: the peak number of individual events and concurrent events of low temperature and drought is significantly different, the time trend of the number of concurrent events in each growth period is not the same, and the interannual difference is significant; low-temperature severity and maize yield loss rate, low-temperature severity, and drought severity had significant correlation. Based on this, a 3 C-vine-copula model was established and passed the test. The interaction between the joint probability of concurrent event intensity and the loss rate leads to complex changes in vulnerability. When low temperature and drought are concurrent, the vulnerability analysis cannot be simply superimposed by low temperature and drought intensity. The vulnerability distribution of maize at different growth stages in Songliao Plain shows noticeable regional differences. The central and southern parts of the study area are the most vulnerable areas under low-temperature and drought stress. This study’s results can help managers adjust agricultural resources and adopt more effective irrigation policies under combined stress conditions.

Identification of Extreme Droughts Based on a Coupled Hydrometeorology Index from GRACE-Derived TWSA and Precipitation in the Yellow River and Yangtze River Basins

Global climate change and human activities have exacerbated droughts’ environmental and socioeconomic threats. However, there is still a lack of effective techniques to consider their combined impacts on drought identification. Therefore, a new copula-based multivariate standardized drought index (CMSDI) was proposed, which integrates precipitation data and terrestrial water storage anomaly (TWSA) data observed by Gravity Recovery and Climate Experiment (GRACE) satellites. The applicability of the CMSDI was assessed compared with the water storage deficits index (WSDI), the self-calibration Palmer drought severity index (sc-PDSI), the standardized precipitation evapotranspiration index (SPEI), and the standardized precipitation index (SPI) in the Yellow River Basin (YRB) and the Yangtze River Basin (YZRB) for 2002–2020. The assessments were conducted regarding both temporal evolution and spatial distribution. The results showed that the CMSDI was more synchronized with the WSDI and SPI than with the other two indices and presented different trends and correlations in the YRB and YZRB. The CMSDI outperformed the other drought indices due to the limitations of the sc-PDSI, SPEI, and SPI in detecting certain drought events, and the greater inaccuracy of the WSDI in identifying extreme droughts. Furthermore, the CMSDI revealed a clear upward trend in parts of the middle and lower YRB and a clear downward trend in the upper YZRB, emphasizing the need for more attention to droughts in the YRB. This study presents a new perspective on the integrated use of satellite and measured data in drought monitoring across different regions.

Spatial-Temporal Characteristics of Spring Maize Drought in Songnen Plain, Northeast China

With the intensification of global warming, food production will face serious drought risk. In view of the insufficient applicability of the existing crop drought index, a standardized crop water deficit index (SCWDI) was constructed based on the construction idea of the standardized precipitation evapotranspiration index (SPEI) and the crop water deficit index (CWDI) in this study. On this basis, the spatial and temporal characteristics of spring maize drought in Songnen Plain were explored by the slope trend analysis and Morlet wavelet analysis methods. The results show the following: (1) Compared with the existing drought index, the SCWDI shows obvious advantages in drought monitoring of spring maize. (2) In the whole growth stage of spring maize, the change trend of SCWDI was small in the temporal series (−0.012/10a). Spatially, the drought trend of spring maize was mainly decreasing (−0.14~0/10a). The drought frequency of spring maize in each growth stage was mainly light drought in most regions. (3) The three main drought cycles of spring maize in Songnen Plain were 29 years, 10 years, and 4 years. In the next few years, the drought of spring maize in Songnen Plain was controlled by the first main cycle, and the drought years may increase, which should be prevented. The research was expected to provide technical support for crop drought monitoring and agricultural disaster prevention.

Irrigation Scheduling for Maize under Different Hydrological Years in Heilongjiang Province, China.

