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Study region: The Xilin River Basin in the Inner Mongolia Autonomous Region, China. Study focus: This study aims to identify the components and change patterns of runoff in the basin, and quantitatively assess the impacts of climate change and overgrazing on runoff. Future changes in river basin runoff were simulated under three climate change scenarios. New hydrological insights for the region: This study quantitatively assessed the impacts of climate change and overgrazing on runoff in the Xilin River Basin using the HYPE model and five attribution scenarios. The assessment results are as follows: (1) Monthly runoff in the basin shows typical bimodal characteristics. Snowmelt is the primary contributor to runoff from March to mid-April, while precipitation has the largest contribution in the remaining months. (2) During the growing season, overgrazing has a more significant impact in reducing runoff than climate change, and runoff changes caused by climate change are more complex on a monthly scale than at the annual scale. (3) Among the three development scenarios, SSP126 with low levels of greenhouse gas emissions appeared to be the most suitable for the sustainable and healthy development of the grassland inland river basin.

The increasing penetration of non-programmable renewable energy sources (RES) is enforcing the need for accurate power production forecasts. In the category of hydroelectric plants, Run of the River (RoR) plants belong to the class of non-programmable RES. Data-driven models are nowadays the most widely adopted methodologies in hydropower forecast. Among all, the Artificial Neural Network (ANN) proved to be highly successful in production forecast. Widely adopted and equally important for hydropower generation forecast is the HYdrological Predictions for the Environment (HYPE), a semi-distributed hydrological Rainfall–Runoff model. A novel hybrid method, providing HYPE sub-basins flow computation as input to an ANN, is here introduced and tested both with and without the adoption of a decomposition approach. In the former case, two ANNs are trained to forecast the trend and the residual of the production, respectively, to be then summed up to the previously extracted seasonality component and get the power forecast. These results have been compared to those obtained from the adoption of a ANN with rainfalls in input, again with and without decomposition approach. The methods have been assessed by forecasting the Run-of-the-River hydroelectric power plant energy for the year 2017. Besides, the forecasts of 15 power plants output have been fairly compared in order to identify the most accurate forecasting technique. The here proposed hybrid method (HYPE and ANN) has shown to be the most accurate in all the considered study cases.

This study is focused on water quality and quantity modelling in the Berze River basin located in the Zemgale region of Latvia. The contributing basin area of 872 km2 is furthermore divided into 15 sub-basins designated according to the characteristics of hydrological network and water sampling programme. The river basin of interest is a spatially complex system with agricultural land and forests as two predominant land use types. Complexity of the system reflects in the discharge intensity and diffuse pollution of nitrogen compounds into the water bodies of the river basin. The presence of urban area has an impact as the load from the existing wastewater treatment plants consist up to 76 % of the total nitrogen load in the Berze River basin. Representative data sets of land cover, agricultural field data base for crop distribution analysis, estimation of crop management, soil type map, digital elevation model, drainage conditions, network of water bodies and point sources were used for the modelling procedures. The semi-distributed hydro chemical model HYPE has a setup to simulate discharge and nitrogen transfer. In order to make the model more robust and appropriate for the current study the data sets previously stated were classified by unifying similar spatially located polygons. The data layers were overlaid and 53 hydrological response units (SLCs) were created. Agricultural land consists of 48 SLCs with the details of soils, drainage conditions, crop types, and land management practices. Manual calibration procedure was applied to improve the performance of discharge simulation. Simulated discharge values showed good agreement with the observed values with the Nash-Sutcliffe efficiency of 0.82 and bias of −6.6 %. Manual calibration of parameters related to nitrogen leakage simulation was applied to test the most sensitive parameters.

This study investigates the impacts of climate change on the hydrological response of a Mediterranean mesoscale catchment using a hydrological model. The effect of climate change on the discharge of the Alata River Basin in Mersin province (Turkey) was assessed under the worst-case climate change scenario (i.e., RCP8.5), using the semi-distributed, process-based hydrological model Hydrological Predictions for the Environment (HYPE). First, the model was evaluated temporally and spatially and has been shown to reproduce the measured discharge consistently. Second, the discharge was predicted under climate projections in three distinct future periods (i.e., 2021–2040, 2046–2065 and 2081–2100, reflecting the beginning, middle and end of the century, respectively). Climate change projections showed that the annual mean temperature in the Alata River Basin rises for the beginning, middle and end of the century, with about 1.35, 2.13 and 4.11 °C, respectively. Besides, the highest discharge timing seems to occur one month earlier (February instead of March) compared to the baseline period (2000–2011) in the beginning and middle of the century. The results show a decrease in precipitation and an increase in temperature in all future projections, resulting in more snowmelt and higher discharge generation in the beginning and middle of the century scenarios. However, at the end of the century, the discharge significantly decreased due to increased evapotranspiration and reduced snow depth in the upstream area. The findings of this study can help develop efficient climate change adaptation options in the Levant’s coastal areas.

