Daily Time Step Research Articles

A comprehensive assessment of RCP4.5 projections and bias-correction techniques in a complex coastal karstic aquifer in the Mediterranean

This study aims to assess the projected precipitation and temperature changes at the coastal karstic aquifer of Salento (Apulia, Southern Italy) under the Representative Concentration Pathway RCP4.5. For this purpose, an ensemble of twelve Regional Climate Models (RCMs) driven by several General Circulation Models (GCMs) were collected. Eight bias-correction (BC) methods were applied at daily time steps, and their results were assessed on monthly and annual time steps, using daily records from 19 and 11 precipitation and temperature (minimum and maximum) stations, respectively, for the period 1960–2005. Missing data in the observed dataset were filled-in applying the best performing techniques out of the 5 that were employed and tested. The Linear Scaling and the Power Transformation were found to be the most effective methods for precipitation BC at the case study, while all methods performed equally well in correcting air temperature datasets. Results of future climate projections show a decrease in precipitation of about 6% and an increase in temperature of 2°C until the end of this century, compared to the historical period (1971–2005). This study forms the first comprehensive attempt to test the scientific literature’s most widely used bias-correction methods over the study area. The case study may be considered a benchmark for circum-Mediterranean regions because of its high geomorphological and structural complexity, regional size, surface water scarcity, and significant water withdrawals for human activities.

Simple Bayesian reconstruction and forecasting of stream water temperature for ecologists-A tool using air temperature, optionally flow, in a time series decomposition approach.

Mitigating the impacts of global warming on wildlife entails four practical steps. First, we need to study how processes of interest vary with temperature. Second, we need to build good temperature scenarios. Third, processes can be forecast accordingly. Only then can we perform the fourth step, testing mitigating measures. While having good temperature data is essential, this is not straightforward for stream ecologists and managers. Water temperature (WT) data are often short and incomplete and future projections are currently not routinely available. There is a need for generic models which address this data gap with good resolution and current models are partly lacking. Here, we expand a previously published hierarchical Bayesian model that was driven by air temperature (AT) and flow (Q) as a second covariate. The new model can hindcast and forecast WT time series at a daily time step. It also allows a better appraisal of real uncertainties in the warming of water temperatures in rivers compared to the previous version, stemming from its hybrid structure between time series decomposition and regression. This model decomposes all-time series using seasonal sinusoidal periodic signals and time varying means and amplitudes. It then links the contrasted frequency signals of WT (daily and six month) through regressions to that of AT and optionally Q for better resolution. We apply this model to two contrasting case study rivers. For one case study, AT only is available as a covariate. This expanded model further improves the already good fitting and predictive capabilities of its earlier version while additionally highlighting warming uncertainties. The code is available online and can easily be run for other temperate rivers.

Modeling of surface energy balance for Icelandic glaciers using remote-sensing albedo

Abstract. During the melt season, absorbed solar energy, modulated at the surface by albedo, is one of the main governing factors controlling surface melt variability for glaciers in Iceland. An energy balance model was applied with the possibility of utilizing spatiotemporal Moderate Resolution Imaging Spectroradiometer (MODIS) satellite-derived daily surface albedo driven by high-resolution climate forcing data to reconstruct the surface energy balance (SEB) for all Icelandic glaciers for the period 2000–2021. The SEB was reconstructed from April through September for 2000–2021 at a daily time step with a 500 m spatial resolution. Validation was performed using observations from various glaciers spanning distinct locations and elevations with good visual and statistical agreement. The results show that spatiotemporal patterns for the melt season have high annual and interannual variability for Icelandic glaciers. The variability was influenced by high climate variability, deposition of light-absorbing particles (LAPs) from volcanic eruptions and dust hotspots in pro-glacial areas close to the glaciers. Impacts of LAPs can lead to significant melt enhancement due to lowering of albedo and increased short-wave radiative energy forced at the surface. Large impacts on the SEB were observed for years with high LAP deposits, such as the volcanic eruption years of 2004, 2010 and 2011 and the sand- and dust-rich year of 2019. The impacts of volcanic eruptions and other LAP events were estimated using historical mean albedo under the same climatology forcing to provide estimations of melt energy enhancements. The impact of LAPs was often significant even though the glaciers were far away from the eruption location. On average, the melt enhancements due to LAPs were ∼27 % in 2010, ∼16 % in 2011 and ∼14 % in 2019 for Vatnajökull, Hofsjökull and Langjökull.

