We study the number of flux vacua for type IIB string theory on an orientifold of the Calabi-Yau expressed as a hypersurface in WCP 4 [1, 1, 2, 2, 6] by evaluating a suitable integral over the complex-structure moduli space as per the conjecture of Douglas and Ashok. We show that away from the singular conifold locus, one gets a power law, and that the (neighborhood) of the conifold locus indeed acts as an attractor in the (complex structure) moduli space. In the process, we evaluate the periods near the conifold locus. We also study (non)supersymmetric solutions near the conifold locus, and show that supersymmetric solutions near the conifold locus do not support fluxes.
Soil temperature plays an important role in understanding hydrological, ecological, meteorological, and land surface processes. However, studies related to soil temperature variability are very scarce in various parts of the world, especially in the Indian Himalayan Region (IHR). Thus, this study aims to analyze the spatio-temporal variability of soil temperature in two nested hillslopes of the lesser Himalaya and to check the efficiency of different machine learning algorithms to estimate soil temperature in the data-scarce region. To accomplish this goal, grassed (GA) and agro-forested (AgF) hillslopes were instrumented with Odyssey water level and decagon soil moisture and temperature sensors. The average soil temperature of the south aspect hillslope (i.e., GA hillslope) was higher than the north aspect hillslope (i.e., AgF hillslope). After analyzing 40 rainfall events from both hillslopes, it was observed that a rainfall duration of greater than 7.5 h or an event with an average rainfall intensity greater than 7.5 mm/h results in more than 2 °C soil temperature drop. Further, a drop in soil temperature less than 1 °C was also observed during very high-intensity rainfall which has a very short event duration. During the rainy season, the soil temperature drop of the GA hillslope is higher than the AgF hillslope as the former one infiltrates more water. This observation indicates the significant correlation between soil moisture rise and soil temperature drop. The potential of four machine learning algorithms was also explored in predicting soil temperature under data-scarce conditions. Among the four machine learning algorithms, an extreme gradient boosting system (XGBoost) performed better for both the hillslopes followed by random forests (RF), multilayer perceptron (MLP), and support vector machine (SVMs). The addition of rainfall to meteorological and meteorological + soil moisture datasets did not improve the models considerably. However, the addition of soil moisture to meteorological parameters improved the model significantly.
Soil moisture dynamics have a significant effect on overland flow generation. Catchment aspect is one of the major controlling factors of overland flow and soil moisture behaviour. A few experimental studies have been carried out in the uneven topography of the Himalayas. This study presents plot‐scale experiments using portable rainfall simulator at an altitude of 1,230 m above mean sea level and modelling of overland flow using observed datasets. Two plots were selected in 2 different aspects of Aglar watershed of Lesser Himalaya; the agro‐forested (AF) plot was positioned at the north aspect whereas the degraded (DE) plot was located at the south aspect of the hillslope. HS flumes and rain gauges were installed to measure the runoff at the outlet of the plot and the rainfall depth during rainfall simulation experiments. Moreover, 10 soil moisture sensors were installed at upslope and downslope locations of both the plots at 5, 15, 25, 35, and 45 cm depth from ground level to capture the soil moisture dynamics. The tests were conducted at intensities of 79.8 and 75 mm/hr in AF plot and 82.2 and 72 mm/hr in the DE plot during Test 1 and Test 2, respectively. The observed data indicate the presence of reinfiltration process only in the AF plot. The high water holding capacity and the presence of reinfiltration process results in less runoff volume in the AF plot compared with the DE plot. The Hortonian overland flow mechanism was found to be the dominant overland flow mechanism as only a few layers of top soil get saturated during all of the rainfall–runoff experiments. The runoff, rainfall, and soil moisture data were subsequently used to calibrate the parameters of HYDRUS‐2D overland flow module to simulate the runoff hydrograph and soil moisture. The components of hydrograph were evaluated in terms of peak discharge, runoff volume and time of concentration, the results were found to be within the satisfactory range. The goodness of fit of simulated hydrographs were more than 0.85 and 0.95 for AF and DE plot, respectively. The model produced satisfactory simulation results of soil moisture for all of the rainfall–runoff experiments. The HYDRUS‐2D overland flow module was found promising to simulate the runoff hydrograph and soil moisture in plot‐scale research.
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