INTRODUCTION<\/h2>\n
Water is essential for the\u00a0existence of\u00a0all living organisms and determines the\u00a0functioning of\u00a0human society. As a result of\u00a0climate change, extreme weather fluctuations are increasingly occurring, leading to a lack of\u00a0precipitation and the\u00a0occurrence of\u00a0droughts or, conversely, to extreme precipitation and floods. One of\u00a0the\u00a0causes of\u00a0these changes is human society and its ever-increasing demand for water and other strategic raw materials\u00a0[1].<\/p>\n
The\u00a0flow rate of\u00a0most watercourses in\u00a0Czechia is influenced by anthropogenic activity, and since the\u00a01950s there has been an extreme increase in\u00a0pressure on water resources worldwide\u00a0[2]. The\u00a0values measured at water-gauging stations more or less reflect human activities, which include the\u00a0withdrawal of\u00a0surface water and groundwater for agricultural purposes, especially irrigation, and for supplying the\u00a0population and industry. On the\u00a0other hand, there is the\u00a0wastewater disposal into surface waters (and rarely into groundwater), as well as the\u00a0deliberate increase or decrease in\u00a0the\u00a0flow rate of\u00a0a watercourse by operating reservoirs\u00a0[3].<\/p>\n
Water is often abstracted from one catchment and disposed into another several kilometres away. For example, in\u00a0the\u00a0Svitava catchment, groundwater is abstracted to supply the\u00a0Brno agglomeration with drinking water and, after use, is led to the\u00a0Brno-Mod\u0159ice wastewater treatment plant, which flows into the\u00a0Svratka\u00a0[4]. This means that the\u00a0measured values in\u00a0both catchments are strongly influenced by anthropogenic activity, and therefore natural discharge cannot be measured directly, but must be calculated\u00a0[2].<\/p>\n
Since a watercourse is the\u00a0main\u00a0variable that connects ecosystem components through hydrological, biological, geomorphological, and water quality processes, the\u00a0estimate of\u00a0natural discharge (in\u00a0our case, the\u00a0so-called unaffected discharge; further generally referred to as QNE) is usually used as a reference quantity for estimating hydrological response to the\u00a0climate regime, for assessing the\u00a0ecological status of\u00a0a river, and for estimating the\u00a0amount of\u00a0potentially available water\u00a0[5].<\/p>\n
This paper focuses on the analysis of the impact of water withdrawal, disposal, and accumulation on flow rates at water-gauging stations in Czechia for the reference period 1991\u20132020. It also includes an assessment of regional differences in the hydrological regime of Czech river catchments and the identification of areas where significant changes in water availability occur. It also\u00a0assesses areas with potential water resource deficits, based on the\u00a0SSP2-4.5 and SSP5-8.5 climate scenarios. The\u00a0results of\u00a0this analysis will contribute to effective water resource management and a deeper understanding of\u00a0changes in\u00a0the\u00a0water regime.<\/p>\n
METHODOLOGY AND DATA USED<\/h2>\nAnalysis of\u00a0the\u00a0influence of\u00a0water use on discharge (DC 1.1)<\/span><\/h3>\nIn\u00a0addition to the\u00a0activities of\u00a0other consortium members already presented in\u00a0[6], the\u00a0main\u00a0task of\u00a0the\u00a0CHMI in\u00a0DC 1.1 was to analyse the\u00a0impact of\u00a0water use on discharge in\u00a0Czechia. The\u00a0basis was monthly data on total influence on discharge at water-gauging stations, expressed in\u00a0percentage as the\u00a0ratio of\u00a0discharge changes to QNE. In\u00a0practice, this variable (and its time series) was designated by the\u00a0abbreviation OVLTOT. Formally, its calculation can be expressed as<\/p>\n![\"\"](\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\")
<\/a>\nwhere:<\/p>\n
<\/p>\n
DELTA\u00a0\u00a0\u00a0\u00a0 represents
\nthe\u00a0total impact of\u00a0operated reservoirs in\u00a0the\u00a0catchment above the\u00a0given station (or\u00a0the\u00a0difference between volumes at the\u00a0beginnings of\u00a0the\u00a0months)<\/p>\n
SUMA\u00a0\u00a0\u00a0 is
\nthe\u00a0sum of\u00a0impacts by withdrawals and\u00a0disposals<\/p>\n
Each variable related to the\u00a0influence was first converted to m3<\/sup>\u00a0\u00b7\u00a0s\u20131<\/sup> and given an adequate sign. Negative values of\u00a0OVLTOT then indicated a predominance of\u00a0withdrawals (including water accumulation in\u00a0reservoirs), while positive values were associated with a predominant disposal (including water discharge from reservoirs). These data are regularly uploaded once a year to a CHMI database together with other available data on impacts valid for water-gauging stations (in\u00a0accordance with Act No. 254\/2001 Coll., on waters, as amended; with Decree of\u00a0the\u00a0Ministry of\u00a0Agriculture No. 431\/2001\u00a0Coll., on the\u00a0content of\u00a0the\u00a0water balance, its method of\u00a0compilation and on data for the\u00a0water balance, and to a certain\u00a0extent also with Decree of\u00a0the\u00a0Ministry of\u00a0Agriculture No. 252\/2013 Coll., on the\u00a0scope of\u00a0data in\u00a0records of\u00a0the\u00a0state of\u00a0surface and groundwater and on the\u00a0method of\u00a0processing, storing, and transmitting this data to public administration information systems). When calculating QNE, special attention is paid to the\u00a0distinction between withdrawals only from surface waters (which is reflected in\u00a0the\u00a0SUMAY characteristic, which results in\u00a0unaffected discharge values in\u00a0the\u00a0database abbreviated as QNEY) and total withdrawals (i.e. withdrawals from surface water including groundwater; which is reflected in\u00a0the\u00a0SUMAX characteristic, which results in\u00a0unaffected discharge values in\u00a0the\u00a0database abbreviated as QNEY). To maintain\u00a0homogeneity of\u00a0the\u00a0time series, only territorially relevant objects with a permit for the\u00a0withdrawal or disposal of\u00a0more than 6,000\u00a0m3<\/sup> per year or 500\u00a0m3<\/sup> per month are included in\u00a0the\u00a0calculation of\u00a0the\u00a0SUMA characteristic. The\u00a0DELTA characteristic only considers reservoirs with a permitted volume of\u00a0surface water accumulated or dammed greater than 1,000,000\u00a0m3<\/sup>. The\u00a0reference period 1991\u20132020 was selected for the\u00a0current analyses, with a total of\u00a0346 gauging stations meeting the\u00a0criterion for the\u00a0completeness of\u00a0the\u00a0time series.<\/p>\nSimultaneously, an R script for the\u00a0actual calculation of\u00a0QNE series was developed at the\u00a0CHMI. The\u00a0script functionality depends on using coordinates for the\u00a0correct location of\u00a0the\u00a0influencing object, and therefore it was necessary to check the\u00a0coordinates of\u00a0the\u00a0input data of\u00a0the\u00a0influence. Special attention was paid to appropriate location of\u00a0the\u00a0beginnings and ends of\u00a0the\u00a0conduits in\u00a0the\u00a0system of\u00a0watershed divides so that the\u00a0point of\u00a0withdrawal (or disposal) logically falls into the\u00a0catchment area with the\u00a0loss (or gain) of\u00a0water. At\u00a0the\u00a0end of\u00a0work on WP1 (June 2024), the\u00a0most detailed vector layer published as\u00a0of\u00a01\u00a0July\u00a02024 on the\u00a0CHMI website with open spatial data\u00a0[7, 8] was already considered for localising the\u00a0objects. This layer was constructed on top of\u00a0the\u00a0fifth generation Digital Terrain\u00a0Model of\u00a0the\u00a0Czech Republic (DMR 5G;\u00a0[9]). The\u00a0resulting QNE values were compared with the\u00a0values obtained at the\u00a0TGM WRI, which has been performing their annual calculation and submitted them to the\u00a0CHMI.<\/p>\n
For the\u00a0purposes of\u00a0developing the\u00a0R script and calculating the\u00a0M-day discharge cadastre for the\u00a0reference period 1991\u20132020, input data on discharge influences were compared between three main\u00a0sources, which were the\u00a0Integrated System for Fulfilling Reporting Obligations (ISPOP system), files (exports) from the\u00a0River Basin\u00a0Authorities, state enterprises, and geographic layers from the\u00a0VODA Water Management Information Portal available at https:\/\/voda.gov.cz\/. It was found that the\u00a0sources differ in\u00a0the\u00a0number of\u00a0objects and the\u00a0values themselves; however, updating (also in\u00a0terms of\u00a0error corrections) of\u00a0these sources is somewhat decentralised, which can be seen as a great uncertainty. Therefore, a general check of\u00a0the\u00a0location of\u00a0objects, their duplicates, and the\u00a0values of\u00a0withdrawals and disposals was performed\u00a0[10].<\/p>\n
Data from the\u00a0VODA Water Management Information Portal was considered more as supplementary because, at the\u00a0time of\u00a0processing (i.e. at the\u00a0end of\u00a0work on WP1), it only reached the\u00a0year 2020, compared to the\u00a0status on the\u00a0portal from August 2024 with data reaching back to 2014. Based on gaps in\u00a0the\u00a0time series, or changes in\u00a0the\u00a0object name and other attributes, objects were selected that could potentially be combined or divided. An analysis of\u00a0water bodies was also carried out, where both water body and watercourse withdrawals can be reported at the\u00a0same time. Therefore, objects situated in\u00a0close proximity to reservoirs were located. All this information about selected objects was subsequently sent to the\u00a0CHMI regional offices for manual checks via the\u00a0PostgreSQL database with support for GIS tools (i.e. PostGIS).<\/p>\n
