Space-time variability of soil moisture droughts in the Himalayan region
Bibliographic record
Abstract
Abstract. Soil water is a major requirement for biomass production and therefore one of the most important factors for agriculture productivity. As agricultural droughts are related to declining soil moisture, this paper examines soil moisture drought in the transboundary Koshi River basin in the Central Himalayan region. By applying the J2000 hydrological model, daily spatially distributed soil moisture is derived for the entire basin over a 28-year period, 1980–2007. A multi-site and multi-variable approach – streamflow data at one station and evapotranspiration data at three stations – was used for the calibration and validation of the J2000 model. In order to identify drought conditions based on the simulated soil moisture, the Soil Moisture Deficit Index (SMDI) was then calculated, considering the derivation of actual from long-term soil moisture on a weekly timescale. To spatially sub-divide the variations in soil moisture, the river basin is partitioned into three distinct geographical areas, trans-Himalaya, the high and middle mountains, and the plains. Further, the SMDI is aggregated temporally to four seasons – winter, pre-monsoon, monsoon, and post-monsoon – based on wetness and dryness patterns observed in the study area. The results indicate that the J2000 model can simulate the hydrological cycle of the basin with good accuracy. Considerable variation in soil moisture was observed in the three physiographic regions and across the four seasons due to high variation in precipitation and temperature conditions. Droughts have been increasing in frequency in the later years of the period under study, most visibly in the pre-monsoon season. Comparing the SMDI with the standardized precipitation index (SPI) suggests that SMDI can reflect a higher variation of drought conditions than SPI. The novel contribution of this study is that a spatial and temporal variation of SMDI is calculated for the first time in the Central Himalayan region and for the Koshi River basin. This calculation is based on a high-resolution spatial representation of soil moisture, which was simulated using a fully distributed hydrological model. Our results suggest that both the occurrence and severity of droughts have increased in the Koshi River basin over the last three decades, especially in the winter and pre-monsoon seasons. The insights provided into the frequency, spatial coverage, and severity of drought conditions can provide valuable inputs towards an improved management of water resources and greater agricultural productivity in the region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".