Drought, a climate-related disaster impacting a variety of sectors, poses challenges for millions of people in South Asia. Accurate and complete drought information with a proper monitoring system is very important in revealing the complex nature of drought and its associated factors. In this regard, deep learning is a very promising approach for delineating the non-linear characteristics of drought factors. Therefore, this study aims to monitor drought by employing a deep learning approach with remote sensing data over South Asia from 2001–2016. We considered the precipitation, vegetation, and soil factors for the deep forwarded neural network (DFNN) as model input parameters. The study evaluated agricultural drought using the soil moisture deficit index (SMDI) as a response variable during three crop phenology stages. For a better comparison of deep learning model performance, we adopted two machine learning models, distributed random forest (DRF) and gradient boosting machine (GBM). Results show that the DFNN model outperformed the other two models for SMDI prediction. Furthermore, the results indicated that DFNN captured the drought pattern with high spatial variability across three penology stages. Additionally, the DFNN model showed good stability with its cross-validated data in the training phase, and the estimated SMDI had high correlation coefficient R² ranges from 0.57~0.90, 0.52~0.94, and 0.49~0.82 during the start of the season (SOS), length of the season (LOS), and end of the season (EOS) respectively. The comparison between inter-annual variability of estimated SMDI and in-situ SPEI (standardized precipitation evapotranspiration index) showed that the estimated SMDI was almost similar to in-situ SPEI. The DFNN model provides comprehensive drought information by producing a consistent spatial distribution of SMDI which establishes the applicability of the DFNN model for drought monitoring.

The headwaters of the Yangtze River are located on the Qinghai Tibetan Plateau, which is affected by climate change. Here, treamflow trends for Tuotuohe and Zhimenda sub-basins and relations to temperature and precipitation trends during 1961–2015 were investigated. The modified Mann–Kendall trend test, Pettitt test, wavelet analysis, and multivariate correlation analysis was deployed for this purpose. The temperature and precipitation significantly increased for each sub-basin, and the temperature increase was more significant in Tuotuohe sub-basin as compared to the Zhimenda sub-basin. A statistically significant periodicity of 2–4 years was observed for both sub-basins in different time spans. Higher flow periodicities for Tuotuohe and Zhimenda sub-basin were found after 1991 and 2004, respectively, which indicates that these are the change years of trends in streamflows. The influence of temperature on streamflow is more substantial in Tuotuohe sub-basin, which will ultimately impact the melting of glaciers and snowmelt runoff in this sub-basin. Precipitation plays a more critical role in the Zhimenda streamflow. Precipitation and temperature changes in the headwaters of the Yangtze River will change the streamflow variability, which will ultimately impact the hydropower supply and water resources of the Yangtze Basin. This study contributes to the understanding of the dynamics of the hydrological cycle and may lead to better hydrologic system modeling for downstream water resource developments.

The quantitative attribution of changes in streamflow to climate change (CC) and land cover change (LCC) for the Yangtze River Source Region (YRSR), China, was assessed. We used a combination of the SWAT model along with the statistical technique one factor at a time (OFAT) and innovative trend analysis (ITA) to achieve the study objectives. The climate and hydrology data from 1961 to 2016 and land-cover maps of 5 years’ difference from 1985 to 2015 were used. The model was calibrated (1964–1989) using a land-cover map of 1985 and validated for 1990–2016. This validated model was further validated for all other land-cover maps used in this study. The SWAT model simulation showed that streamflow had been significantly influenced by CC compared to LCC using land-cover maps of 1985–1990, 1990–1995. However, the SWAT model simulations did not result in further changes in streamflow for land cover maps of 2000–2005, 2005–2010, and 2010–2015 because there have not been any significant changes in land cover after 2000 while the main contributing factor was climate change. The SWAT model simulations showed that the main driver of changes in streamflow in the Yangtze River Source Region is climate change. This study shows that the individual impacts are more critical than combined impacts for designing hydraulic structures, water resources planning and management, and decision-making policies at the regional/basin scale.

This study investigated the influence of land use change and climate change on changes in streamflow in the Bengawan Solo River (BSR, 16 389 km²) basin, Indonesia. We applied the excess water and energy approach to three tributaries of the BSR, namely Pidekso, Madiun and Kening rivers, which represent the up-, mid-, and downstream parts of the BSR, respectively. This study used hydrological data from 1982−2013 and land use data for the years 1994 and 2013. The results show that from 1982 to 2013, the annual streamflow of the Pidekso and Madiun River increased, while in the Kening River it decreased. Based on attribution measures, the increasing streamflow trend in the Pidekso and Madiun rivers is mainly attributed to land use change, while the decreasing streamflow trend in the Kening River is mainly attributed to climate change. These conclusions are supported by statistical analyses and land use change analysis.