Data-scarce Regions Research Articles

Unsupervised active–transfer learning for automated landslide mapping

Detailed landslide inventories are required for multiple purposes including disaster damage assessments, susceptibility mapping for spatial planning, and disaster risk reduction. Active learning is an artificial intelligence strategy that can achieve good performances in landslide mapping by training a machine-learning model with a reduced number of landslide/non-landslide observations, which can save time and effort in labeling training instances. Nevertheless, active-learning models are unstable at the beginning of sample selection due to the limited initial knowledge of landslide distribution. Transfer learning can help make the learner robust by transferring a landslide model trained on an existing landslide inventory from a different, but geographically similar source area, to the unseen target area. In order to adjust a transferred machine-learning model to the possibly unique environmental characteristics of the unseen area, we proposed a new framework called Unsupervised Active-Transfer Learning (UATL). This framework used a weight function to combine the landslide model transferred from the source area, with a model trained on a small, but increasing number of landslide/non-landslide observations from the target area to efficiently build a more robust learner. We examined two methods, adaptive UATL and regular UATL, which differed in the way they assign weights to the combined learners. We evaluated our proposed new methods by comparing them with three benchmark methods (active learning only, model transfer only, and the model trained in the unseen area itself) by means of the partial area under the receiver operating characteristic (ROC) curve (AUROC) as the evaluation criterion. The results showed that the new methods, and especially adaptive UATL, can achieve good predictive performances. With only about 235 training instances from the target area, the partial AUROC obtained from adaptive UATL was only 2% lower than that obtained from the model trained in the target area itself, and consistently outperformed the other two benchmarks. Overall, we suggest that the framework proposed can be applied to the natural hazards management workflow for assisting in emergency response, especially in data-scarce regions (e.g., mountainous areas and developing countries).

Regional evaluation of groundwater-surface water interactions using a coupled geohydrological model (SWAT+gwflow)

Study regionThe research is conducted for the Scheldt river basin, where seven major watersheds located in Belgium and partly in France are included in the analysis. Study focusA proper representation of groundwater–surface water interactions with (geo)hydrological models is possible via a coupled model. However, such models have disadvantages, such as complex code modifications and new tunings, and are computationally expensive. Therefore, their application on large spatial scales is limited. A newly developed model, SWAT+gwflow integrates the Soil Water Assessment Tool (SWAT+) with the groundwater module gwflow and has the potential to overcome these limitations. However, this coupled model has not yet been evaluated at a regional scale; hence, we present the evaluation of this model for regional studies using a global aquifer data over the seven watersheds in the Scheldt basin. Furthermore, we have investigated and quantified water balance components within the basin, with a focus on groundwater-surface water exchange. New Hydrological Insights for the RegionFrom the results (Nash-Sutcliffe efficiency (NSE) of 0.8–0.9 for all catchments based on monthly average streamflow during calibration and validation periods), we consider the model to be a good simulator of hydrology in the basin. In addition, the simulated groundwater head shows good agreement with observed well data (with a mean absolute error of less than 0.42 m). Also, the rivers in five of the seven watersheds are found to be strongly dependent on groundwater discharge to the streams. We conclude that (1) the SWAT+gwflow model is capable of accurately modeling hydrological processes and state variables in the seven watersheds using global aquifer data and limited computational time, (2) the climate-gridded dataset can successfully be used for (geo)hydrological studies, and (3) the groundwater-surface water interaction increases over the years (from 1975 to 2021) with a strong increment found in the Grote Nete (3.7 fold) and upper Scheldt (2.3 fold) watersheds. These results are, moreover, promising for data-scarce regions where geohydrological modeling relies on the use of global datasets, but the mere success of this modeling application does not guarantee the accuracy of the dataset for other locations, hence, further verification is required. Furthermore, although in this study, the gwflow module is integrated into the SWAT+ model, it could also be integrated into other surface water models for other studies.

