Display omitted
•Impact of climate and landuse changes on water balance in future.•SWAT model is used to estimate the water yield, ET and its change in the future and Markov Chain used for land use ...prediction.•Runoff is affected more by climate change and future rainfall estimated by downscaling climate parameter.•ET is affected more by landuse change due to changed curve number.•Integrated effects of climate and landuse project higher runoff and lower ET in future.
Both the climate and land use changes have considerable impact on the water balance of a basin and sub-watersheds influencing various parameters such as water yield, surface runoff, evapotranspiration (ET), etc. Therefore, to predict the trend in the future water balance, individual and combined impact of both should be considered. The major objective of the study is to assess changes in the future water balance by investigating the independent and integrated impacts of climate and land use changes using SWAT (Soil and Water Assessment Tool) model in a part of the Narmada river basin in Madhya Pradesh, India. This includes the sub-objectives of future projection of rainfall and minimum and maximum temperatures by LS-SVM (Least Square Support Vector Machine) and SDSM (Statistical Downscaling Model) models to estimate climate change impact, and prediction of land use change of the basin area by the Markov Chain model. The present and future climate of the 2020s, 2050s and 2080s have been projected along with the past, present and future land use projection (1990, 2000, 2011, 2020 and 2050). The individual and combined effects on water balance have been shown in 12 sub-watersheds (SW) of the basin projecting increased water yield and decreased ET in the future. Individual impact of climate change shows high water yield, surface runoff and ET, while the individual impact of land use change shows increased water yield and surface runoff but decreased ET in the future. The SW 1–7 indicate comparatively higher surface runoff and water yield due to the presence of bare lands, agricultural lands and settlements and lower ET, while southern SW of 8–12 show low water yield and surface runoff but higher ET due to the presence of more vegetation and forest areas. The impact of climate change is found to have a more prominent effect on the water yield while impact of land use change is more on ET.
Digital Elevation Model （DEM） is one of the important parameters for soil erosion assessment. Notable uncertainties are observed in this study while using three high resolution open source DEMs. The ...Revised Universal Soil Loss Equation （RUSLE） model has been applied to analysis the assessment of soil erosion uncertainty using open source DEMs （SRTM, ASTER and CARTOSAT） and their increasing grid space （pixel size） from the actual. The study area is a part of the Narmada river basin in Madhya Pradesh state, which is located in the central part of India and the area covered 20,558 km2. The actual resolution of DEMs is 30 m and their increasing grid spaces are taken as 90, 150, 210, 270 and 330 m for this study. Vertical accuracy of DEMs has been assessed using actual heights of the sample points that have been taken considering planimetric survey based map （toposheet）. Elevations of DEMs are converted to the same vertical datum from WGS 84 to MSL （Mean Sea Level）, before the accuracy assessment and modelling. Results indicate that the accuracy of the SRTM DEM with the RMSE of 13.31, 14.51, and 18.19 m in 30, 150 and 330 m resolution respectively, is better than the ASTER and the CARTOSAT DEMs. When the grid space of the DEMs increases, the accuracy of the elevation and calculated soil erosion decreases. This study presents a potential uncertainty introduced by open source high resolution DEMs in the accuracy of the soil erosion assessment models. The research provides an analysis of errors in selecting DEMs using the original and increased grid space for soil erosion modelling.
The present study has illustrated the estimation of the soil organic carbon (SOC) distribution from point survey data (prepared after laboratory test) by a hybrid interpolation method, viz. ...regression kriging (RK) in a part of the Narmada river basin in the central India. In this study, eight selected predictor variables are used such as, brightness index (BI), greenness index (GI), wetness index (WI), normalized difference vegetation index (NDVI), vegetation temperature condition index (VTCI), digital elevation model (DEM), and slope and compound topographic index (CTI). The RK method has given satisfactory results as observed from the level of accuracy. Finally, the amount of SOC content in varied slope, soil and landuse categories has been analysed. Concentration of SOC has been observed to be more in low elevated areas in clay soil with mainly agricultural and vegetated lands.
Climate change affects the environment and natural resources immensely. Rainfall, temperature and evapotranspiration are major parameters of climate affecting changes in the environment. Evapotrans- ...piration plays a key role in crop production and water balance of a region, one of the major parameters affected by climate change. The reference evapotranspiration or ETo is a calculated parameter used in this research. In the present study, changes in the future rainfall, minimum and maximum temperature, and ETo have been shown by downscaling the HadCM3 （Hadley Centre Coupled Model version 3） model data. The selected study area is located in a part of the Narmada river basin area in Madhya Pradesh in central India. The downscaled outputs of projected rainfall, ETo and temperatures have been shown for the 21st century with the HADCM3 data of A2 scenario by the Least Square Support Vector Machine （LS-SVM） model. The efficiency of the LS-SVM model was measured by different statistical methods. The selected predictors show considerable correlation with the rainfall and temperature and the application of this model has been done in a basin area which is an agriculture based region and is sensitive to the change of rainfall and temperature. Results showed an increase in the future rainfall, temperatures and ETo. The temperature increase is projected in the high rise of minimum temperature in winter time and the highest increase in maximum temperature is projected in the pre-monsoon season or from March to May. Highest increase is projected in the 2080s in 2081-2091 and 2091-2099 in maximum temperature and 2091-2099 in minimum temperature in all the stations. Winter maximum temperature has been observed to have increased in the future. High rainfall is also observed with higher ETo in some decades. Two peaks of the increase are observed in ETo in the April-May and in the October. Variation in these parameters due to climate change might have an impact on the future water resource of the study area, which is mainly an agricultural based region, and will help in proper planning and management.
