Drought Assessment Based on Fused Satellite and Station Precipitation Data: An Example from the Chengbi River Basin, China

Drought poses a significant constraint on economic development. Drought assessment using the standardized precipitation index (SPI) uses only precipitation data, eliminating other redundant and complex calculation processes. However, the sparse stations in southwest China and the lack of information on actual precipitation measurements make drought assessment highly dependent on satellite precipitation data whose accuracy cannot be guaranteed. Fortunately, the Chengbi River Basin in Baise City is rich in station precipitation data. In this paper, based on the evaluation of the accuracy of IMERG precipitation data, geographically weighted regression (GWR), geographic difference analysis (GDA), and cumulative distribution function (CDF) are used to fuse station precipitation data and IMERG precipitation data, and finally, the fused precipitation data with the highest accuracy are selected to evaluate the drought situation. The results indicate that the accuracy of IMERG precipitation data needs to be improved, and the quality of CDF-fused precipitation data is higher than the other two. The drought analysis indicated that the Chengbi River Basin is in a cyclical drought and flood situation, and from October to December 2014, the SPI was basically between +1 and −1, showing a spatial pattern of slight flooding, normal conditions, and slight drought.

Groundwater Drought Analysis under Data Scarcity: The Case of the Salento Aquifer (Italy)

Salento is a regional coastal karst aquifer located in Southern Italy with a highly complex geological, geomorphological, and hydrogeological structure. High and unruly exploitation of groundwater from licensed and unlicensed wells for irrigation and drinking purposes affects groundwater, with consequent degradation of its qualitative and quantitative status. The increased frequency of meteorological droughts and rising temperatures may only worsen the already compromised situation. The absence of complete and enduring monitoring of groundwater levels prevents the application of some methodologies, which require long time series. The analysis of climate indexes to describe the groundwater level variation is a possible approach under data scarcity. However, this approach may not be obvious for complex aquifers (in terms of scale, intrinsic properties, and boundary conditions) where the response of the groundwater to precipitation is not necessarily linear. Thus, the proposed research deals with the assessment of the response of the Salento aquifer to precipitation variability based on correlations between the Standardized Precipitation Index (SPI) and Standardized Precipitation and Evapotranspiration Index (SPEI) and groundwater levels for nine monitoring wells from July 2007 to December 2011. The study aims at evaluating the ability of the above indicators to explain the behavior of groundwater on complex aquifers. Moreover, it has the general aim to verify their more general reliable application. Results of three different correlation factors outline direct and statistically significant correlations between the time series. They describe the Salento aquifer as a slow filter, with a notable inertial behavior in response to meteorological events. The SPI 18-months demonstrates to be a viable candidate to predict the groundwater response to precipitation variability for the Salento aquifer.

A Study on Sensitivities of Tropical Forest GPP Responding to the Characteristics of Drought—A Case Study in Xishuangbanna, China

Droughts that occur in tropical forests (TF) are expected to significantly impact the gross primary production (GPP) and the capacity of carbon sinks. Therefore, it is crucial to evaluate and analyze the sensitivities of TF-GPP to the characteristics of drought events for understanding global climate change. In this study, the standardized precipitation index (SPI) was used to define the drought intensity. Then, the spatially explicit individual-based dynamic global vegetation model (SEIB-DGVM) was utilized to simulate the dynamic process of GPP corresponding to multi-gradient drought scenarios—rain and dry seasons × 12 level durations × 4 level intensities. The results showed that drought events in the dry season have a significantly greater impact on TF-GPP than drought events in the rainy season, especially short-duration drought events. Furthermore, the impact of drought events in the rainy season is mainly manifested in long-duration droughts. Due to abundant rainfall in the rainy season, only extreme drought events caused a significant reduction in GPP, while the lack of water in the dry season caused significant impacts due to light drought. Effective precipitation and soil moisture stock in the rainy season are the most important support for the tropical forest dry season to resist extreme drought events in the study area. Further water deficit may render the tropical forest ecosystem more sensitive to drought events.

