BULL Database - Spanish Basin attributes for Unravelling Learning in Large-sample hydrology.

We present a novel basin dataset for large-sample hydrological studies in Spain. BULL comprises data for 484 basins, combining hydrometeorological time series with several attributes related to geology, soil, topography, land cover, anthropogenic influence and hydroclimatology. Thus, we followed recommendations in the CARAVAN initiative for generating a truly open global hydrological dataset to collect these attributes. Several climatological data sources were used, and their data were validated by hydrological modelling. One of the main novelties of BULL compared to other national-scale datasets is the analysis of the hydrological alteration of the basins included in this dataset. This aspect is critical in countries such as Spain, which are characterised by rivers suffering from the highest levels of anthropisation. The BULL dataset is freely available at https://zenodo.org/records/10605646 .

Assessing the effectiveness of potential best management practices for science-informed decision support at the watershed scale: The case of the Mar Menor coastal lagoon, Spain.

Coastal lagoons are ecosystems of high environmental importance but are quite vulnerable to human activities. The continuous inflow of pollutant loads can trigger negative impacts on the ecological status of these water bodies, which is contrary to the European Green Deal. One example is the Mar Menor coastal lagoon in Spain, which has experienced significant environmental degradation in recent years due to excessive external nutrient input, especially from non-point source (NPS) pollution. Mar Menor is one of the largest coastal lagoons of the Mediterranean region and a site of great ecological and socio-economic value. In this study, the highly anthropogenic and complex watershed of Mar Menor, known as Campo de Cartagena (1244 km2), was modelled with the Soil and Water Assessment Tool (SWAT) to analyse potential options for recovery of this unique system. The model was used to simulate several best management practices (BMP) proposed by recent Mar Menor regulations, such as vegetative filter strips, shoreline buffers, contour farming, removal of illegal agriculture, crop rotation management, waterway vegetation restoration, fertiliser management and greenhouse rainwater harvesting. Sixteen scenarios of individual and combined BMPs were analysed in this study. We found that, as individual measures, vegetative filter strips and contour farming were most effective in nutrient reduction: approximately 30 % for total nitrogen (TN) and 40 % for total phosphorus (TP). Moreover, waterway vegetation restoration showed the highest sediment (S) reduction at approximately 20 %. However, the combination of BMPs demonstrated clear synergistic effects, reducing S export by 38 %, TN by 67 %, and TP by 75 %. Selecting the most appropriate BMPs to be implemented at a watershed scale requires a holistic approach considering effectiveness in reducing NPS pollution loads and BMP implementation costs. Thus, we have demonstrated a way forward for enabling science-informed decision-making when choosing strategies to control NPS contamination at the watershed scale.

Application of an integrated catchment-lake model approach for simulating effects of climate change on lake inputs and biogeochemistry.

Climate change is simultaneously affecting lakes and their catchments, resulting in altered runoff patterns in the catchment and modified mixing and biogeochemical dynamics in lakes. The effects of climate change in a catchment will eventually have an impact on the dynamics of a downstream water body as well. An integrated model would allow considering how changes in the watershed affect the lake, but coupled modelling studies are rare. In this study we integrate a catchment model (SWAT+) and a lake model (GOTM-WET) to obtain holistic predictions for Lake Erken, Sweden. Using five different global climate models, projections of climate, catchment loads and lake water quality for the mid and end of the 21st century have been obtained under two future scenarios (SSP 2-45 and SSP 5-85). Temperature, precipitation and evapotranspiration will increase in the future, overall resulting in an increase in water inflow to the lake. An increasing importance of surface runoff will also have consequences on the catchment soil, hydrologic flow paths, and the input of nutrients to the lake. In the lake, water temperatures will rise, leading to increased stratification and a drop in oxygen levels. Nitrate levels are predicted to remain unchanged, while phosphate and ammonium levels increase. A coupled catchment-lake configuration such as that illustrated here allows prediction of future biogeochemical conditions of a lake, including linking land use changes to changing lake conditions, as well as eutrophication and browning studies. Since climate affects both the lake and the catchment, simulations of climate change should ideally take into account both systems.

Assessment of future hydrologic alteration due to climate change in the Aracthos River basin (NW Greece).

