Hydrologic Evaluation Research Articles

Statistical and Hydrological Evaluations of Multiple Satellite Precipitation Products in the Yellow River Source Region of China

Comprehensively evaluating satellite precipitation products (SPPs) for hydrological simulations on watershed scales is necessary given that the quality of different SPPs varies remarkably in different regions. The Yellow River source region (YRSR) of China was chosen as the study area. Four SPPs were statistically evaluated, namely, the Tropical Rainfall Measurement Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42V7, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Record (PERSIANN-CDR), Integrated Multisatellite Retrievals for Global Precipitation Measurement final run (IMERG-F), and gauge-corrected Global Satellite Mapping of Precipitation (GSMaP-Gauge) products. Subsequently, the hydrological utility of these SPPs was assessed via the variable infiltration capacity hydrological model on a daily temporal scale. Results show that the four SPPs generally demonstrate similar spatial distribution pattern of precipitation to that of the ground observations. In the period of January 1998 to December 2016, 3B42V7 outperforms PERSIANN-CDR on basin scale. In the period of April 2014 to December 2016, GSMaP-Gauge demonstrates the highest precipitation monitoring capability and hydrological utility among all SPPs on grid and basin scales. In general, 3B42V7, IMERG-F, and GSMaP-Gauge show a satisfactory hydrological performance in streamflow simulations in YRSR. IMERG-F has an improved hydrological utility than 3B42V7 in YRSR.

Evaluation and Hydrological Application of CMADS Reanalysis Precipitation Data against Four Satellite Precipitation Products in the Upper Huaihe River Basin, China

Satellite- and reanalysis-based precipitation products are important data source for precipitation, particularly in areas with a sparse gauge network. Here, five open-access precipitation products, including the newly released China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) reanalysis dataset and four widely used bias-adjusted satellite precipitation products [SPPs; i.e., Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis 3B42 Version 7 (TMPA 3B42V7), Climate Prediction Center (CPC) morphing technique satellite-gauge blended product (CMORPH-BLD), Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR)], were assessed. These products were first compared with the gauge observed data collected for the upper Huaihe River basin, and then were used as forcing data for streamflow simulation by the Xin’anjiang (XAJ) hydrological model under two scenarios with different calibration procedures. The performance of CMADS precipitation product for the Chinese mainland was also assessed. The results show that: (1) for the statistical assessment, CMADS and CMORPH-BLD perform the best, followed by TMPA 3B42V7, CHIRPS, and PERSIANN-CDR, among which the correlation coefficient (CC) and root-mean-square error (RMSE) values of CMADS are optimal, although it exhibits certain significant negative relative bias (BIAS; −22.72%); (2) CMORPH-BLD performs the best in capturing and detecting rainfall events, while CMADS tends to underestimate heavy and torrential precipitation; (3) for streamflow simulation, the performance of using CMADS as input is very good, with the highest Nash-Sutcliffe efficiency (NSE) values (0.85 and 0.75 for calibration period and validation period, respectively); and (4) CMADS exhibits high accuracy in eastern China while with significant negative BIAS, and the performance declines from southeast to northwest. The statistical and hydrological evaluations show that CMADS and CMORPH-BLD have high potential for observing precipitation. As high negative BIAS values showed up in CMADS evaluation, further study on the error sources from original data and calibration algorithms is necessary. This study can serve as a reference for selecting precipitation products in data-scarce regions with similar climates and topography in the Global Precipitation Measurement (GPM) era.

