Mean Monthly Streamflow Research Articles

Application of coupled MIKE SHE/MIKE HYDRO on streamflow dynamics under climate change in Lake Tana sub basin, Abay Basin, Ethiopia

AbstractWater availability and quality are fluctuating due to climate change, which has disastrous effects on life. Modeling climate change impact on streamflow in the Lake Tana sub‐basin (LTSB) in selected watersheds was the main goal of the research. This research is unique in that it applies the coupled MIKE SHE/MIKE HYDRO model technique, which has not been applied before to investigate changes in streamflow due to climate change in LTSB. Streamflow in the LTSB was forecasted using the MIKE SHE/MIKE HYDRO model from seven ensembles of GCMs under two emission scenarios (SSP2–4.5 and SSP5–8.5) between 2041 and 2,100. According to the calibration and validation results in the period of 1985–2007, MIKE SHE performs well while simulating streamflow in LTSB. During calibration and validation, the Nash‐Sutcliffe efficiency (NSE) values in all watersheds were greater than 0.8, except for the Ribb watershed calibration, where it was 0.75. The mean monthly changes in temperatures indicate an incremental tendency in the next decades. According to the simulation result, the monthly mean rainfall for SSP2–4.5 and SSP5–8.5 will increase from 0.05 to 61.9% and 2.02 to 46.26% in the 2050s and 8.7 to 44.38% to 6.44 to 66.24% in the 2080s, respectively. Gilgle Abay, Gumara, and Ribb watersheds will lose 56.2, 56.65, 57.01% of their mean monthly streamflow in the 2050s, according to SSP2–4.5. For the Gilgle Abay, Gumara, and Ribb watersheds, streamflow will increase by 6.7% to 21.94%, 6.08% to 23.26%, and 6.51% to 22.88%, accordingly, in the 2050s under SSP5–8.5. Increased rainfall in the watershed could result in floods in the already‐flooded Gumara and Ribb flood plains. The government and community should implement coping mechanisms to lessen the impact of climate change like early warring and planting more trees. Overall, the study showed that it is expected that conditions related to streamflow may change in the future due to climate alteration. By offering a better understanding of current and future climates, the integrated modeling system developed can assist environmentalists, hydrologists, and policymakers in water management and policy intervention.

Future Projection of Water Resources of Ruzizi River Basin: What Are the Challenges for Management Strategy?

This study investigates the impact of climate change on hydrological dynamics in the Ruzizi River Basin (RRB) by leveraging a combination of observational historical data and downscaled climate model outputs. The primary objective is to evaluate changes in precipitation, temperature, and water balance components under different climate scenarios. We employed a multi-modal ensemble (MME) approach to enhance the accuracy of climate projections, integrating historical climate data spanning from 1950 to 2014 with downscaled projections for the SSP2-4.5 and SSP5-8.5 scenarios, covering future periods from 2040 to 2100. Our methodology involved calibrating and validating the SWAT model against observed hydrological data to ensure reliable simulations of future climate scenarios. The model’s performance was assessed using metrics such as R2, NSE, KGE, and PBIAS, which closely aligned with recommended standards. Results reveal a significant decline in mean annual precipitation, with reductions of up to 37.86% by mid-century under the SSP5-8.5 scenario. This decline is projected to lead to substantial reductions in surface runoff, evapotranspiration, and water yield, alongside a marked decrease in mean monthly stream flow, critically impacting agricultural, domestic, and ecological water needs. The study underscores the necessity of adaptive water resource management strategies to address these anticipated changes. Key recommendations include implementing a dynamic reservoir operation system, enhancing forecasting tools, and incorporating green infrastructure to maintain water quality, support ecosystem resilience, and ensure sustainable water use in the RRB. This research emphasizes the need for localized strategies to address climate-driven hydrological changes and protect future water resources.

