Data-poor Regions Research Articles

Evaluating the suitability of large-scale datasets to estimate nitrogen loads and yields across different spatial scales

Decision makers are often confronted with inadequate information to predict nutrient loads and yields in freshwater ecosystems at large spatial scales. We evaluate the potential of using data mapped at large spatial scales (regional to global) and often coarse resolution to predict nitrogen yields at varying smaller scales (e.g., at the catchment and stream reach level). We applied the SPAtially Referenced Regression On Watershed attributes (SPARROW) model in three regions: the Upper Midwest part of the United States, New Zealand, and the Grande River Basin in southeastern Brazil. For each region, we compared predictions of nitrogen delivery between models developed using novel large-scale datasets and those developed using local-scale datasets. Large-scale models tended to underperform the local-scale models in poorly monitored areas. Despite this, large-scale models are well suited to generate hypotheses about relative effects of different nutrient source categories (point and urban, agricultural, native vegetation) and to identify knowledge gaps across spatial scales when data are scarce. Regardless of the spatial resolution of the predictors used in the models, a representative network of water quality monitoring stations is key to improve the performance of large-scale models used to estimate loads and yields. We discuss avenues of research to understand how this large-scale modelling approach can improve decision making for managing catchments at local scales, particularly in data poor regions.

Achieving SOC Conservation without Land-Use Changes between Agriculture and Forests

Global land-use changes impact soil’s ability to perform essential functions. This study investigates whether soil organic carbon (SOC) can be conserved without altering land use in traditional farming systems and degraded natural forests, focusing on ‘disturbed’ agricultural soils and ‘undisturbed’ forest soils. We also examine the influence of dominant crops on SOC within the top 30 cm of soil in data-deficient regions of Nepal. Using a multi-stage cluster sampling design, we tested 12 regression models to identify the best relationships among variables such as SOC, soil bulk density (BD), pH, dominant crops, climate, topography, and management practices. Our analysis revealed similar SOC levels in both disturbed and undisturbed soils, indicating significant degradation in forested areas, whereas traditional farming systems could support SOC and preserve farm-based indigenous knowledge alongside food security. Further, SOC stocks varied significantly (p < 0.05) across different cropping systems, suggesting that managing dominant crops could be a strategy to optimize SOC, with these crops serving as indicators. Additionally, our results show that the weak linear correlation between SOC and BD in regularly disturbed soils, such as farmlands, where anthropogenic activities frequently alter soil bulk density, may be misleading when estimating bulk density-dependent SOC. This finding suggests the need for further research into varying degrees of anthropogenic disturbance in soil to confirm these results. While the site-specific nature of the findings warrants caution with respect to generalization, they provide valuable insights for carbon monitoring, climate actions, ecosystem health, and land-use management in similar traditional farming systems and degraded forests, particularly in data-poor regions.

A novel voting ensemble model empowered by metaheuristic feature selection for accurate flash flood susceptibility mapping

This study addresses the challenges of flash flood susceptibility mapping in Yemen’s Qaa’Jahran Basin, characterized by complex terrain and limited hydro-meteorological data. To enhance predictive accuracy, we integrate metaheuristic feature selection with ensemble learning models. Initially, fifteen flash flood variables were retrieved using Geographic Information System (GIS) based remote sensing, setting the stage for a novel feature selection algorithm. Then, the Memo Search Algorithm (MSA), a metaheuristic approach is proposed to efficiently reduce feature space. Through comprehensive comparisons with established algorithms such as the Artificial Bee Colony (ABC) and Gray Wolf Optimizer (GWO), MSA refined the selection, identifying 'elevation’ and 'distance to streams’ as optimal factors. Statistical validations using the Friedman and Wilcoxon signed-rank tests confirmed the significant superiority of MSA over competing algorithms. Ensemble classifiers (bagging, boosting, stacking) were then applied to the reduced feature space. Comprehensive evaluation revealed the boosting ensemble with MSA outperformed traditional techniques reaching 98.75% accuracy, 0.9896 Area Under the Curve (AUC), and 98.95% the harmonic mean of the precision and recall (F1-score). Precision in identifying high-risk flash flood zones was underlined via spatial prediction, confirming the integrated framework’s ability to significantly improve forecast accuracy. The findings aid disaster management with powerful geographic mapping in data-poor regions. The proposed framework is adaptable globally for flash flood-prone areas with similar constraints. As climate change is expected to increase extreme rainfall events, communities globally will need robust data-driven methodologies for flash flood susceptibility mapping. The Key recommendations of the current study include investigating hybrid feature selection methods to better enhance predictive inputs and analyzing transferability across hydro-climatic zones.