Hydrological evaluation of global gridded precipitation datasets in a heterogeneous and data-scarce basin in Iran

Accurate estimation of the precipitation characteristics, including the value, temporal pattern, and spatial distribution, plays a significant role in the input uncertainty reduction for rainfall-runoff models. In many basins, the improper spatial distribution of rain gauge stations or their limited historical recorded data causes many challenges, especially in heterogeneous catchments which due to the impact of the drastic geographical alterations on the rainfall distribution pattern, the cover of the ground stations cannot estimate the actual precipitation rate. This challenge can be potentially solved by adopting rainfall products as alternative or complementary data sources. In this research, three rainfall products (PERSIANN-CCS, CMORPH and ERA-Interim), were compared against rain gauge stations for calibration of a daily conceptual lumped rainfall-runoff model (CRFM) in a data-scarce and heterogeneous basin located in southwestern Iran. The results indicated that ERA-Interim has the best performance among other datasets. Better performance of this dataset compared to the in-situ data also suggests a better estimation of the basin average as well as the temporal pattern of precipitation. The KGE value was obtained as 0.8 and 0.74, respectively, for a rainfall-runoff model that utilized the ERA-Interim as input in the calibration and validation periods. The results showed that the performance of satellite-based data of CMORPH and PERSIANN-CCS is not acceptable in simulating the daily flow. Also, the seasonal assessment showed that ERA-Interim has a better performance compared to other datasets, during fall and winter. However, in the spring, the performance of all datasets significantly reduces, and the range of BIAS variation increases. Generally, all datasets were shown to perform better in simulating the flow in terms of the transition from dry to wet periods, rather than wet to dry periods.

Exploring the best sequence LSTM modeling architecture for flood prediction

Accurate and efficient models for rainfall–runoff (RR) simulations are crucial for flood risk management. Recently, the success of the recurrent neural network (RNN) applied to sequential models has motivated groups to pursue RR modeling using RNN. Existing RNN based methods generally use either sequence input single output or unsynced sequence input and output architectures. In this paper, we propose a synced sequence input and output long short-term memory (LSTM) network architecture for hydrologic analysis and compare it to existing methods (sequence input single output LSTM). We expect the model will improve RR prediction in terms of accuracy, calibration training time, and computational cost. The key idea is to efficiently learn the long term dependency of runoff on past rainfall history. To be more specific, we use the indigenous ability of the LSTM network to preserve long term memory instead of artificially setting a time window for input data. In this way, we can avoid losing long term memory of the input, the calibration of the time window length, and excessive computation. The whole procedure mimics the traditional process-driven methods and is closer to the physics interpretation of the RR process. We conducted experiments on real-world hydrologic data from the Brays Bayou in Houston, Texas. Extensive experimental results clearly validate the effectiveness of our proposed method in terms of various statistical and hydrological related evaluation metrics. Notably, our experiment shows that some rainfall events could affect the runoff process in the test watershed for at least a week. For fine temporal resolution prediction, this long term effect needs to be carefully handled, and our proposed method is superior in this case.

Hydrological Simulation of Runoff for a Watershed in Punpun Basin using SWAT

Runoff is a very important phenomenon of hydrological cycle and it is relevant for the watershed management programme for conservation and development or natural resources and its management. However, In India the availability of accurate information on runoff is very scarce and needs to be calculated empirically for further developments. The Soil and Water Assessment Tool (SWAT) is a physical parameter model which has been developed to estimate values for the runoff, sediment and nutrient carry off from the agricultural watersheds under various distinct land management practices. For the present study, a small agricultural watershed has been selected for runoff assessment. Watershed is considered to be the ideal unit for management of the natural resources. Extraction of watershed parameters using Remote Sensing and Geographical Information System (GIS) and use of mathematical models is one of the current trends for hydrologic evaluation of these watersheds monitored. The Soil and Water Assessment Tool (SWAT) having an interface with Quantum GIS (QGIS) software (QSWAT version 1.3) was selected for the estimation of surface runoff from an area of Punpun basin near Patna an intermediate watershed of Punpun river, located in southern Bihar region. Maps of the region obtained from the Bihar Remote Sensing Application Centre (BIRSAC) were used for computation. The model was run and validated with the observed runoff and for the years 2005-2010. The performance of the model was evaluated using statistical and graphical methods to assess the capability of the model in simulating the surface runoff from the study area. According to the model estimates, the value for the surface runoff was maximum for the year 2007 as 710 mm and was minimum for the year 2005 with about 185 mm. As per the observed values of discharge from the Central Water Commission (CWC), the values for surface runoff for these years were different by about 10 to 11%.

