A statistical bias correction technique for global climate model predicted near-surface temperature in India using the generalized regression neural network

Global climate models (GCMs) developed by the numerical simulation of physical processes in the atmosphere, ocean, and land are useful tools for climate prediction studies. However, these models involve parameterizations and assumptions for the simulation of complex phenomena, which lead to random and structural errors called biases. So, the GCM outputs need to be bias-corrected with respect to observed data before applying these model outputs for future climate prediction. This study develops a statistical bias correction approach using a four-layer feedforward radial basis neural network – a generalized regression neural network (GRNN) to reduce the biases of the near-surface temperature data in the Indian mainland. The input to the network is the CNRM-CM5 model output gridded data of near-surface temperature for the period 1951–2005, and the target to the model used for bias correcting the input data is the gridded near-surface temperature developed by the Indian Meteorological Department for the same period. Results show that the trained GRNN model can improve the inherent biases of the GCM modelled output with significant accuracy, and a good correlation is seen between the test statistics of observed and bias-corrected data for both the training and testing period. The trained GRNN model developed is then used for bias correction of CNRM-CM5 modelled projected near-surface temperature for 2006–2100 corresponding to the RCP4.5 and RCP8.5 emission scenarios. It is observed that the model can adapt well to the nature of unseen future temperature data and correct the biases of future data, assuming quasi-stationarity of future temperature data for both emission scenarios. The model captures the seasonal variation in near-surface temperature over the Indian mainland, having diverse topography appreciably, and this is evident from the bias-corrected output.

Appraisal of hydro-ecology, geomorphology, and sediment behavior during low and high floods in the Lower Indus River Estuary

The riverine ecosystem is beholden by the freshwater; however, morphological changes and sediment load destabilize the natural river system which deteriorates the ecology and geomorphology of the river ecosystem. The Lower Indus River Estuary (LIRE) geomorphological response was synthesized using satellite imagery (1986–2020) and evaluated against the field measurements. The estuary sinuosity index has an increasing trend from 1.84 (1986) to 1.92 (2020) and the estuary water area is increased from 101.41 km2 (1986) to 110.24 km2 (2020). The sediment load investigation at Kotri barrage indicated that the median size of bed material samples during the low-flow period falls between 0.100 and 0.203 mm and the bed material after the high flow has clay and silt (<0.0623 mm) ranging from 17–95% of the total weight of samples. The vegetated land loss on the banks is positively correlated with the peak runoff at Kotri barrage (r2=0.92). The bank erosion was computed with high precision (r2=0.84) based on an improved connection of the coefficient of erodibility and excess shear stress technique. This study will be helpful for policymakers to estimate the ecological health of LIRE, and sediment fluxes play an essential role in the mega-delta system and coastal management.

Estimation of greenhouse gas emissions of a tropical reservoir in Colombia

Tropical reservoirs are generally flooded in soils with a high content of organic matter. This, combined with high temperatures, favors the generation of carbon dioxide (CO2) and methane (CH4) by biological degradation, contributing to the impact on climate change. A tropical reservoir in Colombia was monitored for 7 years in the pre-fill, fill and post-fill stages, for the last of these during the day and night. Emissions from diffusive fluxes at the surface of the water were measured using a floating static chamber, while inverted funnel methodology was used to measure the fluxes by bubbling. The samples collected in the field were analyzed in the laboratory using a gas chromatograph with a mass detector. The results showed average emissions of 70,892.51 ± 41,079.16-ton CO2eq/year for pre-filling; 178,254.53 ± 105,838.01-ton CO2eq/year for filling; and 466,946.57-ton CO2eq/year for post-filling (for 5 years), concluding that the weather conditions and the filling percentage (Area surface and volume) had an impact on the generation of greenhouse gases at filling and post-filling stages, as did the organic matter present in the area of influence of the sampling point. Higher greenhouse gas emissions were found during the day compared to the results at night, indicating that temperature affects these processes, especially in tropical reservoirs. This study, currently unique in Colombia, will allow directing efforts towards mitigating the impacts of greenhouse gas emissions in tropical reservoirs.

