Forecast of streamflows to the Arctic Ocean by a Bayesian neural network model with snowcover and climate inputs

Increasing water flowing into the Arctic Ocean affects oceanic freshwater balance, which may lead to the thermohaline circulation collapse and unpredictable climatic conditions if freshwater inputs continue to increase. Despite the crucial role of ocean inflow in the climate system, less is known about its predictability, variability, and connectivity to cryospheric and climatic patterns on different time scales. In this study, multi-scale variation modes were decomposed from observed daily and monthly snowcover and river flows to improve the predictability of Arctic Ocean inflows from the Mackenzie River Basin in Canada. Two multi-linear regression and Bayesian neural network models were used with different combinations of remotely sensed snowcover, in-situ inflow observations, and climatic teleconnection patterns as predictors. The results showed that daily and monthly ocean inflows are associated positively with decadal snowcover fluctuations and negatively with interannual snowcover fluctuations. Interannual snowcover and antecedent flow oscillations have a more important role in describing the variability of ocean inflows than seasonal snowmelt and large-scale climatic teleconnection. Both models forecasted inflows seven months in advance with a Nash–Sutcliffe efficiency score of ≈0.8. The proposed methodology can be used to assess the variability of the freshwater input to northern oceans, affecting thermohaline and atmospheric circulations

Twenty-four buried ice masses remotely mapped in Transantarctic Mountains, Antarctica

Glacial ice contains information on paleoclimate, but typically is less than 1 Myrs old because glaciers continuously flow and melt. A recently discovered buried ice mass in Antarctica is dated to 3-5 Myrs and highlights the potential for long preservation of ancient ice under a layer of debris. Only two such ice masses are so far known in Antarctica. Given the significant scientific potential and lack of systematic mapping, we set out to locate all buried ice masses in Transantarctic Mountains (TAM). We visually analyzed >8,000 high-resolution satellite images covering much of the TAM. We searched for the polygonal patterned ground that signifies ice in the ground and once detected the corresponding digital elevation model was inspected for presence of a convex landform. When both features coincide, they indicate the presence of a buried ice body. We identified 22 new sites that are likely to conceal massive buried ice masses

Spatially Variable Precipitation and Its Influence on Water Balance in a Headwater Alpine Basin, Nepal

The current knowledge of the spatial variability of precipitation in High Mountain Asia is based on the remotely-sensed estimates (coarse spatial and temporal resolution) or data from sparsely-distributed rain gauges. However, as precipitation is strongly affected by topography in mountainous terrain, the spatially varying precipitation and the resulting water balances are currently poorly understood. To fill this gap in knowledge, we studied the spatial variation of the precipitation and its impact on water balance in a small headwater basin located in the foothills of the Himalaya, Nepal. We deployed ten rain gauges and climate stations, spanning the whole elevation range 700–4500 m above sea level (masl) for a period of four years. Our results show a quadratic polynomial relationship between annual precipitation and station elevation, which are used to produce annual precipitation maps. The performance of the elevation-based precipitation estimates is adequate in closing the water balance while the performances of average precipitation and Thiessen polygon method are poor and inconsistent in closing the water balance. We also demonstrate that precipitation estimates from one or two gauges at the lowest basin elevation substantially underestimate the water balance. However, the precipitation from one or two rain gauges at 2000–3000 masl provide a significantly better estimate of the water balance of a small headwater basin

Impacts of climatic variability on surface water area observed by remotely sensed imageries in the Red River Basin

Recent wetting in the Northern Great Plain (NGP) exerted strong influences on lakes and wetlands. However, the influence of recent increase in precipitation on spatiotemporal variation of surface water area is poorly understood in the Red River Basin (RRB, northern United States and southern Canada). Here, we used a high-resolution global surface water dataset to understand spatiotemporal dynamics of the annual, total, permanent, and seasonal water extent in RRB. Monthly surface water area is investigated to detect the change in seasonal surface water extent. We found four distinct phases of variation in surface water: Phase 1 (1990–2001, wetting); Phase 2 (2002- 2005, dry); Phase 3 (2006–2013, recent wetting); and Phase 4 (2014–2019, recent drying). A bare land to a permanent and seasonal water area switch is observed during Phase 1, while the other phases have experienced relatively little fluctuation. Findings have implications for nutrient concentration assessment in lakes and wetlands

Spatially Variable Precipitation and Its Influence on Water Balance in a Headwater Alpine Basin, Nepal

