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Understanding the variability in extreme meteorological events due to climate change and anthropogenic activities is crucial to ensure sustainable development. In this paper, the variation in precipitation and temperature in the Upper Narmada Basin (UNB), a humid subtropical region of central India, during 1901–2002, was analyzed using the Mann-Kendall (MK) test with the Sen’s slope (SS) estimator and innovative trend analysis (ITA). As a result, a significant negative trend in the annual precipitation occurred at two stations (12.5%) in the UNB, whereas no well-defined significant seasonal trends were detected. Moreover, in the annual mean temperature, a significant increasing trend occurred in the basin, with the highest and lowest rates of 0.470 °C/10 year and 0.287 °C/10 year, respectively. The seasonal temperature exhibited a significant increasing trend in the spring and autumn seasons. The trends in the extreme values were investigated using the ITA method. Furthermore, a quantitative study was carried out to measure the suitability of the ITA by comparing it with nonparametric methods (the MK test and SS estimator). The results endorse the reliability of the ITA method and show strong agreement between the trends and statistics of the parametric (ITA) and nonparametric approaches. To visualize the extreme events in meteorological series, a discrete wavelet transform (DWT) was employed with the Daubechies (db3) mother wavelet. In addition, change points were detected using the sequential Mann-Kendall (SQMK) and cumulative sum (CUSUM) tests. Significant change points in the annual precipitation were detected at nine stations (56.25%) during 1955–1958, while only three stations (18.75%) exhibited the annual mean temperature during 1960. The findings of this study could provide support to understand the variability in the meteorological trend over the basin and can help with water resource planning in future development.

Glaciers are retreating and thinning in the high altitude of the Himalayas due to global warming, causing into formation of numerous glacial lakes. It is necessary to monitor these glacial lakes consistently to save properties and lives downstream from probable disastrous glacial lake outburst flood. In this study, image processing software ArcGIS and ERDAS Imagine have been used to analyse multispectral image obtained by Earth resource satellite Landsat for delineating the glacial lakes with the help of image enhancement technique like NDWI. Landsat data since 1972 through 2013 have been used and maximum seven glacial lakes (L1–L7) have been detected and delineated in Dhauliganga catchment, they are situated above 4000 masl. The Glacial Lake L2 (Lat 30°26′45″E and Long 80°23′16″N) is the largest whose surface area was 132,300 m 2 in Sept 2009, and L6 (Lat 30°23′27″E and Long 80°31′52″N) is highly unstable with variation rate −55 to +145 % with increasing trend. Additionally, glacial lakes L2 (Lat 30°26′45″E and Long 80°23′16″N) and L6 (Lat 30°23′27″E and Long 80°31′52″N) have been identified as potentially hazardous. These lakes may probably burst; as a result, huge reserve of water and debris may be released all on a sudden. This may transform into hazardous flash flood in downstream causing loss of lives, as well as the destruction of houses, bridges, fields, forests, hydropower stations, roads, etc. It is to note that Dhauliganga river considered in this study is a tributary of Kaliganga river, and should not be confused with its namesake the Dhauliganga river, which is a tributary of Alaknanda river.

Assessment of actual evapotranspiration (ET) is essential as it controls the exchange of water and heat energy between the atmosphere and land surface. ET also influences the available water resources and assists in the crop water assessment in agricultural areas. This study involves the assessment of spatial distribution of seasonal and annual ET using Surface Energy Balance Algorithm for Land (SEBAL) and provides an estimation of future changes in ET due to land use and climate change for a portion of the Narmada river basin in Central India. Climate change effects on future ET are assessed using the ACCESS1-0 model of CMIP5. A Markov Chain model estimated future land use based on the probability of changes in the past. The ET analysis is carried out for the years 2009-2011. The results indicate variation in the seasonal ET with the changed land use. High ET is observed over forest areas and crop lands, but ET decreases over crop lands after harvest. The overall annual ET is high over water bodies and forest areas. ET is high in the premonsoon season over the water bodies and decreases in the winter. Future ET in the 2020s, 2030s, 2040s, and 2050s is shown with respect to land use and climate changes that project a gradual decrease due to the constant removal of the forest areas. The lowest ET is projected in 2050. Individual impact of land use change projects decreases in ET from 1990 to 2050, while climate change effect projects increases in ET in the future due to rises in temperature. However, the combined impacts of land use and climate changes indicate a decrease in ET in the future.

