Search

Recorded seasonal variation and uneven distribution of rainfall is one of the major issues to the agrarian society and the domestic water users today. For strategic planning of water use, scientific estimation of water balance in different spatio-temporal scale is necessary. For this, the present paper intends to investigate monthly and annual water balance state of Mayurakshi River basin (including 45 sub-basins) using precipitation (P), runoff (Q), evapotranspiration (ET) and recharge/soil moisture. P, Q, ET, respectively, vary from 1625.97 mm to 2037.93 mm, 182.19 mm to 1503.15 mm and 1050 mm to 1500 mm for the entire basin. The monthly pattern shows significant variation attributed to strong seasonality of P and temperature (T). Monsoon months recorded heavy maximum P, Q and actual ET and on the other hand, all these are very poor during pre-monsoon season. Groundwater recharge annually differs spatially, but annual average recharge is 307mmwith main contribution in monsoon months. Water deficit condition prevails in pre-monsoon season; strong groundwater lowering and drying of top soil are some explicit evidences of the same. This seasonal water scarcity withstands against multi-cropping strategies of the agrarian communities. To cope with this emerging situation, pressure on groundwater is mounting but it is the fact that this resource is also highly limited and precious. Quantification of monthly water balance and water deficit will be inputs to the strategy development for sustainable water resource management. [ABSTRACT FROM AUTHOR]

Remote sensing has revolutionized the assessment of evapotranspiration by continuous monitoring of the variable at a global scale. However, it is difficult to accurately estimate actual evapotranspiration (AET) in areas with high spatial heterogeneity along with very little ancillary data available. The presence of spatial heterogeneity is observed over India and being a country, whose economy largely depends on agriculture, estimation of AET is crucial for efficient water management. This study proposed a new framework to evaluate AET at a fine spatial scale suitable for such heterogeneous and data-sparse environments, with spatial and temporal validation using global datasets. Landsat 8 data were used to estimate AET for eight cloud-free days of 2014 over the Malaprabha River Basin in India using the modified surface energy balance algorithm for land (M-SEBAL) and two-source energy balance (TSEB) models. These two AET estimates were compared with Moderate resolution Imaging Spectroradiometer (MODIS) and Global Land Evaporation Amsterdam Model (GLEAM) AET for spatial and temporal validation respectively. M-SEBAL outperformed TSEB in capturing the magnitude and spatial variability of AET (e.g., spatial correlation between M-SEBAL and MODIS AET was 0.56 and 0.45 for day of year (DOYs) 040 and 136, respectively, whereas for the same DOYs the correlation between TSEB and MODIS AET was 0.16 and 0.36). The results demonstrate the challenge in AET estimation at a fine spatial resolution and highlight the importance of choosing a suitable algorithm. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is one of the major water exchange processes between the earth's surface and the atmosphere. ET is a combined process of evaporation from open water bodies, bare soil and plant surfaces, and transpiration from vegetation. Remote sensing‐based ET models have been developed to estimate spatially distributed ET over large regions, however, many of them reportedly underestimate ET over semi‐arid regions (Jamshidi et al., Journal of Hydrometeorology, 2019, 20, 947–964). In this work, we show that underestimation of ET can occur due to the open water evaporation from flooded rice paddies ignored in the existing ET models. To address the gap in ET estimation, we have developed a novel approach that accounts for the missing ET component over flooded rice paddies. Our method improved ET estimates by a modified Penman‐Monteith algorithm that considered the fraction of open water evaporation from flooded rice paddies. Daily ET was calculated using ground based meteorological data and the MODIS satellite data over the Krishna River Basin. Seasonal and annual ET values over the Krishna Basin were compared with two different ET algorithms. ET estimates from these two models were also compared for different crop combinations. Results were validated with flux tower‐based measurements from other studies. We have identified a 17 mm/year difference in average annual ET over the Krishna River Basin with this new ET algorithm. This is very critical in basin scale water balance analysis and water productivity studies. [ABSTRACT FROM AUTHOR]

