Data-sparse Regions Research Articles

Bias correction of 20 years of IMERG satellite precipitation data over Canada and Alaska

Study regionWe define two northern study areas: one covering all of Canada and Alaska and a second, smaller subregion surrounding the Peace-Athabasca Delta for testing. Study focusThis study aims to use bias correction to improve satellite precipitation data over a relatively data-sparse high latitude region using a network of in-situ rain gauges. We evaluate the satellite data and derive a linear bias-elevation relationship and apply the correction with a digital elevation model at a monthly scale, and further disaggregate it to produce corrected data at a daily scale. New hydrological insights for the regionWe find that the underestimation in the satellite data increases linearly with increasing elevation, above 500 m a.s.l. at the continental scale and for all elevations at the regional scale. Bias also varies seasonally, with higher bias in summer and lower bias in winter. Compared with uncalibrated data, the monthly continental correction reduces absolute bias by 16% and the root mean squared error by 6%, while the daily continental correction improves absolute bias by 17% but degrades root mean squared error slightly by 2%. We conclude that applying elevation-based bias correction reduces systematic elevational bias in northern high-latitude satellite precipitation data.

Assessing the performance of satellite derived and reanalyses data in capturing seasonal changes in extreme precipitation scaling rates over the Indian subcontinent

Intensity and frequency of short duration precipitation extremes are expected to increase under a warming climate at ≈7%K-1, following the Clausius-Claypeyron scaling relationship. Recent studies have analyzed global and regional scaling rates for precipitation extremes against temperature over an annual period, however assessment of seasonal variations of precipitation-temperature scaling, is largely unexplored, especially over the Indian subcontinent. Satellite derived and reanalysis based precipitation data sets can function as a suitable alternative to rain-gauge based data sets over data-sparse regions. In the present study, the performance of three high resolution data sets - GPM-IMERG satellite derived, ERA5 and IMDAA reanalysis precipitation - in determining the seasonal variations in precipitation-temperature scaling rates are investigated. When compared with the IMD data, IMERG and IMDAA capture the spatial variations and magnitude of scaling rates of daily precipitation extremes much better than ERA5. Two calibrated datasets - AIMERG and AERA5 are also analyzed and compared to IMD scaling rates. Following this, we further probe into the performance of the IMERG, ERA5 and IMDAA data sets over the entire Indian subcontinent, by including the results of scaling rates over oceans. The scaling rates of all three are found to be comparable in terms of magnitude and spatial distribution when analyzed over the entire Indian subcontinent. Significant seasonal variations in scaling rates are found over the Indian subcontinent, with the highest scaling rates in the post-monsoon and pre-monsoon seasons, and weaker rates in the monsoon season. Land–ocean contrast in scaling rates is evident across all seasons, but weak ocean scaling rates are found in the monsoon season for all three data sets.

Assessment of remotely sensed precipitation products for climatic and hydrological studies in arid to semi-arid data-scarce region, central-western Morocco

The lack of ground-based data remains the main challenge for hydrologists working in semi-arid to arid regions. In this context, remotely sensed precipitation products (SPPs) are crucial for widespread hydrological and climatic applications. The core benefit of SPPs is their high spatial-temporal resolution with evenly distributed precipitation estimates. However, their accuracy can vary considerably with climatic conditions and location and should be concisely evaluated beforehand. Therefore, this study is a first attempt to exhaustively compares and evaluates the performance of three widely used SPPs (CHIRPS, TRMM and PERSIANN) against ground-based observations at daily scale over the Souss data-sparse region (Central-western Morocco). Our proposed approach consisted of three main steps. The reference gauge method was adopted to evaluate daily precipitation data. Next, the hydrological simulation method was used to assess the performance of SPPs in simulating the observed streamflow in four locations of the SRB using multiple models. The non-parametric methods were applied to assess the capacity of SPPs in capturing precipitation extremes and characterizing the hydrological signature of SRB based on streamflow extreme detection. Our findings indicated that the three SPPs were not able to well reproduce the total amount of daily ground-based observations over the Souss region (CCmedian < 0.51; PBIAS > ±35%). The HEC-HMS model coupled with the TRMM product is the most appropriate combination for daily river flow restitution and for hydrologic signatures detection in SRB. For extreme precipitation events, CHIRPS showed satisfactory performance in capturing nearly all extreme events indices while PERSIANN and TRMM demonstrated reasonable ability to capture the wet extreme events. Therefore, it is still rather difficult to replace completely the ground-based data. Consequently, we recommend to further improve the satellite-based algorithms to adapt to the complex topography and special characteristics of semi-arid data-scarce regions.