Meteorological Drought Assessment in Sharjah, UAE Using Drought Indices

Drought is a natural disaster that significantly affects environmental and socio-economic conditions. It occurs when there is a period of below average precipitation in a region, and it results in water supply shortages affecting various sectors and life adversely. Droughts impact the ecosystems, crop production, and erode livelihoods. Monitoring drought is essential especially in the United Arab Emirates (UAE) due to the scarcity of rainfall for an extended period of time. In this study, drought is assessed in Sharjah UAE using monthly precipitation and average temperature data recorded for 35 years (1981-2015) at the Sharjah International Airport. The standardized precipitation Index (SPI), and the Reconnaissance Drought Index (RDI) are selected to predict future droughts in the region. SPI and RDI are fitted to the statistical distribution functions (gamma and lognormal) in an annual time scale and then, a trend analysis of index values is carried out using Mann-Kendal test. The correlation between SPI and RDI indices was found to be high where both showed high drought frequencies and a tendency to get drier over time, thus indicating the need of appropriate drought management and monitoring.

Evaluation of Seasonal, Drought, and Wet Condition Effects on Performance of Satellite-Based Precipitation Data over Different Climatic Conditions in Iran

The Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Mission (GPM) are the most important and widely used data sources in several applications—e.g., forecasting drought and flood, and managing water resources—especially in the areas with sparse or no other robust sources. This study explored the accuracy and precision of satellite data products over a span of 18 years (2000–2017) using synoptic ground station data for three regions in Iran with different climates, namely (a) humid and high rainfall, (b) semi-arid, and (c) arid. The results show that the monthly precipitation products of GPM and TRMM overestimate the rainfall. On average, they overestimated the precipitation amount by 11% in humid, by 50% in semi-arid, and by 43% in arid climate conditions compared to the ground-based data. This study also evaluated the satellite data accuracy in drought and wet conditions based on the standardized precipitation index (SPI) and different seasons. The results showed that the accuracy of satellite data varies significantly under drought, wet, and normal conditions and different timescales, being lowest under drought conditions, especially in arid regions. The highest accuracy was obtained on the 12-month timescale and the lowest on the 3-month timescale. Although the accuracy of the data is dependent on the season, the seasonal effects depend on climatic conditions.

A global-scale relationship between crop yield anomaly and multiscalar drought index based on multiple precipitation data

Drought impact on crop production is well known as crop yield is strongly controlled by climate variation. Previous studies assessed the drought impact using a drought index based on a single input data set, while the variability of the drought index to the input data choice is notable. In this study, a drought index based on the Standardized Precipitation Index with multiple timescales using several global precipitation datasets was compared with the detrended anomaly based on the global dataset of historical yield for major crops over 1981-2016. Results show that the drought index based on the ensemble precipitation dataset correlates better with the crop yield anomaly than a single dataset. Based on the drought index using ensemble datasets, global crop areas significantly affected by drought during the study period were around 23, 8, 30, and 29% for maize, rice, soybean, and wheat, respectively, induced mainly by medium to longer drought timescale (5 – 12-months). This study indicates that most crops cultivated in dry regions were affected by droughts worldwide, while rice shows less correlation to drought as it is generally irrigated and cultivated in humid regions with less drought exposure. This study provides a valuable framework for data choices in drought index development and a better knowledge of the drought impact on agriculture using different timescales on a global scale towards understanding crop vulnerability to climate disruptions.

Relationship between meteorological and hydrological droughts in the upstream regions of the Lancang–Mekong River

Information on the relationship between meteorological drought (MD) and hydrological drought (HD) can serve as the basis for early warning and mitigation of HD. In this study, the standardized precipitation index and standardized streamflow index were applied to characterize MD and HD, respectively, and the evolution characteristics of MD and HD were assessed in the upstream regions of the Lancang–Mekong River (ULMR) from 1961 to 2015. Furthermore, the relationship between MD and HD was investigated using the Pearson correlation and wavelet analysis. The results revealed that (1) there was no significant change in the annual precipitation and streamflow; however, the ULMR experienced successive alternations of wet and dry episodes; (2) the average duration and magnitude of MD and HD increased with an increase in the time scale, while the duration and magnitude of MD lengthened and amplified in HD; (3) MD more likely propagated to HD as the time scale increased, and the propagation time exhibited marked seasonality, which was shorter in the wet season and longer in the dry season; and (4) there was a positive correlation between MD and HD; these two types of drought exhibited similar resonance frequency and phase-shift characteristics, and HD lagged behind MD.