Effects of global change on streamflow, water demand and supply: a case study from the Pyrenees

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However, changes in climate, population, consumption patterns, land use and urbanization are affecting its quality and future availability. In Andorra, a country located in the middle of the Pyrenees, the confluence of climate change and a socioeconomic model with an important weight of the tourism industry based on an intensive use of water could threaten the future sustainability of water resources. This paper analyses the water resources of Andorra and its future sustainability using the Water Evaluation and Planning system (WEAP) modelling tool. The WEAP-Andorra model presents an initial estimate of the national water demand segregated into the main water consumers in the country (i.e. tourism, residential, primary sector, snowmaking, and hydro power production). It explores the future evolution of water resources combining climatic, including an extreme drought scenario, and socioeconomic variables (i.e. demography, tourism, irrigation, and snowmaking trends). The model includes an Adaptation scenario to assess the impact of some strategic adaptation measures. The results indicate a significant decrease in annual streamflow across all simulated scenarios by 2050. In the global change scenarios, yearly streamflow is projected to decrease between 5.3% and 8.5%, while in an extreme drought scenario, the loss reaches 65.4%. The impact of global change on future water demand at the country scale is not expected to be compromised. However, in an extreme drought scenario, it could be affected. The sectors most affected by the combination of global change and drought could be ski resorts, especially to ensure snowmaking and hydropower production. The future frequency and duration of droughts will determine the severity of the unmet demand. Water resource Sustainability Global change Adaptation Pyrenees Andorra Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Global change is one of the world’s greatest challenges. Warming temperatures, droughts, socio-economic and land-use changes are likely to affect the hydrological cycle, modify regimes and consequently alter water availability (Devkota & Gyawali, 2015 ; Xie et al., 2015a ). Changes in mountain hydrological regimes may have implications for water management, especially in densely populated areas, but also for water uses that are highly dependent on the hydrological regime, such as hydropower generation (Bombelli et al., 2019 ; Ravazzani et al., 2016 ; Schaefli, 2015 ). Numerous studies have been carried out both internationally and, in the Pyrenees (Beniston, 2012 ; García-Ruiz et al., 2011 ; IPCC, 2014 c;pez-Moreno, Revuelto, et al., 2013 ; López-Moreno, Vicente-Serrano, et al., 2013 ; Nogués-Bravo et al., 2007 ; Viviroli et al., 2003 ) to analyse the impacts of climate change on water resources in mountain areas. However, most studies address the resource and demand dimensions separately, reaching partial scientific conclusions that make it difficult to fully understand the dynamics of global change. Others focus on specific sectors of activity, ignoring the impact on the whole system. Other studies point to an increase in droughts in the following years in Europe, especially in summer (Manning et al., 2019 ; Spinoni et al., 2018 ), when the co-occurrence of extremes, such as meteorological drought and high temperatures is known as a compound event. The Mediterranean and Southern Europe seem to be one of the most affected areas (Spinoni et al., 2018 ), where an increase in both the magnitude and the duration of droughts (extremely long dry spells) is predicted (Lemus Casanovas, 2021 ). Recent literature has reported a decrease in streamflow in Mediterranean rivers in recent decades (Beguería, 2023 ; García-Ruiz et al., 2011 ; Hervé Le Treut, 2013 ; López-Moreno et al., 2006 ; Lorenzo-Lacruz et al., 2012 ; Vicente-Serrano & López-Moreno, 2005 ) with a slight increase in streamflow in winter and a decrease in summer (García-Ruiz et al., 2011 ; Senatore et al., 2011 ). In mountain ranges, streamflow and snow accumulation have decreased in recent decades, and further reductions could threaten the sustainability of downstream regions (Beguería et al., 2019 ; Haro-Monteagudo et al., 2020 ; Zabaleta et al., 2020 ). Global change also includes changes in land use and socio-economic activities, especially population growth, which leads to an increase in the demand for water. The expansion of woodlands results in increased evapotranspiration and water demand in the landscape (Buendia et al., 2016 ; Xie et al., 2015b ). As a result, the whole set of impacts related to global change can put the hydrological system and its uses at risk. Understanding the interaction between global change and water resources can help society address and diminish harmful effects by introducing better water management plans and integrating adaptation strategies. This paper proposes an integrated approach that combines hydrological and water resources management modelling to improve understanding of the future impacts of physical and socioeconomic changes on water resources. The model focuses on the Valira river catchment in the Principality of Andorra, generating scenarios of water availability and identifying the most vulnerable sectors in case of increased water scarcity. Andorra is a small, mountainous country located in the Central Pyrenees between France and Spain, with a population of 81588 inhabitants as of 2022 and an area of 468 km 2 . Over the last 50 years, the country has transitioned from a traditional agricultural and stock farming system to a tourism focused system. This shift has resulted in significant changes in land use, including a considerable increase in woodland, expansion of urban areas and infrastructure, and a reduction in crop cultivation (Caritg, 2009 ). During the reference period (1981–2010), Andorra experienced an average annual precipitation of 1054 mm and an average temperature of 5.6ºC. In 2019, the water resources were estimated to be 366 hm 3 /year (Govern d’Andorra, 2020 ). The resources are mainly concentrated in three rivers: Valira, Valira del Nord and Valira d’Orient, which are the focus of this study (Fig. 1 ). The hydrological regime of Andorra is typical of a mountainous area with Mediterranean influence and a nival regime. The flow is high in spring and low in summer and winter. As shown in Fig. 1 , streamflow is measured and recorded at the main station gauge in the Valira river (A403-Borda Sabaté), which belongs to the Ebro Basin Agency ( Confederación Hidrográfica del Ebro -CHE). The paper is organized as follows. The material and methods are presented in Section 2. Section 3 provides the results and discussion. Finally, conclusions and some prospects for future work are presented in Section 4. 2. Material and methods The paper presents an integrated model developed using the Water Evaluation and Planning 1 (WEAP) (Sieber & Purkey, 2015 ) tool in two main steps. Firstly, hydrological and water demand modelling were conducted to represent current physical and socioeconomic dimensions. Then, the integrated model was projected up to 2050, guided by climatic and socioeconomic factors representing various global change scenarios. These different scenarios enable the quantification of impacts on streamflow and on different socioeconomic water users (i.e. main activity sectors of the country). Finally, in order to quantify their potential impact on the conservation and sustainability of water resources, some adaptation strategies focusing on demand-side efficiency have been analysed. 2.1. WEAP model The implemented WEAP model estimates the water balance in the Valira river catchment inside Andorra from 2015 to 2050 in a monthly timestep. It includes the upper Valira catchment, Valira del Nord and Valira d’Orient sub-catchments, with a total area of 475 km 2 . The principle algorithm of WEAP is a spatially-resolved water balance calculated on a monthly basis by balancing water supply and demand at each node and link in the system (Höllermann et al., 2010 ). Figure 2 shows the WEAP structure with 4 river segments, 100 sub-catchments, 33 demand sites, 1 reservoir with hydropower generation, transmission links and flow requirements to fix ecological flows. Nodes represent demand sites from different socioeconomical sectors (red dots) and water inputs from catchments from hydrological balance (green dots), while links connect them to rivers. The model combines the hydrological and demand modelling for conducting an integrated water resources assessment. The base year (current accounts) of the model is established in 2015 but, in terms of climate, it is constructed using the 30-year standard reference period 1981–2010 to avoid the interannual variability of specific years. Model calibration (2015–2017) and validation (2018–2019) have been conducted in a separate simulation. 2.1.1. Hydrological modelling The hydrological processes were internally calculated using the WEAP Soil Moisture method. This scheme, based on empirical functions that describe evapotranspiration, surface runoff, sub-surface runoff (i.e. interflow) and deep percolation. It represents the catchment in two soil layers, as well as the potential for snow accumulation (Sieber & Purkey, 2015 ). The water mass balance is represented in lumped portions of the catchment, each divided into N (i.e. eight in this study) fractional areas representing different land cover types (Angarita et al., 2018 ). The model is forced using climate data that is assumed to be uniform over each defined hydrological unit, which in this study consists of. 100 sub-catchments. Base year precipitation and temperature are based on the spatial interpolation of the climatic reference period (1981–2010) monthly data. The interpolation was performed using the fixed interpolation method (Ninyerola et al., 2000 ), considering the Andorran meteorological stations and the geographical factors with influence on the climate of the area (Batalla, Esteban, et al., 2016). Monthly mean values for the 100 modelled sub-catchments were calculated using the available data in a 90 meters cell size grid 2 . Humidity and wind are established in the same way but, in this case, using the PIRAGUA_atmos_analysis 3 regional dataset (Palazón et al., 2022 ). It contains observation-based meteorological data with a cell size resolution of 2.5 km suited for hydrological simulation of the Pyrenees range, for the historical period (1981–2010). Land use types and its area have been defined in the modelled sub-catchments using the 2012’s Andorran Land Cover Map 4 and the 2009’s Catalan Land Cover Map 5 to complete the Ós river sub-catchment outside Andorran administrative limits. Land cover classification includes dense forest, open forest, cropland, permanent meadows and grassland, rocky areas, bare areas, urban areas, and shrublands. As detailed in Section 2.1.3, land use parameters (i.e. soil water capacity, deep water capacity, runoff resistance factor, etc.) are the base for the model calibration. 