An object-based image analysis approach for comparing tree detection from satellite imagery at different scales; A case study in Sukumba Mali

This paper combines an object-based image analysis (OBIA) approach, with markov-random field based-super resolution mapping (MRF-SRM) technique to extract individual tree objects from satellite imagery. Extraction of individual trees in urban and developing cities using satellite imageries has been quite challenging and a subject of active research with many approaches to it. The study area is a part of Sukumba village located in Koutiala district, Mali. The aim is to evaluate the performance comparison of the combined MRF-SRM approach, which was demonstrated for each combination of scale factor and class separability. Due to varying contrast sensitivity, there exist a general limitation in the spatial distribution of land cover data sets derived from most coarse and fine resolution satellite imageries. A 10 m spatial resolution Sentinel-2 and 2 m spatial resolution Worldview-3 satellite images were used for the study. First, the pixel based MRF-SRM classification technique of Tolpekin and Stein, 2009 was applied on both images. Subsequently, the classified SRM thematic results were partitioned into objects (segments) using region growing segmentation algorithm as post a classification procedure. Finally, validation and comparison of the resulting tree objects was done using an object based image analysis approach to determine existential (TP), extensional (FP) and positional (PA) accuracy. For Sentinel-2 image and out of 1787 reference objects, 1214 (TP) was detected and 573 (FP) was undetected. The PA is 6.504 m out of 10 m, total detection error is 0.467 m and the final detection accuracy is 68%. Similarly, for Worldview-3 image and out of 1787 reference objects, 1772 (TP) was detected and 15 (FP) was undetected. The PA is 1.714 m out of 2 m, total detection error is 0.318 m and the final detection accuracy is 99%. The successful approach used in this study will support the capacity of monitoring agricultural change in data sparse regions and developing countries.

Visual Stratigraphy-Based Age Scale Developed for the Shallow Mount Siple Firn Core, Antarctica

Here we present a case study for using visual stratigraphy to date a shallow firn core from coastal West Antarctica. The Mount Siple ice core has the potential to reconstruct climate in this data-sparse region over recent decades. Line scanned images of the 24 m firn core were used to generate a grey-scale, which displays variability consistent with annual cycles. The resulting Mount Siple age scale spans from 1998 ± 1 to 2017 CE. This study demonstrates that the seasonal changes in the grey-scale record provide an independent method of dating firn cores. However, the presence of melt layers at this site has introduced an error of ±1 year. Visual line stratigraphy has the unique advantage over traditional annual layer counting, based on chemical or isotopic species, of being non-destructive and relatively inexpensive. Visual line stratigraphy has proved to be an effective dating method for this site.

Coupling physical constraints with machine learning for satellite-derived evapotranspiration of the Tibetan Plateau

