Total Streamflow Research Articles

Large-scale baseflow index prediction using hydrological modelling, linear and multilevel regression approaches

Baseflow is critical for water balance budget, water resources management, and environmental evaluation. Prediction of baseflow index (BFI), the ratio of baseflow to total streamflow, has a great significance in unravelling the baseflow characteristics for large scale trajectory. Therefore, this study compares BFI predictive performance derived from a new multilevel regression approach along with two other commonly used approaches: hydrological modelling (SIMHYD, a simplified version of the HYDROLOG model, and Xinanjiang model), and linear regression (traditional linear regression, and alternative traditional regression considers the second-order interaction). The multilevel regression approach does not only group the catchments into the four climate zones (arid, tropics, equiseasonal and winter rainfall), but also considers inter-catchment and inter-climate zone variances. Likewise, calibration and two regionalisation techniques namely spatial proximity and integrated similarity are used to obtain the BFI from hydrological modelling approach. Correspondingly, the traditional linear regression technique estimates BFI establishing linear regressions between catchment attributes and four climate zones. Then, all the three approaches are evaluated against combined average estimation from four well-parameterised baseflow separation methods (Lyne-Hollick (LH), United Kingdom Institute of Hydrology (UKIH), Chapman-Maxwell (CM) and Eckhardt (ECK)) at 596 catchments across Australia for 1980–2012. The findings show that the multilevel regression has greatly improved the performance of BFI prediction in comparison to other methods. In particular, the two calibrated and regionalised hydrological models perform worst in predicting BFI with a Nash-Sutcliffe Efficiency (NSE) of −8.44 and −2.58 along with an absolute percent bias (PBIAS) of 81% and 146% (overestimation of baseflow), respectively. However, the traditional linear regression remains in intermediate position with the NSE of 0.57 and bias of 25. In addition, alternative traditional regression also shows very close proximity. In contrast, the multilevel regression approach shows the best performance with the NSE of 0.75 and bias of 19%. The study also demonstrates that the multilevel regression approach can improve BFI prediction, and shows potential for being used in the prediction of other hydrological signatures in large-scale.

Streamflow into Beijing and Its Response to Climate Change and Human Activities over the Period 1956–2016

Streamflow is likely affected by climate change and human activities. In this study, hydro-meteorological data from six rivers upstream of Beijing, namely, the Yongdinghe, Baihe, Heihe, Chaohe, Juhe, and Jumahe Rivers, were analyzed to quantify the spatial and temporal variability of streamflow and their responses to climate change and human activities over the period of 1956–2016. The Mann–Kendall test and moving t-test were used to detect trends and changing points of the annual streamflow. Results showed that the streamflow into Beijing experienced a statistically significant downward trend (p < 0.05), abruptly changing after the early 1980s, owing to climate and human effects. The climate elasticities of the streamflow showed that a 10% decrease in precipitation would result in a 24.5% decrease in total streamflow, whereas a 10% decrease in potential evapotranspiration would induce a 37.7% increase in total streamflow. Human activities accounted for 87% of the reduction in total streamflow, whereas 13% was attributed to climate change. Lastly, recommendations are provided for adaptive management of water resources at different spatial scales.

Beyond binary baseflow separation: a delayed-flow index for multiple streamflow contributions

Abstract. Understanding components of the total streamflow is important to assess the ecological functioning of rivers. Binary or two-component separation of streamflow into a quick and a slow (often referred to as baseflow) component are often based on arbitrary choices of separation parameters and also merge different delayed components into one baseflow component and one baseflow index (BFI). As streamflow generation during dry weather often results from drainage of multiple sources, we propose to extend the BFI by a delayed-flow index (DFI) considering the dynamics of multiple delayed contributions to streamflow. The DFI is based on characteristic delay curves (CDCs) where the identification of breakpoint (BP) estimates helps to avoid rather subjective separation parameters and allows for distinguishing four types of delayed streamflow contributions. The methodology is demonstrated using streamflow records from a set of 60 mesoscale catchments in Germany and Switzerland covering a pronounced elevation gradient of roughly 3000 m. We found that the quickflow signal often diminishes earlier than assumed by two-component BFI analyses and distinguished a variety of additional flow contributions with delays shorter than 60 d. For streamflow contributions with delays longer than 60 d, we show that the method can be used to assess catchments' water sustainability during dry spells. Colwell's predictability (PT), a measure of streamflow periodicity and sustainability, was applied to attribute the identified delay patterns to dynamic catchment storage. The smallest dynamic storages were consistently found for catchments between approx. 800 and 1800 m a.s.l. Above an elevation of 1800 m the DFI suggests that seasonal snowpack provides the primary contribution, whereas below 800 m groundwater resources are most likely the major streamflow contributions. Our analysis also indicates that dynamic storage in high alpine catchments might be large and is overall not smaller than in lowland catchments. We conclude that the DFI can be used to assess the range of sources forming catchments' storages and to judge the long-term sustainability of streamflow.

