Performance Of Drought Indices Research Articles

The Performance of Drought Indices on Maize Production in Northern Nigeria Using Artificial Neural Network Model

Drought is widely known to put the ecosystem at risk. It ensues when there is a major rainfall shortage that causes hydrological discrepancies and alters the land productive structures. The degree of rainfall influences the growth and harvests of maize, particularly where irrigation is not practicable. In some parts of northern Nigeria, rainfall is unpredictable and often lower than the quantity needed for a viable crop. For the detection, classification, and control of drought conditions, drought indices are used. There has been notable progress in the last few years in terms of modelling droughts by utilizing statistical or physical models. Despite the successes documented by most of these approaches; a plain, effective, and well-built statistical model is the artificial neural network (ANN). The use of artificial neural networks (ANN) to evaluate the impact of drought indices on maize output in the 17 northern Nigerian states is presented in this research. For a 25-year period from 1993 to 2018, observed annual data of drought indices, RDI, and the Palmer drought indices, which comprise SCPDSI, SCPHDI, and SCWLPM, as well as maize yield (measured in tonnes) in Northern states of Nigeria. The ANN model was evaluated using several activation functions (sigmoid, hyperbolic tangent, and rectified linear unit), hidden layers (1, 2, and 3), and training sets (70%, 80%, and 90%). The Mean Square Error (MSE) was employed to evaluate each ANN model's performance. In summary, most of the states' lowest mean square errors (MSEs) were generated via RELU. Also, as the training percentage increases, the mean square error increases.

Performance of Drought Indices in Assessing Rice Yield in North Korea and South Korea under the Different Agricultural Systems

Drought affects a region’s economy intensively and its severity is based on the level of infrastructure present in the affected region. Therefore, it is important not only to reflect on the conventional environmental properties of drought, but also on the infrastructure of the target region for adequate assessment and mitigation. Various drought indices are available to interpret the distinctive meteorological, agricultural, and hydrological characteristics of droughts. However, these drought indices do not consider the effective assessment of damage of drought impact. In this study, we evaluated the applicability of satellite-based drought indices over North Korea and South Korea, which have substantially different agricultural infrastructure systems to understand their characteristics. We compared satellite-based drought indices to in situ-based drought indices, standardized precipitation index (SPI), and rice yield over the Korean Peninsula. Moderate resolution imaging spectroradiometer (MODIS), tropical rainfall measuring mission (TRMM), and global land data assimilation system (GLDAS) data from 2001 to 2018 were used to calculate drought indices. The correlations of the indices in terms of monitoring meteorological and agricultural droughts in rice showed opposite correlation patterns between the two countries. The difference in the prevailing agricultural systems including irrigation resulted in different impacts of drought. Vegetation condition index (VCI) and evaporative stress index (ESI) are best suited to assess agricultural drought under well-irrigated regions as in South Korea. In contrast, most of the drought indices except for temperature condition index (TCI) are suitable for regions with poor agricultural infrastructure as in North Korea.

Estimation of annual regional drought index considering the joint effects of climate and water budget for Krishna River basin, India.

The Standardized Precipitation and Evapotranspiration Index (SPEI) became one of the popular drought indices due to the consideration of difference between precipitation (P) and potential evapotranspiration (PET), which represents the energy-based climatic water balance. Implementation of actual evapotranspiration (AET), which accounts for both water and energy-based climatic evaporative demand in drought characterization studies, is limited. This study proposes a meteorological drought index with the structure of the SPEI and actual evapotranspiration modeled with empirical formulations and remote sensing data integrated with surface energy models at annual scale. The proposed drought index imposes the effect of precipitation, PET, and AET using operational meteorological data sets of precipitation and temperatures. The present study aimed to test how a drought index based on PET and P can outperform with the inclusion of AET at a river basin scale at 12-month scale. The proposed hypothesis was tested considering Krishna River basin, India, as a case study for which most of the basin is in arid climate. The performance of drought indices was compared using historical droughts in terms of severity, areal extent, frequency, and duration based on empirical AET models along with satellite-based land surface ET data-based drought indices. The proposed AET-based drought indices have effectively captured the historical drought years over the Krishna River basin. The empirical AET formulation-based drought index was identified as a more reliable measure in the estimation of drought characteristics by comparing with satellite-based land surface AET-based drought index. The AET-based drought indices were able to drive the areas into moderate, which or otherwise categorized under severe drought regions with PET-based drought indices. Inclusion of AET in the drought characterization along with precipitation and PET can drive the highly intensified drought events determined by SPEI into moderate and less frequent droughts with short durations over a large river basin with arid climate.