Long-term droughts significantly impact surface and groundwater resources in India, however, observed changes in major river basins have not been well explored. Here we use Standardized Precipitation ...Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) at three different time scales (24, 48, and 60 months) to identify long-term droughts in India for the observed record of 1951-2015. Drought characteristics (extent, events, frequency, and intensity) are analyzed for different river basins in India. Increasing trend in the areal extent of droughts is observed in two methods with three time scales in the maximum area (63.66%) in India. We use the data from the Gravity Recovery and Climate Experiment (GRACE) to estimate the changes in the terrestrial water storage (TWS) during the period 2002-2015. We identify that major long-term droughts in India occurred from 1966 to 1969, 1972, 1986-1987, and 2002-2004. The all-India average TWS shows a negative trend from 2002 to 2015 with prominent decline in north Indian river basins and positive trend in south Indian river basins. SPI and SPEI at longer time scales are positively associated with TWS indicating the adverse impacts of droughts on surface and groundwater resources in such a populated region.
The thermal status of northeast Indian cities has a great impact on the sustainability of these places. To meet the research gap in this area, a study is performed in Imphal city, India by ...investigating the relationship between land surface temperature (LST) and four spectral indices in the summer and winter seasons from 1991 to 2021. The mean LST of the city increases at >1% rate per decade in both seasons. The urban heat island (UHI) develops mostly along the central Imphal. A considerable difference in the mean LST between UHI and non-UHI in summer (3.05 °C in 1991, 2.46 °C in 2001, 3.13 °C in 2011, and 2.49 °C in 2021) and winter (2.01 °C in 1991, 2.63 °C in 2001, 2.64 °C in 2011, and 2.57 °C in 2021) reflects the continuous warming status of the city. Some urban hot spots develop inside the UHI of the central and north Imphal. The dynamic nature of the relationships of spectral indices with LST (moderate negative for MNDWI and NDVI, strongly positive for NDBI, and moderate negative for NDBaI) will be helpful for proper sustainable urban planning. Urban thermal field variance index map shows that the south Imphal attains more ecological comfort than the rest of the parts.
The Vietnamese Mekong Delta (VMD) is located in Vietnam
The Vietnamese Mekong Delta (VMD) region has one of the leading productions of rice in the world and it stands at the intersection of extreme ...anthropogenic activity and climate change. To this end, the major focus of this study is to explore the changes in land use, climate, water resources, and their inter-relationship, which are intended to showcase the ability of publicly available earth observations and models in improving understanding of the past changes and future scenarios and contribute to improved decision making. We analyzed the change of agricultural crops (single, double, and triple) and climatic parameters (precipitation, and land surface temperature, and evapotranspiration). Consequently, we used Soil and Water Assessment Tool Model (SWAT) and selected six GCMs for extreme climate to investigate the change of streamflow.
Our results indicated that double rice crop and aquaculture are the top two land use categories in the VMD, the areas of triple rice crop have increased significantly, especially for the An Giang and Dong Thap provinces. However, by examining the climate, water, and land data analytics, we see challenges in the expansion of triple rice crop over VMD. The spatio-temporal changes in climate variables and future streamflow projections provide strong evidence to water resources managers and decision-makers in the VMD.
Display omitted
•The areas of triple rice crop have increased significantly (2000–2018).•Trend of climatic variables (precipitation, temperature, and ET) are analyzed.•We selected extreme climatic scenarios for future projection on the VMD.•We simulated the projected streamflow due to extreme climatic conditions.
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.
Temporal change in rainfall erosivity varies due to the rainfall characteristic (amount, intensity, frequency, duration), which affects the conservation of soil and water. This study illustrates the ...variation of rainfall erosivity due to changing rainfall in the past and the future. The projected rainfall is generated by SDSM (Statistical DownScaling Model) after calibration and validation using two GCMs (general circulation model) data of HadCM3 (A2 and B2 scenario) and CGCM3 (A1B and A2 scenario). The selected study area is mainly a cultivable area with an agricultural based economy. This economy depends on rainfall and is located in a part of the Narmada river basin in central India. Nine rainfall locations are selected that are distributed throughout the study area and surrounding. The results indicate gradually increasing projected rainfall while the past rainfall has shown a declined pattern by Mann–Kendall test with statistical 95% confidence level. Rainfall erosivity has increased due to the projected increase in the future rainfall (2080s) in comparison to the past. Rainfall erosivity varies from −32.91% to 24.12% in the 2020s, −18.82 to 75.48% in 2050s and 20.95–202.40% in 2080s. The outputs of this paper can be helpful for the decision makers to manage the soil water conservation in this study area.
The study focused on investigating the seasonal and spatiotemporal relationship between the relationships of LST with four spectral indices (MNDWI, NDBaI, NDBI, and NDVI) in and around Manipur City ...of India using eight cloud-free Landsat data from the summer and winter seasons for 1991, 2001, 2011, and 2021. These spectral indices respond differently to the change of LST in an urban landscape. Pearson’s linear correlation coefficient was the basis of the correlation analysis. The study finds that LST builds a moderate negative relationship with NDVI (R = -0.42) and MNDWI (R = -0.42), a moderate positive relationship with NDBaI (R=0.48), and NDBI (R = 0.61). The relationship is more stable in the winter season (CV = 7.31, 7.04, 10.45, and 28.71 for MNDWI, NDBaI, NDBI, and NDVI, respectively) than in summer (CV = 44.46, 36.09, 23.67, and 29.71 for MNDWI, NDBaI, NDBI, and NDVI, respectively). The strength of the relationship is gradually increasing in the winter season while there is no such effect noticed on the trend in the summer season. The LST-NDBI relationship is the most consistent (CV = 18.19), while the LST-NDVI relationship is the most variable (CV = 30.37).
PDF