Appropriate irrigation schedules could minimize the existing imbalance between agricultural water supply and crop water requirements (ETc), which is severely impacted by climate change. In this study, different hydrological years (a wet year, normal year, dry year, and an extremely dry year) in Heilongjiang Province were calculated by hydrological frequency methods. Then, the single crop coefficient method was used to calculate the maize ETc, based on the daily meteorological data of 26 meteorological stations in Heilongjiang Province from 1960 to 2020. Afterward, the CROPWAT model was used to calculate the effective precipitation (Pe) and irrigation water requirement (Ir), and formulate the irrigation schedules of maize in Heilongjiang Province under different hydrological years. The results showed that ETc and Ir decreased first and then increased from west to east. The Pe and crop water surplus deficit index increased first and then decreased from west to east in Heilongjiang Province. Meanwhile, the average values of the Ir in were 171.14 mm, 232.79 mm, 279.08 mm, and 334.47 mm in the wet year, normal year, dry year, and extremely dry year, respectively. Heilongjiang Province was divided into four irrigation zones according to the Ir of different hydrological years. Last, the irrigation quotas for the wet year, normal year, dry year, and extremely dry year were 0~180 mm, 20~240 mm, 60~300 mm, and 80~430 mm, respectively. This study provides reliable support for maize irrigation practices in Heilongjiang Province, China.

Influence factors and mechanisms of 2015–2016 extreme flood in Pearl River Basin based on the WSDI from GRACE

Study regionPearl River Basin (PRB) has a tropical to subtropical climate in China. Study focusAn unprecedented extreme flood broke out in PRB during 2014–2016 super El Niño period. We construct the water storage deficit index (WSDI) to quantify this extreme flood event. The principal component analysis approach is used to analyze the WSDI in PRB and El Niño-Southern Oscillation (ENSO) and the sea surface temperature (SST) anomaly in the Tropical Indian Ocean (TIOS) to identify the influence factors. Furthermore, the underlying influence mechanisms are explored via correlation analysis. New hydrological insights for the regionThe severity of the extreme flood event was 483.11 km3 with a maximum surplus of terrestrial water storage in February 2016 (76.11 km3). The extreme flood was mainly influenced by the super El Niño and enhanced by TIOS. The WSDI is significantly correlated with ENSO and TIOS, for the first and second principal components, the correlation coefficients are 0.90 and 0.41 for ENSO after a 3-month lag, and 0.86 and 0.66 for TIOS after a 2-month lag, respectively. The super El Niño favored the extreme flood event by intensifying the anticyclones over the South China Sea, the Philippine Sea, and the Bay of Bengal, further promoting the attenuation of the South China Sea Monsoon and the westward extension of West Pacific Subtropical High, inducing more moisture transport towards PRB.

A Modified Temperature Vegetation Dryness Index (mTVDI) for Agricultural Drought Assessment Based on MODIS Data: A Case Study in Northeast China

Satellite-based drought indices have been shown to be effective and convenient in detecting drought conditions. The temperature vegetation dryness index (TVDI) is one of the most frequently used drought indices; however, it is not suitable for areas with high fractional vegetation cover (FVC). In this study, a modified temperature vegetation dryness index (mTVDI) was constructed by using the multispectral vegetation dryness index (MVDI) proposed by a PROSAIL simulation and water stress experiments which was based on the theory of the TVDI and utilized MODIS data. Compared with the TVDI, the mTVDI presents a more triangular feature space, as well as obviously increased R2 values for dry and wet edges (from 0.37–0.90 to 0.53–0.91 for dry edges and from 0.00–0.77 to 0.24–0.80 for wet edges). The mTVDI was evaluated using standardized precipitation evapotranspiration indices (SPEIs), precipitation, potential evapotranspiration (PET), and the crop water deficit index (CWDI), and the results confirmed that the mTVDI can better reflect the actual spatial changes, compared to the TVDI, under high FVC, as well as presenting an increased Pearson correlation coefficient (by 0.06–0.10) when compared with SPEIs. Moreover, the good performance of the mTVDI in major drought events indicates its reliability and accuracy for drought monitoring. Overall, the mTVDI is a reliable and accurate satellite-based dryness index suitable for high FVC conditions.

