Flood hydrometeorological situations associated with monsoon floods on the Par River in Western India

The objective of present work is to understand flood hydrometeorological situations associated with monsoon floods on the Par River, therefore, the analyses of synoptic conditions connected with large floods was carried out. This encompasses analysis of interannual rainfall variability and associated floods, analysis of storm tracts, investigation of normalized accumulated departure from mean (NADM) and evaluation of the relation between El Niño and monsoon rainfall. In order to accomplish above analyses, the annual rainfall data of the Par Basin have been obtained for 118 years from India Meteorological Department (IMD), Pune and Chennai. The annual maximum series (AMS)/stage data were procured for a gauging site namely Nanivahial for 45 years from Irrigation Department of Gujarat State, Ahmedabad. The results indicate that the interannual variability was characterized by increased frequency and magnitude of floods on the Par River primarily after 1930s. Majority of the large floods in the basin were connected with low pressure systems. It is observed that most of the floods were associated with positive departure from mean rainfall in the basin. The NADM graph shows epochal behaviour of high and low rainfall of the basin and floods. The analysis of El Niño and Southern Oscillation indicates that the probability of the occurrence of the floods in the Par Basin is high during the average SST index and majority of the floods in the basin have occurred during above normal conditions of rainfall. The present study can, therefore, prove to be a significant contribution towards the Par-Tapi-Narmada link project of the Government of Gujarat and water divergent projects of the Government of Maharashtra in association with Government of India.

Oceanic Drivers and Empirical Prediction of Interannual Rainfall Variability in Late Summer Over Northeast China

Northeast China (NEC) is located between the subtropical monsoon and temperate-frigid monsoon regions and exhibits two successive rainy seasons with different natures: the northeast cold vortex rainy season in early summer (May–June) and the monsoon rainy season in late summer (July–August). Summer rainfall over NEC (NECR) has a fundamental influence on society, yet its successful seasonal prediction remains a long-term scientific challenge to current dynamical models. The poor NECR prediction skill is partly attributed to the large NECR variability at both the interannual and interdecadal time scales. Here, we focus on the oceanic drivers of the late summer NECR variability and associated physical processes at interannual time scale. Then, we establish an empirical prediction model to predict the interannual variability of summer NECR at one-month lead time (in June). The analysis of observations spanning 40 years (1963–2002) reveals three physically and synergistically influencing predictors of the late summer NECR interannual variability. Above-normal NECR is preceded in the previous spring by (a) warm sea surface temperature (SST) anomalies in the tropical northern Indian Ocean, (b) a positive thermal contrast tendency in the tropical West–East Pacific SST, and (c) a positive tendency of the North Atlantic tripolar SST. These precursors enhance the anomalous low-level anticyclone over the Northwest Pacific and southerly anomalies over NEC in late summer, which are beneficial to enhancing NECR. An empirical prediction model built on these three predictors achieves a forecast temporal correlation coefficient (TCC) skill of 0.72 for 1961–2019, and a 17-year (2003–2019) independent forecast shows a significant TCC skill of 0.70. The skill is substantially higher than that of five state-of-the-art dynamical models and their ensemble mean for 1979–2019 (TCC=0.24). These results suggest that the proposed empirical model is a very meaningful approach for the prediction of NECR, although the dynamical prediction of NECR has considerable room for improvement.

Near-term impact of climate variability on yam rot incidence over a humid tropical region: projections in CORDEX-Africa scenarios

The global population is projected to be enormous by the mid-21st century, whereas, most essential crops being sustained by the rain-fed agriculture are threatened by climate change. Therefore, the study investigated the projected near-future effect of rainfall variability on rot incidence and yam production in humid tropical Nigeria. Production data from the Food and Agriculture Organization and the Nigeria National Bureau of Statistics showed the significant increasing trend in the annual yam output. The field survey conducted in 2018 showed that the maximum percentage of rot incidence occurred in July. Climate Research Unit observational rainfall data from 1979 to 2018 showed the nonsignificant trend in the interannual rainfall variability; however, it showed low variability and a significant decreasing trend in the July rainfall. A pathogenicity test on yam samples confirmed rot by fungi, bacteria and nematodes as virulent pathogens, whereas, the nutritional qualities of the rotted yams were indicated. Monthly rainfall and rot incidence showed positive correlation (r = 0.84, significant at 99% from t-test). The positive characteristic impact values indicated that increase (decrease) in the monthly rainfall corresponds to increase (decrease) in the magnitude of monthly percentage rot incidence. Thus, the significantly decreasing rainfall reduced the quantity of rot incidence and consequently increased the annual yam production for the period. Selected CoOrdinated Regional Downscaling EXperiment-Africa models and the ensemble mean showed a good measure of agreement with observational rainfall in the historical experiments. The efficiencies of the bias-corrected outputs in the representative concentration pathway (RCP) 4.5 and 8.5 indicated improved ‘reasonable’ performances. Bias-corrected projections of the July rainfall showed an increasing trend in both the RCPs, which indicate a potential increase in rot incidence and the consequent decline in annual yam production. The findings are imperative in sustaining the global food supply.

