In humid regions, the chemical flux and cycling of elements is intimately linked to the hydrologic cycle. This insight opened in the late s. XX a worldwide avenue for the use of small watersheds as ecological units to study the hydrological and biogeochemical functioning of ecosystems at the small catchment scale. The Montseny catchment research, starting in 1978, initially addressed the forest response to acid rain. But continuous recording for about 4 decades in two small catchments allowed to describe the changes in streamwater chemistry related to changes in atmospheric deposition (with particular emphasis to S, N and P deposition), to climate change and to the inputs of African dust. Further research and new hypothesis testing may take advantage of the collected data series in these long-term study sites at a Mediterranean site. This is the motivation for the publication of the quality-checked original stream and atmospheric deposition chemistry files whose links accompany this paper.

Flooding in coastal areas can result from the joint occurrence of multiple individual flood variables, resulting in compound flooding (CF) events. Individual variables may not be extreme but can result in a severe coastal impact if they occur in close succession or simultaneous. Bivariate joint distribution analysis is ineffective in assessing the likelihood of joint occurrence, thus demanding a more advanced higher dimensional probability framework. Incorporating higher dimensional joint simulation via traditional symmetric 3-D Archimedean or Elliptical copulas has statistical limits and would be incapable of preserving all lower-level dependencies. The heterogeneous dependency in hydrologic consequences can be modelled effectively via the fully nested Archimedean (FNA) copulas. Incorporating FNA under parametric distribution settings is not flexible enough since it is restricted by the prior distributional assumption of the function type for both its marginal density functions and copulas in parametric fittings. This study introduces a multivariate FNA copula under semiparametric distribution settings. The presented approach is based on relaxing the modelling of univariate marginal behaviour without any distributional assumption via the nonparametric kernel density estimation (KDE). The univariate marginal distribution of all the flood characteristics is constructed via normal KDE. The performance of FNA with nonparametric marginals outperforms the FNA copula built under parametric settings. The derived semiparametric FNA is applied to a case study in compounding the joint impact of rainfall, storm surge and river discharge observations on the west coast of Canada. The presented copula-based joint modelling is employed in multivariate analysis of flood risks in trivariate primary joint and conditional joint return periods. The trivariate hydrologic risk associated with compound events is analyzed using the failure probability (FP) statistics. Investigation reveals that trivariate hydrologic events produce a higher failure probability than bivariate (or univariate) events; neglecting trivariate joint analysis would underestimate FP. Also, it indicates that trivariate hydrologic risk values would increase with an increase in service time of the hydraulic facilities.

Gully erosion leads to soil loss and poses significant threats to availability of land for cultivation, food security and development of infrastructure. Hydrological changes driven by land-use changes are often identified as drivers of gully erosion. The aims of this paper are to assess the influence of changes in land use on gully catchment hydrology and relate these changes to gully erosion. To achieve these aims, we used The Soil Water and Assessment Tool (SWAT, 2012 version) to assess hydrological changes driven by land-use changes in 22 gully catchments in the Orlu region of southeast Nigeria between 2009 and 2018. In the absence of measured hydrological data, we used a mixed-method approach to validate modelling results: literature review, field observations and focus group meetings. Increase in surface runoff estimates was observed in 21 catchments where reductions in fallow cover were observed. In one catchment where increased fallow was observed, we identified reduction in surface runoff. Surface runoff erosion was identified as a dominant process of gully expansion in the study area, but there was no significant relationship between changes in gullied area and changes in runoff volumes (r 2 = 0.15, p = 0.08). Within-catchment variations in land-use configuration influences surface runoff volumes and pathways, and by extension, gully erosion. This was evident in the slow gully expansion rate/gully reduction identified at gullies with vegetated adjacent lands as opposed to active runoff incision observed in gullies with bare adjacent lands. Therefore, the key finding of this study is that it is important to identify and incorporate the uniqueness of gully catchments in gully management as a successful management technique in a particular catchment may not work in another.