A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses

A large number of historical simulations and future climate projections are available from Global Climate Models, but these are typically of coarse resolution, which limits their effectiveness for assessing local scale changes in climate and attendant impacts. Here, we use a novel statistical downscaling model capable of replicating extreme events, the Bias Correction Constructed Analogues with Quantile mapping reordering (BCCAQ), to downscale daily precipitation, air-temperature, maximum and minimum temperature, wind speed, air pressure, and relative humidity from 18 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6). BCCAQ is calibrated using high-resolution reference datasets and showed a good performance in removing bias from GCMs and reproducing extreme events. The globally downscaled data are available at the Centre for Environmental Data Analysis (https://doi.org/10.5285/c107618f1db34801bb88a1e927b82317) for the historical (1981–2014) and future (2015–2100) periods at 0.25° resolution and at daily time step across three Shared Socioeconomic Pathways (SSP2-4.5, SSP5-3.4-OS and SSP5-8.5). This new climate dataset will be useful for assessing future changes and variability in climate and for driving high-resolution impact assessment models.

Fire risk modeling: an integrated and data-driven approach applied to Sicily

Abstract. Wildfires are key not only to landscape transformation and vegetation succession, but also to socio-ecological values loss. Fire risk mapping can help to manage the most vulnerable and relevant ecosystems impacted by wildfires. However, few studies provide accessible daily dynamic results at different spatio-temporal scales. We develop a fire risk model for Sicily (Italy), an iconic case of the Mediterranean Basin, integrating a fire hazard model with an exposure and vulnerability analysis under present and future conditions. The integrated model is data-driven but can run dynamically at a daily time step, providing spatially and temporally explicit results through the k.LAB (Knowledge Laboratory) software. This software provides an environment for input data integration, combining methods and data such as geographic information systems, remote sensing and Bayesian network algorithms. All data and models are semantically annotated, open and downloadable in agreement with the FAIR principles (findable, accessible, interoperable and reusable). The fire risk analysis reveals that 45 % of vulnerable areas of Sicily have a high probability of fire occurrence in 2050. The risk model outputs also include qualitative risk indexes, which can make the results more understandable for non-technical stakeholders. We argue that this approach is well suited to aiding in landscape and fire risk management, under both current and climate change conditions.

Evaluation of flash drought under the impact of heat wave events in southwestern Germany

Flash droughts are a recently recognised type of extreme drought defined by the rapid onset and strong intensification of drought conditions. Our understanding of flash drought processes under the influence of heat waves needs to be improved in the context of global warming. Here, we applied a physically based hydrological model, i.e., TRAnspiration and INterception (TRAIN) model to simulate root zone soil moisture (RZSM) and evapotranspiration (ET) with daily time steps and at a 1 × 1 km resolution to identify and assess flash droughts. Two states, Baden-Württemberg (BW) and Rhineland-Palatinate (RP), located in southwestern Germany, were selected as the study areas. Three datasets, the Global Land Evaporation Amsterdam Model (GLEAM) dataset, ERA5-Land (land component of the fifth generation of European ReAnalysis) dataset, and SMAP-L4 (Soil Moisture Active Passive Level-4) dataset, were selected to evaluate the TRAIN simulated RZSM and ET from 1961 to 2016. The results show that the simulated RZSM had the highest correlation with the ERA5-Land products, followed by SMAP-L4 and GLEAM, with regional average correlation coefficients (CC) of 0.765, 0.762, and 0.746, respectively. The CC of the TRAIN simulated ET with ERA5-Land and GLEAM ET were 0.828 and 0.803, respectively. The results of the trend analyses showed a significant increase (p < 0.05) in the number of flash droughts and heat waves in both the BW and RP states. A comparative analysis revealed that the mean duration and onset speed of flash droughts in BW (RP) without heat waves were 10.42 (10.67) pentads and 19.69th percentile/pentad (17.16th percentile/pentad), respectively, while associated with heat waves they were 8.95 (9.53) pentads and 21.77th percentile/pentad (19.91th percentile/pentad), respectively. This indicates that flash droughts under the influence of heat waves are generally shorter in duration but faster in occurrence. The findings of this study have important implications for flash drought assessment, monitoring, and mitigation under the impact of heat waves.

