Indian Rainfall Research Articles

Testing heavy rainfall change in Jeju Island, Korea and its linkage to typhoon activity over East Asia during the Holocene

Understanding the spatiotemporal variability of extreme hydrologic events, especially those driven by typhoons over Asia, and their controlling factors is essential to testing and predicting their future changes under global warming. Here, we present a series of direct heavy-rainfall records over the past ca. 10,000 years based on lake sediments from a volcanic cone (Sara Oreum) on Jeju Island, Korea, where orographic amplification of precipitation is dominant and sensitive to present typhoon activity. The reconstructed heavy rainfall index based on the high-resolution elemental ratios (e.g., Ti/Al and Sr/Si) shows clear variability at centennial timescales and is similar to records of coastal flooding events along the southern coast of Korea, typhoon-driven overwash events along the south-western coast of Japan, indicating strong typhoon influence on the heavy rainfall events in Jeju Island during the Holocene. Furthermore, this heavy rainfall-typhoon linkage has been influenced by past El Niño–Southern Oscillation (ENSO) activity and sea surface temperature (SST) in the western Tropical Pacific, showing time-varying influences. This study suggests that on decadal to multi-centennial timescales, stronger ENSO activity is responsible for eastward shift in the location of typhoon genesis, resulting in more frequent recurving tracks of typhoon and increased landfalls in Korea and Japan, whereas SST affects the intensification rate of typhoon. The heavy rainfall index reconstructed from the volcanic cones of Jeju Island may be used as an indicator of past frequency and track changes of typhoon over highly populated East Asia.

Understanding Short-Term and Long-Term Price Fluctuations of Main Staple Food Commodities in Aceh Province, Indonesia: An ARDL Investigation

Aceh Province still relies on external sources from other regions for its main staple food commodities, resulting in unpredictable price fluctuations. To address this issue, it is essential to identify the key determinants responsible for these fluctuations and implement suitable preventive measures and policies. Utilizing monthly time-series data from January 2016 to December 2020 and employing the Autoregressive Distributed Lag (ARDL) approach, we investigate the short-term and long-term impact of variables like raw material prices, rainfall, and price index received by farmers on the price fluctuations. The results of the ARDL estimation reveal that all selected independent variables play a crucial role and significant in influencing the price fluctuations of main staple food commodities. Armed with these findings, we suggest that policymakers can provide necessary resources to farmers, strengthen weather monitoring systems, and enhance market transparency, thus better controlling future price fluctuations of regional staple food commodities.

THE USE OF GEOGRAPHIC INFORMATION SYSTEM (GIS) AND REMOTE SENSING (RS) FOR POTENTIAL UNCONFINED GROUNDWATER IN STRUCTURAL AND VOLCANO LANDFORM

The Northern Bandung area covers two landforms, namely volcano and structural landforms. Unconfined groundwater has become the water source for local people’s daily needs in both landforms. It is necessary to map the potential unconfined groundwater for both volcano and structural landforms due to the significant role of springs for the local people living in those areas. This research aims to map the unconfined groundwater on the volcano and structural landforms. This study employed the approaches of Analytical Hierarchy Process (AHP), Geographic Information System (GIS), and Remote Sensing (RS) using the variables of lineament density, rainfall, slope, and Topographic Wetness Index (TWI), hydrogeology, drainage density, and land use. The result shows that each variable has the Consistency Ratio (CR) below 0,1, resulting in consistent research variables and appropriate for discussion. The classification of the potential groundwater is divided into three categories: low, medium, and high. The survey validation finds that 147 springs spread at 86 high lands, 55 medium lands, and six lowlands. This model can be an alternative to map the potential unconfined groundwater in both volcano and structural areas.