Australia’s Future Energy Resources project: the untapped potential of onshore low carbon energy resources

The ‘Australia’s Future Energy Resources’ (AFER) project, funded under the Government’s ‘Exploring for the Future’ (EFTF) program has been completed. The project’s four modules have evaluated a mixture of energy resource commodities, including natural gas, hydrogen, subsurface storage opportunities for carbon dioxide and hydrogen. They are complemented by several targeted basin inventories which outline the current geological knowledge of energy resources in underexplored, data-poor regions. Several publicly available data sets have been generated and published under the AFER project, including 3750 line-km of reprocessed 2D seismic data, acquired in the Pedirka and western Eromanga basins, of which key lines have been interpreted and integrated with geological and petrophysical well log data. Relative prospectivity maps have been produced for five energy resource commodities from 14 play intervals to show the qualitative variability in prospectivity of these resources, including quantitative resource assessments where warranted. Results from the AFER project have helped to identify and geologically characterise the required energy resource commodities to accelerate Australia’s path to net zero emissions.

Partnering with the commercial fishing sector and Aotearoa New Zealand’s ocean community to develop a nationwide subsurface temperature monitoring program

Coastal regions of the world’s oceans are critical to supporting the fishing sector, recreation, tourism, and the global blue economy. However, there is a paucity of subsurface, in situ ocean measurements in coastal and shelf regions worldwide that corresponds to the region where a majority of commercial fishing occurs. In Aotearoa New Zealand, the Moana Project and technology partner ZebraTech, Ltd. have co-designed a fully automatic system that measures, transmits, processes, and disseminates temperature observations in near real-time with a goal of providing broad-scale coverage of New Zealand’s coastal and shelf seas. In the first two years, more than 300 sensors were deployed by over 250 vessels with the cooperation and support of the commercial fishing sector, providing more than one million temperature measurements per month throughout New Zealand’s exclusive economic zone. Participation by the fishing sector is critical to program success with continuous improvement based on fishing sector feedback. Here we introduce the fishing-vessel-based temperature and pressure data collection on a national scale and present initial results showcasing a step change in research quality ocean temperature data collection. Next, we highlight the full-circle data pathway including improved ocean forecasts and near real-time return of the data to the vessels that obtained them. Finally, a discussion of key partnerships, use cases, and lessons learned in Aotearoa New Zealand provides a potential framework for deploying similar systems in data-poor regions worldwide with the support of the commercial fishing fleet and citizen scientists.

Climate and biodiversity change constrain the flow of cultural ecosystem services to people: A case study modeling birding across Africa under future climate scenarios

Global change is currently impacting ecosystems and their contributions to people (i.e. ecosystem services). These impacts have consequences for societies and human well-being, especially in Africa. Historically, efforts have focused on assessing global change from a social or biophysical perspective, treating them as separate entities. Yet, our understanding of impacts to social-ecological systems remains limited, particularly in the Global South, due to a lack of data, tools, and approaches accounting for social and ecological aspects of ecosystem services. This is especially relevant for cultural ecosystem services as they are less tangible. We use a simple indicator and important provider of a multitude of cultural ecosystem services, birding, to understand how climate, biodiversity, and land use change will impact cultural ecosystem services across Africa. We explore how emerging tools and data can overcome limitations in mapping and modeling cultural ecosystem services, particularly in analyzing human preferences and behavior at large spatiotemporal scales and in data-poor regions. Leveraging crowdsourced data from eBird and using machine learning techniques we map and model recreational birding to assess the underlying social-ecological relationships and the impact of future climate and environmental change. We show that bird species richness, protected areas, accessibility, and max temperature contribute most to birding suitability across the continent. Further, we show spatial shifts in the suitability of birding under three future climate scenarios (SSP126, 370, and 585). Models suggest climate and biodiversity change will increasingly constrain the flow of birding related cultural ecosystem services across Africa. This has implications for human-nature interactions, development of countries, management of protected areas, and overall human well-being in the future. More generally, we highlight opportunities for crowdsourced datasets and machine learning to integrate non-material ecosystem services in models and thus, enhance the understanding of future impacts to ecosystem services and human well-being.