Change in low flows due to catchment management dynamics—Application of a comparative modelling approach

AbstractUnderstanding the natural low flow of a catchment is critical for effective water management policy in semi‐arid and arid lands. The Geba catchment in Ethiopia, forming the headwaters of Tekeze‐Atbara basin was known for its severe land degradation before the recent large scale Soil and Water conservation (SWC) programs. Such interventions can modify the hydrological processes by changing the partitioning of the incoming rainfall on the land surface. However, the literature lacks studies to quantify the hydrological impacts of these interventions in the semi‐arid catchments of the Nile basin. Statistical test and Indicators of Hydrological Alteration (IHA) were used to identify the trends of streamflow in two comparatives adjacent (one treated with intensive SWC intervention and control with fewer interventions) catchments. A distributed hydrological model was developed to understand the differences in hydrological processes of the two catchments. The statistical and IHA tools showed that the low flow in the treated catchment has significantly increased while considerably decreased in the control catchment. Comparative analysis confirmed that the low flow in the catchment with intensive SWC works was greater than that of the control by >30% while the direct runoff was lower by >120%. This implies a large proportion of the rainfall in the treated catchment is infiltrated and recharge aquifers which subsequently contribute to streamflow during the dry season. The proportion of soil storage was more than double compared to the control catchment. Moreover, hydrological response comparison from pre‐ and post‐intervention showed that a drastic reduction in direct runoff (>84%) has improved the low flow by >55%. This strongly suggests that the ongoing intensive SWC works have significantly improved the low flows while it contributed to the reduction of total streamflow in the catchment.

Relating hydroclimatic change to streamflow, baseflow, and hydrologic partitioning in the Upper Rio Grande Basin, 1980 to 2015

Understanding how changing climatic conditions affect streamflow volume and timing is critical for effective water management. In the Rio Grande Basin of the southwest U.S., decreasing snowpack, increasing minimum temperatures, and decreasing streamflow have been observed in recent decades, but the effects of hydroclimatic changes on baseflow, or groundwater discharge to streams, have not been investigated. In this study, we determine how trends in precipitation, snowpack accumulation, and snowmelt rate relate to total streamflow, baseflow, and the hydrologic partitioning of baseflow and runoff at 12 sites in the Upper Rio Grande Basin (URGB) during 1980 to 2015. Total streamflow was partitioned into baseflow and runoff components at a daily time step using conductivity-mass-balance hydrograph separation. Trends in annual total streamflow, baseflow, runoff, baseflow index, precipitation, snowmelt rate, and peak snow water equivalent (SWE) were evaluated from 1980 to 2015 using the non-parametric Mann-Kendall trend test. Results indicate that baseflow forms a large component of total streamflow, contributing an average of 49% of total discharge upstream of Albuquerque, NM. During 1980 to 2015, decreasing trends in total streamflow occurred at 9 of 12 sites and were almost always associated with decreases in baseflow, suggesting that baseflow volumes can respond to changing climatic and anthropogenic conditions within decades. Decreasing snowmelt rates were more frequently associated with decreases in baseflow and total streamflow than were decreases in precipitation and peak SWE, highlighting the importance of snowmelt rate as a process controlling streamflow generation. If snow accumulation and snowmelt rates continue to decrease in the future, results indicate that total streamflow and baseflow volumes will decline, and that baseflow will become a larger fraction of total streamflow in the URGB.