More accurate and process-based satellite evapotranspiration (ET) estimation for the Tibetan Plateau (TP)—the Third Pole of the world—have long been of major interest in hydrometeorology. Combining recent advances in satellite-based ET mechanistic algorithms and data-oriented methods allows ET hybrid modeling by coupling physical constraints with machine learning (ML). Specifically, we developed two hybrid models, a surface conductance-based ML model (ML-Gs) and a soil evaporation-based ML model (ML-Es), to estimate regional ET on the TP. These hybrid models have biophysical framework, under which one of the parameters or components is modeled using ML. Hybrid models make ML complementary to the process-based ET framework, which to find an optimal junction between well physical mechanism and high model performance. The daily ET estimates were evaluated at 28 eddy covariance flux tower sites, as well as by comparison with two process-based ET algorithms (a Penman–Monteith-based ET-PM algorithm and a Priestley-Taylor-based ET-PT algorithm) and a data-oriented pure ML method. The hybrid models decreased the root-mean-square-error (RMSE) of two physical algorithms (1.11 mm/day for ET-PM, 1.09 mm/day for ET-PT) to 0.50 mm/day, and increased the Kling-Gupta efficiency (KGE) (0.35 for ET-PM, 0.36 for ET-PT) to 0.92. Our hybrid models also showed improved performance (KGE of 0.65) than pure ML (KGE of 0.62) at data-sparse regions as well as for the responses to extreme weather events. It indicates that our approach does not only boost the ET simulation accuracy, but also improve the physical understanding of ML-based ET estimation. More importantly, ML-Es focuses on the ET components on the TP and is more well-defined than ML-Gs. An innovation of our approach is that for data-sparse regions and extreme cases, the more robust physical mechanism was coupled, the better generalization performance of hybrid model could achieve. The spatiotemporal ET patterns based on our hybrid models were consistent with the variations in local climatic regions and could provide critical information on the understanding of hydrological processes under the global and regional climate changes.

Multiple Spatial and Temporal Scales Evaluation of Eight Satellite Precipitation Products in a Mountainous Catchment of South China

Satellite precipitation products (SPPs) have emerged as an important information source of precipitation with high spatio-temporal resolutions, with great potential to improve catchment water resource management and hydrologic modelling, especially in data-sparse regions. As an indirect precipitation measurement, satellite-derived precipitation accuracy is of major concern. There have been numerous evaluation/validation studies worldwide. However, a convincing systematic evaluation/validation of satellite precipitation remains unrealized. In particular, there are still only a limited number of hydrologic evaluations/validations with a long temporal period. Here we present a systematic evaluation of eight popular SPPs (CHIRPS, CMORPH, GPCP, GPM, GSMaP, MSWEP, PERSIANN, and SM2RAIN). The evaluation area used, using daily data from 2007 to 2020, is the Xiangjiang River basin, a mountainous catchment with a humid sub-tropical monsoon climate situated in south China. The evaluation was conducted at various spatial scales (both grid-gauge scale and watershed scale) and temporal scales (annual and seasonal scales). The evaluation paid particular attention to precipitation intensity and especially its impact on hydrologic modelling. In the evaluation of the results, the overall statistical metrics show that GSMaP and MSWEP rank as the two best-performing SPPs, with KGEGrid ≥ 0.48 and KGEWatershed ≥ 0.67, while CHIRPS and SM2RAIN were the two worst-performing SPPs with KGEGrid ≤ 0.25 and KGEWatershed ≤ 0.42. GSMaP gave the closest agreement with the observations. The GSMaP-driven model also was superior in depicting the rainfall-runoff relationship compared to the hydrologic models driven by other SPPs. This study further demonstrated that satellite remote sensing still has difficulty accurately estimating precipitation over a mountainous region. This study provides helpful information to optimize the generation of algorithms for satellite precipitation products, and valuable guidance for local communities to select suitable alternative precipitation datasets.

Harnessing Insights from Indicators-Based Resilience Assessment for Enhancing Sustainability in the Gurage Socio-Ecological Production Landscape of Ethiopia.

Even though the mosaic of different land-use/land-cover types has long contributed to the resilience of socio-ecological production landscapes and seascapes in Ethiopia, recent data indicate that their sustainability is under threat. This study aims to evaluate landscape resilience by adopting a set of indicators for enhancing sustainability in the Gurage socio-ecological production landscape in Ethiopia. The authors employed a toolkit of indicators in the production landscape through a community-based scoring approach (1-5 Likert scale). The information from household surveys, land-use/land-cover analysis, and satellite-based drought incidents assessment was integrated with the ranking analysis to support the evaluations. The results revealed that landscape diversity, ecosystem protection, local governance, and social equity indicators had the highest landscape resilience ranks. In contrast, lower ranks are associated with knowledge, innovation, livelihoods, and well-being indicators. The overall resilience of the Gurage socio-ecological production landscape was estimated to be below average. Thus, strategies that enhance the resilience and sustainability of this socio-ecological landscape are essential. The findings could help draw the attention of policymakers and natural resource managers to building and strengthening the resilience of the landscape. This study demonstrates that indicators could aid in evaluating landscape resilience status along with other ancillary information, particularly in data-sparse regions. Methods of assessing resilience must be creative in such regions, and this paper may inform such efforts. In addition, the study recommends that landscape resilience indicators be improved by reducing subjective matter and including spatial-explicit dimensions for evaluating resilience.

