Dynamic rain model for linear stochastic environments

To develop modem agriculture, a vision of an integral management is required, where the complexity of interactions between climatic, biological, economical, social and political factors involved in the food production must systematically be analyzed in a context of regional conditions. At the same time, it is necessary to develop the ability to forecast both the climatic variations and their possible impact on society. The minimization of this impact on agriculture through consistent practices adequate to local climates, is not only commendable, but basically necessary, besides, the usefulness of these studies in acquiring a better knowledge of those areas with an inversion risk for agricultural and cattle rising development is high. In this paper a statistical model is used to accomplish the objectives above mentioned. The rainfall variability in several areas of the Tlaxcala State (Mexico) is analyzed with due regard to both inter- and intra-annual relations, considering that the cumulative rainfall, in the former case, follows a logistic curve and in the latter it follows a linear, first order, stochastic process.

Analysis of rainfall pattern and extreme events during southwest monsoon season over Varanasi during 1971-2010

An attempt has been made to detect the pattern of rainfall and examine the trends and variations of extreme events of rainfall over Varanasi (Uttar Pradesh, India) through seasonal, monthly and decadal analysis during southwest monsoon season (June-September) using the daily rainfall data of 40 years period from 1971-2010. The results show that cumulative rainfall during 1971-2010 is overall decreasing in monsoon season as well as in all the months June, July, August and September. In general, the observed rainfall events in all categories (Non rainy day, 0-2.4 mm; Category I, 2.5-64.4; Category II, 64.5 to 124.4; Category III, 124.5 mm or more) have a decreasing trend in all the months and monsoon season over the entire period of study. However, decadal analysis reveals that in general frequency of rainfall events in almost every category is decreasing in recent decade. Different results are seen in August, as cumulative rainfall is decreasing in this month, whereas very heavy and exceptionally heavy rainfall events and their contribution have increased in recent decade as well as over total period.

Evolution of Surface Drainage Network for Spoil Heaps under Simulated Rainfall

Spoil heaps laid from the infrastructure building sites or the mining sites are confoundedly prone to accelerated soil erosion and inducing debris flows on extreme rainfall occasion, thus threatening water quality and personal safety. In present study, the roughness and drainage network evolution of the loess spoil heap (a 33° slope gradient) were investigated via indoor simulation experiment under three rainfall intensities (60, 90, and 120 mm/h). A detailed scan of the slope using laser scanner, topographic analysis based on ArcGIS software, and statistical analyses were the main methods utilized in the study. The results showed that surface roughness increased with cumulative rainfall. For three rainfall intensity treatments, the proneness of shallow landslide under 90 mm/h intensity resulted in the largest roughness. The drainage density and stream frequency of the spoil heap slope both decreased with cumulative rainfall and negatively correlated with surface roughness, which indicated the convergence of the drainage network. Meanwhile, the individual flow paths presented an increasing sinuosity and a decreasing gradient with cumulative rainfall. However, drainage network features varied in a less marked degree during different rainfall intensities, showing comparable fractal dimensions of 1.350–1.454, 1.305–1.459, and 1.292–1.455 for the three rainfall intensities. Evaluating the response of four hydrodynamic characteristics of runoff to the drainage network evolution, stream power was found to be most sensitive. The linearity of the relationships between stream power and drainage density and that between stream sinuosity and gradient were estimated to have R2 between 0.961 and 0.979.

Drought monitoring over India through Normalized Difference Vegetation Index (NDVI)

Normalized Difference Vegetation Index (NDVI) is a simple index to monitor the state of vegetation (stressed/unstressed) which can be derived from satellite data. Hence an attempt is made to find out the vegetation responses to rainfall through NDVI over the study area. Applicability of NDVI in drought monitoring is discussed using the NDVI and rainfall data for the period 1982-2003. The anomaly of NDVI is compared with the percentage departure of rainfall of corresponding years. Results showed a significant relation between the NDVI with the percentage departure of rainfall. The time series plots of averaged NDVI and seasonal rainfall (June-September) are done for NW India (21° N - 31° N, 68° E - 78° E), Central India (22° N - 27° N, 70° E - 77° E) and Peninsular India (16° N - 21° N, 74° E - 79° E) over the period 1982-2003 to analyze changes in vegetation pattern of India during the last two decades. Results indicated a clear linear relationship over NW and Central India. NDVI anomalies and the corresponding cumulative rainfall showed significantly linear correlation of 0.69 over NW India and 0.57 over Central India significant at 1% level but the correlation is found to be insignificant over Peninsular India which was only 0.04. Trend analysis of averaged NDVI over India showed that during last two decades the vegetation status had quite improved over the dry farming tracts of India.

Characteristics of Landslide and Rainfall Areas in Majalengka Regency, West Java Province

Majalengka Regency is one of the districts with a high landslide hazard in West Java Province. They are mostly affected by heavy rainfall or prolonged rain. This study aims to classify landslide events in 2018-2019 based on physical factors consisting of slopes, soil types, lithology, land use, and vegetation density using the K-Means Clustering analysis method. To analyze the characteristics of rainfall that triggered landslides in 2018-2019 using the Thiessen polygon method. The results showed that the clustering of landslide events in 2018-2019 in Majalengka Regency was formed five clusters with the highest rainfall on the D-Day average in cluster 5, which is 49 mm/day. The highest average cumulative rainfall 3 days before the landslide events was in cluster 4, which is 80 mm/day. The highest average cumulative rainfall 5 days before the landslide events was in cluster 3 is 112 mm/day. The highest average cumulative rainfall 10 days before the landslide events was in cluster 1, which is 174 mm/day.

