Improved representation of agricultural land use and crop management for large-scale hydrological impact simulation in Africa using SWAT+

To date, most regional and global hydrological models either ignore the representation of cropland or consider crop cultivation in a simplistic way or in abstract terms without any management practices. Yet, the water balance of cultivated areas is strongly influenced by applied management practices (e.g. planting, irrigation, fertilization, and harvesting). The SWAT+ (Soil and Water Assessment Tool) model represents agricultural land by default in a generic way, where the start of the cropping season is driven by accumulated heat units. However, this approach does not work for tropical and subtropical regions such as sub-Saharan Africa, where crop growth dynamics are mainly controlled by rainfall rather than temperature. In this study, we present an approach on how to incorporate crop phenology using decision tables and global datasets of rainfed and irrigated croplands with the associated cropping calendar and fertilizer applications in a regional SWAT+ model for northeastern Africa. We evaluate the influence of the crop phenology representation on simulations of leaf area index (LAI) and evapotranspiration (ET) using LAI remote sensing data from Copernicus Global Land Service (CGLS) and WaPOR (Water Productivity through Open access of Remotely sensed derived data) ET data, respectively. Results show that a representation of crop phenology using global datasets leads to improved temporal patterns of LAI and ET simulations, especially for regions with a single cropping cycle. However, for regions with multiple cropping seasons, global phenology datasets need to be complemented with local data or remote sensing data to capture additional cropping seasons. In addition, the improvement of the cropping season also helps to improve soil erosion estimates, as the timing of crop cover controls erosion rates in the model. With more realistic growing seasons, soil erosion is largely reduced for most agricultural hydrologic response units (HRUs), which can be considered as a move towards substantial improvements over previous estimates. We conclude that regional and global hydrological models can benefit from improved representations of crop phenology and the associated management practices. Future work regarding the incorporation of multiple cropping seasons in global phenology data is needed to better represent cropping cycles in areas where they occur using regional to global hydrological models.

Implications of salinity normalization of seawater total alkalinity in coral reef metabolism studies

Salinity normalization of total alkalinity (TA) and dissolved inorganic carbon (DIC) data is commonly used to account for conservative mixing processes when inferring net metabolic modification of seawater by coral reefs. Salinity (S), TA, and DIC can be accurately and precisely measured, but salinity normalization of TA (nTA) and DIC (nDIC) can generate considerable and unrecognized uncertainties in coral reef metabolic rate estimates. While salinity normalization errors apply to nTA, nDIC, and other ions of interest in coral reefs, here, we focus on nTA due to its application as a proxy for net coral reef calcification and the importance for reefs to maintain calcium carbonate production under environmental change. We used global datasets of coral reef TA, S, and modeled groundwater discharge to assess the effect of different volumetric ratios of multiple freshwater TA inputs (i.e., groundwater, river, surface runoff, and precipitation) on nTA. Coral reef freshwater endmember TA ranged from -2 up to 3032 μmol/kg in hypothetical reef locations with freshwater inputs dominated by riverine, surface runoff, or precipitation mixing with groundwater. The upper bound of freshwater TA in these scenarios can result in an uncertainty in reef TA of up to 90 μmol/kg per unit S normalization if the freshwater endmember is erroneously assumed to have 0 μmol/kg alkalinity. The uncertainty associated with S normalization can, under some circumstances, even shift the interpretation of whether reefs are net calcifying to net dissolving, or vice versa. Moreover, the choice of reference salinity for normalization implicitly makes assumptions about whether biogeochemical processes occur before or after mixing between different water masses, which can add uncertainties of ±1.4% nTA per unit S normalization. Additional considerations in identifying potential freshwater sources of TA and their relative volumetric impact on seawater are required to reduce uncertainties associated with S normalization of coral reef carbonate chemistry data in some environments. However, at a minimum, researchers should minimize the range of salinities over which the normalization is applied, precisely measure salinity, and normalize TA values to a carefully selected reference salinity that takes local factors into account.

Understanding process controls on groundwater recharge variability across Africa through recharge landscapes

Groundwater is critical in supporting current and future reliable water supply throughout Africa. Although continental maps of groundwater storage and recharge have been developed, we currently lack a clear understanding on how the controls on groundwater recharge vary across the entire continent. Reviewing the existing literature, we synthesize information on reported groundwater recharge controls in Africa. We find that 15 out of 22 of these controls can be characterised using global datasets. We develop 11 descriptors of climatic, topographic, vegetation, soil and geologic properties using global datasets, to characterise groundwater recharge controls in Africa. These descriptors cluster Africa into 15 Recharge Landscape Units for which we expect recharge controls to be similar. Over 80% of the continents land area is organized by just nine of these units. We also find that aggregating the Units by similarity into four broader Recharge Landscapes (Desert, Dryland, Wet tropical and Wet tropical forest) provides a suitable level of landscape organisation to explain differences in ground-based long-term mean annual recharge and recharge ratio estimates. Furthermore, wetter Recharge Landscapes are more efficient in converting rainfall to recharge than drier Recharge Landscapes as well as having higher annual recharge rates. In Dryland Recharge Landscapes, we found that annual recharge rates largely varied according to mean annual precipitation, whereas recharge ratio estimates increase with increasing monthly variability in P-PET. However, we were unable to explain why ground-based estimates of recharge signatures vary across other Recharge Landscapes, in which there are fewer ground-based recharge estimates, using global datasets alone. Even in dryland regions, there is still considerable unexplained variability in the estimates of annual recharge and recharge ratio, stressing the limitations of global datasets for investigating ground-based information.

Urban flood susceptibility evaluation and prediction during 2010–2030 in the southern watersheds of Mashhad city, Iran

Background Urban flood susceptibility evaluation (FSE) can utilize empirical and rational procedures to focus on the urban flood evaluation using physical coefficients and land-use change ratios. The main aim of the present paper was to evaluate a flood susceptibility model in the southern watersheds of Mashhad city, in Iran, for 2010, 2020, and 2030. The construction of the model depended on the utilization of some global datasets to estimate the runoff coefficients of the watersheds, peak flood discharges, and flood susceptibility evaluations. Results and conclusions Based on the climatic precipitation and urban sprawl variation, our results revealed the mean values of the runoff coefficient (Cr) from 0.50 (2010) to 0.65 (2030), where the highest values of Cr (> 0.70) belonged to the watersheds with real estate cover, soil unit of the Mollisols, and the slope ranges over 5–15%. The averagely cumulative flood discharges were estimated from 2.04 m3/s (2010) to 5.76 m3/s (2030), revealing an increase of the flood susceptibility equal 3.2 times with at least requirement of an outlet cross-section by > 46 m2 in 2030. The ROC curves for the model validity explained AUC values averagely over 0.8, exposing the very good performance of the model and excellent sensitivity.