Evaluating the Impact of Statistical Bias Correction on Climate Change Signal and Extreme Indices in the Jemma Sub-Basin of Blue Nile Basin

This study evaluates the effect of the statistical bias correction techniques of distribution mapping and linear scaling on climate change signals and extreme rainfall indices under different climate change scenarios in the Jemma sub-basin of the Upper Blue Nile Basin. The mean, cumulative distribution function (CDF), mean absolute error (MAE), probability of wet days (Prwet (%)), and 90th percentile (X90 (mm)) of observed rainfall and the regional climate model (RCM) simulations of rainfall with and without statistical bias correction were compared with the historical climate (1981–2005). For future (2071–2100) climate scenarios, the change in climate signal and extreme rainfall indices in the RCM simulations with and without bias correction were also evaluated using different statistical metrics. The result showed that the statistical bias correction techniques effectively adjusted the mean annual and monthly RCM simulations of rainfall to the observed rainfall. However, distribution mapping is effective and better than linear scaling for adjusting the probability of wet days and the 90th percentile of RCM simulations. In future climate scenarios, RCM simulations showed an increase in rainfall. However, the statistically bias-adjusted RCM outputs revealed a decrease in rainfall, which indicated that the statistical bias correction techniques triggered a change in climate signal. Statistical bias correction methods also result in changes in the extreme rainfall indices, such as frequency of wet days (R1mm), number of heavy precipitation days (R10mm), number of very heavy rainfall days (R20mm), and other intensity and frequency indices.

Assessing the performance of façade-integrated transparent photovoltaic panels for daylight and energy generation in high rise buildings of hot & dry climate

Indian cities are witnessing a boom in the growth of high-rise buildings to accommodate the ever-intensifying population. The modern trend of applying glass on the façades of tall buildings holds a threat due to Indian climates. Though it allows the daylight, reduces the artificial lighting, and provides a good view, the heat gained through the glass façades increases a building’s energy consumption. Therefore, an innovative, advanced strategy is required that replaces the glass with alternatives which can potentially minimize the energy demands either by controlling the heat gain or producing the electricity by itself, to match the demand. Transparent photovoltaic facades can ideally solve this purpose, as it allows the daylight and are multi-functional in nature. It produces electricity and fulfils several other tasks of solar protection and glare protection. But, the presence of solar cells may cause undesirable shading or low illuminance levels; hence, it is important to comprehensively assess the daylight performance and also calculate its energy performance. The aim of the current research paper is to determine the effectiveness of integrating the transparent photovoltaic panels over window/glass façades of daytime-occupied high-rise buildings in a Hot & dry climate and the form configurations required to achieve a considerable amount of energy on-site, without affecting the quantity of daylight. In this study, the performance of the amorphous silicon (a-Si) PV window, with 26.7% visibility, was evaluated through simulation using Rhino+Honeybee software over different building sizes & aspect ratios. The results showed that there was a gradual decline in the energy intensity with increase in length and the energy intensity got increased with an increase in floor height. The form with a high aspect ratio had the highest EPI of 228kwh/m2/year. Further, the form with the least aspect ratio achieved a better daylight performance. The findings of this research are potentially valuable for building designers and decision-makers to foresee the behaviour pattern of BIPV facades in energy generation, while affirming a comfortable and luminous environment.

An Analytical Model for Prediction of Floods Using Machine Learning

Abstract: One of the prodigious perquisites of Python is that you can build solutions for real time glitches. This applies in almost every manufacturing. From constructing models to forecast sicknesses to constructing web apps that can estimate the future sales of your online store, predict demand, predict future outcomes. In this paper we proposed a binary logistic model which can forecast floods grounded on the regular rainfall index for every year in Kerala, India.

Development of design guidelines for thermal, visual, and noise insulation performance of building envelope of low-rise commercial office buildings in the Indian tropical climate

Development of design guidelines for thermal, visual, and noise insulation performance of building envelope of low-rise commercial office buildings in the Indian tropical climate

Bias correction and spatial disaggregation of satellite-based data for the detection of rainfall seasonality indices

