Spatio-Temporal Trends in Precipitation, Temperature, and Extremes: A Study of Malawi and Zambia (1981–2021)

Analyzing long-term climate changes is a prerequisite for identifying hotspot areas and developing site-specific adaptation measures. The current study focuses on assessing changes in precipitation, maximum and minimum temperatures, and potential evapotranspiration in Zambia and Malawi from 1981 to 2021. High-resolution precipitation and temperature datasets are used, namely, Climate Hazards Group InfraRed Precipitation with Station data (0.05°) and Multi-Source Weather (0.1°). The Mann–Kendall trend test and Sen’s Slope methods are employed to assess the changes. The trend analysis shows a non-significant increase in annual precipitation in many parts of Zambia and Central Malawi. In Zambia and Malawi, the average annual and seasonal maximum and minimum temperatures show a statistically significant increasing trend (up to 0.6 °C/decade). The change in precipitation during the major rainy seasons (December–April) shows a non-significant increasing trend (up to 3 mm/year) in a large part of Zambia and Central Malawi. However, Malawi and Northern Zambia show a non-significant decreasing trend (up to −5 mm/year). The change in December–April precipitation significantly correlates with El Niño–Southern Oscillation (Indian Ocean Dipole) in Southern (Northern) Zambia and Malawi. To minimize the impact of the observed changes, it is imperative to develop adaptation measures to foster sustainability in the region

Performance of state-of-the-art C3S European seasonal climate forecast models for mean and extreme precipitation over Africa

Seasonal hydrological forecasts at high spatial and temporal resolution can help manage water resources and mitigate impacts of extreme events but are dependent on skillful and operational seasonal forecasts from climate models. In this study, we evaluate precipitation forecasts from five operational climate models with a potential to drive hydrological forecasts: European Centre for Medium-Range Weather Forecasts (ECMWF), UK Met Office (UK-Met), Météo France, Deutscher Wetterdienst, and Centro Euro-Mediterraneo sui Cambiamenti Climatici. The Multi-Source Weighted-Ensemble Precipitation is used as a reference data set to evaluate the model skill. The performance of individual models is evaluated on daily, weekly, monthly, seasonal, and climatological periods, and for selected target months, lead-times and drought events, and compared to unweighted and skill-weighted multi-model ensemble mean forecast. For all models, the lead 1-month forecast can replicate the climatological mean, monthly mean, and monthly anomaly precipitation, although much of this skill originates from the first week of the forecast. The skill drops rapidly for lead 2-month and longer and is highest in drier regions and seasons. The forecast skill of monthly meteorological drought events at lead 1-month is modest. All models represent the monthly variation in the length of wet and dry spell days at lead 1-month, but the skill is weak for heavy and very heavy precipitation days. ECMWF is found to be the most skillful model, followed by the UK-Met, although the multi-model weighted average provides the highest performance compared to individual models and the un-weighted multi-model mean

Hydrological investigation of climate change impact on water balance components in the agricultural terraced watersheds of Yemeni highland

Hydrological models serve as valuable instruments for assessing the impact of climate change on water resources and agriculture as well as for developing adaptation measures. In Yemen, climate change and variability are imposing a significant impact on the most important sectors such as agriculture and economy. The current study evaluates the influence of future climate on hydrology and water balance components in Yemen’s highlands using a semi-distributed physical-based hydrologic model Soil Water Assessment Tool (SWAT) and employing high-resolution climate projections. The SWAT was calibrated and verified using observed streamflow data from 1982 to 2000 in three large catchments. Ground data from 24 stations and statistically downscaled future climate data for the period 2010–2100 under RCP2.6 and RCP8.5 are used. SWAT performance was assessed using multiple statistical methods, which revealed the commendable performance of SWAT during the calibration (average NSE = 0.80) and validation (NSE = 0.72) periods. The outcome indicates an increase in future seasonal and annual rainfall, maximum temperature, and minimum temperature in the 2020s and the 2080s under both RCP2.6 and RCP8.5 scenarios. This projected increase in the rainfall and the local temperature will result in increased averages of surface runoff, evapotranspiration, soil water, and groundwater recharge in the representative three catchments up to 6.5%, 21.1%, 7.6%, and 6.4%, respectively. Although, the projected increase in the water balance components will benefit the agriculture and water sector, specific adaptation measures will be crucial to mitigate potential flood impacts arising from the increased precipitations as well as to minimize the consequences of the increased temperature. Likewise, demand for supplementary irrigation is expected to increase to offset the higher evapotranspiration rates in the future

