Hydrological Model Simulations Research Articles

Enhancing real-time flood forecasting and warning system by integrating ensemble techniques and hydrologic model simulations

ABSTRACT Flooding poses a severe threat to communities and infrastructure worldwide, which requires advanced flood forecasting warning systems. In this research paper, a real-time flood forecasting and warning system for the Dharoi Dam in the state of Gujarat, India is developed. This novel system combines ensemble techniques and hydrological modeling simulations to enhance flood prediction accuracy and provides a timely warning. The study focuses on critical gaps in the current flood forecasting capabilities, recognizes the need for improved flood management in the region, and builds upon the existing research conducted globally and in India. The real-time flood forecasting and warning system uses information from various sources such as rainfall, river flows, and water level observations. The system enhances the accuracy of flood forecasts with a 1–5-day lead time by utilizing ensemble techniques, which incorporate multiple models and their corresponding forecasts. The 2-day and 3-day lead times combined with postprocessing techniques yield excellent results, as evidenced by the reservoir inflow correlation value of 0.86 and the receiver operating characteristic-area under the curve (ROC-AUC) value of 0.93. This work aims to reduce the impact of floods in this region and can be used by decision-makers as a disaster management tool.

Street Community-Level Urban Flood Risk Assessment Based on Numerical Simulation

Urban waterlogging is a serious urban disaster, which brings huge losses to the social economy and environment of the city. As an important means of urban rainfall inundation analysis, numerical simulation plays an important role in promoting the risk assessment of urban waterlogging. Scientific and accurate assessment of waterlogging disaster losses is of scientific significance for the formulation of disaster prevention and mitigation measures and the guidance of post-disaster recovery and reconstruction. In this study, the SCS-CN hydrological model and GIS coupling numerical simulation method were used to simulate the inundation of urban waterlogging under four different rainfall return periods and to realize the visualization of the inundation range and waterlogging depth in Zhengzhou. At the same time, based on the numerical simulation results, the building is used as the basic assessment unit to construct a refined assessment framework for urban waterlogging risk at the street community level based on hazard, exposure, and vulnerability analysis. The refined risk assessment results have an important reference value for optimizing the working ideas of waterlogging control and providing a reference for local management departments to effectively deal with waterlogging disasters.

Enhancing the streamflow simulation of a process-based hydrological model using machine learning and multi-source data

Streamflow simulation is crucial for flood mitigation, ecological protection, and water resource planning. Process-based hydrological models and machine learning algorithms are the mainstream tools for streamflow simulation. However, their inherent limitations, such as time-consuming and large data requirements, make achieving high-precision simulations challenging. This study developed a hybrid approach to simultaneously improve the accuracy and computational efficiency of streamflow simulation, which integrates Block-wise use of the TOPMODEL (BTOP) model into the eXtreme Gradient Boosting (XGBoost), i.e., BTOP_XGB. In this approach, BTOP generates simulated streamflow using the Latin hypercube sampling algorithm instead of the time-consuming calibration algorithms to reduce computational costs. Then, XGBoost combines BTOP simulated streamflow with multi-source data to reduce simulation errors. In which, serval input variable selection algorithms are employed to choose relevant inputs and remove redundant information for model. The hybrid approach is validated and compared with a standalone model at three hydrological stations in the Jialing River basin, China. The results show that the performance of BTOP_XGB is significantly better than the BTOP and XGBoost models. The NSE of BTOP_XGB at Beibei, Xiaoheba, and Luoduxi stations increases by 54%, 21%, and 83%, respectively. Meanwhile, the computational time of BTOP_XGB is saved by >90% compared to the original calibrated BTOP. BTOP_XGB is less affected by parameter sample sizes and data amounts, demonstrating the robustness of the hybrid model. This study simplifies the complexity of the hydrological model and enhances the stability of machine learning, jointly improving the reliability of streamflow simulation. The hybrid approach provides a potential shortcut for streamflow simulation over basins with large areas or limited observed data.

