Rainfall Distribution Patterns Research Articles

Yield Enhancement of Pigeonpea [Cajanus cajan (L.) Millsp.] through Drip Irrigation and Fertigation Management

Background: Pigeonpea an indeterminate pulse crop with profuse branching responds well to both irrigation and fertilizer. Erratic rainfall distribution pattern exposes this crop to dry spell during its vegetative stage and terminal drought at reproductive stage and the poor crop nutrition further results in to low yield. Under such circumstances it is very difficult to sustain the yield of pigeonpea. Agronomic practices like precise and timely application of drip irrigation along with judicious use of nutrients play a vital role to boost the yield of any crop. Thus the attempts were made to explore the yield potential of pigeonpea under drip irrigation and fertigation management. Methods: The present study was conducted at the experimental farm of AICRP on Irrigation Water Management, VNMKV, Parbhani (MS) during kharif 2018 and 2019. The experiment was laid out in split plot design with main plots comprising of four drip irrigation levels viz. 0.6, 0.8, 1.0 ETc (crop evapotranspiration) and conventional method and sub plots were allotted to four fertigation levels viz. control (no fertilizer), 80% RDF, 100% RDF (25: 50: 25 NPK kg ha-1) and 120% RDF. Result: Drip irrigation at 0.8 ETc recorded higher seed yield, harvest index, water use efficiency, nutrient use efficiency and net returns of pigeonpea followed by 1.0 ETc except in case of water use efficiency. As regards to fertigation studies, higher values of seed yield, harvest index and water use efficiency were recorded with drip fertigation @ 25:50:25 NPK kg ha-1 closely followed by 20:40:20 NPK kg ha-1. However higher nutrient use efficiency and net returns were obtained in drip fertigation @ 20:40:20 NPK kg ha-1.

Improved Rainfall Data in the Philippines through Concurrent Use of GPM IMERG and Ground-Based Measurements

The availability of accurate and reliable rainfall data that are applicable to various phenomenological, climatological, and modeling studies is important, especially in the Philippines, which is considered to be highly vulnerable to natural hazards and a changing climate. The presented strategy involved constructing a dataset consisting of synoptic data, automatic rain gauge (ARG) measurements, and satellite data that are co-registered, consistent, and formatted in the same manner. Although sparse in number, the synoptic stations provide the most accurate rainfall information and were used as the baseline for creating the dataset. The ARGs that are within a distance of 1 km to the synoptic stations were used to determine the correction factors needed to make the synoptic and ARG data consistent. Subsequently, the corrected ARGs were used to make the satellite IMERG data consistent with both ARG and synoptic data. In case of the latter, only IMERG pixels with at least 10 ARGs within the relatively large footprint of the satellite sensor were used in estimating the required correction parameters derived from a combination of a power transform and linear regression correction techniques. The final results show good agreement of synoptic and corrected ARG data with correlation coefficients of 0.94 and 0.97 for the 10 day and monthly data, respectively, and improvement in the linear regression slope from 0.67 to 0.90 for 10 day data, and 0.70 to 0.94 for monthly data. In addition, the corrected ARG data agree well with the corrected IMERG data, with correlation coefficients of 0.88 and 0.93 for the 10 day and monthly data, respectively, and an improvement in slope from 0.66 to 0.87 for 10 day data, and 0.74 to 0.99 for monthly data. The merit of using a combined dataset is illustrated through comparative analyses of the IMERG data and spatially interpolated synoptic and ARG data. The results show general agreements in spatial patterns of rainfall across the datasets, especially in areas where in situ measurements are recorded. The observed discrepancy when ground data is limited emphasizes the need for satellite IMERG data to obtain the true spatial patterns of rainfall distribution.

