Search

• Parameter uncertainty varies over time scales due to land use change (LUC). • The calibrated daily runoff model is deficient for the extreme flow changes. • Model structure impacts the extreme runoff direction change for moderate LUC. Hydrological modeling has provided key insights into the mechanisms of land-use change impacts on runoff. However, the uncertainty of this impact is poorly understood. This paper examines the uncertainty originated from hydrological models' parameters and structure in simulating hydrological responses to land use change on daily, monthly and annual time scales. Two hydrological models, SWAT and HSPF, were applied to simulate the runoff response in the Xitiaoxi basin of eastern China. The effects of three land use scenarios (in 1985, 2002 and 2014) were analyzed with respect to the expansion of urban areas and reduction of cropland and forests. The changes in streamflow with multi-timescales between two models were compared at the whole basin and the sub-basin scales. The parameter uncertainty was estimated based on a sequential uncertainty analysis method (SUFI-2). The results indicate that the two models (SWAT and HSPF) could reproduce the observed daily and seasonal flows well, but they cannot accurately reconstruct the extreme flows (such as annual 7-day minimum discharge, R_7dMIN). The effect of parameter uncertainty on streamflow varies over time scales. The simulated annual and monthly runoff change values show large discrepancies but with same trends. The simulated changes for annual maximum discharge (R_1dMAX) and R_7dMIN often show different signs. The average width of the predictive interval in the relative change of different flow characteristics exceeded 100% of the average relative changes for both models. HSPF tends to present larger relative changes for the annual and monthly runoffs compared to SWAT. Moreover, the model choice could affect the direction change in R_1dMAX and R_7dMIN. However, when land use change was significant to a certain degree, both models simulated increased R_1dMAX and decreased R_7dMIN, but with different significance levels. The study suggests that the model structure represents an additional uncertainty, which should be accounted for in the land-use change impact assessment. [ABSTRACT FROM AUTHOR]

Highlights: [•] This paper examines the impacts of rain gauge densities on runoff simulations. [•] Variation of rain gauge densities impacts the areal mean rainfall considerably. [•] The locations of rain gauges impact the model performances considerably. [•] Great impacts on model performances occurring when rain gauges fall below 0.4 station per 1000km2. [Copyright &y& Elsevier]

Summary: In this study a rigorous evaluation and comparison of the difference in water balance simulations resulted from using different downscaling techniques, GCMs and hydrological models is performed in upper Hanjiang basin in China. The study consists of the following steps: (1) the NCEP/NCAR reanalysis data for the period 1961–2000 are used to calibrate and validate the statistical downscaling techniques, i.e. SSVM (Smooth Support Vector Machine) and SDSM (Statistical Downscaling Model); (2) the A2 emission scenarios from CGCM3 and HadCM3 for the same period are used as input to the statistical downscaling models; and (3) the downscaled local scale climate scenarios are then used as the input to the Xin-anjiang and HBV hydrological models. The results show that: (1) for the same GCM, the simulated runoffs vary greatly when using rainfall provided by different statistical downscaling techniques as the input to the hydrological models; (2) although most widely used statistics in the literature for evaluation of statistical downscaling methods show SDSM has better performance than SSVM in downscaling rainfall except the Nash–Sutcliffe efficiency (NSC) and root mean square error-observations standard deviation ratio (RSR), the runoff simulation efficiency driven by SDSM rainfall is far lower than by SSVM; and (3) by comparing different statistics in rainfall and runoff simulation, it can be concluded that NSC and RSR between simulated and observed rainfall can be used as key statistics to evaluate the statistical downscaling models’ performance when downscaled precipitation scenarios are used as input for hydrological models. [Copyright &y& Elsevier]

