Rainfall Classes Research Articles

Rainfall classification and forecasting based on a novel voting adaptive dynamic optimization algorithm

Environmental issues of rainfall are basic in terms of understanding and management of ecosystems and natural resources. The rainfall patterns significantly affect soil moisture, vegetation growth and biodiversity in the ecosystems. In addition, proper classification of rainfall types helps in the evaluation of the risk of flood, drought, and other extreme weather events’ risk, which immensely affect the ecosystems and human societies. Rainfall classification can be improved by using machine learning and metaheuristic algorithms. In this work, an Adaptive Dynamic Puma Optimizer (AD-PO) algorithm combined with Guided Whale Optimization Algorithm (Guided WOA) introduces a potentially important improvement in rainfall classification approaches. These algorithms are to be combined to enable researchers to comprehend and classify rain events by their specific features, such as intensity, duration, and spatial distribution. A voting ensemble approach within the proposed (AD-PO-Guided WOA) algorithm increases its predictive performance because of the combination of predictions from several classifiers to localize the dominant rainfall class. The presented approach not only makes the classifying of rain faster and more accurate but also strengthens the robustness and trustworthiness of the classification in this regard. Comparison to other optimization algorithms validates the effectiveness of the AD-PO-Guided WOA algorithm in terms of performance metrics with an outstanding 95.99% accuracy. Furthermore, the second scenario is applied for forecasting based on the long short-term memory networks (LSTM) model optimized by the AD-PO-Guided WOA algorithm. The AD-PO-Guided WOA- LSTM algorithm produces rainfall prediction with an MSE of 0.005078. Wilcoxon rank test, descriptive statistics, and sensitivity analysis are applied to help evaluating and improving the quality and validity of the proposed algorithm. This intensive method facilitates rainfall classification and is a base for suggested measures that cut the hazards of extreme weather events on societies.

Use of Spatial Data in Mapping Landslide Prone Areas in Luhurjaya and Surrounding Areas, Lebak Regency, Banten Province

Landslides are a natural process that occurs in a natural landscape on earth which can cause various environmental damage and can cause loss of life. An increase in the risk of landslides can be caused by uncontrolled land conversion due to the increasing population so that land development is necessary. The research was conducted in Luhurjaya and surrounding areas, Cipanas District, Lebak Regency, Banten with an area of 9 x 9 km. The spatial integration method was used to map landslide-prone zones using a scoring and weighting method, using five parameters, namely slope, rainfall, lineament density, rock type, and NDVI. Those five parameters are then overlaid to form a landslide susceptibility map. As a result of analyzing the research area based on these five parameters, it was found that this area has five slope classes, one rainfall class, five lineament density classes, three rock type classes, and four NDVI classes. After all the datas are scored and weighed, it was found that this research area can be classified to three zones, namely low landslide susceptibility zone, medium landslide susceptibility zone, and high landslide susceptibility zone. From the results of this analysis, disaster mitigation should be needed that can affect the community in the Lebak Regency area, Banten Province, namely in the form of providing provisions in the form of knowledge to the community regarding disaster management which is useful for minimizing the risk of the consequences of landslides

Machine learning classification of rainfall forecasts using Austin weather data

The paper examines the machine learning classification of rainfall forecasts using Austin weather data. Rain is a natural phenomenon that is essential for the Earth's water cycle. Rain brings benefits to daily lives and also causes disasters, such as floods, which will endanger lives in addition to causing great losses. Due to this, many methods have been studied and experimented with to find a solution to predict rainfall and prevent tragedies from happening. In this research, the Austin weather dataset is applied to make predictions of rainfall through the implementation of machine learning models. The models used to predict rainfall based on the data set were Extreme Gradient Boosting, Support Vector Machine, Long Short-Term Memory, and Random Forest models. 21 variables with 1319 records were present in the dataset, but the variables used for the modelling were 18 variables from the original data, and 1 variable, “Precipitation Sum,” was converted to the variable “Precipitation Range,” which contained the classes “no rain,” “small rain,” “moderate rain,” and “heavy rain” based on specific value ranges. After training and predicting the data on the models, it was shown that Extreme Gradient Boosting gave the best results of 85.17% accuracy, 83.19% F1 score, 85.17% recall score, and 82.14% precision score, and was able to give predictions on all 4 classes of rainfall. This study and the way to implement machine learning models for rainfall prediction have the potential to provide new insights and methodologies for future studies and pave the way for finding a high-accuracy rainfall prediction method to avoid disaster.

