Assessment of drought trend and variability in India using wavelet transform

ABSTRACT

This paper presents an analysis of trends in six drought variables at 566 stations across India over the period 1901–2002. Six drought variables were computed using standardized precipitation index (SPI). The Mann-Kendall (MK) trend test and Sen’s slope estimator were used for trend analysis of drought variables. Discrete wavelet transform (DWT) was used to identify the dominant periodic components in trends, whereas the significance of periodic components was examined using continuous wavelet transform (CWT) based global wavelet spectrum (GWS). Our results show an increasing trend in droughts in eastern, northeastern and extreme southern regions, and a decreasing trend in the northern and southern regions of the country. The periodic component influencing the trend was 2–4 years in south, 4–8 years in west, east and northeast, 8–64 years in central parts and 32–128 years in the north; however, most of the periodic components were not statistically significant.     

Time–frequency characterization of sub-divisional scale seasonal rainfall in India using the Hilbert–Huang transform

Time–frequency characterization is useful in understanding the nonlinear and non-stationary signals of the hydro-climatic time series. The traditional Fourier transform, and wavelet transform approaches have certain limitations in analyzing non-linear and non-stationary hydro-climatic series. This paper presents an effective approach based on the Hilbert–Huang transform to investigate time–frequency characteristics, and the changing patterns of sub-divisional rainfall series in India, and explored the possible association of monsoon seasonal rainfall with different global climate oscillations. The proposed approach integrates the complete ensemble empirical mode decomposition with adaptive noise algorithm and normalized Hilbert transform method for analyzing the spectral characteristics of two principal seasonal rainfall series over four meteorological subdivisions namely Assam-Meghalaya, Kerala, Orissa and Telangana subdivisions in India. The Hilbert spectral analysis revealed the dynamic nature of dominant time scales for two principal seasonal rainfall time series. From the trend analysis of instantaneous amplitudes of multiscale components called intrinsic mode functions (IMFs), it is found that both intra and inter decadal modes are responsible for the changes in seasonal rainfall series of different subdivisions and significant changes are noticed in the amplitudes of inter decadal modes of two seasonal rainfalls in the four subdivisions since 1970s. Further, the study investigated the links between monsoon rainfall with the global climate oscillations such as Quasi Bienniel Oscillation (QBO), El Nino Southern Oscillation (ENSO), Sunspot Number (SN), Atlantic Multidecadal Oscillation (AMO) etc. The study noticed that the multiscale components of rainfall series IMF1, IMF2, IMF3, IMF4 and IMF5 have similar periodic structure of QBO, ENSO, SN, tidal forcing and AMO respectively. As per the seasonal rainfall patterns is concerned, the results of the study indicated that for Assam-Meghalaya subdivision, there is a likelihood of extreme rare events at ~0.2 cycles per year, and both monsoon and pre-monsoon rainfall series have decreasing trends; for Kerala subdivision, extreme events can be expected during monsoon season with shorter periodicity (~2.5 years), and monsoon rainfall has statistically significant decreasing trend and post-monsoon rainfall has a statistically significant increasing trend; and for Orissa subdivision, there are chances of extremes rainfall events in monsoon season and a relatively stable rainfall pattern during post-monsoon period, but both monsoon and post-monsoon rainfall series showed an overall decreasing trend; for Telangana subdivision, there is a likelihood of extreme events during monsoon season with a periodicity of ~4 years, but both monsoon and post-monsoon rainfall series showed increasing trends. The results of correlation analysis of IMF components of monsoon rainfall and five climate indices indicated that the association is expressed well only for low frequency modes with similar evolution of trend components.     

