Tigris Basin Landscapes: Sensitivity of Vegetation Index NDVI to Climate Variability Derived from Observational and Reanalysis Data

Abstract

The primary aim of this work is to study the response of the normalized difference vegetation index (NDVI) of landscapes in the Lower Tigris Basin to current global and regional climate variability presented, respectively, by the global circulation indices and monthly temperatures and precipitation extracted from five observational/reanalysis datasets. The second task is to find the dataset that best reflects the regional vegetation and climate conditions. Comparison of the Köppen-Trewartha bioclimatic landscapes with the positions of botanical districts, land cover types, and streamflow estimates led to the conclusion that only two datasets correctly describe regional climatic zones. Therefore, searching for the NDVI response to regional climate variability requires the use of normalized analogues of temperatures and precipitations, as well as the Spearman Rank Correlation. We found that March/April NDVI, as proxies of the maximum biological productivity of the regional landscapes, are strongly correlated with October- March precipitation derived from three datasets and January-March temperatures derived from one dataset. We discovered the significant impact of autumn-winter El-Niño-Southern Oscillation and winter Indian Oceanic Dipole states on regional weather (e.g. all recent five severe droughts occurred during strong La Niña events). However, the strength of this impact on the vegetation was clearly linked to the zonal landscape type. By selecting pairs of the temperature/precipitation time-series that best correlated with NDVI at a given landscape, we have built a synthetic climate dataset. The landscape approach presented in this work can be used to validate the viability of any dataset when assessing the impacts of climate change and variability on weather-dependent components of the Earth's surface.

Predicting Time Series of Temperature in Nineveh Using The Conversion Function Models

“…After the diagnostic stage in which the function of the TFM rank (r, s, k) is determined and the chain of disturbance of the ARMA model, the parameters of the TFM described in equation (2) are evaluated as follows:…”

“…Moreover, in many prediction situations, other events lead to a regular impact on the time series that we want to predict (dependent variables), so we need to use multivariate prediction models, and here we must build a prediction model that includes more than one-time series and shows the dynamic characteristics of the system. Such a model is called a transformation function model(TFM) [1], [2].…”

Vegetation response to climate and climatic extremes in northwest Bangladesh: a quantile regression approach

“…The availability of satellite NDVI data for a longer period allows vegetation changes assessment at global, regional and local scales (Measho et al, 2021). In the last three decades, it has been utilized for monitoring crop yield (Mkhabela et al, 2011), biodiversity (Madonsela et al, 2018), environmental degradation (Zhang et al, 2021), vegetation dynamics (Zarei et al, 2020), forest re susceptibility (Van Le et al, 2021), biological productivity (Alhumaima and Abdullaev, 2020), ecological environment (Sun et al, 2021), deserti cation (Li et al, 2021). Besides, it has been used to examine the in uence of climate variability on vegetation dynamics, droughts and environmental sustainability (Parmesan, C. 2006, Walther et al 2002, Bi et al 2013, Jiang et al 2017, Qi et al 2019Zhu et al 2019).…”

Climatic Temperature Data Forecasting in Nineveh Governorate Using the Recurrent Neutral Network Method

“…The temperature forecasting can be studied for many seasonal cycles such as monthly, daily, hourly, or other periods. The temperature data is time-series data, and its observations are often by the nearest previous observation [2]. A previous study suggested forecasting temperature data in the literature using exponential smoothing models [3].…”