Identification of the most appropriate adaptation for rice and wheat in the face of climate change in eastern India

The changing climate affects natural resources that impart a negative impact on crop yield and food security. It is thus imperative to identify agro-climate wise, area-specific adaptation options to ensure food security. This study, therefore, evaluated some feasible adaptation options for two staple food grain crops, rice and wheat, in different agro-climatic regions (ACRs) of Eastern India. Alteration in transplanting date, seedling age, and fertilizer management (rate and split of fertilizer) for rice; and sowing date, fertilizer management, and deficit irrigation scheduling for wheat, are assessed as adaptation options. Crop environment and resource synthesis (DSSAT) model is used to simulate the crop yield using different plausible adaptation options to projected climate scenarios. Findings show that shifting transplanting/sowing date, and nitrogen fertilizer application at 120% of recommended nitrogen dose with four splits could be an effective adaptation for rice and wheat crops. Results also emphasize that transplanting of 18 days older seedlings may be beneficial in rice cultivation. In contrast, irrigation at a 30–40% deficit of maximum available water would sustain the wheat yield under climate change conditions. This study suggests the best combination of adaptation options under climate change conditions in diverse ACRs, which may assist agriculturists in coping with climate change.

Runoff modeling in Kolar river basin using hybrid approach of wavelet with artificial neural network

In this paper, Kolar River watershed, Madhya Pradesh is taken as the study area. This study area is located in Narmada River in Central India. The data set consists of monthly rainfall of three meteorological stations, Ichhawar, Brijesh Nagar, and Birpur rainfall stations from 2000 to 2018, runoff data at Birpur and temperature data of Sehore district. In this paper, radial basis function neural network models have been studied for generation of rainfall–runoff modeling along with wavelet input and without wavelet input to the RBF neural network. A total of 15 models was developed in this experiment based on various combinations of inputs and spread constant of RBF model. The evaluation criteria for the best models selected are based on R2, AARE, and MSE. The best predicting model among the networks is model 8, which has input of R(t-1), R(t-2), R(t-3), R(t-4), and Q(t-1). For RBFNN model, maximum value of R2 is 0.9567 and least value of AARE and MSE is observed. Similarly, for WRBFNN model, maximum value of R2 is 0.9889 and least value of AARE and MSE is observed. WRBF performs better than RBF with any data processing techniques which shows model proposed possess better predictive capability.

An estimation of the evapotranspiration of typical steppe areas using Landsat images and the METRIC model

Remote sensing excels in estimating regional evapotranspiration (ET). However, most remote sensing energy balance models require researchers to subjectively extract the characteristic parameters of the dry and wet limits of the underlying surfaces. The regional ET accuracy is affected by wrong determined ideal pixels. This study used Landsat images and the METRIC model to evaluate the effects of different dry and wet pixel combinations on the ET in the typical steppe areas. The ET spatiotemporal changes of the different land cover types were discussed. The results show that the surface temperature and leaf area index could determine the dry and wet limits recognition schemes in grassland areas. The water vapor flux data of an eddy covariance system verified that the relative error between the ETd,METRIC and ETd,GES of eight DOYs (day of the year) was 18.8% on average. The ETMETRIC values of the crop growth season and the ETIMS of eight silage maize irrigation monitoring stations were found to have a relative error of 11.1% on average. The spatial distribution of the ET of the different land cover types in the study area was as follows: ETwater > ETarable land > ETforest land > ETunutilized land > ETgrassland > ETurban land.

Future climate change and impacts on water resources in the Upper Blue Nile basin

This study aims to assess the impact of climate change on the water resources of the Upper Blue Nile basin using an integrated climate and hydrological model. The impact of climate change on water resources is being assessed using the regional climate model (RCM) under the representative concentration pathway (RCP4.5 and RCP8.5) scenarios and the Soil and Water Assessment Tool (SWAT) hydrological model. Future climate scenarios have been developed for the 2030s (2021–2040) and the 2050s (2041–2060). The study found that the projected rainfall shows a decreasing trend and is not statistically significant, while the temperature shows an increasing trend and is statistically significant. Due to the sharp rise in temperature, the annual evapotranspiration increased by about 10.4%. This and the declining trend of rainfall will reduce streamflow up to 54%, surface runoff up to 31%, and water yield up to 31%. Climate change causes seasonal and annual fluctuations in the water balance components. However, the projected seasonal changes are much greater than the annual changes. Therefore, the results of this study will be useful to basin planners, policymakers, and water resources managers in developing adaptation strategies to offset the adverse effects of climate change in the Upper Blue Nile basin.

