Shifts in Irrigation Water Demand and Supply Patterns during Critical Crop Growth Stages under Changing Impacts of Climate and Socio-Economic Dynamics in South Asia
Bibliographic record
Abstract
Changing irrigation water demand (IWD) and supply (IWS) patterns (size and time) under increased climate variability and socio-economic development is significantly effecting the water and food production in the densely populated South Asia (SA). Considering food security paradigm of SA, where rice and wheat are major staple and water-intensive crops, this study aims to investigate the linkages in IWD by crops and IWS by sources (surface and groundwater) using integrated climate and socio-economic projections. The novel aspect of this study is to explore IWD and IWS pattern shifts during critical crop growth stages (CW’s), which is previously less studied with no remarkable research evidence for IGB region. Quantification of shifts in IWD and IWS patterns in future is crucial for long-term integrated water resources and agricultural planning. For this, LPJmL crop-water model is forced with an ensemble of eight state of the art downscaled GCM at 5 arc-min resolution. To assess the combined impacts of climate and socio-economic changes, RCP-SSP framework is used. Our statistical analysis results show that IWD is higher in vegetative stage (CW1) than the reproductive stage (CW2) during both Rabi and Kharif cropping seasons. Water demand is decreasing in future for wheat while increasing for rice. IWS is decreasing substantially from surface while increasing largely from groundwater resources during Rabi. Though, IWS during kharif season is increasing largely from both surface and groundwater resources. There is mismatch in demand and supply as evident from the results suggesting 10 days early wheat planting reduces IWD by 8.0% in F1, 18.7% in F2 and 28.4% in F3 during CW1 with a decrease of 7%, 30 % and 62.56% during F1, F2 and F3 in CW2. Increased IWS with larger contribution from groundwater resources is projected for both crops in future. Water gap between demand and supply during both CW’s in future is increasing for Rabi and Kharif suggesting 10 days early planting of wheat while 20 days delay in kharif planting. Estimation of IWS by sources helped in assessing shifts in percent (%) dependency of water supply from different sources. Moreover, Spatio-temporal mismatch between water demand and supply help exploring geospatially driven water gap trends consequently, highlighting water stress hotspots during CW’s in future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".