Analysis Of Rainfall Data Research Articles

Rainfall trend and its Implications for Sustainable Crop Production and Water Resources Management: A Case Study of Iwo, Nigeria

Studies have revealed that the pattern of rainfall will change in view of climate change scenario being experienced globally. Thus, a thirty-year (1989-2018) rainfall data analysis was conducted for Iwo in Osun State, southwestern Nigeria to determine the trend pattern of rainfall. One parameter Mann-Kendall statistic was used in the analysis of the data. The results showed that while five months (February, April, July, August and December) showed negative trend (Negative y-values) indicating declining rainfall over the period, seven months (January, March, May, June, September, October and November) revealed a positive trend (positive y-values) implying increasing rainfall over the period for those months. However, the general analysis of the 30-year data showed that there was negative trend in rainfall in the study area which means that there is general fall in the rainfall incidence over the period in Iwo. The results of monthly trend indicated that rainfall is no longer dependable for a viable agricultural practice, and so water conservation methodologies need to be embarked upon in order to salvage crops from the shortfall in the rain caused by climate change. It is therefore, recommended that shifting in the planting dates, irrigation, planting cover crops and planting heat tolerant crops and early matured crop varieties among others should be considered by farmers, especially the small scale farmers. Stakeholders in water resources management holds the responsibility on averting water scarcity and the challenge of water pollution in view of the erratic rainfall to ensure human survival.

A SYNTHETIC PRECIPITATION MODEL TO DETERMINE THE USAGE OF THE DISTRIBUTION OF RAIN AS A DATA INPUT IN CALCULATING THE DESIGN FLOOD

In calculating the design-flood discharge, engineers often use the frequency-distribution analysis of rainfall data as the basis for obtaining the magnitude of the design flood. The distribution of rain stations is an important factor in determining the distribution of regional rainfall; however, not all catchments have sufficient rain stations to represent the distribution of precipitation in a watershed. This results in a flood hydrograph—obtained using a rainfall–runoff model that is based on the calculation of the estimated rainfall—with a low level of correlation with the observed hydrograph. This research aims to set up a synthetic rain station that corrects the difference between the simulated hydrograph and observed hydrograph to achieve a better correlation level. Usually, researchers use the available rain stations for analyzing the design rainfall and design flood; however, in this research, a synthetic rain station has been built. Rainfall–runoff modeling methods for ungauged catchments (UCs), i.e., watersheds without observation stations, are widely available. Each method has different parameters to determine the unit hydrograph in a watershed. During the development of rainfall–runoff models at UCs, the precipitation in the upstream of a watershed is often assumed to be uniform, but it is not the case. The methodology used in this study collects hourly rainfall data from the ARR and hourly flood-discharge data from the AWLR in one catchment, then calculates the Thiessen coefficient before and after the set up of the synthetic rain station, and finally, calculates the rainfall at the synthetic rain station by trial and error. From the results of the synthetic rainfall modeling, the equation for the synthetic rainfall is obtained:ArRR=(-0.127A-0.260FB-0.307Re+1.227Q+0.51)/(C.K_Thiessen ) with a correlation coefficient of R = 0.818 in a watershed area below 300 km2.

Integrated approach for landslide hazard assessment in the High City of Antananarivo, Madagascar (UNESCO tentative site)