Exploring climate-change impacts on streamflow and hydropower potential: insights from CMIP6 multi-GCM analysis

ABSTRACT The generation of hydropower is profoundly influenced by shifts in streamflow patterns induced by climate change. This research examines changes in streamflow and the potential surge in hydropower generation over a span of 35 years (2015–2050) at the Bhakra Dam site within the Upper Sutlej River basin. Employing a deep learning methodology, particularly the long short-term memory (LSTM) model, in conjunction with Coupled Model Intercomparison Project (CMIP) 6 multi-global climate model (GCM), facilitates a thorough analysis of these dynamics. Six out of 14 bias-corrected statistically downscaled datasets (0.25° × 0.25° grid resolution) from CMIP6 multi-GCM were selected based on entropy and combined compromise solution techniques. This innovative approach is utilized to assess streamflows and project potential increases in hydropower at the Bhakra Dam site under shared socioeconomic pathways (SSP) scenarios, specifically SSP245 and SSP585. The results indicate a maximum increase of approximately 15% and 17% in mean monthly streamflow under SSP245 and SSP585, respectively. Moreover, dependability flows calculated at Q50, Q75, and Q90 show respective rises of 13%, 16%, and 17% under SSP245 and 21%, 17%, and 18% under SSP585. The projected hydropower potential exhibits an increase of up to 15.9% and 17.3% under SSP245 and SSP585, respectively.

Temporal and spatial patterns of hydroclimate variability related to the Pacific Decadal Oscillation in Michigan, USA

ABSTRACT Teleconnections play an influential role in driving atmospheric circulation and hydroclimatic variability at regional and global scales. While hydroclimatic conditions are associated with the Pacific Decadal Oscillation (PDO) throughout North America, to date there has been limited study on the influence of the PDO on the hydroclimate of Michigan. Using statistical analyses, this study quantified the mean monthly streamflow, precipitation and temperature characteristics in climate divisions and stream gage stations in both the Upper and Lower Peninsulas of Michigan during the warm (+) and cool (-) phases of the PDO from 1967 to 2018. Streamflow is greater statewide during PDO+ phases compared to during PDO- phases, and most prominent from August to February. During the peak month of streamflow difference by PDO phase, November, there was a streamflow increase of nearly 26% during PDO+ phases compared to the long-term mean. In relation, mean monthly precipitation increased (decreased) statewide from August to November during PDO+ (PDO-) phases, with decreased (increased) monthly precipitation from December to July. This study advances our understanding of the PDO’s influence on hydroclimatic variability in Michigan, presenting novel results at intra-seasonal scales in this highly impactful and populated region.

Modelling the Impacts of the Changing Climate on Streamflow in Didesa Catchment, Abay Basin, Ethiopia

The Didesa catchment, which is the second largest subbasin of the Abay basin, significantly contributes to the Blue Nile’s outflow. Understanding the dynamics of water availability under the changing climate in such a basin assists in the proper planning of land use and other development activities. This study presents changes in climatic elements such as rainfall, temperature, and evapotranspiration using observation data and regional climate models (RCMs) under two representative concentration pathways (RCPs) for three future periods. We use a calibrated hydrological model to further assess climate change’s effects on streamflow. We select three RCMs and their ensemble’s mean by evaluating their performance with respect to observations. We apply the modified Mann–Kendall test to detect trends in each dataset. The result shows that annual mean maximum and minimum temperatures increase in the catchment for the 2021–2040, 2041–2070, and 2071–2100 periods as compared to baseline (1989–2018) under both RCP4.5 and RCP8.5 scenarios. Annual mean maximum temperature and potential evapotranspiration experienced a significant decreasing trend during the year from 1989 to 2018. Furthermore, there was an increasing trend in annual rainfall from 1989 to 2018, which could be related to the cooling of sea surface temperature over the equatorial Pacific. We detect an increasing trend in temperature in both scenarios and all periods; however, no clear trend pattern is found in rainfall. The result from hydrological model simulations reveals that the mean monthly streamflow slightly increases in the winter season while it decreases during the main rainy season. Further study of detailed weather systems, which affect the subbasin’s climate, is recommended.