Nitrate prediction in groundwater of data scarce regions: The futuristic fresh-water management outlook

Nitrate contamination in groundwater poses a significant threat to water quality and public health, especially in regions with limited data availability. This study addresses this challenge by employing machine learning (ML) techniques to predict nitrate (NO3−-N) concentrations in Mexico's groundwater. Four ML algorithms—Extreme Gradient Boosting (XGB), Boosted Regression Trees (BRT), Random Forest (RF), and Support Vector Machines (SVM)—were executed to model NO3−-N concentrations across the country. Despite data limitations, the ML models achieved robust predictive performances. XGB and BRT algorithms demonstrated superior accuracy (0.80 and 0.78, respectively). Notably, this was achieved using ∼10 times less information than previous large-scale assessments. The novelty lies in the first-ever implementation of the ‘Support Points-based Split Approach’ during data pre-processing. The models considered initially 68 covariates and identified 13–19 significant predictors of NO3−-N concentration spanning from climate, geomorphology, soil, hydrogeology, and human factors. Rainfall, elevation, and slope emerged as key predictors. A validation incorporated nationwide waste disposal sites, yielding an encouraging correlation. Spatial risk mapping unveiled significant pollution hotspots across Mexico. Regions with elevated NO3−-N concentrations (>10 mg/L) were identified, particularly in the north-central and northeast parts of the country, associated with agricultural and industrial activities. Approximately 21 million people, accounting for 10 % of Mexico's population, are potentially exposed to elevated NO3−-N levels in groundwater. Moreover, the NO3−-N hotspots align with reported NO3−-N health implications such as gastric and colorectal cancer. This study not only demonstrates the potential of ML in data-scarce regions but also offers actionable insights for policy and management strategies. Our research underscores the urgency of implementing sustainable agricultural practices and comprehensive domestic waste management measures to mitigate NO3−-N contamination. Moreover, it advocates for the establishment of effective policies based on real-time monitoring and collaboration among stakeholders.

Big data in Antarctic sciences – current status, gaps, and future perspectives

Abstract. This paper was initiated by a multidisciplinary Topic Workshop in the frame of the Deutsche Forschungsgemeinschaft Priority Program 1158 “Antarctic Research with Comparative Investigations in Arctic Ice Areas”, and hence it represents only the national view without claiming to be complete but is intended to provide awareness and suggestions for the current discussion on so-called big data in many scientific fields. The importance of the polar regions and their essential role for the Earth system are both undoubtedly recognized. However, dramatic changes in the climate and environment have been observed first in the Arctic and later in Antarctica over the past few decades. While important data have been collected and observation networks have been built in Antarctica and the Southern Ocean, this is a relatively data-scarce region due to the challenges of remote data acquisition, expensive labor, and harsh environmental conditions. There are many approaches crossing multiple scientific disciplines to better understand Antarctic processes; to evaluate ongoing climatic and environmental changes and their manifold ecological, physical, chemical, and geological consequences; and to make (improved) predictions. Together, these approaches generate very large, multivariate data sets, which can be broadly classified as “Antarctic big data”. For these large data sets, there is a pressing need for improved data acquisition, curation, integration, service, and application to support fundamental scientific research. Based on deficiencies in crossing disciplines and to attract further interest in big data in Antarctic sciences, this article will (i) describe and evaluate the current status of big data in various Antarctic-related scientific disciplines, (ii) identify current gaps, (iii) and provide solutions to fill these gaps.

UAV-based thermography reveals spatial and temporal variability of evapotranspiration from a tropical rainforest

Evapotranspiration (ET) from tropical forests plays a significant role in regulating the climate system. Forests are diverse ecosystems, encompass heterogeneous site conditions and experience seasonal fluctuations of rainfall. Our objectives were to quantify ET from a tropical rainforest using high-resolution thermal images and a simple modeling framework. In lowland Sumatra, thermal infrared (TIR) images were taken from an uncrewed aerial vehicle (UAV) of upland and riparian sites during both dry and wet seasons. We predicted ET from land surface temperature data retrieved from the TIR images by applying the DATTUTDUT energy balance model. We further compared the ET estimates to ground-based sap flux measurements for selected trees and assessed the plot-level spatial and temporal variability of ET across sites and seasons. Average ET across sites and seasons was 0.48 mm h–1, which is comparable to ET from a nearby commercial oil palm plantation where this method has been validated against eddy covariance measurements. For given trees, a positive correlation was found between UAV-based ET and tree transpiration derived from ground-based sap flux measurements, thereby corroborating the observed spatial patterns. Evapotranspiration at upland sites was 11% higher than at riparian sites across all seasons. The heterogeneity of ET was lower at upland sites than at riparian sites, and increased from the dry season to the wet season. This seasonally enhanced ET variability can be an effect of local site conditions including partial flooding and diverse responses of tree species to moisture conditions. These results improve our understanding of forest-water interactions in tropical forests and can aid the further development of vegetation-atmosphere models. Further, we found that UAV-based thermography using a simple, energy balance modeling scheme is a promising method for ET assessments of natural (forest) ecosystems, notably in data scarce regions of the world.

