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At subdaily resolution, rain intensity exhibits a strong variability in space and time, which is favorably modeled using stochastic approaches. This strong variability is further enhanced because of the diversity of processes that produce rain (e.g., frontal storms, mesoscale convective systems and local convection), which results in a multiplicity of space–time patterns embedded into rain fields and in turn leads to the nonstationarity of rain statistics. To account for this nonstationarity in the context of stochastic weather generators and therefore preserve the relationships between rainfall properties and climatic drivers, we propose to resort to rain type simulation. In this paper, we develop a new approach based on multiple-point statistics to simulate rain type time series conditional to meteorological covariates. The rain type simulation method is tested by a cross-validation procedure using a 17-year-long rain type time series defined over central Germany. Evaluation results indicate that the proposed approach successfully captures the relationships between rain types and meteorological covariates. This leads to a proper simulation of rain type occurrence, persistence and transitions. After validation, the proposed approach is applied to generate rain type time series conditional to meteorological covariates simulated by a regional climate model under an RCP8.5 (Representative Concentration Pathway) emission scenario. Results indicate that, by the end of the century, the distribution of rain types could be modified over the area of interest, with an increased frequency of convective- and frontal-like rains at the expense of more stratiform events. [ABSTRACT FROM AUTHOR]

The accuracy of hydrological assessments in mountain regions is often hindered by the low density of gauges coupled with complex spatial variations in climate. Increasingly, spatial datasets (i.e. satellite and other products) and new computational tools are merged with ground observations to address this problem. This paper presents a comparison of approaches of different complexities to spatially interpolate monthly precipitation and daily temperature time series in the upper Aconcagua catchment in central Chile. A generalised linear mixed model (GLMM) whose parameters are estimated through approximate Bayesian inference is compared with simpler alternatives: inverse distance weighting (IDW), lapse rates (LRs), and two methods that analyse the residuals between observations and WorldClim (WC) data or Climate Hazards Group Infrared Precipitation with Station data (CHIRPS). The assessment is based on a leave-one-out cross validation (LOOCV), with the root-mean-squared error (RMSE) being the primary performance criterion for both climate variables, while the probability of detection (POD) and false-alarm ratio (FAR) are also used for precipitation. Results show that for spatial interpolation of temperature and precipitation, the approaches based on the WorldClim or CHIRPS residuals may be recommended as being more accurate, easy to apply and relatively robust to tested reductions in the number of estimation gauges. The GLMM has comparable performance when all gauges were included and is better for estimating occurrence of precipitation but is more sensitive to the reduction in the number of gauges used for estimation, which is a constraint in sparsely monitored catchments. [ABSTRACT FROM AUTHOR]

Timely forecasts of the onset or possible evolution of droughts are an important contribution to mitigate their manifold negative effects. In this paper we therefore analyse and compare the performance of the first month of the probabilistic extended range forecast and of the seasonal forecast from the European Centre for Medium-range Weather Forecasts (ECMWF) in predicting droughts over the European continent. The Standardized Precipitation Index (SPI- 1) is used to quantify the onset or likely evolution of ongoing droughts for the next month. It can be shown that on average the extended range forecast has greater skill than the seasonal forecast, whilst both outperform climatology. No significant spatial or temporal patterns can be observed, but the scores are improved when focussing on large-scale droughts. In a second step we then analyse several different methods to convert the probabilistic forecasts of SPI into a Boolean drought warning. It can be demonstrated that methodologies which convert low percentiles of the forecasted SPI cumulative distribution function into warnings are superior in comparison with alternatives such as the mean or the median of the ensemble. The paper demonstrates that up to 40% of droughts are correctly forecasted one month in advance. Nevertheless, during false alarms or misses, we did not find significant differences in the distribution of the ensemble members that would allow for a quantitative assessment of the uncertainty. [ABSTRACT FROM AUTHOR]

A climatology of snow water equivalent (SWE) based on data collected at 240 gauging sites was performed for the Italian Alps over the 1967–2020 period, when Enel routinely conducted snow depth and density measurements with homogeneous methods. Six hydrological sub-regions were investigated spanning from the eastern Alps to the western Alps at altitudes ranging from 1000 to 3000 m a.s.l. Measurements were conducted at fixed dates at the beginning of each month from 1 February to 1 June and on 15 April. To our knowledge, this is the most comprehensive and homogeneous dataset of measured snow depth and density for the Italian Alps. Significant decreasing trends over the years at all fixed dates and elevation classes were identified for both snow depth, equal to -0.12 ± 0.06 m per decade, and snow water equivalent, equal to -51 ± 37 mm per decade, on average in the six macro-basins we selected. The analysis of bulk snow density data showed a temporal evolution along the snow accumulation and melt season, but no altitudinal trends were found. A Moving Average and Running Trend Analysis (MARTA triangles), combined with a Pettitt's test change-point detection, highlighted a decreasing change of snow climatology occurring around the end of the 1980s. The comparison with winter temperature and precipitation data from the HISTALP dataset identified a major role played by temperature on the long-term decrease and changing points of snow depth and SWE with respect to precipitation, mainly responsible for its variability. Correlation with climatic indexes indicates significant negative values of the Pearson correlation coefficient with winter North Atlantic Oscillation (NAO) and positive values with winter Western Mediterranean Oscillation (WeMO) for some areas and elevation classes. Results of this climatology are synthesized in a temporal polynomial model that is useful for climatological studies and water resources management in mountain areas. [ABSTRACT FROM AUTHOR]

There are indications that the reference climatology underlying meteorological drought has shown nonstationarity at seasonal, decadal, and centennial timescales, impacting the calculation of drought indices and potentially having ecological and economic consequences. Analyzing these trends in meteorological drought climatology beyond 100 years, a time frame which exceeds the available period of observation data, contributes to a better understanding of the nonstationary changes, ultimately determining whether they are within the range of natural variability or outside this range. To accomplish this, our study introduces a novel approach to integrate unevenly scaled tree-ring proxy data from the North American Seasonal Precipitation Atlas (NASPA) with instrumental precipitation datasets by first temporally downscaling the proxy data to produce a regular time series and then modeling climate nonstationarity while simultaneously correcting model-induced bias. This new modeling approach was applied to 14 sites across the continental United States using the 3-month standardized precipitation index (SPI) as a basis. The findings showed that certain locations have experienced recent rapid shifts towards drier or wetter conditions during the instrumental period compared to the past 1000 years, with drying trends generally found in the west and wetting trends in the east. This study also found that seasonal shifts have occurred in some regions recently, with seasonality changes most notable for southern gauges. We expect that our new approach provides a foundation for incorporating various datasets to examine nonstationary variability in long-term precipitation climatology and to confirm the spatial patterns noted here in greater detail. [ABSTRACT FROM AUTHOR]

Comparative analysis has been a little used approach to the teaching of hydrology. Instead, hydrology is often taught by introducing fundamental principles with the assumption that they are sufficiently universal to apply across most any hydrologic system. In this paper, we illustrate the value of using comparative analysis to enhance students" insights into the degree and predictability of future nonstationarity in flood frequency analysis. Traditionally, flood frequency analysis is taught from a statistical perspective that can offer limited means of understanding the nature of non-stationarity. By visually comparing graphics of mean daily flows and annual peak discharges (plotted against Julian day) for watersheds in a variety of locales, distinct differences in the timing and nature of flooding in different regions of the US becomes readily apparent. Such differences highlight the dominant hydroclimatological drivers of different watersheds. When linked with information on the predictability of hydroclimatic drivers (hurricanes, atmospheric rivers, snowpack melt, convective events) in a changing climate, such comparative analysis provides students with an improved physical understanding of flood processes and a stronger foundation on which to make judgments about how to modify statistical techniques for making predictions in a changing climate. We envision that such comparative analysis could be incorporated into a number of other traditional hydrologic topics. [ABSTRACT FROM AUTHOR]

The paper compares different approaches to regional frequency analysis with the main focus on the implementation of the region-of-influence (ROI) technique for the modelling of probabilities of heavy precipitation amounts in the area of the Western Carpathians. Unlike the conventional regional frequency analysis where the at-site design values are estimated within a fixed pooling group (region), the ROI approach as a specific alternative to focused pooling techniques makes use of flexible pooling groups, i.e. each target site has its own group of sufficiently similar sites. In this paper, various ROI pooling schemes are constructed as combinations of different alternatives of sites' similarity (pooling groups defined according to climatological characteristics and geographical proximity of sites, respectively) and pooled weighting factors. The performance of the ROI pooling schemes and statistical models of conventional (regional and at-site) frequency analysis is assessed by means of Monte Carlo simulation studies for precipitation annual maxima for the 1-day and 5-day durations in Slovakia. It is demonstrated that a) all the frequency models based on the ROI method yield estimates of growth curves that are superior to the standard regional and at-site estimates at most individual sites, and b) the selection of a suitable ROI pooling scheme should be adjusted to the dominant character of the formation of heavy precipitation. [ABSTRACT FROM AUTHOR]