For map outputs of\u00a0the\u00a0total influence, a third-order catchment layer was selected, including 346 selected water-gauging stations with a complete time series of\u00a0the\u00a0total percentage of\u00a0influence for the\u00a0hydrological period 1991\u20132020. The\u00a0time series of\u00a0other studied elements were also complete. For each station, the\u00a0total area of the\u00a0catchment above it and its share of\u00a0the\u00a0area of the\u00a0third-order catchment in\u00a0which the\u00a0station is located were first calculated. The\u00a0total percentage of\u00a0influence for each catchment was calculated as the\u00a0sum of\u00a0total influence at all stations in\u00a0the\u00a0given catchment, with the\u00a0weight of\u00a0each station being the\u00a0calculated share of\u00a0the\u00a0catchment area above the\u00a0given station. The\u00a0total percentage of\u00a0influence in\u00a0individual catchments therefore corresponds primarily to the\u00a0stations at the\u00a0mouth or near the\u00a0mouth, where the\u00a0largest area is drained.<\/p>\n
In\u00a0the\u00a0next phase, a trend analysis was performed to determine whether there were statistically significant gradual changes in\u00a0the\u00a0time series of\u00a0elements affecting the\u00a0discharge of\u00a0Czech rivers in\u00a0the\u00a0selected period. Two statistical significance levels were chosen, namely \u03b1\u00a0=\u00a00.05 and \u03b1\u00a0=\u00a00.01. The\u00a0Mann\u2013Kendall test for the\u00a0presence of\u00a0a trend\u00a0[11\u201313] and its modification proposed in\u00a0the\u00a0article\u00a0[14] were applied so that, in\u00a0the\u00a0case of\u00a0a significant autoregressive coefficient with the\u00a0assumed first-order autoregressive model, variance of\u00a0the\u00a0test statistic was corrected\u00a0[15\u201317]. Results for each station and month were summarised in\u00a0the\u00a0value of\u00a0the\u00a0standardised test statistic\u00a0Z (indicating the\u00a0direction of\u00a0a possible trend), the\u00a0p-value, and Sen\u2019s nonparametric estimate of\u00a0the\u00a0trend direction, denoted SEN\u00a0[18]. These analyses were performed for both monthly and annual time series.<\/p>\n
The results were processed using the R package modifiedmk [19]. Cases where the p-value fell below the chosen significance level were plotted on maps using arrows at the locations of water-gauging stations. The arrow deviating from\u00a0the\u00a0horizontal direction, depending on the\u00a0sign of\u00a0the\u00a0Z or SEN values, represented an increasing trend (number of\u00a0values without a sign) or decreasing trend (number with a minus sign), similar to what was done in\u00a0other papers dealing with trends in\u00a0the\u00a0components of\u00a0the\u00a0hydrological cycle in\u00a0Czechia\u00a0[20\u201322]. Map outputs were subsequently created from these analyses.<\/p>\n
Identification of\u00a0areas with deficient water resources (DC 1.2)<\/span><\/h3>\nUnlike precipitation, air temperature is, as expected, more evenly distributed between individual river catchments, which allows for analysis of\u00a0its changes for entire Czechia. Compared to the\u00a0normal from 1991\u20132020, changes in\u00a0the\u00a0average monthly temperature oscillate between 0 \u00b0C and +2 \u00b0C for both scenarios until approximately 2055 (Fig.\u00a03<\/span><\/em>). From this year, a more significant increase in\u00a0the\u00a0change in\u00a0air temperatures can be observed for both scenarios, especially for the\u00a0more pessimistic scenario SSP5-8.5. This is also confirmed by the\u00a0calculated average temperatures for individual decades of\u00a0the\u00a021st century. While the\u00a0change in\u00a0average monthly air temperature compared to the\u00a0normal in\u00a0the\u00a0first four decades (between 2020\u20132060) on a nationwide scale is around +1 \u00b0C, between 2060 and 2070 it exceeds +2 \u00b0C in\u00a0the\u00a0SSP5-8.5 scenario and continuously increases to an extreme +5 \u00b0C towards the\u00a0end of\u00a0the\u00a0century. In\u00a0contrast, according to the\u00a0SSP2-4.5 scenario, a more moderate increase in\u00a0air temperature can be expected, with a maximum change of\u00a0+2.4 \u00b0C between 2080 and 2090.<\/p>\nFor precipitation, predictions are more variable, with the\u00a0course of\u00a0events according to different scenarios differing significantly (Fig.\u00a04<\/span><\/em>). From a nationwide perspective, according to the\u00a0SSP2-4.5 scenario, monthly precipitation totals have been around the\u00a0average of\u00a0the\u00a0reference period 1991\u20132020 (59.9\u00a0mm\/month) in\u00a0the\u00a0long term. From approximately 2040, there is a positive change in\u00a0precipitation totals, which lasts almost until the\u00a0end of\u00a0the\u00a0century.<\/p>\nIn\u00a0contrast, the\u00a0SSP5-8.5 scenario suggests more significant changes, similar to the\u00a0development of\u00a0air temperatures. Around 2055, there is a positive change in\u00a0monthly precipitation compared to the\u00a01991\u20132020 normal. According to this scenario, average monthly totals will increase by up to 15\u00a0%, with this growth trend continuing constantly until the\u00a0end of\u00a0the\u00a0century.<\/p>\n
Although the\u00a0outlook for the\u00a0nationwide average monthly precipitation totals may seem relatively optimistic, averages for individual decades show significant differences between third-order river catchments. From the\u00a0map outputs for both analysed scenarios (Figs. 5<\/span><\/em> and 6<\/span><\/em>), a recurring pattern can be seen at first glance across individual decades. This is the\u00a0transition of\u00a0higher precipitation totals in\u00a0the\u00a0west of\u00a0Czechia through a precipitation-poorer area that stretches from north to south, back to the\u00a0precipitation-richer east of\u00a0the\u00a0country. This transition is especially evident in\u00a0the\u00a0SSP2-4.5 scenario. While in\u00a0the\u00a0west of\u00a0the\u00a0country, a positive change in\u00a0monthly precipitation compared to the\u00a0normal prevails across all decades, in\u00a0the\u00a0northern, central, and southern parts of\u00a0Czechia this change is slightly negative. The\u00a0exception is the\u00a0decade 2020\u20132030, characterised by a negative change in\u00a0almost all catchments (national average -7.4\u00a0%) and, conversely, the\u00a0precipitation-rich decade 2070\u20132080 (average +8.4\u00a0%). The\u00a0scenario SSP5-8.5 predicts an increasing negative change in\u00a0average monthly precipitation for the\u00a0north-south belt of\u00a0Czechia in\u00a0the\u00a0first three decades compared to the\u00a01991\u20132020 normal, while in\u00a0the\u00a0east and west there is a transition from slightly positive to zero values. However, for the\u00a0rest of\u00a0the\u00a0century, the\u00a0prediction ranges exclusively in\u00a0positive changes in\u00a0total precipitation across all river catchments. The\u00a0nationwide average will not fall below +10\u00a0% from 2050, with maxima in\u00a0the\u00a0decades 2070\u20132080 (+16.3\u00a0%) and 2090\u20132100 (+17.4\u00a0%).<\/p>\nTo clarify the\u00a0map outputs of\u00a0both air temperatures and precipitation, it\u00a0should be added that values in\u00a0some border catchments can sometimes differ significantly from values in\u00a0neighbouring catchment. This difference is caused by cropping a raster of\u00a0a certain\u00a0size by a smaller area of the\u00a0catchment, which can result in\u00a0the\u00a0extraction of\u00a0only one value\/pixel (i.e. the\u00a0value of\u00a0the\u00a0average monthly precipitation\/air temperature) for the\u00a0part of\u00a0the\u00a0studied catchment. This is therefore not a calculation error, but the\u00a0result of\u00a0the\u00a0necessary raster extraction.<\/p>\n
The\u00a0last analysis performed was the\u00a0SPI index calculation, which is used to estimate wet\/dry conditions based on total precipitation. Specifically, it\u00a0is the\u00a0standard deviation by which observed precipitation would differ from the\u00a0long-term average and, before calculation, the\u00a0precipitation time series must first be transformed into a quantity with a standard Gaussian distribution (through its quantile function) using the\u00a0cumulative distribution function of\u00a0the\u00a0probabilistic model which is assumed to be a good description of\u00a0empirical values\u00a0[33]. In\u00a0this case, the\u00a0index calculated for a twelve-month time window SPI12 (with potential removal of\u00a0seasonality) with a gamma distribution was chosen.<\/p>\n
According to simulated values of\u00a0total precipitation using the\u00a0scenario SSP2-4.5, extremely dry periods can be estimated during the\u00a02020s and around 2065, 2082, 2091, and 2094. Exceptionally to extremely dry conditions occur less frequently in\u00a0the\u00a0Oder catchment, while the\u00a0B\u00edlina and Oh\u0159e catchments show a more significant episode around 2058. Extremely and exceptionally wet conditions across the\u00a0catchments were simulated in\u00a0the\u00a0late 2040s and especially in\u00a0the\u00a0second half of\u00a0the\u00a02070s, and in\u00a0the\u00a0Morava catchment especially in\u00a0the\u00a0early 2070s. According to the\u00a0more pessimistic SSP5-8.5 scenario, dry and wet episodes do not alternate as much. Two more significant periods were simulated. The\u00a0first period, 2035\u20132050, includes four significant dry episodes, while the\u00a0second period, 2075\u20132095, includes six significant wet episodes. Here, obvious differences can be found between the\u00a0catchments. For example, the\u00a0dry episode around 2040 is not evident in\u00a0the\u00a0Ostravice, Opava and Morava catchments, while the\u00a0wet episode in\u00a02055 is not evident in\u00a0the\u00a0Oder catchment and in\u00a02086 in\u00a0the\u00a0Elbe catchment.<\/p>\n
