Hydrological Modelling Results Research Articles

Karst modelling challenge 1: Results of hydrological modelling

The complexity of karst groundwater flow modelling is reflected by the amount of simulation approaches. The goal of the Karst Modelling Challenge (KMC) is comparing different approaches on one single system using the same data set. Thirteen teams with different computational models for simulating discharge variations at karst springs have applied their respective models on one single data set coming from the Milandre Karst Hydrogeological System (MKHS). The approaches include neural networks, reservoir models, semi-distributed models and fully distributed groundwater models. Four and a half years of hourly or daily meteorological input and hourly discharge data were provided for model calibration. The validation comprised forecasting one year of discharge, without the observed discharge data. The model performance was evaluated using the volume conservation, Nash-Sutcliffe efficiency (NSE) and the Kling-Gupta efficiency (KGE) applied on the total discharge and individual flow components. As a result, the comparison of model performances is a challenging task due to the differences in the model architecture but also required time steps: some of the models require aggregated daily steps while others could be run using hourly data, which provided some interesting differences depending on how the data was transformed. The use of instantaneous data (e.g. value at noon) produces less bias that averaging hourly data over one day. The transformation of hourly into daily data produces a decrease of Nash and KGE of 0.05 to 0.08 (i.e. from 1 to ~0.93). The resulting simulations (forecasted values for year 2016) produced KGEs ranging between 0.83 and 0.37 (0.83 to −0.24 for NSE). Although the simulations matched the monitored flows reasonably well, most models struggled to simulate baseflow conditions accurately. In general, the models that performed the best for this exercise were the global ones (Gardenia and Varkarst), with a limited number of parameters, which can be calibrated using automatic calibration procedures. The neural network models also showed a fair potential, with one providing reasonable results despite the relatively short dataset available for warming-up (4.5 years). Semi-and fully distributed models also suggested that with some more effort they could perform well. The accuracy of model predictions does not seem to increase by using models with more than 9–12 calibration parameters. An evaluation of the relative errors between the forecasted and the observed values revealed that for most models, 50% of the forecasted values contained more than 50% of difference against the observed discharge rate, with 25% having a difference larger than 100%. A significant part of the poorly forecasted values corresponded to base-flow which was surprising given that as base-flow is generally much easier to predict than peak flow. Hence, this shows that modelling approaches and criteria for the calibration are too oriented towards peak-flow sections of the hydrographs, and that improvements could be gained by more focus on the base-flow.

Evaluation and Application of Quantitative Precipitation Forecast Products for Mainland China Based on TIGGE Multimodel Data

AbstractWe evaluated 24-h control forecast products from The International Grand Global Ensemble center over the 10 first-class water resource regions of Mainland China in 2013–18 from the perspective of precipitation processes (continuous) and precipitation events (discrete). We evaluated the forecasts from the China Meteorological Administration (CMA), the Centro de Previsão de Tempo e Estudos Climáticos (CPTEC), the Canadian Meteorological Centre (CMC), the European Centre for Medium-Range Weather Forecasts (ECMWF), the Japan Meteorological Agency (JMA), the Korea Meteorological Administration (KMA), the United Kingdom Met Office (UKMO), and the National Centers for Environmental Prediction (NCEP). We analyzed the differences among the numerical weather prediction (NWP) models in predicting various types of precipitation events and showed the spatial variations in the quantitative precipitation forecast efficiency of the NWP models over Mainland China. Meanwhile, we also combined four hydrological models to conduct meteo-hydrological runoff forecasting in three typical basins and used the Bayesian model averaging (BMA) method to perform the ensemble forecast of different scenarios. Our results showed that the models generally underestimate and overestimate precipitation in northwestern China and southwestern China, respectively. This tendency became increasingly clear as the lead time rose. Each model has a high reliability for the forecast of no-rain and light rain in the next 10 days, whereas the NWP model only has high reliability on the next day for moderate and heavy rain events. In general, each model showed different capabilities of capturing various precipitation events. For example, the CMA and CMC forecasts had a better prediction performance for heavy rain but greater errors for other events. The CPTEC forecast performed well for long lead times for no-rain and light rain but had poor predictability for moderate and heavy rains. The KMA, UKMO, and NCEP forecasts performed better for no-rain and light rain. However, their forecasting ability was average for moderate and heavy rain. Although the JMA model performed better in terms of errors and accuracy, it seriously underestimated heavy rain events. The extreme rainstorm and flood forecast results of the coupled JMA model should be treated with caution. Overall, the ECMWF had the most robust performance. Discrepancies in the forecasting effects of various models on different precipitation events vary with the lead time and region. When coupled with hydrological models, NWP models not only control the accuracy of runoff prediction directly but also increase the difference among the prediction results of different hydrological models with the increase in NWP error significantly. Among all the single models, ECMWF, JMA, and NCEP have better effects than the other models. Moreover, the ensemble forecast based on BMA is more robust than the single model, which can improve the quality of runoff prediction in terms of accuracy and reliability.