2.1.2. Water demand modelling Water demand of the main activity sectors in Andorra were implemented in the WEAP model using transmission links from the river to the demand sites or to the Engolasters reservoir. To characterize the final monthly demand of each main sector (i.e. domestic, touristic, agricultural and livestock, ski resorts and hydropower generation), the national annual water use official data (Govern d’Andorra, 2021a ) have been supplemented (see Table 1 ). The monthly domestic water demand is characterised for each of Andorra's parishes (i.e. Andorran regional governments) using the 2019 annual water consumption reports. The domestic use of water during the 2019 is estimated to be at 220 l/person·day (Govern d’Andorra, 2021a ). Touristic water demand was defined at parish level using the number, capacity, and category of the tourism accommodations in Andorra (from camping sites, hostels to luxury hotels) in the year 2019 6 . Tourism accommodation with SPA is estimated with a 20% increase in water consumption. The dataset used for the monthly data consumption variation and total occupancy was a 10-year period (2011–2020) published annually by Unió Hotelera d’Andorra 7 . The annual water use rate was assigned according to the tourism accommodations categories (Gössling et al., 2012 ). The water requirements for snowmaking of the Andorran ski resorts were obtained from the Andorran Government’s annual water consumption publication (Govern d’Andorra, 2021a ). Monthly variations in water consumption were extracted from one of the ski resorts (Pesado, 2021 ). Agricultural water demand was obtained from the Department of Agriculture of the Andorran Government database, which registers the different grassland and crop types and their representative surfaces 8 . The main grassland types are tobacco, ray grass and alfalfa. Crop water requirements are limited in the modelling from May to September (i.e. the natural vegetative period in the Pyrenees) and the specific crop water demand was obtained from the official Food and Agriculture Organization of the United Nation database 9 . Livestock water demand was obtained from the number and type of cattle heads from Government registration database 10 and its daily water needs (INRA, 2018 ). The monthly dataset used for the research was from 2015 to 2021. This data was converted to monthly livestock consumption requirement in all the catchments. Hydropower water demand was characterised in WEAP with the information provided by FEDA 11 , the public company responsible for importing, generating, distributing, and commercialising electricity in Andorra. This information includes the maximum turbine flow, the storage capacity of the reservoir, the volume elevation curve, the tailwater elevation and intakes from the river to the reservoir named Engolasters . Transmission links from different catchments were used to implement the latter, while also considering the regulatory environmental flow requirement (see Fig. 2 ). Table 1 summarises the assumptions made in characterising the water demands of the various sectors in Andorra. Description Source Domestic water demand Standard domestic water demand in 2019 (220 l/person*day) Govern d’Andorra ( 2021a ) Monthly domestic water demand Touristic water demand Number, capacity, and category of the tourism accommodations Unpublished DB provided by Andorran Government (2021) Monthly data consumption variation and total occupancy 20% of water demand increasing in tourism accommodation with SPA Unió hotelera ( http://www.uha.ad ) Veure com justificar valor alçat Snowmaking water requirement All Andorran ski resorts 2019 annual consumption (m 3 ) Govern d’Andorra ( 2021a ) Monthly data consumption variation (30% November, 36 December, 25% January, 1% February) One ski resort personal communication Agricultural water requirement Crop types and surface area Mean water requirements of Andorran crops: 575 mm/m 2 Mean water requirements of Andorran crops minus the contribution of precipitation: 338 mm/m 2 Monthly data requirements variation Andorran Government database 12 FAO database 9 , Govern d’Andorra ( 2021a ) Batalla, Ninyerola, et al. ( 2016 ) Pesado ( 2021 ) Livestock water demand 20 m 3 /day for standard livestock units INRA ( 2018 ) Cattle: number and type Andorra Government livestock database 10 Andorra's official equivalences of livestock type BOPA, núm. 47, 2015 Hydropower water demand Monthly water catchment to Hydropower FEDA 13 Hydropower characteristics (storage capacity = 0.6 hm 3 , and volume elevation curve) FEDA 13 Table 1 . Summary of the assumptions made in characterising water demands of the various sectors in Andorra. 2.1.3. Model calibration and validation The WEAP model, which incorporates hydrology and water management, was calibrated (2015–2017) and validated (2018–2019) for the monthly streamflow at the most representative discharge gauge (see A403 station in Fig. 1 ) located on the Valira river outlet from Andorra. Although automatic calibration approaches are often preferred, they are not always successful. The main concern is that automatic calibration fails to attach physical reality to the parameters, and the resulting modelling may not make hydrologic sense (Pomeroy et al., 2022). In addition, some parameters are highly influenced by different land cover and seasonality, such as runoff resistance or crop coefficient, which requires experienced manual calibration. However, automatic calibration is less time-consuming and more objective than manual calibration. Therefore, a hybrid calibration of the parameters for all land covers has been performed. First, manual adjustment was developed using qualitative and quantitative techniques based on visual inspection of the model output (i.e. streamflow) and the use of objective functions to provide a numerical assessment of performance. Different literature provides a helpful range values to initiate the manual calibration for all the parameters (Amin et al., 2018 ; Keith et al., 2010 ; Sieber & Purkey, 2015 ; Vicuña et al., 2011 ). Then an automatic calibration with Parameter Estimation 14 (PEST) was used for the freezing point and melting point parameters. Further information on the used parameters and their optimal values can be found in Table 2 . Table 2 Calibration ranges provided by literature and their optimal values. Parameter Range provided by literature Optimal value Soil water capacity 0-higher (mm) 2000 Root zone conductivity 0-1000 (mm/month) 50 Deep water capacity 0-higher (mm) 20000 Deep water conductivity 0.1-higher (mm/month) 1 Runoff resistance factor 0-1000 (no dimension) 2–20 (depending on land cover and seasonality) Preferred flow direction 0–1 (no dimension) 0.3 Crop factor 0–1 (no dimension) (depending on land cover and seasonality) Freezing point - − 2ºC Melting point - + 7.5ºC To evaluate the performance of the calibration results, the statistical parameters percent bias (PBIAS), Nash–Sutcliffe efficiency (NSE) and the ratio of root-mean-square error to the observations standard deviation (RSR) were used in this paper. According to Moriasi et al. ( 2007 ), the statistical parameters show a very good performance in terms of NSE (> 75) and RSR (< 0.50), and a satisfactory ratio in terms of PBIAS ( ± 15%). The monthly model calibration and validation presented in Fig. 3 showed that the WEAP model could be a useful tool for evaluating the impacts of global change on the streamflow of the study area. 2.2. Scenario analysis Table 3 shows the implementation of three future scenarios, allowing for climate and socioeconomic changes. The reference scenario serves as a realistic baseline, while the Global change scenario incorporates higher levels of climate and socioeconomic changes in the projections up to 2050. The scenario known as Historical drought year assumes the temperature increase and socioeconomic changes of the Global Change scenario, as well as the precipitation decrease of the driest year on record. Scenario Climate change (2050 anomaly) Socioeconomic changes in the main sectors (2050) RCP T (ºC) Pp (%) Domestic Tourism Agriculture Ski resorts Reference 4.5 T 1 P 1 ▲Demographic: 1,18%/year ▲Accommodations: 1,18%/year Global change 8.5 T 2 P 2 ▲Demographic: 1,62%/year ▲Accommodations: 1,62%/year Occupancy: 70% ▲Irrigation demand 10%* ▲Snow making 15% Historical drought year 8.5 T 2 P 3 ▲Demographic: 1,62%/year ▲Accommodations: 1,62%/year Occupancy: 70% ▲Irrigation demand 10%* ▲Snow making 15% Table 3 . Summary of the implemented scenarios up to 2050. T 1 , T 2 , P 1 , P 2 and P 3 represent the projected monthly distributions of temperature and precipitation in each scenario (see Table 4 ). Monthly climatic future changes presented in Table 2 are based on the Andorran average values of the PIRAGUA_atmos_climate 15 regional dataset (Palazón et al., 2022 ). It is a statistical downscaling at the Pyrenees range of six global climatic models for the historical and future periods (1981-2010-2100). The average of the six climatic models in terms of temperature and precipitation were used to implement the future scenarios (Table 4 ). To represent the impact of extreme drought on water resources, the Historical drought year scenario is based on the year with the lowest precipitation record, representing an extreme climate scenario by 2050. The analysed series, within the framework of the CLIMPY 16 project, covers the period 1950–2015. The year with the lowest precipitation was 2007. Therefore, for the 2050 horizon, the precipitation of that year has been calculated as a percentage deviation (i.e. P 3 in Table 4 ) from the reference meteorological period (1981–2010). Table 4 Projected changes in monthly average temperature and precipitation in 2050 for the different scenarios Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. Dec. T 1 (ºC) 1.36 0.92 1.21 1.08 1.37 2.18 1.92 2.33 1.50 1.44 1.03 1.14 T 2 (ºC) 1.66 1.61 1.66 2.05 2.09 3.32 2.96 2.83 2.50 2.11 2.03 1.95 P 1 (%) 1.01 1.50 3.39 -4.75 -3.62 3.05 -1.90 6.10 -3.34 2.24 -2.82 -5.16 P 2 (%) -5.62 7.35 3.74 -1.13 -1.89 0.41 3.11 14.55 4.38 1.75 -12.1 -5.80 P 3 (%) -71.23 13.71 -1.48 -29.36 -12.33 -44.47 -54.64 1.47 -88.57 -59.46 -52.99 -65.75 In addition to climate variation, some socioeconomic changes are also considered. In the reference scenario, the population growth and tourism activity remain stable as the current trend (i.e. population and accommodations increase of 1.18%/year). According to Recaño ( 2021 ), the Global change scenario considers a more pronounced increase in population and tourism, at a rate of 1.62% per year. Additionally, this scenario assumes an increase in snowmaking of 15% (Gerbaux et al., 2020 ), a 10% increase in irrigation requirements (FAO, 2008 ), and an increase in tourist occupancy of up to 70%,. The Adaptation scenario was implemented while maintaining the same climatic and socioeconomic assumptions as the Historical drought year. This scenario includes certain adaptation measures, such as reduced water demand and improved water supply infrastructure. The details of these measures are provided below. Measures related to domestic consumption were extracted from the Andorran Circular Economy Law (Govern d’Andorra, 2021c ). The law mandates a reduction in consumption to 150 litres per person per day, which represents a 30% decrease from the current average consumption. Additionally, public investment is assumed to improve water distribution and transport canalizations, reducing water losses by 20% from the current rate of 40% (Govern d’Andorra, 2021b ). In this scenario, there is also an improvement in the efficiency measures for water usage in tourist accommodations, allowing for a 25% reduction in consumption. This improvement follows the guidelines of LIFE Watsavereuse 17 . Lastly, there is assumed to be an improvement in crop irrigation and snowmaking techniques, resulting in a 15% increase in water storage efficiency (Cognard & François, 2019 ). 3. Results and discussion This section presents the results of the impact on streamflow and demand in the implemented scenarios using the WEAP model. 