Comparison of short-term streamflow forecasting using stochastic time series, neural networks, process-based, and Bayesian models

Streamflow forecasts are essential for water resources management. Although there are many methods for forecasting streamflow, real-time forecasts remain challenging. This study evaluates streamflow forecasts using a process-based model (Soil and Water Assessment Tool-Variable Source Area model-SWAT-VSA), a stochastic model (Artificial Neural Network -ANN), an Auto-Regressive Moving-Average (ARMA) model, and a Bayesian ensemble model that utilizes the SWAT-VSA, ANN, and ARMA results. Streamflow is forecast from 1 to 8 d, forced with Quantitative Precipitation Forecasts from the US National Weather Service. Of the individual models, SWAT-VSA and the ANN provide better predictions of total streamflow (NSE 0.60–0.70) and peak flow, but underpredicted low flows. During the forecast period the ANN had the highest predictive power (NSE 0.44–0.64), however all three models underpredicted peak flow. The Bayesian ensemble forecast streamflow with the most skill for all forecast lead times (NSE 0.49–0.67) and provided a quantification of prediction uncertainty.

Feasibility/Design Study for a Cloud Seeding Program in the Yadkin River Basin, North Carolina

North American Weather Consultants (NAWC) was contracted by Cube Hydro Partners, LLC with headquarters in Bethesda, Maryland, to conduct a cloud seeding feasibility/design study for the Yadkin River Basin located in North Carolina. This study was prepared in conformance with American Society of Civil Engineer’s Guidelines and Standards. The goal of the program would be to augment precipitation over the Yadkin River Basin to increase streamflow into a series of Cube Hydro reservoirs along the Yadkin River. This increased streamflow would then be used to generate additional hydropower (clean energy) from facilities installed on project dams. NAWC compiled a site-specific climatology for this Basin. Both ground-based and airborne seeding was considered, but only airborne seeding was recommended due to the lack of significant topographic features in the Basin and the rather high average height of the -5° C level in potentially “seedable” storms (630 mb), as well as other meteorological factors. Regression equations relating precipitation to streamflow suggest that seeding only during the cool season produces streamflow increases not only during the cool season, but would also have a residual effect that increases streamflow throughout the remainder of the year. Overall, cool-season seeding is likely to have about 1.4 times as much impact on total streamflow as warm-season seeding (assuming the same increase percentage-wise in precipitation), with the cool season increase more evenly distributed throughout the year in terms of the resultant streamflow. Therefore, only a cool season program was recommended. Detailed examination of a 3-year cool season storm climatology in the Yadkin Basin suggest that aircraft-based cloud seeding during the cool season could enhance precipitation by 4.1%, resulting in an estimated 5.2% increase in annual streamflow (109,200 acre-feet). Cost estimates were prepared for a seasonal program. Once the estimated costs were established, NAWC was able to develop benefit/cost ratio estimates. Using information provided by Cube Hydro, NAWC calculated a potential benefit/cost ratio of 5/1. NAWC concluded that the proposed program was feasible based upon both technical and economic considerations as recommended in the “Guidelines for Cloud Seeding to Augment Precipitation”, Third Edition by the American Society of Civil Engineers (ASCE,2016).

CLUES model calibration: residual analysis to investigate potential sources of model error

ABSTRACT This paper presents the results of the latest calibrations of the CLUES model for each of total nitrogen, total phosphorus and Escherichia coli. The model provides estimates of in-stream annual loads of these contaminants for every river reach in the New Zealand River Environments Classification and has been used in New Zealand for catchment planning and policy development. CLUES has been calibrated nationally against loads estimated from data collected from water quality monitoring stations located across the country which could lead to parameter bias. To ascertain whether there is any systematic bias, the calibration residuals were evaluated against regional (geographical) location and a range of upstream catchment characteristics, namely land use, soil drainage properties, slope, network order, proportion of baseflow in total stream flow, climate, geology and source of flow. We found that CLUES gives reasonable load estimates at the catchment scale (Nash-Sutcliff efficiencies >0.8 for all the contaminants). However, there was significant uncertainty in the parameterisation. While several significant relationships were found between upstream catchment characteristics and the model residuals, these relationships were weak and are unlikely to point to any systematic bias in the calibration.