Relevance of climatological information on spatial and temporal variability of Indian Summer monsoon rainfall (ISMR) in recent El Niño years and its impact on four important kharif crops over India

The spatial and temporal variability of Indian Summer Monsoon Rainfall (ISMR) during El Niño years (2002, 2004 & 2009) and its impact on district-wise yield of four important kharif crops viz., rice, maize, pearlmillet and sorghum over India were analysed. The number of districts fall under different rainfall classes are different in normal and El Niño years and found that there is a small shifting of spine curve towards left, which indicate the number of districts under lower rainfall classes increased in El Niño years. As far as National level analysis indicated that the yield of these major kharif crops grown steadily during the study period with higher rate of 43 kg/ha in maize and lower rate of 6.2 kg/ha in sorghum due to technological advancement. Identified high and medium vulnerable districts with respect to above crops in El Niño years based on yield changes > −10 per cent compared to average of previous and succeeding years. 77 and 36 districts identified as highly vulnerable with respect to rice and sorghum, respectively. Whereas 36 and 65 districts in the case of sorghum and maize, respectively. Thus, the results of this study provide an opportunity to policy makers/ researchers/other stakeholders to prepare short and long term contingency plan to cope up with the situation, if there is any forecast of El Niño in future.

Rainfall Modifies the Disturbance Effects on Regulating Ecosystem Services in Tropical Forests of Bangladesh

Tropical forests are critical for ecosystem functioning and are the major provider of global ecosystem services. The combined effects of climate change and anthropogenic disturbances in the tropics, however, have caused a mass decline in major ecosystem services in recent decades. Nevertheless, most previous studies focused entirely on these disturbances’ effects on forest ecosystem services without taking into consideration the combined effects of climate change and disturbances on key ecosystem services’ indicators. This is more pronounced in Bangladesh, which is one of the countries most adversely affected by global climate change. We investigated anthropogenic disturbances’ effects on regulating ecosystem services under different rainfall regimes in Khadimnagar National Park (KNP) and Satchari National Park (SNP), two major forested, protected areas in Northeast Bangladesh. We considered twelve indicators for five aboveground ecosystem services (i.e., primary production, carbon storage, biodiversity, wildlife habitat, and timber production) and four indicators for four belowground ecosystem services (i.e., organic carbon storage, soil aggregate stability, soil physical support, and soil water regulation) in our study. The effects of disturbances on the indicators of four aboveground ecosystem services (i.e., primary production, tree carbon storage, biodiversity conservation, and timber production) and one belowground ecosystem service (soil physical support) differed in terms of rainfall regimes. Disturbances negatively influenced indicators of primary production (p = 0.03, r = −0.28), tree carbon storage (p = 0.02, r = −0.29), and timber production (p = 0.002, r = −0.39), and positively impacted soil organic carbon storage (p = 0.04, r = 0.27) only under a medium-rainfall regime. Interestingly, a significant positive impact of disturbances on soil physical support (p = 0.001, r = 0.39) was observed under heavy rainfall in our study. In addition, disturbances were significantly associated with biodiversity conservation (p < 0.05) and wildlife habitat quality (p < 0.05) under both rainfall classes. Our findings provide critical insights into the synergistic role of anthropogenic perturbations in regulating ecosystem services with rainfall pattern as a key climatic parameter, which can be useful in restoring large tracts of degraded tropical forests in the United Nations (UN) Decade on Ecosystem Restoration.

