Spatio-temporal analysis of flood events using GIS and remote sensing-based approach in the Ukrina River Basin, Bosnia and Herzegovina
-
Luka Sabljić
,
Tin Lukić
Abstract
The subject of this research is the analysis of flood dynamics in the Ukrina River Basin, Bosnia and Herzegovina, using a remote sensing-based approach and geographic information systems during the period 2016–2019. The aim is to identify the spatial and temporal patterns of floods by integrating satellite-derived precipitation data, hydrological data, and Sentinel-1 imagery processed through Google Earth Engine. The methodology included the use of CHIRPS precipitation data and the Standardized Precipitation Index (SPI) for identifying meteorological anomalies, while Sentinel-1 SAR data were used to map flood extent based on radar backscatter change detection. The approach combined temporal analysis with spatial overlays of land use and administrative boundaries to assess affected areas. Flood events were identified in January 2016 (89.98 ha), March 2017 (179.85 ha), March 2018 (58.81 ha), and May 2019 (195.38 ha), coinciding with periods of above-average precipitation (>125%), positive SPI values, and elevated water levels. The spatial analysis of flooded areas, overlaid with land use data, revealed that agricultural land was the most affected category, with 79.21 ha flooded in 2016, 169.15 ha in 2017, 48.89 ha in 2018, and 184.90 ha in 2019. Built-up areas were also significantly impacted, posing risks to infrastructure and economic stability. The cities and municipalities of Derventa, Prnjavor, and Stanari were most frequently affected by floods during the study period. The findings highlight the role of cumulative precipitation and hydrological conditions in triggering flood events and provide insights for flood risk management, including adaptive strategies, early warning, and sustainable land use planning.
1 Introduction
Climate change represents a threat to people worldwide [1]. According to Klein et al. [2] and Meresa et al. [3], changes in the intensity and frequency of climate change lead to the occurrence of extreme events, such as heavy and intense precipitation, which significantly impact water resource management and flood risk occurrence. Asadieh and Krakauer [4] highlight that, globally, the annual maximum daily precipitation increased by an average of 5.73 mm during the period from 1901 to 2010. Furthermore, the global precipitation average showed a pronounced trend toward more intense rainy seasons and drier periods without precipitation between 1979 and 2010 [5]. Additionally, research results [6,7,8] indicate that the frequency of extreme precipitation has significantly increased over the last few decades. Similarly, recent studies [9] suggest that the observed rate of increase in extreme precipitation at the global level aligns with the well-known Clausius–Clapeyron relationship [10]. In Bosnia and Herzegovina (BH), trends in extreme precipitation share similar characteristics with trends in the region, where an increase in the frequency and intensity of precipitation has been recorded [11]. According to the conclusions of the Intergovernmental Panel on Climate Change [12], areas with increasing average precipitation will experience more intense wet events in the future, while areas with decreasing average precipitation will face more pronounced extreme dry periods [13]. Kundzewicz et al. [14] emphasize that prolonged precipitation (>1 day) of high intensity is considered the most common cause of floods in large basins, while floods in smaller basins are often caused by short-term but extremely intense precipitation.
Under the influence of extreme precipitation [15], floods typically occur when a river receives more water than it can handle, causing its channel capacity to be exceeded, and the excess water spills out, flooding surrounding low and vulnerable areas [16,17]. Numerous studies indicate that floods are one of the most devastating and frequent natural hazards [9,18,19,20,21], affecting more people than any other natural hazard [17] and thus leading to increased mortality, as well as significant economic and environmental damage [22,23]. According to the World Meteorological Organization (WMO), floods have caused an average of 6,500 fatalities over the last 20 years [17], as well as economic damage exceeding 30 billion USD annually [24].
On the other hand, in Europe, flood risks and consequences follow trends observed at the global level. Over the past 30 years, Europe has recorded more than 400 major floods (many of which were catastrophic), affecting over 8.7 million people, claiming more than 2,000 lives, and causing material losses exceeding 72 billion euros [25,26]. According to Ionita and Nagavciuc [26], among the most devastating and costly floods in various parts of Europe are the winter floods of 1993 and 1995 in Germany, the Netherlands, and France [27,28,29,30] the 2002 and 2013 floods in the Elbe River Basin [31,32,33]; the 2005, 2008, and 2010 floods in Eastern Europe [34,35,36]; the 2010 floods in Central Europe [37]; the UK floods of 2000, 2007, and 2014 [38,39,40,41]; and the 2014 floods in the Balkan Peninsula [42,43,44,45]. Among recent floods in Europe, the 2021 floods stand out. During that year, extreme precipitation affected Germany, Belgium, Luxembourg, and neighboring countries, resulting in catastrophic floods, particularly pronounced in western Germany, along the Meuse River and its tributaries in Belgium and the Netherlands [46]. In BH, exceptionally high precipitation amounts have caused catastrophic floods multiple times in recent years [11]. Between 2000 and 2021, floods were recorded in 2000, 2001, 2009, 2010, 2014, 2018, 2019, 2020, and 2021 [47]. The most devastating floods occurred in May and August 2014, when 17 and 14% of the average annual precipitation from the 1961–1990 period fell within just a few days [48]. During that year, all historical records in the instrumental period were broken [47]. For example, the maximum water level of the Vrbas River at Delibašino Selo (Banja Luka, BH) on May 16, 2014, was 816 cm, nearly 2 m higher than the previous maximum water level [49]. Before that, similar weather conditions were recorded in June 2010, when 20% of the total annual precipitation occurred within just 2 days [48]. The intensity and frequency of extreme climate events, including floods, are increasing, and according to the RCP8.5 climate scenario, these events are expected to become even more pronounced in the future in BH and the region [47].
For flood risk management, information about the intensity, frequency, and various factors that can influence floods during flood events is essential [17,50,51]. The response to floods requires careful preparation and effective communication and is typically conducted during emergency situations, where a clear understanding of the event’s characteristics is necessary to implement an intervention plan in the affected area [52]. Unfortunately, determining the extent of floods through field data collection (e.g., observing flood perimeters by citizens) is often unreliable, impractical, time-consuming, and financially exhausting [53]. Moreover, data collected using this method usually have limited spatial coverage and low collection frequency [52].
Therefore, data obtained through remote sensing methods are of exceptional importance, as they enable continuous and up-to-date monitoring, as well as access to historical imagery and synoptic representations of large areas [52]. The availability of multi-functional sources (instruments operating in the visible, thermal, and microwave parts of the electromagnetic spectrum), different spatial scales, and resolutions is key for assessing the extent of flooded areas [54]. Additionally, the application of geographic information systems (GIS) is significant, as they, together with remote sensing technologies, enable rapid access to satellite data and efficient management of large data volumes [55,56,57,58] for analyzing natural disasters such as floods. Numerous studies dedicated to flood mapping or identifying flood-prone areas have been conducted using GIS technologies and remote sensing at the global level [59,60,61,62,63], the European level [64,65,66], and in the BH region [67,68,69,70]. Although flood studies based on geographic information systems (GIS) and remote sensing in BH are still relatively rare, there is a growing number of studies indicating increasing interest and potential for applying these technologies [71,72,73,74].
The main research questions this study aims to address are:
What are the key meteorological and hydrological factors that most significantly contribute to the occurrence of floods in the Ukrina River Basin, and how can these factors be effectively identified and monitored using remote sensing and GIS technologies?
How can satellite data, such as Climate Hazards Center InfraRed Precipitation with Station data (CHIRPS), be used to calculate the Standardized Precipitation Index (SPI) and analyze periods with increased flood risk, and what are their limitations in the context of the Ukrina River Basin?
How do floods affect different land categories in the Ukrina River Basin, particularly agricultural areas, and what are the implications for local communities and land management practices?
To what extent can GIS and remote sensing technologies improve flood monitoring and management in areas with limited infrastructure for traditional monitoring?
How can the integration of remote sensing data into flood risk management strategies contribute to more effective prevention, monitoring, and mitigation of flood consequences in the Ukrina River Basin?
The subject of this research is the identification, monitoring, and analysis of floods using advanced remote sensing techniques and GIS. The research aims to identify the factors that lead to flood occurrences, analyze their spatial and temporal characteristics, and examine the socio-geographical consequences, including impacts on the population and land use practices. A key component of the research includes mapping the spatial extent of floods, identifying the most vulnerable areas, and analyzing land use patterns. The study applies advanced technologies, including remote sensing “products” and modern GIS technologies, to provide a comprehensive understanding of the spatial and temporal flood patterns. The results of the research form the basis for developing flood risk management strategies, improving early warning systems, and planning preventive measures to mitigate adverse effects on the environment and local communities.
2 Study area
The study area is the Ukrina River Basin, located in the northern part of BH. The basin covers a total area of 1498.66 km2 [75,76], extending from 44°35′09″N to 45°05′12″N and from 17°23′53″E to 18°07′53″E (Figure 1). The basin belongs to the southern part of the Pannonian Basin and lies at the junction of two macro-regional units: the Pannonian Basin and the mountain ranges [71]. Specifically, it is situated between the Vrbas River Basin to the west and the Bosna River Basin to the east. According to Tošić [77], the basin’s topographical divide is formed by the Dinaric watershed mountains: the Borja and Uzlomac mountains to the south, as well as mountains stretching across the central and northern parts of the basin, including Ljubić, Čavka, Javorovo, Careva Gora, Motacija, and Vučjak at the far east.
The study area with locations of MS whose data were used to assess the validity of satellite-based precipitation estimates.
The Ukrina River Basin is geologically diverse, with Mesozoic sedimentary, magmatic, and volcano-sedimentary rocks [78], Paleozoic formations linked to the Motajica Massif [79], and extensive Cenozoic deposits. Neogene sediments from the Miocene and Pliocene epochs cover large lowland areas [71], while Quaternary gravel and sand terraces dominate river valleys [80]. Based on the Basic Geological Map at a scale of 1:100,000 [81], the main geological characteristics of the study area are shown in Figure 2a.