Karst springs are the natural outflow of karst water to the surface. These springs occur where the water table can reach the surface unimpeded. This study examines the effect of alluvial deposits with varying thickness and permeability, covering the main outlet (karst spring) of a karst network on karst drainage (e.g., development of the karst water, drainage patterns, conduit-matrix interaction) as a result of a positive base level shift. This was realized with a numerical conceptual model (FEFLOW) of a hypothetical karst aquifer with 6 model configurations (inactive vs. active conduit flow, free vs. confined spring conditions with 20 m and 50 m sediment cover, respectively, with low and high hydraulic conductivity). Conduit flow and coupled conduit-matrix interactions were incorporated into the model with one-dimensional discrete feature elements. The results show that the permeability of the sediments has a more distinctive effect on conduit discharge than their thickness. The conduit network significantly contributes to the drainage even with a fully confined spring outlet. The conduit system acts as a water collector from the matrix in the recharge zone. The buried outlet increases the hydrostatic pressure farther along the conduit, and water is pushed upwards back into the matrix in the vicinity of the stratigraphic contact. Depending on the depositional setting, this results in the evolution of one to multiple new flow systems towards new potential spring sites. The results obtained here provide insight into the likely responses of natural karst systems.

Predicting extreme storm and flood events requires analysis to predict probable rainfall in target years. We present a non-stationary frequency analysis for 6 meteorological stations in Korea and Japan: Gangneung, Kwangju, Pohang, Seoul, Kochi, Iida. Non-stationary analysis results in higher estimated rainfall than stationary analysis for all stations. Increased probable rainfall in Korean stations was higher than in Japanese stations (i.e. Z-values of Korean stations were larger than for Japanese stations). Using rainfall data at the 6 sites with increasing trends, we estimate 3 types of probably predicted rainfall for the target years 2020, 2050 and 2070. According to the results of applicability analysis, in the case of a 100-year return period, the probable rainfall estimated by non-stationary methods has a residual of 1.6~2.5% in Kochi, 11.98~16.01% in Gangneung, 4.3~4.9% in Kwangju, and 3.2~5.3% in Seoul. This study indicates that non-stationary methods provide better results in terms of confidence than stationary methods for representing rainfall with increasing trends. The non-stationary rainfall frequency analysis provided more reasonable and well-directed estimates of probable rainfall for the target year. Results show that non-stationary methods estimate probable rainfall well over short timescales based on linear regression of observed data. Further, the probable rainfall estimator for target years reflects the increasing temporal pattern of rainfall and predicts future rainfall. Results from this study can inform the design of flood prevention approaches and effective hydraulic structures.

The spatial pattern of soil water content (SWC) determines the success of vegetation restoration in semi- and arid regions. The Qilian Mountains in China are in a semi-arid area where decades of environmental degradation prompted large-scale restoration efforts with a native constructive species Picea crassifolia. However, the relationships between SWC and landcover are not clear in this area, hindering woodland restoration. In this study, we determined spatial distribution and characteristics of SWC in the Qilian Mountains in four main types of landcover. Our results revealed that 1. SWC decreased in the order of natural forest, brushland, grassland, planted forest, and mixed forest, but it was significantly different only in natural forest (p<0.05); 2. planted forest exhibited a lower SWC at young ages (less than 50 a) than natural forest, brushland or grassland, and the ecohydrological effect of afforestation exhibited hysteresis at long time scales; 3. a variogram analysis and Kriging interception of the spatial pattern of SWC showed that shrubland and grassland exhibited superior adaptability to local SWC compared with afforestation. The differences in SWC among various landcover types indicated that strengthening the maintenance and protection of natural forests is more important for restoration efforts than afforestation; furthermore, in semi-arid regions, shrubs and grasses are more suitable for use in ecological restoration than forest plantations.