Development of a Conceptual Snow Sub-Model: Application in Meteorological Stations, Slovakia

Abstract A semi-distributed rainfall-runoff Approximate Redistributive Balance (ARB) model is currently in development as a tool for the assessment and analysis of the water management balance at the level of micro-basins on the territory of Slovakia. For the winter season, it is necessary to supplement the model with a sub-model for calculating the snow water equivalent (SWE) with a comparatively low amount of the input data necessary. Since SWE models generally operate in a daily time step, a new sub-model was developed and tested in monthly and weekly time steps in 30 meteorological stations in the north of Slovakia. When compared in a weekly time step with the snow sub-model of the HBV rainfall-runoff model and when the impact of switching from a monthly to weekly time step on the quality of the runoff simulation was evaluated, the results showed that the snow sub-model does react to sudden snowmelt better when compared to the modified version of the HBV snow sub-model used. Using a weekly time step for the snow sub-model in a monthly ARB model runoff simulation showed an increase of accuracy (NSE change from 0.89 to 0.92) in one case, while maintaining the same level of accuracy in the second one.

Simulation Models for Hydro-Electric Energy by Steady-Rate and Night-Shift-Pumped-Storage Operations

New operation simulation models for hydroelectric energy and its financial benefit over an N-year period in daily time steps by the steady-rate and the open-loop night-shift-pumped-storage rules are developed. These models are applied on 11 existing dams in Türkiye, which reflect a wide range of hydrologic and hydraulic peculiarities, for regulations between 90% through 40% and the outputs are compared. Regulation is the ratio of (outflow for energy generation, hm3/day) / (average inflow, hm3/day). The present worths of energy benefits and of pumping costs computed with a discount rate of 9.5% over a 35-year period yield that the night-shift pumped-storage operations are more profitable than the steady-rate rule. Finally, generalized regression equations for average annual produced energy and for present worth of net benefits by both operation rules against statistically significant explanatory variables are developed using the results of these 11 dams, which are all meaningful by relevant statistical criteria.

Developing a real-time self-organizing algorithm for irrigation planning of rapeseed cultivation

Abstract Sustainable planning of water allocation in the agricultural sector requires attention to soil, plant, climate and their limitations. This study was conducted in order to develop a real-time framework for simulating soil–water balance in the root zone, crop growth curve and irrigation planning of rapeseed cultivation in Henan Province, China during a cropping season from March to October 2022. Simulation of production functions with field information calibration at daily time step was developed to accurately estimate the simulation of crop growth and soil water balance. Particle swarm optimization (PSO) algorithm is incorporated as an efficient tool to evaluate the water productivity as objective function in a self-organizing framework. Choosing the appropriate planting date for rapeseed cultivation at the beginning of the growing season was evaluated to increase the use of precipitation for canopy cover growth and thus reduce irrigation water consumption. The results showed that the proposed model increased water productivity by 23% as the objective function, and evaporation from the soil surface decreased by 16%. The maximum difference between the irrigation depth in the optimal and existing strategies was 41 mm in the germination stages until the seed-filling stage, which caused a decrease in final biomass and plant transpiration.

Effect of land use land cover changes on hydrological response of Punpun River basin.

