Impact of climate change on hydrology components using CORDEX South Asia climate model in Wunna, Bharathpuzha, and Mahanadi, India

Sediment load assessments under climate change scenarios and a lack of integration between climatologists and environmental modelers

Increasing precipitation accelerates soil erosion and boosts sediment loads, especially in mountain catchments. Therefore, there is significant pressure to deliver plausible assessments of these phenomena on a local scale under future climate change scenarios. Such assessments are primarily drawn from a combination of climate change projections and environmental model simulations, usually performed by climatologists and environmental modelers independently. Our example shows that without communication from both groups the final results are ambiguous. Here, we estimate sediment loads delivered from a Carpathian catchment to a reservoir to illustrate how the choice of meteorological data, reference period, and model ensemble can affect final results. Differences in future loads could reach up to even 6000 tons of sediment per year. We suggest there must be a better integration between climatologists and environmental modelers, focusing on introducing multi-model ensembles targeting specific impacts to facilitate an informed choice on climate information.

Prediction of soil erosion and sediment yield in an ungauged basin based on land use land cover changes

Soil erosion is a significant problem in the agriculture sector and the environment globally. Susceptible soil erosion zones must be identified and erosion rates evaluated to decrease land degradation problems and increase crop productivity by protecting soil fertility. Therefore, a research study has been carried out in the Ponnaniyar River basin, an ungauged tributary of the Cauvery basin in India, primarily used for agriculture. The main purpose of this study is to assess soil erosion (SE) and sediment yield (SY) for the future in an ungauged basin by utilizing the projected land use/land cover (LULC) map of the study area. Additionally, Landsat 8 satellite dataset was only used for the classification and prediction of LULC to eliminate the variation between the resolution, bands and its wavelength of different satellites datasets. To achieve the goals of this study, three phases were followed. First, the LULC of the study area was classified using a Random Trees Classifier (RTC), a machine learning technique, followed by the projection of land cover using a Cellular Automata-based Artificial Neural Network (CA-ANN) model. The driving factors for this model include digital elevation model (DEM), slope, distance to roads, settlements, and water bodies. The accuracy level of the projected LULC map was determined by comparing it with the classified LULC map of the study area, and the results showed an overall accuracy (OA) of 85.35 percentage and a kappa coefficient (K) of 0.74, respectively. Second, the projected LULC map was used in the land management factor (C) and conversation practice factor (P) of the Revised Universal Soil Loss Equation (RUSLE) model to assess soil erosion. The model was integrated with the sediment delivery ratio (SDR) to estimate sediment yield within the study area. The accuracy of the generated erosion map based on the classified and projected LULC for the year 2022 was determined using the receiver operating characteristic curve (ROC) curve, and it was found to be in satisfactory agreement. Finally, for effective soil and water conservation measures, the basin was divided into 13 sub-watersheds (SWs) using terrain analysis in geographical information system (GIS). The SWs were prioritized based on the mean soil loss in the 4-year interval from 2014 to 2030 and integrated using the weighted average method to determine the final prioritization. From these findings, SW 11, SW 9, SW 12, and SW 1 are extremely affected by soil erosion, and immediate implementation of water harvesting structures is required for soil conservation. Also, this research might be useful for decision-makers and policymakers in land management.

Soil Conservation Service-Curve Number method-based historical analysis of long-term (1936-2016) temporal evolution of city-scale potential natural groundwater recharge from precipitation: case study Algiers (Algeria)

Managing groundwater resources in urban areas requires an adequate understanding and assessment of urban hydrogeological systems (structure, components, connections, and imposed conditions) as a part of a larger, dynamically evolving environment. Urbanization and climate change are amongst the widely recognized signs of such a continuous evolution. Within this context, the present study gives a quantitative assessment of the impact of these two factors threatening water resources in urban environments. The Soil Conservation Service-Curve Number (SCS-CN) method is used to conduct a long-term quantitative analysis of the temporal evolution of the potential natural groundwater recharge from precipitation at the scale of Algiers city for an 80-year-long period (1936-2016). The length of the study period allowed us to account for and analyze important changes in urban settings and climatic conditions within the study zone. Overall, two trend shifts over three distinct periods were found to characterize the temporal evolution of precipitation, several climate change indicators defined for the study, and the potential natural aquifer recharge. A strong, approximately 1:4, linear correlation between the estimated city-scale potential natural aquifer recharge and precipitation was observed for the studied period (R2 = 0.748). Moreover, even though the urban area has known a rapid (2nd order polynomial) increase from 1936 to 2016, climate change (accounted for via the changes in precipitation regime) impacted the city-scale potential natural groundwater recharge with higher magnitudes than urbanization. Finally, the computed climate change indicators show that starting in the mid-1980s, Algiers has started receiving less precipitations, with fewer heavy rain events and longer dry condition periods.