Trends and variability in African long-term precipitation

African precipitation trends are commonly analyzed using short-term data observed over small areas. This study analyzed changes in long-term (1901–2015) annual and seasonal precipitation of high spatial (0.5°?×?0.5° grid) resolution covering the entire African continent. To assess an acceleration/deceleration of the precipitation increase/decrease, trend magnitude (mm/year) over the period 1991–2015 was subtracted from that of 1965–1990 to obtain Slope Difference (SD, mm/year). Co-variation of precipitation sub-trends with changes in large-scale ocean–atmosphere conditions was investigated. Regardless of the trend significance, in most parts of Africa, annual precipitation exhibited negative (positive) trends over the period 1965–1990 (1991–2015). Thus, the continent was, on average, recently (from 1991 to 2015) wetter than it was over the period 1965–1990. From 1901 to 2015, the null hypothesis H₀ (no trend) was rejected (p < 0.05) for annual precipitation decrease over West Africa especially along the coastal areas near the Gulf of Guinea. The H₀ was also rejected (p < 0.05) for the increase in annual and September–November precipitation of some areas along the Equatorial region (such as in Gabon and around Lake Victoria). For both annual and seasonal precipitation, the least SD values in the range ??1 to 1 mm/year were obtained in areas north of 10° N. The SD value went up to about 20 mm/year over the Sahel belt especially for the peak monsoon (June–August season). For the March–May precipitation, positive SD values were obtained in the Western part of Southern Africa. However, negative SD values (around ??5 mm/year) were obtained in the Horn of Africa. Variation in sub-trends of the East African precipitation was found to be driven by changes in Sea Surface Temperature (SST) of the Indian and Atlantic Oceans. Variability in sub-trends of the West African precipitation is linked to changes in SST of the Atlantic Ocean. Changes in sub-trends of the South African precipitation correspond to anomalies in SST from the Pacific and Indian Oceans. Knowledge of precipitation changes and possible drivers is vital for predictive adaptation regarding the impacts of climate variability on hydro- or agro-meteorology.     

Observed and Forecasted Intraseasonal Activity of Southwest Monsoon Rainfall over India During 2010, 2011 and 2012

The monsoon seasons of 2010 and 2011, with almost identical seasonal total rainfall over India from June to September, are associated with slightly different patterns of intraseasonal rainfall fluctuations. Similarly, the year 2012, with relatively less rainfall compared to 2010 and 2011, also witnessed different intraseasonal rainfall fluctuations, leading to drought-like situations over some parts of the country. The present article discusses the forecasting aspect of monsoon activity over India during these 3 years on an extended range time scale (up to 3 weeks) by using the multimodel ensemble (MME), based on operational coupled model outputs from the ECMWF monthly forecasting system and the NCEP’s Climate Forecast System (CFS). The average correlation coefficient (CC) of weekly observed all-India rainfall (AIR) and the corresponding MME forecast AIR is found to be significant, above the 98 % level up to 2 weeks (up to 18 days) with a slight positive CC for the week 3 (days 19–25) forecast. However, like the variation of observed intraseasonal rainfall fluctuations during 2010, 2011 and 2012 monsoon seasons, the MME forecast skills of weekly AIR are also found to be different from one another, with the 2012 monsoon season indicating significant CC (above 99 % level) up to week 2 (12–18 days), and also a comparatively higher CC (0.45) during the week 3 forecast (days 19–25). The average CC between observed and forecasted weekly AIR rainfall over four homogeneous regions of India is found to be the lowest over the southern peninsula of India (SPI), and northeast India (NEI) is found to be significant only for the week 1 (days 5–11) forecast. However, the CC is found to be significant over northwest India (NWI) and central India (CEI), at least above the 90 % level up to 18 days, with NWI having slightly better skill compared to the CEI. For the individual monsoon seasons of 2010, 2011 and 2012, there is some variation in CC and other skill scores over the four homogeneous regions. Thus the slight variations in the characteristics of intraseasonal monsoon rainfall over India is associated with variations in predictive skill of the coupled models and the MME-based predictions of intraseasonal monsoon fluctuations for 2–3 weeks, providing encouraging results. The MME forecast in 2010 is also able to provide useful guidance, well in advance, about an active September associated with a delayed withdrawal of the monsoon and also the heavy rainfall over north Pakistan.     