The present study assessed the hydrological response of land use land cover (LULC) change on the Punpun River basin. High-resolution gridded rainfall and temperature data from the years 1995 to 2020 have been used in the Soil and Water Assessment Tool (SWAT) in the Geographic Information System (GIS) to analyze the hydrological response of the Punpun River basin and water balance components. Hydrological Response Units (HRUs) have been created for the basin. Each HRU is based on a distinct combination of soil, slope, and land use. Five SWAT models have been prepared based on the LULC of every 5-year interval to simulate the basin's hydrological response. The period selected for calibration is 1995-2015 and for validation is 2016-2020 for the modeling of daily streamflow data. The observed and simulated streamflow was checked for performance indices of coefficient of determination (R2), Nash-Sutcliffe Efficiency (NSE), and percent bias (PBIAS) on daily time steps. The results were found to be good with R2 = 0.72, NSE = 0.68, and PBIAS = 23.2 for calibration and R2 = 0.93, NSE = 0.77, and PBIAS = 19.8 for validation. The study reveals that 7.01% of evapotranspiration (ET) was increased from 1995 to 2020 with increase in agricultural area of 21.86%. It was also found that built-up area, surface runoff, and water yield have been increased by 9.14, 14.43, and 17.40%, respectively. Further, the groundwater contribution of the basin was decreased.

Improving model capability in simulating spatiotemporal variations and flow contributions of nitrate export in tile-drained catchments

It is essential to identify the dominant flow paths, hot spots and hot periods of hydrological nitrate-nitrogen (NO3-N) losses for developing nitrogen loads reduction strategies in agricultural watersheds. Coupled biogeochemical transformations and hydrological connectivity regulate the spatiotemporal dynamics of water and NO3-N export along surface and subsurface flows. However, modeling performance is usually limited by the oversimplification of natural and human-managed processes and insufficient representation of spatiotemporally varied hydrological and biogeochemical cycles in agricultural watersheds. In this study, we improved a spatially distributed process-based hydro-ecological model (DLEM-catchment) and applied the model to four tile-drained catchments with mixed agricultural management and diverse landscape in Iowa, Midwestern US. The quantitative statistics show that the improved model well reproduced the daily and monthly water discharge, NO3-N concentration and loading measured from 2015 to 2019 in all four catchments. The model estimation shows that subsurface flow (tile flow + lateral flow) dominates the discharge (70–75%) and NO3-N loading (77–82%) over the years. However, the contributions of tile drainage and lateral flow vary remarkably among catchments due to different tile-drained area percentages and the presence of farmed potholes (former depressional wetlands that have been drained for agricultural production). Furthermore, we found that agricultural management (e.g. tillage and fertilizer management) and catchment characteristics (e.g. soil properties, farmed potholes, and tile drainage) play important roles in predicting the spatial distributions of NO3-N leaching and loading. The simulated results reveal that the model improvements in representing water retention capacity (snow processes, soil roughness, and farmed potholes) and tile drainage improved model performance in estimating discharge and NO3-N export at a daily time step, while improvement of agricultural management mainly impacts NO3-N export prediction. This study underlines the necessity of characterizing catchment properties, agricultural management practices, flow-specific NO3-N movement, and spatial heterogeneity of NO3-N fluxes for accurately simulating water quality dynamics and predicting the impacts of agricultural conservation nutrient reduction strategies.

Reliability of gridded temperature datasets to monitor surface air temperature variability over Bolivia

AbstractSix gridded temperature datasets (T‐datasets) were evaluated for the first time over the South American continent, through the case study of Bolivia, by comparing them with temperature records acquired from 82 meteorological stations spanning the 1995–2010 period. The comparisons were carried out at the daily time step considering different seasons (annual scale, austral summer and austral winter) and regions (Amazon, La Plata and Altiplano basins). Overall, the climate hazards group infrared temperature with stations (CHIRTS) and the climate prediction centre (CPC) T‐datasets provided the most reliable mean daily temperature (Tmean) and also described well the temporal variability of minimum and maximum daily temperature estimates (Tn and Tx). Tmean, Tn and Tx trends were analysed over the 1983–2016 period to observe temperature temporal evolution across the three regions. Despite some general agreements between the trends (Tmean, Tx and Tn), large discrepancies are also observed. It was found that CPC overestimates and CHIRTS underestimates mean temperature trends and that CPC (CHIRTS) was better than CHIRTS (CPC) to estimate Tx (Tn) trends, both in magnitude and space. Furthermore, opposing trends (i.e., warming and cooling) are described by CPC and CHIRTS for some specific regions, which call into question their reliability for such analyses. These findings highlight the need to validate gridded temperature products with reliable ground data for the regions under study, particularly if they have a wide elevation range.