Detection of Spatial Shift in Flood Regime of the Kabul River Basin in Pakistan, Causes, Challenges, and Opportunities

Recent evidence of regional climate change impacts on hydrological cycle directed us to study the floods in a high elevated and rapidly urbanized river basin, the Kabul River basin (KRB), Pakistan, which is susceptible to frequent flooding. Therefore, we analyzed the changes in flood regime at various spatial and temporal scales and their possible causes, which is accomplished by using flood indicators, trend analysis, change point analysis, and hydrological modeling. The results showed that the northern and northwestern parts of the KRB were more exposed to flood hazard than the southern parts under long-term scenario (1961/64-2015). However, after the change points, the flood risk decreased in the northern and increased in the southern regions. This spatial shift increased the vulnerability of population to the flood hazard, because the majority of population resides in the southern region. The extreme precipitation has also increased, especially the maximum one-day rainfall and maximum five-day rainfall throughout the basin. Particularly, the major cause of the decrease in different flood indicators in the northern parts of the KRB is the corresponding decrease in the annual and monsoonal rainfall and corresponding positive mass balance of glaciers in the northern region after the occurrence of change point in flood regime. However, the major cause of the increase in flood hazard on the southern part of the KRB is associated with maximum five-day rainfall. A 68% variability of annual maximum flood for the Kabul River at Nowshera and an 84% variability of annual maximum flood for Bara River at Jhansi post are explained by maximum five-day rainfall. In addition, a considerable decrease in forests (–5.21%) and increase in the urban area (88.26%) from 1992–2015 also amplifies the risk of higher flood peaks. The results of hydrological modeling suggest that the six-hourly flood peak increased by 6.85% (1992–2010) and 4.81% (2010–2015) for the extreme flood of 2010 for the Kabul River at Nowshera. The flood peak per decade will increase by 8.6%, as compared to the flood peak under the land use scenario of 2010. Therefore, consideration of proper land use planning is crucial for sustainable flood management in the KRB.

Simulation of rainfall–runoff process for an ungauged catchment using an event-based hydrologic model: A case study of koraiyar basin in Tiruchirappalli city, India

The Koraiyar River basin is located in Tiruchirappalli city, one of the fastest-growing medium-sized cities in south India. Over the decades, the city has experienced sporadic flooding in vast areas due to the breaching of bunds when the river is in spate. The objective of this investigative study is to simulate rainfall–runoff in the Koraiyar basin watershed through the reliable Hydrologic Modelling System developed by the Hydrologic Engineering Center (HEC-HMS). Land-use and land-cover constantly change due to increasing population taken as one of parameter, its influence on CN, and their impact on surface runoff is analyzed in this study. The specified hyetograph method is adopted for the meteorological modelling; the Soil Conservation Services-Curve Number (SCS-CN) is selected to calculate the loss rate, and the SCS unit hydrograph method is adopted to simulate the runoff rate. Calibration and validation of the model are done to simulate assessed peak discharge values for comparison with actual observed values. The Nash–Sutcliffe efficiency coefficient is between 0.5 and 0.6, which indicates that the hydrological modelling results are satisfactory and acceptable for simulation of rainfall–runoff. The peak discharge is obtained for the single maximum rainfall event of about 100 mm in a day over the past 40 years, and hydrographs are generated.