Urban Flood Management through Urban Land Use Optimization Using LID Techniques, City of Addis Ababa, Ethiopia

In recent years, many urban areas in Ethiopia have experienced frequent flood events as a result of climate change and urban sprawl. Unplanned and unsustainable poor urban storm water management strategies will aggravate the impact and frequency of flood occurrence. In this study, impacts of urbanization and climate change on generated flood magnitude are analyzed using the urban hydrological model of Storm Water Management Model (SWMM) and Low Impact Development (LID) sustainable land use optimization techniques. Three rainfall distribution patterns (TS1, TS2 and TS3) in combination with rainfall duration periods of 10, 30 and 60 min and a pessimistic climate change scenario of RCP 4.5 compared to RCP 8.5 are used for the analysis purpose for selected infiltration and storage LID techniques (Bio-Retention Cell, Infiltration Trench and Rain Barrel). The study results showed that combined LID techniques have a significant impact on urban flood reduction of up to 75%. This significant amount of flood reduction is greater than the amount of excess flood magnitude which occurred as a result of climate change using the most pessimistic climate change scenario. The study results also confirmed that rainfall patterns have a significant impact on peak discharge for shorter rainfall durations. This study highly recommends using cost effective, easy and environmental adaptive and sustainable LID techniques for urban flood management in addition to existing drainage structures.

The FLOOD DISASTER MANAGEMENT BASED ON EXTREME TROPICAL RAINFALL IN DECADES OF CLIMATE CHANGE IN INDONESIA

Indonesia's climate classification is divided into three rainfall patterns. The three patterns are Seasonal Pattern, Equatorial Pattern, and Local Pattern (Anti Seasonal). Flood Disaster Management based on extreme rainfall is very much needed, as the analysis was taken as a case study on January 22, 2019, a flood disaster occurred in South Sulawesi. The flood event indicated that there was heavy rain that flushed the South Sulawesi region for several days, which is classified as monsoonal rainfall. This study aims to analyze the characteristics of heavy rain with atmospheric anomalies during these events by calculating rainfall intensity to determine future flooding patterns and using the WRF model to analyze cloud distribution patterns and rainfall distribution. The method used in this research is Mononobe and Weather Research and Forecasting (WRF) using the Fabric Fritsch cumulus parameterization scheme. The analysis showed that the intensity and duration of rainfall of 2, 5 10, 25, and 50 years were obtained from the Mononobe model, as well as from the atmospheric dynamics data, there was rain for 3 consecutive days caused by cumulonimbus type rain clouds. Based on the WRF model, it can be seen that the CAPE value before the onset of rain is quite significant, thus supporting the growth of rain clouds as an important variable in flood disaster management in the South Sulawesi region in particular and the tropical zone in. general.
 Motivation/Background: Indonesia is included in a tropical climate where extreme rainfall is important to analyze. The majority of flood disasters in the tropics occur in decades of extreme atmosphere, this is an important reason in this study.
 Method: The Mononobe method can be used to calculate the distribution pattern of rainfall intensity throughout 2, 5, 10, 25, and 50 years, as a prediction of future rainfall intensity patterns. The WRF model is used to calculate the cloud distribution pattern and the spatial distribution of rainfall.
 Results: The results of this study obtained patterns of rainfall intensity and duration of 2, 5, 10, 25, and 50 years from the Mononobe model, as well as from the atmospheric dynamics data, there was rain for 3 consecutive days caused by cumulonimbus rain clouds. The pattern of cloud distribution and rainfall at the time of the incident at the WRF model research location.
 Conclusions: Analysis of the distribution pattern of rainfall intensity for the periods of 2, 5, 10, 25, and 50 years, as well as the distribution pattern of clouds and rainfall, is very necessary for disaster identification, especially hydrometeorology. This is very important as a variable in flood disaster management, especially in the tropics

Study of Rainfall and Water Discharge Spatial Variability Using Exploratory Spatial Data Analysis Method in Bondowoso Regency

The difference in rainfall intensity affects the hydrological cycle as a process that greatly determines the amount of water discharge. Thus, in water resources management, it is important to determine the distribution pattern of rainfall and discharge. By studying the characteristics of rainfall distribution patterns and water discharge, the potential of water resources can be illustrated well. This study uses the Exploratory Spatial Data Analysis method to examine spatial variability of rainfall intensity and water discharge in Bondowoso Regency. Rainfall and discharge data are collected from 35 rain stations and 227 weirs in 2008 until 2018. This study produces monthly average rainfall distribution values between 190 mm / month with monthly average discharge between 7300lt/sec/month. Meanwhile, the obtained average annual rainfall distribution values are between 2300 mm/year with annual average discharge values between 105000 lt/sec/month. The spatial distribution map using IDW method produces information on the potential of water resources as follow: the higher the height of a place, the higher the average monthly rainfall, while the lower the height of a place, the higher the average monthly discharge. As for the obtained correlation value between rainfall and discharge is R² = 0.665.

