Modelling the relationship between groundwater depth and NDVI using time series regression with Distributed Lag M

Groundwater plays a key role in hydrological processes, including in determining aboveground vegetal growth characteristics and species distribution. This study aimed at estimating time-series data of Normalized Difference Vegetation Index (NDVI) using groundwater depth as a predictor in two land cover types: grassland and shrubland. The study also investigated the significance of past (lagged) groundwater and NDVI in estimating the current NDVI. Results showed that lagged groundwater depth and vegetation conditions influence the amount of current NDVI. It was also observed that first lags of groundwater depth and NDVI were significant predictors of NDVI in grassland. In addition, first and second lags of NDVI were consistently significant predictors of NDVI in shrubland. This shows the importance of vegetation type when modelling the relationship between groundwater depth and NDVI.Keywords: Groundwater depth; Landsat NDVI; Time-series analysis; Distributed Lag Model

Regime shifts in the COVID-19 case fatality rate dynamics: A Markov-switching autoregressive model analysis

The 2019 novel coronavirus disease (COVID-19) has spread rapidly to many countries around the world from Wuhan, the capital of China’s Hubei province since December 2019. It has now a huge effect on the global economy. As of 13 September 2020, more than 28, 802, 775, and 920, 931 people are infected and dead, respectively. The mortality of COVID-19 infections is increasing as the number of infections increase. Many countries published control measures to contain its spread. Even though there are many drugs and vaccines under trial by pharmaceutical companies and research groups, no specific vaccine or drug has yet been found. Therefore, it is necessary to explain the behaviour of the case fatality rate (CFR) of COVID-19 using the most updated COVID-19 epidemiological data before 13 September 2020. The dynamics in the CFR were analyzed using the Markov-switching autoregressive (MSAR) models. Results showed that the two-regime and three-regime MSAR approach better captured the non-linear dynamics in the CFR time series data for each of the top heavily infected countries including the world. The results also showed that rises in CFRs are more volatile than drops. We believe that this information can be useful for the government to establish appropriate policies in a timely manner

Monthly geographically weighted regression between climate and vegetation in the Eastern Cape Province of South Africa: Clustering pattern shifts and biome-dependent accuracies

Remote sensing provides cost-effective and unbiased data and thus is ideal for assessing climate–vegetation relationships. Such relationships can be quantified using geographically weighted regression (GWR) approach to account for variations of the relationships across space. This approach was applied in the Eastern Cape province of South Africa that is rich in biodiversity hosting 10 of the country's 11 biomes. The study aimed to determine if the GWR accuracy for relating Enhanced Vegetation Index (EVI) with rainfall and Land Surface Temperature (LST) shows an optimal pattern with time and space. and to explore if the correlation of EVI with rainfall and LST varies with biome type. Monthly data covering February 2000 to December 2017 were used for the three variables. The coefficient of determination (R2) was greater than 0.5 for 75% of the locations, with month-to-month change of R2 exceeding 25% for many locations. Optimized Hot Spot Analysis returned well-defined broad clusters of high and low R2 values separated by clusters of randomly distributed R2 values. These clusters shifted with month, further stressing the benefit of modelling at the monthly scale. Assessment of R2 by biome showed the importance of biomes in characterizing GWR of climate and vegetation, with better correlations found in low biodiversity (Succulent Karoo and Nama-Karoo biomes) than in higher biodiversity (Forest and Indian Ocean Coastal Belt biomes) zones. Further, the estimation residuals of the Forest Biome varied significantly from 3 to 5 other biomes across the year indicating the complex interaction of this biome with rainfall and LST. The study encourages further research by using high temporal resolution data for detailed monitoring within the GWR framework

Forecasting monthly soil moisture at broad spatial scales in sub-Saharan Africa using three time-series models: evidence from four decades of remotely sensed data

ABSTRACTSoil moisture is a critical environmental variable that determines primary productivity and contributes to climatic processes. It is, therefore, important to forecast soil moisture to inform expectations of derivative outputs reliably. While forecasting soil moisture continues to advance, there is a need to extend it to different geoclimatic regions, including in sub-Saharan Africa, where livelihoods predominantly rely on subsistence agriculture. We used remotely sensed soil moisture data produced by the European Space Agency – Climate Change Initiative (ESA CCI). The data, which covered the period 1978 to 2019, were used to forecast monthly soil moisture in different agroecological zones and land cover types. The Seasonal Random Walk, Exponential Smoothing and Seasonal Autoregressive Integrated Moving Average (SARIMA) forecasting models were trained on 70% of the data (November 1978 – August 2007) and subsequently applied to a test dataset (September 2007 – December 2019). All models showed solid prediction accuracies for all agroecological zones (unbiased root mean square error, ubRMSE ≤ 0.05 m3 m−3) and land cover types (ubRMSE ≤ 0.04 m3 m−3). This was corroborated by similarities in season-adjusted anomalies between observed and forecasted soil moisture for nearly all agroecological zones and land cover types, with a correlation coefficient of r > 0.5 for most locations). The broad-scale interpretation of soil moisture forecasting can inform moisture availability and variability by regions; however, more research is encouraged to improve forecasting at spatially and temporally detailed levels to assist small-scale farming practices in the continent

Biome-level relationships between vegetation indices and climate variables using time-series analysis of remotely-sensed data

Climatic factors such as rainfall and temperature play a vital role in the growth characteristics of vegetation. While the relationship between climate and vegetation growth can be accurately predicted in instances where vegetation is homogenous, this becomes complex to determine in heterogeneous vegetation environments. The aim of this paper was to study the relationship between remotely-sensed monthly vegetation indices (i.e. Normalized Difference Vegetation Index and Enhanced Vegetation Index) and climatic variables (temperature and precipitation) using time-series analysis at the biome-level. Specifically, the autoregressive distributed lag model (ARDL1 and ARDL2, corresponding respectively to one month and two month lags) and the Koyck-transformed distributed lag model were used to build regression models. All three models estimated NDVI and EVI fairly accurately in all biomes (Relative Root-Mean-Squared-Error (RMSE): 12.0–26.4%). Biomes characterized by relative homogeneity (Grassland, Savanna, Indian Ocean Coastal Belt and Forest Biomes) achieved the most accurate estimates due to the dominance of a few species. Comparisons of lag size (one month compared to two months) generally showed similarities (Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and log-likelihood) with quite high comparability in certain biomes – this indicates the utility of the ARDL1 and ARDL2 model, depending on the availability of appropriate data. These findings demonstrate the variation in estimation linked to the biome, and thus the validity of biome-level correlation of climatic data and vegetation indices