The MODerate-resolution Imaging Spectroradiometer (MODIS) snow cover product was evaluated by Parajka and Blösch (2006) over the territory of Austria. The spatial and temporal variability of the MODIS snow product classes are analyzed, the accuracy of the MODIS snow product against numerous in situ snow depth data are examined and the main factors that may influence the MODIS classification accuracy are identified in their studies. The authors of this paper would like to provide more discussion to the scientific community on the "Validation of MODIS snow cover images" when similar methodology is applied to mountainous regions covered with abundant snow but with limited number of ground survey and automated stations. Daily snow cover maps obtained from MODIS images are compared with ground observations in mountainous terrain of Turkey for the winter season of 2002-2003 and 2003- 2004 during the accumulation and ablation periods of snow. Snow depth and density values are recorded to determine snow water equivalent (SWE) values at 19 points in and around the study area in Turkey. Comparison of snow maps with in situ data show good agreement with overall accuracies in between 62 to 82 percent considering a 2-day shift during cloudy days. Studies show that the snow cover extent can be used for forecasting of runoff hydrographs resulting mostly from snowmelt for a mountainous basin in Turkey. MODIS-Terra snow albedo products are also compared with ground based measurements over the ablation stage of 2004 using the automated weather operating stations (AWOS) records at fixed locations as well as from the temporally assessed measuring sites during the passage of the satellite. Temporarily assessed 20 ground measurement sites are randomly distributed around one of the AWOS stations and both MODIS and ground data were aggregated in GIS for analysis. Reduction in albedo is noticed as snow depth decreased and SWE values increased. [ABSTRACT FROM AUTHOR]

Despite its local and regional importance, hydrometeorological data on the Sudd (one of Africa's largest wetlands) is very scanty. This is due to the physical and political situation of this area of Sudan. The areal size of the wetland, the evaporation rate, and the influence on the micro and meso climate are still unresolved questions of the Sudd hydrology. The evaporation flux from the Sudd wetland has been estimated using thermal infrared remote sensing data and a parameterization of the surface energy balance (SEBAL model). It is concluded that the actual spatially averaged evaporation from the Sudd wetland over 3 years of different hydrometeorological characteristics varies between 1460 and 1935mm/yr. This is substantially less than open water evaporation. The wetland area appears to be 70% larger than previously assumed when the Sudd was considered as an open water body. The temporal analysis of the Sudd evaporation demonstrated that the variation of the atmospheric demand in combination with the inter-annual fluctuation of the groundwater table results into a quasi-constant evaporation rate in the Sudd, while open water evaporation depicts a clear seasonal variability. The groundwater table characterizes a distinct seasonality, confirming that substantial parts of the Sudd are seasonal swamps. The new set of spatially distributed evaporation parameters from remote sensing form an important dataset for calibrating a regional climate model enclosing the Nile Basin. The Regional Atmospheric Climate Model (RACMO) provides an insight not only into the temporal evolution of the hydro-climatological parameters, but also into the land surface climate interactions and embedded feedbacks. The impact of the flooding of the Sudd on the Nile hydroclimatology has been analysed by simulating two land surface scenarios (with and without the Sudd wetland). The paper presents some of the model results addressing the Sudd's influence on rainfall, evaporation and runoff of the river Nile, as well as the influence on the microclimate. The paper presents a case study that confirms the feasibility of using remote sensing data (with good spatial and poor temporal coverage) in conjunction with a regional climate model. The combined model provides good temporal and spatial representation in a region characterized by extremely scarce ground data. [ABSTRACT FROM AUTHOR]

This paper presents the result of the regional coupled climatic and hydrologic model of the Nile Basin. For the first time the interaction between the climatic processes and the hydrological processes on the land surface have been fully coupled. The hydrological model is driven by the rainfall and the energy available for evaporation generated in the climate model, and the runoff generated in the catchment is again routed over the wetlands of the Nile to supply moisture for atmospheric feedback. The results obtained are quite satisfactory given the extremely low runoff coefficients in the catchment. The paper presents the validation results over the sub-basins: Blue Nile, White Nile, Atbara river, the Sudd swamps, and the Main Nile for the period 1995 to 2000. Observational datasets were used to evaluate the model results including radiation, precipitation, runoff and evaporation data. The evaporation data were derived from satellite images over a major part of the Upper Nile. Limitations in both the observational data and the model are discussed. It is concluded that the model provides a sound representation of the regional water cycle over the Nile. The sources of atmospheric moisture to the basin, and location of convergence/divergence fields could be accurately illustrated. The model is used to describe the regional water cycle in the Nile basin in terms of atmospheric fluxes, land surface fluxes and land surface-climate feedbacks. The monthly moisture recycling ratio (i.e. locally generated/total precipitation) over the Nile varies between 8 and 14%, with an annual mean of 11%, which implies that 89% of the Nile water resources originates from outside the basin physical boundaries. The monthly precipitation efficiency varies between 12 and 53%, and the annual mean is 28%. The mean annual result of the Nile regional water cycle is compared to that of the Amazon and the Mississippi basins. [ABSTRACT FROM AUTHOR]

Flux towers provide essential terrestrial climate, water, and radiation budget information needed for environmental monitoring and evaluation of climate change impacts on ecosystems and society in general. They are also intended for calibration and validation of satellite-based Earth observation and monitoring efforts, such as assessment of evapotranspiration from land and vegetation surfaces using surface energy balance approaches. In this paper, 15 years of Skukuza eddy covariance data, i.e. from 2000 to 2014, were analysed for surface energy balance closure (EBC) and partitioning. The surface energy balance closure was evaluated using the ordinary least squares regression (OLS) of turbulent energy fluxes (sensible (H) and latent heat (LE)) against available energy (net radiation (Rn) less soil heat (G)), and the energy balance ratio (EBR). Partitioning of the surface energy during the wet and dry seasons was also investigated, as well as how it is affected by atmospheric vapour pressure deficit (VPD), and net radiation. After filtering years with low-quality data (2004-2008), our results show an overall mean EBR of 0.93. Seasonal variations of EBR also showed the wet season with 1.17 and spring (1.02) being closest to unity, with the dry season (0.70) having the highest imbalance. Nocturnal surface energy closure was very low at 0.26, and this was linked to low friction velocity during night-time, with results showing an increase in closure with increase in friction velocity. The energy partition analysis showed that sensible heat flux is the dominant portion of net radiation, especially between March and October, followed by latent heat flux, and lastly the soil heat flux, and during the wet season where latent heat flux dominated sensible heat flux. An increase in net radiation was characterized by an increase in both LE and H, with LE showing a higher rate of increase than H in the wet season, and the reverse happening during the dry season. An increase in VPD is correlated with a decrease in LE and increase in H during the wet season, and an increase in both fluxes during the dry season. [ABSTRACT FROM AUTHOR]

In response to flood risk, design flood estimation is a cornerstone of planning, infrastructure design, setting of insurance premiums, and emergency response planning. Under stationary assumptions, flood guidance and the methods used in design flood estimation are firmly established in practice and mature in their theoretical foundations, but under climate change, guidance is still in its infancy. Human-caused climate change is influencing factors that contribute to flood risk such as rainfall extremes and soil moisture, and there is a need for updated flood guidance. However, a barrier to updating flood guidance is the translation of the science into practical application. For example, most science pertaining to historical changes to flood risk focuses on examining trends in annual maximum flood events or the application of non-stationary flood frequency analysis. Although this science is valuable, in practice, design flood estimation focuses on exceedance probabilities much rarer than annual maximum events, such as the 1 % annual exceedance probability event or even rarer, using rainfall-based procedures, at locations where there are few to no observations of streamflow. Here, we perform a systematic review to summarize the state-of-the-art understanding of the impact of climate change on design flood estimation in the Australian context, while also drawing on international literature. In addition, a meta-analysis, whereby results from multiple studies are combined, is conducted for extreme rainfall to provide quantitative estimates of possible future changes. This information is described in the context of contemporary design flood estimation practice to facilitate the inclusion of climate science into design flood estimation practice. [ABSTRACT FROM AUTHOR]

Traditionally, long-term predictions of river discharges and their extremes include constant relationships between landscape properties and model parameters. However, due to the co-evolution of many landscape properties more sophisticated methods are necessary to quantify future landscape-hydrological model relationships. As a first step towards such an approach we use the Brutsaert and Nieber (1977) analysis method to characterize streamflow recession behaviour of ≈ 200 Swedish catchments within the context of global change and landscape co-evolution. Results suggest that the Brutsaert-Nieber parameters are strongly linked to the climate, soil, land use, and their interdependencies. Many catchments show a trend towards more non-linear behaviour, meaning not only faster initial recession but also slower recession towards base flow. This trend has been found to be independent from climate change. Instead, we suggest that land cover change, both natural (restoration of natural soil profiles in forested areas) and anthropogenic (reforestation and optimized water management), is probably responsible. Both change types are characterised by system adaptation and change, towards more optimal ecohydrological conditions, suggesting landscape co-evolution is at play. Given the observed magnitudes of recession changes during the past 50 years, predictions of future river discharge critically need to include the effects of landscape co-evolution. The interconnections between the controls of land cover and climate on river recession behaviour, as we have quantified in this paper, provide first-order handles to do so. [ABSTRACT FROM AUTHOR]