Understanding the association between global forest fire products and hydrometeorological variables

Climate change and anthropogenic activities have influenced the frequency and magnitude of forest fires both globally and regionally. While skilful short- to extended-range prediction of forest fires is essential for effective mitigation in local communities, it is also important to identify the implications of forest fires on different sectors, including water resources and sustainable development. Limited studies have investigated the association between forest fires and hydrometeorological variables at the regional scale in developing countries due to the lack of necessary datasets, which can now be leveraged using the newly hosted global reanalysis of fire danger indices (referred to as fire indices). The current study presents a comprehensive analysis of the spatio-temporal variations of eight fire indices across India, as well as their association with hydro-meteorological variables, such as precipitation, temperature, and the streamflow of a major river basin (Mahanadi) in India. The accuracy of these indices in capturing real fire events and the potential benefit of incorporating fire indices into long-term hydrologic simulations are also explored. The results show that fire indices can accurately yield fire seasons (i.e., post-monsoon and summer) in India. Furthermore, forest fires are found to be strongly associated with hydro-meteorological variables, typically resulting in low streamflow regimes. Fire indices can also capture actual fire events, maintaining high scalar accuracy. Finally, an improvement in uncalibrated hydrologic model simulations is observed when simulated streamflow is post-processed using the fire indices as predictors. Overall, the current study has valuable implications for fire indices forecasting and hydrologic simulations in ungauged basins.

A process-driven deep learning hydrological model for daily rainfall-runoff simulation

Although deep learning (DL) models, especially long-short-term memory (LSTM), demonstrate greater accuracy than process-based models in rainfall-runoff simulation, the predictions from process-based models are more physical than DL models. The main reason is that DL models have almost no process understanding capabilities like process-based models beyond their fitting capability. In this study, we developed a process-driven DL model under a unified DL architecture to improve the process awareness of DL models. To implement the model, a conceptual hydrological model (EXP-HYDRO) is implanted into a recurrent neural network (RNN) cell as a process driver for providing multi-sub-process variables related to the runoff process, and an Entity-Aware LSTM (EA-LSTM) cell is incorporated as a post-processor layer, resulting in the Process-driven RNN-EA-LSTM (PRNN-EA-LSTM). Under the assistance of the process driver, the model performance of PRNN-EA-LSTM on the 531 catchments from the Catchment Attributes and Meteorology for Large-sample Studies dataset is more robust than the pure DL model, and better than using only EXP-HYDRO as the input of EA-LSTM (i.e., EXP-HYDRO-EA-LSTM). Specifically, the median Nash-Sutcliffe efficiency (NSE) of PRNN-EA-LSTM in local and regional simulation is 0.03 and 0.02 higher than LSTM and 0.01 higher than EXP-HYDRO-EA-LSTM. Additionally, PRNN-EA-LSTM significantly enhances the low flow simulations and reduces the catchments number with negative NSE. This study demonstrates that process-based models can help DL models better represent the rainfall-runoff relationship under a unified architecture. Consequently, integrating the adaptability of process-based models into the DL architecture is anticipated to bolster the process understanding capabilities of DL models.

Validation of the MGB-IPH hydrological model for flows simulation in paired watershed in minas gerais, Brazil.

Simulating hydrological behavior makes it possible to analyze and predict water availability in river basins, anticipate potential events and carry out quantitative water balances using various scenarios. These scenarios arise from changes in land use and landscape over the years, the occurrence of extreme events and the effects of climate change. In this context, the aim of this study was to evaluate the effectiveness of the Large Basin Hydrological Model (MGB-IPH) for representing rainfall-runoff in the paired watershed of the Piracicaba River and Piranga River in the state of Minas Gerais, Brazil, using the usual calibration and validation procedures. The results of the average statistical performance indices NASH, NASH-Log, BIAS, R², D and Dr showed that the model was able to simulate the seasonality of the watersheds and presented good adjustments, with a tendency to overerestimate the flow, but within acceptable limits. It is concluded that the model performed satisfactorily in its ability to simulate the hydrological behaviour of the Piracicaba River and Piranga River watersheds, despite its limitations and the lack of input data, making it an applicable tool in local contexts.