Exploring the Potential of OpenStreetMap Data in Regional Economic Development Evaluation Modeling

In regional development studies, GDP serves as an important indicator for evaluating the developing levels of a region. However, due to statistical methods and possible human-induced interfering factors, GDP is also a commonly criticized indicator for less accurately assessing regional economic development in a dynamic environment, especially during a globalized era. Moreover, common data collection approaches are often challenging to obtain in real-time, and the assessments are prone to inaccuracies. This is especially true in economically underdeveloped regions where data are often less frequently or accurately collected. In recent years, Nighttime Light (NTL) data have emerged as a crucial supplementary data source for regional economic development evaluation and analysis. We adapt this approach and attempt to integrate multiple sources of spatial data to provide a new perspective and more effective tools for economic development evaluation. In our current study, we explore the integration of OpenStreetMap (OSM) data and NTL data in regional studies, and apply a Geographically and Temporally Weighted Regression model (GTWR) for modeling and evaluating regional economic development. Our results suggest that: (1) when using OSM data as a single data source for economic development evaluation, the adjusted R2 value is 0.889. When using NTL data as a single data source for economic development evaluation, the adjusted R2 value is 0.911. However, the fitting performance of OSM data with GDP shows a gradual improvement over time, while the fitting performance of NTL data exhibits a gradual decline starting from the year 2014; (2) Among the economic evaluation models, the GTWR model demonstrates the highest accuracy with an AICc value of 49,112.71, which is 2750.94 lower than the ordinary least squares (OLS) model; (3) The joint modeling of OSM data with NTL data yields an adjusted R2 value of 0.956, which is higher than using either one of them alone. Moreover, this joint modeling approach demonstrates excellent fitting performance, particularly in economically underdeveloped regions, providing a potential alternative for development evaluation in data-poor regions.

Millet yield estimations in Senegal: Unveiling the power of regional water stress analysis and advanced predictive modeling

Crop yield is usually affected by impending weather, climate conditions, and human interventions like irrigation. Hence, the prompt detection of regions experiencing water scarcity can aid in implementing effective mitigation strategies. Our study utilized a data-driven approach to compute a Water Demand Index (WDI), which incorporates crucial first-order geophysical variables like ambient temperature, vegetation status, and soil moisture, to identify water-stressed fields in Senegal’s agricultural regions during the millet planting, growing, and harvesting periods. We have also explored various scenarios for enhancing the accuracy of millet yield prediction by incorporating other drought indices, soil characteristics, and a bias correction factor. The novel aspects of this research include the development of regional crop yield predictive models based on sub-seasonal and unaggregated crop information to reflect local variances. To optimize the hyperparameters of machine learning (ML) models, various techniques were utilized. Meanwhile, the performance of these ML models was evaluated using a nested cross-validation approach. The outcomes of the analysis demonstrate that the Random Forest Regressor model exhibits superior predictive performance. The results imply that a holistic approach, encompassing diverse environmental factors and crop growth stages, could result in more precise and dependable millet yield predictions. These results mark a leap forward in crop yield prediction for data-poor regions, paving the way for more accurate and efficient agricultural management. By emphasizing the critical role of detailed, spatio-temporal data, this work substantially enhances model precision, informing targeted strategies to boost yields. This advancement stands to serve producers and consumers alike, fostering sustainable, high-yield agriculture.

Using deep transfer learning and satellite imagery to estimate urban air quality in data-poor regions

Urban air pollution is a critical public health challenge in low-and-middle-income countries (LMICs). At the same time, LMICs tend to be data-poor, lacking adequate infrastructure to monitor air quality (AQ). As LMICs undergo rapid urbanization, the socio-economic burden of poor AQ will be immense. Here we present a globally scalable two-step deep learning (DL) based approach for AQ estimation in LMIC cities that mitigates the need for extensive AQ infrastructure on the ground. We train a DL model that can map satellite imagery to AQ in high-income countries (HICs) with sufficient ground data, and then adapt the model to learn meaningful AQ estimates in LMIC cities using transfer learning. The trained model can explain up to 54% of the variation in the AQ distribution of the target LMIC city without the need for target labels. The approach is demonstrated for Accra in Ghana, Africa, with AQ patterns learned and adapted from two HIC cities, specifically Los Angeles and New York.