Analysis of long-term rainfall trends in India

Abstract

The study of precipitation trends is critically important for a country like India whose food security and economy are dependent on the timely availability of water. In this work, monthly, seasonal and annual trends of rainfall have been studied using monthly data series of 135 years (1871–2005) for 30 sub-divisions (sub-regions) in India. Half of the sub-divisions showed an increasing trend in annual rainfall, but for only three (Haryana, Punjab and Coastal Karnataka), this trend was statistically significant. Similarly, only one sub-division (Chattisgarh) indicated a significant decreasing trend out of the 15 sub-divisions showing decreasing trend in annual rainfall. In India, the monsoon months of June to September account for more than 80% of the annual rainfall. During June and July, the number of sub-divisions showing increasing rainfall is almost equal to those showing decreasing rainfall. In August, the number of sub-divisions showing an increasing trend exceeds those showing a decreasing trend, whereas in September, the situation is the opposite. The majority of sub-divisions showed very little change in rainfall in non-monsoon months. The five main regions of India showed no significant trend in annual, seasonal and monthly rainfall in most of the months. For the whole of India, no significant trend was detected for annual, seasonal, or monthly rainfall. Annual and monsoon rainfall decreased, while pre-monsoon, post-monsoon and winter rainfall increased at the national scale. Rainfall in June, July and September decreased, whereas in August it increased, at the national scale.

Citation Kumar, V., Jain, S. K. & Singh, Y. (2010) Analysis of long-term rainfall trends in India. Hydrol. Sci. J. 55(4), 484–496.     

Interannual climate variability in South America: impacts on seasonal precipitation, extreme events, and possible effects of climate change

Interannual variability is an important modulator of synoptic and intraseasonal variability in South America. This paper seeks to characterize the main modes of interannual variability of seasonal precipitation and some associated mechanisms. The impact of this variability on the frequency of extreme rainfall events and the possible effect of anthropogenic climate change on this variability are reviewed. The interannual oscillations of the annual total precipitation are mainly due to the variability in austral autumn and summer. While autumn is the dominant rainy season in the northern part of the continent, where the variability is highest (especially in the northeastern part), summer is the rainy season over most of the continent, thanks to a summer monsoon regime. In the monsoon season, the strongest variability occurs near the South Atlantic Convergence Zone (SACZ), which is one of the most important features of the South American monsoon system. In all seasons but summer, the most important source of variability is ENSO (El Ni?o Southern Oscillation), although ENSO shows a great contribution also in summer. The ENSO impact on the frequency of extreme precipitation events is also important in all seasons, being generally even more significant than the influence on seasonal rainfall totals. Climate change associated with increasing emission of greenhouse gases shows potential to impact seasonal amounts of precipitation in South America, but there is still great uncertainty associated with the projected changes, since there is not much agreement among the models’ outputs for most regions in the continent, with the exception of southeastern South America and southern Andes. Climate change can also impact the natural variability modes of seasonal precipitation associated with ENSO.     

Analysis of trends in reference evapotranspiration data in a humid climate

Abstract

Statistically significant FAO-56 Penman-Monteith (FAO-56 PM) and adjusted Hargreaves (AHARG) reference evapotranspiration (ET₀) trends at monthly, seasonal and annual time scales were analysed by using linear regression, Mann-Kendall and Spearman’s Rho tests at the 1 and 5% significance levels. Meteorological data were used from 12 meteorological stations in Serbia, which has a humid climate, for the period 1980–2010. Web-based software for conducting the trend analyses was developed. All of the trends significant at the 1 and 5% significance levels were increasing. The FAO-56 PM ET₀ trends were almost similar to the AHARG trends. On the seasonal time scale, for the majority of stations significant increasing trends occurred in summer, while no significant positive or negative trends were detected by the trend tests in autumn for the AHARG series. Moreover, 70% of the stations were characterized by significant increasing trends for both annual ET₀ series.

Editor Z.W. Kundzewicz; Associate editor S. Grimaldi

Citation Gocic, M. and Trajkovic, S., 2013. Analysis of trends in reference evapotranspiration data in a humid climate. Hydrological Sciences Journal, 59 (1), 165–180.     

Spatio-temporal trends of precipitation,its aggressiveness and concentration,along the Pacific coast of South America (36–49°S)

ABSTRACT

Precipitation is the most critical climatic element that directly affects the availability of water resources. The objective of this study was to describe and discuss spatio-temporal patterns of annual precipitation, its aggressiveness, and its concentration along the southwest coast of South America (36°–49°S) from 1930 to 2006. An annual and multi-decadal analysis was applied to 107 sampling stations distributed throughout this region, using the Mann-Kendall test (MK), and the Sampling Uncertainty Analysis (SUA) coupled with Gumbel probability density function (SUA-Gumbel). The analysis revealed positive but not significant trends in annual precipitation and aggressiveness for the region between 36° and 44°S, at least during the last 50 years of the analysed period. However, a significant decrease in annual precipitation and aggressiveness was observed between 44° and 49°S during the same period. The annual concentration of precipitation became slightly more seasonal in the last 50 years within the entire study area.     