Modeling Hydrological Responses to Land Use Dynamics, Choke, Ethiopia

The main objective of this study was to assess the hydrological response of the Choke mountain range to land use dynamics. Two watersheds, Muga and Suha watersheds, were selected for detailed evaluation and analysis. The study was conducted using integrated applications of remote sensing and the Soil Water Assessment Tool (SWAT). The SWAT model was calibrated using Sequential Uncertainty Fitting (SUFI-2) algorithm in SWAT-CUP. Decadal land use maps (1985, 1995, and 2005) were used to simulate the hydrological responses. Simulated model results showed that over the past two decades (1985–2004), the total annual surface and lateral streamflows in the watershed increased at a rate of 1.2 mm/year and 0.57 mm/year, whereas the annual total groundwater flow and percolation in the basin decreased at a rate of 1.6 mm/year and 1.77 mm/year respectively. The decrease in the streamflow was more pronounced during the dry season (October to May), for which statistically significant declines of the base flow or the low flow at a rate of 0.37 m3/year and 0.73 m3/year in the Muga and Suha watersheds, respectively, were found. In the wet season (June to September), the peak flow has increased by 50% in Muga and 94% in Suha watersheds. Results of this study showed that the SWAT model can be an effective and useful tool for the assessment of response of watersheds to land use alterations.

Understanding surface water–groundwater interaction, submarine groundwater discharge, and associated nutrient loading in a small tropical island watershed

Submarine groundwater discharge (SGD) is recognized as an important nutrient delivery mechanism in coastal ecosystems. However, water quality management in these settings is typically focused on surface waters, often ignoring SGD and nearshore groundwater-surface water interaction. In this study, we integrate a comprehensive radionuclide tracer based field investigation with watershed modeling to examine groundwater – surface water partitioning and to quantify nutrient loading from fresh SGD and streamflow in a small embayment located in American Samoa. Measurements included streamflow, SGD rate, and environmental tracers, including 222Rn concentrations, nutrient levels, and nitrogen isotope values in groundwater and surface water samples. We then used the Soil and Water Assessment Tool (SWAT) to validate measured baseflow and SGD rates, and also to estimate storm flows, which were not measured in the field. Field results showed SGD was a significant delivery mechanism for coastal nutrient loads, whereas baseflow-nutrient loading from the upper-watershed was minimal during the study period. Seepage run measurements informed a conceptual hydrogeologic model of groundwater, surface water, and coastal water interaction, which we applied in developing the watershed model. The SWAT model simulated flow observations satisfactorily, and indicated baseflow accounts for only 39% of the total annual stream flow with surface runoff and lateral flow (i.e. interflow) making up the rest. By examining water and nutrient exchange between groundwater, surface water, and SGD, this study provides a more complete understanding of the fate and transport of water and nutrients in small-island watersheds where anthropogenic activities potentially threaten the health of coastal ecosystems.

Ensemble streamflow projections for a small watershed with HSPF model.

A watershed modeling tool, Hydrological Simulation Program-FORTRAN (HSPF), was utilized to model the hydrological processes in the agricultural Sarısu watershed in western Turkey. The meteorological input data were statistically downscaled time series from General Circulation Model simulations. The input data were constructed as an ensemble of 400 individual time series of temperature, precipitation, dewpoint temperature, solar radiation, potential evapotranspiration, cloudiness, and wind velocity, as required by HSPF. The ensemble was divided into four subsets, each comprising of 100 time series, of different Special Report on Emissions Scenarios. Yearly and monthly total streamflow time series were obtained from the calibrated and validated HSPF model spanning a period of 116 years between the water years of 1984 and 2099. The projections in the watershed showed a median increase of 3 °C in yearly average temperatures between the beginning and end 30-year periods of the 116-year simulation periods based on 400 ensemble members while the corresponding change in total yearly precipitation was - 71 mm. These changes led to a decrease in yearly streamflows by 40% which reflected itself to varying degrees in monthly flows. Correlations were established between the principal drivers of the watershed hydrological cycle, namely temperature and precipitation, and streamflow. The results showed that the changes in the climatic conditions will greatly affect water-related issues in the watershed and emphasize the necessity of preparing carefully to adapt to a warmer and drier climate.

