How Extreme Were The Torrential Floods In Smaller Watersheds In Serbia In September 2014

The September torrential floods in 2014 in the Eastern Serbia were a real disaster for local residents in municipalities of Kladovo, Negotin and Majdanpek. Meteorological extreme event caused the hydrological extreme event which led to declaration of the emergency situation in all three municipalities. The combined method of Soil Conservation Service and synthetic unit triangular hydrograph (SCS-SUH) is employed to compute the maximal discharges in small watersheds of Dupljanska reka and Manastiricki potok, in order to assess the extremeness of September 2014 torrential flood events. Since the surface runoff is accompanied by intensive soil erosion on watershed slopes and the maximal discharges by sediment transport in river beds, estimation of mean annual sediment transport is also presented in this work. The September 2014 floods will remain historic given that they took 5 human lives and caused enormous material damage for local municipalities, so presented hydrological analysis should be taken as very important part of flood event documentation along with reports of municipalities’ emergency headquarters.

First experiences with an absolute quantum gravimeter during field campaigns

<p>Recent technological advances in the field of quantum gravimetry led to the first commercially available absolute quantum gravimeters (AQG) that are designed for deployment in field surveys (AQG by Muquans, B series). Limitations of other relative or absolute gravimeters currently used for environmental applications which require highly accurate and precise data (e.g. monitoring subsurface water storage changes), are expected to be at least partly overcome with AQGs.</p><p>In this contribution, we report on the first experiences gained with the Muquans AQG-B02 during a gravimetric field survey in the vicinity of the Geodetic Observatory Wettzell (Bavarian Forest, Germany). The instrument is part of MOSES, a novel observing system of the German Helmholtz Association, comprising flexible and mobile observation modules for event-based investigation of hydrological extreme events, among other processes. To our knowledge, this is the first use of an AQG in an outdoor field campaign. During the 4-day survey, measurements with the AQG were performed on small concrete pillars at 4 field locations and partly repeated on consecutive days. In between the field measurements, reference measurements were carried out on a laboratory pillar of the Geodetic Observatory. We present the AQG field deployment with regard to transport, site design and power supply. The AQG survey is evaluated with respect to its technical and operational feasibility and the data are assessed in terms of their sensitivity, accuracy and reproducibility. Parallel recordings of environmental conditions such as wind speed and air temperature allow for assessing their potential disturbing effect on the gravity measurements. Observations with an A10 absolute gravimeter on the same sites few days before or after the AQG measurements were used for comparing the absolute gravity values.</p>

Mekong Delta Runoff Prediction Using Standardized Remotely-Sensed Water Balance Variables

A suitable routing model for predicting future monthly water discharge (WD) is essential for operational hydrology, including water supply, and hydrological extreme management, to mention but a few. This is particularly important for a remote area without a sufficient number of in-situ data, promoting the usage of remotely sensed surface variables. Direct correlation analysis between ground-observed WD and localized passive remotely-sensed surface variables (e.g., indices and geometric variables) has been studied extensively over the past two decades. Most of these related studies focused on the usage of constructed correlative relationships for estimating WD at ungauged locations. Nevertheless, temporal prediction performance of monthly runoff (R) (being an average representation of WD of a catchment) at the river delta reconstructed from the basin’s upstream remotely-sensed water balance variables via a standardization approach has not been explored. This study examined the standardization approach via linear regression using the remotely-sensed water balance variables from upstream of the Mekong Basin to reconstruct and predict monthly R time series at the Mekong Delta. This was subsequently compared to that based on artificial intelligence (AI) models. Accounting for less than 1% improvement via the AI-based models over that of a direct linear regression, our results showed that both the reconstructed and predicted Rs based on the proposed approach yielded a 2–6% further improvement, in particular the reduction of discrepancy in the peak and trough of WD, over those reconstructed and predicted from the remotely-sensed water balance variables without standardization. This further indicated the advantage of the proposed standardization approach to mitigate potential environmental influences. The best R, predicted from standardized water storage over the whole upstream area, attained the highest Pearson correlation coefficient of 0.978 and Nash–Sutcliffe efficiency of 0.947, and the lowest normalized root-mean-square error of 0.072.

CAMELS-GB: Hydrometeorological time series and landscape attributes for 671 catchments in Great Britain

We present the first large-sample catchment hydrology dataset for Great Britain, CAMELS-GB (Catchment Attributes and MEteorology for Large-sample Studies). CAMELS-GB collates river flows, catchment attributes and catchment boundaries from the UK National River Flow Archive together with a suite of new meteorological timeseries and catchment attributes. These data are provided for 671 catchments that cover a wide range of climatic, hydrological, landscape and human management characteristics across Great Britain. Daily timeseries covering 1970–2015 (a period including several hydrological extreme episodes) are provided for a range of hydro-meteorological variables including rainfall, potential evapotranspiration, temperature, radiation, humidity and river flow. A comprehensive set of catchment attributes are quantified including topography, climate, hydrology, land cover, soils and hydrogeology. Importantly, we also derive human management attributes (including attributes summarising abstractions, returns and reservoir capacity in each catchment), as well as attributes describing the quality of the flow data including the first set of discharge uncertainty estimates for Great Britain. CAMELS-GB (Coxon et al, 2020; available at https://doi.org/10.5285/8344e4f3-d2ea-44f5-8afa-86d2987543a9) is intended for the community as a publicly available, easily accessible dataset to use in a wide range of environmental and modelling analyses.