Managing flood flow connectivity to landscapes to build buffering capacity to disturbances: An ecohydrologic modeling framework for drylands

Increased flooding, droughts, and sediment transport are watershed-scale problems negatively impacting agriculture and ecosystems in drylands worldwide. Vegetation loss in upland watersheds is leading to scouring floods, which in turn decreases infiltration, soil moisture levels, and downstream groundwater recharge. Management to confront these intractable problems has been hindered by a lack of accessible decision support tools for both land and water managers that synthesize the watershed processes that buffer against dryland disturbances. Flood flow connectivities across the landscape create buffer zones through replenishing soil moisture and reducing flood energy, which in turn support multiple functions. This study developed a decision support tool, the Flood Flow Connectivity to the Landscape (FlowCon) framework that quantifies the most efficient management efforts to increase the key watershed buffering functions of increasing infiltration and reducing flow energy. FlowCon links three spatially explicit, process-based, and predictive models to answer two critical management questions: what key processes acting in what optimal areas are drivers of infiltration dynamics and what roles do peak flows of differing scales of energy play. The spatial models delineated the buffer zone to characterize the heterogeneous and optimal infiltration dynamics across the landscape. The hydrologic process model, using a curve number technique, identified the key ecohydrologic processes that affect infiltration and characterized peak flows and flow regime variability. The predictive flood routing model quantified the potential management benefits. We calibrated the models with measured runoff and the corresponding rainfall events for a six-year period, which included thirty-six flow events. The synthesized ecohydrologic indicators provided critical calibrations, improving the relationship between the hydrologic modeling results and observed data by 12% for the linear regression R2 and 69% for the root mean square error (RMSE). Implementation of prioritized management is estimated to reduce peak flow by half, with interventions focused on 24% of floodplains that infiltrate three times the flow volume per area than the floodplain average. FlowCon provides an efficient assessment framework that integrates watershed process understanding in an accessible decision support tool to achieve tangible improvements in dryland watershed management.

SIGNIFICANCE OF REMOTE SENSING BASED PRECIPITATION AND TERRAIN INFORMATION FOR IMPROVED HYDROLOGICAL AND HYDRODYNAMIC SIMULATION IN PARTS OF HIMALAYAN RIVER BASINS

Abstract. The Himalayan region are home to the world’s youngest and largest mountains, and origins of major rivers systems of South Asia. The present work highlight the importance of remote sensing (RS) data based precipitation and terrain products such as digital elevation models, glacier lakes, drainage morphology along with limited ground data for improving the accuracy of hydrological and hydrodynamic (HD) models in various Himalayan river basins such as Upper Ganga, Beas, Sutlej, Teesta, Koshi etc. The satellite based rainfall have mostly shown under prediction in the study area and few places have are also showing over estimation of rainfall. Hydrological modeling results were most accurate for Beas basin, followed by Upper Ganga basin and were least matching for Sutlej basin. Limited ground truth using GNSS measurements showed that digital elevation model (DEM) for carto version 3.1 is most accurate, followed by ALOS-PALSAR 12.5 DEM as compared to other open source DEMs. Major erosion and deposition was found in Rivers Bhagirathi, Alakhnanda, Gori Ganga and Yamuna in Uttarakhand state and Beas and Sutlej Rivers in Himachal Pradesh using pre and post flood DEM datasets. The terrain data and river cross section data showed that river cross sections and water carrying capacity before and after 2013 floods have changed drastically in many river stretches of upper Ganga and parts of Sutlej river basins. The spatio-temporal variation and evolution of glacier lakes was for lakes along with GLOF modeling few lakes of Upper Chenab, Upper Ganga, Upper Teesta and Koshi river basin was done using time series of RS data from Landsat, Sentinel-1 and Google earth images.

