In\u00a0the\u00a0second stage, a\u00a0specialized software tool called SCR (Sklony_\u010cR) was developed to identify specific river reaches with usable head and flow. The\u00a0tool integrates topographic and hydrological data with user-defined technical parameters (e.g., minimum head, flow rate, or desired power output) and allows for rapid screening of\u00a0suitable locations without the\u00a0need for extensive field surveys. The\u00a0methodology was validated through pilot testing in\u00a0the\u00a0Otava catchment, confirming its practical applicability for regional energy planning, project development, and academic research.<\/span><\/p>\n The\u00a0results show that the\u00a0highest hydropower potential is concentrated in\u00a0the\u00a0northern and northeastern regions of\u00a0the\u00a0Czech Republic, particularly in\u00a0the\u00a0catchments of\u00a0the\u00a0Morava, Jizera, \u00dapa, Ol\u0161e, and Lu\u017eick\u00e1 Nisa rivers. The\u00a0combination of\u00a0spatial modelling and interactive analysis offers a\u00a0scalable and user-friendly approach to utilizing the\u00a0previously overlooked potential of\u00a0small streams, which can\u00a0significantly contribute to the\u00a0sustainable development of\u00a0decentralized hydropower in\u00a0mountainous and rural areas. In\u00a0the\u00a0next phases of\u00a0the\u00a0project, the\u00a0methodology will be verified through demonstration studies, including legal and environmental assessments of\u00a0selected locations.<\/span><\/p>\n The\u00a0use of\u00a0renewable energy sources constitutes one of\u00a0the\u00a0main\u00a0directions of\u00a0current European\u00a0and national energy policy\u00a0[1, 2]. While most past investment has focused on large, centralised sources, interest is growing in\u00a0decentralised, low-cost, and spatially dispersed solutions that enhance the\u00a0energy self-sufficiency of\u00a0local communities. This approach is also being applied in\u00a0hydropower, particularly in\u00a0the\u00a0form of\u00a0micro- and pico-hydropower plants that are capable of\u00a0efficiently utilising even low hydropower potential on small watercourses.<\/p>\n In\u00a0connection with ongoing climate change, the\u00a0characteristics of\u00a0precipitation and runoff regimes in\u00a0the\u00a0Czech Republic are also changing. Although total annual precipitation does not change significantly in\u00a0the\u00a0long term, there are substantial changes in\u00a0its temporal and spatial distribution\u00a0[3], as well as an\u00a0increase in\u00a0the\u00a0intensity of\u00a0short-term precipitation events, which are more frequent and extreme than\u00a0in\u00a0the\u00a0past\u00a0[4]. The\u00a0proportion of\u00a0intense precipitation events is increasing, and the\u00a0interval between drought episodes and flash floods is shortening. These changes are reflected in\u00a0the\u00a0variability of\u00a0flows in\u00a0small watercourses and affect their dynamics, stability, and potential for energy utilisation.<\/p>\n Small watercourses (SWC) constitute a\u00a0significant part of\u00a0the\u00a0river network in\u00a0the\u00a0Czech Republic, and their distribution also covers areas where other renewable energy sources \u2013 such as wind or photovoltaic energy \u2013 are not sufficiently effective or feasible. Although their flows and heads often do not allow for direct energy utilisation in\u00a0the\u00a0sense of\u00a0conventional hydropower, the\u00a0overall SWC hydropower potential can\u00a0be significant in\u00a0the\u00a0context of\u00a0decentralised energy systems. For its effective use, however, it is essential to have appropriate tools for the\u00a0systematic identification of\u00a0suitable locations and for the\u00a0preliminary technical and energy assessment of\u00a0their parameters.<\/p>\n The\u00a0Pico-Hydropower project<\/em> (TA CR, No. TK04030223) responds to the\u00a0challenge of\u00a0efficiently utilising SWC by developing a\u00a0methodology for determining their hydropower potential. This article builds on a\u00a0previous article in\u00a0VTEI\u00a0[5], in\u00a0which a\u00a0methodology for interpolating flows in\u00a0catchments without direct measurements was addressed, forming a\u00a0key input for potential calculations. In\u00a0the\u00a0previous phase of\u00a0the\u00a0project, the\u00a0theoretical hydropower potential (HPP) of\u00a0all fourth-order catchments in\u00a0the\u00a0Czech Republic was calculated using a\u00a0combination of\u00a0a\u00a0digital elevation model and interpolated values of\u00a0mean\u00a0annual flow (Qa). The\u00a0result is a\u00a0spatial layer that allows for the\u00a0prioritisation of\u00a0catchments with above-average potential, serving as an\u00a0initial filter for more detailed analyses at the\u00a0level of\u00a0specific stream sections\u00a0[6].