Identification of Drought Events in Major Basins of Africa from GRACE Total Water Storage and Modeled Products

Terrestrial water storage (TWS) plays a vital role in climatological and hydrological processes. Most of the developed drought indices from the Gravity Recovery and Climate Experiment (GRACE) over Africa neglected the influencing roles of individual water storage components in calculating the drought index and thus may either underestimate or overestimate drought characteristics. In this paper, we proposed a Weighted Water Storage Deficit Index for drought assessment over the major river basins in Africa (i. e., Nile, Congo, Niger, Zambezi, and Orange) with accounting for the contribution of each TWS component on the drought signal. We coupled the GRACE data and WaterGAP Global Hydrology Model through utilizing the component contribution ratio as the weight. The results showed that water storage components demonstrated distinctly different contributions to TWS variability and thus drought signal response in onset and duration. The most severe droughts over the Nile, Congo, Niger, Zambezi, and Orange occurred in 2006, 2012, 2006, 2006, and 2003, respectively. The most prolonged drought of 84 months was observed over the Niger basin. This study suggests that considering the weight of individual components in the drought index provides more reasonable and realistic drought estimates over large basins in Africa from GRACE.

Assessment of Meteorological and Agricultural Drought Indices under Climate Change Scenarios in the South Saskatchewan River Basin, Canada

Climate change has amplified the severity of droughts with potentially adverse impacts on agriculture in western Canada. This study assessed meteorological and agricultural drought in the Southern Saskatchewan River Basin (SSRB) using an array of drought indices, including the Standardized Precipitation Index (SPI), the Standardized Precipitation-Evapotranspiration Index (SPEI), the Self-Calibrated Palmer Drought Severity Index (scPDSI), the Soil Moisture Deficit Index (SMDI), and the Evapotranspiration Deficit Index (ETDI). These indices were evaluated using multiple regional climate model (RCM) projections assuming 1.5, 2.0, and 3.0 °C thresholds of global warming. A modified Soil and Water Assessment Tool (SWAT-M) was used to simulate the soil water content (SWC), actual evapotranspiration (AET), and potential evapotranspiration. The results of a sensitivity analysis using the SUFI-2 method in SWAT-CUP showed that the model performed well with BIAS lower than 10% and NSE and R higher than 0.7, and the range of SWC output closely matched the observed SWC. According to the RCM projections, the annual precipitation increases for all three global temperature thresholds while the annual mean temperature increases at a greater rate than the rise in global mean temperature. The projected PDSI and the SPEI suggest that drought duration and severity will exceed historical values while SPI will remain largely unchanged. Furthermore, severe drought conditions (SMDI &lt; 2.0) are more frequent under the 3.0 °C global temperature scenario. The mean ETDI was historically 0.58 while the projected value is 0.2, 0.1, and −0.2 for the first to third scenarios, respectively. Simulated values, spatial maps, and heat maps of SMDI and ETDI illustrated that Canesm2.CRCM5 projects the driest conditions among all the RCMs. Agricultural drought indices, which incorporate SWC data, show more significant effects than meteorological drought indices. The increasing dryness will potentially impact agricultural crop production, particularly under the third scenario (3 °C) in the SSRB.

Improved the Characterization of Flood Monitoring Based on Reconstructed Daily GRACE Solutions over the Haihe River Basin