Rainfall-induced landslide identification using numerical modelling: A southern Chile case

Worldwide, Rainfall-Induced Landslide (RIL) affects the population with significant socioeconomic damage and high economic losses in developing countries. Nowadays, RIL hazard has gained attention from the scientific community in developing countries where low instrumental is available for identification and monitoring. However, RIL-prone zones identification is a complex task, mostly in zones with an increase of extreme hydrometeorological events due to climate change. We propose a new coupled methodology to identify RIL-prone zones based on slope instability (SI) during an extreme hydrometeorological event. We consider the Northern Slope of the Biobío River (NSBR, centered at ~36°S, 72°W) in the south of Chile, a densely populated area with historic RIL events. A one-way coupled system was carried out using the Weather Research and Forecasting (WRF) model to a hydrological model over an experimental area of 172 km2. Hydrological model results were used to calculate the factor of safety (FS) evaluating their temporal variations. Our results were compared with in-situ observations and remote sensing of slope-stability to evaluate their performance based on previous studies with good performance. Our results show that areas with low FS values represented shallow RIL and hyper-concentrated flows. Therefore, our approach could identify RIL-prone areas without the need for extensive in-situ information, which will reduce resources. Finally, our methodology allows implementation in countries with scarce or non-existent resources for the instrumental monitoring of hillslopes.

Investigate the Applicability of CMADS and CFSR Reanalysis in Northeast China

Reanalysis datasets can provide alternative and complementary meteorological data sources for hydrological studies or other scientific studies in regions with few gauge stations. This study evaluated the accuracy of two reanalysis datasets, the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) and Climate Forecast System Reanalysis (CFSR), against gauge observations (OBS) by using interpolation software and statistical indicators in Northeast China (NEC), as well as their annual average spatial and monthly average distributions. The reliability and applicability of the two reanalysis datasets were assessed as inputs in a hydrological model (SWAT) for runoff simulation in the Hunhe River Basin. Statistical results reveal that CMADS performed better than CFSR for precipitation and temperature in NEC with the indicators closer to optimal values (the ratio of standard deviations of precipitation and maximum/minimum temperature from CMADS were 0.92, 1.01, and 0.995, respectively, while that from CFSR were 0.79, 1.07, and 0.897, respectively). Hydrological modelling results showed that CMADS + SWAT and OBS + SWAT performed far better than CFSR + SWAT on runoff simulations. The Nash‒Sutcliffe efficiency (NSE) of CMADS + SWAT and OBS + SWAT ranged from 0.54 to 0.95, while that of CFSR + SWAT ranged from −0.07 to 0.85, exhibiting poor performance. The CMADS reanalysis dataset is more accurate than CFSR in NEC and is a suitable input for hydrological simulations.

Evaluation of gridded climate datasets over Canada using univariate and bivariate approaches: Implications for hydrological modelling

This study evaluates the univariate and bivariate characteristics of five gridded products with respect to the Adjusted and Homogenized Canadian Climate Data for 1980–2010. Spatial variations of temperature and precipitation biases including the ones corresponding to the magnitude and frequency of extremes are assessed over Canada. The dependence structure between the two variables is analysed across each dataset using a goodness of fit test based on copulas. The propagation of univariate and bivariate biases into streamflow simulations is then investigated by driving a semi-distributed hydrological model for three watersheds with distinct characteristics in western Canada. The input data include original (unadjusted) and bias-corrected climate data based on the univariate quantile delta mapping and multivariate bias correction approaches. The univariate analyses show that all datasets have relatively significant cold and wet biases to the west of the Rocky Mountains and hot and dry biases over the Prairies. Bivariate evaluations using copulas show that all products fail to capture the dependence structure between temperature and precipitation at the majority of the locations, which can undermine their suitability for compound event assessments. The differences between univariate and multivariate bias correction approaches are highlighted by the significant differences in the interrelationships between precipitation and temperature. Hydrological modelling results show major improvements in the detection of extremes after correcting the bivariate biases of the input datasets.