Hydrological and geochemical evaluation of fluoride-safe groundwater from Rampurhat-II block in fluoride-laden Birbhum district, West Bengal, India: a multivariate statistical approach

ABSTRACT It has been reported earlier that Birbhum district is often contaminated with groundwater fluoride and many studies have been conducted to decipher that. Our present study area (Rampurhat-II block) is fluoride-safe (<1.5 mg/L) and located in fluoride-laden Birbhum district. The present investigation mainly focused on the reason behind the occurrence of a low concentration of fluoride (0.24–0.37 mg/L) in groundwater fluoride contaminated Birbhum district. It is very interesting to know that groundwater from the study area contains fluoride in groundwater below the permissible limit (1.5 mg/L) though the aquifer sediments contain fluoride bearing minerals (mainly fluorite). To understand the anomaly, multivariate statistical analysis (Pearson correlation matrix, factor analysis and Agglomerative Hierarchical Clustering) has been performed for the physiochemical data of the collected groundwater samples. Moreover, the mineral phase’s equilibrium studies have been done to recognise the actual scenario of water-rock interaction in the aquifers of the present study area. The dissolution of carbonate minerals (mainly calcite) in saturated to super-saturated conditions hinders the dissolution of the fluoride bearing minerals resulting in fluoride-safe groundwater. Again, ion-exchange (between Ca2+/Mg2+ and Na+/K+) is one of the principal mechanistic pathways in the aquifers which maintain the lower content of fluoride in the groundwater.

Monthly data of stable isotopic composition (δ18O, δ2H) and tritium activity in precipitation from 2004 to 2017 in the Mecsek Hills, Hungary.

The stable isotopic composition (δ18O, δ²H) and tritium activity of monthly aggregated precipitation samples were collected between April 2004 and December 2017 at six sites representing the first published precipitation isotope dataset from the Mecsek Hills (Hungary). The dataset includes 697 stable isotopic and 653 tritium activity concentration data of monthly precipitation samples collected across the Mecsek Hills. At the beginning of the monitoring period, the isotopic composition values suggest an insufficient protection against evaporation and this issue has occasionally reappeared later only in limited periods. These data are presented in brackets in the Supplementary Table and should be disregarded from further analysis until additional verification. This dataset provides isotope hydrological benchmark in comparison with other local and regional datasets of stable isotopes and tritium activities in surface water and groundwater not only in the Mecsek Hills but also in the surroundings. It can support water resource management, and paleoclimatological research. Isotope hydrological evaluation and further discussion on the seasonal trends in the precipitation isotopic characteristics are in progress and the tritium data were used in the derivation of a gridded database (1 × 1 km) of amount-weighted annual mean precipitation tritium activity for the Adriatic-Pannonian Region (AP3H_v1, [1]).

Hydrologic Model Evaluation and Assessment of Projected Climate Change Impacts Using Bias-Corrected Stream Flows

Hydrologic models driven by downscaled meteorologic data from general circulation models (GCM) should be evaluated using long-term simulations over a historical period. However, simulations driven by GCM data cannot be directly evaluated using observed flows, and the confidence in the results can be relatively low. The objectives of this paper were to bias correct simulated stream flows from calibrated hydrologic models for two basins in New Jersey, USA, and evaluate model performance in comparison to uncorrected simulations. Then, we used stream flow bias correction and flow duration curves (FDCs) to evaluate and assess simulations driven by statistically downscaled GCMs for the historical period and the future time slices 2041–2070 and 2071–2099. Bias correction of stream flow from simulations increased confidence in the performance of two previously calibrated hydrologic models. Results indicated there was no difference in projected FDCs for uncorrected and bias-corrected flows in one basin, while this was not the case in the second basin. This result provided greater confidence in projected stream flow changes in the former basin and implied more uncertainty in projected stream flows in the latter. Applications in water resources can use the methods described to evaluate the performance of GCM-driven simulations and assess the potential impacts of climate change with an appropriate level of confidence in the model results.

Hydrological evaluation of open-access precipitation data using SWAT at multiple temporal and spatial scales

Abstract. 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.

