Estimation of soil water holding capacity with Random Forests for drought monitoring

Soil moisture is a key variable for drought monitoring but soil moisture measurements networks are very scarce. Land-surface models can provide a valuable alternative to simulate soil moisture dynamics, but only a few countries have such modelling schemes implemented for monitoring soil moisture at high spatial resolution. In this study, a soil moisture accounting model (SMA) was regionalized over the Iberian Peninsula, taking as a reference the soil moisture simulated by a high-resolution land surface model. To estimate soil water holding capacity, the parameter required to run the SMA model, two approaches were compared: the direct estimation from European soil maps using pedotransfer functions, or an indirect estimation by a Machine Learning approach, Random Forests, using as predictors altitude, temperature, precipitation, evapotranspiration and land use. Results showed that the Random Forest model estimates are more robust, especially for estimating low soil moisture levels. Consequently, the proposed approach can provide an efficient way to simulate daily soil moisture and therefore monitor soil moisture droughts, in contexts where high-resolution soil maps are not available, as it relies on a set of covariates that can be reliably estimated from global databases.

Optical and Thermal Remote Sensing for Monitoring Agricultural Drought

By effectively observing the land surface and obtaining farmland conditions, satellite remote sensing has played an essential role in agricultural drought monitoring over past decades. Among all remote sensing techniques, optical and thermal remote sensing have the most extended history of being utilized in drought monitoring. The primary goal of this paper is to illustrate how optical and thermal remote sensing have been and will be applied in the monitoring, assessment, and prediction of agricultural drought. We group the methods into four categories: optical, thermal, optical and thermal, and multi-source. For each category, a concise explanation is given to show the inherent mechanisms. We pay special attention to solar-induced chlorophyll fluorescence, which has great potential in early drought detection. Finally, we look at the future directions of agricultural drought monitoring, including (1) early detection; (2) spatio-temporal resolution; (3) organic combination of multi-source data; and (4) smart prediction and assessment based on deep learning and cloud computing.

Drought Monitoring over Nepal for the Last Four Decades and Its Connection with Southern Oscillation Index

This study identified summer and annual drought events using the Standard Precipitation Index (SPI) for 107 stations across Nepal from 1977 to 2018. For this, frequency, duration, and severity of drought events were investigated. The SPI4 and SPI12 time scales were interpolated to illustrate the spatial patterns of major drought episodes and their severity. A total of 13 and 24 percent of stations over the country showed a significant decreasing trend for SPI4 and SPI12. Droughts were recorded during El Niño and non-El Niño years in Nepal. Among them, 1992 was the worst drought year, followed by the drought year, 2015. More than 44 percent of the locations in the country were occupied under drought conditions during these extreme drought events. Droughts have been recorded more frequently in Nepal since 2005. The areas of Nepal affected by extreme, severe, and moderate drought in summer were 8, 9, and 18 percent, while during annual events they were 7, 11, and 17 percent, respectively. Generally, during the drought years, the SPI and Southern Oscillation Index (SOI) have a strong phase relation compared to the average years.

Prediction for Various Drought Classes Using Spatiotemporal Categorical Sequences

Drought frequently spreads across large spatial and time scales and is more complicated than other natural disasters that can damage economic and other natural resources worldwide. However, improved drought monitoring and forecasting techniques can help to minimize the vulnerability of society to drought and its consequent influences. This emphasizes the need for improved drought monitoring tools and assessment techniques that provide information more precisely about drought occurrences. Therefore, this study developed a new method, Model-Based Clustering for Spatio-Temporal Categorical Sequences (MBCSTCS), that uses state selection procedures through finite mixture modeling and model-based clustering. The MBCSTCS uses the functional structure of first-order Markov model components for modeling each data group. In MBCSTCS, the suitable order K of the components is selected by Bayesian information criterion (BIC). In MBCSTCS, the estimated mixing proportions and the posterior probabilities are used to compute probability distribution associated with the future steps of transitions. Furthermore, MBCSTCS predicts drought occurrences in future time using spatiotemporal categorical sequences of various drought classes. The MBCSTCS is applied to the six meteorological stations in the northern area of Pakistan. Moreover, it is found that MBCSTCS provides expeditious information for the long-term spatiotemporal categorical sequences. These findings may be helpful to make plans for early warning systems, water resource management, and drought mitigation policies to decrease the severe effects of drought.

Drought monitoring over India through Normalized Difference Vegetation Index (NDVI)

Normalized Difference Vegetation Index (NDVI) is a simple index to monitor the state of vegetation (stressed/unstressed) which can be derived from satellite data. Hence an attempt is made to find out the vegetation responses to rainfall through NDVI over the study area. Applicability of NDVI in drought monitoring is discussed using the NDVI and rainfall data for the period 1982-2003. The anomaly of NDVI is compared with the percentage departure of rainfall of corresponding years. Results showed a significant relation between the NDVI with the percentage departure of rainfall. The time series plots of averaged NDVI and seasonal rainfall (June-September) are done for NW India (21° N - 31° N, 68° E - 78° E), Central India (22° N - 27° N, 70° E - 77° E) and Peninsular India (16° N - 21° N, 74° E - 79° E) over the period 1982-2003 to analyze changes in vegetation pattern of India during the last two decades. Results indicated a clear linear relationship over NW and Central India. NDVI anomalies and the corresponding cumulative rainfall showed significantly linear correlation of 0.69 over NW India and 0.57 over Central India significant at 1% level but the correlation is found to be insignificant over Peninsular India which was only 0.04. Trend analysis of averaged NDVI over India showed that during last two decades the vegetation status had quite improved over the dry farming tracts of India.