Evaluation of land use/land cover effect on streamflow: a case of Robigumero watershed, Abay Basin, Ethiopia

Land use land cover change has an impact on hydrology of the watershed on the Robigumero watershed. The study mainly focused on estimating land use change and stream flow under different land use land cover changes of the Robigumero watershed. Land use land cove maps of 1996, 2006 and 2016 were collected from Ethiopian water irrigation and energy office. The soil and water assessment tool model (SWAT) was used to simulate LULC effects on the streamflow of Robigumero Watershed. The SWAT model performance was evaluated through sensitivity analysis, calibration, and validation. During the study period the land use land cover has changed due to growth in population of the study area. The Agricultural land increased by 22.4% and while grass land & forestland decreased by 17.5 and 5.3% Respectively in the year between 1996 to 2016. The findings of the stream flow simulation were used to assess the seasonal variability in stream flow caused by changes in land use and land cover. Both the calibration and validation result shows very good agreement between observed and simulated stream flow with NSE values of 0.81 and R2 values of 0.83 for calibration and NSE Values of 0.86 and R2 values of 0.87 for validation. The result of this study indicated that mean monthly stream flow were increased by 44.1m3/s for wet season and decreased by 2.3m3/s in dry season over 21 years’ period. In general redaction of agricultural land and increment of forest land on the degraded land reduce stream flow which shows the reduction of soil erosion. Therefore, this study results can be used to encourage different users and policymakers for planning and management of water resources in the Robigumero watershed as well as in other regions of Ethiopia.Graphical abstract

Evaluation of long-term monthly mean streamflow trend in the Mediterranean basins using different methods

Evaluation of long-term monthly mean streamflow trend in the Mediterranean basins using different methods

Simulating future flood risks under climate change in the source region of the Indus River

AbstractPakistan experiences extreme flood events almost every year during the monsoon season. Recently, flood events have become more disastrous as their frequency and magnitude have increased due to climate change. This situation is further worsened due to the limited capacity of existing water reservoirs and their ability to absorb and mitigate peak floods. Thus, the simulation of stream flows using projected data from climate models is essential to assess flood events and proper water resource management in the country. This study investigates the future floods (in near future and far future periods) using the integrated flood analysis system (IFAS) model under the RCP2.6, RCP4.5, and RCP8.5 climate change scenarios. Downscaled and bias corrected climatic data of six general circulation models and their ensemble were used in this study. The IFAS model simulated the stream flow efficiently (R2 = 0.86–0.93 and Nash–Sutcliffe efficiency = 0.72–0.92) in the Jhelum River basin (JRB), Kabul River basin (KRB), and upper Indus River basin (UIRB) during the calibration and validation periods. The simulation results of the model showed significant impact of projected climate change on stream flows that will cause the mean monthly stream flow in the JRB to be lower, while that of the KRB and UIRB to be higher than that of the historical period. The highest flow months are expected to shift from May–June (Jhelum basin) and June–July (Kabul basin) to April–May with no changes in the UIRB. Higher frequencies of low to medium floods are projected in the KRB and UIRB, while the JRB expects fewer flood events. Based on the results from the IFAS model, it is concluded that stream flow in the study area will increase with several flood events.

Forecasting of Streamflow and Comparison of Artificial Intelligence Methods: A Case Study for Meram Stream in Konya, Turkey

The planning and management of water resources are affected by streamflow. The analysis of the sustainability of water resources has used well-grounded methods such as artificial neural networks, used for streamflow forecasting by researchers in recent years. The main aim of this study is to evaluate the performance of various methods for long-term forecasting from the data of the mean monthly streamflows between 1981 and 2017 from the Kucukmuhsine station on the Meram Stream in the Turkish province of Konya. For that reason, the multilayer perceptron (MLP), long short-term memory (LSTM), and adaptive neuro-fuzzy inference system (ANFIS) artificial intelligence techniques were employed in this study for the long-term forecasting of streamflow for 12 months, 24 months, and 36 months. The mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) were used to evaluate the performance of the models developed to make predictions using the data from 1981 to 2017, and the Mann-Whitney test was applied to examine the differences between the actual data from 2018 to 2020 and each model’s forecasted results for those three years. The LSTM model showed superiority based on the values of R2 (calculated as 0.730) and RMSE (lowest value of 0.510), whereas the MLP yielded better prediction accuracy as reflected by the value of MAE (lowest value of 0.519). The ANFIS model did not have the best prediction ability for any of the criteria. In accordance with the Mann-Whitney test results, LSTM and MLP indicated no significant difference between the actual data from 2018 to 2020 and the forecasted values; whereas, there was a significant difference for the ANFIS model at a confidence level of 95%. The results showed that the LSTM model had a better prediction performance, surpassing the MLP and ANFIS models, when comparing mean monthly streamflow forecasts.