River estuaries are characterized by mixing processes between isotopically depleted freshwater inflows and isotopically more enriched marine water masses. Therefore, they often show linear correlation between salinity and water isotopes (δ18O and δ2H values). Here we evaluated spatial and seasonal isotope dynamics along three estuarine lagoon transects at the northern German Baltic Sea coast. The data show strong seasonality of isotope values even at locations located furthest from the river mouths. They further reveal a positive and linear salinity-isotope correlation in spring, but hyperbolic and partially even reverse correlation in summers. We conclude that additional physical processes, such as evaporation from the shallow lagoons, overprint the two-phase mixing correlation during summers. Understanding of those water isotope dynamics are crucial in context of ecological studies, for example when interpreting oxygen and hydrogen isotope data in aquatic organisms that depend on ambient estuarine waters.

Hydrological processes such as evaporation, infiltration, and runoff are affected not only by natural climate change but also by land cover and soil conditions. The impact of urbanization on the key elements of the hydrological process is worth studying in context of rapid urbanization. This paper combines the soil-land use index grid and the GSSHA model to quantitatively study the impact of land use on urban hydrological processes under the background of the changing urbanization stage. The results show that with the increase in land development and utilization activities, the hydrological process will transform. When grassland and woodland are converted to construction land, the changes in runoff, infiltration, and evaporation are the largest. The runoff depth increased by 0.94×10-1~2.42×10-1mm/km², infiltration depth decreased by 0.80×10-1~ 2.18×10-1mm/km², evaporation decreased by 0.14×10-1~ 0.28×10-1mm/km². In the transition from forest land to grassland, from cultivated land to forest land, and from cultivated land to grassland, the increase of infiltration contributed over 80% to the decrease of runoff process. This provides a scientific basis for future urban planning and sponge city construction.

Climate change causes fluctuations in temperature and precipitation. As a result, it affects the discharge of rivers, the most important consequence of which is the tendency toward extreme events such as torrential rains and widespread droughts. River discharge is one of the most important climatic and hydrological parameters. Investigating the changes in this parameter is one of the main prerequisites in the management and proper use of water resources and rivers. Most trend detection studies are based on analyzing changes in the mean or middle of the data. They do not provide information on how changes occur in different data ranges. Therefore, to investigate parameter changes in a different range of the data series, various regression models were proposed. Frequentist quantile regression and Bayesian quantile regression models were used to estimate their trend and trend slope in different quantiles of discharge in different seasons of the year for Arazkouseh, Tamar, and Galikesh stations of Gorganroud basin in northern Iran with the statistical period of 1346–1396 (1966–2016). The results show that in most seasons of the year, high discharge rates for all 3 stations have decreased with a steep slope, and only in summer, Tamar and Galikesh stations have had an increasing trend, but low discharge rates have not changed significantly. Spatially, the discharge values at Arazkouseh station have a decreasing trend with a higher slope rate, and in terms of time, the most decreasing trend has been in spring. Comparing the models also shows that the Bayesian quantile regression model provides more accurate and reliable results than the frequency-oriented quantile regression model. In general, quantile regression models are useful for predicting and estimating extreme high and low discharge changes for better management to reduce flood and drought damage.

Understanding how droughts propagate through the hydrological cycle from precipitation to streamflow and groundwater is important for improving water and risk management policies. At the catchment scale, the analysis of drought propagation and classification into drought types is usually done manually, which can be time consuming and difficult to replicate. Here, we developed an automated, objective procedure for classification of different drought types with the aim to study drought propagation in the tropics. The method was applied to the Savegre catchment in Costa Rica as a proof-of-concept. We first confirmed that drought events in the catchment could be classified into the process-based typology from the literature: classical rainfall deficit drought, wet-to-dry season drought, and composite drought. The automation algorithm was able to replicate the classification obtained with the manual typology with the exception of two events, and thus it is a development towards objective and time efficient hydrological drought analysis in tropical catchments. Most of the detected hydrological droughts (80% and 76% of all river discharge and baseflow droughts, respectively) were classical rainfall deficit droughts, which suggests that climate plays a more important role in drought development than catchment characteristics in this catchment. However, the importance of catchment characteristics was revealed by the presence of severe composite drought events and by the attenuation of significant precipitation droughts.