Sensitivity and model reduction of simulated snow processes: Contrasting observational and parameter uncertainty to improve prediction

The hydrology of high-elevation, mountainous regions is poorly represented in Earth Systems Models (ESMs), yet these ecosystems play an important role in the storage and land-atmosphere exchange of water. As much of the western United States’ water comes from water stored in the snowpack (snow water equivalent, SWE), model representation of these regions is important. This study assesses how uncertainty in both model parameters and forcing affect simulated snow processes through sensitivity analysis (active subspaces) on model inputs (meteorological forcing and model input parameters) for a widely used snow model. Observations from an AmeriFlux tower at the Niwot Ridge research site are used to force an integrated, single-column hydrologic model, ParFlow-CLM. This study finds that trees can mute the effects of snow albedo causing the evergreen needleleaf scenarios to be sensitive primarily to hydrologic forcing while bare ground simulations are more sensitive to the snow parameters. The bare ground scenarios are most sensitive overall. Both forcing and model input parameters are important for obtaining accurate hydrologic model results.

A Comparison of Methods for Calculating Monthly Flows on Small Catchments

The paper deals with a determination of the monthly time series of flows in catchments where this quantity is not measured. While determining unknown flows, the authors have assumed that the historical climatic data for the given area and flows in nearby and similar catchments are available. So-called “analogy” methods are possible to use for such a defined task. In these methods, unknown flows are determined based on an “analogy” with known data from similar, nearby catchments. The authors have compared hydrological modelling and statistical and machine learning regression methods. They have determined that if at least short-term measurements are available for the river catchment studied, the most suitable method is a regression with LASSO regularisation, as LASSO eliminates the problem of multicollinearity in the input data. The use of the Support Vector Machines and Neural Network with Bayesian regularisation seems to be other suitable methods. The precision of hydrological modelling results is slightly less than the results from regression methods, but the authors have demonstrated that these results are still suitable in the context of water management calculations.

On the Use of Adaptive Ensemble Kalman Filtering to Mitigate Error Misspecifications in GRACE Data Assimilation

AbstractThe ensemble Kalman filter (EnKF) has been proved as a useful algorithm to merge coarse‐resolution Gravity Recovery and Climate Experiment (GRACE) data with hydrologic model results. However, in order for the EnKF to perform optimally, a correct forecast error covariance is needed. The EnKF estimates this error covariance through an ensemble of model simulations with perturbed forcing data. Consequently, a correct specification of perturbation magnitude is essential for the EnKF to work optimally. To this end, an adaptive EnKF (AEnKF), a variant of the EnKF with an additional component that dynamically detects and corrects error misspecifications during the filtering process, has been applied. Due to the low spatial and temporal resolutions of GRACE data, the efficiency of this method could be different than for other hydrologic applications. Therefore, instead of spatially or temporally averaging the internal diagnostic (normalized innovations) to detect the misspecifications, spatiotemporal averaging was used. First, sensitivity of the estimation accuracy to the degree of error in forcing perturbations was investigated. Second, efficiency of the AEnKF for GRACE assimilation was explored using two synthetic and one real data experiment. Results show that there is considerable benefit in using this method to estimate the forcing error magnitude and that the AEnKF can efficiently estimate this magnitude.

Water availability in Pakistan from Hindukush–Karakoram–Himalayan watersheds at 1.5 °C and 2 °C Paris Agreement targets

Highly seasonal water supplies from the Himalayan watersheds of Jhelum, Kabul and upper Indus basin (UIB) are critical for managing the world's largest contiguous irrigation system of the Indus basin and its dependent agrarian economy of Pakistan. Here, we assess changes in the contrasting hydrological regimes of these Himalayan watersheds, and subsequent water availability under the Paris Agreement 2015 targets that aim of limiting the mean global warming to 1.5 °C (Plus1.5), and further, well below 2.0 °C (Plus2.0) relative to pre-industrial level. For this, calibrated semi-distributed hydrological model setups of the University of British Columbia are coupled with the 80-member ensemble experiments of Plus1.5 and Plus2.0 performed under the half a degree additional warming, projections, prognosis and impacts (HAPPI) framework. Our results suggest that during October to February, HAPPI ensembles project a median warming of up to 1 °C under Plus1.5 and of more than 1.5ºC under Plus2.0 relative to the historical period (2006–2015). Such warming is generally higher in daily minimum than daily maximum temperature and over high altitudes. Both scenarios project drying of the monsoon (July–September) and the main snowpack accumulation/melt seasons (March-June). Results of hydrological modeling suggest significant changes in the timings of contrasting hydrological regimes that collectively reflect on changes in the overall hydrology of the Himalayan watersheds. Median changes generally indicate strengthening of the nival and glacial regimes for all basins under Plus1.5, except for the nival regime of the Jhelum basin, whereas such changes are more pronounced under Plus2.0. Subsequently, annual water availability from three Himalayan watersheds will increase by 34% and 43% under Plus1.5 and Plus2.0, respectively. These findings provide an overall picture of the surface water availability in Pakistan under the aspirations of the Paris Agreement 2015 and can inform on policy decisions together with the country's first-ever national water policy approved recently.