Air Surface Temperature Correlation with Greenhouse Gases by Using Airs Data Over Peninsular Malaysia

The main objective of this study is to develop algorithms for calculating the air surface temperature (AST). This study also aims to analyze and investigate the effects of greenhouse gases (GHGs) on the AST value in Peninsular Malaysia. Multiple linear regression is used to achieve the objectives of the study. Peninsular Malaysia has been selected as the research area because it is among the regions of tropical Southeast Asia with the greatest humidity, pockets of heavy pollution, rapid economic growth, and industrialization. The predicted AST was highly correlated (R = 0.783) with GHGs for the 6-year data (2003–2008). Comparisons of five stations in 2009 showed close agreement between the predicted AST and the observed AST from AIRS, especially in the wet season (within 1.3 K). The in situ data ranged from 1 to 2 K. Validation results showed that AST (R = 0.776–0.878) has values nearly the same as the observed AST from AIRS. We found that O₃ during the wet season was indicated by a strongly positive beta coefficient (0.264–0.992) with AST. The CO₂ yields a reasonable relationship with temperature with low to moderate beta coefficient (?0.065 to 0.238). The O₃, CO₂, and environmental variables experienced different seasonal fluctuations that depend on weather conditions and topography. The concentration of gases and pollution were the highest over industrial zones and overcrowded cities, and the dry season was more polluted compared with the wet season. These results indicate the advantage of using the satellite AIRS data and a correlation analysis to investigate the effect of atmospheric GHGs on AST over Peninsular Malaysia. An algorithm that is capable of retrieving Peninsular Malaysian AST in all weather conditions with total uncertainties ranging from 1 to 2 K was developed.     

Analysis of the rainfall variability and change in the Republic of Benin (West Africa)

ABSTRACT

The temporal variation and trends of annual rainfall distribution in Benin were examined using data from 1940 to 2015 at six meteorological stations and three raingauges stationed throughout the country. The nonparametric modified Mann-Kendal (MK) and Levene tests were applied to detect trends and heteroscedasticity, respectively. For six of the time series, no significant trends were detected. A Bayesian multiple change points detection approach was applied to the rainfall time series, and most (six of nine) exhibited abrupt change points, corresponding to the alternation between wet (before 1968 and after 1990) and dry (1969–1990) periods. No significant trends or breakpoints and changes in the variance were observed for the spatial average rainfall time series. Seven modified MK trend tests were applied; the trends are affected by the selected MK method and rainfall statistics. Oceanic and/or atmospheric influences on the rainfall in Benin were examined by investigating the correlation between the precipitation time series and several indices. Negative seasonal correlations were determined for the North Atlantic Oscillation, Pacific Decadal Oscillation and Niño3, while positive seasonal correlations were observed for the Southern Oscillation, Antarctic Oscillation and Dipole Mode Index.     

Effect of autocorrelation on temporal trends in rainfall in a valley region at the foothills of Indian Himalayas

This study examines the effect of autocorrelation on step and monotonic trends in seasonal and annual rainfall. Initially, for step change, modified-Pettitt test is applied in two ways. First, using the corrected and unbiased trend-free-pre-whitening (TFPWcu) approach. Second, using a new approach in which time series is modelled by intervention analysis for modified Pettitt test. Subsequently, for monotonic trends, Mann–Kendall (MK) and six approaches of modified Mann–Kendall (MMK) test are applied to NCDC data for period 1901–2012 and its sub-periods. Approaches of MMK include pre-whitening (PW), trend-free-pre-whitening (TFPW), TFPWcu, two Variance Correction Approaches (VCAs) based on empirical formula (VCA:CF1) and Monte-Carlo-Simulations (VCA:CF2) and long term persistence (MK-LTP). A single change point is identified in 1970 for annual and monsoon rainfall from original and modified-Pettitt’s test using TFPWcu, while time series modelling approach has not exhibited any change point. Process shift in rainfall series is also studied using CUSUM and multiple change points are identified using Segment-Neighbourhood method. Outcomes of MMK show that TFPWcu is able to efficiently limit the effect of autocorrelation and may be preferred over PW and TFPW. The VCA:CF2 is not dependent on whole autocorrelation structure and corrects variance of all data series using lag-1 autocorrelation and may be preferred over VCA:CF1. MK-LTP considers long term persistence and it has exhibited presence of weaker trends than exhibited by other approaches. VCA:CF2 and MK-LTP are used to study trends of rainfall in Dehradun.     