Comparison of different spectral indices to differentiate the impact of insect attack on planted forest stands

Eucalyptus plantations are an important contributor to the South African economy and are geographically concentrated in the productive Zululand region. However, this area is becoming increasingly vulnerable to various forest disturbances such as insect attacks and drought. Information of the location, extent and duration of insect infestation is therefore crucial for sustainable timber production and forest management planning, and remote sensing approaches have been successful in the retrieval and analysis of such information along with field surveys. Sappi Forests frequently conduct field surveys to establish the nature of disturbances to improve the performance of trees, as these may be difficult to distinguish from other disturbances such as droughts. Great effort has been made to remotely detect the impact of G. scutellatus using single-date high-resolution commercial satellites, whose application is restricted by high costs. Here, we compare the performance of spectral indices for detecting the impact of G. scutellatus infestation in KwaMbonambi plantations using publicly available Landsat data and also apply anomaly detection framework to further test their performance for detecting the impact of insect damage on trees and other disturbances that occurred on the same compartment over time. Our results showed high performance of short-wave infrared (SWIR) band for detecting the impact of insect damage, followed by the normalized difference infrared index (NDII) and normalized difference vegetation index NDVI, when anomaly detection was applied. Without anomaly detection, the spectral indicators showed changes even to drought impact in 2015, which did not when anomaly detection was used. We believe that indices containing the SWIR would perform better for detecting anomalous changes, and further research is required to explore this. Anomaly detection only showed the impact of insect damage and clearcutting as anomalous changes. Overall, we demonstrated the benefit of identifying vegetation anomalies caused by the impact of insect damage and other disturbances using freely available Landsat time-series data, particularly in data-poor regions that have restricted computational resources.

Habitat overlap among native and introduced cold-water fishes in the Himalayas

Fish invasions threaten native freshwater ecosystems worldwide, yet methods to map biodiversity in data-deficient regions are scarce. Rainbow trout (Oncorhynchus mykiss) and brown trout (Salmo trutta fario) have been introduced to the Himalayan ecoregion where they are sympatric with vulnerable native snow trout Schizothorax plagiostomus and Schizothorax richardsonii. We aim to evaluate potential habitat overlap among snow trout and non-native trout in the Indus and Ganges River basins, Himalayan ecoregion. We transferred maximum entropy (MaxEnt) models developed with spatially continuous freshwater-specific environmental variables to map the distribution of potentially suitable habitats for rainbow and brown trout in the Himalayas. We adopted a similar procedure to map suitable habitats for snow trout species. There were substantial habitat overlaps (up to 96%) among snow trout and non-native trout. Yet, the physiography of receiving basins could play a role minimizing the impacts of each non-native trout on native snow trout. We generate high-resolution classified stream suitability maps as decision support tools to help managers in habitat allocation and policy formation to balance recreational fisheries with conservation of snow trout. Our workflow can be transferred to other basins and species for mapping freshwater biodiversity patterns in species-rich yet data-poor regions of the world.

Monitoring of a Coastal Protection Scheme through Satellite Remote Sensing: A Case Study in Ghana

Earth observation can provide managers with valuable information on ongoing coastal processes and major trends in coastline evolution, especially in data-poor regions. This paper examines the use of optical satellite images in the mapping of the changes in shoreline position before, during, and after the implementation of a protection scheme. The aim of this paper is twofold: (i) to demonstrate the potential of satellite imagery as an effective, robust, and low-cost tool to remotely monitor the effectiveness of protective structures based on a large-scale case study in West Africa; and (ii) to compile lessons learned from this case study that can be used in the design of future interventions. The analysis shows that before the implementation of the protection scheme, the coastal sector was retreating at a rate of −1.6 m/year, which is in line with the average retreat rates reported in other studies for the region. After project implementation, this trend reversed into shoreline accretion at a rate of +1.0 m/year, locally experiencing positive and negative oscillations in the short term. Furthermore, the shoreline-extracted positions proved useful in assessing the impact of differences in the groynes’ permeability with respect to temporary leeside erosion. Finally, it is recommended to continue this monitoring to assess long-term trends.