The major problem of estimating snowmelt runoff for Beas river basin is inadequacy of observed meteorological data distributed across the basin. In this study, ERA-Interim global reanalysis data have been used for assessing the stream flow and sediment yield in Beas river basin of North Western Himalaya. The snow module of ARCSWAT hydrology model has been simulated by integration of subbasin-wise elevation band files for modeling snowmelt runoff process including sediment yield due to rainfall and temperature change for different elevation bands varying from 361 to 6188 m. The gridded reanalysis (0.125° × 0.125°) dataset produces a decreased maximum and minimum temperature and increased precipitation at higher elevation in comparison with IMD gridded weather data. The outcome of this study conveys that the reanalysis data represent better snowmelt runoff (NSE = 0.76, 0.70 and R2 = 0.80, 0.70) and sediment yield (NSE = 0.50, 0.53 and R2 = 0.72, 0.57) mechanism at Pong and Pandoh dams than IMD gridded weather data (NSE = 0.50, 0.47 and R2 = 0.65, 0.60) for stream flow and (NSE = 0.50, 0.53 and R2 = 0.65, 0.60) sediment yield during the period 1996–1999 and 1999–2002 for these two locations.

Statistical downscaling (SD) is preferable to dynamic downscaling to derive local-scale climate change information from large-scale datasets. Many statistical downscaling models are available these days, but comparison of their performance is still inadequately addressed for choosing a reliable SD model. Thus, it is desirable to compare the performance of SD models to ensure their adaptability in future climate studies. In this study, a statistical downscaling model (SDSM) or multi-linear regression and the Least Square Support Vector Machine (LS-SVM) were used to do downscaling and compare the results with those obtained from general circulation model (GCM) for identifying the best SD model for the Indira Sagar Canal Command area located in Madhya Pradesh, India. The GCM, Hadley Centre Coupled Model version 3 (HadCM3), was utilized to extract and downscale precipitation, maximum temperature (Tmax), and minimum temperature (Tmin) for 1961–2001 and then for 2001–2099. Before future projections, both SD models were initially calibrated (1961–1990) and validated (1991–2001) to evaluate their performance for precipitation and temperature variables at all gauge stations, namely Barwani, East Nimar, and West Nimar. Results showed that the precipitation trend was under-predicted owing to large errors in downscaling, while temperature was over-predicted by SD models. HIGHLIGHTS Precipitation values are under-predicted, while temperature values are over-predicted by statistical downscaling models.; Large errors in downscaling precipitation are observed, since downscaling of precipitation is more problematic than temperature.; Statistical measures (R2, RMSE, SSE, NSE, and MAE) showed good agreement between observed and downscaled climate variables for SDSM and LS-SVM.;

In ancient India, air, water, and solar radiation were represented as gods and key components of water availability, food production, and energy generation. However, the linkage between modern science and ancient wisdom has not been well explored. In this study, we estimate the water–food–energy potential using various openly available data products and perform statistical analysis to establish the scientific basis behind the geographical alignment of eight prominent Shiva temples along the 79° meridian east to the north, known as the Shiva Shakti Aksh Rekha (SSAR). Results indicate a strong correlation between the SSAR belt and water–energy–food productivity potential, where 18.5% of the area can produce 44 × 10 6 tons of rice annually. With an estimated renewable energy generation potential of 596.6 GW, the SSAR belt could significantly contribute to India’s target of 500 GW of renewable energy production by 2030. This study highlights the potential role of traditional geographical alignments in supporting future water, energy, and food security in highly populated countries such as India.