Climate change is a worldwide reality with significant effects on hydrological processes. It has already produce alterations in streamflow regime and is expected to continue in the future. To counteract the climate change impact, a better understanding of its effects is necessary. Hydrological models in combination with Indicators of Hydrologic Alteration (IHA) suppose an up-to-date approach to analyze in detail the impacts of climate change on rivers. In this study, the Soil and Water Assessment Tool (SWAT) model and Indicators of Hydrologic Alteration in Rivers (IAHRIS) software were successfully applied in Aracthos River basin, an agricultural watershed located in the north-western area of Greece. Statistical indices showed an acceptable performance of the SWAT model in both calibration (R2 = 0.74, NSE = 0.54, PBIAS = 17.06%) and validation (R2 = 0.64, NSE = 0.36, PBIAS = 12.31%) periods on a daily basis. To assess the future hydrologic alteration due to climate change in Aracthos River basin, five Global Climate Models (GFDL-ESM2, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM and NorESM1-M) were selected and analyzed under two different emission scenarios (RCP 4.5 and RCP 8.5) for a long-term period (2070-2099). Results indicate that precipitation and flow is expected to be reduced and maximum and minimum temperature to be increased, compared to the historical period (1970-1999). IHA, obtained from IAHRIS software, revealed that flow regime can undergo a severe alteration, mainly on droughts that are expected to be more significant and longer. All these future hydrologic alterations could have negative consequences on the Aracthos River and its surroundings. The increase of droughts duration in combination with the reduction of flows and the alteration of seasonality can affect the resilience of riverine species and it can produce the loss of hydraulic and environmental diversity. Therefore, this study provides a useful tool for decision makers to develop strategies against the impact of climate change.

Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain).

The Mar Menor is a hypersaline coastal lagoon with high environmental value and a characteristic example of a highly anthropized hydro-ecosystem located in the southeast of Spain. An unprecedented eutrophication crisis in 2016 and 2019 with abrupt changes in the quality of its waters caused a great social alarm. Understanding and modeling the level of a eutrophication indicator, such as chlorophyll-a (Chl-a), benefits the management of this complex system. In this study, we investigate the potential machine learning (ML) methods to predict the level of Chl-a. Particularly, Multilayer Neural Networks (MLNNs) and Support Vector Regressions (SVRs) are evaluated using as a target dataset information of up to nine different water quality parameters. The most relevant input combinations were extracted using wrapper feature selection methods which simplified the structure of the model, resulting in a more accurate and efficient procedure. Although the performance in the validation phase showed that SVR models obtained better results than MLNNs, experimental results indicated that both ML algorithms provide satisfactory results in the prediction of Chl-a concentration, reaching up to 0.7 R2CV (cross-validated coefficient of determination) for the best-fit models.

A comparative study of fire weather indices in a semiarid south-eastern Europe region. Case of study: Murcia (Spain).

Forest fires are an important distortion in forest ecosystems, linked to their development and whose effects proceed beyond the destruction of ecosystems and material properties, especially in semiarid regions. Prevention of forest fires has to lean on indices based on available parameters that quantify fire risk ignition and spreading. The present study was conducted to compare four fire weather indices in a semiarid region of 11,314km2 located in southern Spain, characterised as being part of the most damaged area by fire in the Iberian Peninsula. The studied period comprises 3033 wildfires in the region during 15years (2000-2014), of which 80% are >100m2 and 14% >1000m2, resulting around 40km2 of burnt area in this period. The indices selected have been Angström Index, Forest Fire Drought Index, Forest Moisture Index and Fire Weather Index. Likewise, four selection methods have been applied to compare the results of the studied indices: Mahalanobis distance, percentile method, ranked percentile method and Relative Operating Characteristic curves (ROC). Angström index gives good results in the coastal areas with higher temperatures, low rainfall and wider range of variations while Fire Weather Index has better results in inland areas with higher rainfall, dense forest mass and fewer changes in meteorological conditions throughout the year. ROC space rejects all the indices except Fire Weather Index with good performance all over the region. ROC analysis ratios can be used to assess the success (or lack thereof) of fire indices; thus, it benefits operational wildfire predictions in semiarid regions similar to that of the case study.

The economic value of conjoint local management in water resources: Results from a contingent valuation in the Boquerón aquifer (Albacete, SE Spain).

In the field of water resources management, the Water Framework Directive is the first directive to adopt an ecosystem approach, establishing principles and economic tools for an integrated management of water resources to protect, conserve and restore all water bodies. The incorporation of local authorities in this management involves quality benefits that are perceived by users in an effective and lasting way. The purpose of this paper is to present the economic value of the environmental recovery of the overexploited Boquerón aquifer in Hellín (Albacete, SE Spain) and all of its associated ecosystems. This aquifer operates as a regulating reservoir for the surface waters of the Hellín Canal. The contingent valuation method (CVM) applied in this environmental assessment of the aquifer showed that its non-use value was €147,470 per year, due to the high environmental awareness of the Hellín people, which is enough to ensure the survival of the ecosystems linked to the aquifer.