Modeling climate change impacts on blue and green water of the Kobo-Golina River in data-scarce upper Danakil basin, Ethiopia

Study regionKobo-Golina River, Upper Danakil Basin, Ethiopia. Study focusesIt is crucial to understand the spatiotemporal distribution of blue water (BW) and green water (GW) for optimal use of water resources, especially in data-scarce regions. This study aims to evaluate the extent to which future climate is changing, and its impact on blue-green water resources in the study area. Projected changes were predicted based on the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models (GCMs) for three future periods (2015–2044, 2045–2075, 2076–2100) under two shared socio-economic pathways (SSP2–4.5 & SSP5–8.5). Compromise programming technique was employed to rank and select best performing GCMs. The multi-variable calibrated SWAT+ model was forced with climate projections from the top-ranked CMIP6 GCMs ensemble to simulate projected blue water (BW) and green water (GW) in the study area. New hydrological insights for the regionCompared to the baseline period (1984–2014), blue water declined while green water exhibited an increasing trend in all future periods under two SSPs. It is also noted that the spatial distribution of BW and GW remains uneven in the study area. Precipitation significantly impacted BW than GW resources. This study provides valuable insights into the utilization of the recent CMIP6 Global Climate Model coupled with multi-variable calibrated SWAT+ hydrological models for better simulation of Blue-Green water in data-scarce basins under changing climate.

Global prediction of extreme floods in ungauged watersheds

Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks1. Accurate and timely warnings are critical for mitigating flood risks2, but hydrological simulation models typically must be calibrated to long data records in each watershed. Here we show that artificial intelligence-based forecasting achieves reliability in predicting extreme riverine events in ungauged watersheds at up to a five-day lead time that is similar to or better than the reliability of nowcasts (zero-day lead time) from a current state-of-the-art global modelling system (the Copernicus Emergency Management Service Global Flood Awareness System). In addition, we achieve accuracies over five-year return period events that are similar to or better than current accuracies over one-year return period events. This means that artificial intelligence can provide flood warnings earlier and over larger and more impactful events in ungauged basins. The model developed here was incorporated into an operational early warning system that produces publicly available (free and open) forecasts in real time in over 80 countries. This work highlights a need for increasing the availability of hydrological data to continue to improve global access to reliable flood warnings.

Modeling run-off flow hydrographs using remote sensing data: an application to the Bashar basin, Iran

Abstract Precipitation is hard to access in countries like Iran, due to inadequate number of rain gauge stations. Remote sensing provides an alternative source of rainfall estimation. In this study, the effectiveness of the HEC-HMS model was evaluated using GPM (Global Precipitation Measurement Mission) satellite and rain gauge station data. The model was calibrated and validated using 5 flood events' data of a hydrometric station at the outlet of Bashar basin. Important flood parameters including peak discharge (QP), flood volume (V) and time of concentration (TC) were used to evaluate and compare the application of satellite and ground station data in the model, using various statistical indices. The accuracy of QP and V estimations using rain gauge data was higher than those obtained by satellite data. However, the difference between mean relative error (MRE) in QP estimation was about 1% (9.9% and 10.6% for rain gauge and satellite data, respectively). Conversely, higher accuracies were met for TC estimation using satellite (with MRE 9.1% and 10.2% for GPM and rain gauge data, respectively). Such results imply the sole utilization of satellite precipitation data is reliable for modeling hydrological key parameters, which can be helpful in areas with limited ground station coverage.