Dryspells and Minimum Air Temperatures Influence Rice Yields and their Forecast Uncertainties in Rainfed Systems

Crop yield predictions on inter-seasonal to inter-annual horizons are subject to a diverse set of uncertainties associated with climate forecast scenarios. However, the uncertainties associated with climate-related parameters can be marginally controlled (reduced) over the growing season, i.e., from planting through harvest, leveraging a suite of climate forecast ensembles as forcings. In this study, we present a novel approach that combines a coupled hydrologic-crop modeling framework with probabilistic forecasts to characterize and reduce uncertainties in seasonal rice yields predictions. By weighting real-time (nowcast) and forecast climate forcings, the comprehensive framework accurately quantifies uncertainties associated with climate forecasts. At a provincial scale, the crop model extensively captured the uncertainties in yields whilst significantly complementing observations over the growing season. We observed that the spread of yield predictions gradually decreased over time (i.e., towards harvest) as subsequent timeframes incorporated a higher degree of present conditions/forcings and reduced reliance on forecasts. Furthermore, we investigated the information exchange between yields and hydrologic/drought variables over different time frames within the season. Notably, we found a higher synchronization of information transfer between yields, dryspells, and minimum air temperatures towards the end of season, indicating strong explicit links between these variables and crop yields. These outcomes have significant implications on crop yield forecasting and nowcasting, particularly in data-poor regions. By providing a better understanding of the uncertainties associated with seasonal climate forecasts and the interplay between hydrologic variables, drought conditions, and crop yields, this research can aid in improving decision-making processes related to agricultural planning, and risk management. Moreover, these insights can inform assessments of economic, social, and environmental impacts of drought in agricultural systems.

Regional offshore ground motion prediction model from a referenced empirical approach: A case study in the Japan Trench area

With increasing resource exploitation and engineering construction offshore, the seismic safety of offshore structures has received attention from researchers and designers, and an offshore ground motion prediction model (GMPM) can be used to estimate the possible earthquake response of structures and provide the foundation for seismic fortification of structures. Because there are few strong motion records recorded by offshore stations, there are insufficient empirical data in the magnitude-distance range to directly develop a GMPM using empirical regression techniques. To develop a reliable GMPM for data-poor offshore regions, we used a referenced empirical approach based on an onshore empirical prediction model. We collected 2605 records from 138 crustal earthquake events, 2669 records from 125 slab earthquake events and 5693 records from 267 interface earthquake events recorded by S-net stations. We used the existing Japanese onshore GMPM as a reference model. Then, we analyzed the residuals of the reference model for offshore data and calculated its adjustment factors. Finally, we developed a GMPM for the Japan Trench area and compared it with the existing onshore models. The proposed model fit the data subsets at hypocentral distances of 20–300 km reasonably well. There were different spectral accelerations (SAs) for different source types, with considerably larger SAs for short periods of offshore motions from crustal events than for the motions from the other two types, and the difference becomes more obvious with the increase of magnitude. At short periods, the SAs of the offshore motions from slab events were slightly larger than those of the motions from the interface events. The discrepancy of the offshore and onshore GMPM was significant, and the peak ground accelerations (PGAs) and SAs of the offshore ground motions were apparently greater than those of onshore ground motions of the same magnitude and distance. The offshore GMPM established by the reference empirical approach contribute to studying the characteristics of offshore strong ground motions, and this approach can be applied to data-poor regions.

Hurdles and opportunities in implementing marine biosecurity systems in data-poor regions.

Managing marine nonindigenous species (mNIS) is challenging, because marine environments are highly connected, allowing the dispersal of species across large spatial scales, including geopolitical borders. Cross-border inconsistencies in biosecurity management can promote the spread of mNIS across geopolitical borders, and incursions often go unnoticed or unreported. Collaborative surveillance programs can enhance the early detection of mNIS, when response may still be possible, and can foster capacity building around a common threat. Regional or international databases curated for mNIS can inform local monitoring programs and can foster real-time information exchange on mNIS of concern. When combined, local species reference libraries, publicly available mNIS databases, and predictive modeling can facilitate the development of biosecurity programs in regions lacking baseline data. Biosecurity programs should be practical, feasible, cost-effective, mainly focused on prevention and early detection, and be built on the collaboration and coordination of government, nongovernment organizations, stakeholders, and local citizens for a rapid response.