Characteristics of the study area: (a) geology, (b) elevation, (c) slope, (d) average sum precipitation, (e) average air temperature, and (f) hydrographic network. The markings (a) of the geological classification from the legend have the following meanings: 1 – Albite granites; 2 – Siltstones, clays, flood facies; 3 – Amphibolites; 4 – Amphibolite gneisses; 5 – Amphibolite schists, amphibolite facies; 6 – Amphibolite schists, epidote-amphibolite facies; 7 – Breccias (predominantly diabase), rarely greywackes; 8 – Brecciated limestones; 9 – Brecciated limestones, marly limestones, sandstones; 10 – Dacites; 11 – Deluvial-proluvial sediments: scree, gravels, and clays; 12 – Diabase-chert formation, metamorphosed ophiolitic melange; 13 – Diabase-chert formation: ophiolitic melange; 14 – Diabase-chert formation: sandstones, shales, cherts with small bodies of diabase and spilite; 15 – Diabases; 16 – Diopside amphibolites; 17 – Dolomites and limestones with megalodonts; 18 – Channel facies: alluvium, river islands, and river beaches; 19 – Flysch: massive and thick-bedded sandstones, shales, clayey marls, and conglomerates; 20 – Flysch: sandstones, siltstones, and clay shales with glauconitic mass; 21 – Flysch: fine-grained and medium-grained sandstones with graded bedding in alternation with siltstones and rarely marls; 22 – Gabbros, predominantly olivine gabbro; 23 – Gabbroperidotites; 24 – Clayey and sandy marls, marly-sandy clays, weathered ferruginous sandstones; 25 – Clayey and sandy gravels and scree with loams; 26 – Garnet-diopside amphibolites; 27 – Coarse-clastic formations: sands, coarse-grained sandstones, conglomerates, sandy clays, platy marly limestones, and tuffs; 28 – Keratophyres; 29 – Clinozoisite-amphibolites; 30 – Conglomerates, sandstones, clays, and marls; 31 – Conglomerates, sandstones, subordinately marls and clays, coal (footwall zone); 32 – Quartz-carbonate rocks (listvenites); 33 – Quartz-sericite schists and phyllites; 34 – Marls, calcareous marls, and limestones; 35 – Marlstones, marls, limestones, sandstones, subordinately conglomerates, tuffs, coal; 36 – Marly clays, marly limestones, and sands; 37 – Marly-sandy clays, marls, oolitic limestones, carbonate sandstones, and conglomerates; 38 – Marly micrites, marls, and arenites; 39 – Lithothamnium and Amphistegina limestones, marls, sandy-clayey marls, clays, sands, conglomerates, and tuffs; 40 – Massive, rarely thin-bedded limestones; 41 – Metamorphosed volcano-sedimentary formations; 42 – Lower river terrace; 43 – Ophitic gabbros; 44 – Ophitic normal gabbros; 45 – Olistostrome breccias; 46 – Peridotites; 47 – Sand and gravel; 48 – Sand, clays, and gravels; 49 – Sandstones, fine-grained conglomerates, marls, and shales; 50 – Sandstones, marls, and sands; 51 – Sands, gravels, partially clayey; 52 – Platy to thick-bedded limestones with brachiopods; 53 – Platy marly limestones and organogenic-reef limestones; 54 – Platy marly micrites; 55 – First river terrace; 56 – Variegated sands and clays and chert gravels; 57 – Variegated sands, subordinately gravels; 58 – Cherts, shales, and limestones; 59 – Cherts, greywacke sandstones, shales, and fine-grained conglomerates with lenses of limestone; 60 – Serpentinites; 61 – Scree/Talus; 62 – Fine-grained sands, interbeds of sandy clays, lenses of gravel; 63 – Gravel and sand; 64 – Bedded marly micrites, marls, and subordinately arenites; 65 – Solifluction; 66 – Spilites; 67 – Thin-platy marly and sandy limestones, shaly limestones, shales, and quartz sandstones; 68 – Weathered, coarse conglomerates, gravels, sandy and gravelly loams; 69 – Limestones, marls, conglomerate-breccia.
Elevation within the basin ranges from 88 to 950 m (Figure 2b). The northern and central parts are dominated by low-lying terrain along the main course of the Ukrina River and its major tributaries, forming typical river valleys. In contrast, the southern and southwestern parts are characterized by higher terrains and steep slopes. The slope map (Figure 2c) shows that flat and gently sloping terrain is located along the riverbanks of the Ukrina. According to the Köppen–Geiger classification [82], the Ukrina River Basin is partly classified as Cfb (southwestern part), characterized by mild winters and moderately warm summers, while the northeastern part falls under the Cfc type, marked by mild winters and short, cool summers. The climatic characteristics of the study area are illustrated through the spatial distribution of the average annual sum of precipitation (Figure 2d) and air temperature (Figure 2e) for the period 2000–2020. The highest precipitation values are recorded in the southern parts of the basin, where the average annual total reaches nearly 996 mm [83], which can be attributed to the orographic influence of elevated areas. In contrast, the northern part of the basin receives lower amounts of precipitation, averaging around 785 mm [83], consistent with the predominantly lowland character of this region. Temperatures range from 12.58°C in the southern, higher-altitude areas to 13.53°C in the northern part of the basin [84]. The spatial distribution of air temperature largely reflects the morphological structure of the terrain, with cooler values associated with higher elevations and warmer areas located in the lower valleys along the Ukrina River.
The Ukrina River Basin represents a hydrologically complex area characterized by a dense and well-developed river network (Figure 2f). The main watercourse, the Ukrina River, originates in the southern part of the basin from the confluence of the Velika and Mala Ukrina rivers and flows from south to north until it joins the Sava River as a right tributary. The total length of the Ukrina River is 134.9 km, while the length of the main watershed ridge within the basin reaches 248.9 km [77]. The horizontal asymmetry of the basin affects the spatial distribution of tributaries and runoff dynamics [77], which is particularly relevant in the formation of rapid and intense flood waves.
According to hydrological indicators, the average annual discharge of the Ukrina at its confluence is 19.8 m3/s, while the maximum discharge during a 100-year flood event (Q1/100) reaches 635.4 m3/s [71,85].
The river network (Figure 2f) is dense, especially in the southern and central parts of the basin, where tributaries such as the Vijaka, Lupljanica, and Ilova contribute to the hydrological system [71]. In addition to natural hydrographic elements (Figure 2f), hydrotechnical structures (dams and embankments) have been mapped within the basin.
The Ukrina River Basin encompasses a total of 405 settlements, which had 242,655 inhabitants and 91,394 households [86]. It is important to emphasize that the population within the basin is smaller than the stated number, as the basin does not cover the entire area of the listed cities and municipalities. Also, according to the Corine Land Cover (CLC) geospatial database (https://land.copernicus.eu/) from 2018, the dominant land use in the Ukrina River Basin consists of agricultural land, which covers 818.10 km2 (54.59%), followed by forested areas 649.47 km2 (43.34%), built-up areas 22.80 km2 (1.52%), and water bodies 8.27 km2 (0.55%). In terms of employment structure, the largest share of employed persons (approximately 40%) is in the economy and manufacturing industries, which rely on agriculture.
3 Methodology and data
The spatial and temporal investigation of floods in the study area is based on the application of various geoinformation technologies, including GIS and remote sensing. The application of these technologies involves the processing and analysis of remote sensing “products” in the form of satellite imagery. The purpose of processing and analyzing satellite data is to identify the occurrence of extreme precipitation, monitor the effects of extreme precipitation on water level rise, and spatially map floods that result from these causal factors. Additionally, this approach aims to assess the population exposed to flood risk and analyze the patterns of land use affected by flooding. The processing of satellite data is conducted using the Google Earth Engine platform and QGIS 3.40 “Bratislava” software (https://qgis.org/). A comprehensive methodological approach to the spatial and temporal investigation of floods is presented in Figure 3, and the same will be explained in detail in the following sections.
Algorithm for processing satellite and meteorological data (step 1 – validation of Climate Hazards Group InfraRed Precipitation with Station Data; step 2 – calculation of SPI; step 3 – identification of high water levels; step 4 – mapping of flooded areas and assessment of adverse impacts).
3.1 Assessment of the validity of satellite precipitation data
The primary input data for identifying extreme precipitation in the study area are satellite-based precipitation estimates known as CHIRPS. CHIRPS is a relatively new precipitation database, developed by the Climate Hazards Group at the University of California, Santa Barbara [83]. It is based on a combination of rain gauge data, satellite imagery, and (re)analysis products [87]. The data are available from 1981 to near-real-time [88], with a relatively high spatial resolution of 0.05° [89]. Before use, they have been validated and applied in scientific research across various parts of the world, including Cyprus [90,91,92], Argentina [93], Nepal [94], Italy [95], Mozambique [96], Vietnam [97], Pakistan [88], China [98,99], and BH [74,100,101].
The validation of CHIRPS satellite data in the Ukrina River Basin is based on the methodological framework of the study by Sabljić et al. [74], which involves comparing CHIRPS data with meteorological station (MS) data on monthly precipitation for the period 1981–2023. Data from the MS were obtained from the Republic Hydrometeorological Service of the Republic of Srpska. The analysis included data from MS located within the basin boundaries, as well as those in close proximity to the basin (Figure 1, Table 1). The inclusion of stations outside the basin boundaries is justified by the limited number of stations within the basin relative to its area, the short operational period of certain stations, and the relatively “low” spatial resolution of satellite data compared to the extent of the study area.
MS whose data were used for the validation assessment
| Name of MS | Location | Time period | Elevation (m) |
|---|---|---|---|
| Derventa | 44°58′N, 17°54′E | 1981–2012 | 108 |
| Stanari | 44°45′N, 17°47′E | 2006–2018 | 169 |
| Banja Vrućica | 44°35′N, 17°53′E | 2009–2023 | 206 |
| Prnjavor | 44°52′N, 17°39′E | 2019–2023 | 186 |
| Čelinac | 44°43′N, 17°19′E | 2017–2023 | 199 |
| Srbac | 45°05′N, 17°31′E | 2000–2023 | 107 |
| Modriča | 44°57′N, 18°18′E | 1997–2002 | 111 |
The validation of CHIRPS data at the monthly level is based on the following formula:
where V month,i represents the percentage agreement for month (i), expressed as a percentage; P sat,i is the precipitation value based on satellite data for month (i); P met,i represents the precipitation value based on meteorological data for month (i); and |Psat,i − P met,i | is the absolute difference between satellite and meteorological data for month (i).