Shift in the quantitative relationship between sedimentary records and catchment floods influenced by dam construction

Interpretation of sedimentary records is a crucial method for studying paleo-floods. However, the response intensity of sedimentary records to floods may be altered by reservoir storage. Therefore, depicting flood intensity in sedimentary records and alterations in the response relationship is the primary challenge in paleo-flood reconstruction. This study used the Yalu River catchment–estuary–coast as the research area and quantitatively reconstructed the response relationship between sediment grain size composition and flood intensity. This was conducted through a combination of forward simulation of the HYDROTREND hydrological model and reconstruction of sedimentary records, as well as evaluating the impact of reservoir regulation on the quantitative relationship. The findings showed that the lower Yalu River channel and tidal flat were both efficient at preserving flood records. However, there were significant differences in the response of different geomorphic units to floods. The river channel showed a significant increase in the coarse-sensitive component, whereas the tidal flat exhibited a substantial increase in the fine-sensitive component. Further study showed that there was a significant logarithmic relationship between the content of the fine-sensitive fraction and flood intensity. The sensitivity of the fine-sensitive fraction towards small floods was much higher than that toward extreme floods. In addition, reservoir construction caused a significant shift in the relationship between flood intensity and flood-related sensitive sediment fractions. Consequently, the reconstructed flood intensities of the pre-dam construction showed diverse levels of magnification based on the quantitative relationship between the sedimentary record and flood intensity of the post-dam. Meanwhile, this shift led to an increase in flood intensity corresponding to the critical value of the flood record that could be detected: from floods with a return period exceeding two years before dam construction to floods with a return period exceeding five years after dam construction. This study has quantitatively reconstructed the relationship between sediment grain size fractions and flood intensity and assessed the impact of reservoir construction on the quantitative relationship. It has also provided new ideas for more accurate reconstruction of paleo-flood intensity changes in the future.

Modelling the future climate impacts on hydraulic infrastructure development in tropical (peri-)urban region: Case of Kigali, Rwanda

The current global climate has shown a significant change, mostly resulting from human-induced activities. Frequent experiences of extreme rainstorms, deadly landslides, and floods followed by the destruction of roads, bridges, drainage, buildings, agriculture, and other infrastructures have been appearing across the globe along with extensive socio-economic effects including human lives losses whereby tropical Africa is among the greatly affected regions. Several studies in the region acclaim the increase of climate-related extremes due to a gradual climate variation. Hence, this study aimed to evaluate how existing water structures might respond to the future climate, which is getting more severe and frequent in the region. The study was conducted on the Nyabugogo River catchment (NRC), covering a huge part of the Kigali metropolitan area. It was carried out through a downscaled global climate model (CMIP6 GCM) projection coupled with a joint SWAT + hydrological model and HEC-RAS hydrodynamic simulation. The study showed that the annual precipitation in Kigali might keep increasing, resulting in increased risks of extreme weather events. The study identified up to 38% (+514.9 mm) annual precipitation increment, which resulted in more than a doubled flow rate (+28.0 m3/s) increment by the end of the century under a high greenhouse gas emission scenario (ssp585). As a result, hydrodynamic simulations revealed that the Bridge-1 in NRC might fail to accommodate the 50-year return peak storm under ssp585. Henceforth, there is a need to adopt high GHG emission scenarios in critical infrastructure development. Further, enforcing green-grey infrastructures in flood risk-low resilient areas is recommended to improve climate resilience. Thus, the results of this study might prove useful in climate-resilient infrastructure development and other pre-emptive adaptation practices, most importantly building anticipated resilience against climate-related hazards.