Comparing spatially explicit approaches to assess social vulnerability dynamics to flooding

Madagascar is one of the poorest countries in the world, and therefore highly vulnerable to tropical cyclone-induced coastal and inland flooding. This study aims to assess Madagascar's social vulnerability to flooding resulting from tropical storms, in a spatially explicit manner ,accounting for dynamics in vulnerability over time. For this, the research applied three different social vulnerability models in a data-scarce context, for the years 2009 and 2018. Around these years, two tropical cyclones hit the island (in 2008 and 2018). The three models are 1) inductive (social vulnerability index: SoVI), 2) deductive (Weighted Median, WM), and 3) social vulnerability profiling (SVP). Our results show that the most vulnerable regions are in the south of Madagascar, which is consistent across all three models. While the calculated vulnerability score (based on hazard and exposure data) indicated a decrease in vulnerability over time, only the SVP predicted a similar vulnerability decrease, but this comparison is surrounded by uncertainty since the 2008 and 2018 events differ in flood hazard characteristics. The application of the SoVI method in such regions has some limitations, for example, introducing subjective modeling decisions. The application of the WM method could be suitable, but only if relations with variables and social vulnerability are known and understood. The SVP model seems a suitable approach for a first scoping study of social vulnerability, but it provides less insight into spatial variation. The main recommendation from this study is to further focus future research on model validation in data-scarce regions and to assess model accuracy by exploring validation data.

IMPROVING THE ACCURACY OF SATELLITE-BASED NEAR SURFACE AIR TEMPERATURE AND PRECIPITATION PRODUCTS

Abstract. In this study, we evaluate the performance of several reanalyses and satellite-based products of near-surface air temperature and precipitation to determine the best product in estimating daily and monthly variables across the complex terrain of Turkey. Each product’s performance was evaluated using 1120 ground-based gauge stations from 2015 to 2019, covering a range of complex topography with different climate classes according to the Köppen-Geiger classification scheme and land surface types according to the Moderate Resolution Imaging Spectroradiometer (MODIS). Furthermore, various traditional and more advanced machine learning downscaling algorithms were applied to improve the spatial resolution of the products. We used distance-based interpolation, classical Random Forest, and more innovative Random Forest Spatial Interpolation (RFSI) algorithms. We also investigated several satellite-based covariates as a proxy to downscale the precipitation and near-surface air temperature, including MODIS Land Surface Temperature, Vegetation Index (NDVI and EVI), Cloud Properties (Cloud Optical Properties, Cloud Effective Radius, Cloud Water Path), and topography-related features. The agreement between the ground observations and the different products, as well as the downscaled temperature products, was examined using a range of commonly employed measures. The results showed that AgERA5 was the best-performing product for air temperature estimation, while MSWEP V2.2 was superior for precipitation estimation. Spatial downscaling using bicubic interpolation improved air temperature product performance, and the Random Forest (RF) machine learning algorithm outperformed all other methods in certain seasons. The study suggests that combining ground-based measurements, precipitation products, and features related to topography can substantially improve the representation of spatiotemporal precipitation distribution in data-scarce regions.

Radar remote sensing reveals potential underestimation of rainfall erosivity at the global scale.

Rainfall kinetic energy (RKE) constitutes one of the most critical factors that drive rainfall erosivity on surface soil. Direct measurements of RKE are limited, relying instead on the empirical relations between kinetic energy and rainfall intensity (KE-I relation), which have not been well regionalized for data-scarce regions. Here, we present the first global rainfall microphysics-based RKE (RKEMPH) flux retrieved from radar reflectivity at different frequencies. The results suggest that RKEMPH flux outperforms the RKE estimates derived from a widely used empirical KE-I relation (RKEKE-I) validated using ground disdrometers. We found a potentially widespread underestimation of RKEKE-I, which is especially prominent in some low-income countries with ~20% underestimation of RKE and the resultant rainfall erosivity. Given the evidence that these countries are subject to greater rainfall-induced soil erosion, these underestimations would mislead conservation practices for sustainable development of terrestrial ecosystems.