Predicting the Effects of Land Use Land Cover and Climate Change on Munneru River Basin Using CA-Markov and Soil and Water Assessment Tool

It is important to understand how changing climate and Land Use Land Cover (LULC) will impact future spatio-temporal water availability across the Munneru river basin as it aids in effective water management and adaptation strategies. The Munneru river basin is one of the important sub-basins of the Krishna River in India. In this paper, the combined impact of LULC and Climate Change (CC) on Munneru water resources using the Soil and Water Assessment Tool (SWAT) is presented. The SWAT model is calibrated and validated for the period 1983–2017 in SWAT-CUP using the SUFI2 algorithm. The correlation coefficient between observed and simulated streamflow is calculated to be 0.92. The top five ranked Regional Climate Models (RCMs) are ensembled at each grid using the Reliable Ensemble Averaging (REA) approach. Predicted LULC maps for the years 2030, 2050 and 2080 using the CA-Markov model revealed increases in built-up and kharif crop areas and decreases in barren lands. The average monthly streamflows are simulated for the baseline period (1983–2005) and for three future periods, namely the near future (2021–2039), mid future (2040–2069) and far future (2070–2099) under Representation Concentration Pathway (RCP) 4.5 and 8.5 climate change scenarios. Streamflows increase in three future periods when only CC and the combined effect of CC and LULC are considered under RCP 4.5 and RCP 8.5 scenarios. When compared to the CC impact in the RCP 4.5 scenario, the percentage increase in average monthly mean streamflow (July–November) with the combined impact of CC and LULC is 33.9% (near future), 35.8% (mid future), and 45.3% (far future). Similarly, RCP 8.5 increases streamflow by 33.8% (near future), 36.5% (mid future), and 38.8% (far future) when compared to the combined impact of CC and LULC with only CC. When the combined impact of CC and LULC is considered, water balance components such as surface runoff and evapotranspiration increase while aquifer recharge decreases in both scenarios over the three future periods. The findings of this study can be used to plan and develop integrated water management strategies for the basin with projected LULC under climate change scenarios. This methodology can be applied to other basins in similar physiographic regions.

Critical role of groundwater discharge in sustaining streamflow in a glaciated alpine watershed, northeastern Tibetan Plateau

As the hydrologic buffering capacity of glaciers diminishes on climate warming, groundwater stored in the glaciated alpine watersheds becomes an important source of streamflow, quantifying the groundwater contribution to this streamflow is significant for better predictions of the impact of rapidly disappearing glaciers on regional water resources. However, the role of groundwater in sustaining streams remains unclear. Here, we selected the upper Shule River Basin (USRB) on the northeast Tibetan Plateau (NETP) as a case to address this knowledge gap through a comprehensive study of geochemistry and stable isotopes data, the application of an end member mixing model and the baseflow hydrograph separation program (HYSEP). Our results indicate that even though the potential sources of streamflow exhibited distinct monthly differences during December 2012‐December 2013, the groundwater was the dominant contributor to streamflow generation in the USRB. The groundwater contributed 45% to 100% of the monthly mean streamflow, and the annual mean value of 70%. By contrast, the glacier-snow meltwater and precipitation only contributed 12% and 18% of the annual discharge. The volume contributed by groundwater was calculated as 9.93 × 108 m3, approximately six times higher than the input of the glacier-snow meltwater (i.e., 1.63 × 108 m3). From 1954 to 2018, the volume of the groundwater discharge to the streamflow in the USRB continuously increased from 4.83 × 108 m3/a (65% of total streamflow) in the 1950s to 10.34 × 108 m3/a (71% of total streamflow) in the 2010s, an increase of 2.14 times. The retreating glacier, as well as increasing precipitation and temperature, were determined to be the main reasons for the increase in groundwater discharge to the streamflow. Our findings suggest that groundwater contribution is more important than was previously thought in the glaciated alpine watersheds on the NETP.