Demonstrating the Use of the Yield-Gap Concept on Crop Model Calibration in Data-Poor Regions: An Application to CERES-Wheat Crop Model in Greece

Yield estimations at global or regional spatial scales have been compromised due to poor crop model calibration. A methodology for estimating the genetic parameters related to grain growth and yield for the CERES-Wheat crop model is proposed based on yield gap concept, the GLUE coefficient estimator, and the global yield gap atlas (GYGA). Yield trials with three durum wheat cultivars in an experimental farm in northern Greece from 2004 to 2010 were used. The calibration strategy conducted with CERES-Wheat (embedded in DSSAT v.4.7.5) on potential mode taking into account the year-to-year variability of relative yield gap Yrg (YgC_adj) was: (i) more effective than using the average site value of Yrg (YgC_unadj) only (the relative RMSE ranged from 10 to 13% for the YgC_adj vs. 48 to 57% for YgC_unadj) and (ii) superior (slightly inferior) to the strategy conducted with DSSAT v.4.7.5 (DSSAT v.3.5—relative RMSE of 5 to 8% were found) on rainfed mode. Earlier anthesis, maturity, and decreased potential yield (from 2.2 to 3.9% for 2021–2050, and from 5.0 to 7.1% for 2071–2100), due to increased temperature and solar radiation, were found using an ensemble of 11 EURO-CORDEX regional climate model simulations. In conclusion, the proposed strategy provides a scientifically robust guideline for crop model calibration that minimizes input requirements due to operating the crop model on potential mode. Further testing of this methodology is required with different plants, crop models, and environments.

Characterizing sediment load variability in the red river system using empirical orthogonal function analysis: Implications for water resources management in data poor regions

This paper introduces a novel data-based modelling approach, Empirical Orthogonal Function (EOF), to characterize the sediment load variability at the downstream section of the Red River system in Vietnam. We modified the original EOF and performed it on the demeaned datasets of monthly total sediment load. Rating curves between the discharge and sediment load were used to validate the performance of EOF analysis. The results showed significant similarity between simulated and observed patterns. A dramatic decrease in sediment load has been observed via both the EOF and rating curve methods across the observation period (∼1958–2021), with two dramatic decreasing phases identified (1988–2008 and 2009-present). The variation in the sediment load was mainly related to the seasonal and inter-annual hydrological conditions in the basin, as well as the dam-reservoir impact. Before 1988, river discharge and rainfall were well correlated to the first EOF mode, indicating that the natural forces were the main driving factor for the sediment load. However, anthropogenic pressure (dam-reservoir impact) emerged as an important driver to the declining of sediment load since the end of 1988 and became more severe towards the end of 2008. We conclude that the modified EOF method developed in this study successfully demonstrates a simple and efficient means of reconstructing and evaluating the sediment load variability over time in data-poor regions, influenced by complex natural and anthropogenic – drivers. It can, thus, be useful for water resources management in ungauged basins in the region.

Global Horizontal Irradiance in West Africa: Evaluation of the WRF-Solar Model in Convection-Permitting Mode with Ground Measurements

Abstract The number of solar power plants has increased in West Africa in recent years. Reliable reanalysis data and short-term forecasting of solar irradiance from numerical weather prediction models could provide an economic advantage for the planning and operation of solar power plants, especially in data-poor regions such as West Africa. This study presents a detailed assessment of different shortwave (SW) radiation schemes from the Weather Research and Forecasting (WRF) Model option Solar (WRF-Solar), with appropriate configurations for different atmospheric conditions in Ghana and the southern part of Burkina Faso. We applied two 1-way nested domains (D1 = 15 km and D2 = 3 km) to investigate four different SW schemes, namely, the Community Atmosphere Model, Dudhia, RRTMG, Goddard, and RRTMG without aerosol and with aerosol inputs (RRTMG_AERO). The simulation results were validated using hourly measurements from different automatic weather stations established in the study region in recent years. The results show that the RRTMG_AERO_D01 generally outperforms the other SW radiation schemes to simulate global horizontal irradiance under all-sky condition [RMSE = 235 W m−2 (19%); MAE = 172 W m−2 (14%)] and also under cloudy skies. Moreover, RRTMG_AERO_D01 shows the best performance on a seasonal scale. Both the RRTMG_AERO and Dudhia experiments indicate a good performance under clear skies. However, the sensitivity study of different SW radiation schemes in the WRF-Solar model suggests that RRTMG_AERO gives better results. Therefore, it is recommended that it be used for solar irradiance forecasts over Ghana and the southern part of Burkina Faso.