Trends in hydrological and climatic variables affected by four variations of the Mann-Kendall approach in Urmia Lake basin,Iran

ABSTRACT

The trends in hydrological and climatic time series data of Urmia Lake basin in Iran were examined using the four different versions of the Mann-Kendall (MK) approach: (i) the original MK test; (ii) the MK test considering the effect of lag-1 autocorrelation; (iii) the MK test considering the effect of all autocorrelation or sample size; and (iv) the MK test considering the Hurst coefficient. Identification of hydrological and climatic data trends was carried out at monthly and annual time scales for 25 temperature, 35 precipitation and 35 streamflow gauging stations selected from the Urmia Lake basin. Mann-Kendall and Pearson tests were also applied to explore the relationships between temperature, precipitation and streamflow trends. The results show statistically significant upward and downward trends in the annual and monthly hydrological and climatic variables. The upward trends in temperature, unlike streamflow, are much more pronounced than the downward trends, but for precipitation the behaviour of trend is different on monthly and annual time scales. Furthermore, the trend results were affected by the different approaches. Specifically, the number of stations showing trends in hydrological and climatic variables decreased significantly (up to 50%) when the fourth test was considered instead of the first and the absolute value of the Z statistic for most of the time series was reduced. The results of correlations between streamflow and climatic variables showed that the streamflow in Urmia Lake basin is more sensitive to changes in temperature than those of precipitation. The observed decreases in streamflow and increases in temperature in the Urmia Lake basin in recent decades may thus have serious implications for water resources management under the warming climate with the expected population growth and increased freshwater consumption in this region.

Editor Z. W. Kundzewicz; Associate editor Q. Zhang     

Microphysical characteristics of rainfall during different seasons over a coastal tropical station using disdrometer

Drop size distribution (DSD) over the tropical region exhibit pronounced variations during different monsoon seasons. Measurements from an impact type Joss–Waldovgel disdrometer is used for characterization of drop size distribution and its integral parameters over a tropical coastal station (Thiruvananthapuram, 8.31°N, 76.54°E, 20 m asl). Rain events were identified during the winter, premonsoon, summer monsoon and postmonsoon seasons from 8 years, computed rain duration (min) and accumulated rain water (mm). Rain intensity (mm h^?1), mean drop diameter (D_m, mm) and total number concentration of raindrops (N_T, m^?3) were calculated on each sampling interval and classified in to different bins. The different range bins of rain intensity and their relative contributions towards total rainfall are different for different seasons. Maximum events were reported on the R2 (heavy drizzle/light rain) type, but the contribution of rainfall (mm) is mainly registered on R4 (heavy rain) type. Similarly, the N_T and D_m are also showing different characteristics during different monsoon seasons. Frequency of occurrence of D_m is higher in D_m2 (1–2 mm) followed by D_m1 (D_m < 1 mm) and then D_m3 (2–3 mm) with difference in magnitudes for different seasons. On analysing relative rainfall contribution from different mean diameter bins, it can be observed that D_m2 and D_m3 (1–3 mm) are the major contributors to the total rainfall. In the case of N_T, both frequency and accumulated water are almost same or comparable for the different bins during all the seasons. The D_m and N_T are positively related with different intensity bins. The lower rainfall intensity bins show higher duration during the summer monsoon season and lower duration during the premonsoon season, the higher intensity range bins show lower duration for the premonsoon season and higher duration for the postmonsoon season.     