Diversity in the observed functionality of dams and reservoirs

Abstract Knowledge and modeling of the observed functionality of dams and reservoirs are desirable for better water resources management. In this study, we examine the functionality of dams and reservoirs over much of the globe through a hydroclimate assessment over 990 Global Runoff Data Center stations that have at least one dam/reservoir over the corresponding drainage areas and available streamflow records of at least 25 years. To quantify the potential capacity of human disturbance/alteration, annual cumulative maximum storage (CMS) of the dams are computed and then annual potential changes in the residence time of water (PRT; CMS divided by annual mean monthly flow) are assessed. In addition, the Man–Kendall tests for annual maximum, mean, and minimum monthly streamflow, and drainage area-averaged precipitation are conducted. Results show that the size of CMS and the main purpose have an explanatory power of the designed hydrologic response (i.e., flattening of the seasonality) while 6% of dam-affected stations experienced the opposite hydrologic response (intensifying of the seasonality) due to the overwhelming impact of anthropogenic climate change. This study finds that the magnitude of PRT is a potential indicator to identify a considerable impact of dams and reservoirs for the regional hydrologic regime. The findings of this study suggest diversity in the observed functionality of dams and reservoirs, which is still a challenge in global hydrological modeling.

Quantifying the effects of watershed subdivision scale and spatial density of weather inputs on hydrological simulations in a Norwegian Arctic watershed

Abstract The effects of watershed subdivisions on hydrological simulations have not been evaluated in Arctic conditions yet. This study applied the Soil and Water Assessment Tool and the threshold drainage area (TDA) technique to evaluate the impacts of watershed subdivision on hydrological simulations at a 5,913-km2 Arctic watershed, Målselv. The watershed was discretized according to four TDA scheme scales including 200, 2,000, 5,000, and 10,000 ha. The impacts of different TDA schemes on hydrological simulations in water balance components, snowmelt runoff, and streamflow were investigated. The study revealed that the complexity of terrain and topographic attributes altered significantly in the coarse discretizations: (1) total stream length (−47.2 to −74.6%); (2) average stream slope (−68 to −83%); and (3) drainage density (−24.2 to −51.5%). The spatial density of weather grid integration reduced from −5 to −33.33% in the coarse schemes. The annual mean potential evapotranspiration, evapotranspiration, and lateral flow slightly decreased, while areal rainfall, surface runoff, and water yield slightly increased with the increases of TDAs. It was concluded that the fine TDAs produced finer and higher ranges of snowmelt runoff volume across the watershed. All TDAs had similar capacities to replicate the observed tendency of monthly mean streamflow hydrograph, except overestimated/underestimated peak flows. Spatial variation of streamflow was well analyzed in the fine schemes with high density of stream networks, while the coarse schemes simplified this. Watershed subdivisions affected model performances, in the way of decreasing the accuracy of monthly streamflow simulation, at 60% of investigated hydro-gauging stations (3/5 stations) and in the upstream. Furthermore, watershed subdivisions strongly affected the calibration process regarding the changes in sensitivity ranking of 18 calibrated model parameters and time it took to calibrate.