Refined analysis of flood-regional composition under changing environment in the middle reach of Hanjiang River

To investigate the flood-regional composition under changing environmental conditions in the middle reach of Hanjiang River (MHR), a data-driven hydrological simulation model and its related quantitative methods were developed. The flood-regional composition of Huangzhuang(HZ) in the MHR was quantitatively analyzed, and the influence of environmental changes on river flood routing was discussed. The primary research findings are as follows: ① A hydrological simulation model based on support vector regression machine (SVRM) is constructed to simulate the daily average flow process of HZ from 1965 to 2021. The Nash-Sutcliffe Efficiency (NSE) coefficient achieved values above 0.95, and the overall relative error (RE) was within ± 1 %, indicating excellent simulation performance. ② A quantitative analysis method has been proposed to identify the composition of flood areas. The results indicate that the upper reach of Hanjiang River (UHR) is the primary contributor to floods in the MHR, accounting for 60.62 % to 78.05 % of the total. The Tangbai River (TR) contributed between 14.1 % and 27.4 %, whereas the Nan River had a smaller contribution of only 6.83 % to 8.85 %. ③ Trend analysis indicates that the proportion of floods originating from the UHR increases in the summer flood season and decreases in the autumn flood season, while those changes of TR and Nanhe River (NR) are coincidental, especially in the autumn flood season, the proportion of floods in the TR increases significantly. The impact of floods from the UHR and TR cannot be ignored when implementing flood control measures. ④ A comprehensive analysis method has been proposed to quantify the integrated impacts of environmental changes. The results show that environmental changes had a relatively minor impact on flood routing and its flood-regional composition. However, they did affect the flood propagation process, resulting in earlier occurrences in peak flow, increased in peak discharge, and rapid rise and fall of floodwaters for floods exceeding 12,000 m3/s. These research findings provide strong foundational support for designing flood-regional, as well as flood control and disaster reduction systems in the MHR.

Hydrological Simulation Study in Gansu Province of China Based on Flash Flood Analysis

The calibration and validation of hydrological model simulation performance and model applicability evaluation in Gansu Province is the foundation of the application of the flash flood early warning and forecasting platform in Gansu Province. It is difficult to perform the simulation for Gansu Province due to the fact that it covers a wide range, from north to south, with multiple climate types and diverse landforms. The China Flash Flood Hydrological Model (CNFF) was implemented in this study. A total of 11 model clusters and 289 distributed hydrological models were divided based on hydrology, climate, and land-use factors, among others. A spatiotemporally mixed runoff method and the Event-Specific Geomorphological Instantaneous Unit Hydrograph (GIUH) were applied based on large-scale fast parallel computation. To improve model calibration and validation efficiency, the RSA method (Regionalized Sensitivity Analysis) was used for CNFF model parameter sensitivity analysis, which could reduce the number of model parameters that need to be adjusted during the calibration period. Based on the model sensitivity analysis results, the CNFF was established in Gansu Province to simulate flood events in eight representative watersheds. The average NSE, REQ, and ET were 0.76 and 0.73, 9.1% and 12.6%, and 1.2 h and 1.7 h, respectively, in the calibration and validation period. In general, the CNFF model shows a good performance in multiple temporal and spatial scales, thus providing a scientific basis for flash flood early warning and analysis in Gansu Province.

Projected increase in widespread riverine floods in India under a warming climate

Widespread floods simultaneously affect several subbasins in a large river basin and pose severe challenges to disaster management and flood mitigation efforts. India is highly susceptible to widespread flooding as the country receives more than 70% of annual rainfall in the four-month period from June to September. However, the potential impacts of climate change on the drivers and frequency of widespread floods in India remain unexplored. We use observations, climate model simulations, and a hydrological model to examine the drivers and changes in widespread floods under a projected future climate. The observed increase in extreme precipitation does not translate to flooding in most river basins in India due to its fragmented nature. Translation of extreme precipitation to flood depends on the catchment area. We examined the changes in the widespread floods in Indian river basins using a hydrological model and climate projections. Robust increases in intense precipitation area and frequency are projected under the warming climate. Furthermore, hydrological model simulations indicate a profound increase in widespread floods in Indian river basins under a warming climate. The increase in widespread floods in a warming climate can pose challenges for flood mitigation and management in the future. Consideration of spatial extent of extreme precipitation and catchment area can enhance flood monitoring and prediction efforts. In addition, increases in extreme precipitation may not always lead to rise in flooding.