Error Analysis of Rainfall Inversion Based on Commercial Microwave Links With A–R Relationship Considering the Rainfall Features

Rainfall inversion based on commercial microwave links (CMLs) has been extensively studied and experimented. However, as necessary part of inversion, the error caused by the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A - R</i> relationship is lack of systematic evaluation. Based on the measured raindrop size distribution (RSD) data recorded by a disdrometer, the theoretical rain-induced attenuation and rainrate are calculated, and on this basis, the error between the inversed rainrate based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A - R</i> relationship from ITU-R recommendation and the real rainrate is compared under different rainfall types, rainfall classes and spatially heterogeneous rain cell. Firstly, the errors of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A - R</i> relationship under different rainfall classes show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A - R</i> relationship can effectively invert rainrate between 18 and 26 GHz for all classes, while the error increases significantly outside this frequency range, especially for heavy rainfall. In addition, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A - R</i> relationship performance of different rainfall types is evaluated. There is a significant difference in the unbiasedness of convective rainfall and stratiform rainfall, which are positive and negative bias at high frequency (greater than 50 GHz), respectively. Furthermore, aiming at the inhomogeneity of rainfall spatial distribution, the rainrate distribution and the corresponding path rain-induced attenuation are simulated based on an exponential model, and the influence of spatial location difference on the accuracy of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A - R</i> relationship is deeply analyzed. Finally, the error source and influence of localized <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A - R</i> relationship, integration time and link length are discussed.

RETRACTED: Geospatial assessment of potential land suitability for oil palm (Elaeis guineensis Jacq) cultivation in the western parts of Ethiopia

The editors have decided to retract this article for the following reasons: The authors classified the land suitability related to rainfall as follows: “Previous study by Abubakar et al. (2023b) classified rainfall into four classes: (1329–1420mm), (1420–1490mm), (1490–1560mm) and (&gt;1560mm) and categorizes as highly suitable, moderately suitable, marginally suitable and not suitable, respectively for oil palm tree cultivation”. In oil palm literature (including the cited article by Abubakar et al.), however, the order is reversed. Ideally, oil palm requires &gt;2000mm of well-distributed rainfall per year. The lower rainfall class is still not entirely unsuitable, but there will be a very strong seasonality in production and yields will not be large. As this misinterpretation affected their entire analysis, the authors have been asked to revise their article accordingly. In the meantime, an expression of concern was published. In the two successive revised versions received, the authors changed only a few paragraphs (about rainfall...), but they did not re-run their analysis accordingly, nor did they change the suitability assessment at all, leading to the same conclusion as before despite reversed precipitation assumptions.

Performance assessment of SM2RAIN-NWF using ASCAT soil moisture via supervised land cover-soil-climate classification

The SM2RAIN algorithm developed a simple analytical relationship by inverting the soil-water equation to estimate rainfall through the knowledge of soil moisture. The recently developed SM2RAIN-NWF algorithm offers an improvement in estimating rainfall by integrating the SM2RAIN algorithm and the net water flux (NWF) model. The Advanced Scatterometer (ASCAT) soil moisture products were used to estimate rainfall and evaluate the reliability of the SM2RAIN-NWF algorithm compared to the SM2RAIN on a national scale. Besides, the impact of Land cover-Soil texture-Climate (LSC) characteristics and the intensity of rainfall (four classes of intensity) on the performance of algorithms were discussed. Five performance metrics, including Correlation Coefficient (R), Kling–Gupta (KGE), Root Mean Square Error (RMSE), False Alarm Ration (FAR), and Probability of Detection (POD) were used to validate the estimated cumulative 5-, 14-, and 30-day rainfall. Furthermore, the effect of evapotranspiration (ET) and drainage terms were investigated in the performance of rainfall estimation through the SM2RAIN-NWF algorithm for the first time on a national scale. Results showed the rainfall estimations through the SM2RAIN-NWF algorithm improved approximately up to 7.5% in each accumulation (e.g. rainfall aggregation intervals (AGGR) 5 to 14 and 14 to 30) based on R and KGE indices. In addition, the SM2RAIN-NWF improved rainfall estimations up to 50% based on the KGE index in the southern half of Iran (arid and semi-arid climate) compared to the SM2RAIN estimates. The comprehensive evaluation and uncertainty analysis of rainfall estimations under the supervised classification of 11 LSC and 4 rainfall classes also showed the calibration of the SM2RAIN-NWF was highly affected by environmental and climatic circumstances. Uncertainty analysis showed the SM2RAIN-NWF algorithm can estimate rainfall more consistently in the five LSC classes namely 1) barren-clay loam-arid-desert, 2) barren-loam-arid-steppe, 3) barren-clay loam-arid-steppe, 4) urban-clay loam-arid-desert, and 5) urban-loam-arid-steppe. Similarly, estimating rainfall in the region with precipitation under 267 mm/year can be retrieved more reliably through the SM2RAIN-NWF algorithm. Results obtained from the ET analysis revealed an insignificant (<4%) effect of this term in improving the performance of the SM2RAIN-NWF algorithm. Also, the NWF equation confirmed the paramount role of the drainage term in enhancing the accuracy of rainfall estimates compared to the SM2RAIN method.