Water scarcity-risk assessment in data-scarce river basins under decadal climate change using a hydrological modelling approach

Basin-scale water scarcity, an adverse consequence of the imbalance between the available fresh water and water demands, is an alarming situation in many countries worldwide. This necessitates for analyzing the risk of water scarcity forced with the changing climate, land-use alterations, growing population, and industrialization to pave a way for sustainable water resources systems planning and management. This study advocates a hydrological model-based evaluation approach employing Soil Water Assessment Tool (SWAT) to identify the dominating drivers on the decadal streamflow patterns and other basin-scale water balance components, viz., blue water flow (BWF), green water storage (GWS), and green water flow (GWF) in the Brahmani and Baitarani River basins of eastern India. The results reveal that, in these basins, the climate change effect has relatively a higher impact on streamflow than the land-use change. This study surmises that the Brahmani and Baitarani River basins can be categorized under the high- and moderate-risk water scarcity zones, respectively. The developed approach of assessing the water scarcity risk under the non-availability of basin-scale sectoral water demands could be a reliable tool for many data-scarce river basins worldwide.

Hydrological evaluation of satellite and reanalysis precipitation products in the glacier-fed river basin (Gilgit)

Weather conditions and flow are frequently analyzed using the multisource precipitation products, where rain gauge network is not available or dispersed. This study has been designed to evaluate the precipitation and flow using rain gauges, satellite, and reanalysis data (TRMM 3B42RT v-7 and ERA-Interim) over Gilgit (glacier fed), a river basin of northern Pakistan. Statistical matrices, such as Bias, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), coefficient of determination (R2), and percentage (%) difference, were used to report the discrepancies in the precipitation and flow. Moreover, the spatial distribution, rain occurrence, and volume precipitation were used for further analysis. However, both sources showed ability to capture the pattern well. In the rain occurrence, light precipitation ( 10 mm/h) shares more than 40% in total volume (April–September). Overall, the TRMM outperforms for precipitation estimation in the catchment. Moreover, the findings of the study would be helpful in controlling flood-causing disaster in this particular area and provide important information for sustainable development and to overcome food security and energy issues in the country. These findings indicate the potential of satellite and reanalysis data as alternative to rain gauge in many applications where data are not available or very limited.

Variability assessment of simulated recharge resulted from precipitation using different GCMs, case study: west shore of Lake Urmia, Iran

Variations associated with recharge rate prediction are studied by a comparison between the simulated recharge based on weather data generated by Long Ashton Research Station Weather Generator (LARS-WG), which is a downscaling model with spectra of global circulation models (GCMs). The study area included aquifer of Urmia Plain (West Shore of Lake Urmia) located in Iran, Middle East. The aquifer area was classified based on soil composition gradation. Hydrologic Evaluation of Landfill Performance (HELP) model and percolation into vertical column were used for 1-dimensional modelling (pseudo-2-D modelling) of the recharge of various zones of Urmia Plain. The data required for modelling of weather parameter and recharge include average daily temperature, daily rainfall, and solar radiation. The recharge modelling was performed for time periods of 2020–2040, 2041–2060, 2061–2080, and 2081–2100 and five GCMs based on the 5th report issued by Intergovernmental Panel on Climate Change (IPCC), namely, EC-EARTH, GFDL-CM3, HadGEM2, MIROC5, and MPI-ESM-MR, under RCP 8.5 and RCP 4.5 scenarios. The recharge results of periods were compared between the GCMs. This variability in recharge prediction indicated that GCMs are highly important tools for water management planning.