Assessing combinations of artificial neural networks input/output parameters to better simulate daily streamflow: Case of Brazilian Atlantic Rainforest watersheds

This paper presents an assessment of the feasibility of using artificial neural networks (ANNs) with different input and output configurations to simulate streamflows for Brazilian Atlantic Rainforest basins. Hydrological data consisted of potential evapotranspiration (PET), rainfall (P), and streamflow (including runoff and baseflow) daily series (32 years in extent) of 12 watersheds of the Itapemirim River Basin (IRB). As a novelty, the input parameters for the ANNs were chosen basing on the statistical correlation between rainfall and flow data. Daily rainfall amounts until the third antecedent day were strongly correlated to runoff and streamflow. Accumulated rainfall until the 90 antecedent days were strongly correlated to baseflow and streamflow. Also as novelty, the ANNs were trained, for the downstream watershed, considering three different input and output configurations. The 1st configuration (ANNP) simulates daily streamflow considering input variables only related to rainfall. The 2nd (ANNP-PET) also simulates daily streamflow, but considers the differences between precipitation and potential evapotranspiration as input variables. The 3rd (ANN*P_streamflow) also considers input variables only related to rainfall, but the outputs were daily runoff and daily baseflow, which were used to later calculate total streamflow. Better streamflow simulations were provided by ANNP_streamflow, followed by ANNP-PET and ANNP. However, the three ANNs performances were quite similar. Thus, we indicate the use of ANNP for regions with lack of meteorological data (such as IRB) due to the need for fewer input parameters (only rainfall) and no need of splitting runoff and baseflow. After, the trained ANNs were applied to simulate the streamflow of each watershed. The results show that all the ANNs were able to satisfactorily simulate the streamflow of 11 watersheds, with better performances for the largest ones.

Modeling the impact of agricultural crops on the spatial and seasonal variability of water balance components in the Lake Tana basin, Ethiopia

AbstractThe Lake Tana basin hosts more than three million people and it is well known for its water resource potential by the Ethiopian government. The major economic activity in the region is agriculture, but the effect of agricultural crops on water resources is poorly understood. Understanding the crop water interaction is important to design proper water management plans. Therefore, the primary objective of this research is to investigate the effect of different agricultural crops on the spatial and seasonal variability of water balance components of Gilgelabay, Gumara, and Ribb catchment areas of Lake Tana basin, Ethiopia. To this end, the hydrologic model SWAT (Soil and Water Assessment Tool) was used to simulate the water fluxes between 1980 and 2014. The water balance components, which were mapped for each hydrologic response unit, indicated the spatial variations of water fluxes in the study. Cereal crops like teff and millet had significant effect in enhancing groundwater recharge, whereas leguminous crops like peas had significant impact in increasing runoff generation. Moreover, the model outputs showed that the total streamflow is dominated by baseflow and about 13%, 9%, and 7% of the annual rainfall goes to the deep aquifer system of Gilgelabay, Gumara, and Ribb catchment areas, respectively.

The role of GRACE total water storage anomalies, streamflow and rainfall in stream salinity trends across Australia’s Murray-Darling Basin during and post the Millennium Drought

By influencing water tables of saline aquifers, multiyear dry or wet periods can significantly delay or accelerate dryland salinity, but this effect remains poorly quantified at the large river basin scale. The Gravity and Climate Recovery Experiment (GRACE) satellite measures changes in the total water storage of river systems, providing a unique opportunity for better understanding connections between stream salinity and changes in catchment water storages at the large river basin scale. Here, we quantified the role of GRACE total water storage anomalies (TWSA) in stream salinity variability across Australia’s Murray-Darling Basin (∼1 million km2), while also accounting for streamflow and rainfall. We used the MERRA-2 global land surface model to i) place our findings in the context of the longer-term hydroclimatology (1980-present) and ii) to decompose TWSA into groundwater storage as an alternative driver variable. Multivariate time series regression models (generalized additive mixed models or GAMM) showed that the driver variables could explain 20–50% of the variability in stream salinity across 8 sub-catchments in the Murray Darling Basin. TWSA commonly explained as much variability as streamflow, while groundwater storage and TWSA had very similar explanatory power and rainfall only negligible contributions. The 2000–2009 Millennium Drought and the subsequent La Nina Floods had a predominantly decelerating and accelerating effect on stream salinity respectively and these trends were partially explained by trends in TWSA. Our study illustrates that GRACE can be a useful addition for monitoring and modeling dryland salinity over large river basins.

