Drought Spatiotemporal Characteristics Based on a Vegetation Condition Index in Erbil, Kurdistan Region, Iraq

Drought is a complex phenomenon that has severe impacts on the environment. Vegetation and its conditions are very sensitive to drought effects. This study aimed to monitor and assess the drought severity and its relationships to some ecological variables in ten districts of Erbil Governorate (Kurdistan Region), Iraq, throughout 20 years (1998-2017). The results revealed that droughts frequently hit Erbil throughout the study period. The Landsat time-series- based on Vegetation Condition Index (VCI) significantly correlated with precipitation, Digital Elevation Model (DEM), and latitude. Extreme VCI-based drought area percentages were recorded in 1999, 2000, 2008, and 2011 by 43.4%, 67.9%, 43.3%, and 40.0%, respectively. The highest crop yield reduction in the study area occurred mainly in 2000, 2008, and 2012 due to low precipitation rates. These results reveal the capability of the VCI for drought characteristics and highlighting relationships with some ecological variables, which provide vital information to the decision-makers, environmental, and economic sectors.

A Dynamic Hierarchical Bayesian Approach for Forecasting Vegetation Condition

Agricultural drought, which occurs due to a significant reduction in the moisture required for vegetation growth, is the most complex amongst all drought categories. The onset of agriculture drought is slow and can occur over vast areas with varying spatial effects, differing in areas with a particular vegetation land cover or specific agro-ecological sub-regions. These spatial variations imply that monitoring and forecasting agricultural drought require complex models that consider the spatial variations in a given region of interest. Hierarchical Bayesian Models are suited for modelling such complex systems. Using partially pooled data with sub-groups that characterise spatial differences, these models can capture the sub-group variation while allowing flexibility and information sharing between these sub-groups. This paper's objective was to improve the accuracy and precision of agricultural drought forecast in spatially diverse regions with a Hierarchical Bayesian Model. Results showed that the Hierarchical Bayesian Model was better at capturing the variability for the different agro-ecological zones and vegetation land covers compared to a regular Bayesian Auto-Regression Distributed Lags model. The forecasted vegetation condition and associated drought probabilities were more accurate and precise with the Hierarchical Bayesian Model at 4 to 10 weeks lead times. Forecasts from the hierarchical model exhibited higher hit rates with a low probability of false alarms for drought events in semi-arid and arid zones. The Hierarchical Bayesian Model also showed good transferable forecast skills over counties not included in the training data.

Drought assessment for kharif rice using standardized precipitation index (SPI) and vegetation condition index (VCI)

The monthly weather data for 31 years from 1985-2015 was used to analyze the extent of meteorological drought using standardized precipitation index (SPI) over Allahabad, Kanpur and Lucknow. MODIS NDVI data from 2000-2015 was used for monitoring of agricultural drought through NDVI based vegetation condition index (VCI) for all the three districts. The monthly SPI and VCI values from July to October were correlated with productivity index (PI) of kharif rice.Both the indices (SPI and VCI) were positively correlated with PI for all the districts. In Allahabad SPI and VCI during September month showed a significant correlation (0.70**& 0.61*) while in Kanpur VCI during October and SPI of July and August were significantly correlated with PI of kharif Rice. The multiple regression equation developed for predicting kharif rice PI in Allahabad, Kanpur and Lucknow districts explained 69 to 76 per cent variabilityin PI.

What state of the world are we in? Targeted monitoring to detect transitions in vegetation restoration projects.

Monitoring vegetation restoration is challenging because ‘best practice’ monitoring is costly, requires long-term funding, and involves monitoring multiple vegetation variables which are often not linked back to learning about progress toward objectives. There is a clear need for the development of targeted monitoring programs that focus on a reduced set of variables that are tied to specific restoration objectives. In this paper, we present a method to progress the development of a targeted monitoring program, using a pre-existing state-and-transition model. We i) use field data to validate an expert-derived classification of woodland condition states; ii) use this data to identify which variable(s) help differentiate woodland condition states; and iii) identify the target threshold (for the variable) that signifies the desired transition has been achieved. The measured vegetation variables from each site in this study were good predictors of the different states of vegetation condition. We show that by measuring only a few of these variables, it is possible to assign the vegetation condition state for a collection of sites, and monitor if and when a transition to another state has occurred. Out of nine vegetation variables considered, the density of immature trees and percentage of exotic understorey vegetation cover were the variables most frequently specified as effective to define a threshold or transition. We synthesise findings by presenting a decision tree that provides practical guidance for the development of targeted monitoring strategies for woodland vegetation.

