Global high-resolution drought indices for 1981-2022

Droughts are among the most complex and devastating natural hazards globally. High-resolution datasets of drought metrics are essential for monitoring and quantifying the severity, duration, frequency and spatial extent of droughts at regional and particularly local scales. However, current global drought indices are available only at a coarser spatial resolution (>50 km). To fill this gap, we developed five high-resolution (5 km) gridded drought records based on the Standardized Precipitation Evaporation Index (SPEI) covering the period 1981–2022. These multi-scale (1–48 months) SPEI indices are computed based on monthly precipitation (P) from the Climate Hazards group InfraRed Precipitation with Station data (CHIRPS, version 2) and Multi-Source Weighted-Ensemble Precipitation (MSWEP, version 2.8) and potential evapotranspiration (PET) from the Global Land Evaporation Amsterdam Model (GLEAM, version 3.7a) and Bristol Potential Evapotranspiration (hPET). We generated four SPEI records based on all possible combinations of P and PET datasets: CHIRPS-GLEAM, CHIRPS-hPET, MSWEP-GLEAM, and MSWEP-hPET. These drought records were evaluated globally and exhibited excellent agreement with observation-based estimates of SPEI, root zone soil moisture, and vegetation health indices. The newly developed high-resolution datasets provide more detailed local information and be used to assess drought severity for particular periods and regions and to determine global, regional, and local trends, thereby supporting the development of site-specific adaptation measures. These datasets are publicly available at the Centre for Environmental Data Analysis (CEDA): https://dx.doi.org/10.5285/ac43da11867243a1bb414e1637802dec (Gebrechorkos et al., 2023)

A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses

AbstractA large number of historical simulations and future climate projections are available from Global Climate Models, but these are typically of coarse resolution, which limits their effectiveness for assessing local scale changes in climate and attendant impacts. Here, we use a novel statistical downscaling model capable of replicating extreme events, the Bias Correction Constructed Analogues with Quantile mapping reordering (BCCAQ), to downscale daily precipitation, air-temperature, maximum and minimum temperature, wind speed, air pressure, and relative humidity from 18 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6). BCCAQ is calibrated using high-resolution reference datasets and showed a good performance in removing bias from GCMs and reproducing extreme events. The globally downscaled data are available at the Centre for Environmental Data Analysis (https://doi.org/10.5285/c107618f1db34801bb88a1e927b82317) for the historical (1981–2014) and future (2015–2100) periods at 0.25° resolution and at daily time step across three Shared Socioeconomic Pathways (SSP2-4.5, SSP5-3.4-OS and SSP5-8.5). This new climate dataset will be useful for assessing future changes and variability in climate and for driving high-resolution impact assessment models.</jats:p

Emerging Themes and Future Directions of Multi-Sector Nexus Research and Implementation

Water, energy, and food are all essential components of human societies. Collectively, their respective resource systems are interconnected in what is called the “nexus”. There is growing consensus that a holistic understanding of the interdependencies and trade-offs between these sectors and other related systems is critical to solving many of the global challenges they present. While nexus research has grown exponentially since 2011, there is no unified, overarching approach, and the implementation of concepts remains hampered by the lack of clear case studies. Here, we present the results of a collaborative thought exercise involving 75 scientists and summarize them into 10 key recommendations covering: the most critical nexus issues of today, emerging themes, and where future efforts should be directed. We conclude that a nexus community of practice to promote open communication among researchers, to maintain and share standardized datasets, and to develop applied case studies will facilitate transparent comparisons of models and encourage the adoption of nexus approaches in practice

Emerging Themes and Future Directions of Multi-Sector Nexus Research and Implementation

Water, energy, and food are all essential components of human societies. Collectively, their respective resource systems are interconnected in what is called the “nexus”. There is growing consensus that a holistic understanding of the interdependencies and trade-offs between these sectors and other related systems is critical to solving many of the global challenges they present. While nexus research has grown exponentially since 2011, there is no unified, overarching approach, and the implementation of concepts remains hampered by the lack of clear case studies. Here, we present the results of a collaborative thought exercise involving 75 scientists and summarize them into 10 key recommendations covering: the most critical nexus issues of today, emerging themes, and where future efforts should be directed. We conclude that a nexus community of practice to promote open communication among researchers, to maintain and share standardized datasets, and to develop applied case studies will facilitate transparent comparisons of models and encourage the adoption of nexus approaches in practice

Analysis of climate variability and droughts in East Africa using high-resolution climate data products