After the drought of the 1970s in West Africa, the variability in rainfall and land use changes mostly affected flow, and recently flooding has been said to be an increasingly common occurrence throughout the whole of West Africa. These changes have raised many questions about the impact of climate change on the flood regimes in West African countries. This paper investigates whether floods are becoming more frequent or more severe and to what extent climate patterns have been responsible for these changes. We analyzed the trends in the floods occurring in 11 catchments within West Africa's main climate zones. The methodology includes two methods for sampling flood events, namely the AM (annual maximum) method and the POT (peak over threshold), and two perspectives of analysis are presented: long-term analysis based on two long flood time series and a regional perspective involving 11 catchments with shorter series. The Mann-Kendall trend test and the Pettitt break test were used to detect nonstationarities in the time series. The trends detected in flood time series were compared to the rainfall index trends and vegetation indices using contingency tables in order to identify the main driver of change in flood magnitude and flood frequency. The relation between the flood index and the physiographic index was evaluated through a success criterion and the Cramer criterion calculated from the contingency tables. The results show the existence of trends in flood magnitude and flood frequency time series, with two main patterns. Sahelian floods show increasing flood trends and one Sudanian. catchment presents decreasing flood trends. For the overall catchments studied, trends in the maximum 5-day consecutive rainfall index (R5d) show good coherence with trends in flood, while the trends in normalized difference vegetation indices (NDVIs) do not show a significant agreement with flood trends, meaning that this index has possibly no impact on the behavior of floods in the region. [ABSTRACT FROM AUTHOR]

The impact of climate change on water resources is usually assessed at the local scale. However, regional climate models (RCMs) are known to exhibit systematic biases in precipitation. Hence, RCM simulations need to be post-processed in order to produce reliable estimates of local scale climate. Popular post-processing approaches are based on statistical transformations, which attempt to adjust the distribution of modelled data such that it closely resembles the observed climatology. However, the diversity of suggested methods renders the selection of optimal techniques difficult and therefore there is a need for clarification. In this paper, statistical transformations for postprocessing RCM output are reviewed and classified into (1) distribution derived transformations, (2) parametric transformations and (3) nonparametric transformations, each differing with respect to their underlying assumptions. A real world application, using observations of 82 precipitation stations in Norway, showed that nonparametric transformations have the highest skill in systematically reducing biases in RCM precipitation. [ABSTRACT FROM AUTHOR]

The Mekong River in Southeast Asia thanks its regular annual flood to the southwest monsoon. At longer time scales, the monsoon is a spatially and temporally variable circulation, with different annual to millennial variation for different regions. In this paper, the Indian and theWestern Pacific components of the monsoon were analyzed to draw a light on the interannual flood variability of the Mekong River. The focus is on the variance of flood season flows at 8 stations on the Mekong River, as well as on well-known climate indexes that reflect the dynamics of the monsoon circulation and ocean temperature anomalies. An effort was made to identify the temporal resolution that contains most of the interannual variability of both flood regime of the Mekong and monsoon intensity. We found a close connection between the Western Pacific monsoon and the discharge in Kratie and other stations in the Southern Mekong region. In the frequency domain, the interannual to decadal variance of the Mekong discharge closely follows that of the Western Pacific monsoon. More importantly, the well-known regime shift of 1976 in the North Pacific is detectable in the frequency domain for flood discharge and monsoon intensity. This suggests a relationship between Pacific sea surface temperature and monsoon variance, which is a good predictor for flood variance. This dependence influences the probability of occurrence of floods in the Mekong Delta. [ABSTRACT FROM AUTHOR]

This paper investigates issues involved in calibrating hydrological models against observed data when the aim of the modelling is to predict future runoff under different climatic conditions. To achieve this objective, we tested two hydrological models, DWBM and SIMHYD, using data from 30 unimpaired catchments in Australia which had at least 60 yr of daily precipitation, potential evapotranspiration (PET), and streamflow data. Nash-Sutcliffe efficiency (NSE), modified index of agreement (d1) and water balance error (WBE) were used as performance criteria. We used a differential split-sample test to split up the data into 120 subperiods and 4 different climatic sub-periods in order to assess how well the calibrated model could be transferred different periods. For each catchment, the models were calibrated for one sub-period and validated on the other three. Monte Carlo simulation was used to explore parameter stability compared to historic climatic variability. The chi-square test was used to measure the relationship between the distribution of the parameters and hydroclimatic variability. The results showed that the performance of the two hydrological models differed and depended on the model calibration. We found that if a hydrological model is set up to simulate runoff for a wet climate scenario then it should be calibrated on a wet segment of the historic record, and similarly a dry segment should be used for a dry climate scenario. The Monte Carlo simulation provides an effective and pragmatic approach to explore uncertainty and equifinality in hydrological model parameters. Some parameters of the hydrological models are shown to be significantly more sensitive to the choice of calibration periods. Our findings support the idea that when using conceptual hydrological models to assess future climate change impacts, a differential split-sample test and Monte Carlo simulation should be used to quantify uncertainties due to parameter instability and non-uniqueness. [ABSTRACT FROM AUTHOR]

This paper investigates the temporal transposability of hydrological models under contrasted climate conditions and evaluates the added value of using an ensemble of model structures for flow simulation. This is achieved by applying the Differential Split Sample Test procedure to twenty lumped conceptual models on a catchment in the Province of Québec (Canada) and another one in the State of Bavaria (Germany). First, a calibration/validation procedure was applied on four historical non-continuous periods with contrasted climate conditions. Then, model efficiency was quantified individually (for each model) and collectively (for the model ensemble). The individual analysis evaluated model performance and robustness. The ensemble investigation, based on the average of simulated discharges, focused on the twenty-member ensemble and all possible model subsets. Results showed that using a single model may provide hazardous results when the model is to be applied in contrasted conditions. Overall, some models turned out as a good compromise in terms of performance and robustness, but generally not as much as the twenty-model ensemble. Model subsets offered yet improved performance over the twenty-model ensemble, but at the expanse of spatial transposability (i.e. need of site-specific analysis). [ABSTRACT FROM AUTHOR]

This paper outlines a new strategy to derive evaporation from satellite observations. The approach uses a variety of satellite-sensor products to estimate daily evaporation at a global scale and 0.25 degree spatial resolution. Central to this methodology is the use of the Priestley and Taylor (PT) evaporation model. The minimalistic PT equation combines a small number of inputs, the majority of which can be detected from space. This reduces the number of variables that need to be modelled. Key distinguishing features of the approach are the use of microwave-derived soil moisture, land surface temperature and vegetation density, as well as the detailed estimation of rainfall interception loss. The modelled evaporation is validated against one year of eddy covariance measurements from 43 stations. The estimated annual totals correlate well with the stations' annual cumulative evaporation (R = 0.80, N = 43) and present a low average bias (-5%). The validation of the daily time series at each individual station shows good model performance in all vegetation types and climate conditions with an average correlation coefficient of R =0.83, still lower than the R =0.90 found in the validation of the monthly time series. The first global map of annual evaporation developed through this methodology is also presented. [ABSTRACT FROM AUTHOR]

Rain gauges can offer high quality rainfall measurements at their locations. Networks of rain gauges can offer better insight into the space-time variability of rainfall, but they tend to be too widely spaced for accurate estimates between points. While remote sensing systems, such as radars and networks of microwave links, can offer good insight in the spatial variability of rainfall they tend to have more problems in identifying the correct rain amounts at the ground. A way to estimate the variability of rainfall between gauge points is to interpolate between them using fitted variograms. If a dense rain gauge network is lacking it is difficult to estimate variograms accurately. In this paper a 30-year dataset of daily rain accumulations gathered at 29 automatic weather stations operated by KNMI (Royal Netherlands Meteorological Institute) and a one-year dataset of 10 gauges in a network with a radius of 5 km around CESAR (Cabauw Experimental Site for Atmospheric Research) are employed to estimate variograms. Fitted variogram parameters are shown to vary according to season, following simple cosine functions. Semi-variances at short ranges during winter and spring tend to be underestimated, but semi-variances during summer and autumn are well predicted. [ABSTRACT FROM AUTHOR]