The upper Ganga Basin in Uttarakhand, India, has high hydropower potential and plays an important role in the development of the state economy. Thus, an accurate knowledge of annual water yield is of paramount importance to this region. This paper deals with use of contemporary water yield estimation models such as the distributed Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and the Lumped Zhang model and their validation to identify the most suited one for water yield estimation in the upper Ganga Basin. In previous studies utilizing these models, water yield was estimated by considering a single value of some important model parameters for the entire basin, which in fact show distributed variation at a finer (pixel) scale. Therefore, in this study, pixel-level computations are performed to assess and ascertain the need for incorporating the spatial variation of such parameters in model applications. To validate the findings, the observed sub-basin discharge data are analyzed with the computed water yield for 4 decades, i.e., 1980, 1990, 2001 and 2015. The results obtained are in good agreement with the water yield obtained at the pixel scale. [ABSTRACT FROM AUTHOR]

Land use/land cover (LULC) change occurs due to natural and anthropogenic causes. In developing countries, rapid industrialization and urbanization imposes a major threat to natural environment. Present study was carried out to monitor the LULC changes due to urbanization in a rapidly changing river basin, India. The purpose of choosing the river basin was to analyze past changes and predict possible consequences within a defined natural boundary. Multi-temporal images acquired from Landsat and Indian Remote Sensing (IRS) satellites between 1992-2009 as well as a digital elevation model was used to generate historical and current LULC pattern in the basin. An object based image analysis technique was employed for precise classification of multi-temporal images followed by GIS-based change detection studies. The study reveals that the built up area has increased significantly and added 288 km2 between 1992 - 2009. Increase in built up area is attributed to decrease in wastelands and agricultural land. The expansion of built up area along major transportation networks, specifically after the year 2000 shows the rapid rate of urbanization in the basin. [ABSTRACT FROM AUTHOR]

Climate change scenarios generated by general circulation models (GCMs) have too coarse a spatial resolution to be useful in planning disaster risk reduction and climate change adaptation strategies at regional to river/lake basin scales. This paper investigates the performances of existing state-of-the-art rule induction and tree algorithms, namely, single conjunctive rule learner, decision table, M5P model tree, decision stump, and REPTree. Downscaling models are developed to obtain projections of mean monthly maximum and minimum temperatures ( Tmax and Tmin) as well as pan evaporation to lake-basin scale in an arid region in India using these algorithms. The predictor variables, such as air temperature, zonal wind, meridional wind, and geo-potential height, are extracted from the National Centers for Environmental Prediction (NCEP) reanalysis data set for the period 1948-2000 and from the simulations using third-generation Canadian coupled global climate models for emission scenarios for the period 2001-2100. A simple multiplicative shift was used for correcting predictand values. The performances of various models have been evaluated on several statistical performance parameters such as correlation coefficient, mean absolute error, and root mean square error. The M5P model tree algorithm was found to yield better performance among all other learning techniques explored in the present study. An increasing trend is observed for Tmax and Tmin for emission scenarios, whereas no trend has been observed for pan evaporation in the future. [ABSTRACT FROM AUTHOR]

This paper presents a relationship between the runoff curve number (CN) and mean potential evapotranspiration (PET) using long-term daily rainfall-runoff data. To this end, CNs were derived for different durations from rainfall-runoff data of eight different agro-climatic river basins in India, and these were correlated with PET derived for respective watersheds using the standard Penman-Monteith method. The resulting high coefficient of determination () values, viz, 0.99 for Hemawati and Mohegoan; 0.98 for Haridanagar, Kalu, and Seonath; 0.96 for Manot, Ghodahadho, and Ramganga strongly support the versatility of the derived relationship. This relationship also invokes determination of PET from the available published CN values, and therefore may be quite useful in field applications. [ABSTRACT FROM AUTHOR]

Monthly scenarios of relative humidity ( R) were obtained for the Malaprabha river basin in India using a statistical downscaling technique. Large-scale atmospheric variables (air temperature and specific humidity at 925 mb, surface air temperature and latent heat flux) were chosen as predictors. The predictor variables are extracted from the (1) National Centers for Environmental Prediction reanalysis dataset for the period 1978-2000, and (2) simulations of the third generation Canadian Coupled Global Climate Model for the period 1978-2100. The objective of this study was to investigate the uncertainties in regional scenarios developed for R due to the choice of emission scenarios (A1B, A2, B1 and COMMIT) and the predictors selected. Multi-linear regression with stepwise screening is the downscaling technique used in this study. To study the uncertainty in the regional scenarios of R, due to the selected predictors, eight sets of predictors were chosen and a downscaling model was developed for each set. Performance of the downscaling models in the baseline period (1978-2000) was studied using three measures (1) Nash-Sutcliffe error estimate ( E), (2) mean absolute error (MAE), and (3) product moment correlation ( P). Results show that the performances vary between 0.59 and 0.68, 0.42 and 0.50 and 0.77 and 0.82 for E, MAE and P. Cumulative distribution functions were prepared from the regional scenarios of R developed for combinations of predictors and emission scenarios. Results show a variation of 1 to 6% R in the scenarios developed for combination of predictor sets for baseline period. For a future period (2001-2100), a variation of 6 to 15% R was observed for the combination of emission scenarios and predictors. The variation was highest for A2 scenario and least for COMMIT and B1 scenario. [ABSTRACT FROM AUTHOR]