Quantifying evapotranspiration fluxes on green roofs: A comparative analysis of observational methods

Evapotranspiration (ET) is an important process in green stormwater infrastructure (GSI) aiming to reduce urban drainage, to promote cooling and/or to contribute to an urban hydrological balance restoration closer to the natural one. However, on these structures and particularly on green roofs (GR), its evaluation remains challenging and subject to discussion. Estimates of ET by water balance, energy balance, and an ET chamber were performed on five different plots of a full-scale experimental green roof in Trappes (France). Compared to both water balance (90th percentile range of daily ET values: 0.8 mm/d to 3 mm/d) and chamber methods (90th percentile range of daily ET values: 1 mm/d to 1.5 mm/d), the energy balance (90th percentile range of daily ET values is between 1.8 mm and 3.7 mm) produces higher values, 1 to 2 times higher in cumulative values during common periods. The chamber ET displays a similar trend to the energy balance on an hourly basis, and its values remain within the same range as the water balance evaluations on a daily time-step. All three assessments consistently fell below the potential ET values estimated with the Penman-Monteith formula. Critical issues in ET estimation through experimentation have arisen. Sensible heat flux (H) significantly increases ET values when using the energy balance approach compared to the other two methods. The Water Balance method is practical, but on days following rainfall events, the Chamber method may prove more reliable, albeit more time and labour-intensive. The three methods indicated that the substrate thickness was the main contributing factor to increase ET, with well-maintained herbaceous plants providing higher ET values than sedums in thick (15 cm) substrates. In addition, the substrate's nature, especially its organic content, is another factor that promotes ET in green roofs.

Development of a computationally efficient floodplain ecological response model for large-scale, data-sparse riparian environments

Quantitative assessment of floodplain ecological response to flow regimes is challenging but essential for setting targets and estimating impacts for environmental water management. This paper proposes a model that takes long-term (90 years) and large-scale (9 million grid cells) flood maps as input to estimate the response of floodplain vegetation using infinitely differentiable functions. The model, named Floodplain Ecological Response Model (FERM), is calibrated against 1-D temporal Leaf Area Index (LAI) data from the WAVES energy and water balance model at a daily timestep, and validated on the entire floodplain using condition data of the Icon Sites of the Murray River aggregated to a yearly timestep. Results show that FERM can adequately simulate the response of different types of vegetation on the floodplain, while reducing the data requirements and runtime drastically compared to other approaches. The FERM modeling approach is a first step towards a quantitative modeling of floodplain forest ecosystems at large scale with realistic data and computation requirements. It is intended to indicate the potential of such an approach in semi-arid systems where data availability is limited, and to encourage the further research needed to improve our understanding of floodplain forests and our capacity to model the impact of floods on their ecological response.

Large-scale seasonal forecasts of river discharge by coupling local and global datasets with a stacked neural network: Case for the Loire River system