The most important climatological inputs required for the calibration and validation of hydrological models are temperature and precipitation that can be derived from observational records or alternatively from regional climate models (RCMs). In this paper, meteorological station observations and results of the PRECIS (Providing REgional Climate for Impact Studies) RCM driven by the outputs of reanalysis ERA 40 data and HadAM3P general circulation model (GCM) results are used as input in the hydrological model. The objective is to investigate the effect of precipitation and temperature simulated with the PRECIS RCM nested in these two data sets on discharge simulated with the HBV model for three river basins in the Hindukush-Karakorum-Himalaya (HKH) region. Six HBV model experiments are designed: HBV-Met, HBV-ERA and HBV-Had, HBV-MetCRU-corrected, HBV-ERABenchmark and HBV-HadBenchmark where HBV is driven by meteorological stations data, data from PRECIS nested in ERA-40 and HadAM3P, meteorological stations CRU corrected data, ERA-40 reanalysis and HadAM3P GCM data, respectively. Present day PRECIS simulations possess strong capacity to simulate spatial patterns of present day climate characteristics. However, also some quantitative biases exist in the HKH region, where PRECIS RCM simulations underestimate temperature and overestimate precipitation with respect to CRU observations. The calibration and validation results of the HBV model experiments show that the performance of HBV-Met is better than the HBV models driven by other data sources. However, using input data series from sources different from the data used in the model calibration shows that HBV-Had is more efficient than other models and HBV-Met has the least absolute relative error with respect to all other models. The uncertainties are higher in least efficient models (i.e. HBV-MetCRU-corrected and HBV-ERABenchmark) where the model parameters are also unrealistic. In terms of both robustness and uncertainty ranges the HBV models calibrated with PRECIS output performed better than other calibrated models except for HBV-Met which has shown a higher robustness. This suggests that in data sparse regions such as the HKH region data from regional climate models may be used as input in hydrological models for climate scenarios studies. [ABSTRACT FROM AUTHOR]

Urban flooding causes large economic losses, property damage and loss of lives. The impact of environmental changes, mainly urbanization and climatic change, leads to increased runoff and peak flows which the drainage system must be able to cope with to reduce potential damage and inconvenience. Allowing for detention storage to compliment the conveyance capacity of the drainage system network is one of the approaches to reduce urban floods. Contemporary practice is to design systems against stationary environmental forcings - including design rainfall, landuse, etc. Due to the rapid change in the climate- and the urban environment, this approach is no longer appropriate, and explicit consideration of gradual changes during the life-time of the drainage system is warranted. In this paper, a staged cost optimization tool based on the hydraulic performance of the drainage system is presented. A one dimensional hydraulic model is used for hydraulic evaluation of the network together with a genetic algorithm based optimization tool to determine optimal intervention timings and responses over the analysis period. The model was applied in a case study area in the city of Porto Alegre, Brazil. It was concluded that considerable financial savings and/or additional level of flood-safety can be achieved by approaching the design problem as a staged plan rather than one-off scheme. [ABSTRACT FROM AUTHOR]

Olive trees (Olea europaea L.) are commonly grown in the Mediterranean basin where prolonged droughts may occur during the vegetative period. This species has developed a series of physiological mechanisms, that can be observed in several plants of the Mediterranean macchia, to tolerate drought stress and grow under adverse climatic conditions. These mechanisms have been investigated through an experimental campaign carried out over both irrigated and drought-stressed plants in order to comprehend the plant response under stressed conditions and its ability to recover. Experimental results show that olive plants subjected to water deficit lower the water content and water potentials of their tissues, establishing a particularly high potential gradient between leaves and roots, and stop canopy growth but not photosynthetic activity and transpiration. This allows the continuous production of assimilates as well as their accumulation in the various plant parts, so creating a higher root/leaf ratio if compared to well-watered plants. Active and passive osmotic adjustment due to the accumulation of carbohydrates (in particular mannitol and glucose), proline and other osmolytes have key roles in maintaining cell turgor and leaf activities. At severe drought-stress levels, the non-stomatal component of photosynthesis is inhibited and a light-dependent inactivation of the photosystem II occurs. Finally, the activities of some antioxidant enzymes involved in the scavenging of activated oxygen species and in other biochemical pathways increase during a period of drought. The present paper provides an overview of the driving mechanisms adopted by olive trees to face drought stress with the aim of better understanding plant-soil interactions. [ABSTRACT FROM AUTHOR]

Flooding is a major natural hazard in many parts of the world, and its frequency and magnitude are projected to increase with global warming. With increased concern over ongoing climate change, more detailed and precise information about climate-change risks is required for formulating local-scale countermeasures. However, the impacts of biases in climate-model outputs on river-flood simulation have not been fully evaluated, and thus evaluation of future flood risks using hazard maps (high-resolution spatial-distribution maps of inundation depths) has not been achieved. Therefore, this study examined methods for constructing future-flood-hazard maps and discussed their validity. Specifically, we compared the runoff-correction method that corrects for bias in general-circulation-model (GCM) runoff using the monthly climatology of reanalysis runoff with the lookup method, which uses the GCM simulation results without bias correction to calculate changes in the return period and depends on the reanalysis simulation to determine absolute flood depths. The results imply that the runoff-correction method may produce significantly different hazard maps compared to those based on reanalysis of runoff data. We found that, in some cases, bias correction did not perform as expected for extreme values associated with the hazard map, even under the historical climate, as the bias of extreme values differed from that of the mean value. We found that the change direction of a future hazard (increase or decrease) obtained using the runoff-correction method relative to the reference reanalysis-based hazard map may be inconsistent with changes projected by Catchment-based Macro-scale Floodplain Model (CaMa-Flood) simulations based on GCM runoff input in some cases. On the other hand, the lookup method produced future-hazard maps that are consistent with flood-hazard changes projected by CaMa-Flood simulations obtained using GCM runoff input, indicating the possibility of obtaining a reasonable inundated-area distribution. These results suggest that the lookup method is more suitable for future-flood hazard-map construction than the runoff-correction method. The lookup method also has the advantage of facilitating research on efficient construction of future-climate hazard maps, as it allows for improvement of the reanalysis hazard map through upgrading of the model and separate estimation of changes due to climate change. We discuss future changes at the global scale in inundation areas and the affected population within the inundation area. Using the lookup method, the total population living in modeled inundation areas with flood magnitudes exceeding the 100-year return period under a future climate would be approximately 1.86 billion. In the assessment of future-climate risks, we found that an affected population of approximately 0.2 billion may be missed if the historical-hazard map is used as an alternative to constructing future-hazard maps, and only frequency changes are considered. These results suggest that, in global flood-risk studies, future-hazard maps are important for proper estimation of climate-change risks rather than assessing solely changes in the frequency of occurrence of a given flood intensity. [ABSTRACT FROM AUTHOR]

Leaf transpiration and energy exchange are coupled processes that operate at small scales yet exert a significant influence on the terrestrial hydrological cycle and climate. Surprisingly, experimental capabilities required to quantify the energy-transpiration coupling at the leaf scale are lacking, challenging our ability to test basic questions of importance for resolving large-scale processes. The present study describes an experimental set-up for the simultaneous observation of transpiration rates and all leaf energy balance components under controlled conditions, using an insulated closed loop miniature wind tunnel and artificial leaves with pre-defined and constant diffusive conductance for water vapour. A range of tests documents the above capabilities of the experimental set-up and points to potential improvements. The tests reveal a conceptual flaw in the assumption that leaf temperature can be characterized by a single value, suggesting that even for thin, planar leaves, a temperature gradient between the irradiated and shaded or transpiring and non-transpiring leaf side can lead to bias when using observed leaf temperatures and fluxes to deduce effective conductances to sensible heat or water vapour transfer. However, comparison of experimental results with an explicit leaf energy balance model revealed only minor effects on simulated leaf energy exchange rates by the neglect of cross-sectional leaf temperature gradients, lending experimental support to our current understanding of leaf gas and energy exchange processes. [ABSTRACT FROM AUTHOR]

We investigate how evaporation changes depending on the scales in the Altiplano region of the Atacama Desert. More specifically, we focus on the temporal evolution from the climatological to the sub-diurnal scales on a high-altitude saline lake ecosystem. We analyze the evaporation trends over 70 years (1950–2020) at a high-spatial resolution. The method is based on the downscaling of 30 km ERA5 reanalysis data at hourly resolution to 0.1 km spatial resolution data, using artificial neural networks to analyze the main drivers of evaporation. To this end, we use the Penman open-water evaporation equation, modified to compensate for the energy balance non-closure and the ice cover formation on the lake during the night. Our estimation of the hourly climatology of evaporation shows a consistent agreement with eddy-covariance (EC) measurements and reveals that evaporation is controlled by different drivers depending on the time scale. At the sub-diurnal scale, mechanical turbulence is the primary driver of evaporation, and at this scale, it is not radiation-limited. At the seasonal scale, more than 70 % of the evaporation variability is explained by the radiative contribution term. At the same scale, and using a large-scale moisture tracking model, we identify the main sources of moisture to the Chilean Altiplano. In all cases, our regime of precipitation is controlled by large-scale weather patterns closely linked to climatological fluctuations. Moreover, seasonal evaporation significantly influences the saline lake surface spatial changes. From an interannual scale perspective, evaporation increased by 2.1 mmyr-1 during the entire study period, according to global temperature increases. Finally, we find that yearly evaporation depends on the El Niño–Southern Oscillation (ENSO), where warm and cool ENSO phases are associated with higher evaporation and precipitation rates, respectively. Our results show that warm ENSO phases increase evaporation rates by 15 %, whereas cold phases decrease it by 2 %. [ABSTRACT FROM AUTHOR]