Comparison of Multi-Year Reanalysis, Models, and Satellite Remote Sensing Products for Agricultural Drought Monitoring over South Asian Countries

The substantial reliance of South Asia (SA) to rain-based agriculture makes the region susceptible to food scarcity due to droughts. Previously, most research on SA has emphasized the meteorological aspects with little consideration of agrarian drought impressions. The insufficient amount of in situ precipitation data across SA has also hindered thorough investigation in the agriculture sector. In recent times, models, satellite remote sensing, and reanalysis products have increased the amount of data. Hence, soil moisture, precipitation, terrestrial water storage (TWS), and vegetation condition index (VCI) products have been employed to illustrate SA droughts from 1982 to 2019 using a standardized index/anomaly approach. Besides, the relationships of these products towards crop production are evaluated using the annual national production of barley, maize, rice, and wheat by computing the yield anomaly index (YAI). Our findings indicate that MERRA-2, CPC, FLDAS (soil moisture), GPCC, and CHIRPS (precipitation) are alike and constant over the entire four regions of South Asia (northwest, southwest, northeast, and southeast). On the other hand, GLDAS and ERA5 remain poor when compared to other soil moisture products and identified drought conditions in regions one (northwest) and three (northeast). Likewise, TWS products such as MERRA-2 TWS and GRACE TWS (2002–2014) followed the patterns of ERA5 and GLDAS and presented divergent and inconsistent drought patterns. Furthermore, the vegetation condition index (VCI) remained less responsive in regions three (northeast) and four (southeast) only. Based on annual crop production data, MERRA-2, CPC, FLDAS, GPCC, and CHIRPS performed fairly well and indicated stronger and more significant associations (0.80 to 0.96) when compared to others. Thus, the current outcomes are imperative for gauging the deficient amount of data in the SA region, as they provide substitutes for agricultural drought monitoring.

Forecasting Vegetation Condition with a Bayesian Auto-regressive Distributed Lags (BARDL) Model

Droughts form a large part of climate/weather-related disasters reported globally. In Africa, pastoralists living in the Arid and Semi-Arid Lands (ASALs) are the worse affected. Prolonged dry spells that cause vegetation stress in these regions have resulted in the loss of income and livelihoods. To curb this, global initiatives like the Paris Agreement and the United Nations recognised the need to establish Early Warning Systems (EWS) to save lives and livelihoods. Existing EWS use a combination of Satellite Earth Observation (EO) based biophysical indicators like the Vegetation Condition Index (VCI) and socio-economic factors to measure and monitor droughts. Most of these EWS rely on expert knowledge in estimating upcoming drought conditions without using forecast models. Recent research has shown that the use of robust algorithms like Auto-Regression, Gaussian Processes and Artificial Neural Networks can provide very skilled models for forecasting vegetation condition at short to medium range lead times. However, to enable preparedness for early action, forecasts with a longer lead time are needed. The objective of this research work is to develop models that forecast vegetation conditions at longer lead times on the premise that vegetation condition is controlled by factors like precipitation and soil moisture. To achieve this, we used a Bayesian Auto-Regressive Distributed Lag (BARDL) modelling approach which enabled us to factor in lagged information from Precipitation and Soil moisture levels into our VCI forecast model. The results showed a ∼2-week gain in the forecast range compared to the univariate AR model used as a baseline. The R2 scores for the Bayesian ARDL model were 0.94, 0.85 and 0.74, compared to the AR model's R2 of 0.88, 0.77 and 0.65 for 6, 8 and 10 weeks lead time respectively.