Water quality assessment and a study of current palm oil mill effluent (POME) treatment by ponding system method

Palm oil is one of the main drivers of Malaysian economy that becomes major contributor to the Malaysian Gross Domestic Product (GDP) in the agriculture sector. The demand for palm oil-based product is expected to increase proportionally to the growing population, thus palm oil plantations have been growing rapidly to meet the global demand. Hence, to ensure sustainable management of palm oil production, avoid a significant effect to environmental pollution, the Roundtable on Sustainable Palm Oil (RSPO) and Malaysian Sustainable Palm Oil (MSPO) have been established to minimize environmental issues related to the palm oil industry in Malaysia. This paper provides the physicochemical characteristics and treatment techniques currently available for treating palm oil mill effluent (POME) nowadays. In addition, the correspondence between biological oxygen demand (BOD) and suspended solid (SS) of raw POME samples with the rainfall distribution pattern were also evaluated. The sampling took place at the cooling pond of a palm oil mill throughout 2019 and had been analyzed using water quality standard methods. The results indicated that the total monthly rainfall significantly influenced the concentration of BOD and SS parameters, of which in dry season (February) the highest BOD (36,200 mg/L) and lowest SS (10,522 mg/L) were recorded.

A modified correlation in principal component analysis for torrential rainfall patterns identification

This paper presents a modified correlation in principal component analysis (PCA) for selection number of clusters in identifying rainfall patterns. The approach of a clustering as guided by PCA is extensively employed in data with high dimension especially in identifying the spatial distribution patterns of daily torrential rainfall. Typically, a common method of identifying rainfall patterns for climatological investigation employed T mode-based Pearson correlation matrix to extract the relative variance retained. However, the data of rainfall in Peninsular Malaysia involved skewed observations in the direction of higher values with pure tendencies of values that are positive. Therefore, using Pearson correlation which was basing on PCA on rainfall set of data has the potentioal to influence the partitions of cluster as well as producing exceptionally clusters that are eneven in a space with high dimension. For current research, to resolve the unbalanced clusters challenge regarding the patterns of rainfall caused by the skewed character of the data, a robust dimension reduction method in PCA was employed. Thus, it led to the introduction of a robust measure in PCA with Tukey’s biweight correlation to downweigh observations along with the optimal breakdown point to obtain PCA’s quantity of components. Outcomes of this study displayed a highly substantial progress for the robust PCA, contrasting with the PCA-based Pearson correlation in respects to the average amount of acquired clusters and indicated 70% variance cumulative percentage at the breakdown point of 0.4.

Study on rainfall distribution pattern for agricultural planning in Bhatapara region of Chhattisgarh plain

The amount and distribution of rainfall holds the key of the success for agriculture production. Weekly, monthly and seasonal rainfall data are very useful for planning agricultural operations. In view of this, an attempt has been made to evaluate rainfall distribution patterns i.e. weekly, seasonal and annual rainfall, based on 20 years (2000-2019) data of Bhatapara, Chhattisgarh in the year of 2020 at DKS College of Agricultural and Research Station, Indira Gandhi Krishi Vishva Vidylay, Bhatapara, Chhattisgarh. The analysis showed that amongst various weeks of monsoon season, the 26th week received highest rainfall amount equal to 420.00 mm. However, average rainfall as 99.60 mm was found to be highest for 29th week. From the monthly rainfall analysis, it was found that maximum value of average monthly rainfall (349.50 mm) and minimum value (2.0 mm) were recorded in the months of July and March, respectively amounting to 30.44% and 0.27% of the average annual rainfall with CV of 33.60% and 238.05%, respectively. On the basis of crop (Kharif) seasons received the highest rainfall 908.57 mm and contributing 78.57% of the total average annual rainfall. The highest average rainfall equal to 1070.90 mm was observed in monsoon season of the region while the lowest of 21.81 mm was in the winter season. A major part of it generally lost through runoff, which can be stored through in-situ or ex-situ water harvesting structures and can be used during kharif/ rabi/ summer season for growing crops. It can also be utilized as life saving irrigation particularly in dry period of one or two weeks in rainy season also adversely affect standing kharif crops. The annual rainfall varies from 695.00 to 1585.00 mm with an average value of 1135.15 mm. The percentage deviation over mean annual rainfall ranged in between (-) 41.33% and (+) 26.50% at rained region of Bhatpara during period under investigation. Hence the valuable information obtained from the analysis of rainfall in present study can be used for crop planning, designing of soil and water conservation structure in the Bhatapara region.