Model and remote-sensing-guided experimental design and hypothesis generation for monitoring snow-soil–plant interactions

In this study, we develop a machine-learning (ML)-enabled strategy for selecting hillslope-scale ecohydrological monitoring sites within snow-dominated mountainous watersheds, with a particular focus on snow-soil–plant interactions. Data layers rely on spatial data layers from both remote sensing and hydrological model simulations. Specifically, a Landsat-based foresummer drought sensitivity index is used to define the dependency of the annual peak plant productivity on the Palmer drought severity index in the early growing season. Hydrological simulations provide the spatiotemporal dynamics of near-surface soil moisture and snow depth. In this framework, a regression analysis identifies the key hydrological variables relevant to the spatial heterogeneity of drought sensitivity. We then apply unsupervised clustering to these key variables, using the Gaussian mixture model, to group hillslopes into several zones that have divergent relationships regarding soil moisture, snow dynamics, and drought sensitivity. Using the datasets collected in the East River Watershed (Crested Butte, Colorado, United States), results show that drought sensitivity is significantly correlated with model-derived soil moisture and snow-free timing over space and time. The relationship is, however, non-linear, such that the correlation decreases above a threshold elevation and in a heavy snow year due to large snowpacks, lateral flow, and soil storage limitations. Clustering is then able to define the zones that have high or low sensitivity to drought, as well as the mid-elevation regions where sensitivity is associated with the topographic aspect and net potential radiation. In addition, the algorithm identifies the most representative hillslopes with road/trail access within each zone for installing monitoring sites. Our method also aims to significantly increase the use of ML and model-simulation results to guide critical zone and watershed monitoring activities.

Evaluation of the ecohydrological separation of different geomorphological units in a semi-arid area

The ecohydrological separation (ES) phenomenon which states that there are two incompletely mixing subsurface water pools, one of which is supplied to plants for transpiration while the other flows to groundwater and river water, has far-reaching implications for hydrological model simulation and water resources management. Specifically, it is unclear whether it occurs under different geomorphological units (hillside, foothill, and floodplain) within the same climate zone, as well as the reasons for such variations. We collected samples of precipitation, groundwater, river water, soil water, and plant stem water from April to October 2019 and analyzed their stable isotopic composition. Moreover, in order to further confirm whether the ES phenomenon exists in both hillside, foothill, and floodplain, we also calculated the line-conditioned excess and the isotope values of source water. The isotope values of plant stem water were similar to that of soil water rather than groundwater on the hillside. Groundwater on the foothill in April, June, July, and August had considerably different isotope values from plant stem water. The isotopic composition of plant stem water was comparable to that of groundwater in the floodplain, but there were differences in the isotope values of source water and line-conditioned excess. On the hillside, foothill, and floodplain, this study found evidence of the ES phenomenon. As a result of preferential flows through soils that cause soil-water heterogeneity, the ES phenomenon under various geomorphic units differs from one another. This research disproved theories about translational flow across the rooting zone and offered a new perspective on the ES phenomenon.

Global groundwater droughts are more severe than they appear in hydrological models: An investigation through a Bayesian merging of GRACE and GRACE-FO data with a water balance model

Realistic representation of hydrological drought events is increasingly important in world facing decreased freshwater availability. Index-based drought monitoring systems are often adopted to represent the evolution and distribution of hydrological droughts, which mainly rely on hydrological model simulations to compute these indices. Recent studies, however, indicate that model derived water storage estimates might have difficulties in adequately representing reality. Here, a novel Markov Chain Monte Carlo - Data Assimilation (MCMC-DA) approach is implemented to merge global Terrestrial Water Storage (TWS) changes from the Gravity Recovery And Climate Experiment (GRACE) and its Follow On mission (GRACE-FO) with the water storage estimations derived from the W3RA water balance model. The modified MCMC-DA derived summation of deep-rooted soil and groundwater storage estimates is then used to compute 0.5∘ standardized groundwater drought indices globally to show the impact of GRACE/GRACE-FO DA on a global index-based hydrological drought monitoring system. Our numerical assessment covers the period of 2003–2021, and shows that integrating GRACE/GRACE-FO data modifies the seasonality and inter-annual trends of water storage estimations. Considerable increases in the length and severity of extreme droughts are found in basins that exhibited multi-year water storage fluctuations and those affected by climate teleconnections.

