The potential of karst aquifers as a drinking water resource is substantial because of their large storage capacity gained in the course of carbonate dissolution. Carbonate dissolution and consequent development of preferential paths are also the reasons for the complex behavior of these aquifers as regards surface and underground flow. Hydrological modeling is therefore of paramount importance for an adequate assessment of flow components in catchments shaped on karsts. The cross tabulation of such components with geology, soils, and land use data in Geographic Information Systems helps decision makers to set up sustainable groundwater abstractions and allocate areas for storage of quality surface water, in the context of conjunctive water resources management. In the present study, a hydrologic modeling using the JAMS J2000 software was conducted in a karst area of Jequitiba River basin located near the Sete Lagoas town in the state of Minas Gerais, Brazil. The results revealed a very high surface water component explained by urbanization of Sete Lagoas, which hampers the recharge of 7.9 hm³ yr⁻¹ of storm water. They also exposed a very large negative difference (−8.3 hm³ yr⁻¹) between groundwater availability (6.3 hm³ yr⁻¹) and current groundwater abstraction from the karst aquifer (14.6 hm³ yr⁻¹), which is in keeping with previously reported water table declines around drilled wells that can reach 48 m in old wells used for public water supply. Artificial recharge of excess surface flow is not recommended within the urban areas, given the high risk of groundwater contamination with metals and hydrocarbons potentially transported in storm water, as well as development of suffosional sinkholes as a consequence of concentrated storm flow. The surface component could however be stored in small dams in forested areas from the catchment headwaters and diverted to the urban area to complement the drinking water supply. The percolation in soil was estimated to be high in areas used for agriculture and pastures. The implementation of correct fertilizing, management, and irrigation practices are considered crucial to attenuate potential contamination of groundwater and suffosional sinkhole development in these areas.

Rivers are rich in biodiversity and act as ecological corridors for plant and animal species. With climate change and increasing anthropogenic water demand, more frequent and prolonged periods of drying in river systems are expected, endangering biodiversity and river ecosystems. However, understanding and predicting the hydrological mechanisms that control periodic drying and rewetting in rivers is challenging due to a lack of studies and hydrological observations, particularly in non-perennial rivers. Within the framework of the Horizon 2020 DRYvER (Drying River Networks and Climate Change) project, a hydrological modelling study of flow intermittence in rivers is being carried out in three European catchments (Spain, Finland, France) characterised by different climate, geology, and anthropogenic use. The objective of this study is to represent the spatio-temporal dynamics of flow intermittence at the reach level in mesoscale river networks (between 120 and 350 km²). The daily and spatially distributed flow condition (flowing or dry) is predicted using the J2000 distributed hydrological model coupled with a random forest classification model. Observed flow condition data from different sources (water level measurements, photo traps, citizen science applications) are used to build the predictive model. This study aims to evaluate the impact of the observed flow condition dataset (sample size, spatial and temporal representativity) on the performance of the predictive model. Results show that the hybrid modelling approach developed in this study allows the spatio-temporal patterns of drying to be accurately predicted in the three catchments, with a sensitivity criterion above 0.9 for the prediction of dry events in the Finnish and French case studies and 0.65 in the Spanish case study. This study shows the value of combining different data sources of observed flow condition to reduce the uncertainty in predicting flow intermittence.

Climate and land use changes, as well as human water use and flow alteration, are causing worldwide shifts in river flow dynamics. During the last decades, low flows, flow intermittence, and drying have increased in many regions of the world, including Europe. This trend is projected to continue and amplify in the future, resulting in more frequent and intense hydrological droughts. However, due to a lack of data and studies on temporary rivers in the past, little is known about the processes governing the development of flow intermittence and drying, their timing and frequency, or their long-term evolution under climate change. Moreover, understanding the impact of climate change on the drying up of rivers is crucial to assess the impact of climate change on aquatic ecosystems, including the biodiversity and functional integrity of freshwater systems. This study is one of the first to present future projections of drying in intermittent river networks and to analyse future changes in the drying patterns at a high spatial and temporal resolution. Flow intermittence projections were produced using a hybrid hydrological model forced with climate projection data from 1985 until 2100 under three climate scenarios in six European drying river networks. The studied watershed areas are situated in different biogeographic regions, located in Spain, France, Croatia, Hungary, Czechia, and Finland, and their areas range from 150 to 350 km². Additionally, flow intermittence indicators were developed and calculated to assess (1) changes in the characteristics of the drying spells at the reach scale and (2) changes in the spatial extent of drying in the river network at various time intervals. The results for all three climate scenarios show that drying patterns are projected to increase and expand in time and space, despite differences in the amplitude of changes. Temporally, in addition to the average frequency of drying events, the duration increases over the year. Seasonal changes are expected to result in an earlier onset and longer persistence of drying throughout the year. Summer drying maxima are likely to shift to earlier in the spring, with extended drying periods or additional maxima occurring in autumn and extending into the winter season in some regions. A trend analysis of extreme events shows that the extreme dry spells observed in recent years could become regular by the end of the century. Additionally, we observe transitions from perennial to intermittent reaches in the future.