RESULTS AND DISCUSSION<\/h2>\nAnalysis of\u00a0the\u00a0influence of\u00a0water use on discharge (DC 1.1)<\/span><\/h3>\nAs shown in\u00a0Fig.\u00a01<\/span><\/em>, the\u00a0highest values of\u00a0total surface water influence were achieved in\u00a0the\u00a0catchments in\u00a0southern Moravia and the\u00a0Osoblaha catchment, the\u00a0Elbe from the\u00a0Orlice to the\u00a0Lou\u010dn\u00e1, and in\u00a0particular the\u00a0B\u00edlina catchment (however, in\u00a0this catchment, data were entered from only one gauging station); in\u00a0contrast, the\u00a0lowest values were achieved in\u00a0the\u00a0catchments of\u00a0the\u00a0Rybn\u00e1 and the\u00a0Lu\u017enice from the\u00a0Rybn\u00e1 to the\u00a0Ne\u017e\u00e1rka, the\u00a0S\u00e1zava from the\u00a0\u017delivka to the\u00a0mouth, and the\u00a0Dyje from the\u00a0Svratka to the\u00a0mouth. When groundwater withdrawals were included, high values were again\u00a0found in\u00a0the\u00a0catchments in\u00a0southern Moravia and in\u00a0the\u00a0catchments of\u00a0western and northwestern Bohemia. The\u00a0highest values of\u00a0the\u00a0degree of\u00a0influence can be observed in\u00a0the\u00a0catchments of\u00a0the\u00a0Lod\u011bnice, Osoblaha, and Oslava. In\u00a0contrast, the\u00a0lowest values were measured in\u00a0the\u00a0tributaries of\u00a0the\u00a0Freiberg Mulde, \u0160opava and Fl\u00f6ha, in\u00a0the\u00a0catchments of\u00a0the\u00a0Morava from the\u00a0Be\u010dva to the\u00a0Han\u00e1, the\u00a0Rybn\u00e1, and the\u00a0Lu\u017enice from the\u00a0Rybn\u00e1 to the\u00a0Ne\u017e\u00e1rka and the\u00a0Svitava.<\/p>\n<\/a><\/h2>\n<\/a><\/h6>\nFig.\u00a01. Ratio of total discharge influence for third-order catchments (reference period 1991\u20132020)<\/h6>\n
From the\u00a0trend analysis for the\u00a0reference period 1991\u20132020, different behaviours can be observed in\u00a0water withdrawals and disposals at selected water-gauging stations, often creating noticeable clusters in\u00a0several areas (Fig.\u00a02<\/span><\/em>). Overall, however, a zero trend prevails across water withdrawals and disposals. This was found in\u00a0each of\u00a0the\u00a0monitored groups at approximately 230\u00a0of\u00a0the\u00a0total 346 water-gauging stations (around 65\u00a0% of\u00a0all stations).<\/p>\n<\/a>\n <\/p>\n
Fig.\u00a02. Trend analysis for water withdrawals and disposals (reference period 1991\u20132020)<\/h6>\n
In the case of surface water withdrawals, including groundwater, a slightly decreasing and slightly increasing trend was observed at approximately 8 % of all monitored stations. The same ratio was measured at stations with a significantly increasing trend. A significantly decreasing trend was then detected at 47 stations (almost 14 % of all stations), forming noticeable clusters at stations in northern Bohemia (especially the Plou\u010dnice catchment) and eastern Bohemia (the Metuje catchment, the Orlice from the confluence of the Divok\u00e1 and Tich\u00e1 Orlice to the mouth, and the Lou\u010dn\u00e1 and Elbe from the Lou\u010dn\u00e1 to the Chrudimka). Other catchments with a predominance of significantly decreasing trends are the\u00a0catchments of\u00a0the\u00a0Moravsk\u00e1 S\u00e1zava and the\u00a0Morava from the\u00a0Moravsk\u00e1 S\u00e1zava to the\u00a0T\u0159eb\u016fvka, of\u00a0the\u00a0T\u0159eb\u016fvka and Svitava. Clusters of\u00a0slightly decreasing trends can also be observed in\u00a0the\u00a0catchments in\u00a0Silesia (the\u00a0Opava to the\u00a0Moravice, the\u00a0Ol\u0161e and the\u00a0Oder to the\u00a0Opava). In\u00a0contrast, the\u00a0observed increasing trends create clusters in\u00a0the\u00a0Vyso\u010dina region (especially the\u00a0S\u00e1zava catchment to the\u00a0\u017delivka, the\u00a0Svratka to the\u00a0Svitava, and the\u00a0Oslava and the\u00a0Jihlava from the\u00a0Oslava to the\u00a0Rokytn\u00e1) and in\u00a0the\u00a0Dyje catchment.<\/p>\n
In\u00a0the\u00a0case of\u00a0withdrawals considering surface water only, a minimum of\u00a0stations with an increasing trend were found. Decreasing trends were recorded at less than 30\u00a0% of\u00a0the\u00a0monitored stations, which are relatively evenly distributed throughout Czechia. The\u00a0predominance of\u00a0significantly decreasing trends can again\u00a0be observed in\u00a0the\u00a0area of northern Bohemia, especially in\u00a0the\u00a0catchments of\u00a0the\u00a0Lu\u017eick\u00e1 Nisa to the\u00a0Mandava, the\u00a0Jizera, and the\u00a0Kamenice. Other areas with decreasing trends are the\u00a0catchments of\u00a0the\u00a0Berounka and its tributaries, the\u00a0upper and middle reaches of\u00a0the\u00a0Morava, and the\u00a0catchments of\u00a0southern Bohemia (the\u00a0Vltava to the\u00a0Mal\u0161e and the\u00a0Ne\u017e\u00e1rka).<\/p>\n
A slight predominance of\u00a0increasing trends (a total of\u00a062 stations) was found in\u00a0water disposals compared to decreasing trends (34 stations). Areas with a\u00a0predominance of\u00a0increasing trends are the\u00a0catchments of\u00a0western Bohemia (the\u00a0M\u017ee to the\u00a0confluence with the\u00a0Radbuza and the\u00a0Otava to the\u00a0Voly\u0148ka), southern Moravia (the\u00a0Svratka and Svitava) and eastern Moravia (the\u00a0Vset\u00ednsk\u00e1 and Ro\u017enovsk\u00e1 Be\u010dva or the\u00a0Ostravice). Decreasing trends are more point-wise distributed; smaller clusters occur in\u00a0the\u00a0catchments of\u00a0the\u00a0Vltava to the\u00a0Mal\u0161e, the\u00a0Rakovn\u00edk Stream and the\u00a0Metuje.<\/p>\n
Identification of\u00a0areas with deficient water resources (DC 1.2)<\/span><\/h3>\nUnlike precipitation, air temperature is, as expected, more evenly distributed between individual river catchments, which allows for analysis of\u00a0its changes for entire Czechia. Compared to the\u00a0normal from 1991\u20132020, changes in\u00a0the\u00a0average monthly temperature oscillate between 0 \u00b0C and +2 \u00b0C for both scenarios until approximately 2055 (Fig.\u00a03<\/span><\/em>). From this year, a more significant increase in\u00a0the\u00a0change in\u00a0air temperatures can be observed for both scenarios, especially for the\u00a0more pessimistic scenario SSP5-8.5. This is also confirmed by the\u00a0calculated average temperatures for individual decades of\u00a0the\u00a021st century. While the\u00a0change in\u00a0average monthly air temperature compared to the\u00a0normal in\u00a0the\u00a0first four decades (between 2020\u20132060) on a nationwide scale is around +1 \u00b0C, between 2060 and 2070 it exceeds +2 \u00b0C in\u00a0the\u00a0SSP5-8.5 scenario and continuously increases to an extreme +5 \u00b0C towards the\u00a0end of\u00a0the\u00a0century. In\u00a0contrast, according to the\u00a0SSP2-4.5 scenario, a more moderate increase in\u00a0air temperature can be expected, with a maximum change of\u00a0+2.4 \u00b0C between 2080 and 2090.<\/p>\n<\/a>\n <\/p>\n
Fig.\u00a03. Change in average monthly air temperature compared to the 1991\u20132020 normal based on climate scenarios; LOESS regression\u00a0[36] with a 95% confidence interval shown in bold<\/h6>\n
For precipitation, predictions are more variable, with the\u00a0course of\u00a0events according to different scenarios differing significantly (Fig.\u00a04<\/span><\/em>). From a nationwide perspective, according to the\u00a0SSP2-4.5 scenario, monthly precipitation totals have been around the\u00a0average of\u00a0the\u00a0reference period 1991\u20132020 (59.9\u00a0mm\/month) in\u00a0the\u00a0long term. From approximately 2040, there is a positive change in\u00a0precipitation totals, which lasts almost until the\u00a0end of\u00a0the\u00a0century.<\/p>\n<\/a>\n <\/p>\n
Fig.\u00a04. Change in average monthly precipitation totals compared to the 1991\u20132020 normal based on climate scenarios; LOESS regression\u00a0[36] with a 95% confidence interval shown in bold<\/h6>\n
In\u00a0contrast, the\u00a0SSP5-8.5 scenario suggests more significant changes, similar to the\u00a0development of\u00a0air temperatures. Around 2055, there is a positive change in\u00a0monthly precipitation compared to the\u00a01991\u20132020 normal. According to this scenario, average monthly totals will increase by up to 15\u00a0%, with this growth trend continuing constantly until the\u00a0end of\u00a0the\u00a0century.<\/p>\n
Although the\u00a0outlook for the\u00a0nationwide average monthly precipitation totals may seem relatively optimistic, averages for individual decades show significant differences between third-order river catchments. From the\u00a0map outputs for both analysed scenarios (Figs. 5<\/span><\/em> and 6<\/span><\/em>), a recurring pattern can be seen at first glance across individual decades. This is the\u00a0transition of\u00a0higher precipitation totals in\u00a0the\u00a0west of\u00a0Czechia through a precipitation-poorer area that stretches from north to south, back to the\u00a0precipitation-richer east of\u00a0the\u00a0country. This transition is especially evident in\u00a0the\u00a0SSP2-4.5 scenario. While in\u00a0the\u00a0west of\u00a0the\u00a0country, a positive change in\u00a0monthly precipitation compared to the\u00a0normal prevails across all decades, in\u00a0the\u00a0northern, central, and southern parts of\u00a0Czechia this change is slightly negative. The\u00a0exception is the\u00a0decade 2020\u20132030, characterised by a negative change in\u00a0almost all catchments (national average -7.4\u00a0%) and, conversely, the\u00a0precipitation-rich decade 2070\u20132080 (average +8.4\u00a0%). The\u00a0scenario SSP5-8.5 predicts an increasing negative change in\u00a0average monthly precipitation for the\u00a0north-south belt of\u00a0Czechia in\u00a0the\u00a0first three decades compared to the\u00a01991\u20132020 normal, while in\u00a0the\u00a0east and west there is a transition from slightly positive to zero values. However, for the\u00a0rest of\u00a0the\u00a0century, the\u00a0prediction ranges exclusively in\u00a0positive changes in\u00a0total precipitation across all river catchments. The\u00a0nationwide average will not fall below +10\u00a0% from 2050, with maxima in\u00a0the\u00a0decades 2070\u20132080 (+16.3\u00a0%) and 2090\u20132100 (+17.4\u00a0%).<\/p>\n <\/p>\n
<\/a>Fig.\u00a05. Change in average monthly precipitation totals compared to the 1991\u20132020 normal according to the SSP2-4.5 scenario in individual decades for third-order catchments<\/h6>\n<\/a>\n <\/p>\n