The current knowledge of the spatial variability of precipitation in High Mountain Asia is based on the remotely-sensed estimates (coarse spatial and temporal resolution) or data from sparsely-distributed rain gauges. However, as precipitation is strongly affected by topography in mountainous terrain, the spatially varying precipitation and the resulting water balances are currently poorly understood. To fill this gap in knowledge, we studied the spatial variation of the precipitation and its impact on water balance in a small headwater basin located in the foothills of the Himalaya, Nepal. We deployed ten rain gauges and climate stations, spanning the whole elevation range 700–4500 m above sea level (masl) for a period of four years. Our results show a quadratic polynomial relationship between annual precipitation and station elevation, which are used to produce annual precipitation maps. The performance of the elevation-based precipitation estimates is adequate in closing the water balance while the performances of average precipitation and Thiessen polygon method are poor and inconsistent in closing the water balance. We also demonstrate that precipitation estimates from one or two gauges at the lowest basin elevation substantially underestimate the water balance. However, the precipitation from one or two rain gauges at 2000–3000 masl provide a significantly better estimate of the water balance of a small headwater basin

Lithologic mapping of a forested montane terrain from Landsat 5 TM image

Thick forest cover and poor infrastructures are the major hindrances for detailed lithologic mapping in an inaccessible montane landscape. To overcome these limitations, we utilize a Landsat 5 TM image to map lithology using vegetation and drainage pattern as an indicator of underlying rock types in a heavily forested region of the Chittagong Hill Tracts area located in southeastern Bangladesh. We use supervised and unsupervised classifiers for a vegetation-based approach while on-screen digitization is used for drainage patterns-based mapping. Field observations were used for mapping lithology and evaluating accuracy. Overall, our results agree well with the current geologic map and improve it by providing a more spatially detailed distribution of the sandstone and shale. The performances of all approaches are good at the inner and outer flanks of anticlines located in the study area while the drainage pattern mapping performs best at the mid-flank area

Quantifying Uncertainty in Food Security Modeling

Food security is considered as the most important global challenge. Therefore, identifying long-term drivers of food security and their connections is essential to steer policymakers determining policies for future food security and sustainable development. Given the complexity and uncertainty of multidimensional food security, quantifying the extent of uncertainty is vital. In this study, we investigated the uncertainty of a coupled hydrologic food security model to examine the impacts of climatic warming on food production (rice, cereal and wheat) in a mild temperature study site in China. In addition to varying temperature, our study also investigated the impacts of three CO2 emission scenarios—the Representative Concentration Pathway, RCP 4.5, RCP 6.0, RCP 8.5—on food production. Our ultimate objective was to quantify the uncertainty in a coupled hydrologic food security model and report the sources and timing of uncertainty under a warming climate using a coupled hydrologic food security model tested against observed food production years. Our study shows an overall increasing trend in rice, cereal and wheat production under a warming climate. Crop yield data from China are used to demonstrate the extent of uncertainty in food security modeling. An innovative and systemic approach is developed to quantify the uncertainty in food security modeling. Crop yield variability with the rising trend of temperature also demonstrates a new insight in quantifying uncertainty in food security modeling

Mauvais Coulee Basin hydrologic data (observation and simulation)

The authors collected snow water equivalent (SWE) data and compiled a hydrologic model for the Mauvais Coulee Basin using Cold Region Hydrologic Platform. The datasets include observed SWE, observed streamflow, simulated SWE, simulated streamflow, simulated SWE maps (Feb 26, Jan 29, Jan 8 and Mar 26 of 2017), land use maps and hydrological representative unit maps (HRU) maps. This study detected a mechanism of hydrologic change to recent wetting using a cold region hydrologic model

Forecast of streamflows to the Arctic Ocean by a Bayesian neural network model with snowcover and climate inputs

Abstract Increasing water flowing into the Arctic Ocean affects oceanic freshwater balance, which may lead to the thermohaline circulation collapse and unpredictable climatic conditions if freshwater inputs continue to increase. Despite the crucial role of ocean inflow in the climate system, less is known about its predictability, variability, and connectivity to cryospheric and climatic patterns on different time scales. In this study, multi-scale variation modes were decomposed from observed daily and monthly snowcover and river flows to improve the predictability of Arctic Ocean inflows from the Mackenzie River Basin in Canada. Two multi-linear regression and Bayesian neural network models were used with different combinations of remotely sensed snowcover, in-situ inflow observations, and climatic teleconnection patterns as predictors. The results showed that daily and monthly ocean inflows are associated positively with decadal snowcover fluctuations and negatively with interannual snowcover fluctuations. Interannual snowcover and antecedent flow oscillations have a more important role in describing the variability of ocean inflows than seasonal snowmelt and large-scale climatic teleconnection. Both models forecasted inflows seven months in advance with a Nash–Sutcliffe efficiency score of ≈0.8. The proposed methodology can be used to assess the variability of the freshwater input to northern oceans, affecting thermohaline and atmospheric circulations