Downscaling Global Gridded Crop Yield Data Products and Crop Water Productivity Mapping Using Remote Sensing Derived Variables in the South Asia

Local scale crop yield and crop water productivity information is critical for informed decision making, crop yield forecasting and crop model calibration applications. In this study, we have attempted to downscale coarse resolution primary season rice yield datasets to a local scale of 500 m using a minimum-median downscaling approach. Sixteen mainland countries in south and southeast Asia region were considered as study region to downscale global rice yield datasets for 2000-2015. Four medium resolution remote sensing derived vegetation indices such as Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Gross Primary Product (GPP) were used to downscale coarse resolution global rice yield datasets. A kharif season district level rice yield data from International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), India was used as a reference dataset to evaluate the downscaled rice yields at the district scale. The proposed downscaling approach performance was satisfactory with a mean absolute error (MAE) range of 0.85-1.2 t/ha which lies in the error range of 10-15% with respect to actual range of reference rice yield datasets. Furthermore, crop water productivity maps at 500 m scale were also developed with the downscaled rice yield and Moderate Resolution Imaging Spectroradiometer (MODIS) Evapotranspiration (ET) data products. Statistical analysis shows that the rice yield and crop water productivity values across different climate zones were statistically significant. Tropical zone-based crop yield and crop water productivity values were showing higher variation when compared to other climate zones with a range of 1-10 t/ha and 1-12.5 kg/m³, respectively.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s42106-022-00223-2.

A catchment scale assessment of water balance components: a case study of Chittar catchment in South India

The detailed analyses of the water balance components (WBCs) of the catchment help assess the available water resources, especially in the arid climate regions for their sustainable management and development. This paper mainly used the Soil and Water Assessment Tool (SWAT) model to analyze the variation in the WBCs considering the change in the Land Use and Land Cover (LULC) and meteorological variables. For this purpose, the model used the inputs of LULC and meteorological variables between 2001 and 2020 at 5 years and daily time intervals, respectively, from the Chittar river catchment. The developed models were calibrated using SWAT-CUP split-up procedure (pre-calibration and post-calibration). The model was found to be good in calibration and validation, yielding the coefficient of determination (R²) of 0.94 and 0.81, respectively. Furthermore, WBCs of the catchment were estimated for the near future (2021-2030) at the monthly and annual scales. For this endeavor, LULC was forecasted for the years 2021 and 2026 using Cellular Automata (CA)-Artificial Neural Network (ANN), and for the same period, meteorological variables were also forecasted using the smoothing moving average method from the historical data.

Assessment of the spatial-temporal distribution of groundwater recharge in data-scarce large-scale African river basin

The systematic assessment of spatial and temporal distribution of groundwater recharge (GWR) is crucial for the sustainable management of the water resources systems, especially in large-scale river basins. This helps in identifying critical zones in which GWR largely varies and thus leads to negative consequences. However, such analyses might not be possible when the models require detailed hydro-climate and hydrogeological data in data-scarce regions. Hence, this calls for alternate suitable modeling approaches that are applicable with the limited data and, however, includes the detailed assessment of the spatial-temporal distribution of different water balance components especially the GWR component. This paper aimed at investigating the spatial and temporal distribution of the GWR at monthly, seasonal and annual scales using the WetSpass-M physically distributed hydrological model, which is not requiring the detailed catchment information. In addition, the study conducted the sensitivity analysis of model parameters to assess the significant variation of GWR. The large-scale river basins such as the Omo river basin, Ethiopia, were chosen to demonstrate the potential of the WetSpass-M model under limited data conditions. From the modeling results, it was found that the maximum average monthly GWR of 13.4 mm occurs in July. The estimated average seasonal GWR is 32.5 mm/yr and 47.6 mm/yr in the summer and winter seasons, respectively. Further, it was found that GWR is highly sensitive to the parameter such as average rainfall intensity factor.

Indian Summer Monsoon Rainfall: Implications of Contrasting Trends in the Spatial Variability of Means and Extremes

India's agricultural output, economy, and societal well-being are strappingly dependent on the stability of summer monsoon rainfall, its variability and extremes. Spatial aggregate of intensity and frequency of extreme rainfall events over Central India are significantly increasing, while at local scale they are spatially non-uniform with increasing spatial variability. The reasons behind such increase in spatial variability of extremes are poorly understood and the trends in mean monsoon rainfall have been greatly overlooked. Here, by using multi-decadal gridded daily rainfall data over entire India, we show that the trend in spatial variability of mean monsoon rainfall is decreasing as exactly opposite to that of extremes. The spatial variability of extremes is attributed to the spatial variability of the convective rainfall component. Contrarily, the decrease in spatial variability of the mean rainfall over India poses a pertinent research question on the applicability of large scale inter-basin water transfer by river inter-linking to address the spatial variability of available water in India. We found a significant decrease in the monsoon rainfall over major water surplus river basins in India. Hydrological simulations using a Variable Infiltration Capacity (VIC) model also revealed that the water yield in surplus river basins is decreasing but it is increasing in deficit basins. These findings contradict the traditional notion of dry areas becoming drier and wet areas becoming wetter in response to climate change in India. This result also calls for a re-evaluation of planning for river inter-linking to supply water from surplus to deficit river basins.