Can atmospheric reanalysis datasets reproduce flood inundation at regional scales? A systematic analysis with ERA5 over Mahanadi River Basin, India

The prime challenges limiting efficient flood management, especially over large regions, are concurrently related to limited hydro-meteorological observations and exorbitant economics with computational modeling. Reanalysis datasets are a valuable alternative, as they furnish relevant variables at high spatiotemporal resolutions. In recent times, ERA5 has gained significant recognition for its applications in hydrological modeling; however, its efficacy at the inundation scale needs to be understood. The advent of "global flood models" has ensured flood inundation and hazard modeling over large regions, otherwise obscure with regional models. For the first time, the present study explores the fidelity of ERA5 reanalysis at the inundation scale over the Mahanadi River basin, a severely flood-prone region in India. The biases in the discharges within ERA5 are ascertained by comparing them with station-level data at the nascent and extreme levels (i.e., 95th and 99th percentiles). Later, ERA5 is fed to LISFLOOD-FP, an acclaimed global flood model, to reenact the 2006, 2008, 2011, and 2014 flood events. Hit rates exceeding 0.8 compared to MODIS satellite imageries affirm the suitability of ERA5 in accurately capturing flood inundation. Distributed design discharges for 50 yr and 100 yr are derived using a set of extreme value distributions and fed to LISFLOOD-FP to derive design flood inundation and hazards in terms of both "depth" and "product of depth and velocity" of flood waters. Results derived from the study provide vital lessons for efficient land-use planning and adaptation strategies linked to flood protection and resilience.

Impact of climate change on hydrologic components using CORDEX Africa climate model in Gilgel Gibe 1 watershed Ethiopia

This study aimed to assess the impact of climate change on the hydrological components of Gilgel Gibe-1 using the ensemble of Coordinated Regional Climate Downscaling Experiments (CORDEX) Africa Domain namely REMO2009, HIRAM5, CCLM4-8 and RCA4 Regional Climate Models (RCMs) simulations. The performance of these RCM models was evaluated using the observed data from 1985 to 2005 and the ensemble was shown to simulate rainfall and air temperature better than individual RCMs. Then the RCMs ensemble data for historical and future projections from 2026 to 2055 years under RCP4.5 and RCP8.5 were corrected for bias and used to evaluate the impact of climate change. A non-linear bias correction and the monthly mean biases corrections method is used to adjust precipitation and temperature respectively. The future projection shows that; rainfall is expected to increase from August to December with maximum values of 1.97-235.23% under RCP4.5. The maximum temperature is expected to increase with maximum value of 1.62 °C under RCP8.5 in the study area. The calibrated and validated Soil and Water Assessment Tool (SWAT) model was used to investigate the impact of climate change on hydrologic components such as surface runoff, lateral flow, water yield, evapotranspiration and sediment yield. The SWAT model was calibrated and validated using monthly stream flow with the statistical performance of R² value of 0.82 and NSE value of 0.72 for calibration and R² of 0.79 and NSE of 0.67 for validation. Surface runoff and sediment yield are expected to increase from August to December under RCP4.5 and from August to February under RCP8.5. Overall both surface runoff and sediment yield are expected to increase in the future.

Predicting phosphorus and nitrate loads by using SWAT model in Vamanapuram River Basin, Kerala, India

Evaluations of probable environmental impacts of point and diffuse source pollution at regional sizes are essential to achieve sustainable development of natural resources such as land and water. This research focused on how nitrate and phosphorus load varied over time and space in the Vamanapuram River Basin (VRB). Phosphorus and nitrate loads have been evaluated in the VRB using the semi-distributed Soil and Water Assessment Tool (SWAT) hydrological model. SWAT Calibration and Uncertainty Programs (SWAT-CUP) have simulated the developed model using the Sequential Uncertainty Fitting, version 2(SUFI-2). The developed model was simulated for 2001 to 2008, and it was split into two-phase calibration and validation phases. Model performance was evaluated by the percentage of bias (PBAIS) and Nash-Sutcliffe efficiency coefficient (NSE). The simulated performance of nitrate was indicated as NSE = 0.22-0.59 and PBIAS = 51.86-65.88. The simulated performance of phosphorus showed NSE = 0.06-0.33 and PBIAS = 15.14-33.97. Total Phosphorus load was most sensitive to the organic Phosphorus enrichment ratio (ERORGP) and CH_N2 for streamflow simulation. This study concluded that the South-western region was a high potential for nutrient loads. This study will explain the nutrient load and guidelines for land management practice in the study area.

Impact of climate change on snowmelt runoff in a Himalayan basin, Nepal

The Hindu Kush Himalaya (HKH) is one of the major sources of fresh water on Earth and is currently under serious threat of climate change. This study investigates the future water availability in the Langtang basin, Central Himalayas, Nepal under climate change scenarios using state-of-the-art machine learning (ML) techniques. The daily snow area for the region was derived from MODIS images. The outputs of climate models were used to project the temperature and precipitation until 2100. Three ML models, including Gated recurrent unit (GRU), Long short-term memory (LSTM), and Recurrent neural network (RNN), were developed for snowmelt runoff prediction, and their performance was compared based on statistical indicators. The result suggests that the mean temperature of the basin could rise by 4.98 °C by the end of the century. The annual average precipitation in the basin is likely to increase in the future, especially due to high monsoon rainfall, but winter precipitation could decline. The annual river discharge is projected to upsurge significantly due to increased precipitation and snowmelt, and no shift in hydrograph is expected in the future. Among three ML models, the LSTM model performed better than GRU and RNN models. In summary, this study depicts severe future climate change in the region and quantifies its effect on river discharge. Furthermore, the study demonstrates the suitability of the LSTM model in streamflow prediction in the data-scarce HKH region. The outcomes of this study will be useful for water resource managers and planners in developing strategies to harness the positive impacts and offset the negative effects of climate change in the basin.