Persistence and multifractality are two fundamental properties of hydrological time series. This study aims at understanding the origin of the Hurst exponent of streamflow time series. Thus, we have ...obtained streamflow, rainfall, and potential evapotranspiration (PET) time series data pertaining to 101 stations in 17 river basins in India. The persistence and multifractal properties of the time series were estimated using multifractal detrended fluctuation analysis (MFDFA). Results suggest that most of the rainfall time series (85%) exhibit short-term persistence while all the PET time series and 59% of the streamflow time series exhibit long-term persistence. The Hurst exponent exhibited by the river flow series simulated by the dynamic Budyko (DB) model that considers only rainfall and PET as inputs displayed a good agreement with the Hurst exponent of observed streamflow, supporting our hypothesis that climatic factors typically shape the value of the Hurst exponent.
Genetic programming (GP) is an evolutionary regression method that has received considerable interest to model hydro-environmental phenomena recently. Considering the sparseness of ...hydro-meteorological stations on northern areas, this study investigates the benefits and downfalls of univariate streamflow modeling at high latitudes using GP and seasonal autoregressive integrated moving average (SARIMA). Furthermore, a new evolutionary time series model, called GP-SARIMA, is introduced to enhance streamflow forecasting accuracy at long-term horizons in a lake-river system. The paper includes testing the new model for one-step-ahead forecasts of daily mean, weekly mean, and monthly mean streamflow in the headwaters of the Oulujoki River, Finland. The results showed that a combination of correlogram and average mutual information (AMI) analysis might yield in the selection of the optimum lags that are needed to be used as the predictors of streamflow models. With Nash-Sutcliffe efficiency values of more than 99%, both GP and SARIMA models exhibited good performance for daily streamflow prediction. However, they were not able to precisely model the intramonthly snow water equivalent in the long-term forecast. The proposed ensemble model, which integrates the best GP and SARIMA models with the most efficient predictor, may eliminate one-fourth of root mean squared errors of standalone models. The GP-SARIMA also showed up to three times improvement in the accuracy of the standalone models based on the Nash-Sutcliff efficiency measure.
There are a number of empirical methods for determining the width of safe maneuvering areas for ships on waterways. These methods are relatively accurate for straight sections of waterways and less ...accurate for curves of the waterways. When designing waterways in detail or determining the conditions for their safe operation, more accurate methods (e.g., simulations) are used. However, such studies require relatively large expenditure involving the construction of simulation models of various vessels and water areas, and conducting a real-time simulation experiment with a sufficiently large number of maneuvers performed by highly qualified navigators (pilots, captains). This study deals with the problem of determining the width of safe maneuvering areas of ships on the bends of the waterway based on the results of simulation studies. A new empirical method was developed to determine the width of safe maneuvering areas of loaded bulk carriers passing through fairway bends under deteriorated hydro-meteorological conditions. The method was developed on the basis of a generalization of the results of many simulation experiments carried out at various stages of the Świnoujście–Szczecin fairway reconstruction and a determination of the safe operating conditions for ships passing through the fairway.
Abstract
Indonesia’s flash floods have increased significantly since the flash flood in Bohorok, North Sumatra, in 2003 and Jember, East Java, in 2006. Such flash floods have caused negative impacts, ...loss of human life and damage to vital infrastructures such as houses, roads, bridges, or other public facilities. Understanding the factors driving flash floods and their mechanism at the outset is essential in mitigating the negative impacts mentioned above. This paper presents the results of the analysis of the flash flood characteristics of several hydraulic parameters under conditions just before the flash flood incident, including the intensity of the rainfall, the time of concentration, and the time lag of the occurrence. As the object of the study, a flash flood event was selected in Batu City, Upper Brantas River, which occurred on November 4, 2021. The results show that the lag time of the flash flood occurrence at 40 minutes magnitude at Bulukerto Village of Batu City, the application of Kirpich for the time of concentration equation performed better than the Ventura and Watt & Chow equations. Further assessment of precursory characteristics for other similar small catchment and hydro-meteorological conditions is still the subject of beneficial study or research.
Abstract
A novel approach for estimating precipitation patterns is developed here and applied to generate a new hydrologically corrected daily precipitation dataset, called RAIN4PE (for ‘Rain for ...Peru and Ecuador’), at 0.1° spatial resolution for the period 1981-2015 covering Peru and Ecuador. It is based on the application of a) the random forest method to merge multi-source precipitation estimates (gauge, satellite, and reanalysis) with terrain elevation, and b) observed and modeled streamflow data to firstly detect biases and secondly further adjust gridded precipitation by inversely applying the simulated results of the eco-hydrological model SWAT (Soil and Water Assessment Tool). Hydrological results using RAIN4PE as input for the Peruvian and Ecuadorian catchments were compared against the ones when feeding other uncorrected (CHIRP and ERA5) and gauge-corrected (CHIRPS, MSWEP, and PISCO) precipitation datasets into the model. For that, SWAT was calibrated and validated at 72 river sections for each dataset using a range of performance metrics, including hydrograph goodness of fit and flow duration curve signatures. Results showed that gauge-corrected precipitation datasets outperformed uncorrected ones for streamflow simulation. However, CHIRPS, MSWEP, and PISCO showed limitations for streamflow simulation in several catchments draining into the Paċific Ocean and the Amazon River. RAIN4PE provided the best overall performance for streamflow simulation, including flow variability (low-, high- and peak-flows) and water budget closure. The overall good performance of RAIN4PE as input for hydrological modeling provides a valuable criterion of its applicability for robust countrywide hydrometeorological applications, including hydroclimatic extremes such as droughts and floods.