Response of surface water chemistry to reduced levels of acid precipitation: comparison of trends in two regions of New York,USA

In light of recent reductions in sulphur (S) and nitrogen (N) emissions mandated by Title IV of the Clean Air Act Amendments of 1990, temporal trends and trend coherence in precipitation (1984–2001 and 1992–2001) and surface water chemistry (1992–2001) were determined in two of the most acid‐sensitive regions of North America, i.e. the Catskill and Adirondack Mountains of New York. Precipitation chemistry data from six sites located near these regions showed decreasing sulphate (SO₄^2?), nitrate (NO₃^?), and base cation (C_B) concentrations and increasing pH during 1984–2001, but few significant trends during 1992–2001. Data from five Catskill streams and 12 Adirondack lakes showed decreasing trends in SO₄^2? concentrations at all sites, and decreasing trends in NO₃^?, C_B, and H⁺ concentrations and increasing trends in dissolved organic carbon at most sites. In contrast, acid‐neutralizing capacity (ANC) increased significantly at only about half the Adirondack lakes and in one of the Catskill streams. Flow correction prior to trend analysis did not change any trend directions and had little effect on SO₄^2? trends, but it caused several significant non‐flow‐corrected trends in NO₃^? and ANC to become non‐significant, suggesting that trend results for flow‐sensitive constituents are affected by flow‐related climate variation. SO₄^2? concentrations showed high temporal coherence in precipitation, surface waters, and in precipitation–surface water comparisons, reflecting a strong link between S emissions, precipitation SO₄^2? concentrations, and the processes that affect S cycling within these regions. NO₃^? and H⁺ concentrations and ANC generally showed weak coherence, especially in surface waters and in precipitation–surface water comparisons, indicating that variation in local‐scale processes driven by factors such as climate are affecting trends in acid–base chemistry in these two regions. Copyright © 2005 John Wiley & Sons, Ltd.     

Trends in precipitation extremes over the Yellow River basin in North China: Changing properties and causes

The Mann–Kendall test, composite analysis, and 68 high‐quality meteorological stations were used to explore the spatiotemporal variations and causes of precipitation extremes over the Yellow River basin (YRB) during the period of 1960–2011. Results showed that (a) the YRB is characterized by decreases of most precipitation indices, excluding the simple daily intensity index, which has increasing trends in most locations, suggesting that the intensity of rainfall and the probability of occurrence of droughts have increased during the last decades. (b) Trends of extreme precipitation show mixed patterns in the lower reach of the YRB, where drought–flood disasters have increased. The increases in heavy rainfall and decreases in consecutive wet days in recent years over the northwestern portions of the YRB indicate that the intensity and frequency of above‐normal precipitation have been trending upward in domains. In the central‐south YRB, the maximum 1‐day precipitation (RX1day) and precipitation on extremely wet days (R99p) have significantly increased, whereas the number of consecutive dry days has declined; these trends suggest that the intensity of precipitation extremes has increased in those regions, although the frequency of extreme and total rainfall has decreased. (c) The spatial distributions of seasonal trends in RX1day and maximum 5‐day precipitation (RX5day) exhibited less spatial coherence, and winter is becoming the wettest season regionwide, particularly over the central‐south YRB. (d) There were multiple and overlapping cycles of variability for most precipitation indices, indicating variations of time and frequency. (e) Elevation is intimately correlated with precipitation indices, and a weakening East Asian summer monsoon during 1986–2011 compared to that in 1960–1985 may have played an important role in the declines in most indices over the YRB. Therefore, the combined effects from local and teleconnection forcing factors have collectively influenced the variations in precipitation extremes across the YRB. This study may provide valuable evidence for the effective management of water resources and the conduct of agricultural activities at the basin scale.     

Trend analysis using discrete wavelet transform (DWT) for long-term precipitation (1851–2006) over India

This study is an attempt to determine the trends in monthly, annual and monsoon total precipitation series over India by applying linear regression, the Mann-Kendall (MK) test and discrete wavelet transform (DWT). The linear regression test was applied on five consecutive classical 30-year climate periods and a long-term precipitation series (1851–2006) to detect changes. The sequential Mann-Kendall (SQMK) test was applied to identify the temporal variation in trend. Wavelet transform is a relatively new tool for trend analysis in hydrology. Comparison studies were carried out between decomposed series by DWT and original series. Furthermore, visualization of extreme and contributing events was carried out using the wavelet spectrum at different threshold values. The results showed that there are significant positive trends for annual and monsoon precipitation series in North Mountainous India (zone NMI) and North East India (NEI), whereas negative trends were detected when considering India as whole.