Interpolation of rainfall observations during extreme rainfall events in complex mountainous terrain

AbstractThe representation of rainfall in space is important for hydrological modelling. Accurate estimation of rainfall is particularly challenging in mountainous regions where observations are often sparse relative to the spatial variability of rainfall. In these regions, orographic processes lead to complex patterns of rainfall enhancement and rain shadow depletion. This study testsNatural Neighbour Interpolation (NNI),ordinary kriging (OK)andordinary cokriging (CK), to determine ifCKimproves rainfall interpolation during three extreme rainfall events. Three different elevation indices were considered as secondary variables forCK. Preliminary analysis using long‐term annual average rainfall totals, including additional high elevation rainfall observations, showed thatCKwith an effective elevation index (a directionally smoothed elevation, corrected for degree of ‘orographic processing’ and shifted to account for ‘wind‐drift’ of rainfall) as a secondary variable performed better thanNNIandOKwith an overall improvement of around 40%. Using rainfall totals for long‐term wind direction and wind speed rainfall classes,CKperformance was variable but provided an improvement of approximately 15% for wind direction classeswithoutan easterly wind component. For 15‐min timesteps during extreme rainfall events, there were comparatively small differences (cross‐validation usingRMSE) between interpolation methods, partly attributed to having only relatively low elevation rainfall observations, providing weak constraint. Using cross‐validation andmean biasdid, however, show an improvement for both high and low elevation observation classes. Importantly, cross‐variogram estimation provided differing cross‐validation results when estimated for different rainfall accumulation periods: 15‐min, hourly, daily and long‐term. Variograms and cross variograms estimated at a 15‐min timestep frequency were robust for many timesteps, but were difficult to fit automatically for others. Variograms estimated from longer periods were more reliably estimated, but tended to have lower variance and cross‐variance and longer correlation ranges producing a smoother interpolated rainfall field. Given the weak cross‐validation constraint, care must be taken in identifying the most appropriate method and variogram estimation period.

Prediction of Rainfall Classification of Java Island with ANN-Feature Expansion and Ordinary Kriging

Precipitation is one of the most important climatic variables in many aspects of our daily lives. High rainfall intensity can cause floods, landslides, and other natural disasters. Therefore, rainfall prediction is important for predicting natural disasters, assisting farmers in production decisions, and crop harvesting. In this research, a system is built to create a rainfall prediction map using a machine learning approach and spatial interpolation algorithms in Java, Indonesia. In the field of weather prediction, the artificial neural network approach is a popular machine learning method. The artificial neural network (ANN) method is a method that has the advantage of studying connections in the previously unknown hidden layer between input data and output data through training procedures. By using the ANN method, historical weather and climate data can be applied to create a classification model and predict rainfall classes. The classification of data is determined based on the attributes of historical weather and climate data, namely temperature, humidity, air pressure, evaporation, sunlight, and the level of rainfall in the time range per day and month. From the results of the ANN modeling, it was found that the 5C month model with an accuracy value of 89% as the best monthly ANN model, and the 6C day model with an accuracy value of 81% as the best daily ANN model. After going through ANN modeling, there is a spatial interpolation algorithm that is often used to estimate rainfall, namely Ordinary Kriging. The Ordinary Kriging approach is used to reduce the estimated variance and estimate the rainfall value in the case study area. After going through Ordinary Kriging modeling, a rainfall prediction map for the next six months and seven days is made based on the coordinates as a result of the research. The results of this research are rainfall prediction maps for the next six months and the next seven days on Java Island.