Towards early response to desert locust swarming in eastern Africa by estimating timing of hatching

Desert locust (Schistocerca gregaria) plagues threaten agricultural production, food security and the environment across Africa, the Middle East, and Southwest Asia. Control methods targeting adult desert locusts present significant challenges and financial costs. Recognizing this, we developed a ground-breaking fuzzy set Mamdani type inference model that provides an innovative solution for early warning alerts. The model aids in predicting the juvenile stages of locust development, thereby preventing wide-scale locust swarming and mitigating its extensive damages and socioeconomic costs. The novelty of our approach lies in our unique application of environmental variables relevant for locust breeding to estimate the timing and location of desert locust hatching. Additionally, we improved the algorithmic handling of these variables, with localized desert locust bands data used as a proxy for hatching timing with a temporal offset of 35 days. The model's boundary conditions were determined using a training area in Sudan, where comprehensive ground data was available. This rule set was subsequently applied to Turkana County in Kenya, a data-scarce region, demonstrating the model's applicability and success in different contexts. The model's accuracy, assessed by data from the Sudan training site, demonstrated a remarkable score of 82% for true predictions. Furthermore, the model correctly identified the months of highest hatching probabilities in Turkana during 2020, demonstrating its real-world effectiveness and practical value. A correlation analysis affirmed that hatching was associated with increases in chlorophyll levels and precipitation accumulations. Our study marks a significant advancement in predicting the timing of hatching using fuzzy logic in data-scarce environments. By operationalizing more targeted early responses to desert locust infestations, our model facilitates more effective locust control. This study stands as an important contribution to locust management strategies, with substantial implications for agricultural production and food security in affected regions.

A way forward towards the demand-based agriculture water supply using satellite data: a case study of the data-scarce region of Sindh, Pakistan

A way forward towards the demand-based agriculture water supply using satellite data: a case study of the data-scarce region of Sindh, Pakistan

Simulating Changes in Hydrological Extremes—Future Scenarios for Morocco

This paper presents a comprehensive river discharge analysis to estimate past and future hydrological extremes across Morocco. Hydrological simulations with historical forcing and climate change scenario inputs have been performed to better understand the change in magnitude and frequency of extreme discharge events that cause flooding. Simulations are applied to all major rivers of Morocco, including a total of 16 basins that cover the majority of the country. An ensemble of temperature and precipitation input parameter sets was generated to analyze input uncertainty, an approach that can be extended to other regions of the world, including data-sparse regions. Parameter uncertainty was also included in the analyses. Historical simulations comprise the period 1979–2021, while future simulations (2015–2100) were performed under the Shared Socioeconomic Pathway (SSP) 2–4.5 and SSP5–8.5. Clear patterns of changing flood extremes are projected; these changes are significant when considered as a proportion of the land area of the country. Two types of basins have been identified, based on their different behavior in climate change scenarios. In the Northern/Mediterranean basins we observe a decrease in the frequency and intensity of events by 2050 under both SSPs, whereas for the remaining catchments higher and more frequent high-flow events in the form of flash floods are detected. Our analysis revealed that this is a consequence of the reduction in rainfall accumulation and intensity in both SSPs for the first type of basins, while the opposite applies to the other type. More generally, we propose a methodology that does not rely on observed time series of discharge, so especially for regions where those do not exist or are not available, and that can be applied to undertake future flood projections in the most data-scarce regions. This method allows future hydrological hazards to be estimated for essentially any region of the world.

Characterizing hydrological droughts within three watersheds in Yunnan, China from GNSS-inferred terrestrial water storage changes constrained by GRACE data

SUMMARY The spatiotemporal evolution of drought is often modulated by climate and watershed characteristics. While numerous drought studies using space geodesy have been conducted in Yunnan, the scarcity and limited sensitivity of observation instruments have hindered the development of watershed-scale drought analyses. This study aims to accurately characterize hydrological droughts within three watersheds in Yunnan from 2011 January to 2021 May by a Global Navigation Satellite System (GNSS) inversion constrained by Gravity Recovery and Climate Experiment (GRACE) data. Initially, we employed Variational Bayesian Independent Component Analysis to reconstruct the 3-D crustal deformations at 43 GNSS stations resulting from hydrological loadings. We then computed the time-series of vertical displacements caused by GRACE Mascon water products. Subsequently, utilizing the method of least squares, we derived the scaling factors between the vertical crustal displacements (VCD) obtained from GNSS observations and the synthetic displacements derived from GRACE data. By combing scaling-factor-adjusted VCDs derived from GRACE with the GNSS data, we have obtained accurate estimates of water storage for three Yunnan watersheds. Finally, we identified drought events characterized by abnormal decreases in water storage and used climatological methods to quantitatively describe the severity, extent and recovery of these drought extremes. Additionally, we evaluated the influence of various earth elastic structures on the scaling factors, and demonstrated their advantageous contribution to aligning GNSS and GRACE observations. In conclusion, our study introduces a novel approach to integrate GNSS and GRACE retrievals, allowing for accurate characterization of droughts in data-scarce regions, which cannot be achieved by GNSS or GRACE individually. Moreover, our results underscore the potential benefits of watershed-scale drought monitoring and analysis for effective water resource management.