Fig.\u00a06. Change in average monthly precipitation totals compared to the 1991\u20132020 normal according to the SSP5-8.5 scenario in individual decades for third-order catchments<\/h6>\n
To clarify the\u00a0map outputs of\u00a0both air temperatures and precipitation, it\u00a0should be added that values in\u00a0some border catchments can sometimes differ significantly from values in\u00a0neighbouring catchment. This difference is caused by cropping a raster of\u00a0a certain\u00a0size by a smaller area of the\u00a0catchment, which can result in\u00a0the\u00a0extraction of\u00a0only one value\/pixel (i.e. the\u00a0value of\u00a0the\u00a0average monthly precipitation\/air temperature) for the\u00a0part of\u00a0the\u00a0studied catchment. This is therefore not a calculation error, but the\u00a0result of\u00a0the\u00a0necessary raster extraction.<\/p>\n
The\u00a0last analysis performed was the\u00a0SPI index calculation, which is used to estimate wet\/dry conditions based on total precipitation. Specifically, it\u00a0is the\u00a0standard deviation by which observed precipitation would differ from the\u00a0long-term average and, before calculation, the\u00a0precipitation time series must first be transformed into a quantity with a standard Gaussian distribution (through its quantile function) using the\u00a0cumulative distribution function of\u00a0the\u00a0probabilistic model which is assumed to be a good description of\u00a0empirical values\u00a0[33]. In\u00a0this case, the\u00a0index calculated for a twelve-month time window SPI12 (with potential removal of\u00a0seasonality) with a gamma distribution was chosen.<\/p>\n
According to simulated values of\u00a0total precipitation using the\u00a0scenario SSP2-4.5, extremely dry periods can be estimated during the\u00a02020s and around 2065, 2082, 2091, and 2094. Exceptionally to extremely dry conditions occur less frequently in\u00a0the\u00a0Oder catchment, while the\u00a0B\u00edlina and Oh\u0159e catchments show a more significant episode around 2058. Extremely and exceptionally wet conditions across the\u00a0catchments were simulated in\u00a0the\u00a0late 2040s and especially in\u00a0the\u00a0second half of\u00a0the\u00a02070s, and in\u00a0the\u00a0Morava catchment especially in\u00a0the\u00a0early 2070s. According to the\u00a0more pessimistic SSP5-8.5 scenario, dry and wet episodes do not alternate as much. Two more significant periods were simulated. The\u00a0first period, 2035\u20132050, includes four significant dry episodes, while the\u00a0second period, 2075\u20132095, includes six significant wet episodes. Here, obvious differences can be found between the\u00a0catchments. For example, the\u00a0dry episode around 2040 is not evident in\u00a0the\u00a0Ostravice, Opava and Morava catchments, while the\u00a0wet episode in\u00a02055 is not evident in\u00a0the\u00a0Oder catchment and in\u00a02086 in\u00a0the\u00a0Elbe catchment.<\/p>\n
CONCLUSIONS AND RECOMMENDATIONS<\/h2>\n
This paper presented the\u00a0basic results that the\u00a0CHMI reached within\u00a0the\u00a0work package WP1 of\u00a0the\u00a0\u201cWater Centre\u201d<\/span>. In\u00a0addition, the\u00a0shortcomings and uncertainties that accompanied the\u00a0analyses were briefly outlined. For example, the\u00a0situation surrounding data related to the\u00a0influence of\u00a0discharge in\u00a0Czech rivers can be understood as very urgent. There are several sources of\u00a0this data that are not correctly updated, for example, regarding the\u00a0errors found. Moreover, these data are used in\u00a0various other projects, which results in\u00a0the\u00a0creation of\u00a0new web (map) applications offering their presentation, which then worsens the\u00a0orientation of\u00a0the\u00a0processors of\u00a0such data\u00a0[10]. Given that from 2025 (i.e. initially with data for 2024) the\u00a0calculation of\u00a0the\u00a0unaffected mean monthly discharges will be transferred under the\u00a0responsibility of\u00a0the\u00a0CHMI, it will be necessary to carefully consult the\u00a0quality of\u00a0the\u00a0affected data with the\u00a0employees of\u00a0the\u00a0River Basin\u00a0Authorities, state enterprises. Otherwise, the\u00a0developed R script for calculating unaffectedness may be perfect, but it will still not give satisfactory results. For this reason, further maintenance and versioning of\u00a0the\u00a0script is planned via the\u00a0GitHub development platform (e.g. https:\/\/github.com\/ledvinkao). Its variants are also possible, depending on the\u00a0data source.<\/p>\nThe\u00a0analysis of\u00a0the\u00a0influence on discharge and water resources in\u00a0Czechia, both in\u00a0the\u00a0area of surface and groundwater withdrawals and in\u00a0relation to climate change predictions, shows the\u00a0complex and regionally differentiated nature of\u00a0these changes. The\u00a0analysis results show significant variability between river catchments, which underlines the\u00a0need for an individual approach to assessment and management of\u00a0water resources in\u00a0different parts of\u00a0the\u00a0country. In\u00a0areas such as southern Moravia, northwestern Bohemia, and the\u00a0B\u00edlina catchment, significant changes in\u00a0groundwater and surface water withdrawals are being observed, which may have long-term consequences for water availability in\u00a0these regions. Conversely, lower impact values were observed in\u00a0some areas of\u00a0southern and eastern Bohemia \u2013 this may indicate greater water regime stability in\u00a0these regions.<\/p>\n
Climate scenario predictions indicate rising air temperatures throughout the\u00a021st century, with the\u00a0more pessimistic SSP5-8.5 scenario even suggesting a significant temperature increase of\u00a0up to 5\u00a0\u00b0C by the\u00a0end of\u00a0the\u00a0century. Changes in\u00a0air temperature will have a direct impact on the\u00a0water balance in\u00a0Czechia, with regions with higher temperatures facing increased evaporation and changes in\u00a0the\u00a0availability of\u00a0water resources. As for precipitation, the\u00a0scenario SSP2-4.5 shows a rather moderate increase in\u00a0precipitation totals with regional differences, while the\u00a0scenario SSP5 8.5 suggests a significantly higher increase in\u00a0precipitation, especially in\u00a0the\u00a0western and southern parts of\u00a0Czechia.<\/p>\n
SPI index calculation confirms the\u00a0occurrence of\u00a0extreme dry and wet periods during the\u00a021st century and there are obvious differences between individual catchments. The\u00a0scenario SSP2-4.5 predicts periods of\u00a0extreme drought around 2065 and in\u00a0the\u00a02090s, while extremely wet periods will occur in\u00a0the\u00a0late 2040s and 2070s. In\u00a0the\u00a0SSP5-8.5 scenario, these fluctuations are less frequent, but more frequent dry and wet periods are still expected in\u00a0specific decades, with some areas, e.g. the\u00a0Oder catchment, facing more frequent wet episodes.<\/p>\n
Overall, the results indicate the need to adapt water management to the changes that climate change will bring. It is necessary to focus on regional specificities, as climate change will not affect Czechia evenly. It will be necessary to adapt water resource management with regard to the expected development in river catchments, which show different trends in water withdrawal and disposal. This includes not only improving water management policies and strategies for protecting water resources, but also reassessing infrastructure\u00a0projects and measures to mitigate the\u00a0impacts of\u00a0extreme climate conditions such as drought and floods. Preventive measures aimed at retaining water in\u00a0the\u00a0landscape will also play an important role.<\/p>\n
With regard to the\u00a0existence of\u00a0climatological data in\u00a0grid form, the\u00a0number of\u00a0which will certainly rise, Czechia has taken a step in\u00a0the\u00a0right direction. Czech water managers may be assisted by additional information derived from grids, such as antecedent precipitation indices or seasonal hydrological predictions whose development has, in\u00a0fact, already begun with these grids\u00a0[37, 38].<\/p>\n
Acknowledgements<\/h3>\n
The\u00a0article was prepared as part of\u00a0project No. SS02030027 \u201cWater systems and water management in\u00a0the\u00a0Czech Republic in\u00a0conditions of\u00a0climate change\u201d implemented with financial support from the\u00a0Technology Agency of\u00a0the\u00a0Czech Republic within\u00a0Subprogramme 3 \u2013 Long-term Environmental and Climate Perspectives of\u00a0the\u00a0SS programme \u2013 Programme for the\u00a0Support of\u00a0Applied Research, Experimental Development and Innovation in\u00a0the\u00a0Field of\u00a0the\u00a0Environment \u2013 Environment for Life. The\u00a0authors thank both reviewers for their inspiring comments, which significantly contributed to improving the\u00a0manuscript quality.<\/span><\/em><\/p>\nThe\u00a0Czech version of\u00a0this article was peer-reviewed, the\u00a0English version was\u00a0translated from the\u00a0Czech original by Environmental Translation Ltd.<\/p>\n","protected":false},"excerpt":{"rendered":"