Realistic Prognostic Modeling of Specific Attenuation due to Rain at Microwave Frequency for Tropical Climate Region

Absorption and scattering of propagated microwave radio signals by atmospheric variables, particularly rainfall, remained a major cause of propagation attenuation losses and service quality degradation over terrestrial communication links. The International Telecommunications Union Radio (ITU-R) reports and other related works in the literature provided information on attenuation due to rain and microwave propagation data. Such propagation attenuation information in the tropical region of Nigeria is destitute, especially at lower radio waves transmission frequencies. Therefore, this study addresses this problem by employing 12-year rainfall datasets to conduct realistic prognostic modeling of rain rate intensity levels. A classification of the rainfall data into three subgroups based on the depth of rainfall in the region is presented. Additionally, an in-depth estimation of specific rain attenuation intensities based on the 12-year rainfall data at 3.5 GHz is demonstrated. On average, the three rainfall classes produced rain rates of about 29.27 mm/hr, 73.71 mm/hr, and 105.39 mm/hr. The respective attenuation values are 0.89 dB, 1.71 dB, and 2.13 dB for the vertical polarisation and 1.09 dB, 1.20 dB, and 2.78 dB for the horizontal polarisation at 0.01% time percentage computation. Generally, results indicate that higher rain attenuation of 12% is observed for the horizontal polarisation compared to the vertical polarisation. These results can provide valuable first-hand information for microwave radio frequency planning in making appropriate decisions on attenuation levels due to different rainfall depths, especially for lower frequency arrays.

Identifikasi Potensi Longsor Berbasis Sistem Informasi Geografis di Kecamatan Sembalun Kabupaten Lombok Timur

This study aims to assess and map the spatial potential of landslides based on a geographic information system in Sembalun District. Field surveys were carried out from May to July 2020 on a land area of ​​18,318.45 Ha. Field observations include land characteristics, climatic characteristics (rainfall), soil properties in various types of land use. (shrubs, plantations, open land, dry land agriculture, residential and industrial areas, primary and secondary dryland forest. Soil sampling at a depth of 0-15 cm is carried out in a composite manner at 16 sampling points to determine soil properties, namely permeability, texture, structure and status of soil organic matter Data processing (slope, rainfall, geology, land use and erodibility) for the mapping process in the form ofshapefileand generate landslide prone maps from the overlay process. Landslide potential estimation using the method specified by Directorate of Volcanology and Geological Disaster Mitigation/DVMBG (2004). Score = (30% x rainfall class factor) + (20% x geology) + (20% x Erodibility class factor) + (15% x land use) + (15% x slope class factor). The results showed that around 32% (5,901.53 Ha) of the area of ​​Sembalun Subdistrict were in a high vulnerability status to landslide potential, 49% (8,911.39 Ha) were at moderate vulnerability status and 19% (3,505.71 Ha) had low vulnerability. The variables of erodibility and slope are the variables that show the most significant contribution to the potential for landslides in the area.

Characterizing rainfall occurrence in India: Natural variability and recent trends

A deficit in rainy days can affect crop production and escalate groundwater abstraction leading to groundwater depletion, thus may threaten food and water security in the Indian subcontinent. The available researches on rainfall occurrence in India represent inconsistency in several fundamental considerations (spatial scale of analysis, the definition of rainfall classes, methods, dataset, study period, etc.) and lack of studies for non-monsoon seasons over large parts of the subcontinent. Here we attempt to deal with these inconsistencies by examining space–time patterns in seasonal count of rainfall events and their count in different rainfall classes during 1951–2015 for all meteorological seasons of India using high resolution (0.25°) gridded daily rainfall dataset. The results show a widespread decline in monsoon rainy days over major crop-growing regions including north-central, central, and northeast India. A reduction in rainy days is also observed over parts of central India in premonsoon; over north-central and northeast India in postmonsoon; and over peninsular India in winter. These trends are mainly observed in events with rainfall <95th percentile (light to heavy rainfall events) in all the seasons. Occurrence of extreme events (above 95th and 99th percentile) exhibits contrasting trends over different zones with mainly an increase over central and south-central India and decline over central-north India. However, trends in extremes are highly dispersed in space and are conclusive only over a few regions. The observed trends substantially deviate from the patterns reported in past studies for different regions. Rainfall occurrence in different meteorological zones also represents strong interannual and decadal variability, however, the observed trends have emerged beyond the inherent natural variability. The present findings provide an essential foundation for several future explorations with improved understanding of change in rainfall frequency and its possible impacts. It would also assist in developing a comprehensive framework for year-wide management of water resources and agriculture, considering all crop seasons.