Detecting Agricultural and Meteorological Drought With Gross Primary Production Recovery Including Spatiotemporal Statistical Analysis in East Africa's Lake Victoria Basin

Drought imposes severe, long-term effects on global environments and ecosystems. A better understanding of how long it takes a region to recover to pre-drought conditions after drought is essential for addressing future ecology risks. In this study, drought-related variables were obtained using remote sensing and reanalysis products for 2003 to 2016. The meteorological drought index (standardized precipitation evapotranspiration index [SPEI]) and agricultural drought index (vegetation condition index [VCI]) were employed to estimate drought duration time (DDT) and drought recovery time (DRT). To the basin’s west, decreasing rainfall and increasing potential evapotranspiration led to decreasing SPEI. On the east side, decreasing soil moisture from each depth effects vegetation condition, which results in a decreasing gross primary productivity and VCI. Extreme meteorological drought events are likely to occur in the basin’s northeastern and middle western areas, while the southern basin is more likely to suffer from extreme agricultural drought events. The mean SPEI-based DDT (2.45 months) was smaller than the VCI-based DDT (2.97 months); the average SPEI-based DRT (2.02 months) was larger than the VCI-based DRT (1.63 months). Most of the area needs 1 or 2 months to recover from drought except for the basin’s northwestern area, where the DRT is more than 8 months. DDT is the most important parameter in determining DRT. These results provide useful information about regional drought recovery that will help local governments looking to mitigate potential environmental risks and formulate appropriate agricultural policies in Lake Victoria Basin.

Carbon Stock and Soil Characteristics under Expansive Shrubs in the Dry Afromontane Forest in Northern Ethiopia

Increased presence of expansive plant species could bring about various ecological influences on biomass carbon, soil organic carbon, and the physical and chemical properties of the soils. However, their impacts on these ecological parameters could differ due to a wide range of life forms, plant communities of the invaded ecosystems, and abiotic conditions. This work was conducted to examine the impacts of Cadia purpurea and Tarchonanthus camphoratus cover on carbon stock in vegetation and soil and soil physicochemical properties in Desa’a forest, northern Ethiopia. Vegetation and soil data were collected from a total of 150 sampling plots (size 20 m × 20 m) from uninvaded and invaded vegetation conditions. The soil samples were collected from topsoil (0–15 and 15–30 cm) of the uninvaded and invaded vegetation conditions. The statistical difference in carbon stock and soil characteristics P < 0.05 of both invaded and uninvaded vegetation conditions were tested using an independent t-test using an R-software. The mean above- and below-ground biomass carbon stocks of the uninvaded vegetation condition (17.62 Mg·C/ha and 4.14 Mg·C/ha, respectively) were found to be significantly higher than those of the invaded vegetation condition (4.73 Mg·C/ha and 1.11 Mg·C/ha, respectively). The mean soil organic carbons (SOC) were significantly higher P < 0.01 in the uninvaded (122.83 Mg·C/ha) than in the invaded (90.13 Mg·C/ha) vegetation condition. The total carbon stock estimates were significantly higher P < 0.01 in the uninvaded vegetation condition (144.59 Mg·C/ha) than in the invaded vegetation condition (95.97 Mg·C/ha). Furthermore, the result revealed that most of the soil characteristics were significantly lower P < 0.05 under the expansive shrubs invaded vegetation conditions except for significantly high sand content P < 0.05 . Silt, nitrogen, phosphorus, calcium, copper, and zinc did not significantly change with the cover of the expansive shrubs. Our results suggest that increased presence of the expansive species decreased carbon trapping and affected most of the soil nutrients within the forest. Hence, to enhance the carbon storage potential and to maintain the soil nutrient status of the forest, proper conservation, monitoring, and management of the existing PNV and controlling a further expansion of the expansive shrubs are required. Further studies will be required on the factors responsible for the difference in carbon stocks and soil nutrients in each vegetation condition in addition to the impacts of the expansive shrubs expansion.