The question whether the magnitude and frequency of floods have changed due to climate change or other drivers of change is of high interest. The number of flood trend studies is rapidly rising. When changes are detected, many studies link the identified change to the underlying causes, i.e. they attribute the changes in flood behaviour to certain drivers of change. We propose a hypothesis testing framework for trend attribution which consists of essential ingredients for a sound attribution: evidence of consistency, evidence of inconsistency, and provision of confidence statement. Further, we evaluate the current state-of-the-art of flood trend attribution. We assess how selected recent studies approach the attribution problem, and to which extent their attribution statements seem defendable. In our opinion, the current state of flood trend attribution is poor. Attribution statements are mostly based on qualitative reasoning or even speculation. Typically, the focus of flood trend studies is the detection of change, i.e. the statistical analysis of time series, and attribution is regarded as an appendix: (1) flood time series are analysed by means of trend tests, (2) if a significant change is detected, a hypothesis on the cause of change is given, and (3) explanations or published studies are sought which support the hypothesis. We believe that we need a change in perspective and more scientific rigour: detection should be seen as an integral part of the more challenging attribution problem, and detection and attribution should be placed in a sound hypothesis testing framework. [ABSTRACT FROM AUTHOR]

Drought is a natural extreme climate phenomenon that presents great challenges in forecasting and monitoring for water management purposes. Previous studies have examined the use of Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage anomalies to measure the amount of water missing from a drought-affected region, and other studies have attempted statistical approaches to drought recovery forecasting based on joint probabilities of precipitation and soil moisture. The goal of this study is to combine GRACE data and historical precipitation observations to quantify the amount of precipitation required to achieve normal storage conditions in order to estimate a likely drought recovery time. First, linear relationships between terrestrial water storage anomaly (TWSA) and cumulative precipitation anomaly are established across a range of conditions. Then, historical precipitation data are statistically modeled to develop simplistic precipitation forecast skill based on climatology and long-term trend. Two additional precipitation scenarios are simulated to predict the recovery period by using a standard deviation in climatology and long-term trend. Precipitation scenarios are convolved with water deficit estimates (from GRACE) to calculate the best estimate of a drought recovery period. The results show that, in the regions of strong seasonal amplitude (like a monsoon belt), drought continues even with above-normal precipitation until its wet season. The historical GRACE-observed drought recovery period is used to validate the approach. Estimated drought for an example month demonstrated an 80 % recovery period, as observed by the GRACE. [ABSTRACT FROM AUTHOR]

Switzerland has faced extended periods of low river flows in recent years (2003, 2011, 2015 and 2018), with major economic and environmental consequences. Understanding the origins of events like these is important for water resources management. In this work, we provide data illustrating the individual and joint contributions of precipitation and evapotranspiration to low flows in both typical and dry years. To quantify how weather drives low flows, we explore how deviations from mean seasonal climate conditions (i.e., climate anomalies) of precipitation and potential evapotranspiration correlate with the occurrence and magnitude of annual 7 d lowest flows (Qmin) during the warm season (May through November) across 380 Swiss catchments from 2000 through 2018. Most warm-season low flows followed periods of below-average precipitation and above-average potential evapotranspiration, and the lowest low flows resulted from both of these drivers acting together. Low-flow timing was spatially variable across Switzerland in all years, including the driest (2003, 2011, 2015 and 2018). Low flows in these driest years were associated with much longer-lasting climate anomalies than the ≤2 month anomalies which preceded typical warm-season low flows in other years. We found that snow water equivalent and winter precipitation totals only slightly influenced the magnitude and timing of warm-season low flows in low-elevation catchments across Switzerland. Our results provide insight into how precipitation and potential evapotranspiration jointly shape warm-season low flows across Switzerland and potentially aid in assessing low-flow risks in similar mountain regions using seasonal weather forecasts. [ABSTRACT FROM AUTHOR]

Changes in precipitation over the European Alps are investigated with the regional climate model MAR (Modèle Atmosphérique Régional) applied with a 7 km resolution over the period 1903–2010 using the reanalysis ERA-20C as forcing. A comparison with several observational datasets demonstrates that the model is able to reproduce the climatology as well as both the interannual variability and the seasonal cycle of precipitation over the European Alps. The relatively high resolution allows us to estimate precipitation at high elevations. The vertical gradient of precipitation simulated by MAR over the European Alps reaches 33% km -1 (1.21 mm d -1 km -1) in summer and 38 % km -1 (1.15 mm d -1 km -1) in winter, on average, over 1971–2008 and shows a large spatial variability. A significant (p value < 0.05) increase in mean winter precipitation is simulated in the northwestern Alps over 1903–2010, with changes typically reaching 20 % to 40 % per century. This increase is mainly explained by a stronger simple daily intensity index (SDII) and is associated with less-frequent but longer wet spells. A general drying is found in summer over the same period, exceeding 20 % to 30 % per century in the western plains and 40 % to 50 % per century in the southern plains surrounding the Alps but remaining much smaller (<10 %) and not significant above 1500 m a.s.l. Below this level, the summer drying is explained by a reduction in the number of wet days, reaching 20 % per century over the northwestern part of the Alps and 30 % to 50 % per century in the southern part of the Alps. It is associated with shorter but more-frequent wet spells. The centennial trends are modulated over the last decades, with the drying occurring in the plains in winter also affecting high-altitude areas during this season and with a positive trend of autumn precipitation occurring only over the last decades all over the Alps. Maximum daily precipitation index (Rx1day) takes its highest values in autumn in both the western and the eastern parts of the southern Alps, locally reaching 50 to 70 mm d -1 on average over 1903–2010. Centennial maxima up to 250 to 300 mm d -1 are simulated in the southern Alps, in France and Italy, as well as in the Ticino valley in Switzerland. Over 1903–2010, seasonal Rx1day shows a general and significant increase at the annual timescale and also during the four seasons, reaching local values between 20 % and 40 % per century over large parts of the Alps and the Apennines. Trends of Rx1day are significant (p value < 0.05) only when considering long time series, typically 50 to 80 years depending on the area considered. Some of these trends are nonetheless significant when computed over 1970–2010, suggesting a recent acceleration of the increase in extreme precipitation, whereas earlier periods with strong precipitation also occurred, in particular during the 1950s and 1960s. [ABSTRACT FROM AUTHOR]

Multi-year droughts in Mediterranean climates may shift the water balance, that is, the partitioning rule of precipitation across runoff, evapotranspiration, and sub-surface storage. Mechanisms causing these shifts remain largely unknown and are not well represented in hydrologic models. Focusing on measurements from the headwaters of California's Feather River, we found that also in these mixed rain–snow Mediterranean basins a lower fraction of precipitation was partitioned to runoff during multi-year droughts compared to non-drought years. This shift in the precipitation–runoff relationship was larger in the surface-runoff-dominated than subsurface-flow-dominated headwaters (-39 % vs. -18 % decline of runoff, respectively, for a representative precipitation amount). The predictive skill of the Precipitation Runoff Modeling System (PRMS) hydrologic model in these basins decreased during droughts, with evapotranspiration (ET) being the only water-balance component besides runoff for which the drop in predictive skill during drought vs. non-drought years was statistically significant. In particular, the model underestimated the response time required by ET to adjust to interannual climate variability, which we define as climate elasticity of ET. Differences between simulated and data-driven estimates of ET were well correlated with accompanying data-driven estimates of changes in sub-surface storage (ΔS , r=0.78). This correlation points to shifts in precipitation–runoff relationships being evidence of a hysteretic response of the water budget to climate elasticity of ET during and after multi-year droughts. This hysteresis is caused by carryover storage offsetting precipitation deficit during the initial drought period, followed by vegetation mortality when storage is depleted and subsequent post-drought vegetation expansion. Our results point to a general improvement in hydrologic predictions across drought and recovery cycles by including the climate elasticity of ET and better accounting for actual subsurface water storage in not only soil, but also deeper regolith that stores water accessible to roots. This can be done by explicitly parametrizing carryover storage and feedback mechanisms capturing vegetation response to atmospheric demand for moisture. [ABSTRACT FROM AUTHOR]

The Qaidam Basin in the north of the Tibetan Plateau has undergone drastic environmental changes during the last millions of years. During the Pliocene, the Qaidam Basin contained a freshwater mega-lake system although the surrounding regions showed increasingly arid climates. With the onset of the Pleistocene glaciations, lakes began to shrink and finally disappeared almost completely. Today, hyperarid climate conditions prevail in the low-altitude parts of the Qaidam Basin. The question of how the mega-lake system was able to withstand the regional trend of aridification for millions of years has remained enigmatic so far. This study reveals that the mean annual water balance, i.e. the mean annual change in terrestrial water storage in the Qaidam Basin, is nearly zero under present climate conditions due to positive values of net precipitation in the high mountain ranges and shows positive annual values during warmer, less dry years. This finding provides a physically based explanation for how mid-Pliocene climates could have sustained the mega-lake system and that near-future climates not much different from present conditions could cause water storage in reservoirs, raising lake levels and expanding lake areas, and may even result in restoration of the mega-lake system over geological timescales. The study reveals that a region discussed as being an analogue to Mars due to its hyperarid environments is at a threshold under present climate conditions and may switch from negative values of long-term mean annual water balance that have prevailed during the last 2.6 million years to positive ones in the near future. [ABSTRACT FROM AUTHOR]

