Spatial and temporal characteristics of drought and its correlation with climate indices in Northeast China

The spatial and temporal characteristics of drought in Northeast China are investigated, using monthly meteorological data from 140 stations over the period 1970–2014. The study area was divided into three regions using hierarchical cluster analysis based on the precipitation and potential evapotranspiration data. The standardized precipitation evapotranspiration index (SPEI) was calculated for each station on 3-month and 12-month time scales. The Mann-Kendall (MK) trend test and Sen’s slope method were applied to determine the trends for annual and seasonal SPEI time series. Periodic features of drought conditions in each sub-region and possible relationship with large-scale climate patterns were respectively identified using the continuous wavelet transform (CWT) and cross wavelet transform. The results show mitigations in spring and winter droughts and a significant increasing trend in autumn drought. On the annual scale, droughts became more severe and more intense in the western regions but were mitigated in the eastern region. CWT analysis showed that droughts in Northeast China occur predominantly in 14- to 42-month or 15- to 60-month cycles. Annual and seasonal droughts have 2- to 6-year cycles over the three defined regions. Cross wavelet analysis also shows that the statistically significant influence of large-scale climate patterns (the Southern Oscillation, the Atlantic Multidecadal Oscillation, the Arctic Oscillation, and the Polar–Eurasian Pattern) on drought in Northeast China is concentrated in a 16- to 50-month period, possibly causing drought variability in the different regions. The Southern Oscillation, Polar–Eurasia pattern, and Arctic Oscillation are significantly correlated with drought on decadal scales (around 120-month period). The findings of this study will provide valuable reference for regional drought mitigation and drought prediction.

Multi-scale investigation on streamflow temporal variability and its connection to global climate indices for unregulated rivers in India

With the increasing stress on water resources for a developing country like India, it is pertinent to understand the dominant streamflow patterns for effective planning and management activities. This study investigates the spatiotemporal characterization of streamflow of six unregulated catchments in India. Firstly, Mann Kendall (MK) and Changepoint analysis were carried out to detect the presence of trends and any abrupt changes in hydroclimatic variables in the chosen streamflows. To unravel the relationships between the temporal variability of streamflow and its association with precipitation and global climate indices, namely, Niño 3.4, IOD, PDO, and NAO, continuous wavelet transform is used. Cross-wavelet transform and wavelet coherence analysis was also used to capture the coherent and phase relationships between streamflow and climate indices. The continuous wavelet transforms of streamflow data revealed that intra-annual (0.5 years), annual (1 year), and inter-annual (2–4 year) oscillations are statistically significant. Furthermore, a better understanding of the in-phase relationship between the streamflow and precipitation at intra-annual and annual time scales were well-captured using wavelet coherence analysis compared to cross wavelet transform. Furthermore, our analysis also revealed that streamflow observed an in-phase relationship with IOD and NAO, whereas a lag correlation with Niño 3.4 and PDO indices at intra-annual, annual and interannual time scales.

Evaluation and analysis of climate change on the quantity and quality water resources in Lorestan province (Case study of Khorram river)

In recent decades due to many droughts many changes have been made in the quantity and quality of the country’s water resources. this factor has caused many uncertainties in the management of the country’s water resources. The purpose of this study was to improve the understanding of the effects of drought on the quantity and quality of water resources in Lorestsn province in the years 2008 to 2018 by coherence and cross wavelet method. To achieve this goal, first to drought assessment according to precipitation data has been examined using(SPI) index and then the effect of drought on Khorram river water runoff are analyzed. In the next step, the global index of water and the impact of drought on this index in the Khorram river were evaluated. In the next step, the global index of water quality(WQI) and the impact of drought on this index in the Khorram river were evaluated. The results of coherence and cross wavelet indicated which the relative effect of precipitation with a wavelet coherence coefficient of 0.6 on changes in water runoff in the Khorram river is of degree first importance. Also, the relative impact of drought with a wavelet coherence coefficient of 0.4 changes in water quality of Khorram river has been more than other factors. Therefore, climatic factors in reducing the water runoff of Khorram river from factors other are more important. Also, the research results show human factors in changes water quality of Khorram river of degree first importance.

Effects of Climate Change on Cultivation Patterns and Climate Suitability of Spring Maize in Inner Mongolia

Climate change has caused significant alterations in crop cultivation patterns and has affected crop suitability as well as its production. In this study, we investigated the changes in cultivation patterns and climate suitability of spring maize in Inner Mongolia from 1959 to 2018. We used the daily meteorological data from 50 weather stations and growth period data of spring maize from nine agrometeorological stations. In addition, the quantitative and interdecadal relationship between climate suitability of regions and climate-induced crop yield was analyzed using stepwise regression and cross wavelet transform. The results show that: (1) The planting boundaries of different spring maize maturity types extend to the north and east. In the middle part, early maturity maize has been replaced by medium maturity maize. The unsuitable planting areas in Northeast Inner Mongolia are decreasing, and the early maturity areas are increasing. (2) The climate suitability for spring maize planting areas is increasing. However, variations occur between different regions; the eastern region has the highest climate suitability (Sz = 0.67), but the overall trend is decreasing in this region. Whereas the central region has moderate suitability (Sz = 0.62), with a significantly increasing trend (p < 0.05). The western region is lower (Sz = 0.60) and the trend is not significant. (3) Climate suitability and climate-induced yields are generally positively correlated. The primary factors affecting climate-induced yields are sunshine hours, followed by climate suitability, rainfall, and temperature. The cross-wavelet transform shows that climate suitability and climate-induced yield have greater periodicity in the late growth period. Appropriate expansion of the planting range of medium-late maturity spring maize can fully adapt to the impact of climate warming. Therefore, it is necessary to study suitability trends of regions to adopt comprehensive maize production measures.

Investigating the Causal Linkages among Inflation, Interest Rate, and Economic Growth in Pakistan under the Influence of COVID-19 Pandemic: A Wavelet Transformation Approach

This research is the earliest attempt to understand the impact of inflation and the interest rate on output growth in the context of Pakistan using the wavelet transformation approach. For this study, we used monthly data on inflation, the interest rate, and industrial production from January 1991 to May 2020. The COVID-19 pandemic has affected economies around the world, especially in view of the measures taken by governmental authorities regarding enforced lockdowns and social distancing. Traditional studies empirically explored the relationship between these important macroeconomic variables only for the short run and long run. Firstly, we employed the autoregressive distributed lag (ARDL) cointegration test and two causality tests (Granger causality and Toda–Yamamoto) to check the cointegration properties and causal relationship among these variables, respectively. After confirming the long-run causality from the ARDL bound test, we decomposed the time series of growth, inflation, and the interest rate into different time scales using wavelet analysis which allows us to study the relationship among variables for the very short run, medium run, long run, and very long run. The continuous wavelet transform (CWT), the cross-wavelet transform (XWT), cross-wavelet coherence (WTC), and multi-scale Granger causality tests were used to investigate the co-movement and nature of the causality between inflation and growth and the interest rate and growth. The results of the wavelet and multi-scale Granger causality tests show that the causal relationship between these variables is not the same across all time horizons; rather, it is unidirectional in the short-run and medium-run but bi-directional in the long-run. Therefore, this study suggests that the central bank should try to maintain inflation and the interest rate at a low level in the short run and medium run instead of putting too much pressure on these variables in the long-run.