The overall percentage agreement or validation of CHIRPS data for the period 1981–2023 was determined using the following formula:
where V
overall is the overall percentage agreement; ∑ᵢ₌₁12 |Pₛₐₜ,ᵢ − Pₘₑₜ,ᵢ| is the total absolute difference between satellite and meteorological data for all months, and
In addition to the presented methodological framework, the validity of the CHIRPS data was further confirmed by calculating the Pearson correlation coefficient (R p), following the methodology proposed by Dehaghani et al. [102]. The values of this coefficient range between −1 and 1, and it serves as an indicator of the agreement between precipitation measured by MS (P o i ) and satellite-based CHIRPS precipitation estimates (P s i ). The closer the coefficient is to 1, the stronger the agreement between the satellite data and ground-based measurements. The Pearson correlation coefficient was calculated using the following formula [102]:
where
3.2 Meteorological and hydrological characteristics of the flood
Extreme precipitation refers to types of precipitation that significantly deviate from normal values for a specific area or time period. To identify the occurrence of extreme precipitation in the Ukrina River Basin, the average monthly precipitation for a 42-year period (1981–2023) was calculated and compared to the average monthly precipitation during the reference years (2016, 2017, 2018, and 2019). In this study, months in the reference years were considered extreme if their average monthly precipitation exceeded 125% of the corresponding 42-year monthly average. Accordingly, in line with the recommendations of the WMO, a meteorological element encompassing a full climatological cycle was observed [103]. Previously validated CHIRPS satellite precipitation estimates were used as input data for the calculation. The months during the reference years in which precipitation exceeded the 42-year average (>125%) were identified as periods of extreme precipitation that could potentially cause flooding in the study area.
Additional confirmation of the meteorological conditions for flood occurrence was conducted by calculating the SPI. The mentioned index, developed by McKee et al. [104], represents the z-score deviation from the mean expressed in units of standard deviation. In this study, the SPI was calculated following the previously implemented methodological framework of Sabljić et al. [101]. The calculation was performed based on validated CHIRPS precipitation estimates at the location of each pixel for the composite period of each year during the reference period. The formula for calculating the SPI is as follows:
where SPI ijk is the z-value for the pixel (i) during timeframe (j) for year (k), P ijk is the precipitation value for pixel (i) during timeframe (j) for year (k), P ij is the mean for pixel (i) during timeframe (j) over n years, and σ ij is the standard deviation of pixel (i) during week (j) over n years.
The obtained SPI results were classified according to McKee et al. [104] and Moccia et al. [105] into multiple classes representing the severity of wet or dry events (Table 2). Each class has an established probability of occurrence, which is, by definition, the same for both wet and dry events.
Classification of events based on SPI values and their probability of occurrence (according to McKee et al. [104] and Moccia et al. [105])
| SPI values | SPI classes |
|---|---|
| ≥2.0 | Extremely wet |
| 1.5 to 2.00 | Severely wet |
| 1.0 to 1.5 | Moderately wet |
| 0 to 1.0 | Mildly wet |
| −1.0 to 0 | Mildly drought |
| −1.5 to −1.0 | Moderate drought |
| −2.0 to −1.5 | Severe drought |
| ≤2.00 | Extreme drought |
Meteorological extremes can significantly influence the hydrological characteristics of the study area. Unfortunately, these characteristics cannot be monitored at the level of the Ukrina River Basin because there are no active hydrological stations (HS) within the study area. For this reason, to assess the impact of meteorological extremes manifested through extreme precipitation and exceptionally wet events, data from the HS Srbac (Vrbas River Basin), located near the Ukrina River Basin, were used. The Vrbas River Basin and the Ukrina River Basin belong to the same larger Sava River Basin [106], indicating their hydrological and spatial connection. This connection further justifies the use of data from HS Srbac in this study, as it is expected that meteorological and hydrological conditions within the broader Sava River Basin exhibit similar patterns. Although the station is not directly located within the Ukrina River Basin, its proximity and connection to the surrounding environment provide relevant data that can serve as an indicator of the hydrological state of the study area. The inclusion of data from HS Srbac partially compensates for the absence of HS within the Ukrina River Basin and provides a foundation for assessing the impact of extreme precipitation and exceptionally wet conditions on water resources. The obtained data form a basis for evaluating potential floods in the Ukrina River Basin, considering broader hydrological processes within the larger Sava River Basin.
To assess the impact of extreme precipitation on water levels, the average mean water level of the Sava River (HS Srbac) for the period 1997–2023 was calculated. The average mean water level for the 26-year period was compared with the average mean water level during the reference years (2016, 2017, 2018, and 2019). Months in which the mean water level exceeded 100% of the long-term monthly average were considered hydrologically significant. Given the station’s indirect spatial relevance to the study area, a more conservative threshold was adopted to ensure that even moderate hydrological responses to meteorological extremes were not overlooked. Identifying higher water levels at HS Srbac, although not directly within the study area, allows for the establishment of a chronology of events and serves as an indicator of the cause-and-effect relationships between extreme precipitation, high SPI values, and flood occurrence. The periods (months) where these three components coincide – extreme precipitation, high SPI, and elevated water levels – represent key timeframes for further analysis and flood identification. During these periods, using remote sensing methods, it is possible to map flooded areas and determine the spatial extent of floods within the study area, provided that the limitations of the temporal resolution of satellite data are taken into account.
3.3 Flood mapping
The methodology for mapping flooded areas is based on processing remote sensing “products” in the form of radar satellite images known as Sentinel-1 [107]. This satellite mission is part of the European Space Agency’s (ESA) radar mission under the Copernicus Sentinel-1 program. It provides continuous imaging in the C-band, regardless of weather conditions or time of day. The mission consists of two satellites, Sentinel-1A and Sentinel-1B, which operate in the same orbital plane [108]. The first satellite, Sentinel-1A, was launched on April 3, 2014, while Sentinel-1B was launched on April 25, 2016 [109]. Mastrorosa et al. [109] emphasize that the Sentinel-1 mission operates in four unique imaging modes with varying resolutions (up to 5 m) and coverage (up to 400 km), and that it enables dual polarization, very short revisit intervals, and fast product delivery. The main characteristics of the Sentinel-1 satellite mission are provided in Table 3 [110].
Characteristics of the Sentinel-1 satellite mission (according to Fletcher [110])
| Interferometric wide swath mode (IW) | Wave mode | Strip map mode | Extra wide swath mode (EW) | ||
|---|---|---|---|---|---|
| Parameters | Polarization | Dual (HH + VV, VV + VH) | Single (HH, VV) | Dual (HH + HV, VV + VH) | Dual (HH + HV, VV + VH) |
| Azimuth resolution | 20 m | 5 m | 5 m | 40 m | |
| Ground range resolution | 5 m | 5 m | 5 m | 20 m | |
| Azimuth and range looks | Single | Single | Single | Single | |
| Products | Level 2 | Ocean | Ocean | Ocean | Ocean |
| Level 1 | Single look complex | — | Single look complex | Single look complex | |
| Level 0 | Raw data | — | Raw data | Raw data | |
| Characteristic | Lifetime | 7 years (consumables for 12 years) | |||
| Launch date | 1A – 3 April 2014 | 1B – 25 April 2016 timeframe | ||||
| Launcher/location | Soyuz, Kourou (both launches) | ||||
| Orbit | Near-polar, Sun-synchronous, about 690 km, 12-day repeat cycle | ||||
| Orbital period | 98.6 min | ||||
As part of the research aimed at spatial flood identification, Sentinel-1 images were filtered using the GEE platform based on cloud technology [111]. The images were selected with the following characteristics: vertical polarization (VV), interferometric wide swath (IW) imaging mode, and orbital paths in either descending or ascending mode. The study area was defined by the boundaries of the Ukrina River Basin, obtained from the HydroSHEDS geospatial database (https://www.hydrosheds.org/) [75,76]. Data on hydrotechnical structures (dams and embankments) were obtained from the OpenStreetMap geospatial database (https://www.openstreetmap.org/). The satellite images were divided into two temporal groups: pre-flood period and post-flood period (for each reference year of the study). For each time interval, a mosaic was created by merging available images and reducing them to the study area, which enabled a clear depiction of surface conditions before and after the floods. The data processing included a step to re-move “speckle” noise, characteristic of radar images, using the Refined Lee filter. This algorithm uses local statistics, such as mean and variance, within defined “windows”, reducing noise while preserving spatial details. For the filter application, the input satellite data were first converted from dB to natural units, and after processing, they were converted back to dB format. The identification of flooded areas in the study was based on analyzing changes in backscatter signal values (dB). Flooded areas were defined as pixels that had pre-flood values above −18 dB (non-water surfaces) but showed values below −18 dB (water surfaces) after the flood. The threshold of −18 dB was tested on multiple smaller samples within the basin area before applying it across the entire basin to ensure validation. A similar methodology was applied by Arora et al. [112] in the Ghaggar River Basin in India, where Sentinel-1 SAR data and threshold-based classification were used to detect flood-affected areas. Their approach also included testing of backscatter thresholds and integration with land use and elevation data to improve flood impact assessment, especially in agricultural and built-up zones, which supports the methodological choices in this study. Permanent water surfaces in this study were identified as pixels with values below −18 dB in both time intervals. Based on the presented analysis, binary masks were created, and the flooded areas were spatially represented. To enhance the spatial interpretation and visualization of the flood extent, the European Digital Elevation Model (EU-DEM) with a 25 m resolution was used as a topographic base layer for the final flood maps.