River networks contribute disproportionately to the global carbon cycle. However, global estimates of carbon emissions from inland waters are based on perennial rivers, even though more than half of the world's river length is prone to drying. We quantified CO ₂ and CH ₄ emissions from flowing water and dry riverbeds across six European drying river networks (DRNs, 120 reaches) and three seasons and identified drivers of emissions using local and regional variables. Drivers of emissions from flowing water differed between perennial and non‐perennial reaches, both CO ₂ and CH ₄ emissions were controlled partly by the annual drying severity, reflecting a drying legacy effect. Upscaled CO ₂ emissions for the six DRNs at the annual scale revealed that dry riverbeds contributed up to 77% of the annual emissions, calling for an urgent need to include non‐perennial rivers in global estimates of greenhouse gas emissions.

Understanding the impacts of climate change requires the development of hydrological modelling tools. However, data scarcity hinders model application, performance, process simulation and uncertainty, especially for Sub-Saharan Africa. In this study, a multi-catchment approach was used to assess hydrological process variability in the Western Cape (WC) of South Africa using the JAMS/J2000 rainfall–runoff model and a Monte Carlo analysis (MCA). Due to much steeper slopes and lower evapotranspiration, the models suggest that WC is dominated by surface runoff from mountainous regions and regional groundwater flow. The results highlight the impact of the catchment size, availability and position of hydroclimatic and anthropogenic factors and the frequency of the signal-to-noise ratio (water balance). For large catchments (>5000 km²), the calibration was able to achieve a Nash–Sutcliffe efficiency (NSE) of 0.61 to 0.88. For small catchments (<2000 km²), NSE was between 0.23 to 0.39. The large catchments had an overall surface runoff, interflow and baseflow contribution of 44, 19 and 37%, respectively, and lower overall uncertainty. The simulated flow components for the small catchments were variable and these results are less certain. The use of a multi-catchment approach allows for identifying the specific factors impacting parameter sensitivities and in turn provides a means to improve hydrological process simulation.

Realistic projections of the future climate and how this translates to water availability is crucial for sustainable water resource management. However, data availability constrains the capacity to simulate streamflow and corresponding hydrological processes. Developing more robust hydrological models and methods that can circumvent the need for large amounts of hydro‐climatic data is crucial to support water‐related decisions, particularly in developing countries. In this study, we use natural isotope tracers in addition to hydro‐climate data within a newly developed version of the spatially‐distributed J2000iso as an isotope‐enabled rainfall‐runoff model simulating both water and stable isotope (δ²H) fluxes. We pilot the model for the humid tropical San Carlos catchment (2500 km²) in northeastern Costa Rica, which has limited time series, but spatially distributed data. The added benefit of simulating stable isotopes was assessed by comparing different amounts of observation data using three model calibration strategies (i) three streamflow gauges, (ii) three gauges with stream isotopes and (iii) isotopes only. The J2000iso achieved a streamflow Kling–Gupta efficiency (KGE) of 0.55–0.70 across all the models and gauges, but differences in hydrological process simulations emerged when including stable water isotopes in the rainfall‐runoff calibration. Hydrological process simulation varied between the standard J2000 rainfall‐runoff model with a high simulated surface runoff proportion of 37% as opposed to the isotope version with 84%–89% simulated baseflow or interflow. The model solutions that used only isotope data for calibration exhibited differences in simulated interflow, baseflow and model performance but captured bulk water balances with a reasonable match between the simulated and observed hydrographs. We conclude that J2000iso has shown the potential to support water balance modelling for ungauged catchments using stable isotope, satellite and global reanalysis data sets.