Estimation of Base Flow by Optimal Hydrograph Separation for the Conterminous United States and Implications for National-Extent Hydrologic Models

Optimal hydrograph separation (OHS) uses a two-parameter recursive digital filter that applies specific conductance mass-balance constraints to estimate the base flow contribution to total streamflow at stream gages where discharge and specific conductance are measured. OHS was applied to U.S. Geological Survey (USGS) stream gages across the conterminous United States to examine the range/distribution of base flow inputs and the utility of this method to build a hydrologic model calibration dataset. OHS models with acceptable goodness-of-fit criteria were insensitive to drainage area, stream density, watershed slope, elevation, agricultural or perennial snow/ice land cover, average annual precipitation, runoff, or evapotranspiration, implying that OHS results are a viable calibration dataset applicable in diverse watersheds. OHS-estimated base flow contribution was compared to base flow-like model components from the USGS National Hydrologic Model Infrastructure run with the Precipitation-Runoff Modeling System (NHM-PRMS). The NHM-PRMS variable gwres_flow is most conceptually like a base flow component of streamflow but the gwres_flow contribution to total streamflow is generally smaller than the OHS-estimated base flow contribution. The NHM-PRMS variable slow_flow, added to gwres_flow, produced similar or greater estimates of base flow contributions to total streamflow than the OHS-estimated base flow contribution but was dependent on the total flow magnitude.

Analysis of spatial variability of river streamflow at the catchment area of the Kolyma reservoir

Analysis of the streamflow characteristics of fourteen streams and rivers on the catchment area of the Kolyma reservoir showed that spatial variations of the annual total streamflow agree with the variability of precipitation. The maximum daily flow and duration of the flow period depend on the catchment area. The fraction of different landscapes of the watershed is indicator of such processes as the water discharge from the talik aquifers at the river valleys and the cryogenic redistribution of streamflow that affect the variation coefficient of streamflow, the maximum flow and the duration of the flow period.

Changes in glacier mass in the Lenglongling Mountains from 1972 to 2016 based on remote sensing data and modeling

Glacial meltwater-fed streamflow from the Qilian Mountains provides a much-needed source of water for the northeastern margins of the Tibetan Plateau (TP) and surrounding regions. We analyzed changes in glaciers located in the Lenglongling Mountains (LLM) in the eastern Qilian Mountains in terms of glacier area and surface elevation. These analyses were accomplished using multi-temporal satellite images and studying digital elevation models (DEMs) based on topographic maps and ZY-3 data. The results showed that from 1972 to 2016, glacier coverage decreased by 41.1 km2, leaving only 62.1 km2 extant in 2016. The mean rate of change in glacier surface elevation asl was −18.7 ± 1.2 m (−0.43 ± 0.03 m a−1) over the same period. In situ measured data from Ningchan No.1 Glacier (NC01), combined with meteorological records, were also employed to develop a spatially distributed degree-day model for the LLM glaciers. These two approaches yielded similar results, allowing the reconstruction of a consistent time series that detailed annual changes in glacier mass over the past 44 years. In addition, we calculated that the mean annual glacial meltwater-fed streamflow from 1972 to 2013 in the Jiutiaoling Basin (JTLB) northeast of the LLM was equivalent to 1.4 × 107 m3, or 4.6% of the area’s total annual streamflow. It is remarkable that, in years when streamflow was relatively lower, such as 1972, 1991 and 2013, glacial meltwater accounted for a higher proportion (more than 8%) of the streamflow.

Modelling snowmelt runoff in Lidder River Basin using coupled model

ABSTRACTA major proportion of discharge in the Lidder River is contributed by snowmelt runoff in its catchment which is situated in the West Himalayan region. It is, therefore, quite essential to estimate the contribution of snowmelt runoff to discharge along with other hydrological components. The MIKE SHE-MIKE-11 integrated coupled model along with the moderate resolution imaging spectroradiometer (MODIS) remote sensing snow cover products was selected and used in the Lidder Basin to ascertain the snowmelt contribution to runoff along with baseflow and drainage, as well as the percentage of snow-covered areas in the Basin around the year. The model was calibrated by trying to match simulated discharge against observed stream flow discharge at the Sheeshnag gauging station. Sensitivity analysis was carried out by varying three unknown parameters, viz., degree day coefficient, temperature lapse rate and precipitation lapse rate. The three parameters were unknown in the region. The cumulative volume of the simulated runoff was further validated with the observed runoff which showed an excellent comparison. The snow-covered areas obtained from the output of the model were validated with the data obtained from MODIS data sets. The data showed an excellent comparison. The ET losses at Sheeshnag represented 19.65% of the total rainfall, while the runoff at the Sheeshnag catchment of the Lidder Basin represented 40% of the total rainfall. Snowmelt contribution to the total streamflow in the Lidder River at Sheeshnag was found to be 54.19%. The total error percentage of the model was about 0.11 of the total rainfall, indicating a good interaction among different hydrological components of the model. The results obtained suggest that the MIKESHE-MIKE-11 integrated coupled model can be used efficiently in the sub-catchments of the upper Indus Basin in which snowfall and snowmelt runoff are significant.