3.4 Assessment of flood damage impact
To assess the damage impact of floods, the results of the mapped floods were overlaid with the administrative boundaries of cities, municipalities, and their settlements, obtained from the Republic Administration for Geodetic and Property-Legal Affairs of the Republic of Srpska (https://www.rgurs.org/). This analysis enabled the identification of administrative units and an estimation of the population at risk of flooding during the period from 2016 to 2019. Additionally, the impact of floods on different land use types in the study area was assessed. The land use data were obtained from the CLC geospatial database for the year 2018, provided by the Copernicus Land Monitoring Service under the European Environment Agency. The CLC database offers pan-European standardized land cover information with a spatial resolution of 100 m, allowing for consistent and comparable analyses across regions. The 2018 dataset was selected as it corresponds to the mid-point of the study period and provides the most up-to-date, validated classification available at the time of analysis. For the purposes of this study, detailed land cover classes were reclassified into three major categories – built-up areas, agricultural land, and forested areas (Table 4) – to simplify the overlay with mapped flood extents and to assess flood exposure in a spatially clear and policy-relevant manner. The processing and analysis of data were performed using QGIS 3.40 “Bratislava” software (https://qgis.org/). By intersecting flood data with land use categories, the quantitative effects of floods were determined for each class. Spatial–temporal insights into the dynamics of flood events highlighted the most vulnerable categories and their spatial distribution.
Generalization of land use types based on the CLC 2018 geospatial database
| Level 1 | Level 2 | Level 3 | Generalized |
|---|---|---|---|
| 1 Artificial surface | 11 Urban fabric | 111 Continuous urban fabric | Built-up area |
| 112 Discontinuous urban fabric | |||
| 12 Industrial, commercial, and transport units | 121 Industrial or commercial units | ||
| 122 Road and rail networks and associated land | |||
| 123 Port areas | |||
| 124 Airports | |||
| 13 Mine, dump, and transport units | 131 Mineral extraction site | ||
| 132 Dump sites | |||
| 133 Construction site | |||
| 14 Artificial, non-agricultural vegetated areas | 141 Green urban areas | ||
| 142 Sport and leisure facilities | |||
| 2 Agricultural areas | 21 Arable land | 211 Non-irrigated arable land | Agriculture areas |
| 212 Permanently irrigated land | |||
| 213 Rice fields | |||
| 22 Permanent crops | 221 Vineyards | ||
| 222 Fruit trees and berry plantations | |||
| 223 Olive groves | |||
| 23 Pastures | 231 Pastures | ||
| 24 Heterogeneous agricultural areas | 241 Annual crops associated with permanent crops | ||
| 242 Complex cultivation patterns | |||
| 243 Land principally occupied by agriculture, with significant areas of natural vegetation | |||
| 244 Agro-forestry areas | |||
| 3 Forest and semi natural areas | 31 Forests | 311 Broad-leaved forest | Forest areas |
| 312 Coniferous forest | |||
| 313 Mixed forest | |||
| 32 Scrub and/or herbaceous vegetation associations | 321 Natural grasslands | ||
| 322 Moors and heathland | |||
| 323 Sclerophyllous vegetation | |||
| 324 Transitional woodland-shrub | |||
| 33 Open spaces with little or no vegetation | 331 Beaches, dunes, sands | ||
| 332 Bare rocks | |||
| 333 Sparsely vegetated areas | |||
| 334 Burnt areas | |||
| 335 Glaciers and perpetual snow |
4 Results
4.1 Validation of satellite precipitation data
The validation of CHIRPS satellite precipitation estimates was conducted according to the previously described methodology for the time period 1981–2023. A comparison of the CHIRPS data with MS data indicates a high degree of validity of the CHIRPS data (Figure 4a). May and December are characterized by a >85% agreement between CHIRPS data and MS data. April and June exhibit >90% agreement, while 6 out of 12 months (February, March, July, September, October, and November) show >95% agreement. Lower agreement percentages were recorded in August (82.42%) and January (79.69%). The overall percentage agreement of CHIRPS satellite precipitation estimates with MS data for the period 1981–2023 is 94.18%, which confirms that these data are valid for use in the Ukrina River Basin. To further support this conclusion, a Pearson correlation analysis was performed, revealing a very strong and statistically significant positive correlation between the monthly CHIRPS estimates and MS observations (R p = 0.875). This result, presented in Figure 4b, provides additional confirmation of the reliability of CHIRPS satellite precipitation data for the study area.
Validation of CHIRPS data (1981–2023): (a) Monthly precipitation comparison (MS vs CHIRPS) and (b) Pearson correlation analysis (R p = 0.875).
4.2 Identification of meteorological and hydrological preconditions for flood occurrence
To identify extreme precipitation in the Ukrina River Basin, a comparison was made between the CHIRPS average monthly precipitation for the period 1981–2023 and the CHIRPS average monthly precipitation during the reference years (2016, 2017, 2018, and 2019). Months in the reference years with precipitation exceeding 125% of the 42-year monthly average were classified as extreme. This threshold represents a 25% increase over the long-term average and serves as an indicator of significant precipitation anomalies.
In 2016 (Figure 5a), extreme precipitation was recorded in February (190.60%) and July (161.20%). January (127.60%) also exceeded the 125% threshold. March (123.10%) was slightly below but close to the threshold. In 2017 (Figure 5b), extreme values were observed in February (131.40%), April (133.60%), and December (144.10%). March (124.20%) and September (123.60%) were near the threshold. During 2018 (Figure 5c), March (189.60%), May (134.30%), and July (156.40%) exceeded the 125% threshold. February (127.60%) and June (124.40%) also approached or slightly exceeded this value. December recorded 123.00%, close to the extreme threshold. In 2019 (Figure 5d), only May (141.00%) surpassed the 125% threshold. June (117.10%) and November (114.00%) were elevated but below the defined limit.
Percentage-based comparison of CHIRPS monthly precipitation during (a) 2016, (b) 2017, (c) 2018, and (d) 2019 with the 1981–2023 average.
For a more precise analysis of extreme precipitation, the SPI index provides significant insights into moisture intensity, complementing the results of CHIRPS average precipitation data and enabling a better understanding of water conditions on a monthly level. While CHIRPS satellite precipitation estimates identify months with above-average precipitation, the SPI index allows for the assessment of moisture levels and the potential risk of hydrological extremes, such as floods.
During 2016 (Figure 6a), above-average precipitation in January, March, and July corresponds with positive SPI values. Mildly wet conditions were recorded in January (0.59) and March (0.34), while severely wet conditions were observed in July (1.48). In 2017 (Figure 6b), positive SPI values “follow” above-average precipitation over several months. Mildly wet conditions were recorded in February (0.70), March (0.53), May (0.09), September (0.38), October (0.06), and December (0.90). On the other hand, moderately wet conditions were observed in April (1.01). During 2018 (Figure 6c), significant positive SPI values were identified in months with above-average precipitation: mildly wet conditions were recorded in January (0.21), February (0.61), June (0.66), and December (0.49), while moderately wet conditions were observed in May (1.15), and extremely wet conditions in March (2.06). In 2019 (Figure 6d), mildly wet conditions were recorded in April (0.57), June (0.48), July (0.35), August (0.03), November (0.32), and December (0.09), while moderately wet conditions were observed in May (1.32).
Monthly SPI values for (a) 2016, (b) 2017, (c) 2018, and (d) 2019.
Extreme precipitation, as well as high SPI values identified using CHIRPS satellite data, can significantly influence water levels both in the Ukrina River Basin and the surrounding area. Although there is no HS within the Ukrina River Basin, data from the HS Srbac, located near the basin, were used for the purposes of this research. To identify relevant hydrological responses, particular attention was given to months in which recorded water levels exceeded the long-term monthly average (>100%). This approach enables monitoring the reflection of meteorological characteristics on hydrological conditions in the region, providing an approximate picture of the relationship between precipitation and water levels in the study area.
In 2016 (Figure 7a), months with water levels above the long-term average included January (102.78%), February (135.24%), March (166.13%), May (127.71%), June (104.84%), July (118.01%), August (102.62%), and November (133.12%). The most pronounced anomalies occurred in February and March, both significantly exceeding the threshold and indicating a high flood risk. January, May, and July also recorded noticeable increases in water levels. In 2017 (Figure 7b), elevated water levels were observed in February (113.91%), March (104.06%), September (136.98%), and December (143.36%). December recorded the highest value for the year, suggesting cumulative hydrological impacts, while February and March aligned with elevated precipitation and SPI values, reinforcing their hydrological relevance. During 2018 (Figure 7c), significantly elevated water levels were registered in January (154.16%), February (121.18%), March (157.09%), April (154.79%), and July (122.47%). March stands out as the peak month, followed closely by January and April, pointing to a clustering of extreme hydrological responses in the first half of the year. In 2019 (Figure 7d), water levels exceeded the average in February (107.35%), May (154.93%), June (162.55%), November (123.54%), and December (117.71%). May and June recorded the highest values, making them critical months for flood monitoring, while late-year elevations in November and December indicate sustained hydrological activity beyond the main precipitation season.
Percentage-based comparison of average mean water levels during (a) 2016, (b) 2017, (c) 2018, and (d) 2019 with the 1997–2023 average.
4.3 Flood mapping and assessment of flood damage impact
Flood mapping in the study area relies on identifying months characterized by above-average precipitation, positive SPI index values, and elevated water levels. Considering the temporal resolution of Sentinel-1 satellite imagery, the aim of the research was to map at least one flood event in each observed year.
For the purpose of enhancing the spatial interpretation of flood extent, a digital elevation model (DEM) was used as a base layer in Figure 8. The elevation data allow a clearer visualization of flood distribution in relation to terrain morphology. As visible from the Figure 8, flooded areas predominantly occur in low-lying agricultural zones, typically located in alluvial plains along the Ukrina River and its tributaries.
Mapping of flooded areas in the Ukrina River basin during (a) 2016, (b) 2017, (c) 2018, and (d) 2019.
In 2016, favorable conditions for flood mapping were recorded in January, February, and March. Floods in January 2016 were successfully mapped, covering an area of 89.98 ha (Figure 8a). In 2017, favorable conditions were identified in February, March, September, and December. Floods from March 2017 were successfully mapped, covering 179.85 ha (Figure 8b), and their development is associated with favorable wet conditions observed in February, indicating continuity in the processes that led to the floods. In contrast, although wet conditions were recorded in September of the same year, floods were not considered because the preceding months, including June, July, and August, were marked by below-average precipitation, significantly negative SPI values, and falling water levels. It was concluded that the soil during this period was unsaturated, making the September precipitation insufficient to cause flooding. Similarly, flood conditions in December 2017 were deemed inadequate for the same reasons. In 2018, favorable conditions for flood mapping were recorded in January, February, March, and July. Wet conditions from the first three months allowed for the successful mapping of floods in March 2018 (Figure 8c), when the flooded area amounted to 58.81 ha. However, floods from July 2018 could not be mapped due to the temporal resolution limitations of Sentinel-1 imagery. In 2019, favorable conditions for floods were identified in May, June, November, and December. A successfully mapped flooded area of 195.38 ha was recorded in May 2019 (Figure 8d). On the other hand, the conditions in November and December were not analyzed because the preceding months (September and October) were characterized by below-average precipitation and negative SPI values, which indicated unfavorable conditions for the occurrence of floods.