Like many other African countries, Ghana's rain gauge networks are rapidly deteriorating, making it challenging to obtain real-time rainfall estimates. In recent years, significant progress has been made in the development and availability of real-time satellite precipitation products (SPPs). SPPs may complement or substitute gauge data, enabling better real-time forecasting of stream flows, among other things. However, SPPs still have significant biases that must be corrected before the rainfall estimates can be used for any hydrologic application, such as real-time or seasonal forecasting. The daily satellite-based rainfall estimate (CHIRPS-v2) data were bias-corrected using the Bias Correction and Spatial Disaggregation (BSCD) approach. The study further investigated how bias correction of daily satellite-based rainfall estimates affects the identification of seasonality and extreme rainfall indices in Ghana. The results revealed that the seasonal and annual rainfall patterns in the region were better represented after the bias correction of the CHIRPS-v2 data. We observed that, before bias correction, the cessation dates in the country's southwest and upper middle regions were slightly different. However, they matched those of the gauge well after bias correction. The novelty of this study is that, in addition to improving rainfall using CHIRPS data, it also enhances the identification of seasonality indices. The paper suggests the BCSD approach for correcting rainfall estimates from other algorithms using long-term historical records indicative of the rainfall variability area under consideration.

A contrast in biennial variability of rainfall between central India and the Western Ghats and its mechanisms

The Western Ghats (WG) of Peninsular India, an integral part of the Indian summer monsoon rainfall, receives three times the average of all India rainfall. The averaged rainfall over WG is characterized by an intense tropospheric biennial oscillation (TBO) of 2–3 years of periodicity. The rainfall anomalies over WG are almost uncorrelated to rainfall over Central India (CI) in the TBO window. This study characterizes the WG and CI biennial rainfall variability and their governing mechanisms using observation and reanalysis datasets for 1980–2020. A zonally symmetric build-up of heat anomalies from the Iranian Plateau (IP) to the Tibetan Plateau (TP) extending from the surface to the mid-troposphere governs the TBO rainfall of CI. On the other hand, localized heating (cooling) over the IP and Pak-Afghanistan region (PA) and cooling (heating) over the TP governs the phases of TBO rainfall over WG. An increase (decrease) in anomalous heat build-up in the vertical column causes an increase (decrease) in anomalous moist static energy during positive (negative) WG TBO years extending over the IP (TP) region. Increased heating (cooling) over the IP and PA (TP) during positive WG TBO years can shift the center of near-surface cyclonic circulation, anchored over the Indian subcontinent and surrounding areas during the positive CI TBO years, to move westward. This shift in rainfall anomalies and the center of cyclonic circulation is because of the westward shift in anomalous moisture convergence from CI and significant moisture loss along southeastern Peninsular India. Considering the growing number of extreme rainfall events over the WG regions recently, the present work is an attempt to understand the mechanism through which TBO modulates WG and CI rainfall.

Relationship Between the Aerosol Loadings Over the Bay of Bengal and the Arabian Sea in the Early Summer and Asian Monsoon Rainfall Anomalies, and the Role of SST Anomalies in the Indian Ocean

AbstractTwo relatively long‐term satellite aerosol products during 1983–2020 and 2000–2020 are employed to investigate interannual relationships between anomalous aerosol loadings over the Bay of Bengal (BoB) and Arabian Sea (AS) in the early summer and Asian monsoon rainfall anomalies. It is shown that increased aerosol loading (IAL) over the BoB is primarily attributed to an intensified upstream aerosol transport by anomalous subtropical westerly winds and coincides with local sea surface temperature (SST) cooling due to intensified prevailing wind and the aerosol dimming effect, and vice versa. A deficient rainfall in India tends to concur with a BoB IAL while excessive rainfall with an AS IAL in May and June. In June, a BoB IAL coincides with significantly deficient rainfall in the southwestern China‐Huaihe River Basin primarily linked to an anomalous cyclone off the coast of Southeast China. Meanwhile a related overturning of Walker Circulation is identified to be driven by the contrast of anomalous convective activity between the southeastern Indian‐BoB and the South China Sea‐Philippine Sea. An AS IAL is associated with air warming in North India due to an elevated aerosol semi‐direct effect and remote forcing that reinforces the meridional air temperature gradient. The IAL over the respective BoB and AS in June are both preceded by cold SST anomalies in the western Indian Ocean that can be traced back to the preceding winter, but the corresponding atmospheric circulations driving aerosol‐monsoon interactions differ distinctly. The above aerosol‐monsoon relationships are sustained primarily during 2000–2020 and vague during 1983–2020.