Precipitation is a crucial variable for hydro-meteorological applications. Unfortunately, rain gauge measurements are sparse and unevenly distributed, which substantially hampers the use of in situ precipitation data in many regions of the world. The increasing availability of high-resolution gridded precipitation products presents a valuable alternative, especially over poorly gauged regions. This study examines the usefulness of current state-of-the-art precipitation data sets in hydrological modeling. For this purpose, we force a conceptual hydrological model with multiple precipitation data sets in >200 European catchments to obtain runoff and evapotranspiration. We consider a wide range of precipitation products, which are generated via (1) the interpolation of gauge measurements (E-OBS and Global Precipitation Climatology Centre (GPCC) V.2018), (2) data assimilation into reanalysis models (ERA-Interim, ERA5, and Climate Forecast System Reanalysis – CFSR), and (3) a combination of multiple sources (Multi-Source Weighted-Ensemble Precipitation; MSWEP V2). Evaluation is done at the daily and monthly timescales during the period of 1984–2007. We find that simulated runoff values are highly dependent on the accuracy of precipitation inputs; in contrast, simulated evapotranspiration is generally much less influenced in our comparatively wet study region. We also find that the impact of precipitation uncertainty on simulated runoff increases towards wetter regions, while the opposite is observed in the case of evapotranspiration. Finally, we perform an indirect performance evaluation of the precipitation data sets by comparing the runoff simulations with streamflow observations. Thereby, E-OBS yields the particularly strong agreement, while ERA5, GPCC V.2018, and MSWEP V2 show good performances. We further reveal climate-dependent performance variations of the considered data sets, which can be used to guide their future development. The overall best agreement is achieved when using an ensemble mean generated from all the individual products. In summary, our findings highlight a climate-dependent propagation of precipitation uncertainty through the water cycle; while runoff is strongly impacted in comparatively wet regions, such as central Europe, there are increasing implications for evapotranspiration in drier regions. [ABSTRACT FROM AUTHOR]

Temporal and spatial precipitation information is key to conducting effective hydrological-process simulation and forecasting. Herein, we implemented a comprehensive evaluation of three selected precipitation products in the Jialing River watershed (JRW) located in southwestern China. A number of indices were used to statistically analyze the differences between two open-access precipitation products (OPPs), i.e., Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and Climate Prediction Center Gauge-Based Analysis of Global Daily Precipitation (CPC), and the rain gauge (Gauge). The three products were then categorized into subbasins to drive SWAT simulations. The results show the following. (1) The three products are highly consistent in temporal variation on a monthly scale yet distinct on a daily scale. CHIRPS is characterized by an overestimation of light rain, underestimation of heavy rain, and high probability of false alarm. CPC generally underestimates rainfall of all magnitudes. (2) Both OPPs satisfactorily reproduce the stream discharges at the JRW outlet with slightly worse performance than the Gauge model. Model with CHIRPS as inputs performed slightly better in both model simulation and fairly better in uncertainty analysis than that of CPC. On a temporal scale, the OPPs are inferior with respect to capturing flood peak yet superior at describing other hydrograph features, e.g., rising and falling processes and baseflow. On a spatial scale, CHIRPS offers the advantage of deriving smooth, distributed precipitation and runoff due to its high resolution. (3) The water balance components derived from SWAT models with equal simulated streamflow discharges are remarkably different between the three precipitation inputs. The precipitation spatial pattern results in an increasing surface flow trend from upstream to downstream. The results of this study demonstrate that with similar performance in simulating watershed runoff, the three precipitation datasets tend to conceal the identified dissimilarities through hydrological-model parameter calibration, which leads to different directions of hydrologic processes. As such, multiple-objective calibration is recommended for large and spatially resolved watersheds in future work. The main findings of this research suggest that the features of OPPs facilitate the widespread use of CHIRPS in extreme flood events and CPC in extreme drought analyses in future climate. [ABSTRACT FROM AUTHOR]

Fluvial systems in southern Ontario are regularly affected by widespread early-spring flood events primarily caused by rain-on-snow events. Recent studies have shown an increase in winter floods in this region due to increasing winter temperature and precipitation. Streamflow simulations are associated with uncertainties mainly due to the different scenarios of greenhouse gas emissions, global climate models (GCMs) or the choice of the hydrological model. The internal variability of climate, defined as the chaotic variability of atmospheric circulation due to natural internal processes within the climate system, is also a source of uncertainties to consider. Uncertainties of internal variability can be assessed using hydrological models fed by downscaled data of a global climate model large ensemble (GCM-LE), but GCM outputs have too coarse of a scale to be used in hydrological modeling. The Canadian Regional Climate Model Large Ensemble (CRCM5-LE), a 50-member ensemble downscaled from the Canadian Earth System Model version 2 Large Ensemble (CanESM2-LE), was developed to simulate local climate variability over northeastern North America under different future climate scenarios. In this study, CRCM5-LE temperature and precipitation projections under an RCP8.5 scenario were used as input in the Precipitation Runoff Modeling System (PRMS) to simulate streamflow at a near-future horizon (2026–2055) for four watersheds in southern Ontario. To investigate the role of the internal variability of climate in the modulation of streamflow, the 50 members were first grouped in classes of similar projected change in January–February streamflow and temperature and precipitation between 1961–1990 and 2026–2055. Then, the regional change in geopotential height (Z500) from CanESM2-LE was calculated for each class. Model simulations showed an average January–February increase in streamflow of 18 % (±8.7) in Big Creek, 30.5 % (±10.8) in Grand River, 29.8 % (±10.4) in Thames River and 31.2 % (±13.3) in Credit River. A total of 14 % of all ensemble members projected positive Z500 anomalies in North America's eastern coast enhancing rain, snowmelt and streamflow volume in January–February. For these members the increase of streamflow is expected to be as high as 31.6 % (±8.1) in Big Creek, 48.3 % (±11.1) in Grand River, 47 % (±9.6) in Thames River and 53.7 % (±15) in Credit River. Conversely, 14 % of the ensemble projected negative Z500 anomalies in North America's eastern coast and were associated with a much lower increase in streamflow: 8.3 % (±7.8) in Big Creek, 18.8 % (±5.8) in Grand River, 17.8 % (±6.4) in Thames River and 18.6 % (±6.5) in Credit River. These results provide important information to researchers, managers, policymakers and society about the expected ranges of increase in winter streamflow in a highly populated region of Canada, and they will help to explain how the internal variability of climate is expected to modulate the future streamflow in this region. [ABSTRACT FROM AUTHOR]

Climate change has far-reaching implications in permafrost-underlain landscapes with respect to hydrology, ecosystems, and the population's traditional livelihoods. In the Lena River catchment, eastern Siberia, changing climatic conditions and the associated impacts are already observed or expected. However, as climate change progresses the question remains as to how far we are along this track and when these changes will constitute a significant emergence from natural variability. Here we present an approach to investigate temperature and precipitation time series from observational records, reanalysis, and an ensemble of 65 climate model simulations forced by the RCP8.5 emission scenario. We developed a novel non-parametric statistical method to identify the time of emergence (ToE) of climate change signals, i.e. the time when a climate signal permanently exceeds its natural variability. The method is based on the Hellinger distance metric that measures the similarity of probability density functions (PDFs) roughly corresponding to their geometrical overlap. Natural variability is estimated as a PDF for the earliest period common to all datasets used in the study (1901–1921) and is then compared to PDFs of target periods with moving windows of 21 years at annual and seasonal scales. The method yields dissimilarities or emergence levels ranging from 0 % to 100 % and the direction of change as a continuous time series itself. First, we showcase the method's advantage over the Kolmogorov–Smirnov metric using a synthetic dataset that resembles signals observed in the utilized climate models. Then, we focus on the Lena River catchment, where significant environmental changes are already apparent. On average, the emergence of temperature has a strong onset in the 1970s with a monotonic increase thereafter for validated reanalysis data. At the end of the reanalysis dataset (2004), temperature distributions have emerged by 50 %–60 %. Climate model projections suggest the same evolution on average and 90 % emergence by 2040. For precipitation the analysis is less conclusive because of high uncertainties in existing reanalysis datasets that also impede an evaluation of the climate models. Model projections suggest hardly any emergence by 2000 but a strong emergence thereafter, reaching 60 % by the end of the investigated period (2089). The presented ToE method provides more versatility than traditional parametric approaches and allows for a detailed temporal analysis of climate signal evolutions. An original strategy to select the most realistic model simulations based on the available observational data significantly reduces the uncertainties resulting from the spread in the 65 climate models used. The method comes as a toolbox available at https://github.com/pohleric/toe%5ftools (last access: 19 May 2020). [ABSTRACT FROM AUTHOR]