Spatial variance analysis of the actual daily mean rainfall in Riyadh for the period 1970 - 2017

هدفت هذه الدراسة إلى حساب المتوسط الفعلي اليومي للأمطار بمنطقة الرياض وتحليل خصائصه الإحصائية بمحطات المجمعة (R101-460) الرياض المصانع (R001-452) وضرماء (R112-470) والجبيلة (R106-464) وسدوس (R102-461) ورماح (HU103-954) وشقراء (R006-457) وحوطة سدير (R005-456) والحريق (R104-453) وحريملاء (R103-462) بتطبيق العلاقة لنسبية بين كميات الأمطار السنوية ومجموع عدد الأيام الممطرة بالسنة. ولتحقيق أهداف الدراسة اعتمدت هذه الدراسة على المنهج الاستقرائي الذي يتناول تحليل الخصائص الإحصائية لتوزيع الأمطار بتطبيق مقاييس التشتت (المتوسط، الانحراف المعياري، الخطأ المعياري للمتوسط، الخطأ المعياري للانحراف المعياري) من جهة وعلى حساب النسبة بين بين كميات الأمطار السنوية ومجموع عدد الأيام الممطرة بالسنة. كما يعتمد تحليل التوزيع المكاني على استخدام الفئات المتجانسة لكميات الأمطار السنوية ولعدد الأيام الممطرة ولمتوسط الأمطار الفعلي اليومي. وتوصلت هذه الدراسة إلى تحليل الخصائص الإحصائية للأمطار السنوية ولعدد الأيام الممطرة للفترة 1970-2017 بمجموع 10 محطات مطرية واستخدامها في حساب المتوسط الأمطار اليومي الفعلي وتحليل تباين توزيعه المكاني والزماني. كما تم تحديد أفضل نموذج لعلاقة الارتباط (النموذج التكعيبي) بين كميات الأمطار السنوية وعدد الأيام الممطرة. وعلى ضوء النتائج المذكورة قدمت هذه الدراسة مجموعة من التوصيات لتوسيع مجال هذه الدراسة على مختلف مناطق المملكة العربية السعودية ومقارنة نتائجها بنتائج دراسات مماثلة بمختلف المناطق الجافة وشبه الجافة وتوظيفها في الاستدلال على تأثيرات التغير المناخي وتوظيف المتوسط الفعلي اليومي للأمطار في تقدير الميزانية المائية بمنطقة الرياض وفي تقدير الموارد المائية السطحية خلال الأيام الممطرة من السنة للاستفادة منها في مختلف المجالات وفي تصميم قاعدة بيانات مناخية وهيدرولوجية لمختلف مناطق المملكة العربية لتوظيفها في تنفيذ مشاريع الري ودرء مخاطر السيول.

A meta‐analysis of tropical land‐use change effects on the soil microbiome: Emerging patterns and knowledge gaps

AbstractModifications to vegetation and soil due to changes in land use have the potential to alter the soil microbiome, with consequences for carbon and nutrient cycling. Despite the important function of soil microorganisms, little is known about their response to land‐use change, especially in tropical regions where current rates of land conversion are greatest. The aim of this meta‐analysis was to examine how land‐use change influences soil microbial properties in tropical ecosystems and to identify current trends and knowledge gaps in the literature. We identified 83 published paired studies that reported data on microbial biomass, abundance, composition, and enzyme activity under representative land‐use changes in the tropics. We calculated response ratios for studies that compared the following: reference forests to (a) agriculture, (b) pastures, (c) plantations, and (d) secondary forests. Soil microbial biomass decreased with forest conversion to agriculture and plantations. Microbial biomass response to land‐use change depended on rainfall classes, although this was the only microbial variable which had sufficient data to test for a rainfall effect. Microbial abundance and enzyme activity showed variable results depending on the type of forest conversion. Microbial diversity and richness did not show any pattern with forest conversion or recovery to secondary forests. Published studies were not representative of the range of biophysical conditions observed in the tropics. Sites in moist regions in the American tropics were overrepresented. To better predict how land‐use change affects the soil microbiome and its contribution to nutrient cycling, research should reflect observed environmental variation in the tropics.Abstract in Spanish is available with online material.