Flood events have caused huge disasters with regard to human life and economic development, especially short-term flood events that have occurred in recent years. Gravity Recovery and Climate Experiment (GRACE) satellites can directly detect the spatiotemporal characteristics of terrestrial water storage anomalies (TWSA), which play an important role in capturing flood signals. However, the monthly resolution of GRACE-derived TWSA limits its application in monitoring sub-monthly flood events. Therefore, this paper first reconstructs the daily TWSA based on a statistical model with near real-time precipitation and temperature as input variables, and then three daily flood monitoring indexes are developed based on the reconstructed TWSA. Furthermore, these indexes are employed to evaluate the temporal and spatial characteristics of the 2016 short-term flood event in the Haihe River basin (HRB), including the flood potential index (FPI), water storage deficit index (WSDI), and combined climate deviation index (CCDI). In contrast to previous studies, the temporal resolution of TWSA-based indexes is improved from the monthly scale to the daily scale, which largely improves the temporal characterization of flood monitoring. Results demonstrate that (1) among ten kinds of “Temperature-Precipitation” combinations, the reconstructed TWSA based on CN05.1-CN05.1 match well with the GRACE TWSA, as well as publicly available daily TWSA datasets with a Nash-Sutcliffe efficiency coefficient (NSE) of 0.96 and 0.52 ~ 0.81 respectively. (2) The short-term flood characteristics can be better characterized by the reconstructed daily TWSA based on CN05.1-CN05.1, reaching the peak of 216.19 mm on July 20 in the flood center. Additionally, the spatial characteristics of the equivalent water height (EWH) are detected to evolve from southwest to northeast during the short-term flood. (3) FPI, WSDI, and CCDI are proven to be effective in monitoring flood events in the HRB, which validates the reliability of the reconstructed daily TWSA. Moreover, compared to the 56% and 66% coverage of damage quantified by FPI and CCDI, the 45% damage coverage of the flood mapped by WSDI is more consistent with the governmental reports within the HRB. This paper is expected to provide a valuable reference for the assessment of short-term events caused by extreme climate change.

Forecasting Monthly Water Deficit Based on Multi-Variable Linear Regression and Random Forest Models

Forecasting water deficit is challenging because it is modulated by uncertain climate, different environmental and anthropic factors, especially in arid and semi-arid northwestern China. The monthly water deficit index D at 44 sites in northwestern China over 1961−2020 were calculated. The key large-scale circulation indices related to D were screened using Pearson’s correlation (r). Subsequently, we predicted monthly D with the multi-variable linear regression (MLR) and random forest (RF) models at certain lagged times after being strictly calibrated and validated. The results showed the following: (1) The r between the monthly D and the screened key circulation indices varied from 0.71 to 0.85 and the lagged time ranged from 1 to 12 months. (2) The calibrated and validated performance of the established MLR and RF models were all good at the 44 sites. Overall, the RF model outperformed the MLR model with a higher coefficient of determination (R2 &gt; 0.8 at 38 sites) and mean absolute percentage error (MAPE &lt; 50% at 30 sites). (3) The Pacific Polar Vortex Intensity (PPVI) had the greatest impact on D in northwestern China, followed by SSRP, WPWPA, NANRP, and PPVA. (4) The forecasted monthly D values based on RF models indicated that the water deficit in northwestern China would be most severe (−239.7 to −62.3 mm) in August 2022. In conclusion, using multiple large-scale climate signals to drive a machine learning model is a promising method for predicting water deficit conditions in northwestern China.

Characteristics of drought, low temperature, and concurrent events of maize in Songliao Plain

AbstractMaize is susceptible to drought and low temperatures. In recent years, drought, low temperatures, and their composite events have often occurred during the growth period of maize, causing a huge impact. In this study, maize in the Songliao Plain was used as the research object. Based on the crop water surplus deficit index (CWSDI) and heat index (F(T)), combined with the Theil–Sen median and Mann–Kendall tests (Sen–MK) and empirical orthogonal function (EOF), the temporal trends and spatial patterns of drought and low temperatures in different growth periods of maize were obtained. Copula modelling was used to analyse the joint probability distribution and return period of drought and low temperatures. The results showed that the temporal variation trend of the CWSDI was significantly different at different growth periods of maize, and there were different trends and spatial modal distributions. However, the temporal variation trend of F(T) was similar, showing a downward trend over the entire region. There was no obvious asymmetry or skew‐dependent structure in the distribution of concurrent events of drought and low temperature; however, the joint probability contours of different growth periods were biased to the upper left. The recurrence period of drought and low‐temperature concurrent events was within 2 years, and the recurrence period was basically between 2 and 5 years, with a reduction in drought or low temperatures.