Arc SWAT Model Integrated with Arc GIS - Based Evaluation of Land Use /Land Cover Change on the Hydrological Response of Muga Watershed, Abbay Basin, Ethiopia

The study has shown the integration of Arc SWAT with Arc GIS and remote sensing tool are helpful analyze and evaluate both spatial and temporal land use/cover dynamics. It has also shown that Arc SWAT is an effective tool in analyzing the impacts of land use/cover changes on stream flow in areas with limited readily available data. This study is mainly focusing on the investigation of the impacts of land use / land cover changes on the stream flow of Muga watershed which is located in the East Choke Mountains watersheds, Upper Abbay Basin, East Gojjam Zone, Amhara Regional state, Ethiopia. Soil and Water Assessment Tool (SWAT) model were used it investigate the impact of land cover change on the stream flow. For this study SWAT Simulation is used in identifying the most vulnerable sub basins to the stream flow and sediment load changes of Muga watershed. The model was calibrated and validated using historic Stream flow data. The model was calibrated using stream flow data from 1993 to1998, validated from 1999 to2002. The R 2 and NSE values were used to examine model performance and the result indicates 0.81 and 0.87 to R 2 and 0.80 and 0.86 to NSE during calibration and validation respectively. The result of this analysis indicated that the mean monthly stream flow for wet months had increased by 17.75m 3 /s while the dry season decreased by 12.76m 3 /s during the 1995-2013 period due to the land use and land cover change. The highest annual surface runoff was attributed by sub basin 5 whereas sub basin 6 contributes the highest ground water respectively for 1995, 2003 and 2013 land cover maps. In terms of sediment yield, sub basin 1 contributes a maximum load for the study periods. Keywords : Arc SWAT, Arc GIS, Land Use /land cover change, Muga Watershed DOI: 10.7176/CER/13-2-01 Publication date: April 30 th 2021

Predicting mean annual and mean monthly streamflow in Colorado ungauged basins

AbstractRiver managers often need estimates of streamflow for ungauged streams. These estimates can be used in water rights acquisitions, in‐stream flow management, habitat assessment, water quality planning, and stream hazard identification. This publication describes new regression models for predicting mean annual and mean monthly streamflow in Colorado. Unlike previous regional regression studies, the new models incorporate snow persistence (SP), the fraction of time a watershed remains snow covered. Models were developed using streamflow data from 131 watersheds with drainage areas <1,500 km2, no transbasin diversions, and <10% urban area. In addition to SP, topographic, climate, geologic, and hydrologic region variables were used in model predictions. All new models had very good performance, with <6% absolute bias and stronger performance compared to current regional regression models in StreamStats. The mean annual model had the strongest performance, with Nash‐Sutcliffe efficiency coefficient (NSE) of 0.93 and <2% absolute bias. Mean monthly models had best performance during snowmelt runoff months (May‐Jul; NSE ≥0.79; absolute % bias ≤ 4) and weaker performance during low flow months (Aug‐Apr; NSE ≥ 0.59; % bias ≤ 5). Tests of the mean annual model using decadal average streamflow from 1910s to 2000s show very good performance (NSE > 0.75), but predictions were biased low by 14–28% in wetter decades. All equations and coefficients needed to run the models are presented in the publication appendix, and the associated data release includes the spatial data and model code, which can be applied using R or within an R‐based Shiny web app.

Impact of land use/land cover change on stream flow in the Shaya catchment of Ethiopia using the MIKE SHE model

The impact of land use/land cover (LULC) change on the stream flow of the Shaya catchment was assessed by employing the MIKE SHE hydrological model. The digital elevation model, soil, LULC, climate, and stream flow data were used as model inputs. The LULC was analyzed from different years of satellite imageries using ERDAS IMAGINE software package and its accuracy was verified following the standard procedures. Trend and shift detection analysis were also conducted to study the effect of rainfall on stream flow using the Mann–Kendall (MK) and Pettit tests, respectively. The result showed that there is a significant expansion of settlement and agricultural area, while there was a decrease of bare land, forest, and bush land in the catchment. In contrary, rangeland was increased from the year 1987 to 2000 and then decreased from 2000 to 2015. The performance of the MIKE SHE hydrological model was very good both during model calibration and validation periods. It has been observed that rainfall did not show any significant trends and shifts, but the stream flow was increased significantly in the two decades. In this study, the effects of LULC change on the stream flow in the study area have been distinguished: the mean monthly stream flow was increased during the wet season, while it was decreased during the dry season. Therefore, we recommend concerned bodies to monitor and take appropriate control of the LULC changes, especially settlement and deforestation, before it makes acute problems of the hydrologic imbalance in the catchment.