3.1. Effects of global change on the streamflow regime Results showed a clear significative decrease in annual streamflow in all the modelled scenarios (see Fig. 4 ) by 2050, which means an explicit reduction on overall water resources in Andorra, where most of them come from rivers and lakes, and fewer from groundwater. In the reference scenario, the annual streamflow is estimated to decrease by 5.3%, and in the Global change scenario by 8.5%. Despite this, during winter periods, in the Reference and Global change scenarios, the streamflow could increase slightly, except for January where the streamflow could further increase (40–55%). This can be explained by a shift from snow to rain precipitations accordingly to climate change projections. However, the most significant result in streamflow reduction among the different scenarios, is the heavy flow reduction modelled in the Historical drought scenario. In the Global change scenario, nearly 10% of the annual volume of streamflow is lost, whereas in the Historical drought scenario, this loss reaches 65.4%. It appears that the combined effects of rising temperatures and dry years significantly enhance the loss of streamflow, thereby reducing basin water resources. Furthermore, while the Reference and Global change scenarios show a slight increase in flow during winter, the opposite occurs in the Historical drought year scenario. This highlights a significant difference between scenarios, making dry years particularly challenging in terms of water resources in all seasons, especially during winter. Therefore, considering the climate projections for the Pyrenees, in the RCP 8.5 scenario, and the probable increase in the duration, intensity, and magnitude of drought periods (Lemus Casanovas, 2021 ), dry spells and drought future scenarios could have significant impacts on water resources, particularly during specific months of the year. It should be noted that, in the drought scenario, river flow reaches minimum values ​during the winter and summer seasons (see Fig. 5 ). These periods will probably be the most limiting periods for Andorra in terms of water resources. Moreover, in the Historical drought year scenario, the minimum ecological flow defined by regulation 18 could not be met in most of the months of the year. It is of particular concern during the summer when water temperatures reach higher values, exposing the riparian species, especially salmonids a flagship species in Pyrenean rivers (Floury et al., 2021 ). These conditions can expose them to temperatures above 21ºC, concentrations of pollutants, and minimum flows outside their life range (Govern d’Andorra, 1996 ; Lewis, 2006 ). The increase in the frequency of drought episodes indicates non-compliance with minimum ecological flows. Therefore, the ecosystem functions cannot be guaranteed, which may result in changes to the main biological and biochemical parameters of the river. The implementation of measures in the Adaptation scenario reduces streamflow loss in the Historical drought scenario by up to 12.5%. During low streamflow months, such as July, August, and September, adaptation measures could save up to 28.8% of river flow. Additionally, in February, the measures could save around 45.2% of river flow. Therefore, during drought periods, adaptive measures could have a strong effect on water conservation, particularly during periods of low flow. 3.2. Effects of global change on water demand and supply 3.2.1. Water demand Figure 6 shows the monthly water demand for 2050 across all socio-economic sectors, including domestic, agriculture and livestock, tourism, and snowmaking for ski resorts, in the modelled scenarios. The Base year scenario indicates a total demand of 23.3 hm 3 . Domestic consumption is the sector with the highest annual water demand, accounting for 71.8% of the total amount. This is followed by agriculture and livestock at 11.2%, tourism at 10%, and the snowmaking sector at 6.9%. The results presented in Fig. 6 indicate an 88.42% in water demand in the Global change scenario (43.9 hm 3 ) compared to Base year (23.3 hm 3 ). This increase is mainly attributed to projected population growth and other assumed socioeconomic changes. The measures implemented in Adaptation scenario reduce water demand to almost half (41.7%) of the water demand in the Global change scenario. In conclusion, adaptation measures could significantly mitigate the impacts of the projected socioeconomic change on water supply requirements. These measures are necessary to ensure future water resource needs in projected socioeconomic scenarios and to minimize the effects on ecosystems. Hydropower generation is a key energy sector in Andorra, where over 75% of electricity is imported. Non-consumptive water used for hydroelectricity accounts for about 74 hm 3 , which generates approximately 76 GWh annually in the Base year. Figure 7 shows the total annual hydropower production at the main hydropower plant of the country in the modelled scenarios for 2050. The production remains relatively stable in both the Reference and Global change scenarios when compared to the Base year. Therefore, the impact of these scenarios on hydropower production appears negligible on an annual basis. However, during the winter months, hydropower production could increase under these scenarios, while it decreases in June and July. The increase in winter streamflow can be explained by the decrease in the snow/liquid precipitation ratio, accordingly to climate change projections. These results are consistent with other recent studies in different mountain ranges (Bombelli et al., 2019 ; Brunner et al., 2019 ; Van Vliet et al., 2016 ). However, it is important to note that the Historical drought year scenario results in a significant decrease in the annual hydropower generation compared to the Base year, with a reduction of 39.9%. The decrease is observed in every month of the year, with the most significant decreases occurring in June, July, and September, where production could fall 61.6%. In the winter season, the decrease could be around 47%. Additionally, the results indicate that the Adaptation scenario has an insignificant effect on reducing the decline of hydropower generation in the Global Change scenario. This lack of effect could be attributed to the location of the hydropower station in Andorra, which is situated before the main water intakes, making it difficult to obtain the benefits of Adaptation measures. However, a more in-depth analysis is required to evaluate the hydropower exploitation system of Andorra in greater detail and to develop specific adaptation measures and strategies. Despite the limitations of this study, it should be emphasized that a significant loss of hydropower generation capacity must be assumed in drought years. Efforts must be made to improve the hydrological-electricity modelling framework to better understand the linkages between water and electricity supply under future climate variability and change, including droughts. This will contribute to the quantification of the water-energy nexus. 3.2.2. Water supply The WEAP model assesses the probable unmet demands for public water supply locations in future scenarios. Firstly, it analyses the unmet flow requirements and the percentage of monthly demand coverage at the country level. The future demand in the 2050 horizon is guaranteed for all scenarios except for the Historical drought. In this scenario, there is an unmet demand in the snowmaking sector (see Fig. 8 ). In December, the maximum monthly average unmet flow is 187000 m 3 , which represents 36.30% of the required flow. In February 2050, the maximum is 4980 m 3 , which is only 2.59% of the required flow. In the Adaptation scenario the total unmet demand is reduced. Specifically, in February, the flow requirement for snowmaking is fully covered, and in December, the unmet demand is reduced by 12.63% compared to the Historical drought scenario. Therefore, the Adaptation scenario has a significant effect, and efforts in adaptation measures will be crucial for the future sustainability management of water resources. It has also been observed, once again, that the effects of drought periods have a much higher significance on the water resource than a warmer climate. This can be clearly seen here as the Global change scenario does not have any effects on the future flow requirements for snowmaking, while the Historical drought scenario does. As explained in the previous section, mountain global warming, especially in winter season, can facilitate increased flow availability (Lorenzo-Lacruz et al., 2012 ; Oo et al., 2020 ). This fact contributes to explaining why winter demand is fully supplied in the Global change scenario and not in the Historical drought scenario. It is important to note that, according to the results, while the demand for snowmaking does not seem to be guaranteed in Andorra’s future, there are adaptation measures that can help alleviate the effects of water scarcity during December and February. These measures involve local water management through small reservoirs and utilizing water during the spring and summer months. Brunner et al. ( 2019 ) assessed the potential of reservoirs and natural lakes to alleviate water shortages during periods of low seasonal discharge and high water demand. 4. Conclusions The WEAP model was applied in the present study offering a simple process for investigating global change and drought periods impacts on streamflow in a Pyrenean basin. It also assessed the future coverage of water need requirements across the Andorran socio-economic sectors. The results provide detailed estimates of the variations in water resources, including total discharges and seasonal impacts in Andorra. The implemented scenarios enable the study of various responses in streamflow for the 2050 horizon, contributing to a better understanding of streamflow behavioural patterns and dynamics in the most plausible climate change projections and socio-economic pathways. Overall, all scenarios project a reduction in water resources by 2050, with the extreme drought scenario standing out due to a significant reduction. Long and severe droughts are expected to become more frequent in the coming years. These extended periods of low rainfall not only reduce water flow, but also pose a threat to hydropower generation capacity and the ecosystem functions of rivers. Rivers play a crucial role as biogeochemical transformer of energy and water, and in providing diverse ecological habitats, which could be at risk. At this point, it is crucial to implement measures that ensure an appropriate ecological flow regime in each river section. Adaptation measures have been shown to mitigate flow reductions. However, in the case of hydroelectric production, the modelled measures did not have a significant impact in mitigating the expected impacts. The location of water intakes for hydropower plants in the upper parts of the basin strongly influences the effectiveness of adaptive measures. However, further analysis is required to reach more conclusive results and determine the best adaptation strategies for this sector. This study identified that the future demand in the 2050 horizon will not be met in an extreme drought scenario, but will be met in all other scenarios. Drought years may significantly affect the ability to meet the full demand of certain sectors in the future. This is particularly relevant for snowmaking in Andorra and other mountain regions in the Pyrenees where the ski industry is central to the local economy. The implemented scenarios did not consider future changes in land use, an important factor in terms of runoff generation in the Pyrenees (López-Moreno et al., 2014 ). Therefore, the model may underestimate streamflow reductions in global change scenarios. The next steps in the modelling process will consider this factor. Declarations Statements & Declarations Andorra Research + Innovation gratefully acknowledges the Government of the Principality of Andorra for the 2017 complementary grant to the European POCTEFA 2014-2020 Program, Ref. AUEP007-AND/2017. Cristina Pesado Pons acknowledges a predoctoral grant from the Government of the Principality of Andorra (Ref. ATC017 - AND-2017/2019). The authors thank Jordi Ordoñez for his valuable assistance in improving the fitness of the WEAP-Andorra model and Anna Albalat for the support providing some of the climatological data. 