Propagation from meteorological to hydrological drought in the Horn of Africa using both standardized and threshold-based indices

Abstract. There have been numerous drought propagation studies in data-rich countries, but not much has been done for data-poor regions (such as the Horn of Africa, HOA). In this study, we characterize meteorological, soil moisture and hydrological drought and the propagation from one to the other for 318 catchments in the HOA to improve understanding of the spatial variability in the drought hazard. We calculate the standardized precipitation index (SPI), standardized soil moisture index (SSMI) and standardized streamflow index (SSI). In addition, we use the variable threshold method to calculate the duration of drought below a predefined percentile threshold for precipitation, soil moisture and discharge. The relationship between meteorological and soil moisture drought is investigated by finding the SPI accumulation period that has the highest correlation between SPI and SSMI, and the relationship between meteorological and hydrological drought is analysed by the SPI accumulation period that has the highest correlation between SPI and SSI time series. Additionally, we calculated these relationships with the ratio between the threshold-based meteorological-drought duration and soil moisture drought duration and the relation between threshold-based meteorological-drought duration and streamflow drought duration. Finally, we investigate the influence of climate and catchment characteristics on these propagation metrics. The results show that (1) the propagation from SPI to SSMI and the mean drought duration ratio of meteorological to soil moisture drought (P / SM) are mainly influenced by soil properties and vegetation, with the short accumulation periods (1 to 4 months) of SPI in catchments with arable land, high mean annual precipitation, and low sand and silt content, while longer accumulations (5 to 7 months) are in catchments with low mean annual upstream precipitation and shrub vegetation; (2) the propagation from SPI to SSI and precipitation-to-streamflow duration ratio are highly influenced by the climate and catchment control, i.e. geology, elevation and land cover, with the short accumulation times in catchments with high annual precipitation, volcanic permeable geology and cropland and the longer accumulations in catchments with low annual precipitation, sedimentary rocks and shrubland; and (3) the influence of mean annual upstream precipitation is more important for the propagation from SPI to SSI than from SPI to SSMI. Additionally, precipitation accumulation periods of approximately 1 to 4 months in wet western areas of the HOA and of approximately 5 to 7 months in the dryland regions are found. This can guide forecasting and management efforts as different drought metrics are thus of importance in different regions.

Filling the data gaps: Transferring models from data-rich to data-poor deep-sea areas to support spatial management

Spatial management of the deep sea is challenging due to limited available data on the distribution of species and habitats to support decision making. In the well-studied North Atlantic, predictive models of species distribution and habitat suitability have been used to fill data gaps and support sustainable management. In the South Atlantic and other poorly studied regions, this is not possible due to a massive lack of data. In this study, we investigated whether models constructed in data-rich areas can be used to inform data-poor regions (with otherwise similar environmental conditions). We used a novel model transfer approach to identify to what extent a habitat suitability model for Desmophyllum pertusum reef, built in a data-rich basin (North Atlantic), could be transferred usefully to a data-poor basin (South Atlantic). The transferred model was built using the Maximum Entropy algorithm and constructed with 227 presence and 3064 pseudo-absence points, and 200 m resolution environmental grids. Performance in the transferred region was validated using an independent dataset of D. pertusum presences and absences, with assessments made using both threshold-dependent and -independent metrics. We found that a model for D. pertusum reef fitted to North Atlantic data transferred reasonably well to the South Atlantic basin, with an area under the curve of 0.70. Suitable habitat for D. pertusum reef was predicted on 20 of the assessed 27 features including seamounts. Nationally managed Marine Protected Areas provide significant protection for D. pertusum reef habitat in the region, affording full protection from bottom trawling to 14 of the 20 suitable features. In areas beyond national jurisdiction (ABNJ), we found four seamounts that provided suitable habitat for D. pertusum reef to be at least partially protected from bottom trawling, whilst two did not fall within fisheries closures. There are factors to consider when developing models for transfer including data resolution and predictor type. Nevertheless, the promising results of this application demonstrate that model transfer approaches stand to provide significant contributions to spatial planning processes through provision of new, best available data. This is particularly true for ABNJ and areas that have previously undergone little scientific exploration such as the global south.