Evaluation of Grid-Based Rainfall Products and Water Balances over the Mekong River Basin

Gridded precipitation products (GPPs) with wide spatial coverage and easy accessibility are well recognized as a supplement to ground-based observations for various hydrological applications. The error properties of satellite rainfall products vary as a function of rainfall intensity, climate region, altitude, and land surface conditions—all factors that must be addressed prior to any application. Therefore, this study aims to evaluate four commonly used GPPs: the Climate Prediction Center (CPC) Unified Gauge-Based Analysis of Global Daily Precipitation, the Climate Prediction Center Morphing (CMORPH) technique, the Tropical Rainfall Measuring Mission (TRMM) 3B42, and the Global Satellite Mapping of Precipitation (GSMaP), using data collected in the period 1998–2006 at different spatial and temporal scales. Furthermore, this study investigates the hydrological performance of these products against the 175 rain gauges placed across the whole Mekong River Basin (MRB) using a set of statistical indicators, along with the Soil and Water Assessment Tool (SWAT) model. The results from the analysis indicate that TRMM has the best performance at the annual, seasonal, and monthly scales, but at the daily scale, CPC and GSMaP are revealed to be the more accurate option for the Upper MRB. The hydrological evaluation results at the daily scale further suggest that the TRMM is the more accurate option for hydrological performance in the Lower MRB, and CPC shows the best performance in the Upper MRB. Our study is the first attempt to use distinct suggested GPPs for each individual sub-region to evaluate the water balance components in order to provide better references for the assessment and management of basin water resources in data-scarce regions, suggesting strong capabilities for utilizing publicly available GPPs in hydrological applications.

Timing of Landsat Overpasses Effectively Captures Flow Conditions of Large Rivers

Satellites provide a temporally discontinuous record of hydrological conditions along Earth’s rivers (e.g., river width, height, water quality). The degree to which archived satellite data effectively capture the overall population of river flow frequency is unknown. Here, we use the entire archives of Landsat 5, 7, and 8 to determine when a cloud-free image is available over the United States Geological Survey (USGS) river gauges located on Landsat-observable rivers. We compare the flow frequency distribution derived from the daily gauge record to the flow frequency distribution derived from ideally sampling gauged discharge based on the timing of cloud-free Landsat overpasses. Examining the patterns of flow frequency across multiple gauges, we find that there is not a statistically significant difference between the flow frequency distribution associated with observations contained within the Landsat archive and the flow frequency distribution derived from the daily gauge data (α = 0.05), except for hydrological extremes like maximum and minimum flow. At individual gauges, we find that Landsat observations span a wide range of hydrological conditions (97% of total flow variability observed in 90% of the study gauges) but the degree to which the Landsat sample can represent flow frequency distribution varies from location to location and depends on sample size. The results of this study indicate that the Landsat archive is, on average, representative of the temporal frequencies of hydrological conditions present along Earth’s large rivers with broad utility for hydrological, ecologic and biogeochemical evaluations of river systems.

Identifying uncertainties in hydrologic fluxes and seasonality from hydrologic model components for climate change impact assessments

Abstract. Assessing impacts of climate change on hydrologic systems is critical for developing adaptation and mitigation strategies for water resource management, risk control, and ecosystem conservation practices. Such assessments are commonly accomplished using outputs from a hydrologic model forced with future precipitation and temperature projections. The algorithms used for the hydrologic model components (e.g., runoff generation) can introduce significant uncertainties into the simulated hydrologic variables. Here, a modeling framework was developed that integrates multiple runoff generation algorithms with a routing model and associated parameter optimizations. This framework is able to identify uncertainties from both hydrologic model components and climate forcings as well as associated parameterization. Three fundamentally different runoff generation approaches, runoff coefficient method (RCM, conceptual), variable infiltration capacity (VIC, physically based, infiltration excess), and simple-TOPMODEL (STP, physically based, saturation excess), were coupled with the Hillslope River Routing model to simulate surface/subsurface runoff and streamflow. A case study conducted in Santa Barbara County, California, reveals increased surface runoff in February and March but decreased runoff in other months, a delayed (3 d, median) and shortened (6 d, median) wet season, and increased daily discharge especially for the extremes (e.g., 100-year flood discharge, Q100). The Bayesian model averaging analysis indicates that the probability of such an increase can be up to 85 %. For projected changes in runoff and discharge, general circulation models (GCMs) and emission scenarios are two major uncertainty sources, accounting for about half of the total uncertainty. For the changes in seasonality, GCMs and hydrologic models are two major uncertainty contributors (∼35 %). In contrast, the contribution of hydrologic model parameters to the total uncertainty of changes in these hydrologic variables is relatively small (&lt;6 %), limiting the impacts of hydrologic model parameter equifinality in climate change impact analysis. This study provides useful information for practices associated with water resources, risk control, and ecosystem conservation and for studies related to hydrologic model evaluation and climate change impact analysis for the study region as well as other Mediterranean regions.