Variability and predictability of the Northeast India summer monsoon rainfall

AbstractThe mesoscale orography over one of the wettest regions of the world makes the Northeast India (NEI) vulnerable to hydrological disasters while sustaining a biodiversity “hotspot.” The monsoon rainy season over the NEI is known to be longer than June–September (JJAS), but an objective definition has been lacking. Understanding the drivers and predictability of rainfall variability over the region is key for sustainable development planning and adaptation to increasing disasters in the backdrop of a warming climate but remained poor due to lack of a working definition of “summer monsoon season” over NEI. Here, using the relationship between rainfall over the region and winds at 850 hPa over North Bay of Bengal (BoB), we provide an objective definition of the monsoon season over NEI and argue that the “summer monsoon rainy season” over the NEI (NEIR) is from May to September (MJJAS). In contrast to a significant negative relationship between JJAS Central India (CI) rainfall and a JJAS El Niño–Southern Oscillation (ENSO) index, the MJJAS rainfall over the NEI has no relationship with MJJAS or JJAS ENSO. Instead, we unravel that (a) the tropical Northwest‐Pacific (TNWP) sea surface temperature (SST), (b) the South Equatorial Indian Ocean Dipole (SEIOD) and (c) the Atlantic Zonal mode (AZM) are potential drivers of the NEIR variability. Using a causal inference algorithm, namely the Peter and Clark Momentary Conditional Independence (PCMCI) method, we show that SEIOD directly influences NEIR while TNWP SST has a two‐way connection with the NEIR. While “internal variability” may be higher over the NEI, significant modulation of the variances of the subseasonal fluctuations by predictable drivers like the Atlantic Niño and North Atlantic water temperature provides optimism for seasonal prediction of the NEIR.

Unprecedented rainfall index for reducing fatalities caused by sediment-related disasters

This study evaluated effectiveness of the unprecedented rainfall index, TP, proposed by Kosugi (2022) in achieving appropriate evacuation actions by municipalities and residents for reducing victims caused by rainfall-induced landslides and debris flows. TP is defined as the time stamp representing the situation that “current rainfall is the largest since TP”; namely, if we go back to time before TP, the rainfall at the current time is of a magnitude that has been experienced in the past (i.e., the situation is within the historical range) in every evaluation criterion used in analyses. In other words, the rainfall at the current time is of a magnitude never experienced in the period from TP through the present for at least one criterion. TP was computed for all disasters that caused death and missing of people in Japan in 2021: the Atami, Unzen, and Okaya disasters. In every disaster, at the time of the landslide and debris flow occurrences, TP reached the beginning of rainfall record, indicating that the rainfall reached a magnitude never experienced in the past. Based on these results, it was confirmed that (1) TP time series has a large potential to reduce the normalcy bias in all 3 disasters studied, (2) for evaluating anomaly in rainfall, rainfall features should be analyzed based on various evaluation criteria, and (3) the time of past land alteration at an individual location, such as an occurrence time of historical storm event and a construction time of embankment slope, might be set as a TP threshold.

Future changes in the mean and variability of extreme rainfall indices over the Guinea coast and role of the Atlantic equatorial mode