In the present study, trends and variations in climatic variables (i.e. rainfall, wet day frequency, surface temperature, diurnal temperature, cloud cover, and reference and potential evapotranspiration) were analyzed on seasonal (monsoon and non-monsoon) and annual time scales for the Ajmer District of Rajasthan, India. This was done using non-parametric statistical techniques, i.e. the Mann–Kendall (MK) and Modified Mann–Kendall (MMK) tests, over a period of 100 years. The MK test with prewhitening (MK–PW) of climatic series was also applied to climatic variables and the results were compared to those obtained through the MK and MMK tests in order to assess the performance of trend detection methods. The Pettitt–Mann–Whitney (PMW) test was applied to detect the temporal shift in climatic series. The trend analysis revealed that annual and seasonal rainfall did not show any statistically significant trend at a 10% significant level. A noticeable trend increase was found in wet day frequency, surface temperature and reference evapotranspiration ( ) during the non-monsoon season from the three non-parametric statistical tests at a 10% significance level. A statistically significant decrease in maximum temperature was found during the non-monsoon season by the MK–PW test alone. This analysis of several climatic variables at the district scale is helpful for the planning and management of water resources and the development of adaptation strategies in adverse climatic conditions.

Floods adversely affect the life of people and property in the coastal districts. It is important to delineate the flood extent and pattern which helps in the vulnerability assessment and also to find out the intensity of damages to facilitate future planning and management. The study area is a part of the Nuna river basin, which suffers from the flood disasters frequently. The present study applies microwave remote sensing (RADARSAT-1 images) to monitor extent, depth and duration of 2003 and 2008 floods in the Kendrapara district of Odisha, India. RADARSAT-1 images of 4, 11, 13 and 20 September of 2003 and 18, 20, 22 and 24 September of 2008 were used to monitor the flood extent, duration and depth. The threshold method was used to delineate flood extent which was used for calculating flood duration and depth. Further, vulnerability assessment of the paddy crop was done to obtain intensity of damage in the area from the 2003 and 2008 floods. Field survey was done to verify and assess the generated results. Areas affected by more than 15 days of flood duration and depth of more than 3 m faced maximum loss. Both the years witnessed major floods in this area with an estimated damage of around INR 174 million ($3.6 million) in 2003 and INR 75 million ($1.6 million) in 2008.

BackgroundIn the rainfed areas, water, not land, is the foremost restraining resources for better agricultural production to satisfy the growing demand of food and other needs. Water harvesting and enhancing productivity of available water, and not volume of yield per units of land, is therefore a better solution for rainfed agriculture. Under these circumstances, it is necessary toimplement efficient and effective integrated water resources management practices. Elsewhere rainfed dependent highland parts of Ethiopia, in the Beressa watershed most of the rainwater lost in the form of runoff particularly during excess rainfall; therefore, the benefit of rainwater during less rainy season is insignificant. The objective of this study therefore was to assess crop water requirement and net irrigation requirement using CROPWAT8.0 model and Rainfall Contribution Index (RCI) for various crops and to describe the benefits of water harvesting and integrated and sustainable water resources management. Crops growing during the month of February to June (less rainy season) are among the highest irrigation requirements.ResultsBased on RCI (as the value far from 1) reflect rainfall contribution is insufficient to satisfy the crop water requirements for the whole growth stages. Therefore, there is a need for supplemental irrigation and rainwater harvesting and a necessity for integrated water resources management. To reduce the intensity of water shortage therefore conservation planning and water management at watershed level in an integrated manner is critical. Similarly, soil and water conservation management practices is essential for the communities to reduce runoff and other resources for future uses and determine the optimal amount of water resources consumption at the irrigation fields is an urgent issues.ConclusionsAs a result, there is a need to do more efforts related to water harvesting and integrated water resources management for the sustainability of agriculture and over all human survival.