The Langcang-Mekong River Basin is the most important transboundary river basin in Asia. However, over the recent decades, dam construction has been increasingly recognized as having profound effects on hydrological processes, aquatic and riparian ecosystems. Understanding these impacts is critical for the foundation of sustainable runoff surface management. In this study, different methods based on both graphical and numerical techniques were applied to assess the effects of the dams on annual, seasonal, and monthly runoff and to detect hydroclimatic trends in the Upper Mekong Basin during the period 1960–2020. The results reveal two change points with respect to seasonal and annual flow regimes; that is 2003 for the flood season and annual flows, and 2013 for the dry season flow. The duration of the flood season and the volume of annual discharges have both significantly decreased since 2003 and the dry season discharge has significantly increased since 2013 (with both p-values < 0.05). The quantitative assessment suggests that, due to the effect of dams, the monthly discharges increased around 10–450 m3/s during the dry season (December to May), while the flood season’s monthly flows decreased significantly, approximately 1028–2150 m3/s from July to October at Chiang Saen station. The study of hydrological changes in the Mekong watershed is expected to be a significant contribution towards a better understanding of large watersheds in which the hydrological responses are influenced not only by climate change at large spatial and temporal scales but also by changes in the physical environment due to the construction of dams.

Alpine basins are essential to the conservation of water resources. However, they are typically poorly gauged and inaccessible, owing to the harsh prevailing environment and complex terrain. To investigate the influences of different precipitation inputs on hydrological modeling in alpine basins, two representative satellite precipitation products [Tropical Rainfall Measuring Mission (TRMM) and Integrated Multi-Satellite Retrievals for GPM (IMERG)] and two reanalysis precipitation products [China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) and Climate Forecast System Reanalysis (CFSR)] in the Yellow River Source Region (YRSR) were selected for evaluation and hydrological verification against gauge-observed data (GO). Results indicates that the accuracy of these precipitation products in the warm season is higher than that in the cold season, and IMERG has the best performance, followed by CMADS, CFSR, and TRMM. TRMM seriously overestimates high rainfall of greater than 10 mm/day. CFSR overestimates moderate precipitation events of 1–10 mm/d, while CMADS underestimates the effects of precipitation events of 1–20 mm/d. Models using the GO as input yielded satisfactory performance during 2008–2013, and precipitation products have poor simulation results. Although the model using IMERG as input yielded unsatisfactory performance during 2014–2016, this did not affect the use of IMERG as a potential data source for YRSR. After bias correction, the quality of CFSR improves significantly with R2 and NSE increasing by 0.25 and 0.31 at Tangnaihai station, respectively. Model driven by the combination of GO and CMADS precipitation performed the best in all scenarios (R2 = 0.77, NSE = 0.72 at Tangnaihai station; R2 = 0.53, NSE = 0.48 at Jimai station). These results can provide reference data, and research ideas, for improved hydrological modeling of alpine basins.

Plant physiological thresholds, based on the response of plant physiological parameters to soil available water, were proposed to optimize soil drought and desiccation studies. However, further research is lacked to explore the discrepancy between plant physiological thresholds and traditional threshold, stable field capacity (SFC), in prolong drought modelling. This discrepancy may misjudge the occurrence and severity of dry soil layer (DSL) and bring uncertainty to vegetation selection and planting years estimation. In this study, Environment Policy Integrated Climate (EPIC) model was used to predict soil water dynamics and drought yield loss (YL) in apple main production zone on the Chinese Loess Plateau from 2021 to 2080 under four General Circulation Models (GCMs). Subsequently, plant physiological thresholds were determined with leaf net photosynthetic (TPN) and transpiration rate (TTR) and compared with SFC in long-term DSL prediction. The present results showed that TPN and TTR significantly slowed down the formation of DSL and enhanced the correlation between DSL and YL. Forming serious DSL (Quantitative Index, QI > 0.5) has slowed from 20 to 30 years, and R2 of YL and QI increase from 0.265 to 0.528 (QIPN) and 0.409 (QITR). Moreover, future climate change accumulatively reduced 9.95-14.18% of the YL. These results indicated that traditional method overestimated the environmental contradiction between economic benefits and eco-hydrology of apple forests, which could bring unreliable messages to policymaker to restrict further development of apple industries. This study was emphasized that evaluating DSL based on plant physiological threshold reflected better plant growth and water stress, which contributed to further study the sustainable development of fragile ecosystem.