The High City of Antananarivo is one of the most important cultural heritage sites of Madagascar, on the UNESCO Tentative List since 2016. Built on the hilltop of the Analamanga Hill, a granite ridge overlooking the Ikopa River valley, it is renowned for its baroque-style palaces, such as the Rova royal complex, and neo-Gothic cathedrals dating back to the nineteenth century. During the winter of 2015, the twin cyclones Bansi and Chedza hit the urban area of Antananarivo, triggering floods and shallow landslides, as well as causing thousands of evacuees and many casualties. Between 2018 and 2019 several rockfalls occurred from the rock cliffs of the Analamanga hills, destroying housings and killing over 30 people. Both events showed that landslides can pose a high risk to the safety of the inhabitants, the infrastructure, and the cultural heritage of the High City. To assess the landslide hazard in the Analamanga Hill area, an integrated approach was adopted by means of the following actions: (i) creation of a multitemporal detailed scale landslide map; (ii) geotechnical characterization of the involved materials; (iii) analysis of landslide susceptibility in soils/loose deposits; (iv) runout analysis of debris flows channeling within large creek gullies; (v) landslide kinematic analysis of the rockmass; (vi) simulation of rockfall trajectories; (vii) analysis of rainfall data. The results show that the main factors affecting landslides are slope, lithology, creek-gully erosion, and anthropization, while most of the landslide events are clearly triggered by heavy rainfall. The landslide-prone areas (the phenomena include shallow landslides, rock falls, and debris flows) are located primarily along the cliff bounding the western hill slope, the southeastern sector (where abandoned quarries form large slope cuts), and subordinately in the steep creek catchment just east of the Rova. The thematic maps produced represent fundamental land use management tools to be used in Geo Disaster Risk Reduction (GDRR) by scientists, practitioners and the decision-makers involved in the High City protection and conservation. The study conducted represents an important contribution for improving the knowledge on landslide processes in an area with limited data such as Madagascar, and may be reproduced in cultural heritage sites characterized by similar geomorphological and urban scenarios.

Peak discharge mitigation effects in different rainfall patterns at a paddy plot with a runoff control plate

Study regionTawaramoto Town, Nara Prefecture, Japan Study focusIn Japan, steep topography and humid climate make the country vulnerable to flooding and the importance of the flood mitigation function of rice paddy fields has been emphasized. The ‘paddy field dam project’ is being promoted to enhance the flood mitigation function by installing a runoff control plate in a drainage box. However, there are a few studies that have examined the effects of differences in rainfall patterns and rainfall intensity on the function of paddy field dam by examining possible future rainfall patterns. We generated simulated rainfall patterns from the probability-rainfall intensity formula and from the stochastic analysis of historical rainfall data. For each rainfall scale, we investigated the relationship between rainfall characteristics and the function of the runoff control plate. New hydrological insightWhen different rainfall patterns simulated using stochastic methods were input to the model, the peak ponding depth, and difference in peak ponding depth compared to paddy plot without runoff control plate might be larger than when the unimodal rainfall with the almost same 24-h rainfall were input. If the rainfall scale and hourly peak rainfall are determined, we can estimate the possible range of the peak discharge mitigation rate and the difference in the peak ponding depth between the plot with and without the runoff control plate.

Artificial Intelligence Sponge City Wisdom Management Model

Sponge city is currently a new urban rain flood management concept. It refers to the natural disaster that adapts to environmental changes and the natural disasters that is brought by rainwater. It is also known as the sponge, and the alias can also be called as “water elastic city.” The purpose of this article is, based on artificial intelligence, to study the analysis of wisdom management models in sponges. This paper first introduces the artificial intelligence algorithm and sponge city, then compares the traditional sponge city and the wisdom sponge city, then creates a LSTM neural network model, introducing artificial intelligence into sponge city intelligent dynamics in the analysis, and finally compares the rainfall data analysis to the ground. The experimental results show that, at different time points, the training results of rainfall data have shown significant regularity. The entire rainfall process exhibits a rise-decline-rise-increase trend of repetition. The maximum rainfall appeared at the 18th hour, and 7 obvious peaks occurred in 7, 11, 14, 16, 18, 21, and 23 hours.