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Mt Res Dev 23(1):32–40. https://doi.org/10.1659/0276-4741(2003)023[0032:athsot]2.0.co;2 Xie X, Liang S, Yao Y, Jia K, Meng S, Li J (2015a) Detection and attribution of changes in hydrological cycle over the Three-North region of China: Climate change versus afforestation effect. Agric For Meteorol 203:74–87. https://doi.org/10.1016/j.agrformet.2015.01.003 Xie X, Liang S, Yao Y, Jia K, Meng S, Li J (2015b) Detection and attribution of changes in hydrological cycle over the Three-North region of China: Climate change versus afforestation effect. Agric For Meteorol 203:74–87. https://doi.org/10.1016/j.agrformet.2015.01.003 Zabaleta A, Haro-monteagudo D, Antiguedad I, Beguería S (2020) Past hydrological trends on the Pyrenees : towards a higher spatial heterogeneity. EGU Gen Assembly 2020. https://doi.org/https://doi.org/10.5194/egusphere-egu2020-10370 Footnotes https://www.weap21.org/ Data available at http://www.acda.ad/ Database available at http://hdl.handle.net/10261/271111 https://www.iea.ad/mapa-de-cobertes-del-sol-d-andorra-2012 https://www.creaf.uab.es/mcsc/ Personal communication Data available at http://www.uha.ad https://www.agricultura.ad/images/stories/estadistiques/conreus/superficie_conreu_2019.pdf https://www.fao.org/land-water/databases-and-software/crop-information/en/ https://www.agricultura.ad/el-padral www.feda.ad https://www.agricultura.ad/images/stories/estadistiques/conreus/superficie_conreu_2019.pdf Personal communication Further information at https://pesthomepage.org/ Database available at http://hdl.handle.net/10261/271116 https://www.opcc-ctp.org/en/climpy Further information at https://lifewatsavereuse.eu In Andorra, the current regulatory minimum ecological flow is defined as 10% of the average annual flow of, at least, a historical period of 5 years (BOPA, 2005 ). 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(right).\u003c/p\u003e","description":"","filename":"F1.png","url":"https://assets-eu.researchsquare.com/files/rs-3915469/v1/59038eddc8cda7cb5674173d.png"},{"id":50745799,"identity":"9615f208-6838-45c1-bca7-f44d8baae8bc","added_by":"auto","created_at":"2024-02-06 17:04:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":210680,"visible":true,"origin":"","legend":"\u003cp\u003eWEAP schematic view with the modelled hydrographic network (light blue lines), demand nodes (red dots), catchment inflows (green dots), water returns, reservoir (green triangle) and environment flow requirement.\u003c/p\u003e","description":"","filename":"F2.png","url":"https://assets-eu.researchsquare.com/files/rs-3915469/v1/9247627093e6f675a6de1214.png"},{"id":50745795,"identity":"7214499a-4503-4eb7-960e-d657cd89e927","added_by":"auto","created_at":"2024-02-06 17:04:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":57118,"visible":true,"origin":"","legend":"\u003cp\u003eObserved and modelled monthly streamflow in A403 gauging station with the calculated performance statistics to quantify the accuracy of the model.\u003c/p\u003e","description":"","filename":"F3.png","url":"https://assets-eu.researchsquare.com/files/rs-3915469/v1/f01ff4576a90400c433632b1.png"},{"id":50745794,"identity":"4b95df27-8184-49a5-a590-8b30177dab0e","added_by":"auto","created_at":"2024-02-06 17:04:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":84179,"visible":true,"origin":"","legend":"\u003cp\u003eStreamflow variation in the different simulated scenarios in 2050 compared to the base year.\u003c/p\u003e","description":"","filename":"F4.png","url":"https://assets-eu.researchsquare.com/files/rs-3915469/v1/3e6774519f41fa5675c19a20.png"},{"id":50745793,"identity":"e4203d0f-9bc7-47d0-bb0f-fc690341faba","added_by":"auto","created_at":"2024-02-06 17:04:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":66012,"visible":true,"origin":"","legend":"\u003cp\u003eModelled monthly streamflow in A403 gauging station in the different scenarios by 2050, and the regulatory ecological flow (dashed line).\u003c/p\u003e","description":"","filename":"F5.png","url":"https://assets-eu.researchsquare.com/files/rs-3915469/v1/4c8be63b3eb7802a7084549f.png"},{"id":50745800,"identity":"bfedf8e0-c1a5-44fa-9f5f-04763e2d7132","added_by":"auto","created_at":"2024-02-06 17:04:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":82462,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly water demand for 2050 across all socio-economic sectors in the modelled scenarios\u003c/p\u003e","description":"","filename":"F6.png","url":"https://assets-eu.researchsquare.com/files/rs-3915469/v1/e8219187fd4c1cd3d6baaa80.png"},{"id":50747464,"identity":"3ea5eabd-6724-42d2-9409-9f69136cf660","added_by":"auto","created_at":"2024-02-06 17:12:00","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":46190,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly hydropower production at Encamp hydropower plant in the modelled scenarios for 2050.\u003c/p\u003e","description":"","filename":"F7.png","url":"https://assets-eu.researchsquare.com/files/rs-3915469/v1/9a2c2ca2ca8f47ef104198f5.png"},{"id":50747463,"identity":"b2712de8-31b5-46e0-9087-bbc2efba05ef","added_by":"auto","created_at":"2024-02-06 17:12:00","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":47704,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly snowmaking unmet demand (thousands of m3) and demand coverage (%) in the Historical drought and Adaptation scenarios by 2050.\u003c/p\u003e","description":"","filename":"F8.png","url":"https://assets-eu.researchsquare.com/files/rs-3915469/v1/8f66ac1ab1d64e139ab0893e.png"},{"id":75800872,"identity":"3a5adc74-a7a4-4186-95f5-8cf596b4fae6","added_by":"auto","created_at":"2025-02-08 15:27:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1710964,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3915469/v1/8a45f952-ba96-4144-b151-3b7fd80f44da.pdf"}],"financialInterests":"","formattedTitle":"Effects of global change on streamflow, water demand and supply: a case study from the Pyrenees","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGlobal change is one of the world\u0026rsquo;s greatest challenges. Warming temperatures, droughts, socio-economic and land-use changes are likely to affect the hydrological cycle, modify regimes and consequently alter water availability (Devkota \u0026amp; Gyawali, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Xie et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2015a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChanges in mountain hydrological regimes may have implications for water management, especially in densely populated areas, but also for water uses that are highly dependent on the hydrological regime, such as hydropower generation (Bombelli et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ravazzani et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Schaefli, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNumerous studies have been carried out both internationally and, in the Pyrenees (Beniston, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Garc\u0026iacute;a-Ruiz et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; IPCC, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003ec;pez-Moreno, Revuelto, et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; L\u0026oacute;pez-Moreno, Vicente-Serrano, et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Nogu\u0026eacute;s-Bravo et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Viviroli et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) to analyse the impacts of climate change on water resources in mountain areas. However, most studies address the resource and demand dimensions separately, reaching partial scientific conclusions that make it difficult to fully understand the dynamics of global change. Others focus on specific sectors of activity, ignoring the impact on the whole system.\u003c/p\u003e \u003cp\u003eOther studies point to an increase in droughts in the following years in Europe, especially in summer (Manning et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Spinoni et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), when the co-occurrence of extremes, such as meteorological drought and high temperatures is known as a compound event. The Mediterranean and Southern Europe seem to be one of the most affected areas (Spinoni et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), where an increase in both the magnitude and the duration of droughts (extremely long dry spells) is predicted (Lemus Casanovas, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent literature has reported a decrease in streamflow in Mediterranean rivers in recent decades (Beguer\u0026iacute;a, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Garc\u0026iacute;a-Ruiz et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Herv\u0026eacute; Le Treut, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; L\u0026oacute;pez-Moreno et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Lorenzo-Lacruz et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Vicente-Serrano \u0026amp; L\u0026oacute;pez-Moreno, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) with a slight increase in streamflow in winter and a decrease in summer (Garc\u0026iacute;a-Ruiz et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Senatore et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In mountain ranges, streamflow and snow accumulation have decreased in recent decades, and further reductions could threaten the sustainability of downstream regions (Beguer\u0026iacute;a et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Haro-Monteagudo et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zabaleta et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGlobal change also includes changes in land use and socio-economic activities, especially population growth, which leads to an increase in the demand for water. The expansion of woodlands results in increased evapotranspiration and water demand in the landscape (Buendia et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Xie et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2015b\u003c/span\u003e). As a result, the whole set of impacts related to global change can put the hydrological system and its uses at risk. Understanding the interaction between global change and water resources can help society address and diminish harmful effects by introducing better water management plans and integrating adaptation strategies.\u003c/p\u003e \u003cp\u003eThis paper proposes an integrated approach that combines hydrological and water resources management modelling to improve understanding of the future impacts of physical and socioeconomic changes on water resources. The model focuses on the Valira river catchment in the Principality of Andorra, generating scenarios of water availability and identifying the most vulnerable sectors in case of increased water scarcity.\u003c/p\u003e \u003cp\u003eAndorra is a small, mountainous country located in the Central Pyrenees between France and Spain, with a population of 81588 inhabitants as of 2022 and an area of 468 km\u003csup\u003e2\u003c/sup\u003e. Over the last 50 years, the country has transitioned from a traditional agricultural and stock farming system to a tourism focused system. This shift has resulted in significant changes in land use, including a considerable increase in woodland, expansion of urban areas and infrastructure, and a reduction in crop cultivation (Caritg, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDuring the reference period (1981\u0026ndash;2010), Andorra experienced an average annual precipitation of 1054 mm and an average temperature of 5.6\u0026ordm;C. In 2019, the water resources were estimated to be 366 hm\u003csup\u003e3\u003c/sup\u003e/year (Govern d\u0026rsquo;Andorra, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The resources are mainly concentrated in three rivers: Valira, Valira del Nord and Valira d\u0026rsquo;Orient, which are the focus of this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The hydrological regime of Andorra is typical of a mountainous area with Mediterranean influence and a nival regime. The flow is high in spring and low in summer and winter. