Accurate streamflow estimation is essential for effective water resource management and adapting to extreme events in the face of changing climate conditions. Hydrological models have been the conventional approach for streamflow interpolation and extrapolation in time and space for the past few decades. However, their large-scale applications have encountered challenges, including issues related to efficiency, complex parameterization, and constrained performance. Deep learning methods, such as long short-term memory (LSTM) networks, have emerged as a promising and efficient approach for large-scale streamflow estimation. In this study, we have conducted a series of experiments to identify optimal hybrid modeling schemes to consolidate physically based models with LSTM aimed at enhancing streamflow estimation in Denmark. The results show that the hybrid modeling schemes outperformed the Danish National Water Resources Model (DKM) in both gauged and ungauged basins. While the standalone LSTM rainfall–runoff model outperformed DKM in many basins, it faced challenges when predicting the streamflow in groundwater-dependent catchments. A serial hybrid modeling scheme (LSTM-q), which used DKM outputs and climate forcings as dynamic inputs for LSTM training, demonstrated higher performance. LSTM-q improved the mean Nash–Sutcliffe efficiency (NSE) by 0.22 in gauged basins and 0.12 in ungauged basins compared to DKM. Similar accuracy improvements were achieved with alternative hybrid schemes, i.e., by predicting the residuals between DKM-simulated streamflow and observations using LSTM. Moreover, the developed hybrid models enhanced the accuracy of extreme events, which encourages the integration of hybrid models within an operational forecasting framework. This study highlights the advantages of synergizing existing physically based hydrological models (PBMs) with LSTM models, and the proposed hybrid schemes hold the potential to achieve high-quality large-scale streamflow estimations.

Large samples of hydrometeorological time series and catchment attributes are critical for improving the understanding of complex hydrological processes, hydrological model development, and performance benchmarking. CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) datasets have been developed in several countries and regions around the world, providing valuable data sources and test beds for hydrological analysis and new frontiers in data-driven hydrological modeling. Regarding the lack of samples from lowland, groundwater-dominated, small-sized catchments, we develop an extensive repository of a CAMELS-style dataset for Denmark (CAMELS-DK). This CAMELS addition is the first containing both gauged and ungauged catchments as well as detailed groundwater information. The dataset provides dynamic and static variables for 3330 catchments covering all of Denmark from various hydrogeological datasets, meteorological observations, and a well-established national-scale hydrological model. For 304 of those catchments, streamflow observations are provided, whereas simulated streamflow is provided for all 3330 catchments. The dataset contains time series spanning 30 years (1989–2019) with a daily time step, and the data will be updated once new observations and model simulations become available. The dense and full spatial coverage for all 3330 catchments, instead of only gauged catchments, together with the addition of various simulation data from a distributed, process-based model, enhances the applicability of such CAMELS data, for example, for the development of data-driven and hybrid physically informed modeling frameworks or other cases where consistent full spatial coverage is required. We also provide quantities related to the human impact on the hydrological system in Denmark, such as groundwater abstraction and irrigation. The CAMELS-DK dataset is freely available at https://doi.org/10.22008/FK2/AZXSYP (Koch et al., 2024).

The Sanjiangyuan region of Qinghai-Tibetan Plateau is recognized as a biodiversity hotspot of alpine mammals but a barren area in terms of amphibians and reptiles. Here, we describe a new pit viper species, Gloydius rubromaculatus sp. n. Shi, Li and Liu, 2017 that was discovered in this region, with a brief taxonomic revision of the genus Gloydius . The new species can be distinguished from the other congeneric species by the following characteristics: cardinal crossbands on the back, indistinct canthus rostralis, glossy dorsal scales, colubrid-like oval head shape, irregular small black spots on the head scales, black eyes and high altitude distribution (3300-4770 m above sea level). The mitochondrial phylogenetic reconstruction supported the validity of the new species and furthermore reaffirms that G. intermedius changdaoensis , G. halys cognatus , G. h. caraganus and G. h. stejnegeri should be elevated as full species. Gloydius rubromaculatus sp. n. was found to be insectivorous: preying on moths (Lepidoptera, Noctuidae, Sideridis sp.) in the wild. This unusual diet may be one of the key factors to the survival of this species in such a harsh alpine environment.