Hydrological evaluation of merged satellite precipitation datasets for streamflow simulation using SWAT: A case study of Potohar Plateau, Pakistan

Hydrological modeling is a challenging task in poorly gauged catchments, especially in developing countries like Pakistan. Open access precipitation and temperature datasets with different spatial and temporal resolutions provide alternative sources in data-scarce regions. However, individual satellite precipitation datasets (SPDs) have significant uncertainties. Motivated by data scarcity issues, significant spatial and temporal gaps in in-situ observations, and poor performance of individual SPDs in hydrological models, this study evaluates the performance of two merged precipitation datasets (MPDs) to simulate daily streamflow using Soil and Water Assessment Tool (SWAT) in Potohar Plateau, Pakistan. These two MPDs are based on Dynamic Clustered Bayesian Averaging (DCBA) and Dynamic Bayesian Model Averaging (DBMA), respectively, and have 0.25° spatial resolution and daily temporal resolution. Precipitation data from rain gauges (RGs) are also used, and results were compared with MPDs simulated streamflow. Multi-site calibration and validation are performed at seven stations, and the performance of RGs, DCBA, and DBMA in streamflow simulation was evaluated using the coefficient of determination (R2), Nash-Sutcliffe Efficiency (NS), and Percent BIAS (PBIAS). The results demonstrated that precipitation input from RGs presented better performance (very good to good) in streamflow simulation, even with its sparse distribution. The performance of DCBA showed better agreement with the results from RGs; however, DBMA presented satisfactory results on occasional bases. It is concluded from the current study that MPDs combines the advantages of individual SPDs and have higher potential for hydrological applications, significantly reduce the uncertainties of individual SPDs, and can be used as alternatives to RGs in poorly gauged or ungauged catchments.

Evaluation Method of Multiobjective Functions' Combination and Its Application in Hydrological Model Evaluation.

Parameter optimization of a hydrological model is intrinsically a high dimensional, nonlinear, multivariable, combinatorial optimization problem which involves a set of different objectives. Currently, the assessment of optimization results for the hydrological model is usually made through calculations and comparisons of objective function values of simulated and observed variables. Thus, the proper selection of objective functions' combination for model parameter optimization has an important impact on the hydrological forecasting. There exist various objective functions, and how to analyze and evaluate the objective function combinations for selecting the optimal parameters has not been studied in depth. Therefore, to select the proper objective function combination which can balance the trade-off among various design objectives and achieve the overall best benefit, a simple and convenient framework for the comparison of the influence of different objective function combinations on the optimization results is urgently needed. In this paper, various objective functions related to parameters optimization of hydrological models were collected from the literature and constructed to nine combinations. Then, a selection and evaluation framework of objective functions is proposed for hydrological model parameter optimization, in which a multiobjective artificial bee colony algorithm named RMOABC is employed to optimize the hydrological model and obtain the Pareto optimal solutions. The parameter optimization problem of the Xinanjiang hydrological model was taken as the application case for long-term runoff prediction in the Heihe River basin. Finally, the technique for order preference by similarity to ideal solution (TOPSIS) based on the entropy theory is adapted to sort the Pareto optimal solutions to compare these combinations of objective functions and obtain the comprehensive optimal objective functions' combination. The experiments results demonstrate that the combination 2 of objective functions can provide more comprehensive and reliable dominant options (i.e., parameter sets) for practical hydrological forecasting in the study area. The entropy-based method has been proved that it is effective to analyze and evaluate the performance of different combinations of objective functions and can provide more comprehensive and impersonal decision support for hydrological forecasting.

