Seasonal forecasts of Indian summer monsoon rainfall using local polynomial based non-parametric regression model

In this paper, details of new statistical models for forecasting southwest monsoon (June-September) rainfall over India (ISMR) and for northwest India summer monsoon rainfall (NWISMR) are discussed. These models are based on the local polynomial based non-parametric regression method. Two predictor sets (SET-I & SET-II consisting of 4 and 5 predictors respectively) were selected for developing two separate models for making predictions in April and late June respectively. Another predictor set (SET-III) was selected for developing model for monsoon rainfall over NW India (NWISMR). Principle Component Analysis (PCA) of predictor data set was done and the first two principal components were selected for model development. Data for the period 1977-2005 have been used for developing the model and the Jackknife method was used to assess the skill of the model. Both the models showed useful skill in predicting ISMR and showed better performance than the model based on pure climatology. The Hit scores for the three category forecasts during the verification period by April and June models are 0.65 and 0.66 respectively. Root Mean Square Error (RMSE) of these models during the verification period is 5.99 and 6.0% respectively from the Long Period Average (LPA) as against 10.0% from the LPA of the model based on climatology alone. RMSE of the Northwest India model during the independent period is 11.5% from LPA as against 18.5% from the LPA of the model based on the climatology alone. Hit score for the three category forecast for NW India during the verification period is 0.55.

Numerical Analysis of the integrity of potential host rocks – modelling thermo-hydro-mechanical processes in the containment providing rock zone

The disposal of heat-generating nuclear waste in deep geological formations is an internationally accepted concept. Several repository systems are under discussion in Germany, whereby claystone, salt or crystalline rock could act as the host rock. In this contribution we focus on repository systems where the Containment Providing Rock Zone (CRZ) ensures safe enclosure of the waste and thus the geologic barrier is essential. Even though the various rock types considered differ substantially in their mechanical, hydraulic, thermal and chemical behavior, they must all meet the same safety requirements as defined by the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) in 2020. As part of these safety requirements, it must be shown that the integrity of the CRZ is guaranteed for the verification period, i.e. the retention of the properties essential for the containment capacities must be demonstrated over 1 million years. Therefore, the formation of new pathways must be avoided and temperature development must not significantly impair the barrier effect. The anticipated stresses and fluid pressures should not exceed the dilatancy strength and the fluid pressure capacity, respectively. In order to assess the compliance of these requirements, numerical modelling is an essential and powerful tool. Even though great progress has been made regarding the efficiency of computational methods, multiphysical modelling on different length scales over long time periods is still a challenging task. Moreover, since readily available solutions do not exist, adapted methods have to be developed and evaluated, in order to verify concepts and numerical implementations. The BGR gained experience in the field of thermal, hydraulic, mechanical (THM) numerical analysis of the integrity of the CRZ in salt rock and clay stone joined research projects on German disposal options. For crystalline rocks, first concepts are currently being developed within the CHRISTA II project. Compared to clay stone and salt rock, special features have to be taken into account: First of all, crystalline rock is characterized by fractures and other discontinuities. Thus, it cannot be assumed that an undisturbed area of sufficient size can be found for the entire nuclear waste. Consequently, several smaller CRZs must be defined, each providing undisturbed rock. Numerical analysis must deal with smaller CRZs and mechanical and hydraulic boundary conditions that are influenced by fractures. In addition, the processes in the individual CRZs may influence each other (e.g. Temperature distribution). Preliminary modelling approaches and results of numerical THM analyses, considering an upscaled fracture network, are presented.

On the implementation of post-processing of runoff forecast ensembles

Different post-processing techniques are frequently employed to improve the outcome of ensemble forecasting models. The main reason is to compensate for biases caused by errors in model structure or initial conditions, and as a correction for under- or overdispersed ensembles. Here we use the Ensemble Model Output Statistics method to post-process the ensemble output from a continental scale hydrological model, LISFLOOD, as used in the European Flood Awareness System (EFAS). We develop a method for local calibration and interpolation of the post-processing parameters and compare it with a more traditional global calibration approach for 678 stations in Europe based on long term observations of runoff and meteorological variables. For the global calibration we also test a reduced model with only a variance inflation factor. Whereas the post-processing improved the results for the first 1-2 days lead time, the improvement was less for increasing lead times of the verification period. This was the case both for the local and global calibration methods. As the post-processing is based on assumptions about the distribution of forecast errors, we also present an analysis of the ensemble output that provides some indications of what to expect from the post-processing.