Snowmelt‐Radiation Feedback Impact on Western U.S. Streamflow

AbstractOngoing runoff declines in the Colorado River Basin have been shown to be predominately driven by decreasing albedo from warming‐driven snow‐cover loss, especially in late‐spring (hereafter snowmelt‐radiation feedback). Here, we explore the feedback's impact on annual runoff sensitivity to warming across the western U.S. (WUS) using hydrologic model simulations. For 1°C uniform warming, we show that runoff is most sensitive to warming in modestly snow‐covered, interior mountain headwaters, especially the Rocky Mountains. Runoff sensitivities are most associated with the snowmelt‐radiation feedback in basins with runoff coefficients between 0.2 and 0.6, where runoff sensitivity increases with more snow and lower winter temperature. In aggregate, ∼48% of WUS runoff sensitivity is attributable to the snowmelt‐radiation feedback and is especially pronounced in the warming‐sensitive river basins (annual runoff decreases >5%/°C). We also show that the feedback's impact decreases with increasing temperature, which has unresolved implications for streamflow declines in a less‐snow future.

Impacts of small and medium-sized reservoirs on streamflow in two basins of Southeast China, using a hydrological model to separate influences of multiple drivers

Study regionSoutheast coast of China Study focusThe reservoirs in southeast coast of China are mostly small due to the limitations of terrain. In order to analyze the impact of these densely distributed small and medium-sized reservoirs (SMRs) with lacking observation and operational data on runoff, this paper uses a hydrological model & scenario simulation method to separate the impacts of climate variability (CV), land use change (LUC) and SMRs group change on streamflow variation in two river basins of Southeast China. New hydrological insights for the region(1) The streamflow of two basins has changed greatly, with CV and SMRs change being the main influencing factors, while the impact of LUC was relatively small. The contribution rates of CV to the annual runoff change of three hydrological stations in two river basins were 90.19%, 61.75% and 26.64%; Contribution rates of SMRs changes were 9.34%, 37.24% and 71.04%, correspondingly. (2) The regulation of streamflow by SMRs changes led to a decrease in both annual and monthly runoff, which was due to the fact that the main functions of SMRs in this region are agricultural irrigation and domestic water supply. (3) The three-factor separation method based on hydrological modeling, which separated the cumulative effects of SMRs in the form of SMRs group, would be valuable for the study and management of watersheds with numerous SMRs and lack of observation and operational data.

Water quantity–quality assessment in the transboundary river basin under climate change: a case study

Abstract This study combined hydrological and water quality simulation models with a water resources planning model to project future water supply conditions under the dam construction in the Harirud River, located at the Afghanistan, Turkmenistan, and the Iran border. The sustainability requirements and possible conflicts among riparian countries were assessed under climate change and future development in Afghanistan's upstream. The water quantity and quality of the Doosti Dam Basin on the Harirud River were investigated based on a contemporary time (1955–2015) to predict the future condition (2020–2099). The representative concentration pathway scenarios were applied based on five bias-corrected climate models. Results showed that most areas of the study area experienced an increase in temperature (1.5–3.8°C) and a decrease in precipitation (19–24%). The Doosti Dam inflow decreased by about 70% after the Salma Dam construction, and the reliability and sustainability of agricultural water supply in Iran and Turkmenistan will reduce to less than 3% under the representative concentration pathways 8.5 climate change scenario. The results show that the Doosti Dam is not a reliable source to supply the domestic water demand of Mashhad, the second most important city in Iran.