Estimation of Fluctuations and Global Tele-connections in the Monsoonal Rainfall and Associated Floods for Barmer District

Abstract: This paper is about the fluctuations and global teleconnections in the monsoonal rainfall and associated floods of Barmer district, Rajasthan, India. For investigation of precipitation and related floods, the precipitation information for period of 1901-2002 is collected from India Meteorological Department (IMD) for Barmer district. Inter-monsoon rainfall variability analysis, percent departure from mean, Normalized Accumulated Departure from Mean (NADM) and conditional probability technique for El Nino and Southern Oscillation (ENSO) have been used for the analyses of monsoonal rainfall data and its effect on floods. The outcomes demonstrate year to year varieties in the precipitation with huge takeoff from mean precipitation. It is seen that greater part of the floods are related with positive takeoff from mean precipitation. The NADM shows epochal conduct of high and low monsoon precipitation of the basin. For analysis of ENSO Sea Surface Temperature (SST) data is obtained from National Oceanic and Atmospheric Administration (NOAA) for years 1950-2019. It is observed that, during La Nina years the rainfall is higher than average annual rainfall. The analysis of ENSO reveals that the probability of the occurrence of the floods is high during cold (La Nina) events and it is very low during warm (El Nino) events. Keywords: Monsoon rainfall, Global Tele-connection, SST, ENSO effect, floods

Rainfall Analysis for Rainwater Harvesting in Politeknik Ungku Omar

Rainwater Harvesting System (RWHS) is a collection and storage of rainwater for future use and have many benefits related to the economy, environment, technology, and society. Malaysia is well positioned to harvest rainwater because of number of rainy days in Malaysia is high and average of rainfall amount 2500 mm annually. Because of the effects of the topography of Peninsular Malaysia, rainfall distribution varies significantly. Thus, rainfall data analysis for rainwater harvesting potential should be made based on local climate and influences from the monsoon. The objective of this study was to analyse the rainfall data for rainwater harvesting potential in Politeknik Ungku Omar (PUO). To simplify analysis, rainfall distribution is discussed within two monsoons which is Southwest monsoon and Northeast monsoon while rainfall trend is determined under the year 2000 to 2019. The result indicates that PUO will be potential for RWHS regarding to high number of rainfalls event and increasing trend in annual rainfall amount in that area. Further research is clearly needed to investigate the roof size and water consumption to increase the effectiveness of the RWHS.

Modeling and Forecasting of Rainfall Trends based on Historical Data in Bungoma County, Western Kenya using Holt Winters Method

Rainfall is termed as a meteorological phenomenon which is very useful for daily human activities. Most of the population depend on it for domestic purpose and agriculture, hence it is vital for farmers to know rainfall trends and patterns prevailing in their locality. In Kenya, rainfall variability has caused hunger to many people. In agriculture section, water problem is the most critical constraint in food productions. Extreme weather conditions which is occasioned by climatic change and weather variability makes a small scale farmer in Bungoma to face very high risks of reduced productivity. Scarcity of water is a severe environmental constraint to plant productivity hence drought causes loss in crop yield. Majority of the population in Bungoma do depend on agriculture either directly or indirectly therefore analysis of rainfall data for long periods will provide a lot of information about rainfall variability and this will help to better the agricultural activities of Bungoma farmers. The main aim of the study was to model rainfall patterns and hence predict the future rainfall trends in Bungoma region using Holt winters method in the context of the time series. This is because time series analysis plays an important role in modelling, predicting and forecasting meteorological data such as humidity, temperature, rainfall and other environmental variable. Therefore, data for Bungoma monthly and yearly rainfall patterns for the period 1988-2021 was obtained from the Kenya meteorological department. Collected data was analyzed using Holt winters method by R software. Rainfall data was found to be seasonal implying that most of the rainfall occurred in a specific period each year. Forecasted rainfall had increasing and decreasing prediction intervals and this implied that rainfall could either start decreasing or increasing. The data was found to be non-stationary due to presence of seasonality and rainfall trends. Findings of the study will make it possible to facilitate planning and management of water for both domestic and agricultural use in Bungoma region.