<\/p>\n Based on accuracy testing of different types of digital elevation models (DMR 4G, DMR 5G, and their derivatives) [7], a specialised tool, SCR (Sklony_\u010cR), was subsequently developed within the project, enabling the interactive selection\u00a0of\u00a0specific river reaches with usable potential. This tool combines spatial data on heads and flows with user-defined technical parameters (e.g., desired output or minimum head) and allows for the\u00a0rapid identification of\u00a0suitable locations for micro-hydropower utilisation without the\u00a0need for extensive field surveys.<\/p>\n The\u00a0aim of\u00a0this article is to present a\u00a0methodology for locating suitable river reaches of\u00a0SWC using a\u00a0spatial HPP database for fourth-order catchments and the\u00a0SCR tool. It also demonstrates how hydrological catchment analysis can\u00a0be linked with the\u00a0technical design of\u00a0specific sites for the\u00a0installation of\u00a0micro-hydropower sources.<\/p>\n The\u00a0first step in\u00a0assessing the\u00a0exploitable hydropower potential of\u00a0watercourses was the\u00a0spatial delineation and quantitative evaluation of\u00a0all fourth-order catchments in\u00a0the\u00a0Czech Republic. This catchment level was chosen as an\u00a0optimal compromise between hydrological homogeneity and spatial detail, and it also aligns with the\u00a0established catchment classification structure used in\u00a0management databases (e.g., DIBAVOD, CEVT, internal TGM WRI database).<\/p>\n The\u00a0following primary datasets were used for the\u00a0calculations:<\/p>\n Although the\u00a0main\u00a0focus of\u00a0the\u00a0project was on SWC, the\u00a0basic hydrological characteristics were systematically determined for all fourth-order catchments in\u00a0the\u00a0Czech Republic. For each catchment, the\u00a0average stream head and the\u00a0mean\u00a0annual flow Qa<\/sub> (as an\u00a0indicator of\u00a0long-term water availability) were calculated, and these values were subsequently used to derive the\u00a0exploitable HPP. This approach enabled not only a\u00a0comprehensive assessment of\u00a0all fourth-order catchments but also a\u00a0subsequent detailed comparison with results for selected SWC.<\/p>\n The following text describes the procedure for determining the exploitable HPP of watercourses in the Czech Republic. The basis is a spatial division into\u00a0fourth-order catchments and the\u00a0use of\u00a0a\u00a0combination of\u00a0geospatial datasets, a\u00a0digital elevation model, and hydrological data. The\u00a0steps described led to the\u00a0creation of\u00a0a\u00a0unified database containing, for each catchment, values of\u00a0the\u00a0average stream head (H), mean\u00a0annual flow (Qa<\/sub>), and derived hydropower potential (HPP \u2013 P), which is the\u00a0outcome of\u00a0the\u00a0project and is available in\u00a0[7].<\/p>\n The\u00a0value of\u00a0H was derived from the\u00a0elevation difference between the\u00a0start and end of\u00a0the\u00a0so-called main\u00a0stream, defined as the\u00a0longest connected section of\u00a0the\u00a0stream within\u00a0a\u00a0given fourth-order catchment. In\u00a0the\u00a0database, the\u00a0main\u00a0stream line is divided into reaches \u201cfrom tributary to tributary,\u201d or in\u00a0the\u00a0headwater section \u201cfrom the\u00a0source to first confluence.\u201d The\u00a0start and end points of\u00a0each segment within\u00a0a\u00a0catchment were determined by intersecting the\u00a0main\u00a0stream line with the\u00a0drainage divide. The\u00a0elevation values of\u00a0these points were then extracted from the\u00a0DMR 5G digital elevation model raster using spatial analysis tools. The\u00a0head was expressed either in\u00a0metres (total difference) or as the\u00a0average longitudinal slope in\u00a0cm\u00a0\u00b7\u00a0m-1<\/sup>, or as a\u00a0percentage (Fig.