Non‐closure of the surface energy balance is a frequently observed phenomenon of hydrometeorological field measurements, when using the eddy‐covariance method, which can be ascribed to an ...underestimation of the turbulent fluxes. Several approaches have been proposed in order to adjust the measured fluxes for this apparent systematic error. However, there are uncertainties about partitioning of the energy balance residual between the sensible and latent heat flux and whether such a correction should be applied on 30‐min data or longer time scales. The data for this study originate from two grassland sites in southern Germany, where measurements from weighable lysimeters are available as reference. The adjusted evapotranspiration rates are also compared with joint energy and water balance simulations using a physically based distributed hydrological model. We evaluate two adjustment methods: the first one preserves the Bowen ratio and the correction factor is determined on a daily basis. The second one attributes a smaller portion of the residual energy to the latent heat flux than to the sensible heat flux for closing the energy balance for every 30‐min flux integration interval. Both methods lead to an improved agreement of the eddy‐covariance based fluxes with the independent lysimeter estimates and the physically based model simulations. The first method results in a better comparability of evapotranspiration rates, and the second method leads to a smaller overall bias. These results are similar between both sites despite considerable differences in terrain complexity and grassland management. Moreover, we found that a daily adjustment factor leads to less scatter than a complete partitioning of the residual for every half‐hour time interval. The vertical temperature gradient in the surface layer and friction velocity were identified as important predictors for a potential future parameterisation of the energy balance residual.
The Carbonate rock weathering Carbon Sink (CCS) and Silicate rock weathering Carbon Sink (SCS) play a significant role in the carbon cycle and global climate change. However, the spatial‐temporal ...patterns and trends of the CCS and SCS from 1950 to 2099 have not been systematically quantified. Thus, Supported by long‐term hydrometeorological data under the RCP8.5, we use the accepted Suchet and Hartmann models to determine the following. First, we found except for the difference in their weathering rates, the SCS covers 37.2 million km2 more area than the CCS. The CCS Flux (CCSF) and SCS Flux (SCSF) are 5.36 and 1.22 t/km2/yr, respectively. Similarly, the Full CCS (FCCS, 0.3 Pg/yr) is more than the Full SCS (FSCS, 0.08 Pg/yr). Furthermore, the CCS (7.01 kg/km2) and SCS (3.95 kg/km2) are in a state of overall increase. In addition, the mid‐to‐high latitudes of the northern hemisphere are aggravated by warming (0.03°C) and humidity (0.65 mm), while the decrease in runoff in the mid‐latitudes of the southern hemisphere reduces karstification. Specifically, by 2099, the CCSF in the mid‐latitudes of the southern hemisphere will decrease by 5.72%. Instead, the CCSF in the northern hemisphere and lower latitudes of the southern hemisphere will exhibit a gentle upward slope. Particularly, the peak regions of the global FCCS (65.63 Tg/yr) and FSCS (33.01 Tg/yr) are the tropical zone. In conclusion, this study contributes a high‐resolution and long‐time series CS datasets for the CCS and SCS. We provide data and a theory for solving terrestrial carbon sink loss.
Plain Language Summary
The carbon cycle and global‐climate change cannot ignore the Carbonate and Silicate rocks weathering Carbon Sink (CCS and SCS). However, the spatial‐temporal patterns and trends of CCS and SCS from 1950 to 2099 have not been quantified. We use the accepted Suchet and Hartmann models to determine the following. First, we found except for the difference in their weathering rates, the SCS covers 37.2 million km2 more area than the CCS. The CCS Flux (CCSF) and SCS Flux (SCSF) are 5.36 and 1.22 t/km2/yr, respectively. Similarly, the Full CCS (FCCS, 0.3 Pg/yr) is more than the Full SCS (FSCS, 0.08 Pg/yr). Furthermore, the CCS (7.01 kg/km2) and SCS (3.95 kg/km2) are in a state of overall increase. In addition, the mid‐to‐high latitudes of the northern hemisphere are aggravated by warming (0.03°C) and humidity (0.65 mm), while the decrease in runoff in the mid‐latitudes of the southern hemisphere reduces karstification. Specifically, by 2099, the CCSF in the mid‐latitudes of the southern hemisphere will decrease by 5.72%. Instead, the CCSF in the northern hemisphere and lower latitudes of the southern hemisphere will exhibit a gentle upward slope. In conclusion, this study contributes a high‐resolution data set for solving carbon sink loss.