Quantifying uncertainty sources in extreme flow projections for three watersheds with different climate features in China

Extreme flow projections are of considerable necessity for flood and drought disaster prevention and sustainable water resources management to adapt to future climate change. However, these projections are usually associated with large uncertainties arising from different sources. A numerical modeling system including three emission scenarios (ESs), four climate models (CMs), four statistical downscaling (SD) methods, four hydrological modeling (HM) schemes, and four probability distribution (PD) functions was established in this study to project future extreme flow changes at basin scales. This modeling system was employed in the following three watersheds with different climatic and topographical characteristics in China: The Upper Ganjiang River basin (UGRB) in the humid region, the Laoha River basin (LRB) in the semi-arid region, and the Yellow River source region (YRSR) in the semi-humid region. Subsequently, the analysis of variance approach (ANOVA) was used to quantify the contribution of different uncertainty sources in extreme flow projections. Results show that extreme high flow was projected to increase in all basins. By contrast, extreme low flow would decrease in UGRB, and inconsistent change signals were found in LRB and YRSR. The ANOVA-based assessment reveals that CM is generally the dominant uncertainty source for mean monthly streamflow and extreme low flow projections. ES explains a large proportion of total uncertainty for extreme low flow projection in the 2060s in UGRB because of the large difference in non-precipitation frequency projected under different ESs. SD is the main uncertainty source in extreme high flow projection in UGRB where extreme precipitation events frequently occur. Compared with other basins, HM produces relatively high uncertainty in LRB due to its inferior performance in historical streamflow simulations. PD contributes a low percentage of uncertainty in extreme flow projections. Interactions among uncertainty sources even produce larger uncertainties than most individual sources. Although future extreme flow projections are associated with non-negligible uncertainties, the projected remarkable changes in extreme high and low flows indicate that stakeholders should take certain measures to mitigate flood risks in the three basins and alleviate drought risks in UGRB

Impact of climate change on streamflow regime of a large Indian river basin using a novel monthly hybrid bias correction technique and a conceptual modeling framework

The study analyses the impact of climate change on streamflow regime of the Mahanadi River basin (MRB) in projected climate scenarios obtained from CMIP5 models. Projections from a total of 9 Global Climate Models (GCMs) were utilized. Integrated MIKE 11 NAM-HD, a conceptual hydrological model was employed to simulate the streamflow at Hirakud and Mundali gauging sites of upper Mahanadi River basin (UMRB) and middle Mahanadi River basin (MMRB), respectively. Prior to generating the streamflow regimes for the projected scenarios, GCM simulated precipitation and temperature were bias corrected using India Meteorological Department (IMD) observed gridded precipitation and temperature datasets. Precipitation was corrected using quantile mapping technique employing four different approaches, i.e., seasonal, hybrid, monthly non-hybrid, and monthly hybrid approaches. Hybrid approaches explicitly correct the extreme rainfall using Generalized Extreme Value (GEV) distribution whereas Gamma distribution is used for normal rainfall. Temperature data was corrected using Gaussian distribution on daily basis employing monthwise time series. L-moments based frequency analysis method was used to estimate the 50-year return period rainfall for observed and bias corrected projected time series to assess the efficacy of bias correction methods in correcting the extreme events. Monthly hybrid approach was found to resolve rainfall climatology over both UMRB and MMRB with improved skill in extreme rainfall correction. Though, monthly non-hybrid approach produced the seasonal cycle of rainfall with highest accuracy, it performed poorly in correcting the extremes. Under projected climate scenarios mean annual rainfall is found to be increasing for BCC-CSM1.1(m), HadGEM2-AO, GFDL-CM3, and IPSL-CM5A-LR whereas other GCMs show mixed signal compared to their mean state of historic period (1976–2005). Streamflow regime in projected climate was analyzed using ensemble mean of simulated streamflow from identified GCMs. Mean monthly streamflow were overall found to be increasing towards the end of the century i.e., in far-future defined as 2070–2099. Daily high flows (defined at 5% and 10% exceedance probability) were found to be increasing in their magnitude and frequency in far-future whereas occurrences of low flows (defined at 90% and 95% exceedance probability) were found to be predominantly decreasing under projected climate.