Is there a consistency in basin morphometry and hydrodynamic modelling results in terms of the flood generation potential of basins? A case study from the Ulus River Basin (Türkiye)

Floods are natural phenomena that increase their impact every year due to increasing natural and anthropogenic variables, which are effective individually or together. Many research studies have focused on understanding the causative factors. This study aims to reveal whether there is a consistency in the results of the basin morphometry and hydrodynamic model used to understand the flood generation potential of river basins. Both analyses have been discussed individually in many papers but are not comprehensively compared. The Ulus settlement (Türkiye), where Ulus upstream, Suleyman, Alpi, and Eldes tributaries converge, was chosen as a study area. The results of the two analyses were evaluated in terms of their potential ability to produce floods over the subbasins and the Ulus settlement. Eight basin morphometric parameters were applied to the 10 m resolution TopoDEM data, and the results were evaluated according to the Normalised Morphometric Flood Index (NMFI) method, which helps decrease subjectivity in ranking the results. Flow data of the ungauged subbasins were produced using the SWAT rainfall-runoff model, and 2, 5, 10, 20, 50, 100, 200, 500, and 1000 years maximum flow data were generated using flood frequency analysis, and the different flow data were modelled based on 1 m LiDAR DEM using the LISFLOOD-FP 2D-hydrodynamic model. According to the results, the Suleyman subbasin has the highest flood generation potential in basin morphometry and hydrodynamic modelling. It is followed by the Alpi subbasin at high potential, Ulus upstream and Eldes subbasins at medium potential. As a result, we determine there is a consistency in basin morphometry and hydrodynamic modelling results. The basin morphometry and the developed NMFI can be used to determine and understand the flood-generating potential of basins or subbasins in data-scarce regions regarding flood modelling.

Current and future water balance of a mountain subcatchment of Issyk-Kul Lake, Tien Shan range, Kyrgyzstan

Snow and ice dominated basins are particularly vulnerable to climate change but estimating their hydrological balance remains challenging in data-scarce regions like the Tien Shan mountains. With the overall aim of modeling of the large Issyk-Kul Lake basin in Kyrgyzstan, this article focuses on the hydrological balance of the Chon Kyzyl-Suu basin, a representative sub-catchment of the lake basin. The study involved two steps: first, calibration/validation of a distributed hydrological snow model, second, assessment of future trends in runoff, evaporation, snow melt and glacier melt under different climate scenarios. Our results show that the balance of the basin is already upset due to glacier mass loss and that groundwater processes play a significant role in generating discharge. Climate projections for the next 40 years (2020–2060) show no significant trend in precipitation under scenario ssp2–4.5 but an 8.9 % decrease in precipitation under scenario ssp5–8.5. at the same time, air temperature will increase by 0.4 °C under scenario ssp2–4.5, and by 1.8 °C under scenario ssp5–8.5. Under the “business as usual” scenario (ssp2–4.5), the annual river flow of the headwater basins should increase by 13 %, or under the “pessimistic” ssp5–8.5 scenario, by 28 %, mainly due to the increase in glacier runoff. These results make it possible to envisage realistic modeling at the scale of the lake at a daily time step.