EDITOR A. Castellarin ASSOCIATE EDITOR S. Kanae     

Trends in reference evapotranspiration in the humid region of northeast India

In the present study, the trends in the reference evapotranspiration (ET_O) estimated through the Penman‐Monteith method were investigated over the humid region of northeast (NE) India by using the Mann‐Kendall (MK) test after removing the effect of significant lag‐1 serial correlation from the time series of ET_O by pre‐whitening. During the last 22 years, ET_O has been found to decrease significantly at annual and seasonal time scales for 6 sites in NE India and NE India as a whole. The seasonal decreases in ET_O have, however, been more significant in the pre‐monsoon season, indicating the presence of an element of a seasonal cycle. The decreases in ET_O are mainly attributed to the net radiation and wind speed, which are also corroborated by the observed trends in these two parameters at almost all the times scales over most of the sites in NE India. The steady decrease in wind speed and decline in net radiation not only balanced the impact of the temperature increases on ET_O, but may have actually caused the decreases in ET_O over the humid region of northeast India. Copyright © 2011 John Wiley & Sons, Ltd.     

Indian summer monsoon rainfall prediction using artificial neural network

Forecasting the monsoon temporally is a major scientific issue in the field of monsoon meteorology. The ensemble of statistics and mathematics has increased the accuracy of forecasting of Indian summer monsoon rainfall (ISMR) up to some extent. But due to the nonlinear nature of ISMR, its forecasting accuracy is still below the satisfactory level. Mathematical and statistical models require complex computing power. Therefore, many researchers have paid attention to apply artificial neural network in ISMR forecasting. In this study, we have used feed-forward back-propagation neural network algorithm for ISMR forecasting. Based on this algorithm, we have proposed the five neural network architectures designated as BP1, BP2, $\ldots, $ … , BP5 using three layers of neurons (one input layer, one hidden layer and one output layer). Detail architecture of the neural networks is provided in this article. Time series data set of ISMR is obtained from Pathasarathy et al. (Theor Appl Climatol 49:217–224 1994) (1871–1994) and IITM (http://www.tropmet.res.in/, 2012) (1995–2010) for the period 1871–2010, for the months of June, July, August and September individually, and for the monsoon season (sum of June, July, August and September). The data set is trained and tested separately for each of the neural network architecture, viz., BP1–BP5. The forecasted results obtained for the training and testing data are then compared with existing model. Results clearly exhibit superiority of our model over the considered existing model. The seasonal rainfall values over India for next 5 years have also been predicted.     

Integrative stochastic model standardization with genetic algorithm for rainfall pattern forecasting in tropical and semi-arid environments

ABSTRACT

Climate patterns, including rainfall prediction, is one of the most complex problems for hydrologist. It is inherited by its natural and stochastic phenomena. In this study, a new approach for rainfall time series forecasting is introduced based on the integration of three stochastic modelling methods, including the seasonal differencing, seasonal standardization and spectral analysis, associated with the genetic algorithm (GA). This approach is specially tailored to eradicate the periodic pattern effects notable on the rainfall time series stationarity behaviour. Two different climates are selected to evaluate the proposed methodology, in tropical and semi-arid regions (Malaysia and Iraq). The results show that the predictive model registered an acceptable result for the forecasting of rainfall for both the investigated regions. The attained determination coefficient (R²) for the investigated stations was approx. 0.91, 0.90 and 0.089 for Mosul, Baghdad and Basrah (Iraq), and 0.80, 0.87 and 0.94 for Selangor, Negeri Sembilan and Johor (Malaysia).     

Spatial modelling of rainfall trends using satellite datasets and geographic information system

This study developed a standard methodology for identifying spatial trends using satellite-based raster datasets. It involves the novelty of exploring the capabilities of a geographic information system in implementing the procedures of three trend tests, the Spearman rank order correlation (SROC) test, the Kendall rank correlation (KRC) test and the Mann-Kendall (MK) test, on raster datasets of the Tropical Rainfall Measuring Mission at 0.25° × 0.25° resolution. Comparative evaluation of the three tests revealed fair agreement of a major part of the test results for pre-, post- and non-monsoon and one-day maximum rainfall. Also, similar results from KRC and MK tests were obtained over a considerable area for annual, monsoon and monthly maximum rainfall. These findings suggest the importance of selecting the appropriate test depending on rainfall magnitudes at the chosen time scale and emphasize the robustness of the KRC and MK tests.