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, streamflow is measured and recorded at the main station gauge in the Valira river (A403-Borda Sabat\u0026eacute;), which belongs to the Ebro Basin Agency (\u003cem\u003eConfederaci\u0026oacute;n Hidrogr\u0026aacute;fica del Ebro\u003c/em\u003e-CHE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe paper is organized as follows. The material and methods are presented in Section 2. Section 3 provides the results and discussion. Finally, conclusions and some prospects for future work are presented in Section 4.\u003c/p\u003e\n"},{"header":"2. Material and methods","content":"\u003cp\u003eThe paper presents an integrated model developed using the Water Evaluation and Planning\u003csup\u003e1\u003c/sup\u003e (WEAP) (Sieber \u0026amp; Purkey, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) tool in two main steps. Firstly, hydrological and water demand modelling were conducted to represent current physical and socioeconomic dimensions. Then, the integrated model was projected up to 2050, guided by climatic and socioeconomic factors representing various global change scenarios. These different scenarios enable the quantification of impacts on streamflow and on different socioeconomic water users (i.e. main activity sectors of the country). Finally, in order to quantify their potential impact on the conservation and sustainability of water resources, some adaptation strategies focusing on demand-side efficiency have been analysed.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. WEAP model\u003c/h2\u003e \u003cp\u003eThe implemented WEAP model estimates the water balance in the Valira river catchment inside Andorra from 2015 to 2050 in a monthly timestep. It includes the upper Valira catchment, Valira del Nord and Valira d\u0026rsquo;Orient sub-catchments, with a total area of 475 km\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe principle algorithm of WEAP is a spatially-resolved water balance calculated on a monthly basis by balancing water supply and demand at each node and link in the system (H\u0026ouml;llermann et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the WEAP structure with 4 river segments, 100 sub-catchments, 33 demand sites, 1 reservoir with hydropower generation, transmission links and flow requirements to fix ecological flows. Nodes represent demand sites from different socioeconomical sectors (red dots) and water inputs from catchments from hydrological balance (green dots), while links connect them to rivers.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe model combines the hydrological and demand modelling for conducting an integrated water resources assessment. The base year (current accounts) of the model is established in 2015 but, in terms of climate, it is constructed using the 30-year standard reference period 1981\u0026ndash;2010 to avoid the interannual variability of specific years. Model calibration (2015\u0026ndash;2017) and validation (2018\u0026ndash;2019) have been conducted in a separate simulation.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1. Hydrological modelling\u003c/h2\u003e \u003cp\u003eThe hydrological processes were internally calculated using the WEAP Soil Moisture method. This scheme, based on empirical functions that describe evapotranspiration, surface runoff, sub-surface runoff (i.e. interflow) and deep percolation. It represents the catchment in two soil layers, as well as the potential for snow accumulation (Sieber \u0026amp; Purkey, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The water mass balance is represented in lumped portions of the catchment, each divided into \u003cem\u003eN\u003c/em\u003e (i.e. eight in this study) fractional areas representing different land cover types (Angarita et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The model is forced using climate data that is assumed to be uniform over each defined hydrological unit, which in this study consists of. 100 sub-catchments.\u003c/p\u003e \u003cp\u003eBase year precipitation and temperature are based on the spatial interpolation of the climatic reference period (1981\u0026ndash;2010) monthly data. The interpolation was performed using the fixed interpolation method (Ninyerola et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), considering the Andorran meteorological stations and the geographical factors with influence on the climate of the area (Batalla, Esteban, et al., 2016). Monthly mean values for the 100 modelled sub-catchments were calculated using the available data in a 90 meters cell size grid\u003csup\u003e2\u003c/sup\u003e. Humidity and wind are established in the same way but, in this case, using the PIRAGUA_atmos_analysis\u003csup\u003e3\u003c/sup\u003e regional dataset (Palaz\u0026oacute;n et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It contains observation-based meteorological data with a cell size resolution of 2.5 km suited for hydrological simulation of the Pyrenees range, for the historical period (1981\u0026ndash;2010).\u003c/p\u003e \u003cp\u003eLand use types and its area have been defined in the modelled sub-catchments using the 2012\u0026rsquo;s Andorran Land Cover Map\u003csup\u003e4\u003c/sup\u003e and the 2009\u0026rsquo;s Catalan Land Cover Map\u003csup\u003e5\u003c/sup\u003e to complete the \u0026Oacute;s river sub-catchment outside Andorran administrative limits. Land cover classification includes dense forest, open forest, cropland, permanent meadows and grassland, rocky areas, bare areas, urban areas, and shrublands. As detailed in Section 2.1.3, land use parameters (i.e. soil water capacity, deep water capacity, runoff resistance factor, etc.) are the base for the model calibration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2. Water demand modelling\u003c/h2\u003e \u003cp\u003eWater demand of the main activity sectors in Andorra were implemented in the WEAP model using transmission links from the river to the demand sites or to the Engolasters reservoir. To characterize the final monthly demand of each main sector (i.e. domestic, touristic, agricultural and livestock, ski resorts and hydropower generation), the national annual water use official data (Govern d\u0026rsquo;Andorra, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e) have been supplemented (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe monthly domestic water demand is characterised for each of Andorra's parishes (i.e. Andorran regional governments) using the 2019 annual water consumption reports. The domestic use of water during the 2019 is estimated to be at 220 l/person\u0026middot;day (Govern d\u0026rsquo;Andorra, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTouristic water demand was defined at parish level using the number, capacity, and category of the tourism accommodations in Andorra (from camping sites, hostels to luxury hotels) in the year 2019\u003csup\u003e6\u003c/sup\u003e. Tourism accommodation with SPA is estimated with a 20% increase in water consumption. The dataset used for the monthly data consumption variation and total occupancy was a 10-year period (2011\u0026ndash;2020) published annually by \u003cem\u003eUni\u0026oacute; Hotelera d\u0026rsquo;Andorra\u003c/em\u003e\u003csup\u003e7\u003c/sup\u003e. The annual water use rate was assigned according to the tourism accommodations categories (G\u0026ouml;ssling et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe water requirements for snowmaking of the Andorran ski resorts were obtained from the Andorran Government\u0026rsquo;s annual water consumption publication (Govern d\u0026rsquo;Andorra, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e). Monthly variations in water consumption were extracted from one of the ski resorts (Pesado, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAgricultural water demand was obtained from the Department of Agriculture of the Andorran Government database, which registers the different grassland and crop types and their representative surfaces\u003csup\u003e8\u003c/sup\u003e. The main grassland types are tobacco, ray grass and alfalfa. Crop water requirements are limited in the modelling from May to September (i.e. the natural vegetative period in the Pyrenees) and the specific crop water demand was obtained from the official Food and Agriculture Organization of the United Nation database\u003csup\u003e9\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLivestock water demand was obtained from the number and type of cattle heads from Government registration database\u003csup\u003e10\u003c/sup\u003e and its daily water needs (INRA, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The monthly dataset used for the research was from 2015 to 2021. This data was converted to monthly livestock consumption requirement in all the catchments.\u003c/p\u003e \u003cp\u003eHydropower water demand was characterised in WEAP with the information provided by FEDA\u003csup\u003e11\u003c/sup\u003e, the public company responsible for importing, generating, distributing, and commercialising electricity in Andorra. This information includes the maximum turbine flow, the storage capacity of the reservoir, the volume elevation curve, the tailwater elevation and intakes from the river to the reservoir named \u003cem\u003eEngolasters\u003c/em\u003e. Transmission links from different catchments were used to implement the latter, while also considering the regulatory environmental flow requirement (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003esummarises the assumptions made in characterising the water demands of the various sectors in Andorra.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDomestic water demand\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStandard domestic water demand in 2019 (220 l/person*day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGovern d\u0026rsquo;Andorra (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonthly domestic water demand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTouristic water demand\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber, capacity, and category of the tourism accommodations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnpublished DB provided by Andorran Government (2021)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonthly data consumption variation and total occupancy\u003c/p\u003e \u003cp\u003e20% of water demand increasing in tourism accommodation with SPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUni\u0026oacute; hotelera (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.uha.ad\u003c/span\u003e\u003cspan address=\"http://www.uha.ad\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eVeure com justificar valor al\u0026ccedil;at\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSnowmaking water requirement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll Andorran ski resorts 2019 annual consumption (m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGovern d\u0026rsquo;Andorra (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonthly data consumption variation (30% November, 36 December, 25% January, 1% February)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOne ski resort personal communication\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAgricultural water requirement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCrop types and surface area\u003c/p\u003e \u003cp\u003eMean water requirements of Andorran crops: 575 mm/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eMean water requirements of Andorran crops minus the contribution of precipitation: 338 mm/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eMonthly data requirements variation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAndorran Government database\u003csup\u003e12\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eFAO database\u003csup\u003e9\u003c/sup\u003e, Govern d\u0026rsquo;Andorra (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eBatalla, Ninyerola, et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e \u003cp\u003ePesado (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLivestock water demand\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 m\u003csup\u003e3\u003c/sup\u003e/day for standard livestock units\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eINRA (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCattle: number and type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAndorra Government livestock database\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAndorra's official equivalences of livestock type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBOPA, n\u0026uacute;m. 47, 2015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHydropower water demand\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonthly water catchment to Hydropower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFEDA\u003csup\u003e13\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHydropower characteristics (storage capacity\u0026thinsp;=\u0026thinsp;0.6 hm\u003csup\u003e3\u003c/sup\u003e, and volume elevation curve)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFEDA\u003csup\u003e13\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. \u003cem\u003eSummary of the assumptions made in characterising water demands of the various sectors in Andorra.