Spatio-temporal analysis of water requirement of maize (Zea mays L.) in Haryana state of India

Climate change have a considerable impact on crop water demand (ETc). The present study analyzed the spatio-temporal variation of the crop water requirement (ETc), crop water surplus deficit index (CWSDI), and the coupling degree of ETc and effective precipitation (Pe) for maize (Zea mays L.) crop grown in Haryana State of India using ArcMap 10.8 software. ETc was calculated using the Penman-Monteith method and crop coefficient (Kc) approach for 34 years (1985-2018) climatic data. Two statistical models, namely Mann–Kendall test and Sen’s slope estimator, were applied to understand the trend, while Pettitt's test was used to identify abrupt change points by using XLSTAT 2021 software.The result showed that for maize ETc ranged from 571 to 766 mm, CWSDI ranged from -29 to -74% and the degree of coupling of ETc and Pe ranged from 0.26 to 0.71 during 1985-2018. The decadal temporal variation of these indices indicated that ETc during 2007-2018 was relatively higher than it was from 1985-1995 and 1996-2006. The spatio-temporal values of CWSDI indicated that almost all the districts were under water deficit conditions for growing maize crop.Trend analysis showed significantly increasing trend of CWR for most of the districts except Jind, Mewat, Palwal, Panchkula, Rewari, Rohtak and Yamunanagar.The abrupt change point detection analysis of CWR indicates toward different change points, with maximum change points between the years 2004–2008.Such information will help to the farmers in the effective management of water for sustainable production under climate change in the near future.

Spatiotemporal Winter Wheat Water Status Assessment Improvement Using a Water Deficit Index Derived from an Unmanned Aerial System in the North China Plain.

Agricultural droughts cause a great reduction in winter wheat productivity; therefore, timely and precise irrigation recommendations are needed to alleviate the impact. This study aims to assess drought stress in winter wheat with the use of an unmanned aerial system (UAS) with multispectral and thermal sensors. High-resolution Water Deficit Index (WDI) maps were derived to assess crop drought stress and evaluate winter wheat actual evapotranspiration rate (ETa). However, the estimation of WDI needs to be improved by using more appropriate vegetation indices as a proximate of the fraction of vegetation cover. The experiments involved six irrigation levels of winter wheat in the harvest years 2019 and 2020 at Luancheng, North China Plain on seasonal and diurnal timescales. Additionally, WDI derived from several vegetation indices (VIs) were compared: near-infrared-, red edge-, and RGB-based. The WDIs derived from different VIs were highly correlated with each other and had similar performances. The WDI had a consistently high correlation to stomatal conductance during the whole season (R2 between 0.63-0.99) and the correlation was the highest in the middle of the growing season. On the contrary, the correlation between WDI and leaf water potential increased as the season progressed with R2 up to 0.99. Additionally, WDI and ETa had a strong connection to soil water status with R2 up to 0.93 to the fraction of transpirable soil water and 0.94 to the soil water change at 2 m depth at the hourly rate. The results indicated that WDI derived from multispectral and thermal sensors was a reliable factor in assessing the water status of the crop for irrigation scheduling.

Spatial Downscaling of GRACE Data Based on XGBoost Model for Improved Understanding of Hydrological Droughts in the Indus Basin Irrigation System (IBIS)