\u00a01<\/em>).<\/p>\n b) Mean\u00a0annual flow (Qa<\/sub>)<\/strong><\/p>\n The\u00a0mean\u00a0annual flow Qa<\/sub> was determined for all fourth-order catchments in\u00a0the\u00a0Czech Republic. The\u00a0primary source was a\u00a0map of\u00a0specific runoff isolines qa<\/sub>\u00a0[l\u2009\u00b7\u2009s-1<\/sup>\u2009\u00b7\u2009km-2<\/sup>]\u00a0[9, 10]. This map was first vectorised and then reclassified using linear interpolation, with the\u00a0aim of\u00a0refining the\u00a0intervals between individual qa<\/sub> values compared to the\u00a0original printed map, thereby obtaining a\u00a0more detailed spatial distribution. Reclassification was carried out in\u00a0several intervals: in\u00a0the\u00a0range 1.16\u20139 l\u2009\u00b7\u2009s-1<\/sup>\u2009\u00b7\u2009km-2<\/sup> a\u00a0step of\u00a00.5 was used; in\u00a0the\u00a0interval 9\u201320\u00a0l\u2009\u00b7\u2009s-1<\/sup>\u2009\u00b7\u2009km-2<\/sup> a\u00a0step of\u00a01; and in\u00a0the\u00a0interval 20\u201330.5 l\u2009\u00b7\u2009s-1<\/sup>\u2009\u00b7\u2009km-2<\/sup> a\u00a0step of\u00a02.\u00a0Practically, this means that, for example, after the\u00a0minimum value of\u00a01.16 the\u00a0next value was 1.5, followed by 2.0, 2.5, \u2026 up to 9.0 l\u2009\u00b7\u2009s-1<\/sup>\u2009\u00b7\u2009km-2<\/sup> Most of\u00a0the\u00a0Czech Republic falls into this lowest category, whereas the\u00a0interval 9\u201320 is characteristic mainly of\u00a0mountainous areas, and values of\u00a020\u201330.5 occur only sporadically (e.g., in\u00a0the\u00a0Krkono\u0161e Mountains).<\/p>\n The\u00a0values of\u00a0qa<\/sub> were assigned to individual fourth-order catchments based on the\u00a0intersection of\u00a0each catchment polygon with the\u00a0vectorised map of\u00a0isolines. In\u00a0cases where a\u00a0catchment extended across several qa<\/sub> intervals, the\u00a0resulting specific runoff was calculated as an\u00a0area-weighted mean. Subsequently, the\u00a0mean\u00a0annual flow Qa<\/sub>\u00a0[l\u00a0\u00b7\u00a0s-1<\/sup>] was computed for each catchment according to the\u00a0following equation:<\/p>\n where:<\/p>\n A\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 is\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 the\u00a0catchment area\u00a0[km\u00b2]<\/p>\n If a\u00a0catchment was split into multiple parts, the\u00a0resulting flow was determined as the\u00a0sum of\u00a0the\u00a0partial runoff values.<\/p>\n The\u00a0resulting Qa<\/sub> values were further calibrated using data from 137 limnigraph stations operated by the Czech Hydrometeorological Institute (CHMI).<\/p>\n The\u00a0stations were selected so as to avoid excessive spatial density of\u00a0profiles and to ensure that each station represented a\u00a0sufficiently large upstream catchment area. This approach ensured uniform coverage of\u00a0the\u00a0Czech Republic and, at the\u00a0same time, reduced unnecessary workload associated with very small catchments.<\/p>\n Calibration consisted in\u00a0comparing the\u00a0calculated values of\u00a0Qa<\/sub> with the\u00a0measured flows at the\u00a0gauging-station profiles and subsequently adjusting them. The\u00a0differences were apportioned across the\u00a0individual fourth-order catchments proportionally to the\u00a0magnitude of\u00a0the\u00a0originally derived runoff values from the\u00a0isoline map. In\u00a0this way, systematic biases were removed and the\u00a0reliability of\u00a0the\u00a0Qa<\/sub> estimates improved, as shown in\u00a0Fig.\u00a02<\/em>.<\/p>\n <\/p>\n c) Hydropower potential (P)<\/strong><\/p>\n To estimate the\u00a0theoretical hydropower potential P\u00a0[kW], a\u00a0simplified working equation was used:<\/p>\n where:<\/p>\n H\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0is\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 elevation difference [m]<\/p>\n The coefficient 6\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 accounts for unit conversion and gravitational constant<\/p>\n Qa<\/sub>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0represents mean annual flow [m\u00b3\u00b7s-1<\/sup>]<\/p>\n At this stage of\u00a0the\u00a0calculation, turbine efficiency is not considered, so the\u00a0resulting value represents the\u00a0gross theoretical power, primarily useful for comparing the\u00a0relative potential between catchments.