Key Points
High‐resolution data set for global carbonate and spatial diversification carbon sinks was established
Carbon cycle can't ignore CCS (5.36 t/km2/yr) and SCS (1.22 t/km2/yr). Especially CCS (7.01 kg/km2) and SCS (3.95 kg/km2) increased significantly
The peak regions of the global FCCS (65.63 Tg/yr) and FSCS (33.01 Tg/yr) are the tropical zone
•TMPA-3B42 rainfall products are extensively evaluated over India.•Systematic and random error are explored for different seasons.•TMPA-RT has larger random error than TMPA-V7 product.•TMPA products ...have larger systematic error over northeast India.
A comprehensive error characterization of satellite-based precipitation products is vital for advancement of precipitation algorithms, evaluation of numerical model outputs, and their integration in various hydro-meteorological applications. The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) products are in high demand to users for numerous applications. The latest version 7 (V7) of TMPA products are assumed to be better than its previous versions due to substantial changes in input data sets and algorithm. The TMPA algorithm is also a benchmark algorithm for the Global Precipitation Measurement (GPM) mission multisatellite precipitation products. In this study, both near-real-time (TMPA-RT) and research-quality (TMPA-V7) TMPA data sets are extensively evaluated against gridded gauge-based data over India for pre-monsoon, monsoon, and post-monsoon seasons at daily scale for a 13-year (2001–2013) period. The systematic and random error components in TMPA data sets are decomposed for their wider usage in agricultural and hydrological applications. Although TMPA-V7 and RT data sets represent the mean seasonal rainfall characteristics reasonably well, both the satellite-based data sets show an overestimation of rainfall over most parts of the country except over the orographic regions. However, TMPA-V7 is better than TMPA-RT when compared with the reference observations. Both the TMPA products show larger systematic error over the mountainous regions of northeast India. TMPA-RT has larger random error than TMPA-V7 possibly due to difference in calibration methods for the development of these data sets. Both the satellite-based data sets show significant difference from the reference observations for light to moderate rainfall ranges especially in the monsoon season. Larger error associated with systematic component in both the data sets is found during the pre-monsoon season. Furthermore, bias and error in satellite-derived rainfall data show a considerable interannual variation. Overall results suggest that a suitable region and season dependent bias-correction is required in TMPA data sets before its integration in hydrological applications.
Water resources systems in arid regions are sensitive to climate change, which critically impacts the water cycle. In this study, we applied historical hydrometeorological data, CMIP5 data, and a ...large‐scale hydrological model (Community Land Model–Distributed Time‐Variant Gain Model CLM–DTVGM) to study the impact of climate change on the hydrological cycle (i.e., runoff, actual evapotranspiration (ETa), and terrestrial water storage TWS) under different scenarios at the Aksu River basin (ARB) located in the arid region of northwest China. The primary findings of this study: (a) As the determination coefficient (R2) and Nash–Sutcliffe efficiency coefficient (NSE) reached desirable levels (R2 ≥ 0.583, NSE ≥ 0.371, and root‐mean‐square error ≤ 155.727), the CLM–DTVGM achieved a better simulation of runoff in the ARB; under climate change, the runoff depth in the irrigation area became shallower and followed a decreasing trend, with a minimum depth of 0.5 mm and a significant decreasing trend of −8 mm·a−1. (b) Along with changes in the precipitation and temperature of the baseline period (i.e., 1980–2010), runoff, ETa, and TWS were predicted to change significantly in future scenarios. (c) Climate change significantly impacted the historical runoff from the Shaliguilanke and Xiehela hydrological stations, with correlation coefficients larger than 0.9; however, the runoff from the Alaer hydrological station was not affected. However, runoff, ETa, and TWS in the ARB were very closely correlated with climate change in the future scenarios, with correlation coefficients exceeding 0.9. Related research in the future would be important for guiding the sustainable development of the ARB.
There is a significant trend of increasing precipitation and temperature in the Aksu River basin. Temperature and precipitation have significant effects on historical runoff in mountainous areas, but not on that in plain areas. The future hydrological cycle in the Aksu River basin is more significantly influenced by the coupling effect of precipitation and temperature.
Hydrological impacts of global climate change on regional scale are generally assessed by downscaling large-scale climatic variables, simulated by General Circulation Models (GCMs), to regional, ...small-scale hydrometeorological variables like precipitation, temperature, etc. In this study, we propose a new statistical downscaling model based on Recurrent Neural Network with Long Short-Term Memory which captures the spatio-temporal dependencies in local rainfall. The previous studies have used several other methods such as linear regression, quantile regression, kernel regression, beta regression, and artificial neural networks. Deep neural networks and recurrent neural networks have been shown to be highly promising in modeling complex and highly non-linear relationships between input and output variables in different domains and hence we investigated their performance in the task of statistical downscaling. We have tested this model on two datasets—one on precipitation in Mahanadi basin in India and the second on precipitation in Campbell River basin in Canada. Our autoencoder coupled long short-term memory recurrent neural network model performs the best compared to other existing methods on both the datasets with respect to temporal cross-correlation, mean squared error, and capturing the extremes.
PDF