Abstract: Predicting hydrological variables in ungauged catchments has been singled out as one of the major issues in the hydrological sciences. In this study, the conceptual rainfall-runoff model, HBV, was applied to Dongjiang basin and its 13 sub-basins for the purposes of examining the applicability of this well-known model in south China and exploring the possibility of transferring the calibrated parameter values to ungauged basins. For testing the applicability of the model in gauged basins, the model was calibrated for a period of 1978–1983 and validated for a period of 1984–1988. For testing the transferability of parameter values to ungauged basins, two parameter regionalization methods – proxy-basins and global mean – were investigated. The results showed that: 1) the HBV model worked well in the Dongjiang basin with the average indexes of agreement (D) and coefficient of efficiency (ME), respectively, equal to 0.79 and 0.82 in the calibration period, and 0.76 and 0.78 in the validation period; 2) transferring the parameter values from basins that passed the cross-basin test with higher ME values to the hypothetical ungauged catchments produced acceptable results with an average ME value equals to 0.72; 3) compared with the proxy-basin method of parameter estimation, the model produced equally good results for the global mean method with an average ME value equals to 0.74 when using simple arithmetic mean values. Neither the area weighted mean method nor the Thiessen polygon method produced regional parameter values could markedly improve the accuracy of modeling results. It was concluded that both regionalization methods could effectively estimate parameters for ungauged catchments in the Dongjiang basin, and similar model performances were obtained. [Copyright &y& Elsevier]

Summary: Annual runoff and annual suspended sediment loads of hydrological gauging stations along the mainstream of the Yangtze River basin (Pingshan station, Yichang station, Hankou station and Datong station) and main tributaries (Beipei station in Jialingjiang River, Wulong station in Wujiang River and Huangzhuang station in Hanjiang River) were analyzed with the help of Mann–Kendall trend analysis and linear regression analysis. Research results indicate that (1) changing patterns of runoff and sediment loads are different in different parts of the Yangtze River basin. No significant trend is detected for annual runoff at all stations at >95% confidence level. Changes of sediment loads, however, demonstrate different pictures in the Yangtze River basin. The sediment loads are in increasing trend in Pingshan station- the most upstream station on the Yangtze River basin (this increasing trend is significant at >95% confidence level after about 1990), but are in decreasing trend at other stations (including stations in the tributaries studied in this paper). This decreasing trend becomes more obvious from Yichang station to Datong station. (2) Water reservoirs exerted more influences on changes of sediment loads than on runoff, which is the main reason for the decreasing trend of sediment loads found in most stations. (3) Influences of water reservoirs on sediment loads are more obvious in the tributaries than in the mainstream of the Yangtze River basin, while in the mainstream the variation patterns of sediment loads are determined by multiple factors. [Copyright &y& Elsevier]

• A framework was provided for analyzing change causes of runoff and sediment load. • Runoff change attribution analysis was incorporated in quantifying sediment change. • Dam construction obviously disturbed the evolution characteristics of sediment load. Under the background of climate variability and anthropogenic stresses, changes of the runoff and sediment processes have drawn increasing concerns during the past decades. Analyzing the evolution characteristics and causes of runoff and sediment load is critical for the sustainable development of water resource and terrestrial ecosystems of river basins. This study presents an integrated approach for analyzing the evolution characteristics of the annual runoff and sediment load, including the trend, periodicity, change point and their correlations in the Wujiang River basin (WRB) –a typical mountainous river system in southwest China, based on the observed data during the period 1970–2016. On this basis, the contributions of climate variability and human activities on runoff change were first distinguished by using a widely applied Budyko-based hydrological sensitivity method, and then the quantified result was further applied in the attribution analysis of river sediment load change. Research results revealed that the change patterns of runoff and sediment processes, such as the trend, periodicity and potential change point, as well as the relationships of precipitation-runoff and runoff-sediment load are quite different in the WRB. Quantitative assessment revealed that climate variability contributes to 71.5%–85.6% changes of mean annual runoff in 1985–1994, 1995–2004 and 2005–2016 with reference to the baseline period of 1970–1984. In contrary, human activities dominate the reduction of sediment load in the river, and the contribution rate ranged in 67.2%–107.5%. Variations of annual runoff in the WRB was the direct result of climate variability, while land use change played a secondary role. The construction of cascade hydropower reservoirs, particularly in the upper and middle reaches of the Wujiang River is fundamentally responsible for the significant decreasing and the weakened periodicity of sediment load as well as the changed runoff-sediment load correlation at Wulong station in recent years. However, this influence was not big enough to modify the regime of annual runoff. [ABSTRACT FROM AUTHOR]