Projection of Temperature and Precipitation using Multiple Linear Regression and Artificial Neural Network as a Downscaling Methodology for Upper Bhima Basin

Study of Climate change effect on water resources is very important for its effective management. Projection of temperature and precipitation can be performed by using General Circulation Model (GCM) outputs. GCM can make the projections of climate parameters with different emission scenarios at coarser scale. However hydrological models require climate parameters at smaller scale Downscaling technique is used for obtaining small scale climate variables from large scale variables of GCM outputs. In this study downscaling has been carried out by using Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) techniques. Performance of MLR and ANN models has been evaluated considering Coefficient of determination value (R2). It has been observed that ANN performs better against MLR Model, showed the results that rainfall distribution pattern is varied, in monsoon season rainfall decreases while it increases in post monsoon period. Due to its good evaluation performance such techniques can be applicable for downscaling purpose.

Hydrological evaluation of global gridded precipitation datasets in a heterogeneous and data-scarce basin in Iran

Accurate estimation of the precipitation characteristics, including the value, temporal pattern, and spatial distribution, plays a significant role in the input uncertainty reduction for rainfall-runoff models. In many basins, the improper spatial distribution of rain gauge stations or their limited historical recorded data causes many challenges, especially in heterogeneous catchments which due to the impact of the drastic geographical alterations on the rainfall distribution pattern, the cover of the ground stations cannot estimate the actual precipitation rate. This challenge can be potentially solved by adopting rainfall products as alternative or complementary data sources. In this research, three rainfall products (PERSIANN-CCS, CMORPH and ERA-Interim), were compared against rain gauge stations for calibration of a daily conceptual lumped rainfall-runoff model (CRFM) in a data-scarce and heterogeneous basin located in southwestern Iran. The results indicated that ERA-Interim has the best performance among other datasets. Better performance of this dataset compared to the in-situ data also suggests a better estimation of the basin average as well as the temporal pattern of precipitation. The KGE value was obtained as 0.8 and 0.74, respectively, for a rainfall-runoff model that utilized the ERA-Interim as input in the calibration and validation periods. The results showed that the performance of satellite-based data of CMORPH and PERSIANN-CCS is not acceptable in simulating the daily flow. Also, the seasonal assessment showed that ERA-Interim has a better performance compared to other datasets, during fall and winter. However, in the spring, the performance of all datasets significantly reduces, and the range of BIAS variation increases. Generally, all datasets were shown to perform better in simulating the flow in terms of the transition from dry to wet periods, rather than wet to dry periods.

Quiahua, the First Citizen Science Rainfall Monitoring Network in Mexico: Filling Critical Gaps in Rainfall Data for Evaluating a Payment for Hydrologic Services Program

Citizen science data can fundamentally advance the natural sciences, but concerns remain about its accuracy, reliability, and overall value. While some studies have evaluated accuracy of citizen science data, few have also assessed its potential contribution to conservation policy. This study focuses on rainfall data collection, with four goals: (1) to examine motivations of, and barriers for, volunteer participation in citizen science; (2) to evaluate accuracy of citizen science rainfall data in comparison to automatic rain gauge data; (3) to incorporate citizen science rainfall datasets into hydrological models; and (4) to apply the hydrologic model to gauge the contribution of citizen science data to the efficient design of payment for hydrological services (PHS) programs. Twelve citizen science volunteers were trained and collected rainfall data between June 2017 and February 2019 across two watersheds in Veracruz, Mexico. We found that these volunteers were highly motivated by conservation values and learning, while only a few volunteers faced barriers related to time availability for making daily measurements. The mean error in daily rainfall, computed by comparing the manual and automated gauge measurements, was less than 1 mm, or 12% of the average daily rainfall. Approximately one-third (29%) and two-thirds (71%) of the errors were attributed to missing data and misread data, respectively. Spatial patterns of rainfall distribution across the watersheds were similar between citizen science and automatic gauge data, revealing a large fraction of rainfall in middle elevations. Furthermore, the results show that if PHS areas are determined using the existing national rainfall network alone, without citizen science data, critical areas that contribute to dry-season flows would be missed. To our knowledge, this is the first citizen science network for collecting rainfall data in Mexico that has produced results that are relevant to conservation policy design.