It is a common task to partition the synergistic impacts of drivers in the environmental sciences. However, there is no mathematically precise solution to this partition task. Here I present a line-integral-based method, which addresses the sensitivity to the drivers throughout the drivers' evolutionary paths so as to ensure a precise partition. The method reveals that the partition depends on both the change magnitude and pathway (timing of the change) but not on the magnitude alone unless used for a linear system. To illustrate this method, I applied the Budyko framework to partition the effects of climatic and catchment conditions on the temporal change in the runoff for 19 catchments from Australia and China. The proposed method reduces to the decomposition method when assuming a path in which climate change occurs first, followed by an abrupt change in catchment properties. The proposed method re-defines the widely used sensitivity at a point as the path-averaged sensitivity. The total-differential and the complementary methods simply concern the sensitivity at the initial and/or the terminal state, so they cannot give precise results. Although the path-averaged sensitivities varied greatly among the catchments, they can be readily predicted within the Budyko framework. As a mathematically accurate solution, the proposed method provides a generic tool for conducting quantitative attribution analyses. [ABSTRACT FROM AUTHOR]

The amount of lying snow calculated by a land surface model depends in part on the amount of snowfall in the meteorological data that are used to drive the model. We show that commonly used data sets differ in the amount of snowfall, and more generally precipitation, over four large Arctic basins. An independent estimate of the cold-season precipitation is obtained by combining water balance information from the Gravity Recovery and Climate Experiment (GRACE) with estimates of evaporation and river discharge and is generally higher than that estimated by four commonly used meteorological data sets. We use the Joint UK Land Environment Simulator (JULES) land surface model to calculate the snow water equivalent (SWE) over the four basins. The modelled seasonal maximum SWE is 38 % less than observation-based estimates on average, and the modelled basin discharge is significantly underestimated, consistent with the lack of snowfall. We use the GRACE-derived estimate of precipitation to define per-basin scale factors that are applied to the driving data and increase the amount of cold-season precipitation by 28 % on average. In turn this increases the modelled seasonal maximum SWE by 30 %, although this is still underestimated compared to observations by 19 % on average. A correction for the undercatch of precipitation by gauges is compared with the the GRACE-derived correction. Undercatch correction increases the amount of cold-season precipitation by 23 % on average, which indicates that some, but not all, of the underestimation can be removed by implementing existing undercatch correction algorithms. However, even undercatch-corrected data sets contain less precipitation than the GRACE-derived estimate in some regions, and it is likely that there are other biases that are not currently accounted for in gridded meteorological data sets. This study shows that revised estimates of precipitation can lead to improved modelling of SWE, but much more modest improvements are found in modelled river discharge. By providing methods to better define the precipitation inputs to the system, the current study paves the way for subsequent work on key hydrological processes in high-latitude basins. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is one of the most important components in the water cycle. However, there are relatively few direct measurements of ET available (e.g. using flux towers). Nevertheless, various disciplines, ranging from hydrology to agricultural and climate sciences, require information on the spatial and temporal distribution of ET at regional and global scales. Due to the limited data availability, attention has turned toward satellite-based products to fill observational gaps. Various data products, including remote sensing (RS) products, have been developed and provide a large range of ET estimations. Across Africa, only a limited number of flux towers are available; hence, they are insufficient for the systematic evaluation of the available ET products. Thus, in this study, we conduct a methodological evaluation of nine existing RS-derived ET products as well as other available ET products in order to evaluate their reliability at the basin scale. A general water balance (WB) approach is used, where ET is equal to precipitation minus discharge for long-term averages. Firstly, ET products are compared with WB-inferred ET (ET WB) for basins that do not show long-term trends. The ET products and the calculated ET WB are then evaluated against the Budyko equation, which is used as a reference condition. The spatial characteristics of the ET products are finally assessed via the analysis of selected land cover elements across Africa: forests, irrigated areas and water bodies. Additionally, a cluster analysis is conducted to identify similarities between individual ET products. The results show that CMRSET, SSEBop and WaPOR rank highest in terms of the estimation of the long-term average mean ET across basins, with low biases and good spatial variability across Africa. GLEAM consistently ranks lowest in most evaluation criteria, although it has the longest available time period. Each product shows specific advantages and disadvantages. Depending on the study in question, at least one product should be suitable for a particular requirement. The reader should bear in mind that many products suffer from a large bias. Based on the evaluation criteria in this study, the three highest ranked products, CMRSET, SSEBop and WaPOR, would suit many users' needs due to the low biases and good spatial variability across Africa. [ABSTRACT FROM AUTHOR]

A set of complex processes contribute to generate river runoff, which in the hydrological sciences are typically divided into two major categories: surface runoff, sometimes called Hortonian flow, and baseflow-driven runoff or Dunne flow. In this study, we examine the covariance of global satellite-based surface water inundation (SWI) observations with two remotely sensed hydrological variables, precipitation, and terrestrial water storage, to better understand how apparent runoff generation responds to these two dominant forcing mechanisms in different regions of the world. Terrestrial water storage observations come from NASA's Gravity Recovery and Climate Experiment (GRACE) mission, while precipitation comes from the Global Precipitation Climatology Project (GPCP) combined product, and surface inundation levels from the NASA Surface WAter Microwave Product Series (SWAMPS) product. We evaluate the statistical relationship between surface water inundation, total water storage anomalies (TWS; TWSAs), and precipitation values under different time lag and quality control adjustments between the data products. We find that the global estimation of surface inundation improves when considering a quality control threshold of 50 % reliability for the SWAMPS data and after applying time lags ranging from 1 to 5 months. Precipitation and total water storage equally control the majority of surface inundation developments across the globe. The model tends to underestimate and overestimate at locations with high interannual variability and with low inundation measurements, respectively. [ABSTRACT FROM AUTHOR]

Recession analysis is a classical method in hydrology to assess watersheds' hydrological properties by means of the receding limb of a hydrograph, frequently expressed as the rate of change in discharge (-dQ/dt) against discharge (Q). This relationship is often assumed to take the form of a power law -dQ/dt=aQb , where a and b are recession parameters. Recent studies have highlighted major differences in the estimation of the recession parameters depending on the method, casting doubt on our ability to properly evaluate and compare hydrological properties across watersheds based on recession analysis of -dQ/dt vs. Q. This study shows that estimation based on collective recessions as an average watershed response is strongly affected by the distributions of event inter-arrival time, magnitudes, and antecedent conditions, implying that the resulting recession parameters do not represent watershed properties as much as they represent the climate. The main outcome from this work highlights that proper evaluation of watershed properties is only ensured by considering independent individual recession events. While average properties can be assessed by considering the average (or median) values of a and b , their variabilities provide critical insight into the sensitivity of a watershed to the initial conditions involved prior to each recharge event. [ABSTRACT FROM AUTHOR]

Climate warming is changing streamflow regimes and groundwater storage in cold alpine regions. In this study, the Yangbajain headwater catchment in the Lhasa River basin is adopted as the study area to assess streamflow changes and active groundwater storage in response to climate warming. The results show that both annual streamflow and the mean air temperature increase significantly at respective rates of about 12.30 mm per decade and 0.28 ∘ C per decade from 1979 to 2013 in the study area. The results of gray relational analysis indicate that the air temperature acts as a primary factor for the increased streamflow. Due to climate warming, the total glacier volume has retreated by over 25 % during the past 50 years, and the areal extent of permafrost has degraded by 15.3 % over the last 20 years. Parallel comparisons with other subbasins in the Lhasa River basin indirectly reveal that the increased streamflow at the Yangbajain Station is mainly fed by the accelerated glacier retreat. Using baseflow recession analysis, we also find that the estimated groundwater storage that is comparable with the GRACE data increases significantly at rates of about 19.32 mm per decade during the abovementioned period. That is to say, as permafrost thaws, more spaces have been made available to accommodate the increasing meltwater. Finally, a large water imbalance (of more than 5.79×107 m 3 a -1) between the melt-derived runoff and the actual increase in runoff as well as the groundwater storage is also observed. The results from this study suggest that the impacts of glacial retreat and permafrost degradation show compound behaviors on the storage–discharge mechanism due to climate warming, and that this fundamentally affects the water supply and the mechanisms of streamflow generation and change. [ABSTRACT FROM AUTHOR]