Improved Regional Frequency Analysis of rainfall data

Rainfall are subject to local orography features and their intensities can be highly variable. In this context, identifying climatically coherent regions can greatly help interpreting rainfall patterns and improve the inference of return levels. In practice, partitioning a region of interest into homogeneous sub-regions is a delicate statistical task, especially in regards to modeling heavy rainfall features.In this work, our main goal is to propose and study a fast and efficient clustering algorithm. Compared to classical regional frequency analysis techniques, a key aspect is that our algorithm does not rely on the a priori choice of covariates. The proposed numerical scheme is only based on the precipitation dataset at hand, including low, moderate and heavy rainfall. In terms of inference, our approach builds on the easy-to-compute and reliable probability weighted moments commonly used in hydrology. While being in compliance with extreme value theory, we do not impose a parametric form on rainfall distributions and, neither thresholding nor block maxima steps are required in our proposed approach. By construction, our clustering method preserves the scale invariance principle of any classical regional frequency analysis.The performance of our clustering algorithm is assessed on a detailed experimental design based on the extended Generalized Pareto distribution.Sensitivity to the number of clusters is carefully analyzed.We apply our clustering algorithm on Switzerland daily precipitation measured at 191 sites. The found homogeneous regions are consistent with local orography and our approach outperforms the classical regional frequency analysis based on normalized elevation and coordinates as covariates. To complete our analysis of Swiss rainfall, we propose three models based on our clustering outputs. A comparison between our local, semi-regional and regional models indicates that a relatively simple model with two clusters and a spatially varying scale parameter can compete very well against complex models.

Estimation of Intensity Duration Frequency for Ungauged Basin in Lampung Province, Indonesia

Frequency duration intensity (IDF) analysis was conducted to estimate the peak flow rate based on the minimum rainfall data collection station. Rainfall data used is data with high intensity that occurs in a short time from automatic rainfall recording stations. Currently, the availability and distribution of automatic rain recording stations in Lampung Province, Indonesia, are still limited. Therefore, this study aims to use the IDF approach in the ungauged basin area for areas with rainfall data that do not meet the hydrological analysis criteria by interpolating rainfall data from 126 manual rainfall measuring stations in Lampung Province, Indonesia. The research method includes analysis of rainfall intensity using the Mononobe equation at various durations and returns periods. Next, create a rainfall intensity map (isohyet) using ArcGis. Finally, compare the IDF analysis of daily rainfall data at 4 automatic rainfall gauge stations with the estimation results based on the intensity map (isohyet). Based on the results of data analysis, it is known that from the available 126 rainfall climatology stations, there are 113 rainfall climatology stations with complete data for 10 years and 13 rainfall climatology stations with incomplete data for 10 years. In addition, the study results show that 45.24% of the daily rainfall in Lampung province is in the low category, 53.97% is in the medium category, and 0.79% is in the high category. This study indicates that rainfall intensity data from climatological rainfall stations that do not meet the hydrological criteria can be found by interpolating rainfall intensity maps from the nearest rain climatology station that meet the hydrological analysis criteria. The relationship test of the actual rainfall intensity variable at 4 automatic rainfall gauge stations with the rainfall intensity from the map (isohyet) using MAPE showed satisfactory results.

Analysis of Rainfall Variability in the Province of Quirino

The temporal variability of rainfall in Quirino Province was analyzed through the use of rainfall data of seven (7) rain gauges within the neighboring provinces like Nueva Vizcaya and Aurora. The length of record analyzed from 1997 to 2016. In this study, rainfall frequency analysis and consistency of rainfall data from the different stations through the use of double mass curve analysis was performed and analyzed. The annual series was used to screen each station’s annual rainfall data while the province’s average Thiessen rainfall was screened using maximum period series process. It was found out that all the data of the seven (7) rainfall stations were consistent. To attain allowable error in estimation of 10%, 5% and 1% for the mean annual rainfall the number of rain gauge station needed in the province should be 18, 72 and 1799, respectively.