Variability and trends of climate extremes indices from the observed and downscaled GCMs data over 1950–2020 period in Chattogram City, Bangladesh

This study was intended to evaluate the variability and trends of climate extremes by incorporating daily data from Chattogram station and from the high-resolution Coordinated Regional Climate Downscaling Experiment (CORDEX) for two different time series. Here, we also focused on evaluating the performance of the selected RCMs (CanESM2, CSIRO, and GFDL from CORDEX) using Taylor diagrams and heat map analysis. Twenty-two extreme climate indices from ETCCDI were computed for 1950–1989 and 1990–2020 periods. Mann–Kendall and Sen's slope test were performed to estimate the trends from the indices from both station and RCMs data. Highly significant increasing trend for the warm days and warm nights’ frequencies were found, whereas, the frequency of cold days and cold nights indicated significantly decreasing trend. On the other hand, mild increasing trend in 1-day and 5-day maximum rainfall was detected. Also, the average annual precipitation has increased by 6% from the 1950–1989 to 1990–2020 period. During the last three decades, the region has experienced more heavier rainfall in the monsoon but increased water stress in the dry season. The two-fold effects of climate change on the local hydrology revealed by this study need to be addressed properly for the sustainable development of this region.

Exploring the village tank cascade systems (VTCSs) in Vavuniya district, Sri Lanka

An attempt was made to identify, validate the village tank cascade systems (VTCSs) and study the water flow from one village tank to another in each VTCS in the eight Agrarian Service Centre (ASC) divisions in the Vavuniya district from October 2017 to December 2018. VTCS contributes a significant share of available water resources for the livelihoods of households in the Vavuniya district. The 1:10,000 topographic map of the Survey Department, satellite images and the digital elevation model were used to identify the cascades and flow direction map for the study area using ArcGIS 10.2.2. Among 756 village tanks in the district, 80 VTCSs comprising 514 village tanks were identified, and only 69 cascades were validated in the field. In addition, this study identified 111 isolated village tanks without connecting with other village tanks and 131 abandoned village tanks. Further investigation is recommended to explore the possibilities of increasing the cascade areas in the study area by connecting isolated tanks with VTCSs. Initiation taken toward rehabilitation of cascades would enhance the livelihood of farm households in the Vavuniya district and lead to sustainable water resource management.

Simulation-optimization framework in urban flood management for historic and climate change scenarios

A simulation-optimization framework is established by integrating Hydrologic Engineering Center Hydraulic Modeling System (HEC-HMS) for computation of runoff, siting tool EPA System for Urban Storm-water Treatment and Analysis INtegration (EPA-SUSTAIN) for placement of Best Management Practices (BMPs), and Binary Linear Integer Programming (BLIP) for runoff reduction. The framework is applied to an urban catchment, namely Greater Hyderabad Municipal Corporation (GHMC). The rainfall-runoff analysis was conducted for extreme rainfalls for historic (2016) and future events in 2050 and 2064 under Representative Concentration Pathways (RCPs) 6.0 and 8.5. The simulation-optimization approach in the historic scenario yielded 495,607 BMPs occupying 76.99 km2 resulting in runoff reduction of 21.54 mm (198.76–177.22 mm). Achieved runoff reduction is 38.72 (428.35–389.63 mm) and 55.03 (602.65–547.62 mm), respectively, for RCPs 6.0 and 8.5, which could meet the water demands of GHMC for 10.33 and 11.53 days. Impacts of 10 different BMP configurations of varying costs (10–70%) and pollutant load reductions (0–3%) on runoff reduction are accomplished as part of sensitivity analysis.