Increase in ocean-onto-land droughts and their drivers under anthropogenic climate change

Ocean-onto-land droughts (OTLDs)—i.e., droughts originating over the oceans and migrating onto land—are a recently identified phenomenon with severe natural and human impacts. However, the influence of anthropogenic emissions on past and future changes in OTLDs and their underlying mechanisms remain unclear. Here, using precipitation-minus-evaporation deficits to identify global OTLDs, we find OTLDs have intensified due to anthropogenic climate change during the past 60 years. Under a future high-emissions scenario, the OTLDs would become more frequent (+39.68%), persistent (+54.25%), widespread (+448.92%), and severe (+612.78%) globally. Intensified OTLDs are associated with reduced moisture transport driven by subtropical anticyclones in the northern hemisphere and complex circulation patterns in the southern hemisphere. The reduction in moisture transport during OTLDs is mainly caused by the atmospheric thermodynamic responses to human-induced global warming. Our results underscore the importance of improving understanding of this type of drought and adopting climate mitigation measures

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 four 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 hourly 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 can 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://doi.org/10.5285/ac43da11867243a1bb414e1637802dec) (Gebrechorkos et al., 2023)

Making the most of imperfect data: A critical evaluation of standard information collected in farm household surveys

Household surveys are one of the most commonly used tools for generating insight into rural communities. Despite their prevalence, few studies comprehensively evaluate the quality of data derived from farm household surveys. We critically evaluated a series of standard reported values and indicators that are captured in multiple farm household surveys, and then quantified their credibility, consistency and, thus, their reliability. Surprisingly, even variables which might be considered ‘easy to estimate’ had instances of non-credible observations. In addition, measurements of maize yields and land owned were found to be less reliable than other stationary variables. This lack of reliability has implications for monitoring food security status, poverty status and the land productivity of households. Despite this rather bleak picture, our analysis also shows that if the same farm households are followed over time, the sample sizes needed to detect substantial changes are in the order of hundreds of surveys, and not in the thousands. Our research highlights the value of targeted and systematised household surveys and the importance of ongoing efforts to improve data quality. Improvements must be based on the foundations of robust survey design, transparency of experimental design and effective training. The quality and usability of such data can be further enhanced by improving coordination between agencies, incorporating mixed modes of data collection and continuing systematic validation programmes

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

Global-scale evaluation of precipitation datasets for hydrological modelling

Abstract. Precipitation is the most important driver of the hydrological cycle, but it is challenging to estimate it over large scales from satellites and models. Here, we assessed the performance of six global and quasi-global high-resolution precipitation datasets (European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5), Climate Hazards group Infrared Precipitation with Stations version 2.0 (CHIRPS), Multi-Source Weighted-Ensemble Precipitation version 2.80 (MSWEP), TerraClimate (TERRA), Climate Prediction Centre Unified version 1.0 (CPCU), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR, hereafter PERCCDR) for hydrological modelling globally and quasi-globally. We forced the WBMsed global hydrological model with the precipitation datasets to simulate river discharge from 1983 to 2019 and evaluated the predicted discharge against 1825 hydrological stations worldwide, using a range of statistical methods. The results show large differences in the accuracy of discharge predictions when using different precipitation input datasets. Based on evaluation at annual, monthly, and daily timescales, MSWEP followed by ERA5 demonstrated a higher correlation (CC) and Kling–Gupta efficiency (KGE) than other datasets for more than 50 % of the stations, whilst ERA5 was the second-highest-performing dataset, and it showed the highest error and bias for about 20 % of the stations. PERCCDR is the least-well-performing dataset, with a bias of up to 99 % and a normalised root mean square error of up to 247 %. PERCCDR only show a higher KGE and CC than the other products for less than 10 % of the stations. Even though MSWEP provided the highest performance overall, our analysis reveals high spatial variability, meaning that it is important to consider other datasets in areas where MSWEP showed a lower performance. The results of this study provide guidance on the selection of precipitation datasets for modelling river discharge for a basin, region, or climatic zone as there is no single best precipitation dataset globally. Finally, the large discrepancy in the performance of the datasets in different parts of the world highlights the need to improve global precipitation data products. </jats:p