Highlights • The study shows the transferability of regionalization methods with changing climate. • The study compares the uncertainty from climate models and regionalization methods. • The difference between the regionalization methods tends to increase in the future. • In larger precipitation regions, climate models dominate the uncertainty. • Physical similarity method with parameter option produced the most robust result. Abstract Regionalization methods have been extensively discussed as the solution for runoff predictions in ungauged basins (PUB), especially during the PUB decade (2003–2012). At the same time, research topics relevant to climate change appear to be an essential and attractive field for hydrologists in recent decades, because the availability and quality of water resources are strongly affected by climate change. However, it is still unknown whether regionalization methods can be used to predict hydrological impacts of climate change for ungauged catchments or how much uncertainty of future predictions may result from the use of regionalization methods. Therefore, in this study, we investigate the transferability of regionalization methods (i.e. spatial proximity and physical similarity methods, regression method) under changing climate conditions and compare the uncertainty resulting from regionalization methods with that from using climate models. The investigation is based on 108 catchments in Norway, with large variability in climate conditions and geographic characteristics. The study applies a lumped conceptual rainfall-runoff model (WASMOD) with simple structure and six model parameters. Our result shows that (a) the differences in the predictions by the regionalization methods tend to increase in the future, (b) the physical similarity method with parameter option (i.e. the model parameters from the physically-similar donor catchments are first averaged and then used to run the model for the target catchment) shows higher transferability than other methods, (c) the uncertainty contributions from climate models and regionalization methods to future runoff prediction are basin dependent, and (d) the uncertainty of future runoff prediction due to regionalization methods can be higher than that from climate models in low precipitation areas. This study provides insight to the choice of regionalization methods under changing climate conditions and the role of regionalization methods to the uncertainty contributions in future runoff predictions. [ABSTRACT FROM AUTHOR]

Summary This study aims to provide model estimates of changes in hydrological elements, such as EvapoTranspiration (ET) and runoff, in Southern Africa in the near future until 2029. The climate change scenarios are projected by a high-resolution Regional Climate Model (RCM), RegCM4, which is the latest version of this model developed by the Abdus Salam International Centre for Theoretical Physics (ICTP). The hydrological projections are performed by using a large-scale hydrological model (WASMOD-D), which has been tested and customized on this region prior to this study. The results reveal that (1) the projected temperature shows an increasing tendency over Southern Africa in the near future, especially eastward of 25°E, while the precipitation changes are varying between different months and sub-regions; (2) an increase in runoff (and ET) was found in eastern part of Southern Africa, i.e. Southern Mozambique and Malawi, while a decrease was estimated across the driest region in a wide area encompassing Kalahari Desert, Namibia, southwest of South Africa and Angola; (3) the strongest climate change signals are found over humid tropical areas, i.e. north of Angola and Malawi and south of Dem Rep of Congo; and (4) large spatial and temporal variability of climate change signals is found in the near future over Southern Africa. This study presents the main results of work-package 2 (WP2) of the ‘Socioeconomic Consequences of Climate Change in Sub-equatorial Africa (SoCoCA)’ project, which is funded by the Research Council of Norway. [ABSTRACT FROM AUTHOR]