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3. Model calibration and validation\u003c/h2\u003e \u003cp\u003eThe WEAP model, which incorporates hydrology and water management, was calibrated (2015\u0026ndash;2017) and validated (2018\u0026ndash;2019) for the monthly streamflow at the most representative discharge gauge (see A403 station in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) located on the Valira river outlet from Andorra.\u003c/p\u003e \u003cp\u003eAlthough automatic calibration approaches are often preferred, they are not always successful. The main concern is that automatic calibration fails to attach physical reality to the parameters, and the resulting modelling may not make hydrologic sense (Pomeroy et al., 2022). In addition, some parameters are highly influenced by different land cover and seasonality, such as runoff resistance or crop coefficient, which requires experienced manual calibration. However, automatic calibration is less time-consuming and more objective than manual calibration. Therefore, a hybrid calibration of the parameters for all land covers has been performed.\u003c/p\u003e \u003cp\u003eFirst, manual adjustment was developed using qualitative and quantitative techniques based on visual inspection of the model output (i.e. streamflow) and the use of objective functions to provide a numerical assessment of performance. Different literature provides a helpful range values to initiate the manual calibration for all the parameters (Amin et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Keith et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Sieber \u0026amp; Purkey, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Vicu\u0026ntilde;a et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Then an automatic calibration with Parameter Estimation\u003csup\u003e14\u003c/sup\u003e (PEST) was used for the freezing point and melting point parameters. Further information on the used parameters and their optimal values can be found in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCalibration ranges provided by literature and their optimal values.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange provided by literature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOptimal value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSoil water capacity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0-higher (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRoot zone conductivity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0-1000 (mm/month)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDeep water capacity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0-higher (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDeep water conductivity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1-higher (mm/month)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRunoff resistance factor\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0-1000 (no dimension)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u0026ndash;20 (depending on land cover and seasonality)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePreferred flow direction\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;1 (no dimension)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCrop factor\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;1 (no dimension)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(depending on land cover and seasonality)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFreezing point\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;2\u0026ordm;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMelting point\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;7.5\u0026ordm;C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo evaluate the performance of the calibration results, the statistical parameters percent bias (PBIAS), Nash\u0026ndash;Sutcliffe efficiency (NSE) and the ratio of root-mean-square error to the observations standard deviation (RSR) were used in this paper. According to Moriasi et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), the statistical parameters show a very good performance in terms of NSE (\u0026gt;\u0026thinsp;75) and RSR (\u0026lt;\u0026thinsp;0.50), and a satisfactory ratio in terms of PBIAS (\u0026thinsp;\u0026lt;\u0026thinsp;\u0026plusmn;\u0026thinsp;25% and \u0026gt;\u0026thinsp;\u0026plusmn;\u0026thinsp;15%). The monthly model calibration and validation presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e showed that the WEAP model could be a useful tool for evaluating the impacts of global change on the streamflow of the study area.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Scenario analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eshows the implementation of three future scenarios, allowing for climate and socioeconomic changes. The reference scenario serves as a realistic baseline, while the Global change scenario incorporates higher levels of climate and socioeconomic changes in the projections up to 2050. The scenario known as Historical drought year assumes the temperature increase and socioeconomic changes of the Global Change scenario, as well as the precipitation decrease of the driest year on record.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eScenario\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eClimate change (2050 anomaly)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003eSocioeconomic changes in the main sectors (2050)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRCP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT (\u0026ordm;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePp (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDomestic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTourism\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAgriculture\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSki resorts\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReference\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e▲Demographic:\u003c/p\u003e \u003cp\u003e1,18%/year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e▲Accommodations: 1,18%/year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGlobal change\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e▲Demographic:\u003c/p\u003e \u003cp\u003e1,62%/year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e▲Accommodations:\u003c/p\u003e \u003cp\u003e1,62%/year\u003c/p\u003e \u003cp\u003eOccupancy: 70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e▲Irrigation demand\u003c/p\u003e \u003cp\u003e10%*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e▲Snow making\u003c/p\u003e \u003cp\u003e15%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistorical drought year\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e▲Demographic:\u003c/p\u003e \u003cp\u003e1,62%/year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e▲Accommodations: 1,62%/year\u003c/p\u003e \u003cp\u003eOccupancy: 70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e▲Irrigation demand\u003c/p\u003e \u003cp\u003e10%*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e▲Snow making\u003c/p\u003e \u003cp\u003e15%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. \u003cem\u003eSummary of the implemented scenarios up to 2050. T\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eand P\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e \u003cem\u003erepresent the projected monthly distributions of temperature and precipitation in each scenario (see\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cem\u003e).\u003c/em\u003e\u003c/p\u003e \u003cp\u003eMonthly climatic future changes presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e are based on the Andorran average values of the PIRAGUA_atmos_climate\u003csup\u003e15\u003c/sup\u003e regional dataset (Palaz\u0026oacute;n et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It is a statistical downscaling at the Pyrenees range of six global climatic models for the historical and future periods (1981-2010-2100). The average of the six climatic models in terms of temperature and precipitation were used to implement the future scenarios (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo represent the impact of extreme drought on water resources, the Historical drought year scenario is based on the year with the lowest precipitation record, representing an extreme climate scenario by 2050. The analysed series, within the framework of the CLIMPY\u003csup\u003e16\u003c/sup\u003e project, covers the period 1950\u0026ndash;2015. The year with the lowest precipitation was 2007. Therefore, for the 2050 horizon, the precipitation of that year has been calculated as a percentage deviation (i.e. P\u003csub\u003e3\u003c/sub\u003e in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) from the reference meteorological period (1981\u0026ndash;2010).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProjected changes in monthly average temperature and precipitation in 2050 for the different scenarios\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJan.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFeb.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMar.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eApr.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMay\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJun.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eJul.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAug.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSep.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOct.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNov.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eDec.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT\u003c/b\u003e\u003csub\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e(\u0026ordm;C)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eT\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e(\u0026ordm;C)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003csub\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-3.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-1.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-5.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e14.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-5.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003csub\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sub\u003e \u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-71.