The effects of the discharge of wastewater treatment plants (WWTP) on the status of rivers have most commonly been focused on water quality. A very limited number of works have characterised the ability of treatment plants to modify flow patterns in the receiving rivers. This paper presents a methodology for the assessment of the hydrologic alteration caused by WWTP discharges, over a two-fold sequence. The first phase comprises the application of indicators derived from accessible data and informative of the capacity of treatment plants to produce significant flow alterations. The second phase, which may only be carried out when flow data in the receiving river is available, is based on the indicators of hydrologic alteration provided by the free software IAHRIS (6 indicators) and IHA (2 indicators), and on a new indicator proposed in this paper to obtain information of flow alteration at seasonal and monthly time scales. The procedure suggested in this work is applied to the Manzanares River (Central Spain), allowing the quantification of the flow alteration generated by the 12 WWTP which give service to Madrid city (3.8 million inhabitants): Large increases of annual water volumes (from 108 hm3 to 410 hm3); at a monthly scale (increase from 246% to 1516%); variability in flow decreases in wet years by up to 47% and increases in dry years by up to 380%; seasonal patterns is altered within an altered regime. Results of the analysis show: (i) the ability of the proposed methodology to characterise the modification of flow patterns due to WWTP discharges; (ii) the importance of assessing such changes when evaluating the environmental impact of treatment plants; (iii) the importance of designing preventive and mitigation measures which maintain the ecological integrity of river ecosystems in the receiving channels.

Anthropogenic withdraw of groundwater and climatic drought results in the decline of groundwater depth that, in turn, severely limits the water availability for phreatophytic vegetation in arid regions. In this study, a small xeric, phreatophytic tree Haloxylon ammodendron (C.A. Mey.) was investigated to understand the influence of depth to groundwater (DGW) on hydraulic traits and on the trade-off between drought tolerance and leaf area increment. A suite of traits including leaf water potential, pressure–volume (P–V) curves, Huber value, assimilation branch growth, and osmotic regulation substance were measured across five sites with DGW ranges from 3.45 to 15.91 m. Our results indicate that H. ammodendron was subject to greater water stress with increasing DGW, as indicated by decreased predawn (Ψpd) & midday (Ψmd) branch water potential. We also found that growth rate declined as Huber value increased with increasing DGW in the early growing season (EGS). Solute sugar, as a major osmotic substance, drives decreases in osmotic potential at full turgor, and thus constrains assimilation branch growth with increasing DGW in EGS. Therefore, osmotic adjustment accompanied with water potential regulation (Ψpd－Ψmd) and plasticity of Huber value allows this phreatophyte to absorb water from deeper soil layers and tolerate drought. However, these adaptive adjustments cannot fully compensate for nonoptimal water conditions as growth rate continued to decrease as DGW increased in EGS and even became negative in the late growing season (LGS) at almost all sites. Our results provide an insight into how H. ammodendron responds and adapts to changes DGW in a region experiencing hydrological and climatic drought. Greater depth of groundwater had a significant effect on H. ammodendron and may have similar effects for other non-riparian phreatophytic plants in arid regions.