Modelling evolution of a large, glacier-fed lake in the Western Indian Himalaya

In this study, we simulated the evolution of a large glacier-fed lake called the Gepan Gath lake located in Western Himalayas by numerically modelling the evolution of the Gepan Gath glacier that feeds the lake. Due to the extremely large volume and steep lake sidewalls, the lake has been classified as ‘critical’ or prone to hazards such as lake outburst floods in the future, by various scientific investigations. This modelling was carried out by a 1D model that is based on the principle of mass conservation. The 1D model was forced with the glacier surface mass balance (SMB). Due to non-availability of published in-situ estimates, the SMB was estimated using an energy balance-based model on station derived and reanalysis derived meteorological data. Modelled glacier length fluctuations for over 134 years matched reasonably well with that of observed within the RMSE error ~ 320 m. In addition to that, between 2004 and 2019, the modelled and observed lake lengths were in agreement with each other with the RMSE ~ 110 m. Modelled glacier lake lengths also match well with published, satellite imagery derived lengths within 15% uncertainty. The uncertainty in future lake length fluctuations is within 100–200 m. Our ultimate aim is to show that numerical ice-flow modelling can be an asset in modelling glacier-fed lake evolution even in the case of highly data-sparse regions of the IHR.

Bayesian Uncertainty Modeling for P300-Based Brain-Computer Interface.

P300 potential is important to cognitive neuroscience research, and has also been widely applied in brain-computer interfaces (BCIs). To detect P300, many neural network models, including convolutional neural networks (CNNs), have achieved outstanding results. However, EEG signals are usually high-dimensional. Moreover, since collecting EEG signals is time-consuming and expensive, EEG datasets are typically small. Therefore, data-sparse regions usually exist within EEG dataset. However, most existing models compute predictions based on point-estimate. They cannot evaluate prediction uncertainty and tend to make overconfident decisions on samples located in data-sparse regions. Hence, their predictions are unreliable. To solve this problem, we propose a Bayesian convolutional neural network (BCNN) for P300 detection. The network places probability distributions over weights to capture model uncertainty. In prediction phase, a set of neural networks can be obtained by Monte Carlo sampling. Integrating the predictions of these networks implies ensembling. Therefore, the reliability of prediction can be improved. Experimental results demonstrate that BCNN can achieve better P300 detection performance than point-estimate networks. In addition, placing a prior distribution over the weight acts as a regularization technique. Experimental results show that it improves the robustness of BCNN to overfitting on small dataset. More importantly, with BCNN, both weight uncertainty and prediction uncertainty can be obtained. The weight uncertainty is then used to optimize the network through pruning, and the prediction uncertainty is applied to reject unreliable decisions so as to reduce detection error. Therefore, uncertainty modeling provides important information to further improve BCI systems.

Representation and evaluation of southern Africa's seasonal mean and extreme temperatures in the ERA5-based reanalysis products