Parametric and numerical modeling tools to forecast hydrogeological impacts of a tunnel

The project of interest involving a hydroelectrical diversion tunnel through a crystalline rock massif in the Alps required a detailed hydrogeological study to forecast the magnitude of water inflows within the tunnel and possible effects on groundwater flow. The tunnel exhibits a length of 9.5 km and is located on the right side of the Toce River in Crevoladossola (Verbania Province, Piedmont region, northern Italy). Under the geological framework of the Alps, the tunnel is located within the Lower Penninic Nappes in the footwall of the Simplon Normal Fault, and the geological succession is mostly represented by Antigorio gneiss (meta-granites) and Baceno metasediments (metacarbonates). Due to the presence of important mineralized springs for commercial mineral water purposes, the abovementioned hydrogeological study focused on both quantity and quality aspects via rainfall data analysis, monitoring of major spring flow rates, monitoring of hydraulic heads and pumping rates of existing wells/boreholes, hydrochemical and isotopic analysis of springs and boreholes and hydraulic tests (Lefranc and Lugeon). The resulting conceptual model indicated dominant low-permeability (aquitard) behavior of the gneissic rock masses, except under conditions of intense fracturing due to tectonization, and aquifer behavior of the metasedimentary rocks, particularly when interested by dissolution. Groundwater flow systems are mainly controlled by gravity. The springs located near the Toce River were characterized by high mineralization and isotopic ratios, indicating long groundwater flow paths. Based on all the data collected and analyzed, two parametric methods were applied: 1) the Dematteis method, slightly adapted to the case study and the available data, which allows assessment of both potential inflows within the tunnel and potential impacts on springs (codified as the drawdown hazard index; DHI); 2) the Cesano method, which only allows assessment of potential inflows within the tunnel, thereby discriminating between major and minor inflows. Contemporarily, a groundwater flow model was implemented with the equivalent porous medium (EPM) approach in MODFLOW-2000. This model was calibrated under steady-state conditions against the available data (groundwater levels inside wells/piezometers and elevation and flow rate of springs). The Dematteis method was demonstrated to be more reliable and suitable for the site than was the Cesano method. This method was validated considering a tunnel through gneissic rock masses, and this approach considered intrinsic parameters of rock masses more notably than morphological and geomorphological factors were considered. The Cesano method relatively overestimated tunnel inflows, considering variations in the topography and overburden above the tunnel. Sensitivity analysis revealed a low sensitivity of these parametric methods to parameter values, except for the rock quality designation (RQD) employed to represent the fracturing degree. The numerical model was calibrated under ante-operam conditions, and sensitivity analysis evaluated the influence of uncertainties in the hydraulic conductivity (K) values of the different hydrogeological units. The hydraulic head distribution after tunnel excavation was forecasted considering three scenarios, namely, a draining tunnel, tunnel as a water loss source, and tunnel sealed along its aquifer sectors, considering 3 levels of K reduction. Tunnel impermeabilization was very effective, thus lowering the drainage rate and impact on springs. The model quantitatively defined tunnel inflows and the effects on spring flow at the surface in terms of flow rate decrease. The Dematteis method and numerical model were combined to obtain a final risk of impact on the springs. This study likely overestimated the risk because all the values assigned to the parameters were chosen in a conservative way, and the steady-state numerical simulations were also very conservative (the transient state in this hydrogeological setting supposedly lasts 1–3 years). Monitoring of the tunnel and springs during tunnel boring could facilitate the feedback process.