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-29.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-12.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-44.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-54.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-88.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-59.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-52.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-65.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn addition to climate variation, some socioeconomic changes are also considered. In the reference scenario, the population growth and tourism activity remain stable as the current trend (i.e. population and accommodations increase of 1.18%/year). According to Reca\u0026ntilde;o (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the Global change scenario considers a more pronounced increase in population and tourism, at a rate of 1.62% per year. Additionally, this scenario assumes an increase in snowmaking of 15% (Gerbaux et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), a 10% increase in irrigation requirements (FAO, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), and an increase in tourist occupancy of up to 70%,.\u003c/p\u003e \u003cp\u003eThe Adaptation scenario was implemented while maintaining the same climatic and socioeconomic assumptions as the Historical drought year. This scenario includes certain adaptation measures, such as reduced water demand and improved water supply infrastructure. The details of these measures are provided below.\u003c/p\u003e \u003cp\u003eMeasures related to domestic consumption were extracted from the Andorran Circular Economy Law (Govern d\u0026rsquo;Andorra, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021c\u003c/span\u003e). The law mandates a reduction in consumption to 150 litres per person per day, which represents a 30% decrease from the current average consumption.\u003c/p\u003e \u003cp\u003eAdditionally, public investment is assumed to improve water distribution and transport canalizations, reducing water losses by 20% from the current rate of 40% (Govern d\u0026rsquo;Andorra, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e). In this scenario, there is also an improvement in the efficiency measures for water usage in tourist accommodations, allowing for a 25% reduction in consumption. This improvement follows the guidelines of \u003cem\u003eLIFE Watsavereuse\u003c/em\u003e\u003csup\u003e17\u003c/sup\u003e. Lastly, there is assumed to be an improvement in crop irrigation and snowmaking techniques, resulting in a 15% increase in water storage efficiency (Cognard \u0026amp; Fran\u0026ccedil;ois, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cp\u003eThis section presents the results of the impact on streamflow and demand in the implemented scenarios using the WEAP model.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Effects of global change on the streamflow regime\u003c/h2\u003e \u003cp\u003eResults showed a clear significative decrease in annual streamflow in all the modelled scenarios (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) by 2050, which means an explicit reduction on overall water resources in Andorra, where most of them come from rivers and lakes, and fewer from groundwater. In the reference scenario, the annual streamflow is estimated to decrease by 5.3%, and in the Global change scenario by 8.5%. Despite this, during winter periods, in the Reference and Global change scenarios, the streamflow could increase slightly, except for January where the streamflow could further increase (40\u0026ndash;55%). This can be explained by a shift from snow to rain precipitations accordingly to climate change projections.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHowever, the most significant result in streamflow reduction among the different scenarios, is the heavy flow reduction modelled in the Historical drought scenario. In the Global change scenario, nearly 10% of the annual volume of streamflow is lost, whereas in the Historical drought scenario, this loss reaches 65.4%. It appears that the combined effects of rising temperatures and dry years significantly enhance the loss of streamflow, thereby reducing basin water resources. Furthermore, while the Reference and Global change scenarios show a slight increase in flow during winter, the opposite occurs in the Historical drought year scenario. This highlights a significant difference between scenarios, making dry years particularly challenging in terms of water resources in all seasons, especially during winter.\u003c/p\u003e \u003cp\u003eTherefore, considering the climate projections for the Pyrenees, in the RCP 8.5 scenario, and the probable increase in the duration, intensity, and magnitude of drought periods (Lemus Casanovas, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), dry spells and drought future scenarios could have significant impacts on water resources, particularly during specific months of the year. It should be noted that, in the drought scenario, river flow reaches minimum values ​during the winter and summer seasons (see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These periods will probably be the most limiting periods for Andorra in terms of water resources.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMoreover, in the Historical drought year scenario, the minimum ecological flow defined by regulation\u003csup\u003e18\u003c/sup\u003e could not be met in most of the months of the year. It is of particular concern during the summer when water temperatures reach higher values, exposing the riparian species, especially salmonids a flagship species in Pyrenean rivers (Floury et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These conditions can expose them to temperatures above 21\u0026ordm;C, concentrations of pollutants, and minimum flows outside their life range (Govern d\u0026rsquo;Andorra, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Lewis, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The increase in the frequency of drought episodes indicates non-compliance with minimum ecological flows. Therefore, the ecosystem functions cannot be guaranteed, which may result in changes to the main biological and biochemical parameters of the river.\u003c/p\u003e \u003cp\u003eThe implementation of measures in the Adaptation scenario reduces streamflow loss in the Historical drought scenario by up to 12.5%. During low streamflow months, such as July, August, and September, adaptation measures could save up to 28.8% of river flow. Additionally, in February, the measures could save around 45.2% of river flow. Therefore, during drought periods, adaptive measures could have a strong effect on water conservation, particularly during periods of low flow.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Effects of global change on water demand and supply\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. Water demand\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the monthly water demand for 2050 across all socio-economic sectors, including domestic, agriculture and livestock, tourism, and snowmaking for ski resorts, in the modelled scenarios. The Base year scenario indicates a total demand of 23.3 hm\u003csup\u003e3\u003c/sup\u003e. Domestic consumption is the sector with the highest annual water demand, accounting for 71.8% of the total amount. This is followed by agriculture and livestock at 11.2%, tourism at 10%, and the snowmaking sector at 6.9%.\u003c/p\u003e \u003cp\u003eThe results presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e indicate an 88.42% in water demand in the Global change scenario (43.9 hm\u003csup\u003e3\u003c/sup\u003e) compared to Base year (23.3 hm\u003csup\u003e3\u003c/sup\u003e). This increase is mainly attributed to projected population growth and other assumed socioeconomic changes. The measures implemented in Adaptation scenario reduce water demand to almost half (41.7%) of the water demand in the Global change scenario. In conclusion, adaptation measures could significantly mitigate the impacts of the projected socioeconomic change on water supply requirements. These measures are necessary to ensure future water resource needs in projected socioeconomic scenarios and to minimize the effects on ecosystems.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHydropower generation is a key energy sector in Andorra, where over 75% of electricity is imported. Non-consumptive water used for hydroelectricity accounts for about 74 hm\u003csup\u003e3\u003c/sup\u003e, which generates approximately 76 GWh annually in the Base year.\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the total annual hydropower production at the main hydropower plant of the country in the modelled scenarios for 2050. The production remains relatively stable in both the Reference and Global change scenarios when compared to the Base year. Therefore, the impact of these scenarios on hydropower production appears negligible on an annual basis. However, during the winter months, hydropower production could increase under these scenarios, while it decreases in June and July. The increase in winter streamflow can be explained by the decrease in the snow/liquid precipitation ratio, accordingly to climate change projections. These results are consistent with other recent studies in different mountain ranges (Bombelli et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Brunner et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Van Vliet et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, it is important to note that the Historical drought year scenario results in a significant decrease in the annual hydropower generation compared to the Base year, with a reduction of 39.9%. The decrease is observed in every month of the year, with the most significant decreases occurring in June, July, and September, where production could fall 61.6%. In the winter season, the decrease could be around 47%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, the results indicate that the Adaptation scenario has an insignificant effect on reducing the decline of hydropower generation in the Global Change scenario. This lack of effect could be attributed to the location of the hydropower station in Andorra, which is situated before the main water intakes, making it difficult to obtain the benefits of Adaptation measures. However, a more in-depth analysis is required to evaluate the hydropower exploitation system of Andorra in greater detail and to develop specific adaptation measures and strategies.\u003c/p\u003e \u003cp\u003eDespite the limitations of this study, it should be emphasized that a significant loss of hydropower generation capacity must be assumed in drought years. Efforts must be made to improve the hydrological-electricity modelling framework to better understand the linkages between water and electricity supply under future climate variability and change, including droughts. This will contribute to the quantification of the water-energy nexus.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Water supply\u003c/h2\u003e \u003cp\u003eThe WEAP model assesses the probable unmet demands for public water supply locations in future scenarios. Firstly, it analyses the unmet flow requirements and the percentage of monthly demand coverage at the country level.\u003c/p\u003e \u003cp\u003eThe future demand in the 2050 horizon is guaranteed for all scenarios except for the Historical drought. In this scenario, there is an unmet demand in the snowmaking sector (see Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). In December, the maximum monthly average unmet flow is 187000 m\u003csup\u003e3\u003c/sup\u003e, which represents 36.30% of the required flow. In February 2050, the maximum is 4980 m\u003csup\u003e3\u003c/sup\u003e, which is only 2.59% of the required flow.