Impact of Climate Change on Water Resources in the Kilombero Catchment in Tanzania

This article illustrates the impact of potential future climate scenarios on water quantity in time and space for an East African floodplain catchment surrounded by mountainous areas. In East Africa, agricultural intensification is shifting from upland cultivation into the wetlands due to year-round water availability and fertile soils. These advantageous agricultural conditions might be hampered through climate change impacts. Additionally, water-related risks, like droughts and flooding events, are likely to increase. Hence, this study investigates future climate patterns and their impact on water resources in one production cluster in Tanzania. To account for these changes, a regional climate model ensemble of the Coordinated Regional Downscaling Experiment (CORDEX) Africa project was analyzed to investigate changes in climatic patterns until 2060, according to the RCP4.5 (representative concentration pathways) and RCP8.5 scenarios. The semi-distributed Soil and Water Assessment Tool (SWAT) was utilized to analyze the impacts on water resources according to all scenarios. Modeling results indicate increasing temperatures, especially in the hot dry season, intensifying the distinctive features of the dry and rainy season. This consequently aggravates hydrological extremes, such as more-pronounced flooding and decreasing low flows. Overall, annual averages of water yield and surface runoff increase up to 61.6% and 67.8%, respectively, within the bias-corrected scenario simulations, compared to the historical simulations. However, changes in precipitation among the analyzed scenarios vary between −8.3% and +22.5% of the annual averages. Hydrological modeling results also show heterogeneous spatial patterns inside the catchment. These spatio-temporal patterns indicate the possibility of an aggravation for severe floods in wet seasons, as well as an increasing drought risk in dry seasons across the scenario simulations. Apart from that, the discharge peak, which is crucial for the flood recession agriculture in the floodplain, is likely to shift from April to May from the 2020s onwards.

Impact of Future Climate and Vegetation on the Hydrology of an Arctic Headwater Basin at the Tundra–Taiga Transition

Abstract The rapidly warming Arctic is experiencing permafrost degradation and shrub expansion. Future climate projections show a clear increase in mean annual temperature and increasing precipitation in the Arctic; however, the impact of these changes on hydrological cycling in Arctic headwater basins is poorly understood. This study investigates the impact of climate change, as represented by simulations using a high-resolution atmospheric model under a pseudo-global-warming configuration, and projected changes in vegetation, using a spatially distributed and physically based Arctic hydrological model, on a small headwater basin at the tundra–taiga transition in northwestern Canada. Climate projections under the RCP8.5 emission scenario show a 6.1°C warming, a 38% increase in annual precipitation, and a 19 W m−2 increase in all-wave annual irradiance over the twenty-first century. Hydrological modeling results suggest a shift in hydrological processes with maximum peak snow accumulation increasing by 70%, snow-cover duration shortening by 26 days, active layer deepening by 0.25 m, evapotranspiration increasing by 18%, and sublimation decreasing by 9%. This results in an intensification of the hydrological regime by doubling discharge volume, a 130% increase in spring runoff, and earlier and larger peak streamflow. Most hydrological changes were found to be driven by climate change; however, increasing vegetation cover and density reduced blowing snow redistribution and sublimation, and increased evaporation from intercepted rainfall. This study provides the first detailed investigation of projected changes in climate and vegetation on the hydrology of an Arctic headwater basin, and so it is expected to help inform larger-scale climate impact studies in the Arctic.