Biochemical and yield evaluation of onion (Allium cepa L.) genotypes under waterlogging condition

The present investigation entitled “Biochemical and yield evaluation of onion (Allium cepa L.) genotypes under waterlogging condition” was carried out at ICAR-Directorate of Onion and Garlic Research, Rajgurunagar, Pune (Maharashtra). Onion crop is extremely sensitive to waterlogging or soil flooding stress due to its shallow root growth habit. The climate change and rainfall distribution pattern most of the onion growing belt might be subjected to heavy rainfall leading to soil flooding condition and cope up with such situation need to identify the improved onion genotypes with better adaptive traits and tolerance for waterlogging condition Therefore, the main objective of the present study was screening of elite genotypes for water logging tolerance under pot condition. The experiment was laid out in factorial CRD for pot condition during kharif, 2018. The pot experiment was conducted in rainout shelter using plastic pot and plants were treated with creating artificial waterlogging condition with the help of tank pit and another set were kept outside in rainout shelter as control. The experimental materials comprises of 17 onion entries including red and white onion entries. The seedlings of each entry were raised from seeds in nursery for 45 days with recommended cultural practices and transplanted in pot. The biochemical traits leaf antioxidant, leaf flavonoids and phenol content exhibited maximum in genotype RGP 5 under waterlogging condition. The waterlogging condition was increased quality traits which producing more secondary metabolites for defense strategy of the plant. Further, the yield attribute like bulb yield percent was minimum decreased in genotype RGP 5 with the percent reduction of 18.59% and the genotype W 344 recorded maximum percent reduction with decline by 83.49% under water logging treatment. It may due the water logging condition inhibits the translocation of assimilates from source organs to sink organ. Therefore, on the basis of overall yield reduction percentage and other important traits, genotype RGP 5 can be categorized as water logging tolerant genotypes, whereas genotype W 344 and W 448 recognized as water logging sensitive genotypes among all the tested onion genotypes.

Water auditing of a small watershed: A review

Water auditing mainly depends on rainfall analysis and cropping pattern, water balance, watershed development and in-situ moisture conservation. Agriculture is highly dependent on rainfall and many decisions are made based on the amounts of rainfall occurring during crop seasons. The rainfall distribution pattern is most uneven and stochastic, hence knowledge of average annual rainfall is not very useful for deciding the cropping pattern. The watershed development program focus on water, its objective is to enhance crop productivity through in-situ moisture conservation practice.

Model Analysis Faktor Sebaran Data Curah Hujan Tahunan

Rainfall occurs in Watershed (DAS) has a special relationship. Data distribution from measuring devices at several locations in the watershed is predicted to have patterns and close relationships. To know this we need a model to be able to show a relationship between measured rainfall data. This research, a factor analysis model is performed to calculate how much the relationship between rainfall data in the watershed and to determine the shape of rainfall distribution patterns that occur. Based on the calculation results, it is known that pattern of rainfall data distribution from 6 rain stations in Bengawan Solo watershed, namely doplang, randu, menden, kedung, japah, and sambong in 1998 has the same distribution pattern. However, the amount of rain height is different. In addition, the maximum Pearson correlation coefficient is 0.536 at Menden-Randu station. While the minimum Pearson correlation value for the Menden-Sambong station is 0.012. This difference is influenced by the distance between Menden-Randu and Menden-Sambong stations. Based on calculations on the factor analysis model, there are two factors that affect directly between these stations. Factor 1 has coefficient value 0.5. Based on calculations show that rainfall that occurs in each region does not have a strong relationship between one another, but the occurrence of rain that occurs has the same distribution pattern. From this, it provides information that th e pattern of rainfall distribution each year is the same due to the occurrence of rain following climate patterns in global area coverage.