Spatiotemporal analysis of fine particulate matter for India (1980–2021) from MERRA-2 using ensemble machine learning

Particle exposure affects more humans globally than any other air pollutant. However, due to expensive instruments and infrastructural deficiency, a high spatiotemporal network of monitoring stations is not possible, leading to data-scarce regions. Satellite and reanalysis datasets can be implemented to estimate particulate matter, but they do not provide surface concentration and needs to be reconstructed from the components. In this study, a machine learning (ML) framework is implemented to reconstruct PM2.5 from MERRA-2 data components, namely black carbon (BC), organic carbon (OC), dust (DUST), sea salt (SS), and sulfate (SO4) mass concentration. The ground-level data were collected from India's 335 continuous ambient air quality monitoring stations (CAAQMS) and respective MERRA-2 data for 2017–2021 at hourly resolution. Random forest (RF) performs better with train and test scores (R2) of 0.84 and 0.73, respectively, while the empirical equation provides an R2 of only 0.26 on test data. The estimated PM2.5 for Indian states from 1980 to 2021 indicates a significant increase in most cases. However, states in the Indo-Gangetic plain such as Delhi, Punjab, Haryana, and Uttar Pradesh, are the most polluted regions of India. The major shift in concentration is from 2000 onwards, which can be seen as a direct result of the economic liberalization policies implemented in 1991. The results provide evidence for the limitations of the broad application of the empirical equation and the feasibility of ML algorithms as a potential reconstruction technique for developing robust and accurate region-specific models from MERRA-2 data.

Flood hazard potential evaluation using decision tree state-of-the-art models.

Floods occur frequently in Romania and throughout the world and are one of the most devastating natural disasters that impact people's lives. Therefore, in order to reduce the potential damages, an accurate identification of surfaces susceptible to flood phenomena is mandatory. In this regard, the quantitative calculation of flood susceptibility has become a very popular practice in the scientific research. With the development of modern computerized methods such as geographic information system and machine learning models, and as a result of the possibility of combining them, the determination of areas susceptible to floods has become increasingly accurate, and the algorithms used are increasingly varied. Some of the most used and highly accurate machine learning algorithms are the decision tree models. Therefore, in the present study focusing on flood susceptibility zonation mapping in the Trotus River basin, the following algorithms were applied: forest by penalizing attribute-weights of evidence (forest-PA-WOE), best first decision tree-WOE, alternating decision tree-WOE, and logistic regression-WOE. The best performant, characterized by a maximum accuracy of 0.981, proved to be forest-PA-WOE, whereas in terms of flood exposure, an area of over 16.22% of the Trotus basin is exposed to high and very high floods susceptibility. The performances applied models in the present work are higher than the models applied in the previous studies in the same study area. Moreover, it should be noted that the accuracy of the models is similar with the accuracies of the decision tree models achieved in the studies focused on other areas across the world. Therefore, we can state that the models applied in the present research can be successfully used in by the researchers in other case studies. The findings of this research may substantially map the flood risk areas and further aid watershed managers in limiting and remediating flood damage in the data-scarce regions. Moreover, the results of this study can be a very useful for the hazard management and planning authorities.

Geomorphic floodplain mapping in small Mediterranean catchments using LiDAR data

Recent advances in remote sensing technologies along with the increased availability of topographic data have lately encouraged the development of automatic DEM (Digital Elevation Model)-based procedures for floodplain delineation. Geomorphic methods, establishing relationships between flood descriptors and morphologic catchment characteristics, appear particularly suitable to be implemented within a GIS algorithm. In the present work, four simplified geomorphic approaches based on “flow-depth scaling laws” (FD) or “flow-cross-sectional area scaling laws” (FA) with contributing area and two methods employing two different flood descriptors (Hydro-Geomorphic Method, HGM and Geomorphic Flood Index method, GFIM) have been applied for the preliminary evaluation of floodplain extent using high resolution DEMs (i.e. LiDAR at 1 and 2 m resolution) as the main input. Taking as a case study six of the largest basins located in southern Italy, the performances of these methods were evaluated and critically compared using government agency derived flood hazard maps as benchmarks. Results show that the adoption of FD especially when combined with morphology to formulate the GFIM, allows to efficiently predict the flood-prone areas with low computational costs. At the same time, performances of the flood mapping procedures based on “flow-area scaling laws”, although in principle more appealing, seem to be slightly lower. Overall, the proposed approaches can be applied for rough mapping of floodplains in ungauged basins or in data-scarce regions where standard flood hazard maps are unavailable.