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the Adaptation scenario the total unmet demand is reduced. Specifically, in February, the flow requirement for snowmaking is fully covered, and in December, the unmet demand is reduced by 12.63% compared to the Historical drought scenario. Therefore, the Adaptation scenario has a significant effect, and efforts in adaptation measures will be crucial for the future sustainability management of water resources.\u003c/p\u003e \u003cp\u003eIt has also been observed, once again, that the effects of drought periods have a much higher significance on the water resource than a warmer climate. This can be clearly seen here as the Global change scenario does not have any effects on the future flow requirements for snowmaking, while the Historical drought scenario does. As explained in the previous section, mountain global warming, especially in winter season, can facilitate increased flow availability (Lorenzo-Lacruz et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Oo et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This fact contributes to explaining why winter demand is fully supplied in the Global change scenario and not in the Historical drought scenario.\u003c/p\u003e \u003cp\u003eIt is important to note that, according to the results, while the demand for snowmaking does not seem to be guaranteed in Andorra\u0026rsquo;s future, there are adaptation measures that can help alleviate the effects of water scarcity during December and February. These measures involve local water management through small reservoirs and utilizing water during the spring and summer months. Brunner et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) assessed the potential of reservoirs and natural lakes to alleviate water shortages during periods of low seasonal discharge and high water demand.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThe WEAP model was applied in the present study offering a simple process for investigating global change and drought periods impacts on streamflow in a Pyrenean basin. It also assessed the future coverage of water need requirements across the Andorran socio-economic sectors. The results provide detailed estimates of the variations in water resources, including total discharges and seasonal impacts in Andorra.\u003c/p\u003e \u003cp\u003eThe implemented scenarios enable the study of various responses in streamflow for the 2050 horizon, contributing to a better understanding of streamflow behavioural patterns and dynamics in the most plausible climate change projections and socio-economic pathways.\u003c/p\u003e \u003cp\u003eOverall, all scenarios project a reduction in water resources by 2050, with the extreme drought scenario standing out due to a significant reduction. Long and severe droughts are expected to become more frequent in the coming years. These extended periods of low rainfall not only reduce water flow, but also pose a threat to hydropower generation capacity and the ecosystem functions of rivers. Rivers play a crucial role as biogeochemical transformer of energy and water, and in providing diverse ecological habitats, which could be at risk. At this point, it is crucial to implement measures that ensure an appropriate ecological flow regime in each river section.\u003c/p\u003e \u003cp\u003eAdaptation measures have been shown to mitigate flow reductions. However, in the case of hydroelectric production, the modelled measures did not have a significant impact in mitigating the expected impacts. The location of water intakes for hydropower plants in the upper parts of the basin strongly influences the effectiveness of adaptive measures. However, further analysis is required to reach more conclusive results and determine the best adaptation strategies for this sector.\u003c/p\u003e \u003cp\u003eThis study identified that the future demand in the 2050 horizon will not be met in an extreme drought scenario, but will be met in all other scenarios. Drought years may significantly affect the ability to meet the full demand of certain sectors in the future. This is particularly relevant for snowmaking in Andorra and other mountain regions in the Pyrenees where the ski industry is central to the local economy.\u003c/p\u003e \u003cp\u003eThe implemented scenarios did not consider future changes in land use, an important factor in terms of runoff generation in the Pyrenees (L\u0026oacute;pez-Moreno et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Therefore, the model may underestimate streamflow reductions in global change scenarios. The next steps in the modelling process will consider this factor.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eStatements \u0026amp; Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAndorra Research + Innovation gratefully acknowledges the Government of the Principality of Andorra for the 2017 complementary grant to the European POCTEFA 2014-2020 Program, Ref. AUEP007-AND/2017.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCristina Pesado Pons acknowledges a predoctoral grant from the Government of the Principality of Andorra (Ref. ATC017 - AND-2017/2019).\u003c/p\u003e\n\u003cp\u003eThe authors thank Jordi Ordo\u0026ntilde;ez for his valuable assistance in improving the fitness of the WEAP-Andorra model and Anna Albalat for the support providing some of the climatological data.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAmin A, Iqbal J, Asghar A, Ribbe L (2018) Analysis of current and futurewater demands in the Upper Indus Basin under IPCC climate and socio-economic scenarios using a hydro-economic WEAP Model. 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EGU Gen Assembly 2020. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.5194/egusphere-egu2020-10370\u003c/span\u003e\u003cspan address=\"10.5194/egusphere-egu2020-10370\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.weap21.org/\u003c/span\u003e\u003cspan address=\"https://www.weap21.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Data available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.acda.ad/\u003c/span\u003e\u003cspan address=\"http://www.acda.ad/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Database available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://hdl.handle.net/10261/271111\u003c/span\u003e\u003cspan address=\"http://hdl.handle.net/10261/271111\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.iea.ad/mapa-de-cobertes-del-sol-d-andorra-2012\u003c/span\u003e\u003cspan address=\"https://www.iea.ad/mapa-de-cobertes-del-sol-d-andorra-2012\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.creaf.uab.es/mcsc/\u003c/span\u003e\u003cspan address=\"https://www.creaf.uab.es/mcsc/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Personal communication\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Data available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.uha.ad\u003c/span\u003e\u003cspan address=\"http://www.uha.ad\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.agricultura.ad/images/stories/estadistiques/conreus/superficie_conreu_2019.pdf\u003c/span\u003e\u003cspan address=\"https://www.agricultura.ad/images/stories/estadistiques/conreus/superficie_conreu_2019.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fao.org/land-water/databases-and-software/crop-information/en/\u003c/span\u003e\u003cspan address=\"https://www.fao.org/land-water/databases-and-software/crop-information/en/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.agricultura.ad/el-padral\u003c/span\u003e\u003cspan address=\"https://www.agricultura.ad/el-padral\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003cspan\u003ewww.feda.ad\u003c/span\u003e\u003c/span\u003e\u003cspan address=\"http://www.feda.ad\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.agricultura.ad/images/stories/estadistiques/conreus/superficie_conreu_2019.pdf\u003c/span\u003e\u003cspan address=\"https://www.agricultura.ad/images/stories/estadistiques/conreus/superficie_conreu_2019.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Personal communication\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Further information at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pesthomepage.org/\u003c/span\u003e\u003cspan address=\"https://pesthomepage.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Database available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://hdl.handle.net/10261/271116\u003c/span\u003e\u003cspan address=\"http://hdl.handle.net/10261/271116\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.opcc-ctp.org/en/climpy\u003c/span\u003e\u003cspan address=\"https://www.opcc-ctp.org/en/climpy\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Further information at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://lifewatsavereuse.eu\u003c/span\u003e\u003cspan address=\"https://lifewatsavereuse.eu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e In Andorra, the current regulatory minimum ecological flow is defined as 10% of the average annual flow of, at least, a historical period of 5 years (BOPA, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Water resource, Sustainability, Global change, Adaptation, Pyrenees, Andorra","lastPublishedDoi":"10.21203/rs.3.rs-3915469/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3915469/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWater resources have a fundamental value for both ecosystems and society. However, changes in climate, population, consumption patterns, land use and urbanization are affecting its quality and future availability. In Andorra, a country located in the middle of the Pyrenees, the confluence of climate change and a socioeconomic model with an important weight of the tourism industry based on an intensive use of water could threaten the future sustainability of water resources.\u003c/p\u003e \u003cp\u003eThis paper analyses the water resources of Andorra and its future sustainability using the Water Evaluation and Planning system (WEAP) modelling tool. The WEAP-Andorra model presents an initial estimate of the national water demand segregated into the main water consumers in the country (i.e. tourism, residential, primary sector, snowmaking, and hydro power production). It explores the future evolution of water resources combining climatic, including an extreme drought scenario, and socioeconomic variables (i.e. demography, tourism, irrigation, and snowmaking trends). The model includes an Adaptation scenario to assess the impact of some strategic adaptation measures.\u003c/p\u003e \u003cp\u003eThe results indicate a significant decrease in annual streamflow across all simulated scenarios by 2050. In the global change scenarios, yearly streamflow is projected to decrease between 5.3% and 8.5%, while in an extreme drought scenario, the loss reaches 65.4%. The impact of global change on future water demand at the country scale is not expected to be compromised. However, in an extreme drought scenario, it could be affected. The sectors most affected by the combination of global change and drought could be ski resorts, especially to ensure snowmaking and hydropower production. The future frequency and duration of droughts will determine the severity of the unmet demand.\u003c/p\u003e","manuscriptTitle":"Effects of global change on streamflow, water demand and supply: a case study from the Pyrenees","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-06 17:03:55","doi":"10.21203/rs.3.rs-3915469/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ed1b6392-7706-46b3-99b1-7171eaf6b224","owner":[],"postedDate":"February 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-08T15:18:53+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-06 17:03:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3915469","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3915469","identity":"rs-3915469","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}