Estimating the role of climate internal variability and source of uncertainties in hydrological climate-impact projections

Hydrological climate-impact projections in future are limited by large uncertainties from various sources. Therefore, this study aimed to explore and estimate the sources of uncertainties involved in climate changing impacted assessment in a representative watershed of Northeastern China. Moreover, recent researches indicated that the climate internal variability (CIV) plays an important role in various of hydrological climate-impact projections. Six downscaled Global climate models (GCMs) under two emission scenarios and a calibrate Soil and Water Assessment Tool (SWAT) model were used to obtain hydrological projections in future periods. The CIV and signal-to-noise ratio (SNR) are investigated to analyze the the role of internal variability in hydrological projections. The results shows that the internal variability shows a considerable influence on hydrological projections, which need be partitioned and quantified particularly. Moreover, it worth noting the CIV can propagate from precipitation and ET to runoff projections through the hydrological simulation process. In order to partition the CIV and sources of uncertainties, the uncertainty decomposed frameworks based on analysis of variance (ANOVA) are established. The results demonstrate that the CIV and GCMs are the dominate contributors of runoff in rainy season. In contrast, the CIV and SWAT model parameter sets provided obvious uncertainty to runoff in January to May and October to December. The findings of this study advised that the uncertainty is complex in hydrological simulation process hence, it is meaning and necessary to estimate the uncertainty in climate simulation process, the uncertainty analysis results can provide effectively efforts to reduce uncertainty and then give some positive suggestions to stakeholders for adaption countermeasure under climate change.

DOWNSCALING OF SMAP SOIL MOISTURE PRODUCT BY DATA FUSION WITH VIIRS LST/EVI PRODUCT

Soil moisture is an essential variable of environment and climate change, which affects the energy and water exchange between soil and atmosphere. The estimation of soil moisture is thus very important in geoscience, while at same time also challenging. Satellite remote sensing provides an efficient way for large-scale soil moisture distribution mapping, and microwave remote sensing satellites/sensors, such as Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer (AMSR), and Soil Moisture Active Passive (SMAP) satellite, are widely used to retrieve soil moisture in a global scale. However, most microwave products have relatively coarse resolution (tens of kilometres), which limits their application in regional hydrological simulation and disaster prevention. In this study, the SMAP soil moisture product with spatial resolution of 9km is downscaled to 750m by fusing with VIIRS optical products. The LST-EVI triangular space pattern provides the physical foundation for the microwave-optical data fusion, so that the downscaled soil moisture product not only matches well with the original SMAP product, but also presents more detailed distribution patterns compared with the original dataset. The results show a promising prospect to use the triangular method to produce finer soil moisture datasets (within 1 km) from the coarse soil moisture product.

General overestimation of ERA5 precipitation in flow simulations for High Mountain Asia basins

Precipitation is one of the most important input to hydrological models, although obtaining sufficient precipitation observations and accurate precipitation estimates in High Mountain Asia (HMA) is challenging. ERA5 precipitation is the latest generation of reanalysis dataset that is attracting huge attention from various fields but it has not been evaluated in hydrological simulations in HMA. To remedy this gap, we first statistically evaluated ERA5 precipitation with observations from 584 gauges in HMA, and then investigated its potential in hydrological simulation in 11 HMA basins using the Variable Infiltration Capacity (VIC) hydrological model. The ERA5 precipitation generally captures the seasonal variations of gauge observations, and the broad spatial distributions of precipitation in both magnitude and trends in HMA. The ERA5 exhibits a reasonable flow simulation (RB of 5%–10%) at the Besham hydrological station of the UI basin when the contribution from glacier runoff is added to the simulated total runoff. But it overestimates the observations in other HMA basins by 33%–106% without considering glacier runoff, mostly due to the overestimates in the ERA5 precipitation inputs. Therefore, a bias correction is definitely needed before ERA5 precipitation is used for hydrological simulations in HMA basins.

Integrated Surface-Groundwater Modelling of Nitrate Concentration in Mediterranean Rivers, the Júcar River Basin District, Spain

High nutrient discharge from groundwater (GW) into surface water (SW) have multiple undesirable effects on river water quality. With the aim to estimate the impact of anthropic pressures and river–aquifer interactions on nitrate status in SW, this study integrates two hydrological simulation and water quality models. PATRICAL models SW–GW interactions and RREA models streamflow changes due to human activity. The models were applied to the Júcar River Basin District (RBD), where 33% of the aquifers have a concentration above 50 mg NO3−/L. As a result, there is a direct linear correlation between the nitrate concentration in rivers and aquifers (Júcar r2 = 0.9, and Turia r2 = 0.8), since in these Mediterranean basins, the main amount of river flows comes from groundwater discharge. The concentration of nitrates in rivers and GW tends to increase downstream of the district, where artificial surfaces and agriculture are concentrated. The total NO3− load to Júcar RBD rivers was estimated at 10,202 tN/year (239 kg/km2/year), from which 99% is generated by diffuse pollution, and 3378 tN/year (79 kg/km2/year) is discharged into the Mediterranean Sea. Changes in nitrate concentration in the RBD rivers are strongly related to the source of irrigation water, river–aquifer interactions, and flow regulation. The models used in this paper allow the identification of pollution sources, the forecasting of nitrate concentration in surface and groundwater, and the evaluation of the efficiency of measures to prevent water degradation, among other applications.

