Dự đoán các vùng tiềm năng về tái nạp nước ngầm bằng kỹ thuật địa không gian

Sustainable Water Resources Management - Tập 6 - Trang 1-13 - 2020
Aysha Akter1, Abir Md Humam Uddin1, Khalid Ben Wahid1, Shoukat Ahmed2
1Department of Civil Engineering, Chittagong University of Engineering and Technology (CUET), Chittagong, Bangladesh
2Department of Geography, Geology, and Planning, Missouri State University, Springfield, MO, USA

Tóm tắt

Sự đô thị hóa nhanh chóng làm gia tăng các bề mặt không thấm nước, từ đó ngăn cản quá trình nạp nước ngầm tự nhiên, và cuối cùng gây ra mối đe dọa nghiêm trọng đối với nhu cầu nước của thành phố do sự suy giảm mực nước ngầm. Trong bối cảnh này, việc tái nạp nước ngầm nhân tạo (GWR) bằng cách sử dụng lượng mưa dư thừa đang thu hút sự quan tâm tại các khu vực đô thị. Nghiên cứu này chọn Chittagong, thành phố lớn thứ hai và là thủ đô thương mại của Bangladesh, nơi đang đối mặt với vấn đề hạ mực nước ngầm. Tiềm năng của GWR đã được đánh giá bằng việc sử dụng phân tích quyết định đa tiêu chí (MCDA) kết hợp với các kỹ thuật địa không gian. Sau khi đánh giá các tập dữ liệu sơ cấp và thứ cấp, tám lớp chủ đề đã được sử dụng để xác định các vùng tiềm năng cho GWR, bao gồm: độ sâu ngập nước mưa đô thị, mật độ rãnh thoát nước, độ dốc, lượng mưa/lưu lượng nước, kết cấu đất, mực nước ngầm hiện có, đường địa chất và sử dụng/che phủ đất. Bằng việc sử dụng các kỹ thuật phân loại lại, các lớp chủ đề này đã được phân loại thành năm loại riêng biệt và được gán giá trị trọng số chuẩn hóa cụ thể bằng cách sử dụng quy trình phân tích hệ thống và phân cấp (AHP) trong môi trường ArcGIS. Sau đó, phương pháp kết hợp tuyến tính trọng số đã được áp dụng để dự đoán các vùng tiềm năng GWR. Toàn bộ khu vực nghiên cứu sau đó được chia thành năm loại, bao gồm: vùng tiềm năng thấp, trung bình, trung bình-cao, cao và rất cao. Phân tích cho thấy 5,5% tổng diện tích có tiềm năng cao cho GWR. Trong phân tích này, kiến thức chuyên môn và kinh nghiệm thực tiễn đã được sử dụng để phân tích dữ liệu và chuẩn bị bản đồ. Do đó, cùng với dữ liệu mặt đất có độ phân giải cao, các phát hiện của bài báo này dự kiến sẽ cung cấp thông tin hữu ích cho hệ thống hỗ trợ quyết định.

Từ khóa


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