Groundwater depletion will reduce cropping intensity in India

Groundwater depletion is becoming a global threat to food security, yet the ultimate impacts of depletion on agricultural production and the efficacy of available adaptation strategies remain poorly quantified. We use high-resolution satellite and census data from India, the world’s largest consumer of groundwater, to quantify the impacts of groundwater depletion on cropping intensity, a crucial driver of agricultural production. Our results suggest that, given current depletion trends, cropping intensity may decrease by 20% nationwide and by 68% in groundwater-depleted regions. Even if surface irrigation delivery is increased as a supply-side adaptation strategy, which is being widely promoted by the Indian government, cropping intensity will decrease, become more vulnerable to interannual rainfall variability, and become more spatially uneven. We find that groundwater and canal irrigation are not substitutable and that additional adaptation strategies will be necessary to maintain current levels of production in the face of groundwater depletion.

ENSO Teleconnection to Eastern African Summer Rainfall in Global Climate Models: Role of the Tropical Easterly Jet

Global climate models (GCMs) are critical tools for understanding and projecting climate variability and change, yet the performance of these models is notoriously weak over much of tropical Africa. To improve this situation, process-based studies of African climate dynamics and their representation in GCMs are required. Here, we focus on summer rainfall of eastern Africa (SREA), which is crucial to the Ethiopian Highlands and feeds the flow of the Blue Nile River. The SREA region is highly vulnerable to droughts, with El Niño–Southern Oscillation (ENSO) being a leading cause of interannual rainfall variability. Adequate understanding and accurate representation of climate features that influence regional variability is an important but often neglected issue when evaluating models. We perform a process-based evaluation of GCMs, focusing on the upper-troposphere tropical easterly jet (TEJ), which has been hypothesized to link ENSO to SREA. We find that most models have an ENSO–TEJ coupling similar to observed, but the models diverge in their representation of TEJ–SREA coupling. Differences in the latter explain the majority (80%) of variability in ENSO teleconnection simulation across the models. This is higher than the variance explained by rainfall coupling with the Somali jet (44%) and African easterly jet (55%). However, our diagnostics of the leading hypothesized mechanism in the models—variability in divergence in the TEJ exit region—are not consistent across models and suggest that a deeper understanding of the mechanisms of TEJ–precipitation coupling should be a priority for studies of climate variability and change in the region.

Distinct response of gross primary productivity in five terrestrial biomes to precipitation variability

Climate change will impact precipitation variability, potentially accelerating climate-terrestrial carbon feedbacks. However, the response of ecosystems to precipitation variability is difficult to constrain due to myriad physiological and abiotic variables that limit terrestrial productivity. Based on a combination of satellite imagery and a global network of daily precipitation data, we present here a statistical framework to isolate the impact of precipitation variability on the gross primary productivity of five biomes that collectively account for 50% of global land area. The productivity of mesic grasslands and forests decreases by ~28% and ~7% (respectively) in response to more irregular rain within the year, while the sensitivity is halved in response to higher year-to-year variability. Xeric grasslands are similarly impacted by intra-annual rainfall variance, but they show an increase in productivity with higher interannual rainfall variability. Conversely, the productivity of boreal forests increases under higher variability on both timescales. We conclude that projected changes in precipitation variability will have a measurable global impact on the terrestrial carbon sink.

An Overview of the Role of Plant Functional Traits in Tropical Dry Forests

Tropical dry forests (TDFs) are characterized by pronounced seasonality in precipitation, with several months of prolonged drought, 80% of annual precipitation occurring during a four- to six-month rainy season, and high interannual rainfall variability. Surprisingly, there are relatively few studies addressing patterns of functional trait in tropical dry forest (TDF) ecosystems. Functional trait analysis across plant species and the environment is a rapidly developing research field with many possible applications for forest restoration practice. Trait-based ecological research within TDFs will advance our understanding of how these ecosystems interact with and differ from other tropical ecosystems.

Evaluating the shifts in rainfall and pasture-growth variabilities across the pastoral zone of Australia during 1910–2010

Interannual rainfall variability in Australia is a source of risk within agricultural industries. Insights into changes to rainfall and pasture-growth variabilities are essential to inform adaptation strategies for climate risk management within the grazing industry. We investigated shifts in rainfall and pasture-growth variabilities between the periods 1910–1960 and 1961–2010 for the pastoral zone in Australia. Rainfall variability was also assessed for the high-rainfall and wheat–sheep zones. An index of variability was calculated by using gridded rainfall and pasture-growth data for both periods. The percentage change was then calculated as the difference in variation between the two periods. Overall, the variability of annual rainfall has significantly increased (P < 0.01) between the two periods for the pastoral zone. Pastoral regions in the Northern Territory had the greatest increases in pasture-growth variability, with 62–85% of the area affected by a significant increase in variability. Between the periods 1910–1960 and 1961–2010 across the wheat–sheep zone, annual rainfall variability significantly decreased (P < 0.01), with 70% of the area having a negative change, whereas for the high-rainfall zone, the variability did not change significantly. Monitoring ongoing trends in rainfall and pasture-growth variability is important to inform strategic grazing management. Management practices to mitigate the impacts of increased variability in pastoral regions are discussed.