We present results of a regional comparative study of surface mass changes from 2004 to 2008 based on Gravity Recovery and Climate Experiment (GRACE), The Ice, Cloud and Land Elevation Satellite (ICESat) and CHINARE observations over the Lambert Glacier/Amery Ice Shelf system (LAS). Estimation of the ICESat mass change rates benefitted from the density measurements along the CHINARE traverse and a spatial density adjustment method for reducing the effect of spatial density variations. In the high-elevation inland region, a positive trend was estimated from both ICESat and GRACE data, which is in line with the CHINARE accumulation measurements. In the coastal region, there were areas with high level accumulations in both ICESat and GRACE trend maps. In many high flow-speed glacier areas, negative mass change rates may be caused by dynamic ice flow discharges that have surpassed the snow accumulation. Overall, the mass change rate estimate in the LAS of 2004–2008 from the GRACE, ICESat and CHINARE data is 5.41 ± 4.59 Gt a⁻¹, indicating a balanced to slightly positive mass trend. Along with other published results, this suggests that a longer-term positive mass trend in the LAS may have slowed in recent years.

Most operational flood forecasting systems provide predictions of pluvial and fluvial floods, often neglecting groundwater flooding. Groundwater-induced floods can occur when prolonged rainfall, high river stages or elevated sea levels raise the groundwater table above the surface of the land, often occurring in low-lying areas or areas with specific soil and land-surface conditions. This study presents an operational, national-scale, integrated flood forecasting system that combines surface water and groundwater components – such as river discharge and high groundwater levels – to assess flood risk in Denmark. The system has been proven to effectively capture peak river flows and elevated groundwater levels, as it did across the country during the winter of 2024, and provide local-scale insights, as exemplified during a specific flood event in Varde, west Denmark. This study demonstrates how groundwater flooding, often neglected in operational forecasting, can be effectively incorporated at a national scale to support more informed flood management.

Understanding land surface fluxes is essential for sustaining dryland ecosystem functioning and services. However, the scarcity of in situ measurements poses a significant challenge to dryland monitoring. Satellite optical and thermal remote sensing data can provide the instantaneous estimates of land surface fluxes, such as surface temperature (LST), net radiation (Rn), sensible heat flux (H), evapotranspiration (latent heat flux, LE), and gross primary productivity (GPP). However, satellite-based estimates are often limited by sensor revisit frequencies and cloud-cover conditions. To facilitate temporally continuous estimation, process-based land surface models are often used to integrate sparse remote sensing observations and meteorological inputs, thereby generating continuous estimates of energy, water, and carbon fluxes. However, the impact of satellite thermal data accuracy and temporal resolutions on simulating land surface fluxes is under-explored, particularly in dryland ecosystems. Therefore, this study assessed the accuracy of Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data in a dryland tussock grassland ecosystem in southern Spain. We also assessed the incorporation and various temporal frequencies of thermal data into process-based modelling for simulating land surface fluxes. The model simulations were validated against in situ measurements from eddy covariance towers. Results show that MODIS LST has a high correlation but large bias with in situ measurements (R² = 0.81, RMSE = 4.34 °C). After a linear correction of MODIS LST with in situ measurements, we found that the adjusted MODIS LST can effectively improve the half-hourly simulation of LST, Rn, H, LE, SWC, and GPP with relative RMSEs of 7.84, 5.67, 7.81, 11.32, 6.59, and 13.09%, respectively. Such performance is close to the flux simulations driven by in situ LST. We also found that by adjusting the revisit frequency of the satellite sensor to 8 days, the model performance of simulating surface fluxes did not change significantly. This study provides insights into how satellite thermal remote sensing can be integrated with the process-based model to understand dryland ecosystem functioning, which is critical for ecological management and climate adaptation strategies.