Over data-sparse regions such as southern Africa, reanalysis products represent valuable proxies for observed weather data. These products are, however, associated with a range of strengths and weaknesses which cannot be overlooked. Hence, for the period 1979–2021, we explored the performance of three ERA5-based reanalysis products (i.e. AgERA5, ERA5 and ERA5-Land) for their spatiotemporal representation of mean summer (November–March) and winter (May–September) temperatures, and coldwave and heatwave characteristics of the seasonal average number of events and days, and magnitude across southern Africa. Compared to the National Oceanic and Atmospheric Administration Climatology Prediction Centre (NOAA CPC) gridded observation-based temperature reference dataset, the reanalysis datasets adequately reproduced spatiotemporal characteristics of mean daily temperatures, with high area average correlations (r > 0.8). Summer (winter) temperatures were, however, typically under-estimated (over-estimated), with area average biases ranging from −0.22 °C (ERA5) to −0.11 °C (AgERA5) for summer and 0.31 °C (ERA5) to 0.51 °C (ERA5-Land) for winter. These biases were reflected in the general tendency for under-estimation (over-estimation) of the average coldwave (heatwave) magnitudes, with biases ranging from an average of 1.04 °C2 (AgERA5) to 1.76 °C2 (ERA5-Land) for coldwave magnitudes and −0.03 °C2 (AgERA5 and ERA5-Land) to −0.09 °C2 (ERA5) for heatwave magnitudes. Of the coldwave and heatwave indices investigated, the ERA5-based coldwave and heatwave seasonally averaged magnitudes were associated with the poorest performance, with the weakest correlations and most statistically significant biases, especially for regions outside South Africa which have substantially fewer observed temperature records. While the reanalysis products were spatiotemporally consistent in their performance for the seasonally averaged number of coldwave and heatwave days and events, they typically over-estimated the number of days, by almost two days for both indices, contributing to these events, while they generally under-estimated (over-estimated) the number of coldwave (heatwave) events by less than one event for both indices. On average, seasonally averaged heatwave characteristics were somewhat better represented than that for coldwaves. Despite this, the ERA5-based reanalysis products performed relatively well overall and are valuable to apply in further research considering coldwaves and heatwaves across southern Africa, provided their limitations are acknowledged. Impact-based studies exploring heatwave and coldwave influences on human health, crop yields, livestock thermal comfort, and water availability, for instance, will, however, likely require higher grid resolutions offered by the AgERA5 and ERA5-Land products.

Precipitation interpolation, autocorrelation, and predicting spatiotemporal variation in runoff in data sparse regions: Application to Panama

Study regionPanama faces seasonal floods and droughts, and rising freshwater demand for domestic consumption, hydropower, and the operation of the Panama Canal. A process-based hydrological model of the country would complement the existing national water security plan as a scenario planning tool. Study focusIn Panama, as in much of the Global South, sufficient observed data do not exist for all watersheds to calibrate complex hydrological models. Understanding and improving the performance of uncalibrated hydrological models could greatly expand their utility in such regions. In this study, we build and validate an uncalibrated Soil and Water Assessment Tool (SWAT) model for Panama. We extend the default precipitation submodel and demonstrate the importance of accounting for spatial autocorrelation patterns in precipitation inputs: we found large improvements over the default model, not only for monthly means (NSE = 0.88, from 0.69 for default SWAT), but especially for standard deviations (NSE = 0.59, from 0.27) and maxima (NSE = 0.51, from 0.21) of discharge across locations and months. New hydrological insights for regionWe found a strong seasonal trend and regional differences in the spatial autocorrelation of rainfall, suggesting that this phenomenon should not be modeled statically. The resulting precipitation and hydrology models provide important baseline information for Panama, especially on variability and extremes, and could serve as a template for other regions with limited data.

Statistical uncertainty analysis-based precipitation merging (SUPER): A new framework for improved global precipitation estimation

Multi-source merging is an established tool for improving large-scale precipitation estimates. Existing merging frameworks typically use gauge-based precipitation error statistics and neglect the inter-dependence of various precipitation products. However, gauge-observation uncertainties at daily and sub-daily time scales can bias merging weights and yield sub-optimal precipitation estimates, particularly over data-sparse regions. Likewise, frameworks ignoring inter-product error cross-correlation will overfit precipitation observation noise. Here, a Statistical Uncertainty analysis-based Precipitation mERging framework (SUPER) is proposed for addressing these challenges. Specifically, a quadruple collocation analysis is employed to estimate precipitation error variances and covariances for commonly used precipitation products. These error estimates are subsequently used for merging all products via a least-squares minimization approach. In addition, false-alarm precipitation events are removed via a reference rain/no-rain time series estimated by a newly developed categorical variable merging method. As such, SUPER does not require any rain gauge observations to reduce daily random and rain/no-rain classification errors. Additionally, by considering precipitation product inter-dependency, SUPER avoids overfitting measurement noise present in multi-source precipitation products. Results show that the overall RMSE of SUPER-based precipitation is 3.35 mm/day and the daily correlation with gauge observations is 0.71 [−] – metrics that are generally superior to recent precipitation reanalyses and remote sensing products. In this way, we seek to propose a new framework for robustly generating global precipitation datasets that can improve land surface and hydrological modeling skill in data-sparse regions.