Evaluating the Effects of Watershed Characteristics on River Flow for the Case of Fetam River, Ethiopia

Evaluating the effects of watershed characteristics have impacted on the stream flow of the watershed by changing the magnitude of surface runoff and ground water flow. This study is mainly focusing the effects of watershed characteristics on the stream flow by changing SURQ and GWQ for the wet months (June, July, August) and dry months (January, February, March) through satellite Remote Sensing (RS) and Geographic Information System (GIS) integrated with the SWAT model, climate characteristics on stream flow, slope and rainfall effects on stream flow. ArcGIS used to generate land use and cover maps from Landsat TM and ETM+ acquired, respectively, in 1995, 2005 and 2015. The result of this analysis showed that the cultivated land has expanded during the study period of 1975-2002. Using the three generated land cover maps, three SWAT models set up were run to evaluate the effects of watershed characteristics on the stream flow of the study area. The performance of the SWAT model was evaluated through sensitivity analysis, calibration, and validation. Ten flow parameters were identified to be sensitive for the stream flow of the study area and used for model calibration. The model calibration was carried out using observed stream flow data from 1975 to 1993 and a validation period from 1993 to 2002. Both the calibration and validation results showed good match between measured and simulated stream flow data with the coefficient of determination (R2) of 0.89 and Nash-Sutcliffe efficiency (ENS) of 0.78 for the calibration, and R² of 0.91 and ENS of 0.88 of the validation period. The result of this analysis indicated that the mean monthly stream flow increased by 21.92m³/s for the wet months while for the dry months decreased by 13.1 m³/s. Generally, the analysis indicated that flow during the wet months has increased, while the flow during the dry months decreased. The SURQ increased, while GWQ decreased from 1975 to 2002 due to the increment of cultivated lands. The model results showed that the stream flow characteristics changed due to the land cover changes during the study period.

Identifying how future climate and land use/cover changes impact streamflow in Xinanjiang Basin, East China

Climate and land use/cover changes are the main factors altering hydrological regimes. To understand the impacts of climate and land use/cover changes on streamflow within a specific catchment, it is essential to accurately quantify their changes given many possibilities. We propose an integrated framework to assess how individual and combined climate and land use/cover changes impact the streamflow of Xinanjiang Basin, in East China, in the future. Five bias-corrected and downscaled General Circulation Model (GCM) projections are used to indicate the inter-model uncertainties under three Representative Concentration Pathways (RCPs). Additionally, three land use/cover change scenarios representing a range of tradeoffs between ecological protection (EP) and urban development (UD) are projected by Cellular Automata - Markov (CA-Markov). The streamflow in 2021–2050 is then assessed using the calibrated Soil and Water Assessment Tool (SWAT) with 15 scenarios and 75 possibilities. Finally, the uncertainty and attribution of streamflow changes to climate and land use/cover changes at monthly and annual scale are analyzed. Results show that while both land use/cover change alone and combined changes project an increase in streamflow, there is a disagreement on the direction of streamflow change under climate change alone. Future streamflow may undergo a more blurred boundary between the flood and non-flood seasons, potentially easing the operation stress of Xinanjiang Reservoir for water supply or hydropower generation. We find that the impacts of climate and land use/cover changes on monthly mean streamflow are sensitive to the impermeable area (IA). The impacts of climate change are stronger than those induced by land use/cover change under EP (i.e., lower IA); and land use/cover change has a greater impact in case of UD (i.e., higher IA). However, changes in annual mean streamflow are mainly driven by land use/cover change, and climate change may decrease the influence attributed to land use/cover change.