Hydrological evaluation of hourly merged satellite–station precipitation product in the mountainous basin of China using a distributed hydrological model

AbstractOver the past two decades, various satellite‐based precipitation products have been released to provide uninterrupted time‐series precipitation with quasi‐global coverage. The latest Chinese merged satellite–gauge (CMS) rainfall product developed by the China Meteorological Administration (CMA) may reduce the errors in near real time by combining the Climate Prediction Centre's (CPC) morphing algorithm (CMORPH) rainfall estimates with over 30,000 weather station measurements, which therefore offer a potential alternative or supplement to ground‐based rainfall estimates for hydrometeorological applications in sparsely gauged or ungauged basins. In the present study, the characteristics of the CMS precipitation product were comprehensively evaluated for the period 2008–2012 over Danjiangkou basin, central China, and used to drive the Liuxihe (LXH)‐distributed hydrological model for detailed streamflow simulations. The results show that most of the small–median rainfall with an intensity &lt; 25 mm·day–1 can be well captured by the CMS, and the simulated streamflow had relatively better agreement with observations for peak flow in the lower part of the basin. However, the total water amount was 15.7% less than the streamflow driven by rain gauges, on average. In addition, the depths of surface flow and groundwater forced by the CMS rainfall were 22.05% and 21.10% less than rain gauge, respectively. Overall, the study provides new insight into and experience of the hydrological applicability of the high spatiotemporal‐resolution satellite–gauge rainfall product for driving a distributed hydrological model at a daily resolution. This product has the potential to be an alternative data source for data‐sparse or ungauged basins for water resource management and hydrological simulation/prediction purposes.

Hydrological foretelling uncertainty evaluation of water balance components and sediments yield using a multi-variable optimization approach in an external Rif’s catchment. Morocco

Sedimentary and aquatic dynamics are currently causing major environmental problems in the Mediterranean countries, water and soil resources are steadily decreasing over time. Therefore, simulation and estimation of flows and sediments have currently a direct implication in the sustainable management of water and soils. In the present study, a Mediterranean watershed (North-West of Morocco) was benefited from the functionalities of Hydrological Model SWAT, version 2012 (semi-distributed and physically based), by adopting a monthly simulation. The main objective of this study is to calibrate and validate the SWAT model in the Arbaa Ayacha watershed (18,854 ha) between 1980 and 2000 by a multi-objective optimization approach using the Sufi-2 algorithm of the SWAT-CUP program. The performance of the model was tested using three objective functions NSE, PBIAS and R2, while the sensitivity analysis of the model was performed by adjusting 19 parameters of sensitivities related to flow rates and sediment concentrations. The results obtained are satisfactory with a significant correlation between the observed and simulated values of variables flow and sediments. The results also show that evapotranspiration and surface runoff are predominante in the basin compared to the other components of the water balance, while the sediment yield was estimated at 28.09 t/ha/year. Since the model is validated in the study area, it will later be used to simulate the evolution of scenarios based on climate change and land use. It will also help to determine the best managment practices in the watershed.

The spread of nitrogen compounds in an active groundwater exchange zone within a valuable natural ecosystem

The aim of the paper is to present a methodical approach for developing a hydrodynamic model of groundwater flow and transport of nitrogen compounds in the area covered by the precision farming system. The study area covers the part of the Vistula River floodplain terrace near Warsaw (Poland), in the vicinity of nature reserves: Wyspy Zawadowskie, Wyspy Świderskie and Łęgi Oborskie. The components of the water balance in the analysed area were determined using the HELP (Hydrologic Evaluation of Landfill Performance) model. In reference to the doses of mineral nitrogen used during precise fertilisation, the scenarios of the spread of nitrogen ions in the soil-water environment were detailed. The results were presented graphically in the form of hydroisohypses and isolines of nitrate and ammonium concentrations. The activity of overbank flows and the effect of the geological settings on the course of aquifer layers in the active exchange zone were taken into consideration when describing groundwater flow. This allowed us to indicate potential paths of intense nitrate migration resulting from the evolution of the fluvial environment. Moreover, “hydrogeological windows” have been identified, where contaminants can freely migrate to usable aquifer in the major groundwater basin.