Analysis of Rainwater Harvesting (RWH) Capacity in Leuwigajah Urban-Village, South Cimahi

Leuwigajah Urban-Village is an area belonging to Citarum Watershed. Thus, it has a high potential to experience floods during the rainy season. Meanwhile, the area often experiences drought during the dry season due to high surface runoff and low infiltration. This paper discusses the capacity of RWH needed in N.A. of R.W. 19. It discusses hydrological conditions of the Cimahi watershed by presenting an analysis of rainfall data, land use data, and extreme discharge data, both maximum and minimum extreme discharges. Therefore, the Citarum Harum Community Service team decided to build Rainwater Harvesting (RWH) as a water supply source during the dry season. The RWH was built based on people's water needs 10 years later, from 2022-to 2032. Based on the calculations explained in the Rainwater Storage Module book issued by the Ministry of Public Works and Housing, the volume of RWH needed is 125 m3. However, due to limited land, it was decided to build an RWH with dimensions of 2 m x 2 m x 2 m which can accommodate 8 m3 of water. The water in this RWH can fulfil 6.4% of daily water needs, so it is recommended to be used for emergency purposes or secondary purposes such as agriculture and plantations.

Effects of Rainfall Variability on the Occurrence of Crop Pests at Foumbot Subdivision, West Region of Cameroon

Rainfall is an essential climate variable for plant development. Its variability has an impact on the plant phenology. The present study aimed to analyze the variation in rainfall in order to determine the rainfall hazard responsible to the occurrence of pest and diseases in tomato and maize. The study was carried out at Foumbot located in the Western Region of Cameroon, more specifically in the Noun Division. Part of this district occupies the left bank of the river Noun, more precisely the Noun plain. The rainfall data was collected from the period of 1997 to 2019 and analyzed. Monthly data was collected from 1997 to 2019 and daily data from 2010 to 2019.The surveys were made with three groups of actors: the agricultural services (the Agricultural Delegation of Foumbot), the agricultural research services (IRAD of Foumbot), and the farmers' organizations (the Common Initiative Group (CIG) and cooperatives). Stratified random sampling was used for the surveys. The results revealed that the first cropping season from March to July is characterized by an increase in consecutive dry days of 5 to more than 7 days. This situation has been confirmed by 30% of the investigated actors, who mentioned that consecutive days without rain were favorable to the appearances of pest and diseases. The SPI (Standard Precipitation Index) analysis of rainfall data showed that this can be link to the prolongation of the dryness season in the cropping season which started in March. Therefore, the monthly SPI consider March as a dry month. So, March and May are not suitable for the beginning of cropping season due to the multiplication of drought episode. The tomato leaf miner (Tuta absoluta) and the armyworm had been identified as the pest and disease which appear during the consecutive dry days. They are characterized by an increase in temperature. The study is useful for agricultural drought management and crop productivity in an unpredictable environment.

Wet season rainfall onset and flash drought: The case of the northern Australian wet season

AbstractIn this paper, we report on the frequency of false onsets of wet season rainfall in the case of the Northern Australian wet season and investigate the role of large‐scale tropical climate processes such as the El Niño–Southern Oscillation, Indian Ocean Dipole (IOD) and Madden–Julian Oscillation. A false onset occurs when a wet season rainfall onset criterion is met, but follow‐up rainfall is not received for weeks or months later. Our analysis of wet season rainfall data from 1950 through 2020 shows a false onset occurs, on average, between 20 and 30% of wet seasons across all of northern Australia. This increases at a regional and local level such as at Darwin, the Northern Territory (NT), and parts of Queensland's north coast to over 50%. Seasonal climate influences, such as a La Niña pattern and a negative IOD that typically expedite the wet season rainfall onset, also increase the likelihood of a false onset over northern Australia. Our analysis also finds that periods of false onsets can sometimes, but not always, coincide with periods of rapid soil moisture depletion. The false rainfall onsets that develop into flash drought can be potentially disruptive and costly and are of potential significance for agriculture and fire management in northern Australia, and in other monsoonal climates that also typically experience a slow build‐up to the seasonal monsoon. In conclusion, effective rainfall indicates that many seasons experience ‘false onsets’ with dry conditions after early rainfall. We propose that false onsets are a physical characteristic of the climate of northern Australia which occurs with relatively high frequency. In addition, these false onsets may sometimes co‐occur with a flash drought.

Monitoring and mapping of drought in a semi-arid region: case of the Merguellil watershed, central Tunisia.