Sunflower growth and yield response to sewage sludge application under contrasting water availability conditions

Good knowledge of crop response to biosolids is necessary for optimizing their use as soil amendments. The present 2-yr study evaluated the impact of soil application of municipal sewage sludge on growth, dry matter translocation, achene and oil yield as well as on achene Fe, Cu, Zn and Mn content of field-grown sunflower (Helianthus annuus L.) under contrasting water availability in terms of seasonal rainfall. Treatments included three sewage sludge rates (9, 18, and 36 Mg dry weight ha−1 yr−1) compared with inorganic fertilizer (138 kg N plus 24 kg P ha−1 yr−1) and a non-amended control. Adding sewage sludge increased stem diameter, height, and early growth of sunflower plants compared with control. Dry matter at flowering was positively correlated with translocation of dry matter to achenes (r = 0.855**). However, high early dry matter was translated into enhanced achene yield only with adequate water availability along growth cycle, while water shortage resulted in fewer achenes and poor seed-filling. Sewage sludge application increased achene yield even under water shortage, suggesting a beneficial effect of sewage sludge on soil water status. Achene yield with added sewage sludge was similar to or greater than with the inorganic fertilizer. Sewage sludge decreased achene oil concentration and increased achene N concentration, compared with the non-amended control, without affecting Zn, Fe, Mn, and Cu content in achenes even at the high application rate of 18 Mg ha−1. Findings support that sewage sludge addition in soil at the rate of 9 Mg ha−1 was agronomically adequate and could replace inorganic fertilizer in sunflower production, irrespective of seasonal rainfall amount and distribution patterns.

Effects of rainfall manipulation and nitrogen addition on plant biomass allocation in a semiarid sandy grassland

Extreme climate events and nitrogen (N) deposition are increasingly affecting the structure and function of terrestrial ecosystems. However, the response of plant biomass to variations to these global change drivers is still unclear in semi-arid regions, especially in degraded sandy grasslands. In this study, a manipulative field experiment run over two years (from 2017 to 2018) was conducted to examine the effect of rainfall alteration and nitrogen addition on biomass allocation of annuals and perennial plants in Horqin sandy grassland, Northern China. Our experiment simulated extreme rainfall and extreme drought (a 60% reduction or increment in the growing season rainfall with respect to a control background) and N addition (20 g/m2) during the growing seasons. We found that the sufficient rainfall during late July and August compensates for biomass losses caused by insufficient water in May and June. When rainfall distribution is relatively uniform during the growing season, extreme rainfall increased aboveground biomass (AGB) and belowground biomass (BGB) of annuals, while extreme drought reduced AGB and BGB of perennials. Rainfall alteration had no significant impacts on the root-shoot ratio (R/S) of sandy grassland plants, while N addition reduced R/S of grassland species when there was sufficient rainfall in the early growing season. The biomass of annuals was more sensitive to rainfall alteration and nitrogen addition than the biomass of perennials. Our findings emphasize the importance of monthly rainfall distribution patterns during the growing season, which not only directly affect the growth and development of grassland plants, but also affect the nitrogen availability of grassland plants.

Spatial and temporal characteristics of monthly rainfall over Limbang River Basin, Northern Borneo: an evaluation through multivariate statistics

Identification of homogeneous rainfall regime in Limbang River Basin (LRB), Malaysia, Northern Borneo and its temporal characteristics were elucidated in the present study. Multivariate statistical techniques were used to characterize 15 years (2000–2015) monthly rainfall data from twelve rain gauging stations present in LRB. Analysis of monthly accumulated rainfall indicates that LRB receives maximum rainfalls during January, April, November, December and least in July. Three dominant clusters (C1, C2 and C3) of homogeneous rainfall were identified by Ward’s method of hierarchical clustering, and multivariate statistics were applied to understand its temporal characteristics. Comparing the clusters formed in different elevations in LRB, cluster-3 which covers middle catchment region shows the highest accumulated monthly (487 mm in November) and annual rainfall (4571 mm). At the same time, all the clusters shown more or less similar pattern of temporal rainfall distribution with varying dispersion and skewness and these altogether contribute 74% of annual rainfall received by LRB. Though the study area is considered as unique climatic (equatorial tropical rainforest) regime, the observed variation in rainfall among the clusters can be attributed to the factors such as locations of rain gauging stations (elevation and geomorphology), characteristics of hills/mountains, proximity to ocean and wind characteristics in the region. The findings of present research will serve as an essential pre-requisite dataset for any hydrological development program in the river basin.