<\/p>\n This simplified relationship is based on the\u00a0general equation derived from Bernoulli\u2019s\u00a0law for the\u00a0potential energy of\u00a0a\u00a0water column:<\/p>\n where:<\/p>\n \u03c1\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 is\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 water density (1,000\u202fkg\u2009\u00b7\u2009m-3<\/sup>)<\/p>\n g\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 acceleration due to gravity (9.81\u202fm\u00a0\u00b7\u00a0s-2<\/sup>)<\/p>\n Q\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 mean\u00a0annual flow\u00a0[m\u00b3\u00a0\u00b7\u00a0s-1<\/sup>]<\/p>\n H\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 elevation difference\u00a0[m]<\/p>\n \u03b7\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 efficiency (not applied at this stage of\u00a0the\u00a0calculation)<\/p>\n The\u00a0resulting spatial layer, containing the\u00a0attributes of\u00a0mean\u00a0head\u00a0(H), mean\u00a0annual flow (Qa<\/sub>), and derived hydropower potential (HPP), enables a\u00a0uniform and comparable assessment of\u00a0all fourth-order catchments in\u00a0the\u00a0Czech Republic (Fig.\u00a03<\/em>). This layer serves as the\u00a0basis for subsequent analyses and forms the\u00a0primary input for the\u00a0next phases of\u00a0hydropower potential assessment.<\/p>\n While the\u00a0calculation of\u00a0hydropower potential for fourth-order catchments allows for the\u00a0spatial identification of\u00a0areas with a\u00a0higher probability of\u00a0suitable locations, more detailed assessment of\u00a0specific stream sections is necessary for subsequent use. For this purpose, the\u00a0SCR tool (Sklony_\u010cR) was developed within\u00a0the\u00a0project \u2013 a\u00a0specialized software tool for the\u00a0localization and assessment of\u00a0the\u00a0hydropower potential of\u00a0stream sections.<\/p>\n This application is designed as a\u00a0standalone executable (*.exe) and was developed in\u00a0the\u00a0C++ programming language. It is built on a\u00a0geodatabase generated from the\u00a0digital elevation model of\u00a0the\u00a0Czech Republic, interpolated flow characteristics, and topologically connected stream lines. The\u00a0tool allows users to search for and filter specific stream sections in\u00a0the\u00a0user interface based on defined technical parameters, and subsequently to visualize and analyze them according to the\u00a0specified criteria.<\/p>\n The\u00a0operation of\u00a0the\u00a0SCR tool is based on a\u00a0pre-processed geodatabase of\u00a0sections with usable head, created using a\u00a0semi-automatic detection method from DMR 5G. This database contains the\u00a0following types of\u00a0data:<\/p>\n Each segment carries a unique ID (UTOKH_ID), a reference to the corresponding catchment (IDVT), and the stream\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 segment ID (HLGP_ID).<\/p>\n Additionally, the database contains calculated values for:<\/p>\n For the presentation of the SCR user interface [11], the Sp\u016flka (HLPG_ID\u00a0108020180) was selected. The\u00a0application is designed as a\u00a0guided workflow for selecting river reaches, reflecting the\u00a0decision-making logic applied in\u00a0the\u00a0preliminary assessment of\u00a0hydropower potential. In\u00a0the\u00a0first stage, the\u00a0user defines the\u00a0area of\u00a0interest by selecting a\u00a0fourth-order catchment or a\u00a0specific primary stream section.<\/p>\n User-defined technical criteria are subsequently applied to the selected river reach. Filters include minimum segment length (50, 100, 250, and 500 m), minimum elevation difference (1\u20135 m in 1 m steps), minimum flow value Qa<\/sub> (0.1\u20130.5\u00a0m\u00b3\u00a0\u00b7\u00a0s-1<\/sup> in\u00a00.1 m\u00b3\u00a0\u00b7\u00a0s-1<\/sup> steps), and optionally a\u00a0direct requirement for the\u00a0resulting power (1\u20135 kW in\u00a01 kW steps). The\u00a0combination of\u00a0these parameters allows for the\u00a0pre-exclusion of\u00a0segments with insufficient conditions and streamlines the\u00a0search for suitable candidate sites.<\/p>\n The\u00a0results are made available to the\u00a0user as both a\u00a0spatial visualization and a\u00a0tabular output, as shown in\u00a0Fig.