Planning seasonal irrigation water allocation based on an interval multiobjective multi-stage stochastic programming approach

Water managers in arid and semi-arid areas must allocate limited irrigation water to different water use sectors considering the conflicting objectives, seasonal runoff inflow, and multiple uncertainties. To deal with these problems, an interval multiobjective multi-stage stochastic programming (IMMSP) model was proposed for finding reasonable water-storage scale and optimizing limited irrigation-water resources. Key factors in planning irrigation-water resources, such as random seasonal runoff, interval uncertainty in data collection, economic benefits, water leakage loss, and water deficit, were fully considered in the IMMSP model. Additionally, as an important indicator to describe the seasonal water supply capability of local water supply project, including reservoirs, agricultural ponds and etc., the water-storage scale, defined as the ratio of water storage capacity over total streamflow, is proposed to obtain the quantitative relationship between this indicator and objective of IMMSP. This study attempted to obtain the relationship between water-storage scale and objective of IMMSP as well as provide a reference of determining water-storage scale from the perspective of optimization. In addition, to solve the IMMSP model, a modified minimum deviation (MMD) method was proposed for dealing with uncertainties, making tradeoff among conflicting objectives, and reflecting the different importance of objectives. Both the IMMSP model and MMD method were applied to a real-world water-allocation problem in the middle reaches of the Heihe River basin for verifying its validity. The solutions generate a set of decision alternatives under different seasonal runoff scenarios and further guide local water managers identify the optimal management strategies and project construction scale of water conservancy. Moreover, a sound discussion of contribution to water-storage scale planning is made and the comparisons between IMMSP and each single-objective model (economic benefits, water leakage loss, and water deficit) in this study demonstrate that the results obtained by the proposed approach are more practical than a single objective with the same constraints. These results can not only effectively contribute to local irrigation water management and ecological restoration, but also provide more information to plan regional water-storage scale values.

Modelling the impacts of climatic variables on the hydrology of the Upper Crocodile River Basin, Johannesburg, South Africa

The impacts of climatic variables on the hydrology of the Upper Crocodile River Basin (UCRB) were modelled using the Precipitation-Runoff Modelling System (PRMS) for the period between 1996 and 2017. The values for the coefficient of determination (R2) and the Nash–Sutcliffe model efficiency coefficient (EN) gave a satisfactory correlation between the measured and simulated streamflow values, while a higher root mean square error (RMSE) was obtained for the validation period compared to the calibration period. PRMS was confirmed suitable for baseflow simulations because its mean annual recharge estimate of 5.3% was close to that of 7.5%, which was obtained using a separate filter parameter technique, both calculated from baseflow values. The climate scenario simulations revealed that the streamflow components are sensitive to temperature and rainfall. Higher decline in baseflow is observed when temperature increases than when rainfall decreases. Also, a smaller decline is observed in baseflow as compared to total streamflow, indicating the resistance of baseflow to drought. The scenario where temperatures were increased by 1.5 °C and rainfall decreased by 20% resulted in a decrease of 39% in total streamflow and 28% in baseflow. When the natural streamflow decreases by 39%, the wastewater discharge (5 m3/s) exceeds natural streamflow 80% of the simulation period. As urbanisation increases in the UCRB, wastewater will also increase. It is concluded that, while higher amounts of streamflow currently dilute wastewater, with changing climate variables and increasing urbanisation, less natural streamflow will be available to dilute wastewater, leading to highly polluted surface water. Therefore, better treatment and constraints on wastewater discharge are recommended.