Drought is defined as a period of time characterized by below-normal water availability, which may affect crops, animals and the environment. Recently, drought was shown to be more frequent and more intense, implying thereby the need for monitoring and analysis of this natural hazard. The present study aims to examine the spatial extent and temporal variation of droughts in the Merguellil watershed, located in central Tunisia. This contribution was mainly based on the analysis of annual and monthly rainfall time series recorded over the period (1983-2018) in 19 stations spread throughout the study watershed. Rainfall trend was first examined using the Mann-Kendall statistical test. Then, statistical (standard precipitation index (SPI) and Palmer drought severity index (PDSI)), spectral (continuous wavelet transform (CWT)) and mapping (geographical information system (GIS)) techniques were used to identify extreme dry events and to characterize their severity and their spatial and temporal extents. The results obtained revealed the recurrence and frequency of drought conditions in the Merguellil watershed over the study period. Seven drought sequences (1983-1984, 1986-1989, 1992-1995, 1999-2002, 2007-2009, 2013-2015 and 2017-2018), with different levels of severity, were distinguished based on the computed SPI and PDSI values. Spectral analysis of rainfall data also showed the occurrence of significant droughts in recent years. The period starting from 2010 was shown to be marked by recurrent episodes of drought in the Merguellil watershed. Extreme drought events mapping over this period confirmed drought severity at both time and space scales. All of these findings may be helpful for developing programs of water resource management in the study watershed.

Evaluating Coupled Climate Model Parameterizations via Skill at Reproducing the Monsoon Intraseasonal Oscillation

Abstract Empirically generated indices are used to evaluate the skill of a global climate model in representing the monsoon intraseasonal oscillation (MISO). This work adapts the method of Suhas et al., an extended empirical orthogonal function (EEOF) analysis of daily rainfall data with the first orthogonal function indicating MISO strength and phase. This method is applied to observed rainfall and Community Earth System Model (CESM1.2) simulation results. Variants of the CESM1.2 including upper ocean parameterizations for Langmuir turbulence and submesoscale mixed layer eddy restratification are used together with the EEOF analysis to explore sensitivity of the MISO to global upper ocean process representations. The skill with which the model variants recreate the MISO strength and persistence is evaluated versus the observed MISO. While all model versions reproduce the northward rainfall propagation traditionally associated with the MISO, a version including both Langmuir turbulence and submesoscale restratification parameterizations provides the most accurate simulations of the time scale of MISO events.

Study on the Aridity in the North-Western (NW) Part of Bangladesh through Combined Application of Remote Sensing, GIS and Geophysical Data

In the present paper, hydro-environmental behaviour of two adjacent but characteristically different land areas of (i) semi-arid Barind Tract and (ii) Ganges flood plain situated in the north-western part of Bangladesh have been studied and possible mechanisms of variability in the two study sites have been investigated. Time series remote sensing data supplemented with climatic, geophysical and groundwater data and other ancillary information have been analysed for the purpose. Coupling of Landsat TM derived vegetation cover information with meteorological data depicts higher sensitivity of Barind Tract than the other study site towards vegetation cover changes in response to rainfall amount. Temporal sequence of albedo value as derived from Landsat TM data manifests appropriate seasonal trend that is consistent with field observations. Analysis of rainfall data over a period of about 50 years depicts substantially higher rainfall in both the study sites as compared to a typical semiarid region. However, temporal distribution of rainfall events is mostly concentrated within very short period of time in the year. Lithological information on the subsurface configuration depicts the existence of a relatively impermeable clayey layer near the top surface in these areas. Relatively higher thickness of this impermeable clayey layer over the semiarid Barind areas has been noticed that slows down the infiltration process significantly as compared to that in adjacent floodplain areas. Analysis of drainage network exhibits relatively higher drainage density in the Barind Tract area as compared to that in floodplain area. The drainage pattern shows predominantly unidirectional and distributary nature of drainage in the Barind Tract area and in general, distribution is oriented towards basin direction and most of the branches of the drainage system. Ultimately, relatively higher elevation, steeper slope result in relatively quicker surface runoff to carry out excess rain water to the nearest basins, results in decreased infiltration of rainwater. Eventually, the conversion efficiency of Barind Tract area to transform intercepted rainwater into underground reservoir water through infiltration seems to be lower as compared to that of floodplain areas.