Abstract. The occurrence of climate extremes could have dramatic impacts on various sectors such as agriculture, water supply, and energy production. This study aims to understand part of the variability in the extreme rainfall indices over Guinea coast that can be related to the Atlantic equatorial mode (AEM), whose positive phases are associated with an increase in the intensity and frequency of rainfall events. We use six extreme indices computed from six observed rainfall databases and historical and SSP5-8.5 simulations from 24 general circulation models (GCMs) that participate in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to study changes in extreme rainfall events over Guinea coast during July–September. Under present-day conditions, we found that current GCMs clearly overestimate the frequency of wet events and the maximum number of consecutive wet days. The magnitude of the other extreme indices simulated is within the range of the observations which, moreover, present a large spread. Our results confirm the existing studies. However, less attention has been paid to the evaluation of the modelled rainfall extremes associated with the AEM under different climate conditions, while the variability of the AEM is expected to decrease in the future, with a potentially significant impact on the extreme events. Here, we use six (one) observed rainfall (sea surface temperature) data and 24 GCM outputs to investigate the present-day, near-term, mid-term, and long-term future links between the AEM and the extreme rainfall events over the Guinea coast. The biases in the extreme rainfall responses to the AEM are subject to a large spread across the different models and observations. For the long-term future (2080–2099), less frequent and more intense rainfall events are projected. As an illustration, the multimodel ensemble median (EnsMedian) maximum rainfall during 5 consecutive wet days (RX5day) would be 21 % higher than under present-day conditions. Moreover, the variability of the majority of the extreme indices over the Guinea coast is projected to increase (48 % for RX5day in the long-term future). By contrast, the decreased variability of the AEM in a warmer climate leads to a reduced magnitude of the rainfall extreme responses associated with AEM over the Guinea coast. While under present-day conditions the AEM explains 18 % of the RX5day variance in the EnsMedian, this value is reduced to 8 % at the end of 21st century. As a consequence, in absolute, there is a projected increase in the total variability of most of the extreme rainfall indices, but the contribution of the AEM to this variability weakens in a warmer future climate.

APPLICATION OF THE BLACK SCHOLES METHOD FOR COUNTING AGRICULTURAL INSURANCE PREMIUM PRICE BASED ON RAINFALL INDEX IN KAPUAS HULU REGENCY

High-intensity rainfall is one of the factors that can interfere with the state of agriculture. Agricultural insurance is an insurance that can be used to reduce risks related to agricultural losses such as rice production. Climate-based agricultural insurance is a management of climate-related risks. This study aims to determine the rainfall index and calculate the value of agricultural insurance premiums based on the climate index (rainfall) in Kapuas Hulu Regency using the Black Scholes method. In calculating the value of agricultural insurance premiums based on the rainfall index, it starts by calculating the value of the correlation coefficient between rainfall and rice production. Then the value of the rainfall index is obtained, which then the value of the index is tested for lognormality to meet the assumptions on the Black Scholes method, after which it calculates the ln return value of the index value obtained, the last step is to calculate the value of agricultural insurance premiums. Based on case studies, the results obtained are when the risk-free interest rate is 3.5% and rainfall is 54.23 mm the premium paid is Rp 2,386,824 and when the rainfall is 75.39 mm the premium paid is Rp 3,898,142. If the risk-free interest rate is 4% and the bulk is 54.23 mm, the premium paid is IDR 2,383,842, and when the rainfall is 75.39 mm the premium paid is IDR 3,893,272. When the risk-free interest rate is 5% and rainfall is 54.23 mm the premium paid is Rp 2,377,890 and if the rainfall is 75.39 mm the premium paid is Rp 3,883,551. So, the higher the rainfall, the greater the premium value payment. If the risk-free interest rate gets bigger then the premium payment will be smaller.

Cointegration between Agricultural GDP and Inputs in India: An Empirical Analysis

Using Johansen cointegration test, the study analyzes the long-term relationship between the agricultural GDP and its factors (agricultural land, irrigation, fertilizer use, and absolute residual of rainfall and temperature) in India during the period 1960-61 to 2019-20. The analysis considers the absolute residual values of rainfall and temperature as relevant factors. Overall, agricultural production demonstrates a long-term correlation with rainfall and temperature trends due to its capacity to adapt to these variables over extended periods. The findings indicate a significant long-run relationship between these variables, with at least two cointegrating equations. The normalized cointegrating coefficients reveal a positive relationship between agricultural productivity and the size of agricultural land, irrigation, and fertilizer use, while the absolute residual of rainfall and temperature have a negative impact. The adjustment coefficients show that the speed of adjustment to the long-run equilibrium is relatively slow, with the short-term dynamics driven by changes in the residuals. Additionally, the results suggest that policies aimed at increasing agricultural land, irrigation, and fertilizer use in India could positively impact agricultural productivity in the long run. Nevertheless, policymakers should be cautious about the potential adverse effects of policies that impact the residuals, and the implementation of such policies should be done with prudence and patience. These results can provide valuable insights for policymakers and stakeholders in the agricultural sector in India.