Study region: Our study is focussed on a mesoscale catchment, Selke, in central Germany having an area of 463 km2 with spatially diverse land-use from upland to the low-lying areas in the vicinity of the catchment outlet. Study focus: This study used rainfall-runoff data available on daily time step to examine the spatio-temporal variation of runoff coefficients. We then applied a validated semi-distributed hydrological model, HYPE, for examining the spatio-temporal variation of runoff generating mechanisms. HYPE model was modified in a minor fashion and simulations were conducted again to find out the portion of discharge originating from different runoff generation mechanisms. New hydrological insights for the region: We examined the spatio-temporal variation of runoff generating mechanisms on the sub-basin level on seasonal basis. Our analysis reveals that the runoff generation in the Selke catchment is primarily dominated by shallow sub-surface flow and very rarely the contribution from Dunne overland flow exceeds sub-surface flow. Runoff generated by Hortonian mechanism is very infrequent and almost negligible. We also examined the spatio-temporal variation of runoff coefficients on seasonal basis as well as for individual storms. Due to higher precipitation and topographic relief in the upland catchment of Silberhutte, the runoff coefficients were consistently higher and its peak was found in winter months due to lower evapotranspiration.

This investigation reports on a new national model to evaluate the effectiveness of catchment sensitive farming in England, and how pollution mitigation measures have improved water quality between 2006 and 2016. An adapted HYPE (HYdrological Predictions for the Environment) model was written to use accurate farm emissions data so that the pathway impact could be accounted for in the land phase of transport. Farm emissions were apportioned into different runoff fractions simulated in surface and soil layers, and travel time and losses were taken into account. These were derived from the regulator’s ‘catchment change matrix’ and converted to monthly load time series, combined with extensive point source load datasets. Very large flow and water quality monitoring datasets were used to calibrate the model nationally for flow, nitrogen, phosphorus, suspended sediments and faecal indicator organisms. The model was simulated with and without estimated changes to farm emissions resulting from catchment measures, and spatial and temporal changes to water quality concentrations were then assessed.

In this study, the high-resolution polygonal land cover data of EEA Urban Atlas was applied for land-use characterization in the dynamic multi-basin hydrological model, HYPE. The objective of the study was to compare this dedicated urban land cover data in semi-distributed hydrological modelling with the widely used but less detailed EEA CORINE. The model was set up for a basin including a small town named Svedala in southern Sweden. In order to verify the ability of the HYPE model to reproduce the observed flow rate, the simulated flow rate was evaluated based on river flow time series, statistical indicators and flow duration curves. Flow rate simulated by the model based on Urban Atlas generally agreed better with observations of summer storm events than the CORINE-based model, especially when the daily rainfall amount was 10 mm/day or more, or the flow exceedance probability was 0.02 to 0.5. It suggests that the added value of the Urban Atlas model is higher for heavy-to-medium storm events dominated by direct runoff. To conclude, the effectiveness of the proposed approach, which aims at improving the accuracy of hydrological simulations in urbanized basins, was supported.