Analysis of combined and isolated effects of land-use and land-cover changes and climate change on the upper Blue Nile River basin's streamflow

Abstract. Understanding responses by changes in land use and land cover (LULC) and climate over the past decades on streamflow in the upper Blue Nile River basin is important for water management and water resource planning in the Nile basin at large. This study assesses the long-term trends of rainfall and streamflow and analyses the responses of steamflow to changes in LULC and climate in the upper Blue Nile River basin. Findings of the Mann–Kendall (MK) test indicate statistically insignificant increasing trends for basin-wide annual, monthly, and long rainy-season rainfall but no trend for the daily, short rainy-season, and dry season rainfall. The Pettitt test did not detect any jump point in basin-wide rainfall series, except for daily time series rainfall. The findings of the MK test for daily, monthly, annual, and seasonal streamflow showed a statistically significant increasing trend. Landsat satellite images for 1973, 1985, 1995, and 2010 were used for LULC change-detection analysis. The LULC change-detection findings indicate increases in cultivated land and decreases in forest coverage prior to 1995, but forest area increases after 1995 with the area of cultivated land that decreased. Statistically, forest coverage changed from 17.4 % to 14.4%, by 12.2 %, and by 15.6 %, while cultivated land changed from 62.9 % to 65.6 %, by 67.5 %, and by 63.9 % from 1973 to 1985, in 1995, and in 2010, respectively. Results of hydrological modelling indicate that mean annual streamflow increased by 16.9 % between the 1970s and 2000s due to the combined effects of LULC and climate change. Findings on the effects of LULC change on only streamflow indicate that surface runoff and base flow are affected and are attributed to the 5.1 % reduction in forest coverage and a 4.6 % increase in cultivated land areas. The effects of climate change only revealed that the increased rainfall intensity and number of extreme rainfall events from 1971 to 2010 significantly affected the surface runoff and base flow. Hydrological impacts by climate change are more significant as compared to the impacts of LULC change for streamflow of the upper Blue Nile River basin.

Inter-comparison of water balance components of river basins draining into selected delta districts of Eastern India

Quantification of hydrological components in-terms of surface runoff, stream flow and evapotranspiration is important and useful in planning and management of water resources across the river basin, including downstream delta regions. River deltas water availability; management and related disaster risk are largely influenced by the hydrological state of upstream river basins. The paper presents the results of hydrological modelling (SWAT) based long-term water balance components in river basins draining into selected delta Districts of Eastern India. Mahanadi, Brahmani-Baitarani river basins and Hooghly river and adjacent small river basins are considered. The long-term water balance components of Mahanadi and Brahmani-Baitarani river basins are similar and significantly different in Hooghly river and adjacent small river basins. The runoff coefficient is significantly higher in Hooghly river and adjacent small river basins at 0.39 compared to other two river basins (0.247 & 0.256). The evapotranspiration component is relatively low in Hooghly river and adjacent small river basins with smaller range of long-term variation. The time-series model outputs brought out the basin-specific hydrological response variations in low and high rainfall years such as changes in fraction of evapotranspiration and surface runoff. Mahanadi and Brahmani-Baitarani river basins exhibit large inter-annual variation in evapotranspiration, surface runoff fractions. The developed hydrological modelling framework is capable of incorporating future climate data and to predict the basin-scale future water availability, demand, use and to bring out resulting water scenarios that would impact river deltas in-terms of their exposure towards water related adversities, such as drought and flood.