The variation in the timing of the mapped flood events across different months and years reflects the inherent irregularity of hydrometeorological conditions in the Ukrina River Basin. Each flood was associated with a unique combination of meteorological and hydrological drivers (above-average precipitation, positive SPI values, and elevated water levels), and thus, the temporal distribution of events does not follow a strict seasonal pattern. This irregularity highlights the importance of a multi-indicator approach and emphasizes the need for continuous monitoring throughout the year.
The socio-geographical analysis of floods during the period 2016–2019 highlights the vulnerability of the Derventa, Prnjavor, and, in 2017, Stanari municipalities. In the Derventa municipality, floods regularly affected settlements such as Miškovci, Rapčani, Lužani, Begluci, Polje, and Pojezina. In the Prnjavor municipality, the most frequently impacted settlements included Donji Vijačani, Gornji and Donji Štrpci, and Drenova. In the Stanari municipality, the floods of 2017 were recorded in the settlements of Dragalovci, Osredak, Ljeb, Stanari, Cvrtkovci, and Brestovo.
According to the 2013 census [86], the number of people affected by floods in the settlements of Derventa ranged between 15,000 and 20,000, while in Prnjavor, it varied from 3,000 to 10,000, depending on the year. In the Stanari municipality, the 2017 floods impacted settlements with approximately 4,500 residents. The largest spatial extent of floods was recorded in 2019, while the 2017 floods affected the greatest number of settlements across all three municipalities.
The spatial extents of the mapped floods were also analyzed in the context of land use types to assess their impact on different surface categories (Figure 9). The results indicate that agricultural areas were the most affected, while the extent of flooded built-up and forest areas varied depending on the year. During the 2016 floods, 4.90 ha of built-up areas, 79.21 ha of agricultural areas, and 5.87 ha of forest areas were flooded. The 2017 floods brought a significant increase in the extent of flooded agricultural areas (169.15 ha), while the extent of flooded built-up (3.76 ha) and forest areas (6.94 ha) remained relatively low. In 2018, there was a notable decline in the total flood extent, with 2.66 ha of built-up areas, 48.89 ha of agricultural areas, and 7.26 ha of forest areas affected. However, 2019 was characterized by a renewed increase in the flood extent, with 2.84 ha of built-up areas, 184.90 ha of agricultural areas, and 6.64 ha of forest areas flooded.
Flooded land use types.
The analysis of the spatial extent of mapped floods indicates that agricultural areas are the most vulnerable land category in the basin. These areas are primarily located in close proximity to rivers, which is directly related to the soil fertility of alluvial plains. While such a spatial arrangement is favorable for agricultural production, it simultaneously makes these areas particularly susceptible to flood risk. Field photographs (Figure 10a–h) provide visual confirmation of the locations of agricultural areas in river valleys, as well as their connection to the hydrological conditions of the area. In addition to agricultural land, a significant share of built-up areas is exposed to flood risk. Built-up areas in the basin, which include residential, infrastructural, and industrial structures, pose a serious threat to the safety of the local population in the event of flooding. Furthermore, since the basin's population is heavily engaged in agriculture, the loss of crops and damage to residential and industrial structures have a direct impact on the economic and social stability of the community in the study area.
(a–h) Natural characteristics and agricultural areas of the Ukrina River Basin (photos by: L. Sabljić and D. Delić).
The presented results highlight key aspects of the vulnerability of various land categories and settlements to floods, providing a valuable foundation for decision-making in flood risk management. Detailed mapping and analysis of flood-prone areas enable the responsible authorities to more accurately plan preventive measures, such as the development of spatial plans, protection strategies, and emergency interventions. Integrating these results into local and regional policy documents can improve flood management by directing resources to the most vulnerable areas and developing adaptive measures, including the restoration of natural floodplains, infrastructure protection, and public education. The practical application of this data represents an important step toward sustainable flood risk management and reducing their harmful consequences.
5 Discussion
Until a few decades ago, data from rain gauges represented the primary source of precipitation data. According to Michaelides et al. [113] and Brocca et al. [114], these data are characterized by limited spatial coverage and an unrealistic representation of precipitation distribution in study areas. On the other hand, data obtained from meteorological radars have limitations in data quality due to signal distortion [115]. Satellite-based precipitation data cover large areas with high spatial and temporal resolution [74]. However, this type of data also has certain limitations and shortcomings, which can be mitigated through post-processing of satellite data [116]. Accordingly, as highlighted by Loew et al. [117] and Kumar et al. [118], it is necessary to perform a validation assessment of these data using MS data before applying them in research. The CHIRPS satellite precipitation data used in this study have previously been validated in specific areas of BH. According to Sabljić et al. [100], these data were validated with 89.68% accuracy in the municipality of Stanari (BH) for the period 2000–2020. Similarly, the validity of CHIRPS data was confirmed for the Sana River Basin (BH), with accuracy reaching 92.63% for the period 1992–2022 [74] and 94.95% for the period 1981–2023 [101]. The results of CHIRPS data validation for the Ukrina River Basin are consistent with these findings, as a percentage match of 94.18% with MS data was determined for the period 1981–2023.
In recent decades, the impact of extreme precipitation on flood occurrences has been observed not only in BH but throughout Southeastern Europe, including the neighboring Republic of Serbia during May 2014 [119,120]. Precipitation during this period exceeded 200 mm within 72 h, surpassing average May values and causing some of the most catastrophic floods in Serbia's modern history [121,122]. In certain areas, the daily precipitation reached between 190 and 219 l/m2 [123]. On the other hand, Popov et al. [11] highlighted that during the same period in BH, 146.6 mm of precipitation was recorded within a few days, representing 17% of the annual average precipitation for the standard climatological period (1961–1990). More recently, Leščešen et al. [124] identified a positive trend in winter precipitation (December–January–February) in the upper Vardar River Basin in North Macedonia, where one of the most severe flood events was recorded in January and February 2016. In line with these findings, Sabljić et al. [101] identified above-average precipitation in BH during the winter months of 2016. Specifically, extreme precipitation and significantly positive SPI values were recorded in the Sana River Basin, with 0.39 in January and 2.69 in February [101]. Similar to these findings, this study identified extreme precipitation and distinctly positive SPI values during January and February 2016, which ultimately resulted in the occurrence of floods in the Ukrina River Basin. Moreover, during this period, a shift of hydro-meteorological hazards was evident, as both floods and droughts occurred in the same year across BH. This is further supported by the findings of Milanović Pešić et al. [125], who analyzed hydroclimatic trends in the lower courses of the Una, Sana, and Vrbanja rivers (basins geographically adjacent to the Ukrina River Basin). Their study revealed that, despite relatively stable precipitation trends, discharge levels exhibited a declining tendency due to increased air temperatures and enhanced evapotranspiration. These results align with the notion that thermal factors play a crucial role in modulating hydrological responses in small basins of northern BH, particularly under extreme weather conditions.
The results of Sabljić et al. [74] regarding floods caused by extreme precipitation in February 2017 in the Sana River Basin align with the pattern of extreme precipitation and its consequences in the form of floods in the Ukrina River Basin in March 2017. The 1-month difference highlights similar hydrological conditions in both cases, where intensive precipitation over a short period can significantly impact small river basins, causing floods with comparable consequences. In March 2018, extreme precipitation led to a significant rise in the Sana River water level (428 cm), resulting in the river overflowing its banks and flooding 518 ha [73]. These findings align with the occurrences of extreme precipitation and elevated SPI values identified in the Ukrina River Basin, where floods were also successfully spatially identified during March 2018, pointing to the same pattern of hydrological extremes in the region. In May 2019, the water level of the Sana River exceeded the emergency flood defense threshold, reaching a maximum value of 474 cm with a discharge of 902 m3/s [73], which caused floods successfully spatially identified using satellite data [72]. Similarly, during the same period, flooded areas in the Ukrina River Basin were also successfully spatially identified, demonstrating the efficiency of applying satellite data in analyzing hydrological extremes in the region. Additional insight into regional flood dynamics is provided by Gnjato et al. [126], who analyzed trends in the frequency and timing of maximum river discharges across BH. Their results revealed a noticeable increase in flood-prone events over the last two decades, particularly in the northwestern part of the country – including the Una, Sana, Vrbas, and Bosna river basins. These basins are geographically and climatologically comparable to the Ukrina River Basin, which lies between the Vrbas and Bosna. The study highlighted a shift toward more frequent and later spring peak discharges (May–June), attributed to intensified rainfall episodes. These findings reinforce the conclusion that the flood dynamics observed in the Ukrina basin are part of a broader regional hydrological trend affecting small and medium-sized catchments in northern BH.