Spatio-temporal analysis of rainfall pattern in the Western Ghats region of India

This paper investigates the rainfall pattern and its distribution, which is one of the key drivers for the water availability in the Western Ghats region of India. The daily precipitation data from 1901 to 2014 were investigated to understand the rainfall pattern and its variability. The study area has been divided as southern, coastal, central, and northern regions and rainfall amount into 0.2–10 mm, 10–20 mm, and > 50 mm classes. The trends in the annual rainfall, number of rainy days, and various classes within these regions have been investigated. The mean annual rainfall varies between 4000 mm in the coastal areas and greater than 6000 mm at the mountain crest. The Mann–Kendall test indicates significant decrease of annual rainfall, at 5% significance level in central and coastal region. On Contrary, the coastal region recorded significant decrease in the number of rainy days at 5% significance level. An increasing trend in the number of rainy days has been observed in the northern region. No significant trends have been detected in the southern region. A decreasing trend has been detected in the number of rainy days in the rainfall class with 10–20 mm in coastal region. Similarly a significant decrease of rainy days was observed in > 50 mm rainfall class both in the southern and northern regions. The change point probability and homogeneity test indicates that there is a significant increase in rainfall in few stations under the northern region and similarly decrease in the rainfall in central region during the decade of 1970 and normally the rainfall has been homogeneous in the study area. From the precipitation concentration analysis found that Mysore, Kodagu, and adjoining areas are influenced by the bi-modal rainfall, receiving the rainfall during both south–west and north–east monsoons. This analysis helps to understand the spatial variability of rainfall in the Western Ghats of India, which depict mixed trends in the quantum of rainfall and decreasing trend of rainy days across various rainfall classes. These changes detected in the historical rainfall, both in its occurrence and quantum will have significant bearing on the water availability scenario in the Western Ghats region.

Spatial distribution, controlling factors and failure mechanisms of the large-scale landslides in the urban area of Azazga city (northern Algeria)

The city of Azazga is facing extensive and damaging landslide hazard. The aim of this study was to investigate the inventory mapping, the deformation characteristics, the controlling factors and the failure mechanisms of the landslides in the urban area of Azazga based on field surveys, aerial photographs/satellite images interpretation and the exploitation of available data including rainfall database, boreholes, piezometers, inclinometers and laboratory tests. The prepared landslide inventory map indicates that the unstable urban perimeter covers an area of 281.6 ha which represents about 31% of the urban area. Analysis of the inventory data highlighted the action of two types of conditioning factors: (i) susceptibility factors represented by the presence of clay, marl deposits with low mechanical resistance characteristics, the presence of shallow aquifers, the morphology and steep slope, the tectonic features network and a dense hydrographic network; (ii) the triggering factors corresponds to the high intensity rainfall and the uncontrolled human activity. Statistical analysis of the relationship between the landslide occurrence and the landslide-conditioning factors shows that the highest density of landslides occur in quaternary scree and cretaceous clay flysch located on slopes ranging between 5° to 20°. The statistical results indicate that the highest concentration of landslides occurred in the highest rainfall classes ranging between 950 to 1050 mm. Additionally, the results showed the important role of the fluvial incision with more of the landslides occur at a distance of 50 m from the nearest rivers. These data also indicated that the high density of the landslides is concentrated in the farming and urban zones. The inclinometer measurement results reveal a deep rupture surfaces between 4 and 28 m located at the scree–flysch bedrock interface. According to the mechanism modes, landslides are controlled by the following: (i) the dip of the flysch formation layers, the schistosity planes and fractures downward slope direction; and (iii) the interface contact between the quaternary scree and flysch substratum. This work presents an exhaustive understanding of the causes, deformation characteristics and failure mechanisms of the large-scale landslides as a useful information for landslide hazard and risk mitigation.