Environmental risk resulting from historical land degradation in alluvial plains considered for dam planning

AbstractLand degradation of alluvial plains is an environmental risk for groundwater contamination, stemming from the mobilization of contaminants that can occur from waste‐filled gravel pits. This risk can be increased by river damming and consequential rises in groundwater levels. The presence and characteristics of former historical land degradation of alluvial plains should therefore be considered when planning hydroelectric projects. The aim of this study was to assess the hydrological and environmental impacts of the planned damming of the Sava River on the groundwater, which is the drinking water source for the Slovenian capital Ljubljana. It introduces an integrative approach for determining the characteristics of old waste‐filled gravel pits with stereo image processing of historical aerial photographs and electrical resistivity tomography (ERT) in combination with hydrological modelling. Thirty gravel pits in the study area (17 km2) were identified by stereo image analysis, and three were selected to test results in the field using non‐invasive ERT. Integrating this information with the results of hydrological modelling, we assessed the risk of groundwater contamination from the scenario of the planned damming of the River Sava. The results indicate that the thickness of the unsaturated zone below the identified gravel pits will be reduced by an average of 4 m at median groundwater level conditions during damming, which will increase the vulnerability of groundwater to contamination.

Efecto de la subdivisión de cuencas y la estimación de variables climáticas en la simulación de componentes de balance hídrico en cuencas semiáridas mediterráneas

Los resultados de cualquier intento de modelización hidrológica dependen de la calidad y discretización de los datos de entrada. En este trabajo se evalúa cómo la utilización de diferentes capas de variables climáticas de entrada (precipitación y ETP) y diferentes esquemas de discretización de la cuenca influyen en los resultados de un modelo hidrológico bien conocido (SWAT). En concreto se prueban 4 conjuntos de variables de entrada y discretización. Los estadísticos utilizados para evaluar la exactitud obtenida con estos conjuntos son RMSE, PBIAS, NSE y r2. Los resultados muestran que el uso de mejores modelos para obtener series de ETP (Penman Monteith FAO o Hargreaves calibrado) mejora considerablemente la exactitud del modelo respecto a las series obtenidas con el modelo de Hargreaves sin calibrar (opción por defecto en SWAT). Sin embargo, no se aprecian diferencias entre los resultados obtenidos con Penman Monteith FAO y Hargreaves calibrado. Por otro lado, el uso de información climática distribuida mejora notablemente los resultados obtenidos con información agregada. Se observa también la necesidad de calibrar los parámetros de SWAT ya que los valores por defecto están optimizados para ambientes templados de EEUU. The results of hydrological modelling depend on the quality and spatial resolution of the input data. This paper evaluates how the use of different estimations of climatic variables input layers (precipitation and ET P ) and different basin discretization schemes influence affect the results of a contrasted hydrological model (SWAT). Specifically, 4 se ts of input and discretization variables are tested. The statistics used to evaluate the accuracy obtained with these sets are RMSE, PBIAS, NSE and r 2 . The results show that the use of better models to obtain ET P series (Penman Monteith FAO or calibrated Hargreaves) considerably improves the accuracy of the model compared to the series obtained with the uncalibrated Hargreaves model (default option in SWAT). However, there is no difference between the results obtained with Penman Monteith FAO and calibrate d Hargreaves. On the other hand, the use of distributed climate information significantly improves the results obtained with aggregated information. There is also a need to calibrate SWAT parameters as the default values are optimized for temperate environ ments in the USA.

Future climate and runoff projections across South Asia from CMIP5 global climate models and hydrological modelling

Study regionThe South Asia. Study focusThis paper presents future climate and runoff projections for the South Asia region under the RCP8.5 scenario with climate change informed by 42 CMIP5 GCMs. Runoff is projected for 0.5° grids using hydrological models with future climate inputs obtained by empirically scaling the historical climate series. New hydrological insightsThe modelling results indicate that future runoff will increase throughout most of the region except in the far north-east and far north-west. The median projection shows increases in mean annual runoff of 20–30% in the Indian sub-continent for 2046–2075 relative to 1976–2005. The change in runoff is driven mainly by the change in precipitation, moderated (in wetter futures) or intensified (in drier futures) by higher temperature and potential evaporation. The paper also investigates the uncertainties of the projection due to scaling methods and selection of GCMs. The difference in runoff projections from different scaling methods is small relative to the large uncertainty in precipitation projections from the GCMs. Sub-selecting only the “better” performing GCMs shows marginal difference in the uncertainty range of projected runoff. For the broad scale projections presented here, it is best to use projections informed by all the GCMs to provide an indication of the full uncertainty range.