Manifestation of spatially varying demarcations in Indian rainfall trends through change-point analysis (1901-2020).

The rainfall over the Indian region, governed majorly by the monsoonal flow, is a point of research in the perspective of climate change. In this paper, we compute the change points in the rainfall series at every grid of the India Meteorological Department (IMD) daily gridded rainfall data for a period of 120years (1901 to 2020). The map shows clearly demarcated regions indicating different zones, where the rainfall statistics have altered at different periods. It is observed that in a major part of central India, the shift in rainfall intensity is mainly associated with the time frame 1955-1965; in the Indo-Gangetic plain, the changes are found to be more recent (1990), while the latest changes (post 2000) are observed particularly for North Eastern region and some parts along the East Indian coast. The changeover years are significant at a 95% confidence level for most part of the Indian landmass. The causes may be surmised due to moisture transport from the Arabian Sea (Central India), the presence of aerosol (Gangetic Plain), and the possible revival of monsoon due to land-ocean gradient (Eastern coast and North East India). This is the first-ever study which provides a comprehensive daily rainfall change point map over India using 120years of gridded station data.

Potential savings of potable water due to the use of rainwater in a shopping mall in Recife/PE

Water is one of the world's most valuable resources and indispensable for human survival. Given this, climate change and population growth generate an increase in demand for water resources, leading to water scarcity. The use of rainwater is a measure that mitigates environmental impacts, contributes to the retention of rainwater and reduces the demand for drinking water. Therefore, water scarcity stimulates the development of alternatives for water conservation. The objective of this study is to estimate the potential savings of potable water to be replaced by rainwater for non-potable use in a shopping center building in the Northeast of Brazil. The methodology consisted in simulating the volume of the reservoir for storage of collected rainwater. A study of the rainfall indexes and their periodicity was carried out, evaluating the months with highest and lowest precipitation. Subsequently, the Neptune computer program was used to determine the volume of the reservoir. To this end, a study was carried out with different catchment areas, in order to define the area that offers the best potential for supplying the water demands. The results indicated that the most suitable reservoir volume is 117 m³, with potential savings of 6.02% for drinking water and a complete and partial supply of 52.56% for rainwater demand. It was concluded that the potential for drinking water savings justifies the implementation of a rainwater harvesting system in a project of this size, proving to be technically feasible.

A gauge-based sub-daily extreme rainfall climatology for western Europe

We present here the first climatology of extreme sub-daily rainfall for a large part of western Europe, developed using a newly developed set of sub-daily rainfall indices, derived from a recently created global sub-daily rainfall (GSDR) dataset. The indices describe sub-daily (3-hourly) rainfall extremes in terms of their intensity, frequency and timing. Analysis of the frequency index (number of 3hr periods with >20 mm rain (R3hr20mm)) and a suite of intensity indices including the annual maxima (Rx3hr) and values of the 99.9th percentile at each gauge (R99.9p3hr) indicate a peak in the frequency and intensity of short-duration rainfall extremes in summer across most of western Europe, while areas around the north-west Mediterranean experience the highest intensities and frequencies of 3-hr rainfall in autumn. The index of contribution from 3-h annual maxima to the daily total (Rx3hrP) indicates that these events are often produced by very short-duration or very peaked storms, with a tendency towards a late afternoon or evening peak in the time of occurrence. There are also clear differences in the spatiotemporal occurrence of the sub-daily extremes when compared to extreme daily rainfall indices, which could have repercussions for flood management. Additional analysis of the sub-daily rainfall indices within the context of Köppen-Geiger climate zones indicates Mediterranean-type climate zones experience more intense and more frequent sub-daily extremes, while the intensity of rainfall within cooler climate zones is lower and the most intense events are restricted to summer. The climate zones are found to be a relatively good indicator of the extreme rainfall characteristics that can be expected in a region. Being able to compare sub-daily rainfall characteristics across a wide region in this manner greatly enhances our ability to investigate future variability and change in sub-daily extremes and will aid in high-resolution climate model validation.