The article presents the results of the Czech Hydrometeorological Institute (CHMI) obtained when addressing the sub-objectives \u201cScenarios of future water needs for different climate scenarios and individual sectors of water use\u201d (DC 1.1) and \u201cIdentification of areas with deficient water resources\u201d (DC 1.2), which are part of TA CR project No. SS02030027 \u201cWater systems and water management in the Czech Republic in conditions of climate change (Water Centre)\u201d and constitute specific tasks within the work package WP1 focusing on the future of water. The aim of the CHMI was to calculate and analyse how river flows upstream of gauging stations in Czechia are influenced by water use and to determine how this influence may change in relation to climate change. <\/p>\n","protected":false},"author":8,"featured_media":35239,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[2,86,92],"tags":[586,1511,3806,3780,3805,3804,3803,3802],"coauthors":[333,3766,3767,3768],"class_list":["post-35367","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-from-the-world-of-water-management","category-hydraulics-hydrology-and-hydrogeology","category-main","tag-climate-scenarios","tag-czech-republic","tag-natural-discharge","tag-spi-index","tag-unaffected-discharge","tag-water-accumulation","tag-water-disposal","tag-water-withdrawal"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/posts\/35367","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/users\/8"}],"replies":[{"embeddable":true,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/comments?post=35367"}],"version-history":[{"count":7,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/posts\/35367\/revisions"}],"predecessor-version":[{"id":35374,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/posts\/35367\/revisions\/35374"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/media\/35239"}],"wp:attachment":[{"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/media?parent=35367"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/categories?post=35367"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/tags?post=35367"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/coauthors?post=35367"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
In\u00a0addition to the\u00a0activities of\u00a0other consortium members already presented in\u00a0[6], the\u00a0main\u00a0task of\u00a0the\u00a0CHMI in\u00a0DC 1.1 was to analyse the\u00a0impact of\u00a0water use on discharge in\u00a0Czechia. The\u00a0basis was monthly data on total influence on discharge at water-gauging stations, expressed in\u00a0percentage as the\u00a0ratio of\u00a0discharge changes to QNE. In\u00a0practice, this variable (and its time series) was designated by the\u00a0abbreviation OVLTOT. Formally, its calculation can be expressed as<\/p>\n where:<\/p>\n <\/p>\n DELTA\u00a0\u00a0\u00a0\u00a0 represents SUMA\u00a0\u00a0\u00a0 is Each variable related to the\u00a0influence was first converted to m3<\/sup>\u00a0\u00b7\u00a0s\u20131<\/sup> and given an adequate sign. Negative values of\u00a0OVLTOT then indicated a predominance of\u00a0withdrawals (including water accumulation in\u00a0reservoirs), while positive values were associated with a predominant disposal (including water discharge from reservoirs). These data are regularly uploaded once a year to a CHMI database together with other available data on impacts valid for water-gauging stations (in\u00a0accordance with Act No. 254\/2001 Coll., on waters, as amended; with Decree of\u00a0the\u00a0Ministry of\u00a0Agriculture No. 431\/2001\u00a0Coll., on the\u00a0content of\u00a0the\u00a0water balance, its method of\u00a0compilation and on data for the\u00a0water balance, and to a certain\u00a0extent also with Decree of\u00a0the\u00a0Ministry of\u00a0Agriculture No. 252\/2013 Coll., on the\u00a0scope of\u00a0data in\u00a0records of\u00a0the\u00a0state of\u00a0surface and groundwater and on the\u00a0method of\u00a0processing, storing, and transmitting this data to public administration information systems). When calculating QNE, special attention is paid to the\u00a0distinction between withdrawals only from surface waters (which is reflected in\u00a0the\u00a0SUMAY characteristic, which results in\u00a0unaffected discharge values in\u00a0the\u00a0database abbreviated as QNEY) and total withdrawals (i.e. withdrawals from surface water including groundwater; which is reflected in\u00a0the\u00a0SUMAX characteristic, which results in\u00a0unaffected discharge values in\u00a0the\u00a0database abbreviated as QNEY). To maintain\u00a0homogeneity of\u00a0the\u00a0time series, only territorially relevant objects with a permit for the\u00a0withdrawal or disposal of\u00a0more than 6,000\u00a0m3<\/sup> per year or 500\u00a0m3<\/sup> per month are included in\u00a0the\u00a0calculation of\u00a0the\u00a0SUMA characteristic. The\u00a0DELTA characteristic only considers reservoirs with a permitted volume of\u00a0surface water accumulated or dammed greater than 1,000,000\u00a0m3<\/sup>. The\u00a0reference period 1991\u20132020 was selected for the\u00a0current analyses, with a total of\u00a0346 gauging stations meeting the\u00a0criterion for the\u00a0completeness of\u00a0the\u00a0time series.<\/p>\n Simultaneously, an R script for the\u00a0actual calculation of\u00a0QNE series was developed at the\u00a0CHMI. The\u00a0script functionality depends on using coordinates for the\u00a0correct location of\u00a0the\u00a0influencing object, and therefore it was necessary to check the\u00a0coordinates of\u00a0the\u00a0input data of\u00a0the\u00a0influence. Special attention was paid to appropriate location of\u00a0the\u00a0beginnings and ends of\u00a0the\u00a0conduits in\u00a0the\u00a0system of\u00a0watershed divides so that the\u00a0point of\u00a0withdrawal (or disposal) logically falls into the\u00a0catchment area with the\u00a0loss (or gain) of\u00a0water. At\u00a0the\u00a0end of\u00a0work on WP1 (June 2024), the\u00a0most detailed vector layer published as\u00a0of\u00a01\u00a0July\u00a02024 on the\u00a0CHMI website with open spatial data\u00a0[7, 8] was already considered for localising the\u00a0objects. This layer was constructed on top of\u00a0the\u00a0fifth generation Digital Terrain\u00a0Model of\u00a0the\u00a0Czech Republic (DMR 5G;\u00a0[9]). The\u00a0resulting QNE values were compared with the\u00a0values obtained at the\u00a0TGM WRI, which has been performing their annual calculation and submitted them to the\u00a0CHMI.<\/p>\n For the\u00a0purposes of\u00a0developing the\u00a0R script and calculating the\u00a0M-day discharge cadastre for the\u00a0reference period 1991\u20132020, input data on discharge influences were compared between three main\u00a0sources, which were the\u00a0Integrated System for Fulfilling Reporting Obligations (ISPOP system), files (exports) from the\u00a0River Basin\u00a0Authorities, state enterprises, and geographic layers from the\u00a0VODA Water Management Information Portal available at https:\/\/voda.gov.cz\/. It was found that the\u00a0sources differ in\u00a0the\u00a0number of\u00a0objects and the\u00a0values themselves; however, updating (also in\u00a0terms of\u00a0error corrections) of\u00a0these sources is somewhat decentralised, which can be seen as a great uncertainty. Therefore, a general check of\u00a0the\u00a0location of\u00a0objects, their duplicates, and the\u00a0values of\u00a0withdrawals and disposals was performed\u00a0[10].<\/p>\n Data from the\u00a0VODA Water Management Information Portal was considered more as supplementary because, at the\u00a0time of\u00a0processing (i.e. at the\u00a0end of\u00a0work on WP1), it only reached the\u00a0year 2020, compared to the\u00a0status on the\u00a0portal from August 2024 with data reaching back to 2014. Based on gaps in\u00a0the\u00a0time series, or changes in\u00a0the\u00a0object name and other attributes, objects were selected that could potentially be combined or divided. An analysis of\u00a0water bodies was also carried out, where both water body and watercourse withdrawals can be reported at the\u00a0same time. Therefore, objects situated in\u00a0close proximity to reservoirs were located. All this information about selected objects was subsequently sent to the\u00a0CHMI regional offices for manual checks via the\u00a0PostgreSQL database with support for GIS tools (i.e. PostGIS).<\/p>\n For map outputs of\u00a0the\u00a0total influence, a third-order catchment layer was selected, including 346 selected water-gauging stations with a complete time series of\u00a0the\u00a0total percentage of\u00a0influence for the\u00a0hydrological period 1991\u20132020. The\u00a0time series of\u00a0other studied elements were also complete. For each station, the\u00a0total area of the\u00a0catchment above it and its share of\u00a0the\u00a0area of the\u00a0third-order catchment in\u00a0which the\u00a0station is located were first calculated. The\u00a0total percentage of\u00a0influence for each catchment was calculated as the\u00a0sum of\u00a0total influence at all stations in\u00a0the\u00a0given catchment, with the\u00a0weight of\u00a0each station being the\u00a0calculated share of\u00a0the\u00a0catchment area above the\u00a0given station. The\u00a0total percentage of\u00a0influence in\u00a0individual catchments therefore corresponds primarily to the\u00a0stations at the\u00a0mouth or near the\u00a0mouth, where the\u00a0largest area is drained.<\/p>\n In\u00a0the\u00a0next phase, a trend analysis was performed to determine whether there were statistically significant gradual changes in\u00a0the\u00a0time series of\u00a0elements affecting the\u00a0discharge of\u00a0Czech rivers in\u00a0the\u00a0selected period. Two statistical significance levels were chosen, namely \u03b1\u00a0=\u00a00.05 and \u03b1\u00a0=\u00a00.01. The\u00a0Mann\u2013Kendall test for the\u00a0presence of\u00a0a trend\u00a0[11\u201313] and its modification proposed in\u00a0the\u00a0article\u00a0[14] were applied so that, in\u00a0the\u00a0case of\u00a0a significant autoregressive coefficient with the\u00a0assumed first-order autoregressive model, variance of\u00a0the\u00a0test statistic was corrected\u00a0[15\u201317]. Results for each station and month were summarised in\u00a0the\u00a0value of\u00a0the\u00a0standardised test statistic\u00a0Z (indicating the\u00a0direction of\u00a0a possible trend), the\u00a0p-value, and Sen\u2019s nonparametric estimate of\u00a0the\u00a0trend direction, denoted SEN\u00a0[18]. These analyses were performed for both monthly and annual time series.