Hydrological drought evaluation using GRACE satellite-based drought index over the lake basins, East Africa

Long-term hydrological drought monitoring and trend analysis in Blue Nile River basin

This research aims to monitor the hydrological drought trends within the geographical confines of Ethiopia, Sudan, and Egypt in the Blue Nile River Basin. Historical drought circumstances in the basin were analyzed through the utilization of the stream flow drought index (SDI). The long-term historical drought trend was investigated via the application of the Mann - Kendall Sen (MK) test. Streamflow data were collected at the border (GERD) (Ethiopia) and Khartoum and Dongola (Sudan) spanning the period from 1900 to 2001. Four distinct temporal scales were examined, including monthly (SDI1), seasonal (SDI3), bi-annual (SDI6), and annual (SDI12) frequency. Notably, SDI1, SDI3, and SDI6 exhibited a higher frequency of drought occurrences, whereas SDI12 demonstrated lower frequencies, accompanied by the longest duration of drought in all gauged stations. For the preceding 102-year period, two extreme drought events were identified across all stations: 1912/1913 and 1913/1914 in the Border and Dongola stations, and 1912/1913 and 1986/1987 in the Khartoum station. Moreover, the SDI12 results revealed that severe drought events manifested three, six, and four times, in the Border, Khartoum, and Dongola stations, respectively. Furthermore, an investigation of historical extreme and severe drought patterns led to the conclusion that extreme hydrological drought does not pose an imminent threat to downstream nations, including Egypt and Sudan. However, the trend analysis revealed that an increasing drought trend was observed in the Autumn season across all stations, while a positive trend characteristic of a wet condition was observed in the remaining seasons. Annual trend analysis did not show any statistically significant findings. Nevertheless, the study highlighted the imperative role of soil and water conservation measures in upstream countries, such as the Ethiopian highlands, in mitigating the prolonged effects of meteorological drought which gradually propagates into severe hydrological drought. Consequently, downstream nations must engage in cooperative efforts with upstream countries to address this issue collectively, rather than bestowing sole responsibility on the latter.

A hybrid ensemble learning merging approach for enhancing the super drought computation over Lake Victoria Basin

This study introduces a novel Hybrid Ensemble Machine-Learning (HEML) algorithm to merge long-term satellite-based reanalysis precipitation products (SRPPs), enabling the estimation of super drought events in the Lake Victoria Basin (LVB) during the period of 1984 to 2019. This study considers three widely used Machine learning (ML) models, including RF (Random Forest), GBM (Gradient Boosting Machine), and KNN (k-nearest Neighbors), for the emerging HEML approach. The three SRPPs, including CHIRPS (Climate Hazards Group Infra-Red Precipitation with Station), ERA5-Land, and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Record), were used to merge for developing new precipitation estimates from HEML model. Additionally, classification and regression models were employed as base learners in developing this algorithm. The newly developed HEML datasets were compared with other ML and SRPP products for super-drought monitoring. The Standardized precipitation evapotranspiration index (SPEI) was used to estimate super drought characteristics, including Drought frequency (DF), Drought Duration (DD), and Drought Intensity (DI) from machine learning and SRPPs products in LVB and compared with RG observation. The results revealed that the HEML algorithm shows excellent performance (CC = 0.93) compared to the single ML merging method and SRPPs against observation. Furthermore, the HEML merging product adeptly captures the spatiotemporal patterns of super drought characteristics during both training (1984-2009) and testing (2010-2019) periods. This research offers crucial insights for near-real-time drought monitoring, water resource management, and informed policy decisions.

Evaluating flood potential in the Mahanadi River Basin, India, using Gravity Recovery and Climate Experiment (GRACE) data and topographic flood susceptibility index under non-stationary framework

Extreme flood events have been recorded recently in the Mahanadi River basin in India with a high destructive potential that causes large social and economic damages. Because fewer hydrometeorological stations can record the flood magnitude in the basin, exploring new datasets like Gravity Recovery and Climate Experiment (GRACE) becomes important to overcome the barriers of assessing the hydrological extremes. The study estimates the flood potential using the GRACE-based terrestrial water storage (TWS) and analytical hierarchy process (AHP)-based topographic flood susceptibility to model the non-stationary flood frequency. During extreme flood events, the magnitude of the combined flood potential index (CFPI) is high (CFPI > 0.6), which correlates with higher river discharge. The CFPI value for the 2012 flood event with a discharge of 11,000 m3/sec (corresponds to a 35-year return period) is recorded at 0.67. Likewise, the CFPI for the flood event in 2011, which corresponds to a return period of 17 years, also stands at 0.63. The overall correlation between the discharge values of various flood events and CFPI values is above 0.8 for all locations, indicating GRACE-based CFPI's applicability for identifying the flood risk for larger basins like Mahanadi. Furthermore, on integrating CFPI as a covariate in non-stationary flood frequency modeling, the study found its superior performance when compared to both stationary models and non-stationary models with time or other climate indices as covariates, thus, helping in accurate estimation of flood return levels that are very useful in the hydrological design of water resources projects.

A review of recent developments on drought characterization, propagation, and influential factors

Droughts have impacted human society throughout its history. However, the occurrence of severe drought events in the last century and the concerns on the potential effects of climate change have prompted remarkable advances in drought conceptualization and modeling in recent years. This review intends to present the state-of-the-art on drought characterization and propagation, as well as providing insights on how climate dynamics and anthropogenic activities might affect this phenomenon. For this purpose, we first address the distinct concepts of droughts and their relationships. Next, we present two frequently utilized methods based on the run theory for drought characterization and explain the development and recovery stages of droughts. Then, we discuss potential drivers for drought occurrence and propagation, with focus on meteorological factors, catchments' physical characteristics and human activities. Later, we describe how droughts can affect several parameters of water quality. This review also addressed flash droughts, encompassing their definitions, commonly used indices, and potential drivers. Finally, we briefly address the roles of climate change and long-term persistence on future drought scenarios. This review may be useful for researchers and stakeholders for attaining a broader understanding on drought dynamics and impacts.

Reconstructing a long-term water storage-based drought index in the Yangtze River Basin

Drought is a prolonged dry period in the natural climate cycle, and is one of the most costly weather events. The Gravity Recovery and Climate Experiment (GRACE) derived terrestrial water storage anomalies (TWSA) have been widely used to assess drought severity. However, the relatively short cover period of GRACE and GRACE Follow-On limit our knowledge about the characterization and evolution of drought over decades time scale. This study proposes a standardized GRACE reconstructed TWSA index (SGRTI) to assess the drought severity based on a statistical reconstruction method calibrated by GRACE observations. Results show that the SGRTI correlates well with 6-month scale SPI and SPEI, with correlation coefficients reaching 0.79 and 0.81 in the YRB from 1981 to 2019. Soil moisture can capture drought condition like the SGRTI, while cannot further reflect deeper water storage depletion. The SGRTI is also comparable to the SRI and in-situ water level. As a case study for the Yangtze River Basin, its three sub-basins experience more frequent droughts, shorter drought duration, and lower severity drought, as identified by SGRTI during 1992-2019 relative to 1963-1991. The presented SGRTI in this study can provide a valuable supplement to the drought index before the GRACE era.