\u202f4<\/em>. In\u00a0the\u00a0map window, suitable segments are highlighted in\u00a0different colours and can\u00a0be clicked to access detailed information. It is also possible to display an\u00a0interactive longitudinal stream profile, where the\u00a0segments are clearly marked, including their parameters and spatial location. A\u00a0table with key attributes (length, head, Qa<\/sub>) is also available as a\u00a0text. In\u00a0this way, the\u00a0application supports rapid identification of\u00a0stream reaches that meet the\u00a0specified parameters, requiring minimal prior knowledge from the\u00a0user. The\u00a0outputs serve as a\u00a0basis for subsequent project preparation steps or for comparative analyses at the\u00a0regional level.<\/p>\n The\u00a0database of\u00a0hydropower potential at the\u00a0level of\u00a0fourth-order catchments provides a\u00a0unified overview of\u00a0the\u00a0distribution of\u00a0HPP in\u00a0small watercourses and allows for rapid identification of\u00a0regions with higher values for more detailed subsequent assessment. Thanks to visualization in\u00a0maps and tables, preliminary analysis can\u00a0be carried out without the\u00a0need for extensive field surveys. The\u00a0database is complemented by the\u00a0Sklony_\u010cR application, which allows detailed analysis of\u00a0individual river reaches based on user-defined technical parameters (e.g., head, flow, power). Results are presented as maps and tables with longitudinal profiles of\u00a0the\u00a0streams, facilitating the\u00a0identification of\u00a0sites with actual energy potential. The\u00a0combination of\u00a0the\u00a0database and the\u00a0application thus provides a\u00a0comprehensive tool \u2013 from broad-scale assessment to detailed localization of\u00a0suitable reaches.<\/p>\n For demonstration purposes, a\u00a0procedure was carried out where fourth-order catchments with a\u00a0mean\u00a0annual flow below 1\u202fm\u00b3\u00b7s-1<\/sup> were excluded from the\u00a0analysis. The\u00a0remaining catchments were subsequently aggregated to the\u00a0level of\u00a0third-order catchments, and their hydropower potential values were summed. Simultaneously, the\u00a0resulting values were normalized by catchment area, allowing comparison of\u00a0individual units independently of\u00a0their size. This procedure provided an\u00a0overview not only of\u00a0the\u00a0absolute total potential but also of\u00a0its relative intensity per unit area.<\/p>\nINTRODUCTION<\/h2>\n
METHODOLOGY<\/h2>\n
Data inputs<\/h4>\n
\n
Methodology for determining basic parameters (H, Qa<\/sub>, HPP)<\/h3>\n
\u00a0<\/strong>a) Average head of\u00a0the\u00a0main\u00a0stream (H)<\/strong><\/h4>\n
![\"\"](\"https:\/\/www.vtei.cz\/wp-content\/uploads\/2025\/12\/marval-obr-1-1.jpg\")
<\/a>Fig. 1. Mean\u00a0head of\u00a0rivers in\u00a0the\u00a0Czech Republic (H) \u2013 fourth-order catchment scale<\/h6>\n<\/a>\n<\/a>Fig. 2. Mean\u00a0annual streamflow (Qa) \u2013 fourth-order catchment scale<\/h6>\n<\/a>\n<\/a>\n<\/a>Fig. 3. Estimation of\u00a0theoretical hydropower potential (P) \u2013 fourth-order catchment scale<\/h6>\nIdentification of\u00a0subsections using the\u00a0SCR tool (Sklony_\u010cR)<\/h3>\n
\n\n
\nThese are vector segments (primary stream sections) traced along the\u00a0stream axis, created by segmenting the\u00a0main\u00a0line based on significant changes in\u00a0slope. The\u00a0segments have a\u00a0defined start, end, length, elevation difference (\u2206H), and calculated average slope. Refinement was performed using an\u00a0algorithm that detects inflection points in\u00a0the\u00a0elevation profile of\u00a0the\u00a0line derived from DMR 5G.<\/p>\n
\n<\/li>\n
\nEach segment was assigned an\u00a0estimated mean\u00a0annual flow (Qa<\/sub>) based on spatial interpolation from the\u00a0specific runoff map (see section Methodology for determining basic parameters).<\/p>\n
\n<\/li>\n\n
User interface and search algorithm<\/h3>\n
<\/a>Fig. 4. Identification of\u00a0river reaches according to the\u00a0defined parameters (panel 1), longitudinal profile of\u00a0the\u00a0selected river with delineated reaches (panel 2), and input parameters with tabular values of\u00a0the\u00a0selected reach (panel 3)<\/h6>\nRESULTS<\/h2>\n
<\/a><\/h6>\nFig. 5. Hydropower potential of\u00a0third-order catchments recalculated per unit area (km\u00b2)<\/h6>\n