In this paper a knowledge-based model for supplementary irrigation assessment in rainfed agricultural watersheds is presented. The supplementary irrigation assessment problem is divided into different components and is modeled separately. Geographic Information System (GIS) is used to aggregate spatially varying attributes required for the modeling. A graphical user interface is developed in a GIS platform by using the ERDAS macro language tools. The model was applied to two case study areas in India: a subwatershed of Gandheshwari area (West Bengal), and Harsul watershed (Maharashtra). In the Gandheshwari subwatershed, the water availability was found to be inadequate to meet the irrigation requirement and hence the model identified the areas that can be irrigated with different outsource water supply. On the other hand, surface runoff generated in the Harsul watershed was found to be sufficient to meet the supplementary irrigation requirement, thereby showing the feasibility for supplementary irrigation in the area. Using the model, the effect of any rainfall condition can be simulated and hence appropriate measures can be taken in advance to reduce the risk of crop failure. [ABSTRACT FROM AUTHOR]

The analysis of rainstorms is of prime importance for the planning and design of water resources of any region. In the absence of actual records of streamflow data, analysis of rainstorms is useful for assessing flood potentialities as well as the runoff of river catchments. This study provides pertinent information on the spatial distribution characteristics of severe rainstorm events that occurred over the Godavari and its subbasins on the basis of 110 yr (1891–2000) of available rainfall data. The analysis showed that the August 1986 rainstorm had its areal extension over more than a 150 000 km2 area. It is also seen that the August 1913 rainstorm was the most efficient rainstorm over the basin. The rain depth data of severe rainstorms given in this paper can provide a reliable set of information to assess water resource planning on a probability basis. The relationship between the point to areal rainfall and area of the rainstorms from 1- to 3-day durations is also determined. [ABSTRACT FROM AUTHOR]

The coastal regions of India are densely populated and most biological productive ecosystems which are threatened by erosion, natural disaster, and anthropogenic interferences. These threats have made priority in appraisal of shoreline dynamicity as part of sustainable management of coastal zones. The present study assessed the long- to short-term dynamicity of shoreline positions along the coast of Puri district, Odisha, India, during the past 25 years (1990–2015) using open-source multi-temporal satellite images (Landsat TM, ETM + , and OLI) and statistical-based methods (endpoint rate, linear regression rate and weighted linear regression). The long-term assessment during 1990–2015 shows that shoreline accredited at the rate of 0.3 m a−1 with estimated mean accretion and erosional rate of 1.18 m a−1 and 0.64 m a−1, respectively. A significant trend of coastal erosion is primarily observed on the northern side of Puri district coast. A cyclic pattern of accretion (during 1990–1995 and 2000–2004) and erosion (during 1995–2000 and 2009–2015) was observed during the assessment of short-term shoreline change. It exhibited significant correlation with the landfall of severe cyclones and identified cyclic phases after severe cyclonic storms, i.e., phase of erosion, phase of accretion and phase of stabilization. Overall, the natural processes specifically the landfall of tropical cyclones and anthropogenic activities such as the construction of coastal structures, encroachment and recent construction in the coastal regulatory zone, and construction of dams in upper catchment areas are the major factors accountable for shoreline changes. The output of the research undertaken is not only crucial for monitoring the dynamism of coastal ecosystem boundaries but to enable long- to short-term coastal zone management planning in response to recently reported high erosion along the Puri coast. Moreover, the usage of open-source satellite imageries and statistical-based method provides an opportunity in developing cost-effective spatial data infrastructure for shoreline monitoring and vulnerability mapping along the coastal region. [ABSTRACT FROM AUTHOR]