Summary Reservoirs are one of the most efficient infrastructures for integrated water resources development and management; and play a more and more important role in flood control and conservation. Optimal refill operation before the end of flood season is a valuable and effective approach to compromise the flood control, hydropower generation and comprehensive utilization of water resources of river basins. An integrated model consisting of a flood control risk analysis module, a utilization benefits analysis module and a multi-objective evaluation module was proposed in this study to derive joint optimal refill rules for cascade reservoirs. The Jinsha River and Three Gorges cascade reservoirs in the Changjiang River basin of China are selected for a case study. Sixty-one years of observed daily runoff data from 1950 to 2010 have been used to test the model. The results indicate that the proposed model can make an effective tradeoff between flood control and utilization benefits. Joint optimal synchronous and asynchronous refill rules can generate 3.25% and 2.78% more annual average hydropower, respectively and improve the fullness storage rate without increasing flood control risk comparing with the original designed operating rules. [ABSTRACT FROM AUTHOR]

Summary: Limitations in water quantity and quality are among the greatest social and economic problems facing mankind. However, difficulties in estimating the global long-term average runoff have led to differences of as much as 30% when integrated to the whole earth. Model estimates of runoff are especially uncertain for the 50% of the global land surface lacking consistent runoff data. In this study, we present the WASMOD-M global water-balance model, constructed to provide robust runoff estimates both for gauged and ungauged basins. WASMOD-M is a conceptual water-budgeting model with two state-variables and five tunable parameters. A simple parameter-value estimation procedure allowed “acceptable” parameter values to be identified both for the majority of gauged basins, and for most ungauged basins. Acceptable global simulations could be accomplished with continentally constant parameter values but at the cost of compensating errors on a basin scale. A “standard”, spatially-distributed parameter-value set was derived for a ”best” global simulation. Of the simulated 59132 0.5°×0.5° cells, 45% got “good” parameter values as a by-product of regionalisation, 41% from regionalisation, whereas 14% were given a default value set. This global set allowed simulation of the 1915–2000 world water balance. The simulation was in the same range as previously published model results and compilations of runoff measurements. Long-term average within-year runoff variations agreed well with previously published results for most of the studied runoff stations although WASMOD-M was only calibrated against long-term average runoff. Improvement of WASMOD-M and other global water-balance models should be simplified by a common definition of basin boundaries and areas, as well as runoff. Further, modelling progress will depend on improved global datasets of precipitation and runoff regulation. [Copyright &y& Elsevier]

• A systematic scheme to detect and verify abrupt shift of runoff was developed. • A model was proposed to forecast natural streamflow in data-poor areas. • Mechanism of abrupt shift in minor periods in human-impacted runoff was explored. Understanding the long-term variability of streamflow and its response to human activities in water-limited areas is essential for socio-hydrologic models' development. In this study, a framework for the detection and attribution of abrupt shift of minor periods in human-impacted streamflow is proposed. First, the most significant abrupt shift in human-impacted streamflow is detected using the Pettitt test and verified based on different statistical characteristics of streamflow series (trend, periodicity, and different quantiles) and main physical causes (main reservoirs operations). According to the breakpoint, the study period was divided into approximately natural sub-period and human-impacted sub-period. Interestingly, we found the "missing" of minor (2–4-year timescale) periods of the runoff records after abrupt shift points in the study cases. To investigate its mechanisms, we proposed an Improved Multivariate Fuzzy Mean Generating Function (IMFMGF) model to simulate the natural runoff in the human-impacted period and decomposed the observed runoff into natural runoff component and human-impacted changing runoff component. Then, the periodicity of these two components was compared based on Morlet wavelet analysis and Ensemble Empirical Mode Decomposition (EEMD). Results showed that the minor periods' wave crests and troughs of the above two components had excellent negative correspondence. The candidate mechanism is that the offsetting effects (i.e., the regular anthropogenic withdrawal or intake of water.) resulted in the disappearance of minor periods of the human-impacted observed, which can give more certain inputs into the prediction of non-stationary streamflow series. [ABSTRACT FROM AUTHOR]