Flood Frequency Estimation in Data-Sparse Wainganga Basin, India, Using Continuous Simulation

Monsoon-related extreme flood events are experienced regularly across India, bringing costly damage, disruption and death to local communities. This study provides a route towards estimating the likely magnitude of extreme floods (e.g., the 1-in-100-year flood) at locations without gauged data, helping engineers to design resilient structures. Gridded rainfall and evapotranspiration estimates were used with a continuous simulation hydrological model to estimate annual maximum flow rates at nine locations corresponding with river flow gauging stations in the Wainganga river basin, a data-sparse region of India. Hosking–Wallis distribution tests were performed to identify the most appropriate distribution to model the annual maxima series, selecting the Generalized Pareto and Pearson Type III distributions. The L-moments and flood frequency curves of the modeled annual maxima were compared to gauged values. The Probability Distributed Model (PDM), properly calibrated to capture the dynamics of peak flows, was shown to be effective in approximating the Generalized Pareto distribution for annual maxima, and may be useful in modeling peak flows in areas with sparse data. Confidence in the model structure, parameterization, input data and catchment representation build confidence in the modeled flood estimates; this is particularly relevant if the method is applied in a location where no gauged flows exist for verification.

Verification of ERA5 and ERA-Interim precipitation over Africa at intra-annual and interannual timescales

Over data sparse regions such as Africa, reanalysis datasets can provide a useful alternative to enable climate investigations across a range of temporal scales. Here, a detailed comparison of ERA5 and ERA-Interim precipitation over Africa is done at annual and seasonal temporal scales, over various subregions, to identify biases. Additionally, the representation of interannual variability in the reanalyses is evaluated with an emphasis on the ENSO teleconnection to Africa. Overall, there have been several substantial improvements in ERA5 compared to ERA-Interim. ERA-Interim's wet bias in both the annual and seasonal rainfall over tropical Africa has been substantially reduced in ERA5, which is also evident in the area-averaged bias over Africa (ERA5 = 39.097, ERA-Interim = 91.734). ERA5's representation of the annual cycle has improved over most selected subregions, notably over the southwestern Cape (rERA5=0.993, rERAI=0.988) and Cape South Coast (rERA5=0.917, rERAI=0.859) of South Africa, as well as over the Southern Tropics (rERA5=0.998, rERAI=0.971). ERA5 shows higher correlations with observations in the spatial patterns of interannual variability across all seasons, but particularly DJF (Spearman's rERA5=0.504, rERAI=0.475) and MAM (Spearman's rERA5=0.488, rERAI=0.435). Finally, both models capture the ENSO signal remarkably well, although the spatial extent of positive/negative anomalies could be improved in some seasons and regions.

Product- and Hydro-Validation of Satellite-Based Precipitation Data Sets for a Poorly Gauged Snow-Fed Basin in Turkey