Rainfall distribution in relation to flooding in upper Citarum watershed, Indonesia

Disasters caused by weather, such as flood, have become more frequent since the late 1990s. Compared to the previous decade, there are more than twice the number of floods in the world. Essentially, flood often occurs in lowland area with compact river systems. Unlike the case that often happened in the area around the Upper Citarum Watershed, floods occur because of the physical characteristics of the basin in the bowl-like form. In addition, rain that occur consecutively for a certain period of time in a day can result a surplus runoff water falling to the surface of the earth. Observation of rainfall has actually been carried out quantitatively since the 19th century. Satellite-based rainfall observation has the main advantage of being quite accurate in spatial and temporal coverage. This research is intended to see the pattern of rainfall distribution in relation to flooding in the Upper Citarum Watershed using remote sensing methods to process satellite images of PERSIANN CCS in obtaining rainfall data in each sub-watershed. Spatial results show six areas of rainfall distribution as of sub-watershed. Meanwhile, temporal results are analyzed with a graph that shows the pattern of hourly rainfall behaviour.

Relationships among Global Climate Indices and Rainfall Pattern to Detect Impact of Climate Change in Yogyakarta Special Region, Indonesia

Indications of climate change can be known from the changes in rainfall distribution pattern and its volume. Yogyakarta Special Region lately often experiences drought, and has an impact on decreasing agriculture productivity and crop failure in several districts. Some of the research that has been done has not reviewed specifically in the Special Region of Yogyakarta with several Global Climate Indices and has not been spatially displayed. The aims of this research is to determine the relationship between the global climate indices with rainfall patterns in the Special Region of Yogyakarta. Global Climate Indices data used are: Southern Oscillation Index (SOI), Sea Surface Temperature Nino 3.4, Sea Surface Temperature Nino West, and Indian Ocean Basin-wide Warming (IOBW), and rainfall data for 2009-2013. The analysis process uses Principal Component Analysis (PCA) method to determine the relationship between the Global Climate Index and rainfall patterns and then change the data to be spatial using GIS. Based on the results, overall rainfall is negatively correlated with SOI, the area with a quite strong negative correlation (> -0.462) in the northernmost areas namely Cangkringan, Pakem and Ngemplak Districts, and the correlation value continues to decline to the southern regions. The correlation of rainfall with SST Nino 3.4 is dominantly negatively correlated and the area with strong enough negative correlation (> -0.492) in the northern and southernmost regions, namely Pakem, Cangkringan, Ngemplak, Saptosari, Tanjungsari, and Girisubo Districts, and the correlation value continues to decrease to the middle area. Then the correlation of rainfall with Nino West overall has a positive value, the northern region has a strong correlation (> -0.455) in the Districts of Pakem, Cangkringan and Ngemplak, while other regions have a strong enough correlation (> -0.455). The last correlation is with IOBW, it is divided into 2 areas that have positive correlations in the north and south areas and the negative correlation of the middle part is almost entirely, areas with strong positive correlations (> 0.57), namely in Tanjungsari and Girisubo Districts and areas with strong negative correlations (-0,556) in Pundong District. Thus it can be concluded that the Global Climate Indices (SOI, SST Nino 3.4, Nino West, and IOBW) influences the pattern of rainfall distribution in the Special Region of Yogyakarta.

Improved rainfall threshold for landslides in data sparse and diverse geomorphic milieu: a cluster analysis based approach

Rainfall-triggered landslides are the most common type of mass movement seen along the tropical belt due to the prevalence of monsoons. These landslides can be forecasted by understanding the spatial and temporal rainfall distribution patterns, and subsequent generation of rainfall threshold (RT). However, deriving a regional RT in a geologically, geographically and physiographically diverse milieu is a formidable task. The data on spatial and intra-seasonal variability of monsoons can be widely dispersed in such diversified terrains. Clustering analysis provides a promising approach to handle such widely dispersed data. This study intends to develop a methodology using 2-stage clustering process to create RT in such terrains by using daily rainfall versus antecedent rainfall and rainfall versus antecedent rainfall versus soil depth. Sixteen rainfall-induced landslides, located in different terrains in the Western Ghats of India, were subjected to this analysis. Majority of the landslides were modeled, and different RTs were derived for different conditions. The landslides belong to four different classes, viz., landslides occurring at steep slopes; those occurring at knickpoints of highland and midland; in the plateau region and others characterized by a thin veneer of soil. Out of 16 landslides subjected to RT, this method was able to model 13 landslides with a success rate of 81.25%, which is a fair figure.