Highlights • A progressive segmented optimization algorithm (PSOA) for calibration was proposed. • The PSOA calibrated time-variant parameters of Snowmelt Runoff Model effectively. • The PSOA outperformed the segmented optimization algorithm (SOA) Abstract To capture the temporal variability of parameters of hydrological models, the segmented optimization algorithm (SOA) is usually used which subdivides the calibration period into a number of sub-periods and seeks optimal parameters for each sub-period by optimizing the objective function based on the measured and estimated data in the same sub-period. In this paper, we developed a new method that is called a progressive segmented optimization algorithm (PSOA), which seeks optimal parameters by optimizing the objective function based on both the current and all the prior sub-periods. We applied and compared the SOA and PSOA algorithms to the Snowmelt Runoff Model (SRM) in simulating snow-melt streamflow for the Manasi River basin, northwest of China, during snowmelt seasons of 2001–2012. The study showed: (1) PSOA can effectively calibrate the time-variant model parameters while avoiding too much computational time caused by a significant increase of parameter dimensionality. (2) PSOA outperforms SOA for both single-snowmelt-season and multi-snowmelt-season simulations. (3) For single-snowmelt-season simulation, the length of the sub-period has an apparent effect on model performance, the shorter the sub-period is, the better the model performance will be, when the model is calibrated using the PSOA method. (4) For multi-snowmelt-season simulation, an over-short sub-period may cause overfitting problems in some cases such as the situation of taking Nash-Sutcliffe efficiency (NSE) as the objective function. A compromised length of sub-period and objective function may have to be chosen as a trade-off among evaluation criteria and between the importance of calibration and validation. [ABSTRACT FROM AUTHOR]

Summary The Budyko-type equations have begun to be widely adopted to separate the contributions of climate change and human activities to the variation of runoff over long-term periods by using the multi-year averages of hydrological variables. In this study, a two-step framework based on four single-parameter Budyko-type equations is proposed to separate the impacts of climate change and human activities on runoff. First, the relationship of the parameter w in each Budyko-type equation with climatic and human factors is built to reveal the time-variation process of w by using an 11-year moving window. Second, the impacts of climate change and human activities on runoff are separated by using both the decomposition method and the sensitivity method. This separating framework is applied to analyze the variation of the runoff during 1960–2009 in the Weihe River. It is found that the parameter w in each Budyko-type equation is significantly related to both factors of climate and human activities. The results from both the decomposition method and the sensitivity method show that climate change is the main driving factor to the decline in runoff of the Weihe River, while human activities are another important factor. In general, climate change affects runoff not only by changing the hydrological inputs (precipitation and potential evaporation) but also by altering the watershed characteristics as represented by the parameter w ; while the impacts of human activities on runoff are exerted mainly through the alteration of the watershed characteristics. [ABSTRACT FROM AUTHOR]

Summary: In this study, a GIS based simple and easily performed runoff routing approach based on travel time was developed to simulate storm runoff response process with consideration of spatial and temporal variability of runoff generation and flow routing through hillslope and river network. The watershed was discretized into grid cells, which were then classified into overland cells and channel cells through river network delineation from the DEM by use of GIS. The overland flow travel time of each overland cell was estimated by combining a steady state kinematic wave approximation with Manning’s equation, the channel flow travel time of each channel cell was estimated using Manning’s equation and the steady state continuity equation. The travel time from each grid cell to the watershed outlet is the sum of travel times of cells along the flow path. The direct runoff flow was determined by the sum of the volumetric flow rates from all contributing cells at each respective travel time for all time intervals. The approach was calibrated and verified to simulate eight storm runoff processes of Jiaokou Reservoir watershed, a sub-catchment of the Yongjiang River basin in southeast China using available topography, soil and land use data for the catchment. An average efficiency of 0.88 was obtained for the verification storms. Sensitivity analysis was conducted to investigate the effect of the area threshold of delineating river networks and parameter K relating channel velocity calculation on the predicted hydrograph at the basin outlet. The effects of different levels of grid size on the results were also studied, which showed that good results could be attained with a grid size of less than 200m in this study. [Copyright &y& Elsevier]