Satellite-based Precipitation (SBP) products are receiving growing attention, and their utilization in hydrological applications is essential for better water resource management. However, their assessment is still lacking for data-sparse mountainous regions. This study reveals the performances of four available PERSIANN family products of low resolution near real-time (PERSIANN), low resolution bias-corrected (PERSIANN-CDR), and high resolution real-time (PERSIANN-CCS and PERSIANN-PDIR-Now). The study aims to apply Product-Validation Experiments (PVEs) and Hydro-Validation Experiments (HVEs) in a mountainous test catchment of the upper Euphrates Basin. The PVEs are conducted on different temporal scales (annual, monthly, and daily) within four seasonal time periods from 2003 to 2015. HVEs are accomplished via a multi-layer perceptron (MLP)-based rainfall-runoff model. The Gauge-based Precipitation (GBP) and SBP are trained and tested to simulate daily streamflows for the periods of 2003–2008 and 2009–2011 water years, respectively. PVEs indicate that PERSIANN-PDIR-Now comprises the least mean annual bias, and PERSIANN-CDR gives the highest monthly correlation with the GBP data. According to daily HVEs, MLP provides a compromising alternative for biased data sets; all SBP models show reasonably high Nash–Sutcliffe Efficiency for the training (above 0.80) and testing (0.62) periods, while the PERSIANN-CDR-based MLP (0.88 and 0.79) gives the highest performance.

Climatic and altitudinal controls on rainfall extremes and their temporal changes in data-sparse tropical regions

• We used the Simplified MEV to model extreme rainfall quantiles in tropical regions. • Weibull distribution well approximates the tail of daily rainfall distribution. • Statistical parameters and rainfall quantiles significantly depend on latitude. • Homogeneous patterns in regional temporal trends are related to altitude. • Extreme daily rainfall amounts in the Volta basin increased during 1978–2018. Sub-Saharan Africa’s economy and livelihood are primarily dependent on agriculture, which makes it highly vulnerable to the impacts of extreme weather events and climate change. Modelling and quantifying extreme rainfall and its temporal changes in such environments could thus provide crucial information for design, insurance, management, ecology and climate adaptation. Rain gauge networks in the area are relatively sparse, and often characterized by missing data, which hamper the use of extreme-value methods for estimating extreme precipitation quantiles. In this study, we adopted the Simplified Metastatistical Extreme Value approach for the estimation of extreme return levels based on ordinary events (i.e., all the independent realizations of the variable of interest), which was shown to be more accurate than traditional extreme-value methods in the presence of short data records. We examined data from 66 rain gauges covering diverse hydro-climatic regions across Ghana with the aim of (i) validating the robustness of the statistical approach, (ii) characterising the climatic and altitudinal controls on the occurrence, frequency and intensity of rainfall extremes, and (iii) quantifying recent changes in the characteristics of extremes. We found that a two-parameter Weibull distribution well approximates the tail of the daily rainfall distribution throughout the area. Our statistical approach can quantify extremes with largely reduced uncertainties (7–17% uncertainty in the 100-year return levels computed using 10 years of data versus 11–62% of extreme-value based methods). Extreme precipitation statistics (daily intensity distribution, number of wet days, extreme rainfall quantiles) are found to significantly depend on latitude, so that the four latitudinally layered hydro-climatic regions typically adopted in the area well represent spatial variations. Elevation significantly affects the tail heaviness of the daily intensity distribution and thus extreme rainfall quantiles. Temporal changes during the period 1978–2018 are found to be non-homogeneous in the area as well as within the four hydro-climatic regions, but are homogeneous in three altitude-based regions. We report contrasting trends in extreme return levels in low-elevation (<200 m a.s.l.) and hilly regions, related to contrasting changes in the daily intensity distribution. Statistically significant positive trends in extreme daily rainfall amounts are observed in the inland low-elevation region of the Volta river basin, which call for further investigation of changes in future precipitation extremes in this extremely important hydrological region in Sub-Saharan Africa.

Application of Artificial Intelligence Models for Aeolian Dust Prediction at Different Temporal Scales: A Case with Limited Climatic Data