An approach to select optimum inputs for hydrological modeling to improve simulation accuracy in data-scarce regions

Study regionThe source regions of the Yellow River and Yangtze River in the central-eastern part of the Tibetan Plateau. Study focusHydrological model is an important tool in the simulation of watershed hydrology. However, as more and more basic geographic data becomes publicly available and shared globally, researchers are developing a 'symptom' of difficulty in choosing data, so a systematic comparative analysis for model input data selection is necessary. We tested the effects of different types, sources, and resolutions of input data on the model output results based on the SWAT model, and focused on the mechanism of the role of different input data in the model and how to select an appropriate input data for similar studies. New hydrological insights for the regionThe results show that: the meteorological data is crucial in the model's runoff simulation, and ground meteorological observation station data outperforms reanalysis data such as CFSR. Optimizing CFSR significantly improves the model's performance. The DEM resolution minimally impacts runoff simulation, as the difference in results is primarily due to the topographic characteristics of the watershed. DEM selection should consider TWI complexity and its compatibility with the watershed network. The selection of LULC data has little effect on the simulation, and the best input data combination is OBS + 90 m DEM + CNLULC. These findings assist input data selection for similar watersheds using the SWAT model.

Insights on Satellite-Based IMERG Precipitation Estimates at Multiple Space and Time Scales for a Developing Urban Region in India

Abstract Satellite-based rainfall estimates are a great resource for data-scarce regions, including urban regions, because of its finer resolution. Integrated Multi-satellitE Retrievals for GPM (IMERG) is a widely used product and is evaluated at a city scale for the Hyderabad region using two different ground truths, i.e., India Meteorological Department (IMD) gridded rainfall and Telangana State Development Planning Society (TSDPS) automatic weather station (AWS) measured rainfall. The IMERG rainfall estimates are evaluated on multiple spatial and temporal scales as well as on a rainfall event scale. Both continuous and categorical verification metrics suggest good performance of IMERG on the daily scale; however, relatively decreased performance was observed on the hourly scale. Underestimated and overestimated IMERG estimates with respect to IMD gridded rainfall and AWS measured rainfall, respectively, suggest the performance depends on type of ground truth. Unlike categorical metrics, RMSE and PBIAS have a pattern implying a systematic error with respect to rainfall amount. Further, sample size, diurnal variations, and season are found to have a role in IMERG estimates’ performance. Temporal aggregation of hourly to daily time scales showed the improved IMERG performance; however, no spatial-scale dependence was observed among zonewise and Hyderabad region–wise rainfall estimates. Comparison of raw and bias-corrected IMERG rainfall-based intensity–duration–frequency (IDF) curves with corresponding hourly rain gauge IDF curves showcases the value addition via simple bias correction techniques. Overall, the study suggests the IMERG estimates can be used as an alternative data source, and it can be further improved by modifying the retrieval algorithm. Significance Statement Many urban regions are typically data sparse, which limits scientific understanding and reliable engineering designs of various urban hydrometeorology-relevant tasks, including climatological and extreme rainfall characterization, flood hazard assessment, and stormwater management systems. Satellite rainfall estimates come as a great resource and Integrated Multi-satellitE Retrievals for GPM (IMERG) acts as a best alternative. The Hyderabad region, the sixth-largest metropolitan area in India, is selected to analyze the widely used satellite estimates, i.e., retrievals for GPM. The study observed inaccuracies in the IMERG estimates that varied with rainfall magnitudes and space and time scales; nonetheless, the estimates can be used as an alternative data source for decision-making such as whether rain exceeds a certain threshold or not.

Assessing the Surface Downward Longwave Irradiance Models Using ERA5 Input Data in Canada

Abstract Longwave radiation (LR) is one of the energy balance components responsible for warming and cooling water during hot summers. Both downward incoming LR, emitted by the atmosphere, and outgoing LR emitted by the land surface are not widely measured. The influence of clouds on the LR heat budget makes it even harder to establish reliable formulations for all-sky conditions. This paper uses air temperature and cloud cover from the ERA5 reanalysis database to compare 20 models for the downward longwave irradiance (DLI) at Earth’s surface and compare them with ERA5’s DLI product. Our work uses long-time continuous DLI measured data at three stations over Canada, and ERA5 reanalysis, a reliable source for data-scarce regions, such as central British Columbia (Canada). The results show the feasibility of the local calibration of different formulations using ERA5 reanalysis data for all-sky conditions with RMSE metrics ranging from 37.1 to 267.3 W m−2, which is comparable with ERA5 reanalysis data and can easily be applied at broader scales by implementing it into hydrological models. Moreover, it is shown that ERA5 gridded data for DLI shows the best results with RMSE = 31.7 W m−2. This higher performance suggests using ERA5 data directly as input data for hydrological and ecological models.