Spatial model of control of fire prone peatlands based on rainfall data (case study: Kepulauaan Meranti Regency, Riau Province)

Peatland fires in Indonesia occur every year, and the impacts are also known to cause many costs. Fires in Indonesia are 99% caused by humans. Fires can occur when the components of the fire are available, namely fuel, oxygen and heat sources. Triangel fire components are in optimal conditions when the land is dry. Dry conditions are mainly caused by weather, and are reinforced by human activities. The main influence of weather is in the form of rainfall, while the human influence is in the form of canalization. Low rainfall causes dry land and plants. Canalization has the potential to cause the surrounding land to become dry. The canalization is made so that the plant roots are in the aerobic zone, so that the plants can grow well. The purpose of this study is to find out how much rainfall there are still fires and their distribution. By knowing this information, it is hoped that efforts to control forest and land fires can be carried out more effectively. The method used is spatial analysis. Materials used: 2005-2016 rainfall data, 2005-2016 hotspots data, 1: 50,000 scale RBI map. The results of this study indicate that thde chance of occurrence of fires on the low rainfall class (0 - 100 mm) has 70,5 % higher than chance of occurrence of fires than medium raifall class (100 - 300 mm) of 24,5 %.

Clustering of Rainfall Types Using Micro Rain Radar and Laser Disdrometer Observations in the Tropical Andes

Lack of rainfall information at high temporal resolution in areas with a complex topography as the Tropical Andes is one of the main obstacles to study its rainfall dynamics. Furthermore, rainfall types (e.g., stratiform, convective) are usually defined by using thresholds of some rainfall characteristics such as intensity and velocity. However, these thresholds highly depend on the local climate and the study area. In consequence, these thresholds are a constraining factor for the rainfall class definitions because they cannot be generalized. Thus, this study aims to analyze rainfall-event types by using a data-driven clustering approach based on the k-means algorithm that allows accounting for the similarities of rainfall characteristics of each rainfall type. It was carried out using three years of data retrieved from a vertically pointing Micro Rain Radar (MRR) and a laser disdrometer. The results show two main rainfall types (convective and stratiform) in the area which highly differ in their rainfall features. In addition, a mixed type was found as a subgroup of the stratiform type. The stratiform type was found more frequently throughout the year. Furthermore, rainfall events of short duration (less than 70 min) were prevalent in the study area. This study will contribute to analyze the rainfall formation processes and the vertical profile.

Rainfall forecast based on GPS PWV together with meteorological parameters using neural network models

In recent years, it has been found that the Precipitable Water Vapor (PWV) time series derived from ground-based GPS measurements can be used in to forecast precipitation in different regions. However, it is inevitable to consider the impact of several meteorological parameters such as temperature, pressure, relative humidity, water vapor pressure, total cloud cover and day of year (doy) besides PWV on rainfall prediction. In order to predict the precipitation at Tehran station, two types of Artificial Neural Network (ANN), including Multi-Layer Perceptron (MLP) and Nonlinear Auto-Regressive with Exogenous Inputs (NARX) were employed based on mentioned parameters. At first, these neural networks were trained under various circumstances (i.e. with and without PWV) with the help of collocated meteorological and GPS data from years 2007–2010 and then the networks were utilized to forecast different intensities of precipitation over 2011. The results showed that deletion of PWV values from input data will reduce the precision of MLP predictions for the range of rainfalls less than 6 mm. For the range of precipitation above 3 mm, the use of PWV has a positive impact on the output of the NARX model. In addition, the effect of the length of training data on the performance of the proposed models was investigated in terms of Mean Bias Error (MBE), Root Mean Square Error (RMSE) and False Alarm Ratio (FAR) statistics. The best results in the study region were achieved from 4years trained MLP and 2years trained NARX models. Comparing the outputs of the MLP and NARX models with the Global Forecasting System (GFS) 6h forecasts as a standard meteorological forecast showed that the efficiency of the NARX model is higher than the MLP and GFS, especially in moderate and strong rainfall classes. Also, seasonal comparison of these errors showed that both models underestimate rainfall values higher than 3 mm. In almost all seasons, the underestimation of the NARX model was less than MLP. With all pros and cons, the NARX model showed greater performance than MLP for both non-rainfall and rainfall events.