Lovrić et al. [71] emphasized in their research that 54 and 41% of the Ukrina River Basin area, based on the Index-Based Method and Flash Flood Potential Index, is classified into high and very high flood susceptibility categories. The topographical characteristics of the basin significantly contribute to this high susceptibility. As noted in the description of the study area, the higher terrains in the southern and southwestern parts are characterized by steep slopes. Such morphology, while reducing the probability of flooding in those specific zones, contributes to increased erosion potential and accelerated water flow toward lower-lying areas, thereby intensifying hydrological pressure in the valley regions. Furthermore, the flat and gently sloping terrain along the riverbanks of the Ukrina represents zones of increased flood risk. These areas, due to their limited natural drainage capacity and low slope gradients, are characterized by slower water runoff and more frequent occurrences of water stagnation following extreme precipitation events. This aligns with findings in this study, which show that flooded areas are predominantly located in lowland agricultural zones. This high susceptibility, combined with the recorded extreme precipitation and frequent flood occurrences, confirms the importance of continuous analysis of hydro-meteorological extremes in the Ukrina River Basin. By applying advanced satellite technologies, it is possible not only to effectively identify floods but also to better understand their causes and mitigate their consequences on local communities and natural systems. Additionally, a significant segment of flood analysis involves assessing their damage impact. According to the results of flood mapping by Ivanišević et al. [73] in the lower Sana River Basin during 2018 and 2019, >95% of the flooded areas were agricultural land (> 400 ha). These results are expected since areas near watercourses, due to their natural characteristics, are favorable for agriculture [127]. Similarly, the results of this study indicate that agricultural areas are the most affected by floods, highlighting the need for priority planning and flood protection infrastructure development in zones where floods regularly recur. Furthermore, adequate infrastructural measures, such as levees, are needed to protect built-up areas, which, according to this study, are also affected by floods in the study area. While existing hydrotechnical structures such as dams and embankments play a role in water level regulation, particularly in the lower course of the river where flood exposure is greatest, this study recognizes that their current capacity and spatial distribution are often insufficient for effective protection under extreme hydrometeorological conditions. Moreover, areas identified as chronically vulnerable require integration into broader risk management strategies, including the adoption of sustainable land management methods and improving early warning systems. These approaches can not only reduce economic and environmental damages but also enhance the resilience of local communities to hydro-meteorological hazards such as floods.
6 Conclusion
This research demonstrated the effectiveness of integrating Sentinel-1 SAR data, CHIRPS precipitation estimates, SPI index values, and hydrological information for identifying and analyzing flood events in the Ukrina River Basin during 2016–2019. The remote sensing-based approach enabled the detection of flooded areas in January 2016, March 2017 and 2018, and May 2019. Among the different land use types, agricultural areas were the most affected, which is consistent with their spatial concentration in lowland zones along river corridors.
The results highlight the potential of remote sensing and GIS tools to improve flood monitoring in areas where ground-based hydrometeorological infrastructure is limited. By combining flood extent data with administrative boundaries and land use layers, we identified both vulnerable land categories and population exposure. This spatial information can assist decision-makers in prioritizing flood protection measures, infrastructure investment, and adaptation planning.
Nevertheless, the study has certain limitations. The spatial resolution of Sentinel-1 imagery (10 m) may not detect localized flood events in small or narrow floodplains. The temporal resolution of satellite imagery (typically 6–12 days) may result in missed events or delayed mapping, especially during short-duration floods. Additionally, the absence of active HSs within the Ukrina River Basin limits the ability to validate flood peaks and water discharge directly, relying instead on proxy data from nearby stations. The land use dataset (CLC 2018) may also not fully capture land cover changes during the entire study period. Moreover, the adopted methodology, based on coinciding meteorological and hydrological indicators, may overlook certain flood events triggered by rapid runoff over dry antecedent soils. Such events, while lacking pronounced cumulative wetness, can still pose significant flood hazards due to reduced infiltration capacity. Future research could address this by incorporating satellite-derived soil moisture products (e.g., SMAP or ASCAT), which would enable a more comprehensive assessment of antecedent conditions and improve the accuracy of flood detection under diverse hydrometeorological scenarios. Also, future research should focus on integrating higher-resolution satellite products (e.g., Sentinel-2 or commercial imagery), as well as topographic data such as DEMs, to refine floodplain delineation and hydrological modeling. Expanding the analysis toward multi-hazard assessment (including droughts and wildfires) would contribute to the development of an integrated risk management system in the Ukrina River Basin. The presented results form a valuable foundation for designing adaptive land use policies and improving local flood preparedness through spatially informed early warning systems.
Acknowledgments
The authors are grateful to the anonymous reviewers whose comments and suggestions improved the manuscript. The authors would like to thank the Ministry of Agriculture, Forestry, and Water Management of the Republic of Srpska, as well as the Republic Hydrometeorological Institute of the Republic of Srpska, for providing meteorological data (No. 12/1.03-79-1/24). Also, L.S. acknowledges the support of the Ministry of Scientific and Technological Development and Higher Education of the Republic of Srpska through the project “Analysis of Flood Frequency in the Sava River Basin in the Republic of Srpska” (No. 1259111). Furthermore, T.L. and S.B.M. acknowledge the support by the Science Fund of the Republic of Serbia, #17807, The Loess Plateau Margins: Towards Innovative Sustainable Conservation – LAMINATION.
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Funding information: This research received no external funding.
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Author contributions: Conceptualization, L.S., T.L., and D.B.; methodology, L.S.; software, L.S., D.B., and D.A.; validation, L.S., D.B., T.L., and V.S.; formal analysis, L.S. and D.D.; investigation, L.S. and D.D.; resources, T.L.; data curation, L.S., D.B., and D.A.; writing – original draft preparation, L.S. and T.L.; writing – review and editing, L.S., T.L., D.B., S.B.M., V.S., V.M.C., D.D., D.A., B.A., I.M., and G.P.S.; visualization, L.S.; supervision, T.L., D.B., and D.A.; project administration, L.S., B.A., and I.M.; funding acquisition, T.L.
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Conflict of interest: The authors declare no conflicts of interest.
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Data availability statement: The datasets used during the current study are available from the corresponding author on reasonable request.
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Articles in the same Issue
- Research Articles
- Seismic response and damage model analysis of rocky slopes with weak interlayers
- Multi-scenario simulation and eco-environmental effect analysis of “Production–Living–Ecological space” based on PLUS model: A case study of Anyang City
- Remote sensing estimation of chlorophyll content in rape leaves in Weibei dryland region of China
- GIS-based frequency ratio and Shannon entropy modeling for landslide susceptibility mapping: A case study in Kundah Taluk, Nilgiris District, India
- Natural gas origin and accumulation of the Changxing–Feixianguan Formation in the Puguang area, China
- Spatial variations of shear-wave velocity anomaly derived from Love wave ambient noise seismic tomography along Lembang Fault (West Java, Indonesia)
- Evaluation of cumulative rainfall and rainfall event–duration threshold based on triggering and non-triggering rainfalls: Northern Thailand case
- Pixel and region-oriented classification of Sentinel-2 imagery to assess LULC dynamics and their climate impact in Nowshera, Pakistan
- The use of radar-optical remote sensing data and geographic information system–analytical hierarchy process–multicriteria decision analysis techniques for revealing groundwater recharge prospective zones in arid-semi arid lands
- Effect of pore throats on the reservoir quality of tight sandstone: A case study of the Yanchang Formation in the Zhidan area, Ordos Basin
- Hydroelectric simulation of the phreatic water response of mining cracked soil based on microbial solidification
- Spatial-temporal evolution of habitat quality in tropical monsoon climate region based on “pattern–process–quality” – a case study of Cambodia
- Early Permian to Middle Triassic Formation petroleum potentials of Sydney Basin, Australia: A geochemical analysis
- Micro-mechanism analysis of Zhongchuan loess liquefaction disaster induced by Jishishan M6.2 earthquake in 2023
- Prediction method of S-wave velocities in tight sandstone reservoirs – a case study of CO2 geological storage area in Ordos Basin
- Ecological restoration in valley area of semiarid region damaged by shallow buried coal seam mining
- Hydrocarbon-generating characteristics of Xujiahe coal-bearing source rocks in the continuous sedimentary environment of the Southwest Sichuan
- Hazard analysis of future surface displacements on active faults based on the recurrence interval of strong earthquakes
- Structural characterization of the Zalm district, West Saudi Arabia, using aeromagnetic data: An approach for gold mineral exploration
- Research on the variation in the Shields curve of silt initiation
- Reuse of agricultural drainage water and wastewater for crop irrigation in southeastern Algeria
- Assessing the effectiveness of utilizing low-cost inertial measurement unit sensors for producing as-built plans
- Analysis of the formation process of a natural fertilizer in the loess area
- Machine learning methods for landslide mapping studies: A comparative study of SVM and RF algorithms in the Oued Aoulai watershed (Morocco)
- Chemical dissolution and the source of salt efflorescence in weathering of sandstone cultural relics
- Molecular simulation of methane adsorption capacity in transitional shale – a case study of Longtan Formation shale in Southern Sichuan Basin, SW China
- Evolution characteristics of extreme maximum temperature events in Central China and adaptation strategies under different future warming scenarios
- Estimating Bowen ratio in local environment based on satellite imagery
- 3D fusion modeling of multi-scale geological structures based on subdivision-NURBS surfaces and stratigraphic sequence formalization
- Comparative analysis of machine learning algorithms in Google Earth Engine for urban land use dynamics in rapidly urbanizing South Asian cities
- Study on the mechanism of plant root influence on soil properties in expansive soil areas
- Simulation of seismic hazard parameters and earthquakes source mechanisms along the Red Sea rift, western Saudi Arabia
- Tectonics vs sedimentation in foredeep basins: A tale from the Oligo-Miocene Monte Falterona Formation (Northern Apennines, Italy)
- Investigation of landslide areas in Tokat-Almus road between Bakımlı-Almus by the PS-InSAR method (Türkiye)
- Predicting coastal variations in non-storm conditions with machine learning