Water resource management in ungauged catchments is complex due to the uncertainties around the hydrological parameters that dominate the streamflow behaviour. These parameters are usually defined by regionalization approaches in which hydrological response patterns are transferred from gauged to ungauged basins. Regression-based methods using physical properties derived from cartographic data sources are widely used. The current remote sensing techniques offer us new standpoints in regionalisation processing since the hydrological response depends on the physical attributes related to the spectral responses of the territory. Moreover, machine learning approaches have not been specifically applied to the regionalization of hydrologic parameters. This work studies the capability of a catchment’s spectral response based on Sentinel-1 and Sentinel-2 data to address a regression-based regionalization of hydrological parameters using a machine learning approach. Hydrological modelling was conducted by the HBV-light model. We tested the random forest algorithm in several regionalization scenarios: the new approach using the catchments’ spectral signature, the traditional method using physical properties and a fusion of them. The calibration results were excellent (median KGE = 0.83), and the regionalized parameters obtained with the random forest algorithm achieved good performance in which the three scenarios showed almost the same goodness of fit (median KGE = 0.45 to 0.50). We found that the effectiveness depends on the climatic environment and that predictions in humid catchments exhibited better performance than those in the driest catchments. The physical approach (median KGE= 0.71) exhibited better performance than the spectral approach (median KGE= 0.64) in humid catchments, whereas spectral regionalization (median KGE= 0.33) outperformed the physical scenario in the driest catchments (median KGE= 0.25). Herein, our results confirm that regionalization is still challenging in Mediterranean climate variants where the new spectral approach showed promising results and time series of satellite data could improve seasonal regionalization methodologies.

The three dominant processes contributing to runoff as proposed by the Dunne diagram are Hortonian overland flow (HOF), Dunne overland flow (DOF) and subsurface storm flow (SSF). Using a theoretical perspective, we investigate the impact of climate, soil, topography and vegetation on catchment water balance and the probability distribution of the travel times of each runoff generation component in respect of the connected instantaneous response function (CIRF) including the interaction of a partial contributing area connecting to the outlet. A simple distributed hydrologic model is used to capture the effect of the catchment response and to estimate the CIRFs under different possible integration of combined effect of climate, soil, topography and vegetation. A set of dimensionless similarity parameters represent catchment functions and provide a quantitative explanation of the conceptual Dunne diagram. Behavioral catchments are defined from the empirical range of the Budyko curve and mainly compatible to the physical relationship as illustrated in the Dunne diagram. The results consistent with the Dunne diagram are: (1) DOF and SSF dominates in humid for behavioral sand and silt catchments, (2) HOF dominates in arid for behavioral silt and clay catchments. Inconsistent results are: (1) SSF dominates in arid for behavioral sand, silt and clay catchments, (2) HOF dominates in humid for behavioral clay catchment and (3) no dominant HOF for behavioral sand catchment. For HOF and DOF dominates, the distribution of CIRFs can be grouped into similar shapes, which depend on the relative contribution of hillslope scale and catchment scale. For SSF behavioral catchments, the shape of the CIRFs depends on the dryness index. The combined catchment CIRFs of mean travel time for runoff responses consists with the higher first peak from the HOF and/or DOF and the second peak from the SSF.

This paper presents the integrated machine learning and HEC-RAS models for flood inundation mapping in Baro River Basin, Ethiopia. A predictive rainfall-runoff and spatially distributed river simulation models were developed using Artificial Neural Networks (ANNs) and HEC-RAS respectively. Daily rainfall and temperature data of 7-yrs and Topographical Wetness Index (TWI) with a spatial resolution of 50 x 50m were used to train the ANN in R studio. The integration of the spatial and temporal variability in this paper improved the accuracy of the predictive models integrated with ANN and HEC-RAS. The predictive ANN model was tested with the observed daily discharge of the same temporal resolution and the rainfall-runoff result obtained from the tested ANN model was used as input for the HEC-RAS. The flood event of 2005 was used to verify the accuracy of flood generated in the HEC-RAS model by implementing the Normal Difference Water Index (NDWI). The comparison was made between the flood inundation map generated by HEC-RAS and flood events of different periods based on coverage percentage areas and a good agreement was reached with 96 % overlapped areas. The performance of ANN and HEC-RAS models were evaluated with 0.86 and 0.88 values at the training and testing period respectively. Finally, it was concluded that the integration of a machine learning approach with the HEC-RAS model in developing a flood inundation mapping is an appropriate tool to warn residents in this river basin.