Analysis and prediction of hydrological extreme conditions for a small headwater catchment in a German lower mountain range

<p>Headwater catchments with wetlands represent important buffer areas by decreasing peak discharges and providing water in meteorological droughts. Wetlands act also as key feature of the riverine carbon cycle and are able to store significant amounts of carbon. Therefore, understanding and predicting discharge generating processes in the context of climate change is essential for such catchments. We use a Regional Climate Model (RCM) Ensemble to study possible changes in discharge patterns due to climate change at the Lehstenbach catchment, located in the Fichtelgebirge Mountains. Our aim is to quantitatively estimate periods of hydrological droughts and floods, their temporal length and intensity, their recurrence intervals as well as possible connections to snow melt. In order to achieve this goal, we use the process-based model HydroGeoSphere to simulate discharge until 2100 based on the RCM Ensemble. Statistical Analysis, including Trend and Wavelet Analysis aids us in detecting changing discharge conditions. Discharge seems to follow an increasingly variable pattern making droughts and floods more likely in the future. Since the overall length of drought conditions increases although precipitation amounts remain fairly stable, we identified evapotranspiration and altered precipitation patterns as main driving forces of droughts in this headwater. Snow conditions and subsequent spring floods seem to decrease in likelihood until 2100.</p>

Constraining hydrological impact simulations over Europe through weighting the climatic forcing

<p><span>Global hydrological models (GHMs) have become an established tool to simulate water resources on continental scales. To assess the future of water availability and various impacts related to hydrological extreme events, these models usually use sets of atmospheric variables (such as e.g., precipitation, humidity, temperature) obtained from (regional) climate model simulations as input data. The uncertainty associated with the climate projections is transferred onwards into the impact simulations and is usually accounted for through the use of large model ensembles. These ensembles thus enable assessments addressing the robustness of projected hydrological changes and impacts. Given recent efforts within the European Climate Prediction (EUCP) project to test existing and develop new techniques to constrain/weight climate model ensembles, we use here different methods to specify the large-scale meteorological input to an ensemble of regional climate models that provide the input data for a state-of-the-art GHM. The climate models are weighted/constrained based on the key large-scale climatic and meteorological drivers shaping the hydrological characteristics in different regions and large river basins across Europe. To assess the potential benefits of the different techniques, we compare simulation ensembles using unweighted input data obtained from the full ensemble of regional climate models against an ensemble based on constrained/weighted forcing data. Given the large uncertainties usually associated with hydrological impact simulations forced by the full range of available climate models, processing the ensemble output of GHMs based on uncertainty assessments of the underlying climate forcing could lead to more robust projections of water resources in general and hydrological extreme events in particular. </span></p>

Attributing Chinese Hydrological Extreme EventsAttributing Chinese Hydrological Extreme Events

<p>In 2018 & 2019 China was impacted by three extreme hydrological events. Heavy rainfall in Central China during summer 2018, heavy summer rainfall in south eastern China during 2019 and a severe drought in Yunnan in May/June of 2019. Using the Hadley Centre’s state-of-the-art attribution system the role of anthropogenic forcing in the changing risk of these events was studied. The modelling system uses two large ensembles of a 60 km resolution atmospheric model driven with sea-surface temperatures(SST), sea-ice and a package of different forcings. One ensemble  uses observed SSTs and natural and human forcings while the other uses pre-industrialised SSTs and natural forcings.  The studies were done in two week-long workshops held in China which aimed to train early career researchers to carry out event attribution studies. The methodologies used in all studies were similar. In all three cases, anthropogenic forcing reduced the risk of heavy rainfall and increased the risk of drought.  Changes in risk for the three events are surprisingly large with the probability of the  Yunnan drought increasing by a factor of 14, the probability of the summer 2019 heavy rainfall declining by a factor of four, and the probability of the summer 2018 rainfall event declining by a half.  Aerosol induced circulation changes in the model are the likely reason for these changes.</p>

Temporal trend of drought and aridity indices in semi-arid pernambucano to determine susceptibility to desertification

ABSTRACT In semi-arid regions, the use of drought and aridity indices in order to establish diagnoses and prognoses that help in water resources management is crucial, above all, for the evaluation of long-term water availability, and monitoring hydrological extreme events. Therefore, the aim of this study was to evaluate the trends of extreme events to determine susceptibility to desertification in the Brígida river basin, by Drought (RAI, SPI and PDSI) and Aridity (MIA, AI and AIASD) Indices. The results of these indices submitted to statistical analysis (Tukey Test) and to the evaluation of the climate trend (TREND software). The Tukey Test indicated that the PDSI and RAI method are the most suitable for drought analysis, while AI is most appropriate for aridity. The results indicated that regardless of the indices employed, the stations presented significant results in the trend analysis, suggesting intensification of these events over time. Therefore, concluded that drought and aridity indices could help water resources management by managing bodies, indicating the evolution of extreme hydrological phenomena, suggesting the adoption of preventive and mitigating actions regarding the use of water priority. In conclusion, these indices can be used as a tool for indicating areas susceptible to the desertification process.