<\/p>\n The results were processed using the R package modifiedmk [19]. Cases where the p-value fell below the chosen significance level were plotted on maps using arrows at the locations of water-gauging stations. The arrow deviating from\u00a0the\u00a0horizontal direction, depending on the\u00a0sign of\u00a0the\u00a0Z or SEN values, represented an increasing trend (number of\u00a0values without a sign) or decreasing trend (number with a minus sign), similar to what was done in\u00a0other papers dealing with trends in\u00a0the\u00a0components of\u00a0the\u00a0hydrological cycle in\u00a0Czechia\u00a0[20\u201322]. Map outputs were subsequently created from these analyses.<\/p>\n Unlike precipitation, air temperature is, as expected, more evenly distributed between individual river catchments, which allows for analysis of\u00a0its changes for entire Czechia. Compared to the\u00a0normal from 1991\u20132020, changes in\u00a0the\u00a0average monthly temperature oscillate between 0 \u00b0C and +2 \u00b0C for both scenarios until approximately 2055 (Fig.\u00a03<\/span><\/em>). From this year, a more significant increase in\u00a0the\u00a0change in\u00a0air temperatures can be observed for both scenarios, especially for the\u00a0more pessimistic scenario SSP5-8.5. This is also confirmed by the\u00a0calculated average temperatures for individual decades of\u00a0the\u00a021st century. While the\u00a0change in\u00a0average monthly air temperature compared to the\u00a0normal in\u00a0the\u00a0first four decades (between 2020\u20132060) on a nationwide scale is around +1 \u00b0C, between 2060 and 2070 it exceeds +2 \u00b0C in\u00a0the\u00a0SSP5-8.5 scenario and continuously increases to an extreme +5 \u00b0C towards the\u00a0end of\u00a0the\u00a0century. In\u00a0contrast, according to the\u00a0SSP2-4.5 scenario, a more moderate increase in\u00a0air temperature can be expected, with a maximum change of\u00a0+2.4 \u00b0C between 2080 and 2090.<\/p>\n For precipitation, predictions are more variable, with the\u00a0course of\u00a0events according to different scenarios differing significantly (Fig.\u00a04<\/span><\/em>). From a nationwide perspective, according to the\u00a0SSP2-4.5 scenario, monthly precipitation totals have been around the\u00a0average of\u00a0the\u00a0reference period 1991\u20132020 (59.9\u00a0mm\/month) in\u00a0the\u00a0long term. From approximately 2040, there is a positive change in\u00a0precipitation totals, which lasts almost until the\u00a0end of\u00a0the\u00a0century.<\/p>\n In\u00a0contrast, the\u00a0SSP5-8.5 scenario suggests more significant changes, similar to the\u00a0development of\u00a0air temperatures. Around 2055, there is a positive change in\u00a0monthly precipitation compared to the\u00a01991\u20132020 normal. According to this scenario, average monthly totals will increase by up to 15\u00a0%, with this growth trend continuing constantly until the\u00a0end of\u00a0the\u00a0century.<\/p>\n Although the\u00a0outlook for the\u00a0nationwide average monthly precipitation totals may seem relatively optimistic, averages for individual decades show significant differences between third-order river catchments. From the\u00a0map outputs for both analysed scenarios (Figs. 5<\/span><\/em> and 6<\/span><\/em>), a recurring pattern can be seen at first glance across individual decades. This is the\u00a0transition of\u00a0higher precipitation totals in\u00a0the\u00a0west of\u00a0Czechia through a precipitation-poorer area that stretches from north to south, back to the\u00a0precipitation-richer east of\u00a0the\u00a0country. This transition is especially evident in\u00a0the\u00a0SSP2-4.5 scenario. While in\u00a0the\u00a0west of\u00a0the\u00a0country, a positive change in\u00a0monthly precipitation compared to the\u00a0normal prevails across all decades, in\u00a0the\u00a0northern, central, and southern parts of\u00a0Czechia this change is slightly negative. The\u00a0exception is the\u00a0decade 2020\u20132030, characterised by a negative change in\u00a0almost all catchments (national average -7.4\u00a0%) and, conversely, the\u00a0precipitation-rich decade 2070\u20132080 (average +8.4\u00a0%). The\u00a0scenario SSP5-8.5 predicts an increasing negative change in\u00a0average monthly precipitation for the\u00a0north-south belt of\u00a0Czechia in\u00a0the\u00a0first three decades compared to the\u00a01991\u20132020 normal, while in\u00a0the\u00a0east and west there is a transition from slightly positive to zero values. However, for the\u00a0rest of\u00a0the\u00a0century, the\u00a0prediction ranges exclusively in\u00a0positive changes in\u00a0total precipitation across all river catchments. The\u00a0nationwide average will not fall below +10\u00a0% from 2050, with maxima in\u00a0the\u00a0decades 2070\u20132080 (+16.3\u00a0%) and 2090\u20132100 (+17.4\u00a0%).<\/p>\n To clarify the\u00a0map outputs of\u00a0both air temperatures and precipitation, it\u00a0should be added that values in\u00a0some border catchments can sometimes differ significantly from values in\u00a0neighbouring catchment. This difference is caused by cropping a raster of\u00a0a certain\u00a0size by a smaller area of the\u00a0catchment, which can result in\u00a0the\u00a0extraction of\u00a0only one value\/pixel (i.e. the\u00a0value of\u00a0the\u00a0average monthly precipitation\/air temperature) for the\u00a0part of\u00a0the\u00a0studied catchment. This is therefore not a calculation error, but the\u00a0result of\u00a0the\u00a0necessary raster extraction.<\/p>\n The\u00a0last analysis performed was the\u00a0SPI index calculation, which is used to estimate wet\/dry conditions based on total precipitation. Specifically, it\u00a0is the\u00a0standard deviation by which observed precipitation would differ from the\u00a0long-term average and, before calculation, the\u00a0precipitation time series must first be transformed into a quantity with a standard Gaussian distribution (through its quantile function) using the\u00a0cumulative distribution function of\u00a0the\u00a0probabilistic model which is assumed to be a good description of\u00a0empirical values\u00a0[33]. In\u00a0this case, the\u00a0index calculated for a twelve-month time window SPI12 (with potential removal of\u00a0seasonality) with a gamma distribution was chosen.<\/p>\n According to simulated values of\u00a0total precipitation using the\u00a0scenario SSP2-4.5, extremely dry periods can be estimated during the\u00a02020s and around 2065, 2082, 2091, and 2094. Exceptionally to extremely dry conditions occur less frequently in\u00a0the\u00a0Oder catchment, while the\u00a0B\u00edlina and Oh\u0159e catchments show a more significant episode around 2058. Extremely and exceptionally wet conditions across the\u00a0catchments were simulated in\u00a0the\u00a0late 2040s and especially in\u00a0the\u00a0second half of\u00a0the\u00a02070s, and in\u00a0the\u00a0Morava catchment especially in\u00a0the\u00a0early 2070s. According to the\u00a0more pessimistic SSP5-8.5 scenario, dry and wet episodes do not alternate as much. Two more significant periods were simulated. The\u00a0first period, 2035\u20132050, includes four significant dry episodes, while the\u00a0second period, 2075\u20132095, includes six significant wet episodes. Here, obvious differences can be found between the\u00a0catchments. For example, the\u00a0dry episode around 2040 is not evident in\u00a0the\u00a0Ostravice, Opava and Morava catchments, while the\u00a0wet episode in\u00a02055 is not evident in\u00a0the\u00a0Oder catchment and in\u00a02086 in\u00a0the\u00a0Elbe catchment.<\/p>\n As shown in\u00a0Fig.\u00a01<\/span><\/em>, the\u00a0highest values of\u00a0total surface water influence were achieved in\u00a0the\u00a0catchments in\u00a0southern Moravia and the\u00a0Osoblaha catchment, the\u00a0Elbe from the\u00a0Orlice to the\u00a0Lou\u010dn\u00e1, and in\u00a0particular the\u00a0B\u00edlina catchment (however, in\u00a0this catchment, data were entered from only one gauging station); in\u00a0contrast, the\u00a0lowest values were achieved in\u00a0the\u00a0catchments of\u00a0the\u00a0Rybn\u00e1 and the\u00a0Lu\u017enice from the\u00a0Rybn\u00e1 to the\u00a0Ne\u017e\u00e1rka, the\u00a0S\u00e1zava from the\u00a0\u017delivka to the\u00a0mouth, and the\u00a0Dyje from the\u00a0Svratka to the\u00a0mouth. When groundwater withdrawals were included, high values were again\u00a0found in\u00a0the\u00a0catchments in\u00a0southern Moravia and in\u00a0the\u00a0catchments of\u00a0western and northwestern Bohemia. The\u00a0highest values of\u00a0the\u00a0degree of\u00a0influence can be observed in\u00a0the\u00a0catchments of\u00a0the\u00a0Lod\u011bnice, Osoblaha, and Oslava. In\u00a0contrast, the\u00a0lowest values were measured in\u00a0the\u00a0tributaries of\u00a0the\u00a0Freiberg Mulde, \u0160opava and Fl\u00f6ha, in\u00a0the\u00a0catchments of\u00a0the\u00a0Morava from the\u00a0Be\u010dva to the\u00a0Han\u00e1, the\u00a0Rybn\u00e1, and the\u00a0Lu\u017enice from the\u00a0Rybn\u00e1 to the\u00a0Ne\u017e\u00e1rka and the\u00a0Svitava.<\/p>\n From the\u00a0trend analysis for the\u00a0reference period 1991\u20132020, different behaviours can be observed in\u00a0water withdrawals and disposals at selected water-gauging stations, often creating noticeable clusters in\u00a0several areas (Fig.\u00a02<\/span><\/em>). Overall, however, a zero trend prevails across water withdrawals and disposals. This was found in\u00a0each of\u00a0the\u00a0monitored groups at approximately 230\u00a0of\u00a0the\u00a0total 346 water-gauging stations (around 65\u00a0% of\u00a0all stations).