Green Roof Hydrological Modelling With GRU And LSTM Networks

Green Roofs (GRs) are becoming more popular as a low-impact building option. They have the potential to minimize peak stormwater runoff while also increasing the quality of runoff from buildings. Improvement of hydrological models for the simulation of GRs will aid design of individual roofs as well as city scale planning that relies on the predicted impacts of widespread GR implementation. Machine learning (ML) has exploded in popularity in recent years, owing to considerable increases in processing power and data availability. However, there are no studies focusing on the use of ML in hydrological simulation of GRs. We focus on two types of ML-based model: long short-term memory (LSTM) and gated recurrent unit (GRU) in modelling hydrological performance of GRs, with sequence input and a single output (SISO), and synced sequence input and output (SSIO) architectures. According to the results of this paper, LSTM and GRU are useful tools for the modelling of GRs. As the time window length (memory length, time step length of input data) increases, SISO appears to have a higher overall forecast accuracy. SSIO delivers the best overall performance, when the SSIO is close to, or even exceeds, the maximum window size.

Assessment of Historical and Future Water Availability in Himalayan Tamor River Basin Nepal Utilizing CMIP5 CNRM Climate Model Experiments

In this study, the assessment of water availability under climate changing environment has been done in the Himalayan Tamor River Basin, Nepal using physically based, spatially distributed, a continuous model 'Soil and Water Assessment Tool' (SWAT). The hydrological simulation and projection have been performed in the historical (1996-2007) and future times (e.g. 30s, 40s, 50s, 60s, 70s, 80s, 90s). The climate change impact assessment on the hydrology of Tamor river basin has been performed utilizing the CMIP5 CNRM climate model datasets (with RCP4.5 and RCP8.5). The model calibration and parameterization uncertainty evaluation in the simulated and projected flows were done in SWATCUP using SUFI2 algorithm. The results obtained from the model calibration (1996-2004) and validation (2005-2007) showed a reliable estimate of daily streamflow for calibration period (R2 = 0.85, NSE =0.85 and PBIAS=-2.5) and validation period (R2 =0.87, NSE =0.85 and PBIAS=-5.4). The average annual water yield at the main outlet of the basin is computed as 1511.13 mm, and the total annual quantity is recorded as 6.25 BCM. The average annual precipitation over the seleced river basin is projected to be increased in all scenarios. The stations at higher altitude show more temperature rise than those at a lower elevation and thus there would be minimal snowfall has been projected in the basin by 2100 AD under both scenarios (RCP4.5 and RCP8.5). It is expected that the flow pattern in the future would be similar to the baseline pattern under all scenarios. The baseflow will be dramatically increased in all scenarios, but the lowest flow month would be shifted from March to February. Since the base flow during lean months would be increased in future as projected by all scenarios, there would not be adverse impacts on higher percentile flows. This study would be useful for the assessment of the possibility of storage type or run-off-river type hydro-project in the basin in terms of water availability.

Estimating the Effect of Climate Internal Variability and Source of Uncertainty in Climate-Hydrological Projections in a Representative Watershed of Northeastern China

The decomposition and quantification of uncertainty sources in ensembles of climate-hydrological simulation chains is a key issue in climate impact researches. The mainly objectives of this study partitioning climate internal variability (CIV) and uncertainty sources in the climate-hydrological projections simulation process, the climate simulation process formed by six downscaled GCMs under two emission scenarios called GCMs-ES simulation chain, the hydrological simulation process add one calibrate Soil and Water Assessment Tool (SWAT) model called GCMs-ES-HM simulation chain. The CIV and external forcing of climate projections are investigated in each GCMs-ES simulation chain. The CIV of precipitation and ET are large in rainy season, and the single-to-noise ratio (SNR) are also relatively high in rainy season. Furthermore, the uncertainty decomposed frameworks based on analysis of variance (ANOVA) are established. The CIV and GCMs are the dominate contributors of runoff in rainy season. It worth noting the CIV can propagate from precipitation and ET to runoff projections. In additional, the hydrological model parameters are the third uncertainty contributor of runoff, which embody the hydrological model simulate process play important role in hydrological projections in future. The findings of this study advised that the uncertainty is complex in hydrological, hence, it is meaning and necessary to estimate the uncertainty in climate simulation process, the uncertainty analysis results can provide effectively efforts to reduce uncertainty and then give some positive suggestions to stakeholders for adaption countermeasure under climate change.