Climate change may cause severe hydrological droughts, leading to water shortages which will require to be assessed using high-resolution data. Gravity Recovery and Climate Experiment (GRACE) satellite Terrestrial Water Storage (TWSA) estimates offer a promising solution to monitor hydrological drought, but its coarse resolution (1°) limits its applications to small regions of the Indus Basin Irrigation System (IBIS). Here we employed machine learning models such as Extreme Gradient Boosting (XGBoost) and Artificial Neural Network (ANN) to downscale GRACE TWSA from 1° to 0.25°. The findings revealed that the XGBoost model outperformed the ANN model with Nash Sutcliff Efficiency (NSE) (0.99), Pearson correlation (R) (0.99), Root Mean Square Error (RMSE) (5.22 mm), and Mean Absolute Error (MAE) (2.75 mm) between the predicted and GRACE-derived TWSA. Further, Water Storage Deficit Index (WSDI) and WSD (Water Storage Deficit) were used to determine the severity and episodes of droughts, respectively. The results of WSDI exhibited a strong agreement when compared with the Standardized Precipitation Evapotranspiration Index (SPEI) at different time scales (1-, 3-, and 6-months) and self-calibrated Palmer Drought Severity Index (sc-PDSI). Moreover, the IBIS had experienced increasing drought episodes, e.g., eight drought episodes were detected within the years 2010 and 2016 with WSDI of −1.20 and −1.28 and total WSD of −496.99 mm and −734.01 mm, respectively. The Partial Least Square Regression (PLSR) model between WSDI and climatic variables indicated that potential evaporation had the largest influence on drought after precipitation. The findings of this study will be helpful for drought-related decision-making in IBIS.

Progression of frailty as measured by a cumulative deficit index: A systematic review.

Frailty is a risk factor for adverse health outcomes. There is a paucity of literature on frailty progression defined by a cumulative deficit model among community dwelling older people. The objective of this review was to synthesise evidence on these changes in health and mortality among community-dwelling older people. Six databases (Medline, Embase, CINAHL, Cochrane, PsycInfo, Web of Science) and a clinical trials registry were searched in July 2021. The inclusion criteria were studies using a frailty index and providing information on transition between frailty states or to death in community-dwelling older people aged ≥50. Exclusion criteria were studies examining specific health conditions, conference abstracts and non-English studies. To standardise the follow-up period and facilitate comparison, we converted the transition probabilities to annual transition rates. Two reviewers independently screened 5078 studies and 61 studies were included for analysis. Of these, only three used the same frailty state cut-points to facilitate cross-cohort comparison. This review found that frailty tends to increase with time, people who are frail at baseline have greater likelihood to progress in frailty and die, and the main factor that accelerates frailty progression is age. Other risk factors for progression are having chronic disease, smoking, obesity, low-income or/and low-education levels. A frailty index is an accurate predictor of adverse outcomes and death. This systematic review demonstrated that worsening in frailty was a common frailty transition, and older people who are frail at baseline are more likely to die. A frailty index has significant power to predict adverse health outcomes. It is a useful tool for within-cohort comparison but there are challenges comparing different cohorts due to dependence of frailty progression on age and differences in how frailty index is defined and measured.

Drought characterization and severity analysis using GRACE-TWS and MODIS datasets: a case study from the Awash River Basin (ARB), Ethiopia

Abstract Drought is the utmost highly devastating phenomenon in Ethiopia because of societies that are reliant on rainfall-dependent agriculture; thus, it is crucial to characterize drought at the basin scale using new developments in remote sensing products and recently proposed drought indices. This study aimed to quantify drought in the Awash River Basin (ARB) using drought indices of the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Evaporative Demand Drought Index (EDDI), Evaporative Stress Index (ESI), and Water Storage Deficit Index (WSDI). The Mann–Kendall test analysis of annual and seasonal terrestrial water storage (TWS) showed a significant increase from 2002 to 2017 that is beneficial for developmental activities in the ARB. GRACE showed a record high extreme drought that persisted for 15 months was noticed from 2005/01 to 2006/03 with a total water storage deficit of −411.8 mm with a peak shortage of −46.24 mm in 2005/03, representing severe terrestrial water shortage in the ARB. This GRACE-TWS-based quantified extreme water shortage in 2005/30 can be used as a threshold for adaptation. Overall, this study provides a reliable outcome that will be vital for the establishment of climate change adaptation pathways in the future for viable water resources management to minimize the disastrous impacts of drought in the AR.