Qualimetric assessment of metrological characteristics of household gas meters during periodic verification

Informative parameters for statistical estimation of operational error of SAMGAS, METRIX, PREMAGAS household gas meters (HGM) are formulated. These are the values of the measured volume during the verification period of operation and the experimentally determined error of the meter during operation at three normalized flow rates: minimum, maximum and 20% of the maximum. Six ranges of variation of the HGM error at the minimum flow rate were selected to form statistical samples of meters. According to the proposed algorithm, the change in the weighted average HGM error for three normalized flow rates from the measured volume is quantified, taking into account the number of HGMs and their error ranges. It is proposed to apply the concept of generalized weighted average error of HGM, which reflects the operational error of HGMs during their operation in the entire range of consumption when measuring gas volumes up to 60 thousand cubic meters.

Spatiotemporal Model Based on Deep Learning for ENSO Forecasts

El Niño and Southern Oscillation (ENSO) is closely related to a series of regional extreme climates, so robust long-term forecasting is of great significance for reducing economic losses caused by natural disasters. Here, we regard ENSO prediction as an unsupervised spatiotemporal prediction problem, and design a deep learning model called Dense Convolution-Long Short-Term Memory (DC-LSTM). For a more sufficient training model, we will also add historical simulation data to the training set. The experimental results show that DC-LSTM is more suitable for the prediction of a large region and a single factor. During the 1994–2010 verification period, the all-season correlation skill of the Nino3.4 index of the DC-LSTM is higher than that of the current dynamic model and regression neural network, and it can provide effective forecasts for lead times of up to 20 months. Therefore, DC-LSTM can be used as a powerful tool for predicting ENSO events.

Trends inflate estimates of subseasonal skill for surface temperatures

<p>Subseasonal-to-seasonal (S2S) predictions have numerous applications and improving forecast skill on this time scale has become a major effort. Since forecast uncertainty is high on S2S lead times, ensemble prediction systems are essential in order to provide probabilistic forecasts, informing about the range of possible outcomes. For evaluating their performance, these forecasts are routinely compared to a climatological reference forecast. The climatological distribution is commonly assumed to be stationary over the verification period. However, prominent deviations from this assumption exist, especially considering trends associated with climate change. Using synthetic forecast-verification pairs we show that estimates of the probabilistic skill of both continuous and categorical forecasts with a fixed actual level of skill increase as a function of the variance explained by the trend over the hindcast period. The skill of categorical forecasts can be inflated even further when evaluated over a longer forecast period. We also show that this skill enhancement can be observed in the ECMWF extended-range ensemble prediction system. We demonstrate that the effects on the skill in an operational forecast setting are currently strongest in the tropics and mainly relevant for categorical forecasts. This highlights that care needs to be taken when evaluating forecasts that are subject to non-stationarity on time scales much longer than the forecast verification window, especially for categorical forecasts. The results presented in this study are by no means limited to the S2S time scale but have similar implications for the verification of seasonal to decadal predictions, where the existence of trends can further inflate forecast skill.</p>

Prediction of Surface Water Resources in Danjiangkou Basin Based on ESP and Distributed Hydrological Model

<p> The prediction of surface water resources in the Danjiangkou Basin is of great significance for the design of the water transfer plans for the South-to-North Water Diversion Project. However, it is difficult to obtain high-precision simulations for mid- and long-term hydrological forecasting. Based on the thought of extended streamflow prediction (ESP) and distributed hydrological models, this paper proposed a set of forecasting systems for predicting the annual surface water resources in the Danjiangkou Basin. Firstly,  the Wetspa model  was established to forecast the inflow of Danjiangkou reservoir. The Nash efficiency coefficients of the monthly average runoff during the calibration period (2006-2012) and verification period (2013-2016) were 0.97 and 0.95, respectively. Secondly, it was assumed that the rainfall of 2017 could be predicted by the rainfall forecasting model, then the rainfall process was obtained based on the ESP and the runoff process of the basin outlet was calculated through the Wetspa model. Finally, the predicted surface water resources of the Danjiangkou Basin in 2017 was 45.448 billion m<sup>3</sup>, and the actual surface water resources is 40.395 billion m<sup>3</sup>, with a relative error of 12.51%. The results showed that the prediction of surface water resources in Danjiangkou Basin based on ESP and distributed hydrological model could provide a certain reference for the design of water transfer plans of the Danjiangkou Reservoir.</p><p><strong>Key words: </strong>Water resources prediction; ESP; Wetspa model; Nash coefficient</p>