• An improved Budyko complementary relationship (IBCR) is developed. • The IBCR incorporates effects of both climate change and human activities on basin characteristics. • The IBCR is more flexible than the original relation in separating runoff change. Numerous efforts have been made to separate effects of climate change and human activities on long-term runoff change, including a commonly used method based on the Budyko complementary relationship (BCR) to deal with the non-closure problems often encountered in separating the observed runoff change into its contributed components. The BCR was derived by assuming the change of the Budyko parameter reflecting basin characteristics is related to human activities alone. However, some studies show that basin characteristics are related to both climate change and human activities in certain basins, where applying the BCR method could lead to wrong results. In the study, a more flexible form of the BCR, the improved Budyko complementary relationship (IBCR), is developed under the situation that basin characteristics may relate to climate change. A separation method based on the IBCR is also developed. In this IBCR method, the relationship of the basin characteristics parameter to climate and/or human factors is estimated by using a multi-year moving window. To identify the flexibility of the IBCR method relative to the BCR method, both methods were applied to the Weihe basin of China, whose basin characteristics have been found to be affected by both climate change and human activities. Compared with the IBCR method, the BCR method overestimates the climate effect, while underestimates the human effect on the runoff change in the Weihe basin. It is found that whether or not to incorporate the climate effect into the basin characteristics parameter has a significant impact on the runoff change separation results in Weihe basin. Overall, the IBCR method is more reasonable and flexible for separating the long-term runoff change into different components. [ABSTRACT FROM AUTHOR]

• The study evaluated the dependence of regionalization methods on hydrological models. • The performance of regionalization methods assessed in varied climate conditions. • Output averaging option works better than parameter averaging option. • Difference between output and parameter options increases with model complexity. • Simpler models are not superior to complex models in regionalization study. Hydrological models have been widely used to predict runoff in regions with observed discharge data, and regionalization methods have been extensively discussed for providing runoff predictions in ungauged basins (PUB), especially during the PUB decade (2003–2012). Great progress has been achieved in the field of regionalization in previous studies, in which different hydrological models have been coupled with various regionalization methods. However, different conclusions have been drawn due to the use of different hydrological models, regionalization methods, and study regions. In this study, we assessed the performance of the five most widely used regionalization methods (spatial proximity with parameter averaging option (SP-par), spatial proximity with output averaging option (SP-out), physical similarity with parameter averaging option (Phy-par), physical similarity with output averaging option (Phy-out), and regression methods (PCR)) and four daily rainfall-runoff models (GR4J, WASMOD, HBV and XAJ, with 6, 8, 13, and 17 parameters, respectively) at the same time. Our aim was to evaluate how the performance of the regionalization methods depends on (a) the selection of hydrological models, (b) nonstationary climate conditions, and (c) different climatic regions. This investigation used data from 86 independent catchments evenly distributed throughout Norway, covering three different climate zones (oceanic, continental and polar tundra) according to the Köppen-Geiger classification. The results showed that (a) the SP-out and Phy-out methods performed better than the SP-par and Phy-par for all the hydrological models, and the regression method performed worst in most cases; (b) the difference between the parameter averaging option and the output averaging option is positively related to the number of hydrological model parameters, i.e. the greater the number of parameters, the larger the difference between the two options; (c) the XAJ model with the greatest number of parameters produced the best results in most cases, and models with fewer parameters tend to produce similar performance for the different regionalization methods; (d) models with more parameters displayed larger declines in performance than those with fewer parameters for nonstationary conditions; and (e) clear differences in the performance of the regionalization methods exist among the three climatic regions. This study provides insight into the relationship between the complexity of hydrological models and regionalization methods in cold and seasonally snow-covered regions. [ABSTRACT FROM AUTHOR]