- Cross-dimensional adaptivity research on a 3D earth observation data cube model
- Geochronology and geochemistry of late Paleozoic volcanic rocks in eastern Inner Mongolia and their geological significance
- Spatial and temporal evolution of land use and habitat quality in arid regions – a case of Northwest China
- Ground-penetrating radar imaging of subsurface karst features controlling water leakage across Wadi Namar dam, south Riyadh, Saudi Arabia
- Rayleigh wave dispersion inversion via modified sine cosine algorithm: Application to Hangzhou, China passive surface wave data
- Fractal insights into permeability control by pore structure in tight sandstone reservoirs, Heshui area, Ordos Basin
- Debris flow hazard characteristic and mitigation in Yusitong Gully, Hengduan Mountainous Region
- Research on community characteristics of vegetation restoration in hilly power engineering based on multi temporal remote sensing technology
- Identification of radial drainage networks based on topographic and geometric features
- Trace elements and melt inclusion in zircon within the Qunji porphyry Cu deposit: Application to the metallogenic potential of the reduced magma-hydrothermal system
- Pore, fracture characteristics and diagenetic evolution of medium-maturity marine shales from the Silurian Longmaxi Formation, NE Sichuan Basin, China
- Study of the earthquakes source parameters, site response, and path attenuation using P and S-waves spectral inversion, Aswan region, south Egypt
- Source of contamination and assessment of potential health risks of potentially toxic metal(loid)s in agricultural soil from Al Lith, Saudi Arabia
- Regional spatiotemporal evolution and influencing factors of rural construction areas in the Nanxi River Basin via GIS
- An efficient network for object detection in scale-imbalanced remote sensing images
- Effect of microscopic pore–throat structure heterogeneity on waterflooding seepage characteristics of tight sandstone reservoirs
- Environmental health risk assessment of Zn, Cd, Pb, Fe, and Co in coastal sediments of the southeastern Gulf of Aqaba
- A modified Hoek–Brown model considering softening effects and its applications
- Evaluation of engineering properties of soil for sustainable urban development
- The spatio-temporal characteristics and influencing factors of sustainable development in China’s provincial areas
- Application of a mixed additive and multiplicative random error model to generate DTM products from LiDAR data
- Gold vein mineralogy and oxygen isotopes of Wadi Abu Khusheiba, Jordan
- Prediction of surface deformation time series in closed mines based on LSTM and optimization algorithms
- 2D–3D Geological features collaborative identification of surrounding rock structural planes in hydraulic adit based on OC-AINet
- Spatiotemporal patterns and drivers of Chl-a in Chinese lakes between 1986 and 2023
- Land use classification through fusion of remote sensing images and multi-source data
- Nexus between renewable energy, technological innovation, and carbon dioxide emissions in Saudi Arabia
- Analysis of the spillover effects of green organic transformation on sustainable development in ethnic regions’ agriculture and animal husbandry
- Factors impacting spatial distribution of black and odorous water bodies in Hebei
- Large-scale shaking table tests on the liquefaction and deformation responses of an ultra-deep overburden
- Impacts of climate change and sea-level rise on the coastal geological environment of Quang Nam province, Vietnam
- Reservoir characterization and exploration potential of shale reservoir near denudation area: A case study of Ordovician–Silurian marine shale, China
- Seismic prediction of Permian volcanic rock reservoirs in Southwest Sichuan Basin
- Application of CBERS-04 IRS data to land surface temperature inversion: A case study based on Minqin arid area
- Geological characteristics and prospecting direction of Sanjiaoding gold mine in Saishiteng area
- Research on the deformation prediction model of surrounding rock based on SSA-VMD-GRU
- Geochronology, geochemical characteristics, and tectonic significance of the granites, Menghewula, Southern Great Xing’an range
- Hazard classification of active faults in Yunnan base on probabilistic seismic hazard assessment
- Characteristics analysis of hydrate reservoirs with different geological structures developed by vertical well depressurization
- Estimating the travel distance of channelized rock avalanches using genetic programming method
- Landscape preferences of hikers in Three Parallel Rivers Region and its adjacent regions by content analysis of user-generated photography
- New age constraints of the LGM onset in the Bohemian Forest – Central Europe
- Characteristics of geological evolution based on the multifractal singularity theory: A case study of Heyu granite and Mesozoic tectonics
- Soil water content and longitudinal microbiota distribution in disturbed areas of tower foundations of power transmission and transformation projects
- Oil accumulation process of the Kongdian reservoir in the deep subsag zone of the Cangdong Sag, Bohai Bay Basin, China
- Investigation of velocity profile in rock–ice avalanche by particle image velocimetry measurement
- Optimizing 3D seismic survey geometries using ray tracing and illumination modeling: A case study from Penobscot field
- Sedimentology of the Phra That and Pha Daeng Formations: A preliminary evaluation of geological CO2 storage potential in the Lampang Basin, Thailand
- Improved classification algorithm for hyperspectral remote sensing images based on the hybrid spectral network model
- Map analysis of soil erodibility rates and gully erosion sites in Anambra State, South Eastern Nigeria
- Identification and driving mechanism of land use conflict in China’s South-North transition zone: A case study of Huaihe River Basin
- Evaluation of the impact of land-use change on earthquake risk distribution in different periods: An empirical analysis from Sichuan Province
- A test site case study on the long-term behavior of geotextile tubes
- An experimental investigation into carbon dioxide flooding and rock dissolution in low-permeability reservoirs of the South China Sea
- Detection and semi-quantitative analysis of naphthenic acids in coal and gangue from mining areas in China
- Comparative effects of olivine and sand on KOH-treated clayey soil
- YOLO-MC: An algorithm for early forest fire recognition based on drone image
- Earthquake building damage classification based on full suite of Sentinel-1 features
- Potential landslide detection and influencing factors analysis in the upper Yellow River based on SBAS-InSAR technology
- Assessing green area changes in Najran City, Saudi Arabia (2013–2022) using hybrid deep learning techniques
- An advanced approach integrating methods to estimate hydraulic conductivity of different soil types supported by a machine learning model
- Hybrid methods for land use and land cover classification using remote sensing and combined spectral feature extraction: A case study of Najran City, KSA
- Streamlining digital elevation model construction from historical aerial photographs: The impact of reference elevation data on spatial accuracy
- Analysis of urban expansion patterns in the Yangtze River Delta based on the fusion impervious surfaces dataset
- A metaverse-based visual analysis approach for 3D reservoir models
- Late Quaternary record of 100 ka depositional cycles on the Larache shelf (NW Morocco)
- Review Articles
- Humic substances influence on the distribution of dissolved iron in seawater: A review of electrochemical methods and other techniques
- Applications of physics-informed neural networks in geosciences: From basic seismology to comprehensive environmental studies
- Ore-controlling structures of granite-related uranium deposits in South China: A review
- Shallow geological structure features in Balikpapan Bay East Kalimantan Province – Indonesia
- A review on the tectonic affinity of microcontinents and evolution of the Proto-Tethys Ocean in Northeastern Tibet
- Special Issue: Natural Resources and Environmental Risks: Towards a Sustainable Future - Part II
- Depopulation in the Visok micro-region: Toward demographic and economic revitalization
- Special Issue: Geospatial and Environmental Dynamics - Part II
- Advancing urban sustainability: Applying GIS technologies to assess SDG indicators – a case study of Podgorica (Montenegro)
- Spatiotemporal and trend analysis of common cancers in men in Central Serbia (1999–2021)
- Minerals for the green agenda, implications, stalemates, and alternatives
- Spatiotemporal water quality analysis of Vrana Lake, Croatia
- Functional transformation of settlements in coal exploitation zones: A case study of the municipality of Stanari in Republic of Srpska (Bosnia and Herzegovina)
- Hypertension in AP Vojvodina (Northern Serbia): A spatio-temporal analysis of patients at the Institute for Cardiovascular Diseases of Vojvodina
- Regional patterns in cause-specific mortality in Montenegro, 1991–2019
- Spatio-temporal analysis of flood events using GIS and remote sensing-based approach in the Ukrina River Basin, Bosnia and Herzegovina
- Flash flood susceptibility mapping using LiDAR-Derived DEM and machine learning algorithms: Ljuboviđa case study, Serbia
- Geocultural heritage as a basis for geotourism development: Banjska Monastery, Zvečan (Serbia)
- Assessment of groundwater potential zones using GIS and AHP techniques – A case study of the zone of influence of Kolubara Mining Basin
- Impact of the agri-geographical transformation of rural settlements on the geospatial dynamics of soil erosion intensity in municipalities of Central Serbia
- Where faith meets geomorphology: The cultural and religious significance of geodiversity explored through geospatial technologies
- Applications of local climate zone classification in European cities: A review of in situ and mobile monitoring methods in urban climate studies
- Complex multivariate water quality impact assessment on Krivaja River
Articles in the same Issue
- Research Articles
- Seismic response and damage model analysis of rocky slopes with weak interlayers
- Multi-scenario simulation and eco-environmental effect analysis of “Production–Living–Ecological space” based on PLUS model: A case study of Anyang City
- Remote sensing estimation of chlorophyll content in rape leaves in Weibei dryland region of China
- GIS-based frequency ratio and Shannon entropy modeling for landslide susceptibility mapping: A case study in Kundah Taluk, Nilgiris District, India
- Natural gas origin and accumulation of the Changxing–Feixianguan Formation in the Puguang area, China
- Spatial variations of shear-wave velocity anomaly derived from Love wave ambient noise seismic tomography along Lembang Fault (West Java, Indonesia)
- Evaluation of cumulative rainfall and rainfall event–duration threshold based on triggering and non-triggering rainfalls: Northern Thailand case
- Pixel and region-oriented classification of Sentinel-2 imagery to assess LULC dynamics and their climate impact in Nowshera, Pakistan
- The use of radar-optical remote sensing data and geographic information system–analytical hierarchy process–multicriteria decision analysis techniques for revealing groundwater recharge prospective zones in arid-semi arid lands
- Effect of pore throats on the reservoir quality of tight sandstone: A case study of the Yanchang Formation in the Zhidan area, Ordos Basin
- Hydroelectric simulation of the phreatic water response of mining cracked soil based on microbial solidification
- Spatial-temporal evolution of habitat quality in tropical monsoon climate region based on “pattern–process–quality” – a case study of Cambodia
- Early Permian to Middle Triassic Formation petroleum potentials of Sydney Basin, Australia: A geochemical analysis
- Micro-mechanism analysis of Zhongchuan loess liquefaction disaster induced by Jishishan M6.2 earthquake in 2023
- Prediction method of S-wave velocities in tight sandstone reservoirs – a case study of CO2 geological storage area in Ordos Basin
- Ecological restoration in valley area of semiarid region damaged by shallow buried coal seam mining