The behavior of every catchment is unique. Still, we seek for ways to classify them as this helps to improve hydrological theories. In this study, we use hydrological signatures that were recently identified as those with the highest spatial predictability to cluster 643 catchments from the CAMELS dataset. We describe the resulting clusters concerning their behavior, location and attributes. We then analyze the connections between the resulting clusters and the catchment attributes and relate this to the co-variability of the catchment attributes in the eastern and western US. To explore whether the observed differences result from clustering catchments by either climate or hydrological behavior, we compare the hydrological clusters to climatic ones. We find that for the overall dataset climate is the most important factor for the hydrological behavior. However, depending on the location, either aridity, snow or seasonality has the largest influence. The clusters derived from the hydrological signatures partly follow ecoregions in the US and can be grouped into four main behavior trends. In addition, the clusters show consistent low flow behavior, even though the hydrological signatures used describe high and mean flows only. We can also show that most of the catchments in the CAMELS dataset have a low range of hydrological behaviors, while some more extreme catchments deviate from that trend. In the comparison of climatic and hydrological clusters, we see that the widely used Köppen–Geiger climate classification is not suitable to find hydrologically similar catchments. However, in comparison with novel, hydrologically based continuous climate classifications, some clusters follow the climate classification very directly, while others do not. From those results, we conclude that the signal of the climatic forcing can be found more explicitly in the behavior of some catchments than in others. It remains unclear if this is caused by a higher intra-catchment variability of the climate or a higher influence of other catchment attributes, overlaying the climate signal. Our findings suggest that very different sets of catchment attributes and climate can cause very similar hydrological behavior of catchments – a sort of equifinality of the catchment response. [ABSTRACT FROM AUTHOR]

We present a new global land-based daily precipitation dataset from 1950 using an interpolated network of in situ data called Rainfall Estimates on a Gridded Network – REGEN. We merged multiple archives of in situ data including two of the largest archives, the Global Historical Climatology Network – Daily (GHCN-Daily) hosted by National Centres of Environmental Information (NCEI), USA, and one hosted by the Global Precipitation Climatology Centre (GPCC) operated by Deutscher Wetterdienst (DWD). This resulted in an unprecedented station density compared to existing datasets. The station time series were quality-controlled using strict criteria and flagged values were removed. Remaining values were interpolated to create area-average estimates of daily precipitation for global land areas on a 1 ∘ × 1 ∘ latitude–longitude resolution. Besides the daily precipitation amounts, fields of standard deviation, kriging error and number of stations are also provided. We also provide a quality mask based on these uncertainty measures. For those interested in a dataset with lower station network variability we also provide a related dataset based on a network of long-term stations which interpolates stations with a record length of at least 40 years. The REGEN datasets are expected to contribute to the advancement of hydrological science and practice by facilitating studies aiming to understand changes and variability in several aspects of daily precipitation distributions, extremes and measures of hydrological intensity. Here we document the development of the dataset and guidelines for best practices for users with regards to the two datasets. [ABSTRACT FROM AUTHOR]

Seasonality is ubiquitous in nature, and it is closely linked to water quality, ecology, hydrological extremes, and water resources management. Hydrological signatures aim at extracting information about certain aspects of hydrological behaviour. Commonly used seasonal hydro-climatological signatures consider climate or streamflow seasonality, but they do not consider how climate seasonality translates into streamflow seasonality. In order to analyse the translation of seasonal climate input (precipitation minus potential evapotranspiration) into seasonal catchment output (streamflow), we represent the two time series by their seasonal (annual) Fourier mode, i.e. by sine waves. A catchment alters the input sine wave by reducing its amplitude and by shifting its phase. We propose to use these quantities, the amplitude ratio and the phase shift, as seasonal hydrological signatures. We present analytical solutions describing the response of linear reservoirs to periodic forcing to interpret the seasonal signatures in terms of configurations of linear reservoirs. Using data from the UK and the US, we show that the seasonal signatures exhibit hydrologically interpretable patterns and that they are a function of both climate and catchment attributes. Wet, rather impermeable catchments hardly attenuate the seasonal climate input. Drier catchments, especially if underlain by a productive aquifer, strongly attenuate the input sine wave leading to phase shifts up to several months. As an example application, we test whether two commonly used hydrological models (Identification of unit Hydrographs and Component flows from Rainfall, Evaporation and Streamflow – IHACRES; modèle du Génie Rural à 4 paramètres Journalier – GR4J) can reproduce the observed ranges of seasonal signatures in the UK. The results show that the seasonal signatures have the potential to be useful for catchment classification, predictions in ungauged catchments, and model building and evaluation. The use of potential evapotranspiration in the input restricts the applicability of the signatures to energy-limited (humid) catchments. [ABSTRACT FROM AUTHOR]

The Paris Agreement sets a long-term temperature goal to hold global warming to well below 2.0 ∘ C and strives to limit it to 1.5 ∘ C above preindustrial levels. Droughts with either intense severity or a long persistence could both lead to substantial impacts such as infrastructure failure and ecosystem vulnerability, and they are projected to occur more frequently and trigger intensified socioeconomic consequences with global warming. However, existing assessments targeting global droughts under 1.5 and 2.0 ∘ C warming levels usually neglect the multifaceted nature of droughts and might underestimate potential risks. This study, within a bivariate framework, quantifies the change in global drought conditions and corresponding socioeconomic exposures for additional 1.5 and 2.0 ∘ C warming trajectories. The drought characteristics are identified using the Standardized Precipitation Evapotranspiration Index (SPEI) combined with the run theory, with the climate scenarios projected by 13 Coupled Model Inter-comparison Project Phase 5 (CMIP5) global climate models (GCMs) under three representative concentration pathways (RCP 2.6, RCP4.5 and RCP8.5). The copula functions and the most likely realization are incorporated to model the joint distribution of drought severity and duration, and changes in the bivariate return period with global warming are evaluated. Finally, the drought exposures of populations and regional gross domestic product (GDP) under different shared socioeconomic pathways (SSPs) are investigated globally. The results show that within the bivariate framework, the historical 50-year droughts may double across 58 % of global landmasses in a 1.5 ∘ C warmer world, while when the warming climbs up to 2.0 ∘ C, an additional 9 % of world landmasses would be exposed to such catastrophic drought deteriorations. More than 75 (73) countries' populations (GDP) will be completely affected by increasing drought risks under the 1.5 ∘ C warming, while an extra 0.5 ∘ C warming will further lead to an additional 17 countries suffering from a nearly unbearable situation. Our results demonstrate that limiting global warming to 1.5 ∘ C, compared with 2 ∘ C warming, can perceptibly mitigate the drought impacts over major regions of the world. [ABSTRACT FROM AUTHOR]

Identifying and quantifying drought in retrospective is a necessity for better understanding drought conditions and the propagation of drought through the hydrological cycle and eventually for developing forecast systems. Hydrological droughts refer to water deficits in surface and subsurface storage, and since these are difficult to monitor at larger scales, several studies have suggested exploiting total water storage data from the GRACE (Gravity Recovery and Climate Experiment) satellite gravity mission to analyze them. This has led to the development of GRACE-based drought indicators. However, it is unclear how the ubiquitous presence of climate-related or anthropogenic water storage trends found within GRACE analyses masks drought signals. Thus, this study aims to better understand how drought signals propagate through GRACE drought indicators in the presence of linear trends, constant accelerations, and GRACE-specific spatial noise. Synthetic data are constructed and existing indicators are modified to possibly improve drought detection. Our results indicate that while the choice of the indicator should be application-dependent, large differences in robustness can be observed. We found a modified, temporally accumulated version of the indicator particularly robust under realistic simulations. We show that linear trends and constant accelerations seen in GRACE data tend to mask drought signals in indicators and that different spatial averaging methods required to suppress the spatially correlated GRACE noise affect the outcome. Finally, we identify and analyze two droughts in South Africa using real GRACE data and the modified indicators. [ABSTRACT FROM AUTHOR]

A reliable climate dataset is the backbone for modelling the essential processes of the water cycle and predicting future conditions. Although a number of gridded climate datasets are available for the North American content which provide reasonable estimates of climatic conditions in the region, there are inherent inconsistencies in these available climate datasets (e.g., spatially and temporally varying data accuracies, meteorological parameters, lengths of records, spatial coverage, temporal resolution, etc.). These inconsistencies raise questions as to which datasets are the most suitable for the study area and how to systematically combine these datasets to produce a reliable climate dataset for climate studies and hydrological modelling. This study suggests a framework called the REFerence Reliability Evaluation System (REFRES) that systematically ranks multiple climate datasets to generate a hybrid climate dataset for a region. To demonstrate the usefulness of the proposed framework, REFRES was applied to produce a historical hybrid climate dataset for the Athabasca River basin (ARB) in Alberta, Canada. A proxy validation was also conducted to prove the applicability of the generated hybrid climate datasets to hydrologic simulations. This study evaluated five climate datasets, including the station-based gridded climate datasets ANUSPLIN (Australia National University Spline), Alberta Township, and the Pacific Climate Impacts Consortium's (PCIC) PNWNAmet (PCIC NorthWest North America meteorological dataset), a multi-source gridded dataset (Canadian Precipitation Analysis; CaPA), and a reanalysis-based dataset (North American Regional Reanalysis; NARR). The results showed that the gridded climate interpolated from station data performed better than multi-source- and reanalysis-based climate datasets. For the Athabasca River basin, Township and ANUSPLIN were ranked first for precipitation and temperature, respectively. The proxy validation also confirmed the utility of hybrid climate datasets in hydrologic simulations compared with the other five individual climate datasets investigated in this study. These results indicate that the hybrid climate dataset provides the best representation of historical climatic conditions and, thus, enhances the reliability of hydrologic simulations. [ABSTRACT FROM AUTHOR]