Research of Impacts of the 2018 Hokkaido Eastern Iburi Earthquake on Sediment Transport in the Atsuma River Basin Using the SWAT Model

Landslides, debris flows, and other secondary disasters caused by earthquakes threaten the safety and stability of river basins. Earthquakes occur frequently in Japan. Therefore, it is necessary to study the impact of earthquakes on sediment transport in river basins. In this study, considering the influence of reservoirs, the Soil and Water Assessment Tool-calibration and uncertainty program (SWAT-CUP) was employed to analyze the runoff parameter sensitivity and to optimize the parameters. We manually corrected the sediment transport parameters after earthquake, using the Soil and Water Assessment Tool (SWAT) model to assess the process of runoff and sediment transport in the Atsuma River basin before and after the 2018 Hokkaido Eastern Iburi Earthquake. The applicability of the SWAT model to runoff simulation in the Atsuma River basin and the changes of sediment transport process after the earthquake were studied. The research results show that the SWAT model can accurately simulate the runoff process in the Atsuma River basin, the Nash–Sutcliffe efficiency coefficient (NSE) is 0.61 in the calibration period, and is 0.74 in the verification period. The sediment transport increased greatly after the earthquake and it is roughly estimated that the amount of sediment transport per unit rainfall increased from 3.5 tons/mm/year before the earthquake to 6.2 tons/mm/year after the earthquake.

OpenForecast v2: Development and Benchmarking of the First National-Scale Operational Runoff Forecasting System in Russia

Operational national-scale hydrological forecasting systems are widely used in many countries for flood early warning systems and water management. However, this kind of system has never been implemented in Russia. OpenForecast v2—the first national-scale operational runoff forecasting system in Russia—has been developed and deployed to fill this gap. OpenForecast v2 delivers 7 day-ahead streamflow forecasts for 843 gauges across Russia. The verification study has been carried out using 244 gauges for which operational streamflow data were openly available and quality-controlled for the entire verification period (14 March–6 July 2020). The results showed that the developed system provides reliable and skillful runoff forecasts for up to one week. The benchmark testing against climatology and persistence forecasts showed that the system provides skillful predictions for most analyzed basins. OpenForecast v2 is in operational use and is openly available on the Internet.

The Assessment of Climate Change and Land-Use Influences on the Runoff of a Typical Coastal Basin in Northern China

Land use and climate change are the two major driving factors of watershed runoff change, and it is of great significance to study the influence of watershed hydrological processes on water resource planning and management. This study takes the Changyang River basin as the study area, builds a SWAT model and explores the applicability of the SWAT model in the basin. Moreover, we combine data on land use and climate change in different periods to construct a variety of scenario models to quantitatively analyze the impacts of different scenarios on runoff. The results show that the R2 and Ensof the model are 0.71 and 0.68 in the calibration period, respectively, and those in the verification period are 0.68 and 0.65, respectively, indicating that the SWAT model has good applicability in simulating the runoff of the Changyang River basin. Under the comprehensive scenario of land use and climate change on runoff, we found that land use and climate change have a certain contribution to the change in runoff. Therefore, the runoff of the basin increased by 0.22 m3/s, in which land-use change caused the runoff in the basin to increase by 0.07 m3/s attributed to the decreased area of arable land and the increased area of urban land in the basin. Moreover, climate change has caused the runoff in the basin to increase by 0.13 m3/s, mainly influenced by the increased precipitation. The results show that climate change has a more significant effect on runoff in the basin.