<\/p>\n <\/p>\n In the case of surface water withdrawals, including groundwater, a slightly decreasing and slightly increasing trend was observed at approximately 8 % of all monitored stations. The same ratio was measured at stations with a significantly increasing trend. A significantly decreasing trend was then detected at 47 stations (almost 14 % of all stations), forming noticeable clusters at stations in northern Bohemia (especially the Plou\u010dnice catchment) and eastern Bohemia (the Metuje catchment, the Orlice from the confluence of the Divok\u00e1 and Tich\u00e1 Orlice to the mouth, and the Lou\u010dn\u00e1 and Elbe from the Lou\u010dn\u00e1 to the Chrudimka). Other catchments with a predominance of significantly decreasing trends are the\u00a0catchments of\u00a0the\u00a0Moravsk\u00e1 S\u00e1zava and the\u00a0Morava from the\u00a0Moravsk\u00e1 S\u00e1zava to the\u00a0T\u0159eb\u016fvka, of\u00a0the\u00a0T\u0159eb\u016fvka and Svitava. Clusters of\u00a0slightly decreasing trends can also be observed in\u00a0the\u00a0catchments in\u00a0Silesia (the\u00a0Opava to the\u00a0Moravice, the\u00a0Ol\u0161e and the\u00a0Oder to the\u00a0Opava). In\u00a0contrast, the\u00a0observed increasing trends create clusters in\u00a0the\u00a0Vyso\u010dina region (especially the\u00a0S\u00e1zava catchment to the\u00a0\u017delivka, the\u00a0Svratka to the\u00a0Svitava, and the\u00a0Oslava and the\u00a0Jihlava from the\u00a0Oslava to the\u00a0Rokytn\u00e1) and in\u00a0the\u00a0Dyje catchment.<\/p>\n In\u00a0the\u00a0case of\u00a0withdrawals considering surface water only, a minimum of\u00a0stations with an increasing trend were found. Decreasing trends were recorded at less than 30\u00a0% of\u00a0the\u00a0monitored stations, which are relatively evenly distributed throughout Czechia. The\u00a0predominance of\u00a0significantly decreasing trends can again\u00a0be observed in\u00a0the\u00a0area of northern Bohemia, especially in\u00a0the\u00a0catchments of\u00a0the\u00a0Lu\u017eick\u00e1 Nisa to the\u00a0Mandava, the\u00a0Jizera, and the\u00a0Kamenice. Other areas with decreasing trends are the\u00a0catchments of\u00a0the\u00a0Berounka and its tributaries, the\u00a0upper and middle reaches of\u00a0the\u00a0Morava, and the\u00a0catchments of\u00a0southern Bohemia (the\u00a0Vltava to the\u00a0Mal\u0161e and the\u00a0Ne\u017e\u00e1rka).<\/p>\n A slight predominance of\u00a0increasing trends (a total of\u00a062 stations) was found in\u00a0water disposals compared to decreasing trends (34 stations). Areas with a\u00a0predominance of\u00a0increasing trends are the\u00a0catchments of\u00a0western Bohemia (the\u00a0M\u017ee to the\u00a0confluence with the\u00a0Radbuza and the\u00a0Otava to the\u00a0Voly\u0148ka), southern Moravia (the\u00a0Svratka and Svitava) and eastern Moravia (the\u00a0Vset\u00ednsk\u00e1 and Ro\u017enovsk\u00e1 Be\u010dva or the\u00a0Ostravice). Decreasing trends are more point-wise distributed; smaller clusters occur in\u00a0the\u00a0catchments of\u00a0the\u00a0Vltava to the\u00a0Mal\u0161e, the\u00a0Rakovn\u00edk Stream and the\u00a0Metuje.<\/p>\n Unlike precipitation, air temperature is, as expected, more evenly distributed between individual river catchments, which allows for analysis of\u00a0its changes for entire Czechia. Compared to the\u00a0normal from 1991\u20132020, changes in\u00a0the\u00a0average monthly temperature oscillate between 0 \u00b0C and +2 \u00b0C for both scenarios until approximately 2055 (Fig.\u00a03<\/span><\/em>). From this year, a more significant increase in\u00a0the\u00a0change in\u00a0air temperatures can be observed for both scenarios, especially for the\u00a0more pessimistic scenario SSP5-8.5. This is also confirmed by the\u00a0calculated average temperatures for individual decades of\u00a0the\u00a021st century. While the\u00a0change in\u00a0average monthly air temperature compared to the\u00a0normal in\u00a0the\u00a0first four decades (between 2020\u20132060) on a nationwide scale is around +1 \u00b0C, between 2060 and 2070 it exceeds +2 \u00b0C in\u00a0the\u00a0SSP5-8.5 scenario and continuously increases to an extreme +5 \u00b0C towards the\u00a0end of\u00a0the\u00a0century. In\u00a0contrast, according to the\u00a0SSP2-4.5 scenario, a more moderate increase in\u00a0air temperature can be expected, with a maximum change of\u00a0+2.4 \u00b0C between 2080 and 2090.<\/p>\n <\/p>\n For precipitation, predictions are more variable, with the\u00a0course of\u00a0events according to different scenarios differing significantly (Fig.\u00a04<\/span><\/em>). From a nationwide perspective, according to the\u00a0SSP2-4.5 scenario, monthly precipitation totals have been around the\u00a0average of\u00a0the\u00a0reference period 1991\u20132020 (59.9\u00a0mm\/month) in\u00a0the\u00a0long term. From approximately 2040, there is a positive change in\u00a0precipitation totals, which lasts almost until the\u00a0end of\u00a0the\u00a0century.<\/p>\n <\/p>\n In\u00a0contrast, the\u00a0SSP5-8.5 scenario suggests more significant changes, similar to the\u00a0development of\u00a0air temperatures. Around 2055, there is a positive change in\u00a0monthly precipitation compared to the\u00a01991\u20132020 normal. According to this scenario, average monthly totals will increase by up to 15\u00a0%, with this growth trend continuing constantly until the\u00a0end of\u00a0the\u00a0century.<\/p>\n Although the\u00a0outlook for the\u00a0nationwide average monthly precipitation totals may seem relatively optimistic, averages for individual decades show significant differences between third-order river catchments. From the\u00a0map outputs for both analysed scenarios (Figs. 5<\/span><\/em> and 6<\/span><\/em>), a recurring pattern can be seen at first glance across individual decades. This is the\u00a0transition of\u00a0higher precipitation totals in\u00a0the\u00a0west of\u00a0Czechia through a precipitation-poorer area that stretches from north to south, back to the\u00a0precipitation-richer east of\u00a0the\u00a0country. This transition is especially evident in\u00a0the\u00a0SSP2-4.5 scenario. While in\u00a0the\u00a0west of\u00a0the\u00a0country, a positive change in\u00a0monthly precipitation compared to the\u00a0normal prevails across all decades, in\u00a0the\u00a0northern, central, and southern parts of\u00a0Czechia this change is slightly negative. The\u00a0exception is the\u00a0decade 2020\u20132030, characterised by a negative change in\u00a0almost all catchments (national average -7.4\u00a0%) and, conversely, the\u00a0precipitation-rich decade 2070\u20132080 (average +8.4\u00a0%). The\u00a0scenario SSP5-8.5 predicts an increasing negative change in\u00a0average monthly precipitation for the\u00a0north-south belt of\u00a0Czechia in\u00a0the\u00a0first three decades compared to the\u00a01991\u20132020 normal, while in\u00a0the\u00a0east and west there is a transition from slightly positive to zero values. However, for the\u00a0rest of\u00a0the\u00a0century, the\u00a0prediction ranges exclusively in\u00a0positive changes in\u00a0total precipitation across all river catchments. The\u00a0nationwide average will not fall below +10\u00a0% from 2050, with maxima in\u00a0the\u00a0decades 2070\u20132080 (+16.3\u00a0%) and 2090\u20132100 (+17.4\u00a0%).<\/p>\n <\/p>\n <\/p>\n To clarify the\u00a0map outputs of\u00a0both air temperatures and precipitation, it\u00a0should be added that values in\u00a0some border catchments can sometimes differ significantly from values in\u00a0neighbouring catchment. This difference is caused by cropping a raster of\u00a0a certain\u00a0size by a smaller area of the\u00a0catchment, which can result in\u00a0the\u00a0extraction of\u00a0only one value\/pixel (i.e. the\u00a0value of\u00a0the\u00a0average monthly precipitation\/air temperature) for the\u00a0part of\u00a0the\u00a0studied catchment. This is therefore not a calculation error, but the\u00a0result of\u00a0the\u00a0necessary raster extraction.<\/p>\n The\u00a0last analysis performed was the\u00a0SPI index calculation, which is used to estimate wet\/dry conditions based on total precipitation. Specifically, it\u00a0is the\u00a0standard deviation by which observed precipitation would differ from the\u00a0long-term average and, before calculation, the\u00a0precipitation time series must first be transformed into a quantity with a standard Gaussian distribution (through its quantile function) using the\u00a0cumulative distribution function of\u00a0the\u00a0probabilistic model which is assumed to be a good description of\u00a0empirical values\u00a0[33]. In\u00a0this case, the\u00a0index calculated for a twelve-month time window SPI12 (with potential removal of\u00a0seasonality) with a gamma distribution was chosen.<\/p>\n According to simulated values of\u00a0total precipitation using the\u00a0scenario SSP2-4.5, extremely dry periods can be estimated during the\u00a02020s and around 2065, 2082, 2091, and 2094. Exceptionally to extremely dry conditions occur less frequently in\u00a0the\u00a0Oder catchment, while the\u00a0B\u00edlina and Oh\u0159e catchments show a more significant episode around 2058. Extremely and exceptionally wet conditions across the\u00a0catchments were simulated in\u00a0the\u00a0late 2040s and especially in\u00a0the\u00a0second half of\u00a0the\u00a02070s, and in\u00a0the\u00a0Morava catchment especially in\u00a0the\u00a0early 2070s. According to the\u00a0more pessimistic SSP5-8.5 scenario, dry and wet episodes do not alternate as much. Two more significant periods were simulated. The\u00a0first period, 2035\u20132050, includes four significant dry episodes, while the\u00a0second period, 2075\u20132095, includes six significant wet episodes. Here, obvious differences can be found between the\u00a0catchments. For example, the\u00a0dry episode around 2040 is not evident in\u00a0the\u00a0Ostravice, Opava and Morava catchments, while the\u00a0wet episode in\u00a02055 is not evident in\u00a0the\u00a0Oder catchment and in\u00a02086 in\u00a0the\u00a0Elbe catchment.