Analysis of persistent macroaggregates on the surface of agricultural soils

In humid regions, the number of macroaggregates on the soil surface could decline because of rainfall disturbance, or increase due to rainfall-activated chemical and biological processes. We took digital images of macroaggregates at the surface of clay and organic soils six times during a 68-d period with 264 mm natural rainfall. Based on the constant or increasing number of surface macroaggregates during the five time intervals, rainfall did not disturb macroaggregates. Macroaggregate persistence was positively correlated with cumulative rainfall (both soils) and soil moisture (organic soil), so we infer that rainfall promoted macroaggregate assemblage through chemical and biological processes.

Characteristics of Rainfall- Groundwater Level Response in Taipei City, Taiwan

<p>The Taipei Basin, Taiwan has been densely populated and highly economically developed in recent decades. Global climate change has led to frequently flooding and drought events in recent years, formulating suitable measures to mitigate climatic disaster has become a crucial issue in this city. The sponge city concept is one of the most important options for disaster mitigation in highly urbanization areas. However, the city is also potentially threatened by soil liquefaction due to its sedimentary geology and increasing groundwater level. High groundwater level might be a key limiting factor in the promotion of sponge city. The aim of this study was to understand the relationship between rainfall and groundwater level and the impacts of cumulative rainfall, depth to groundwater table, and impervious pavement ratio on the rainfall/groundwater level response in study area. The cross-correlation function (CCF) was applied to analyze the correlation between rainfall and groundwater level data obtained from 20 observed wells and nearby rainfall gages during dry and wet seasons from 2012 to 2017. The significance groundwater recharge response can be found in 61% and 37% of the observation wells during the wet and dry seasons, respectively. Compared with the factors such as cumulative rainfall, and depth to groundwater table, the ratio of surface impervious pavement is the primary affecting factor behind the correlation between rainfall and groundwater level response. The analysis results also show the areas with shallow groundwater level, high imperious pavement ratio, and the groundwater level with no significant response to rainfall, are almost overlapped with the middle and high level liquefaction potential areas in this city. Measures such as the application of the sponge city concept to increase infiltration should be carefully reevaluated in this city. The research results can provide a reference for the future development of urban water resources management and disaster mitigation strategies under the challenge of globe climate change.</p>

Correlation Analysis among Meteorological Factors and PM10 Concentrations in the Atmosphere and Rainwater Quality using Multivariate Methods

<p>This study investigates the effect of meteorological factors on the concentration of PM<sub>10</sub> (particulate matter 10) in the atmosphere and evaluates the variation of chemical quality in rainwater using correlation analysis at Daeyeon dong, Busan, South Korea. The real-time concentration of PM<sub>10</sub> in the atmosphere was measured automatically during eleven rainfall events with a custom-built PM<sub>10</sub> sensor node. The 183 rainfall samples were analyzed for chemical quality (pH and electrical conductivity (EC)). The values of meteorological factors (humidity, wind speed, wind direction, temperature, cumulative rainfall, and rainfall intensity) were obtained from an automatic weather system (AWS) in Nam-gu, Busan. Pearson correlation analysis and principal component analysis (PCA) were performed to identify relationships among PM<sub>10</sub> concentrations, meteorological factors, and chemical quality in rainwater. Cumulative rainfall and rainfall intensity had negative correlations with the concentration of PM<sub>10</sub> (r =  −0.52, and −0.35), and other meteorological factors were shown no correlation with the concentration of PM<sub>10</sub>. When the rainfall intensity was strong (> 5 mm/h), the concentration of PM<sub>10 </sub>showed a negative correlation with the cumulative rainfall (r = −0.55) and pH (r = −0.7). However, for the weak rainfall intensity (< 5 mm/h), there was no correlation between the PM<sub>10</sub> concentration with cumulative rainfall and pH. The results of this study provide an understanding of the interaction between PM<sub>10 </sub>concentrations and hydro-meteorological factors and can be used as a decision tool to evaluate the relative magnitude of PM<sub>10</sub> reduction resulting from various rainfall types.</p>

Abnormal Strain Induced by Heavy Rainfall on Borehole Strainmeters Observed in Taiwan

We found some obvious abnormal strain induced by heavy rainfall from borehole strainmeters deployed in Western Taiwan. The strain induced by rainfall can be divided into two parts, one is the quick response for extra loads of rainwater on the ground, and another one is the slow response for rainwater infiltrating into the strata. The quick and slow rainfall responses of areal strain data are analyzed using the technique of recursive digital filtering. Moreover, the rainfall impact functions of the studied stations are calculated using deconvolution. We found, in most cases, the response strain will reach the maximum in half an hour after heavy rainfall, and then show an exponential decay, it might persist more than 200 h depending on the hydrogeological condition around the station. Whereas the river flowing beside the station will help accelerating the runoff dispersion and reducing rainfall decay time in the hill or mountain region. We also compare the results after calibration in term of isotropic and vertical coupling individually. We found that the response strains are smaller in vertical coupling rather than isotropic coupling. The effects of debris avalanches caused by intensive rainfall in the mountain areas can be viewed as two types of rock deformation: generated only under the influence of rainfall and generated by the increased load in the river channels due to rainfall-induced landslides or debris flow. When the cumulative rainfall exceeds a certain threshold, the strain response curves show a noticeable anomaly likely due to the effects of the debris flow events in places prone to landslides.