Accurate prediction of river discharge is critical for a wide range of sectors, from human activities to environmental hazard management, especially in the face of increasing demand for water resources and climate change. To address this need, a multivariate model that incorporates both local and global data sources, including river and piezometer gauges, sea level, and climate parameters. By employing phase shift analysis, the model optimizes correlations between the target discharge and 12 parameters related to hydrologic and climatic systems, all sampled daily. In addition, a stacked LSTM - a more complex neural network architecture - is used to improve information extraction ability.Exploring river dynamics in the Loire-Bretagne basin and its surroundings, the investigation delves into predictions in daily time steps for one, three, and six months ahead. The resulting forecast features high accuracy and efficiency in predicting river discharge fluctuations, showcasing superior performance in forecasting drought periods over flood peaks. A detailed examination on data used highlights the significance of both local and global datasets in predicting river discharge, where the former dictates short-term predictions, while the latter drives long-range forecasts. Seasonally extended forecasting confirms a strong connection between the forecast leading time and the shift in data correlation, with lower correlation at a lag of 3 months due to seasonal changes affecting forecast quality, compensated by a higher correlation at a longer lag of 6 months. Such mutual effect in this multi-time-step forecasting improves the predictive quality of a six-month horizon, thus encourages progress in long-term prediction to a seasonal scale. The research establishes a practical foundation for effectively utilizing big data to leverage long-term forecasting of environmental dynamics.

Assimilation of Sentinel-2 Biophysical Variables into a Digital Twin for the Automated Irrigation Scheduling of a Vineyard

Decision support systems (DSS) are needed to carry out precision irrigation. Key issues in this regard include how to deal with spatial variability and the adoption of deficit irrigation strategies at the field scale. A software application originally designed for water balance-based automated irrigation scheduling locally fine-tuned through the use of sensors has been further developed with the emerging paradigm of both digital twins and the Internet of Things (IoT). The aim of this research is to demonstrate the feasibility of automatically scheduling the irrigation of a commercial vineyard when adopting regulated deficit irrigation (RDI) strategies and assimilating in near real time the fraction of absorbed photosynthetically active radiation (fAPAR) obtained from Sentinel-2 imagery. In addition, simulations of crop evapotranspiration obtained by the digital twin were compared with remote sensing estimates using surface energy balance models and Copernicus-based inputs. Results showed that regression between instantaneous fAPAR and in situ measurements of the fraction of intercepted photosynthetically active radiation (fIPAR) had a coefficient of determination (R2) ranging from 0.61 to 0.91, and a root mean square deviation (RMSD) of 0.10. The conversion of fAPAR to a daily time step was dependent on row orientation. A site-specific automated irrigation scheduling was successfully adopted and an adaptive response allowed spontaneous adjustments in order to stress vines to a certain level at specific growing stages. Simulations of the soil water balance components performed well. The regression between digital twin simulations and remote sensing-estimated actual (two-source energy balance Priestley–Taylor modeling approach, TSEB-PTS2+S3) and potential (Penman–Monteith approach) evapotranspiration showed RMSD values of 0.98 mm/day and 1.14 mm/day, respectively.

Current and future water balance of a mountain subcatchment of Issyk-Kul Lake, Tien Shan range, Kyrgyzstan

Snow and ice dominated basins are particularly vulnerable to climate change but estimating their hydrological balance remains challenging in data-scarce regions like the Tien Shan mountains. With the overall aim of modeling of the large Issyk-Kul Lake basin in Kyrgyzstan, this article focuses on the hydrological balance of the Chon Kyzyl-Suu basin, a representative sub-catchment of the lake basin. The study involved two steps: first, calibration/validation of a distributed hydrological snow model, second, assessment of future trends in runoff, evaporation, snow melt and glacier melt under different climate scenarios. Our results show that the balance of the basin is already upset due to glacier mass loss and that groundwater processes play a significant role in generating discharge. Climate projections for the next 40 years (2020–2060) show no significant trend in precipitation under scenario ssp2–4.5 but an 8.9 % decrease in precipitation under scenario ssp5–8.5. at the same time, air temperature will increase by 0.4 °C under scenario ssp2–4.5, and by 1.8 °C under scenario ssp5–8.5. Under the “business as usual” scenario (ssp2–4.5), the annual river flow of the headwater basins should increase by 13 %, or under the “pessimistic” ssp5–8.5 scenario, by 28 %, mainly due to the increase in glacier runoff. These results make it possible to envisage realistic modeling at the scale of the lake at a daily time step.