Analysis of climate variability and change as a basis for adaptation and mitigation strategies requires long-term observations. However, the limited availability of ground station data constrains studies focusing on detecting variability and changes in climate and drought monitoring, particularly in developing countries of East Africa. Here, we use high-resolution precipitation (1981–2016) and maximum and minimum temperature (T-max and T-min) (1979–2012) datasets from international databases like the Climate Hazard Group (CHG), representing the most accurate data sources for the region. We assessed seasonal, annual, and decadal variability in rainfall, T-max and T-min and drought conditions using the Standardized Precipitation Index (SPI). The impact of changes in Sea Surface Temperature on rainfall variability and droughts is assessed using the Nino3.4 and Indian Ocean Dipole (IOD) indices. The results show maximum variability in rainfall during October–December (OND, short rainy season) followed by March–May (MAM, long rainy season). Rainfall variability during OND showed a significant correlation with IOD in Ethiopia (69%), Kenya (80%), and Tanzania (63%). In Ethiopia, the period June–September (JJAS) showed a significant negative correlation (−56%) with the Nino3.4. Based on the 12-month SPI, the eastern and western parts of the region are getting drier and wetter, respectively with an average of mild, moderate, and severe droughts of more than 37%, 6%, and 2% of the study period, respectively. The observed severe droughts (e.g., 1999/2000) and extreme floods (e.g., 1997/1998) were found to be linked to respective negative and positive anomalies of the Nino3.4. In general, climate data products with high spatial resolution and accuracy help detect changes and variability in climate at local scale where adaptation is required

Changes in temperature and precipitation extremes in Ethiopia, Kenya, and Tanzania

East Africa is one of the most vulnerable regions of Africa to extreme weather and climate events. Regional and local information on climate extremes is critical for monitoring and managing the impacts and developing sustainable adaptation measures. However, this type of information is not readily available at the necessary spatial resolution. Therefore, here we test trends and variability of temperature (1979–2010) and precipitation (1981–2016) extremes in East Africa, particularly Ethiopia, Kenya, and Tanzania, at a spatial resolution of 0.1 and 0.05°, respectively, using the indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). We use gridded data sets with high accuracy and resolution from the Terrestrial Hydrology Research Group, University of Princeton and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS). Trends of 19 indices are computed by fitting a linear model and using the nonparametric Mann–Kendall test and the magnitude of change is computed using the Sen's slope method. The results show an increasing trend in monthly maximum and minimum values of daily maximum and minimum temperature in large parts of the region. This is accompanied by significant increasing trends in warm nights (TN90p), warm days (TX90p), warm spell duration index (WSDI), and summer days index (SU). In addition, cold days (TX10p) and cold nights (TN10p) showed a significant decreasing trend. In general, the results show an increasing tendency in temperatures extremes, which is in line with rising global mean temperature. In addition, most of the temperature extremes observed after 2000 are warmer than the long‐term mean (1979–2010). Precipitation indices, on the other hand, showed increasing and decreasing trends in Ethiopia, Kenya, and Tanzania, but no general pattern. The outcomes enable identifying hot spot areas and planning of adaptation and mitigation measures at much finer spatial scale than previously possible

Regional climate projections for impact assessment studies in East Africa

In order to overcome limitations of climate projections from Global Climate Models (GCMs), such as coarse spatial resolution and biases, in this study, the Statistical Down-Scaling Model (SDSM) is used to downscale daily precipitation and maximum and minimum temperature (T-max and T-min) required by impact assessment models. We focus on East Africa, a region known to be highly vulnerable to climate change and at the same time facing challenges concerning availability and accessibility of climate data. SDSM is first calibrated and validated using observed daily precipitation, (T-max, and T-min) from 214 stations and predictors derived from the reanalysis data of the National Centers for Environmental Prediction. For projection (2006–2100), the same predictors derived from the second generation Canadian Earth System Model (CanESM2) are used. SDSM projections show an increase in precipitation during the short-rain season (October–December) in large parts of the region in the 2020s (2011–2040), 2050s (2041–2070), and 2080s (2071–2100). During the long-rain season (March–May (MAM)) precipitation is expected to increase (up to 680 mm) in Ethiopia, mainly in the western part, and Kenya and decrease (up to −500 mm) in Tanzania in the 2020s, 2050s, and 2080s. However, the western part of Ethiopia will be much drier than the baseline period (1961–1990) during June–September (JJAS) in the 2020s, 2050s, and 2080s, which indicates a shift in precipitation from JJAS to MAM. Annually, precipitation, T-max, and T-min will be higher than during the baseline period throughout the 21 century in large parts of the region. The projection based on SDSM is in line with the direction of CMIP5 GCMs but differs in magnitude, particularly for T-max and T-min. Overall, we conclude that the downscaled data allow for much more fine-scaled adaptation plans and ultimately better management of the impacts of projected climate in basins of Ethiopia, Kenya, and Tanzania