Accurately predicting ambient dust plays a crucial role in air quality management and hazard mitigation. Dust emission is a complex, non-linear response to several climatic variables. This study explores the accuracy of Artificial Intelligence (AI) models: an adaptive-network-based fuzzy inference system (ANFIS) and a multi-layered perceptron artificial neural network (mlp-NN), over the Southwestern United States (SWUS), based on the observed dust data from IMPROVE stations. The ambient fine dust (PM2.5) and coarse dust (PM10) concentrations on monthly and seasonal timescales from 1990–2020 are modeled using average daily maximum wind speed (W), average precipitation (P), and average air temperature (T) available from the North American Regional Reanalysis (NARR) dataset. The model’s performance is measured using correlation (r), root mean square error (RMSE), and percentage bias (% BIAS). The ANFIS model generally performs better than the mlp-NN model in predicting regional dustiness over the SWUS region, with r = 0.77 and 0.83 for monthly and seasonal fine dust, respectively. AI models perform better in predicting regional dustiness on a seasonal timescale than the monthly timescale for both fine dust and coarse dust. AI models better predict fine dust than coarse dust on both monthly and seasonal timescales. Compared to precipitation, air temperature is the more important predictor of regional dustiness on both monthly and seasonal timescales. The relative importance of air temperature is higher on the monthly timescale than the seasonal timescale for PM2.5 and vice versa for PM10. The findings of this study demonstrate that the AI models can predict monthly and seasonal fine and coarse dust, based on the limited climatic data, with good accuracy and with potential implications for research in data sparse regions.

Evaluating Satellite Soil Moisture Datasets for Drought Monitoring in Australia and the South-West Pacific

Soil moisture (SM) is critical in monitoring the time-lagged impacts of agrometeorological drought. In Australia and several south-west Pacific Small Island Developing States (SIDS), there are a limited number of in situ SM stations that can adequately assess soil-water availability in a near-real-time context. Satellite SM datasets provide a viable alternative for SM monitoring and agrometeorological drought provision in these regions. In this study, we investigated the performance of Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), Soil Moisture Operational Products System (SMOPS), SM from the Advanced Microwave Scanning Radiometer 2 (AMSR-2) and SM from the Advanced Scatterometer (ASCAT) over Australia and south-west Pacific SIDS. Products were first evaluated in Australia, given the presence of several in-situ SM monitoring stations and a state-of-the-art hydrological model—the Australian Water Resources Assessment Landscape modelling system (AWRA-L). We further investigated the accuracy of SM satellite datasets in Australia and the south-west Pacific through Triple Collocation analysis with two other SM reference datasets—ERA5 reanalysis SM data and model data from the Global Land Data Assimilation System (GLDAS) dataset. All datasets have differing observation periods ranging from 1911-now, with a common period of observations between 2015–2021. Results demonstrated that ASCAT and SMOS were consistently superior in their performance. Analysis in the six south-west Pacific SIDS indicated reduced performance for all products, with ASCAT and SMOS still performing better than others for most SIDS with median R values ranging between 0.3–0.9. We conducted a case study of the 2015 El Niño and Positive Indian Ocean Dipole-induced drought in Papua New Guinea. It was shown that ASCAT is a valuable dataset indicative of agrometeorological drought for the nation, highlighting the value of using satellite SM products to provide early warning of drought in data-sparse regions in the south-west Pacific.

Using the ERA5 and ERA5-Land reanalysis datasets for river water temperature modelling in a data-scarce region

It has become apparent in recent decades that river water temperature can have immediate and lasting impacts on aquatic organisms and their lotic habitat. In rivers that are dammed, there is an opportunity and a responsibility to regulate flows in order to control these temperatures to ensure the survival of the fish and other aquatic life. This paper uses a physically based hydraulic model (HEC-RAS) to run a water temperature component, allowing the thermal model to simulate water temperatures at the same hourly time step as the hydraulic model in a data-sparse region using two meteorological reanalysis datasets (ERA5 and ERA5-Land) as inputs allowing for a full representation of the diurnal cycle. This was achieved by making use of the HEC-RAS controller to automate the calibration and subsequent simulation processes. Results show that these products are able to provide high-quality thermal simulations on a 200 km river system in British Columbia, Canada, obtaining mean absolute errors in validation of 0.66 °C and a root mean square error of 0.84 °C. Some of the boundary conditions seemed to have little effect on downstream water temperatures. This is due to the measured point of interest being far enough downstream of the dam that a thermal equilibrium is reached well before. Simulations using shorter river reaches confirm that long lakes in the study region contribute to the thermal equilibrium being attained. There also seems to be a limit to the advantage conveyed by increased spatial density of the data, as results indicate a form of skill plateau after a certain input data density is attained.