Precipitation-type discrimination and changes in related climate indices in the upper Heihe River Basin during 1960–2021

Introduction: Precipitation in the upstream region of the Heihe River basin (UHRB) in the northeastern of the Tibetan Plateau, which is the main water source of the basin, has undergone drastic changes in extreme climate events in recent decades. In addition to the amount of precipitation, the type of precipitation has a substantial impact on hydrological processes.Methods: In this study, we compared the results from three methods aimed at improving precipitation type estimation based on daily precipitation type records for 24 discontinuous years. Based on the precipitation type distinction, we examined the spatial and temporal changes in the total precipitation, rainfall, snowfall and air temperature at the six stations as well as the spatial average of the UHRB during the past 62 years. We also analyzed changes in the quantity, duration magnitude, and frequency of extreme precipitation using the RClimDex model and statistical analysis.Results: The probability of detection value of the T3.6_4.5 method was 1.9%, indicating the estimation was closest to actual records. The analyses based on precipitation type diving showed that rainfall accounts for an average of 81.9% of the total precipitation received in a year. In the context of large scale climate warming, temperatures at all six stations increased significantly, but precipitation changes were only apparent at about half of the stations which were located in regions of higher elevation and influenced by both ENSO and the East Asian monsoon. Analysis of the spatial averages in the UHRB revealed that the annual drought events (CDD) were significantly alleviated, and that the growing season length (GSL) was significantly extended. The annual total precipitation, rainfall, and extreme precipitation indices (P99P, P95P, R95P, and SDII) increased in magnitude, and the frequency of extreme precipitation events (P10mm) also significantly increased.Discussion: The findings of this study indicate that under the background of climate warming, the changes of precipitation patterns in the UHRB which may have resulted in bringing better vegetation growth, but also the increasingly frequent extreme rainfall events may pose challenges to growing extreme rainfall events to agriculture and other human activities in local and downstream areas.

Impact of climate change on climate extreme indices in Kaduna River basin, Nigeria.

This study examined the impact of climate change on climate extreme indices in the Kaduna River basin, Nigeria. Large-scale atmospheric variables derived from the Global Climate Model (GCM), Coupled Model Intercomparison Project Phase 5 (CMIP5) (CanESM2) were used to develop a high-resolution climate using a Statistical Down Scaling Model. The adapted Caussinus-Mestre algorithm for homogenizing networks of temperature series and multivariate bias correction based on an N-dimension probability function were used to homogenize and correct the climate data, respectively. Fifteen climate extreme indices were computed using RClimdex. The coefficient of variance, Kruskal-Wallis test, and the modified Mann-Kendall test were used to assess the variation and trends. Wavelet analysis was used to determine the periodicities of the indices (1980-2020). The findings revealed a significant warming trend with low variability of temperature indices. The moderate variability with an insignificant decreasing trend was found for rainfall indices. Similarly, the future climate indices indicate a continuing positive trend in the temperature extreme indices. The majority of climate indices have a periodicity of less than or equal to 10years for high frequency, except for PRCPTOT, R10MM, R20MM, Rx5day, SDII, TN90p, and TX90p for temperature indices. The findings conclude that the periodicity pattern of climate extreme indices is related to atmospheric phenomena (such as quasi-biennial oscillation, QBO), which indicate the impact of climate change. As a result, this can serve as an early warning for possible extreme event occurrences in the basin. The CMIP6 should be used to compare with the results of this study to provide a detailed assessment of the current implication of climate change on the catchment.