Interpolation of Precipitation Extremes on a Large Domain Toward IDF Curve Construction at Unmonitored Locations

An intensity–duration–frequency (IDF) curve describes the relationship between rainfall intensity and duration for a given return period and location. Such curves are obtained through frequency analysis of rainfall data and commonly used in infrastructure design, flood protection, water management, and urban drainage systems. However, they are typically available only in sparse locations. Data for other sites must be interpolated as the need arises. This paper describes how extreme precipitation of several durations can be interpolated to compute IDF curves on a large, sparse domain. In the absence of local data, a reconstruction of the historical meteorology is used as a covariate for interpolating extreme precipitation characteristics. This covariate is included in a hierarchical Bayesian spatial model for extreme precipitations. This model is especially well suited for a covariate gridded structure, thereby enabling fast and precise computations. As an illustration, the methodology is used to construct IDF curves over Eastern Canada. An extensive cross-validation study shows that at locations where data are available, the proposed method generally improves on the current practice of Environment and Climate Change Canada which relies on a moment-based fit of the Gumbel extreme-value distribution.

Optimising maize sowing under irregular rainfall distribution in northern Thailand: A model approach for improved decision-making

Rainfed maize production in northern Thailand is strongly affected by high rainfall variability and recent apparent shifts of the onset of the rainy season. Hence, decision-making on maize sowing time has become extremely difficult for farmers. This study thus aimed at (i) evaluating the Water, Nutrient and Light Capture in Agroforestry Systems (WaNuLCAS) model for predicting maize performance under rainfed conditions, (ii) assessing maize yield performance under five sowing date options, and (iii) identifying the best sowing option under irregular rainfall. A 2-yr-data set with various maize sowing dates from a field experiment in northern Thailand was used to calibrate and validate the model. WaNuLCAS was able to predict maize yield well (goodness-of-fit statistics: R²=0.83; modelling efficiency: −0.61; root mean square error: 0.14 kg m −2 ; maximum error: 0.16 kg m −2 ; coefficient of residual mass: 0.02; coefficient of determination: 0.56). An analysis of rainfall data (1970–2018) of the Phitsanulok province, Thailand, showed strong interannual variations: 27.1% of the years corresponded with the long-term mean and were moderately dry or moderately wet, while the remaining years were either very wet (10.4%) or very dry (8.3%). The standardized precipitation index indicated both, an earlier rain onset and frequent dry spells towards the end of the rainy season in recent years. Therefore, sowing earlier can be seen as a valuable coping strategy. Simulated sowing date options were: farmers’ practice (FP), 15, 30, and 45 days before FP, and a combination of them (staggered planting). Simulations revealed that under current rainfall conditions water was the most limiting factor for growth and yield of maize, while nutrient limitations had little impact under the current fertilization regime. Maize water uptake was significantly correlated with yield (R²=0.45). The simulation results suggest that sowing maize 30 days before FP or staggered planting are potent alternatives under irregular rainfall, the later particularly when distinct weather forecasts are not possible. Both options reduced the risk of crop failure while maintaining yields under these conditions. Simulations further suggested, under climate scenarios (RCP 4.5) for northern Thailand and the 21st century even earlier sowing seems plausible. • Validation of a model approach to improve decision-making in maize sowing. • Analysis of past precipitation patterns to understand the impact of rainfall variability on maize growth and yield • Testing coping strategies to better adapt to climate change effects on maize cropping. • Early sowing or staggered planting of maize are potent alternatives to cope with climate change projections for northern Thailand.