- Hydrocarbon-generating characteristics of Xujiahe coal-bearing source rocks in the continuous sedimentary environment of the Southwest Sichuan
- Hazard analysis of future surface displacements on active faults based on the recurrence interval of strong earthquakes
- Structural characterization of the Zalm district, West Saudi Arabia, using aeromagnetic data: An approach for gold mineral exploration
- Research on the variation in the Shields curve of silt initiation
- Reuse of agricultural drainage water and wastewater for crop irrigation in southeastern Algeria
- Assessing the effectiveness of utilizing low-cost inertial measurement unit sensors for producing as-built plans
- Analysis of the formation process of a natural fertilizer in the loess area
- Machine learning methods for landslide mapping studies: A comparative study of SVM and RF algorithms in the Oued Aoulai watershed (Morocco)
- Chemical dissolution and the source of salt efflorescence in weathering of sandstone cultural relics
- Molecular simulation of methane adsorption capacity in transitional shale – a case study of Longtan Formation shale in Southern Sichuan Basin, SW China
- Evolution characteristics of extreme maximum temperature events in Central China and adaptation strategies under different future warming scenarios
- Estimating Bowen ratio in local environment based on satellite imagery
- 3D fusion modeling of multi-scale geological structures based on subdivision-NURBS surfaces and stratigraphic sequence formalization
- Comparative analysis of machine learning algorithms in Google Earth Engine for urban land use dynamics in rapidly urbanizing South Asian cities
- Study on the mechanism of plant root influence on soil properties in expansive soil areas
- Simulation of seismic hazard parameters and earthquakes source mechanisms along the Red Sea rift, western Saudi Arabia
- Tectonics vs sedimentation in foredeep basins: A tale from the Oligo-Miocene Monte Falterona Formation (Northern Apennines, Italy)
- Investigation of landslide areas in Tokat-Almus road between Bakımlı-Almus by the PS-InSAR method (Türkiye)
- Predicting coastal variations in non-storm conditions with machine learning
- Cross-dimensional adaptivity research on a 3D earth observation data cube model
- Geochronology and geochemistry of late Paleozoic volcanic rocks in eastern Inner Mongolia and their geological significance
- Spatial and temporal evolution of land use and habitat quality in arid regions – a case of Northwest China
- Ground-penetrating radar imaging of subsurface karst features controlling water leakage across Wadi Namar dam, south Riyadh, Saudi Arabia
- Rayleigh wave dispersion inversion via modified sine cosine algorithm: Application to Hangzhou, China passive surface wave data
- Fractal insights into permeability control by pore structure in tight sandstone reservoirs, Heshui area, Ordos Basin
- Debris flow hazard characteristic and mitigation in Yusitong Gully, Hengduan Mountainous Region
- Research on community characteristics of vegetation restoration in hilly power engineering based on multi temporal remote sensing technology
- Identification of radial drainage networks based on topographic and geometric features
- Trace elements and melt inclusion in zircon within the Qunji porphyry Cu deposit: Application to the metallogenic potential of the reduced magma-hydrothermal system
- Pore, fracture characteristics and diagenetic evolution of medium-maturity marine shales from the Silurian Longmaxi Formation, NE Sichuan Basin, China
- Study of the earthquakes source parameters, site response, and path attenuation using P and S-waves spectral inversion, Aswan region, south Egypt
- Source of contamination and assessment of potential health risks of potentially toxic metal(loid)s in agricultural soil from Al Lith, Saudi Arabia
- Regional spatiotemporal evolution and influencing factors of rural construction areas in the Nanxi River Basin via GIS
- An efficient network for object detection in scale-imbalanced remote sensing images
- Effect of microscopic pore–throat structure heterogeneity on waterflooding seepage characteristics of tight sandstone reservoirs
- Environmental health risk assessment of Zn, Cd, Pb, Fe, and Co in coastal sediments of the southeastern Gulf of Aqaba
- A modified Hoek–Brown model considering softening effects and its applications
- Evaluation of engineering properties of soil for sustainable urban development
- The spatio-temporal characteristics and influencing factors of sustainable development in China’s provincial areas
- Application of a mixed additive and multiplicative random error model to generate DTM products from LiDAR data
- Gold vein mineralogy and oxygen isotopes of Wadi Abu Khusheiba, Jordan
- Prediction of surface deformation time series in closed mines based on LSTM and optimization algorithms
- 2D–3D Geological features collaborative identification of surrounding rock structural planes in hydraulic adit based on OC-AINet
- Spatiotemporal patterns and drivers of Chl-a in Chinese lakes between 1986 and 2023
- Land use classification through fusion of remote sensing images and multi-source data
- Nexus between renewable energy, technological innovation, and carbon dioxide emissions in Saudi Arabia
- Analysis of the spillover effects of green organic transformation on sustainable development in ethnic regions’ agriculture and animal husbandry
- Factors impacting spatial distribution of black and odorous water bodies in Hebei
- Large-scale shaking table tests on the liquefaction and deformation responses of an ultra-deep overburden
- Impacts of climate change and sea-level rise on the coastal geological environment of Quang Nam province, Vietnam
- Reservoir characterization and exploration potential of shale reservoir near denudation area: A case study of Ordovician–Silurian marine shale, China
- Seismic prediction of Permian volcanic rock reservoirs in Southwest Sichuan Basin
- Application of CBERS-04 IRS data to land surface temperature inversion: A case study based on Minqin arid area
- Geological characteristics and prospecting direction of Sanjiaoding gold mine in Saishiteng area
- Research on the deformation prediction model of surrounding rock based on SSA-VMD-GRU
- Geochronology, geochemical characteristics, and tectonic significance of the granites, Menghewula, Southern Great Xing’an range
- Hazard classification of active faults in Yunnan base on probabilistic seismic hazard assessment
- Characteristics analysis of hydrate reservoirs with different geological structures developed by vertical well depressurization
- Estimating the travel distance of channelized rock avalanches using genetic programming method
- Landscape preferences of hikers in Three Parallel Rivers Region and its adjacent regions by content analysis of user-generated photography
- New age constraints of the LGM onset in the Bohemian Forest – Central Europe
- Characteristics of geological evolution based on the multifractal singularity theory: A case study of Heyu granite and Mesozoic tectonics
- Soil water content and longitudinal microbiota distribution in disturbed areas of tower foundations of power transmission and transformation projects
- Oil accumulation process of the Kongdian reservoir in the deep subsag zone of the Cangdong Sag, Bohai Bay Basin, China
- Investigation of velocity profile in rock–ice avalanche by particle image velocimetry measurement
- Optimizing 3D seismic survey geometries using ray tracing and illumination modeling: A case study from Penobscot field
- Sedimentology of the Phra That and Pha Daeng Formations: A preliminary evaluation of geological CO2 storage potential in the Lampang Basin, Thailand
- Improved classification algorithm for hyperspectral remote sensing images based on the hybrid spectral network model
- Map analysis of soil erodibility rates and gully erosion sites in Anambra State, South Eastern Nigeria
- Identification and driving mechanism of land use conflict in China’s South-North transition zone: A case study of Huaihe River Basin
- Evaluation of the impact of land-use change on earthquake risk distribution in different periods: An empirical analysis from Sichuan Province
- A test site case study on the long-term behavior of geotextile tubes
- An experimental investigation into carbon dioxide flooding and rock dissolution in low-permeability reservoirs of the South China Sea
- Detection and semi-quantitative analysis of naphthenic acids in coal and gangue from mining areas in China
- Comparative effects of olivine and sand on KOH-treated clayey soil
- YOLO-MC: An algorithm for early forest fire recognition based on drone image
- Earthquake building damage classification based on full suite of Sentinel-1 features
- Potential landslide detection and influencing factors analysis in the upper Yellow River based on SBAS-InSAR technology
- Assessing green area changes in Najran City, Saudi Arabia (2013–2022) using hybrid deep learning techniques
- An advanced approach integrating methods to estimate hydraulic conductivity of different soil types supported by a machine learning model
- Hybrid methods for land use and land cover classification using remote sensing and combined spectral feature extraction: A case study of Najran City, KSA
- Streamlining digital elevation model construction from historical aerial photographs: The impact of reference elevation data on spatial accuracy
- Analysis of urban expansion patterns in the Yangtze River Delta based on the fusion impervious surfaces dataset
- A metaverse-based visual analysis approach for 3D reservoir models
- Late Quaternary record of 100 ka depositional cycles on the Larache shelf (NW Morocco)
- Review Articles
- Humic substances influence on the distribution of dissolved iron in seawater: A review of electrochemical methods and other techniques
- Applications of physics-informed neural networks in geosciences: From basic seismology to comprehensive environmental studies
- Ore-controlling structures of granite-related uranium deposits in South China: A review
- Shallow geological structure features in Balikpapan Bay East Kalimantan Province – Indonesia
- A review on the tectonic affinity of microcontinents and evolution of the Proto-Tethys Ocean in Northeastern Tibet
- Special Issue: Natural Resources and Environmental Risks: Towards a Sustainable Future - Part II
- Depopulation in the Visok micro-region: Toward demographic and economic revitalization
- Special Issue: Geospatial and Environmental Dynamics - Part II
- Advancing urban sustainability: Applying GIS technologies to assess SDG indicators – a case study of Podgorica (Montenegro)
- Spatiotemporal and trend analysis of common cancers in men in Central Serbia (1999–2021)
- Minerals for the green agenda, implications, stalemates, and alternatives
- Spatiotemporal water quality analysis of Vrana Lake, Croatia
- Functional transformation of settlements in coal exploitation zones: A case study of the municipality of Stanari in Republic of Srpska (Bosnia and Herzegovina)
- Hypertension in AP Vojvodina (Northern Serbia): A spatio-temporal analysis of patients at the Institute for Cardiovascular Diseases of Vojvodina
- Regional patterns in cause-specific mortality in Montenegro, 1991–2019
- Spatio-temporal analysis of flood events using GIS and remote sensing-based approach in the Ukrina River Basin, Bosnia and Herzegovina
- Flash flood susceptibility mapping using LiDAR-Derived DEM and machine learning algorithms: Ljuboviđa case study, Serbia
- Geocultural heritage as a basis for geotourism development: Banjska Monastery, Zvečan (Serbia)
- Assessment of groundwater potential zones using GIS and AHP techniques – A case study of the zone of influence of Kolubara Mining Basin
- Impact of the agri-geographical transformation of rural settlements on the geospatial dynamics of soil erosion intensity in municipalities of Central Serbia
- Where faith meets geomorphology: The cultural and religious significance of geodiversity explored through geospatial technologies
- Applications of local climate zone classification in European cities: A review of in situ and mobile monitoring methods in urban climate studies
- Complex multivariate water quality impact assessment on Krivaja River