The Impact of Climate Change on the Upper Navet Reservoir, Trinidad

A hydrologic study of the Upper Navet Reservoir and its catchment was conducted to investigate and evaluate the potential impacts of climate change on it, using the soil moisture accounting algorithm in HEC-HMS to perform continuous simulations. The catchment is partially gauged, with a single rainfall gauge located within it. With the absence of a stream gauge, recorded stage data from the reservoir was used to evaluate catchment response. The selection of model parameters was based on previous work on the nearby Nariva catchment that was improved by a manual optimisation technique. The model was subject to a split-sample test with a calibration period of 24 months (2003-2004) on a daily time-step followed by validation over a period of 60 months (2005-2009). The validated model was used to evaluate the system’s response to climate change. The meteorological data for this was generated by the PRECIS software for this region. The model was subject to three data sets based on the SRES A1B scenario. The results of simulations conducted for the period 2030-2096 showed that for continuous operation, production rates at the Navet reservoir require a 40% reduction of present values for two of these scenarios and 30% for the most optimistic scenario.

Evaluation of Rainfall Products in Semi-Arid Areas: Application to the Southeast of the Republic of Djibouti and a Focus on the Ambouli Catchment

The Republic of Djibouti is a small country in the Horn of Africa and, as in most developing countries, rain gauges are sparse and data are scarce. This study aims to report on the reliability of gridded precipitation datasets (P datasets) across the Republic of Djibouti through direct comparisons with rain gauge measurements from the annual to the daily time scales. Our specific objective is to be able to use such products in the context of hydrological modeling at a daily time step. Given the scarcity of available data in the Republic of Djibouti, our study was carried out on two time windows (1980–1990 and 2008–2013) and two gauge networks with different spatial resolutions: the southeast of the Republic of Djibouti (5000 km2) and the Ambouli catchment (794 km2), which drains the city of Djibouti. The reliability of these products is analyzed with quantitative metrics and categorical metrics, exclusively at a daily time step for the latter. The performance of the P datasets degrades from the annual time scale to the daily time scale. Even though the same products exhibit the best performance at the various time scales, the performance of most of the products differs from one spatial scale to another. Our results demonstrate the importance of the temporal and spatial windows, as the same products can perform differently according to the scale. For all the spatiotemporal scales, the most reliable product is MSWEP v.2.2. This P dataset is derived from a combination of satellite products (multiple sensors such as infrared and passive microwave), reanalysis products, and rain gauge observations. A strong discrepancy between rain gauge observations and P datasets is revealed according to the categorical metric at a daily time step. The analysis of rainfall events triggering runoff, using a 10 mm rainfall threshold, showed that the most efficient products were unable to accurately detect such events at a daily time step, with a significant underestimation of rainfall events higher than 10 mm. None of these products, even the most reliable, can be used for a calibration/validation of a hydrological model at a daily time step.

Model-based assessment of flood generation mechanisms over Poland: The roles of precipitation, snowmelt, and soil moisture excess

Hydrometeorological variability, such as changes in extreme precipitation, snowmelt, or soil moisture excess, in Poland can lead to fluvial flooding. In this study we employed the dataset covering components of the water balance with a daily time step at the sub-basin level over the country for 1952–2020. The data set was derived from the previously calibrated and validated Soil & Water Assessment Tool (SWAT) model for over 4000 sub-basins. We applied the Mann Kendall test and circular statistics-based approach on annual maximum floods and various potential flood drivers to estimate the trend, seasonality, and relative importance of each driver. In addition, two sub-periods (1952–1985 and 1986–2020) were considered to examine changes in flood mechanism in the recent decades. We show that floods in the northeast Poland were decreasing, while in the south the trend showed a positive behavior. Moreover, the snowmelt is a primary driver of flooding across the country, followed by soil moisture excess and precipitation. The latter seemed to be the dominant driver only in a small, mountain-dominated region in the south. Soil moisture excess gained importance mainly in the northern part, suggesting that the spatial pattern of flood generation mechanisms is also governed by other features. We also found a strong signal of climate change in large parts of northern Poland, where snowmelt is losing importance in the second sub-period in favor of soil moisture excess, which can be explained by the temperature warming and diminishing role of snow processes.