Comparison of Drought Indices in a Semi-Arid River Basin of India

Due to the inherent complexity of drought phenomena, difference in hydro-climatic conditions and watershed characteristics, there is a lack of a universal drought index for assessing drought conditions in a particular region. Hence, the performance evaluation of different drought indices is necessary for identifying a suitable drought index. In this study, the performance of five drought indices was assessed for a semi-arid basin located in western India. The data from nine raingauge stations and four climate stations for the period of 25 years (1985-2009) were used. Based on the data availability, five meteorological drought indices were selected, viz., Percent Departure from Normal (PDN), Effective Drought Index (EDI), Standardized Precipitation Index (SPI), Reconnaissance Drought Index (RDI), and Standardized Precipitation Evapotranspiration Index (SPEI). Among these drought indices, EDI is a time step independent drought index and other four multi-time scale indices were defined at 1-, 3-, 6-, 9- and 12-month scales. The scale for the comparison of these drought indices was chosen based on the correlation with EDI and within among the scales of multi-time scale drought indices. The performance of drought indices during the historical drought was evaluated based on the relative frequency of drought index in a particular drought severity class and the response of these indices with multi-monthly rainfalls. In addition, the performance of these drought indices was assessed using the decision criteria such as Robustness, Tractability, Transparency, Sophistication, and Extendability. Analysis of the results indicated that the 9-month scale is appropriate for comparing drought indices in the study area. SPEI-9 showed maximum relative frequency in the ‘severe dry’ class and was found sensitive to 9-monthly rainfall at most of the stations. Further, the results of the performance evaluation criteria revealed that SPEI-9 has the highest total weighted score (136) followed by RDI-9, SPI-9, EDI, and PDN-9. It is concluded that SPEI-9 is the most suitable drought index for monitoring drought conditions in the study area.

A Monte Carlo Simulation-Based Approach to Evaluate the Performance of three Meteorological Drought Indices in Northwest of Iran

Although meteorological drought indices are considered as important tools for drought monitoring, they are embedded with different theoretical and experimental structures. Regarding the different geographic and climatic conditions around the world, the most meteorological drought indices have been commonly applied for drought monitoring in different parts of the world. Interestingly, it is observed that such indices in the published studies on drought monitoring have usually yielded inconsistent performance. On the other hand, most studies on drought monitoring as well as the performance of drought indices has been based on short-term historical data (less than 50 years). Therefore, this study aimed to analyze and compare the performance of three common indices of SPI, RAI and PNPI to predict long-term drought events using the Monte Carlo procedure and historical data. To do this end, the 50-year recorded or historical rainfall data across 11 synoptic stations in the Northwest of Iran were employed to generate 1000 synthetic data series so that the characteristics of long-term drought might be determined and the performance of those three indices might be analyzed and compared. The results indicated a very high comparative advantage of the SPI in terms of yielding a satisfactory and detailed analysis to determine the characteristics of long-term drought. Also, the RAI indicated significant deviations from normalized natural processes. However, these results could not reasonably and sufficiently predict long-term drought. Finally, the PNPI was determined as the most uncertain and spatial index (depending on average or coefficient of variation of rainfall data) in drought monitoring.

A Hydroclimatological Assessment of Regional Drought Vulnerability: A Case Study of Indiana Droughts

Abstract Characterizing and developing drought climatology continues to be a challenging problem. As decision makers seek guidance on water management strategies, there is a need for assessing the performance of drought indices. This requires the adaptation of appropriate drought indices that aid in monitoring droughts and hydrological vulnerability on a regional scale. This study aims to assist the process of developing a statewide water shortage and assessment plan (WSP) for the state of Indiana by conducting a focused hydroclimatological assessment of drought variability. Drought climatology was assessed using in situ observations and regional reanalysis data. A summary of precipitation and evaporation trends, estimated drought variability, worst-case drought scenarios, drought return period, and frequency and duration was undertaken, using multiple drought indices and streamflow analysis. Results indicated a regional and local variability in drought susceptibility. The worst-case (200-yr return period) prediction showed that Indiana has a 0.5% probability of receiving 45% of normal precipitation over a 12-month drought in any year. Consistent with other studies, the standardized precipitation index (SPI) was found to be appropriate for detecting short-term drought conditions over Indiana. This recommendation has now been incorporated in the 2009 Indiana water shortage plan. This study also highlights the difficulties in identifying past droughts from available climatic data, and the authors’ results suggest a persistent, high degree of uncertainty in making drought predictions using future climatic projections.