<\/p>\n This paper presented the\u00a0basic results that the\u00a0CHMI reached within\u00a0the\u00a0work package WP1 of\u00a0the\u00a0\u201cWater Centre\u201d<\/span>. In\u00a0addition, the\u00a0shortcomings and uncertainties that accompanied the\u00a0analyses were briefly outlined. For example, the\u00a0situation surrounding data related to the\u00a0influence of\u00a0discharge in\u00a0Czech rivers can be understood as very urgent. There are several sources of\u00a0this data that are not correctly updated, for example, regarding the\u00a0errors found. Moreover, these data are used in\u00a0various other projects, which results in\u00a0the\u00a0creation of\u00a0new web (map) applications offering their presentation, which then worsens the\u00a0orientation of\u00a0the\u00a0processors of\u00a0such data\u00a0[10]. Given that from 2025 (i.e. initially with data for 2024) the\u00a0calculation of\u00a0the\u00a0unaffected mean monthly discharges will be transferred under the\u00a0responsibility of\u00a0the\u00a0CHMI, it will be necessary to carefully consult the\u00a0quality of\u00a0the\u00a0affected data with the\u00a0employees of\u00a0the\u00a0River Basin\u00a0Authorities, state enterprises. Otherwise, the\u00a0developed R script for calculating unaffectedness may be perfect, but it will still not give satisfactory results. For this reason, further maintenance and versioning of\u00a0the\u00a0script is planned via the\u00a0GitHub development platform (e.g. https:\/\/github.com\/ledvinkao). Its variants are also possible, depending on the\u00a0data source.<\/p>\n The\u00a0analysis of\u00a0the\u00a0influence on discharge and water resources in\u00a0Czechia, both in\u00a0the\u00a0area of surface and groundwater withdrawals and in\u00a0relation to climate change predictions, shows the\u00a0complex and regionally differentiated nature of\u00a0these changes. The\u00a0analysis results show significant variability between river catchments, which underlines the\u00a0need for an individual approach to assessment and management of\u00a0water resources in\u00a0different parts of\u00a0the\u00a0country. In\u00a0areas such as southern Moravia, northwestern Bohemia, and the\u00a0B\u00edlina catchment, significant changes in\u00a0groundwater and surface water withdrawals are being observed, which may have long-term consequences for water availability in\u00a0these regions. Conversely, lower impact values were observed in\u00a0some areas of\u00a0southern and eastern Bohemia \u2013 this may indicate greater water regime stability in\u00a0these regions.<\/p>\n Climate scenario predictions indicate rising air temperatures throughout the\u00a021st century, with the\u00a0more pessimistic SSP5-8.5 scenario even suggesting a significant temperature increase of\u00a0up to 5\u00a0\u00b0C by the\u00a0end of\u00a0the\u00a0century. Changes in\u00a0air temperature will have a direct impact on the\u00a0water balance in\u00a0Czechia, with regions with higher temperatures facing increased evaporation and changes in\u00a0the\u00a0availability of\u00a0water resources. As for precipitation, the\u00a0scenario SSP2-4.5 shows a rather moderate increase in\u00a0precipitation totals with regional differences, while the\u00a0scenario SSP5 8.5 suggests a significantly higher increase in\u00a0precipitation, especially in\u00a0the\u00a0western and southern parts of\u00a0Czechia.<\/p>\n SPI index calculation confirms the\u00a0occurrence of\u00a0extreme dry and wet periods during the\u00a021st century and there are obvious differences between individual catchments. The\u00a0scenario SSP2-4.5 predicts periods of\u00a0extreme drought around 2065 and in\u00a0the\u00a02090s, while extremely wet periods will occur in\u00a0the\u00a0late 2040s and 2070s. In\u00a0the\u00a0SSP5-8.5 scenario, these fluctuations are less frequent, but more frequent dry and wet periods are still expected in\u00a0specific decades, with some areas, e.g. the\u00a0Oder catchment, facing more frequent wet episodes.<\/p>\n Overall, the results indicate the need to adapt water management to the changes that climate change will bring. It is necessary to focus on regional specificities, as climate change will not affect Czechia evenly. It will be necessary to adapt water resource management with regard to the expected development in river catchments, which show different trends in water withdrawal and disposal. This includes not only improving water management policies and strategies for protecting water resources, but also reassessing infrastructure\u00a0projects and measures to mitigate the\u00a0impacts of\u00a0extreme climate conditions such as drought and floods. Preventive measures aimed at retaining water in\u00a0the\u00a0landscape will also play an important role.<\/p>\n With regard to the\u00a0existence of\u00a0climatological data in\u00a0grid form, the\u00a0number of\u00a0which will certainly rise, Czechia has taken a step in\u00a0the\u00a0right direction. Czech water managers may be assisted by additional information derived from grids, such as antecedent precipitation indices or seasonal hydrological predictions whose development has, in\u00a0fact, already begun with these grids\u00a0[37, 38].<\/p>\n The\u00a0article was prepared as part of\u00a0project No. SS02030027 \u201cWater systems and water management in\u00a0the\u00a0Czech Republic in\u00a0conditions of\u00a0climate change\u201d implemented with financial support from the\u00a0Technology Agency of\u00a0the\u00a0Czech Republic within\u00a0Subprogramme 3 \u2013 Long-term Environmental and Climate Perspectives of\u00a0the\u00a0SS programme \u2013 Programme for the\u00a0Support of\u00a0Applied Research, Experimental Development and Innovation in\u00a0the\u00a0Field of\u00a0the\u00a0Environment \u2013 Environment for Life. The\u00a0authors thank both reviewers for their inspiring comments, which significantly contributed to improving the\u00a0manuscript quality.<\/span><\/em><\/p>\n The\u00a0Czech version of\u00a0this article was peer-reviewed, the\u00a0English version was\u00a0translated from the\u00a0Czech original by Environmental Translation Ltd.<\/p>\n","protected":false},"excerpt":{"rendered":" The article presents the results of the Czech Hydrometeorological Institute (CHMI) obtained when addressing the sub-objectives \u201cScenarios of future water needs for different climate scenarios and individual sectors of water use\u201d (DC 1.1) and \u201cIdentification of areas with deficient water resources\u201d (DC 1.2), which are part of TA CR project No. SS02030027 \u201cWater systems and water management in the Czech Republic in conditions of climate change (Water Centre)\u201d and constitute specific tasks within the work package WP1 focusing on the future of water. The aim of the CHMI was to calculate and analyse how river flows upstream of gauging stations in Czechia are influenced by water use and to determine how this influence may change in relation to climate change. <\/p>\n","protected":false},"author":8,"featured_media":35239,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[2,86,92],"tags":[586,1511,3806,3780,3805,3804,3803,3802],"coauthors":[333,3766,3767,3768],"class_list":["post-35367","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-from-the-world-of-water-management","category-hydraulics-hydrology-and-hydrogeology","category-main","tag-climate-scenarios","tag-czech-republic","tag-natural-discharge","tag-spi-index","tag-unaffected-discharge","tag-water-accumulation","tag-water-disposal","tag-water-withdrawal"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/posts\/35367","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/users\/8"}],"replies":[{"embeddable":true,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/comments?post=35367"}],"version-history":[{"count":7,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/posts\/35367\/revisions"}],"predecessor-version":[{"id":35374,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/posts\/35367\/revisions\/35374"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/media\/35239"}],"wp:attachment":[{"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/media?parent=35367"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/categories?post=35367"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/tags?post=35367"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.vtei.cz\/en\/wp-json\/wp\/v2\/coauthors?post=35367"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![\"\"](\"https:\/\/www.vtei.cz\/wp-content\/uploads\/2025\/04\/Ledvinka-vzorec-1.jpg\")
<\/a>\n
\nthe\u00a0total impact of\u00a0operated reservoirs in\u00a0the\u00a0catchment above the\u00a0given station (or\u00a0the\u00a0difference between volumes at the\u00a0beginnings of\u00a0the\u00a0months)<\/p>\n
\nthe\u00a0sum of\u00a0impacts by withdrawals and\u00a0disposals<\/p>\nIdentification of\u00a0areas with deficient water resources (DC 1.2)<\/span><\/h3>\n
RESULTS AND DISCUSSION<\/h2>\n
Analysis of\u00a0the\u00a0influence of\u00a0water use on discharge (DC 1.1)<\/span><\/h3>\n
<\/a><\/h2>\n<\/a><\/h6>\nFig.\u00a01. Ratio of total discharge influence for third-order catchments (reference period 1991\u20132020)<\/h6>\n
<\/a>\nFig.\u00a02. Trend analysis for water withdrawals and disposals (reference period 1991\u20132020)<\/h6>\n
Identification of\u00a0areas with deficient water resources (DC 1.2)<\/span><\/h3>\n
<\/a>\nFig.\u00a03. Change in average monthly air temperature compared to the 1991\u20132020 normal based on climate scenarios; LOESS regression\u00a0[36] with a 95% confidence interval shown in bold<\/h6>\n
<\/a>\nFig.\u00a04. Change in average monthly precipitation totals compared to the 1991\u20132020 normal based on climate scenarios; LOESS regression\u00a0[36] with a 95% confidence interval shown in bold<\/h6>\n
<\/a>Fig.\u00a05. Change in average monthly precipitation totals compared to the 1991\u20132020 normal according to the SSP2-4.5 scenario in individual decades for third-order catchments<\/h6>\n<\/a>\nFig.\u00a06. Change in average monthly precipitation totals compared to the 1991\u20132020 normal according to the SSP5-8.5 scenario in individual decades for third-order catchments<\/h6>\n
CONCLUSIONS AND RECOMMENDATIONS<\/h2>\n
Acknowledgements<\/h3>\n