Background: Understanding the response of nitrogen fluxes to changes in land use and agricultural practices is crucial for improving the instream water quality prediction. In central Germany, the expansion of bioenergy crops during the last decade led to an increase in fertiliser application rates. The purpose of this study is to investigate the effect of agricultural management changes on the stream nitrogen load of a drinking water reservoir catchment (Weida, 99.5 km) using a hydrological water quality model. Methods: The semi-distributed hydrological water quality model-the HYdrological Predictions for the Environment (HYPE)-was calibrated and validated successfully for discharge and nitrate-N concentrations during the period 1997-2003 (the lowest discharge Nash-Sutcliffe efficiency (NSE) was 0.78). Subsequently, stream nitrogen load of six different land use scenarios and their associated agricultural practice changes were compared to the baseline simulations of the period 2006-2009. Some of these scenarios were designed considering the increased cultivation of bioenergy crops. Results: Results revealed that an increase in mineral fertiliser by 20 % for all crops augmented an increase of monthly stream nitrogen loads in the range of 2-6 % compared to the baseline simulations. Also, it was found that stream nitrogen load increased in scenarios where all or some crop areas were converted to maize and rape, which are the established bioenergy crops in Germany. The increase of nitrogen load resulting from these scenarios differed in terms of magnitude and their temporal patterns, reflecting the importance of timing, the amount of fertiliser applications, and harvesting periods. However, results showed that nitrogen load was reduced in situations when only organic farming or summer barley was used and when rape and maize cropping areas were converted to winter wheat. Conclusions: In this intensively used agricultural catchment, the simulated stream nitrate-N loads quickly responded to fertiliser application changes (increase/decrease). This rapid response could be explained by short residence time of the interflow and baseflow runoff components because of the hardrock geological properties of the catchment. [ABSTRACT FROM AUTHOR]

ABSTRACT Water quality models are increasingly used to predict nutrient losses from anthropogenically impacted catchments, but comprehensive model calibration and transfer of model parameters are often difficult. The objectives of this study are (i) to assess the Hydrological Predictions for the Environment (HYPE) model for simulating runoff and inorganic nitrogen (IN) leaching in nested and spatially heterogeneous mesoscale catchments in central Germany and (ii) to investigate the temporal and spatial variations of IN leaching. A multi-site and multi-objective calibration approach using the parameter estimation tool PEST was employed. Results showed that parameters related to evapotranspiration were most sensitive for runoff simulation. The nitrogen balance was controlled by plant uptake and denitrification. Runoff was well reproduced for both calibration (1994-1999) and validation (1999-2004) periods at all three gauging stations, with the lowest Nash-Sutcliffe efficiency ( NSE) of 0·86 and bias ( PBIAS) of less than 15%. In addition, the model was tested successfully to predict the dynamics of soil water storage during extreme climatological events. IN concentrations showed increase in magnitude and decrease in dynamics from upstream to downstream of the catchment, reflecting the combined effects of nutrient inputs and IN in-stream retention. The dynamics of monthly IN loads were well represented by the model during the whole simulation period, with the lowest NSE and PBIAS of 0·69 and 33%, respectively. Results revealed seasonal dynamics of IN leaching due to the combined effects of hydrological and biogeochemical processes, which is consistent with runoff dynamics. This indicates the controlling role of hydrology on nitrogen transport processes. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This study investigates the impacts of climate change on the hydrological response of a Mediterranean mesoscale catchment using a hydrological model. The effect of climate change on the discharge of the Alata River Basin in Mersin province (Turkey) was assessed under the worst-case climate change scenario (i.e., RCP8.5), using the semi-distributed, process-based hydrological model Hydrological Predictions for the Environment (HYPE). First, the model was evaluated temporally and spatially and has been shown to reproduce the measured discharge consistently. Second, the discharge was predicted under climate projections in three distinct future periods (i.e., 2021–2040, 2046–2065 and 2081–2100, reflecting the beginning, middle and end of the century, respectively). Climate change projections showed that the annual mean temperature in the Alata River Basin rises for the beginning, middle and end of the century, with about 1.35, 2.13 and 4.11 °C, respectively. Besides, the highest discharge timing seems to occur one month earlier (February instead of March) compared to the baseline period (2000–2011) in the beginning and middle of the century. The results show a decrease in precipitation and an increase in temperature in all future projections, resulting in more snowmelt and higher discharge generation in the beginning and middle of the century scenarios. However, at the end of the century, the discharge significantly decreased due to increased evapotranspiration and reduced snow depth in the upstream area. The findings of this study can help develop efficient climate change adaptation options in the Levant's coastal areas. [ABSTRACT FROM AUTHOR]

• The HYPE model reproduces well the spatiotemporal variability of NO 3 - -N and TP. • Multi-site calibration increases the spatial capability of nutrient catchment model. • Hydro-meteorological sub-catchment delineation is important for nutrient prediction. Semi-distributed hydrological and water quality models are increasingly used as innovative and scientific-based management tools. However, their application is usually restricted to the gauging stations where they are originally calibrated, limiting their spatial capability. In this study, the semi-distributed hydrological water quality model HYPE (HYdrological Predictions for the Environment) was tested spatially to represent nitrate-N (NO 3 - - N) and total phosphorus (TP) concentrations and loads of the nested and heterogeneous Selke catchment (463 km2) in central Germany. First, an automatic calibration procedure and uncertainty analysis were conducted using the DiffeRential Evolution Adaptive Metropolis (DREAM) tool to simulate discharge, NO 3 - -N and TP concentrations. A multi-site and multi-objective calibration approach was applied using three main gauging stations, covering the most important hydro-meteorological and physiographical characteristics of the whole catchment. Second, the model's capability was tested to represent further internal stations, which were not initially considered for calibration. Results showed that discharge was well represented by the model at all three main stations during both calibration (1994–1998) and validation (1999–2014) periods with lowest Nash-Sutcliffe Efficiency (NSE) of 0.71 and maximum Percentage BIAS (PBIAS) of 18.0%. The model was able to reproduce the seasonal dynamics of NO 3 - -N and TP concentrations with low predictive uncertainty at the three main stations, reflected by PBIAS values in the ranges from −16.1% to 6.4% and from −20.0% to 11.5% for NO 3 - -N and TP load simulations, respectively. At internal stations, the model could represent reasonably well the seasonal variation of nutrient concentrations with PBIAS values in the ranges from −9.0% to 14.2% for NO 3 - -N and from −25.3% to 34.3% for TP concentration simulations. Overall, results suggested that the spatial validation of a nutrient transport model can be better ensured when a multi-site and multi-objective calibration approach using archetypical gauging stations is implemented. Further, results revealed that the delineation of sub-catchments should put more focus on hydro-meteorological conditions than on land-use features. [ABSTRACT FROM AUTHOR]

This investigation reports on a new national model to evaluate the effectiveness of catchment sensitive farming in England, and how pollution mitigation measures have improved water quality between 2006 and 2016. An adapted HYPE (HYdrological Predictions for the Environment) model was written to use accurate farm emissions data so that the pathway impact could be accounted for in the land phase of transport. Farm emissions were apportioned into different runoff fractions simulated in surface and soil layers, and travel time and losses were taken into account. These were derived from the regulator's 'catchment change matrix' and converted to monthly load time series, combined with extensive point source load datasets. Very large flow and water quality monitoring datasets were used to calibrate the model nationally for flow, nitrogen, phosphorus, suspended sediments and faecal indicator organisms. The model was simulated with and without estimated changes to farm emissions resulting from catchment measures, and spatial and temporal changes to water quality concentrations were then assessed. [ABSTRACT FROM AUTHOR]

Many water quality models have been successfully used worldwide to predict nutrient losses from anthropogenically impacted catchments, but hydrological and nutrient simulations with limited data are difficult considering the transfer of model parameters and complication of model calibration and validation. This study aims: (i) to assess the performance capabilities of a new and relatively more advantageous model, namely, Hydrological Predictions for the Environment (HYPE), that simulates stream flow and nutrient load in agricultural areas by using a multi-site and multi-objective parameter calibration method and (ii) to investigate the temporal and spatial variations of total nitrogen (TN) and total phosphorous (TP) concentrations and loads with crop rotation by using the model for the first time. A parameter estimation tool (PEST) was used to calibrate parameters. Results show that the parameters related to the effective soil porosity were highly sensitive to hydrological modeling. N balance was largely controlled by soil denitrification processes. P balance was influenced by the sedimentation rate and production/decay of P in rivers and lakes. The model reproduced the temporal and spatial variations of discharge and TN/TP relatively well in both calibration (2006-2008) and validation (2009-2010) periods. Among the obtained data, the lowest Nash-Suttclife efficiency of discharge, daily TN load, and daily TP load were 0.74, 0.51, and 0.54, respectively. The seasonal variations of daily TN concentrations in the entire simulation period were insufficient, indicated that crop rotation changed the timing and amount of N output. Monthly TN and TP simulation yields revealed that nutrient outputs were abundant in summer in terms of the corresponding discharge. The area-weighted TN and TP load annual yields in five years showed that nutrient loads were extremely high along Hong and Ru rivers, especially in agricultural lands.